From 6d4c9d5f0cde734c2528541271c984ef8023d1fe Mon Sep 17 00:00:00 2001 From: Till Grutschus Date: Thu, 14 Dec 2023 15:55:05 +0100 Subject: [PATCH 01/13] added data from tensorboard --- ...rop_priority-labeling_k400-hyperparams.csv | 91 ++ ...rop_priority-labeling_k400-hyperparams.csv | 74 ++ ...ing_priority-labeling_k400-hyperparams.csv | 29 + ...ng_priority-labeling_paper-hyperparams.csv | 36 + ...rop_priority-labeling_k400-hyperparams.csv | 1001 +++++++++++++++++ ...rop_priority-labeling_k400-hyperparams.csv | 1001 +++++++++++++++++ ...ing_priority-labeling_k400-hyperparams.csv | 1001 +++++++++++++++++ ...ng_priority-labeling_paper-hyperparams.csv | 1001 +++++++++++++++++ 8 files changed, 4234 insertions(+) create mode 100644 notebooks/result_data/class_0_f1/vit-b_frame-int-8_gaussian-sampling-5s-clips-30-drop_priority-labeling_k400-hyperparams.csv create mode 100644 notebooks/result_data/class_0_f1/vit-b_gaussian-sampling-5s-clips-30-drop_priority-labeling_k400-hyperparams.csv 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+1701404554.622795,31388,0.3071082532405853 From 3200893b49ed3fc455cf020acd3224cf71f81603 Mon Sep 17 00:00:00 2001 From: Till Grutschus Date: Thu, 14 Dec 2023 15:56:21 +0100 Subject: [PATCH 02/13] added result analysis --- notebooks/.gitignore | 1 + notebooks/dataset_label_analysis.ipynb | 109 ++++++--- notebooks/predictions.pkl.dvc | 5 + notebooks/result_analysis.ipynb | 320 +++++++++++++++++++++++++ 4 files changed, 405 insertions(+), 30 deletions(-) create mode 100644 notebooks/.gitignore create mode 100644 notebooks/predictions.pkl.dvc create mode 100644 notebooks/result_analysis.ipynb diff --git a/notebooks/.gitignore b/notebooks/.gitignore new file mode 100644 index 0000000..e517473 --- /dev/null +++ b/notebooks/.gitignore @@ -0,0 +1 @@ +/predictions.pkl diff --git a/notebooks/dataset_label_analysis.ipynb b/notebooks/dataset_label_analysis.ipynb index d4d6287..dc71e98 100644 --- a/notebooks/dataset_label_analysis.ipynb +++ b/notebooks/dataset_label_analysis.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -32,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -41,6 +41,7 @@ " DecordDecode,\n", " SampleFrames,\n", " Resize,\n", + " ThreeCrop,\n", " RandomCrop,\n", " Flip,\n", " FormatShape,\n", @@ -72,17 +73,29 @@ " CenterCrop(crop_size=224),\n", " FormatShape(input_format=\"NCTHW\"),\n", " PackActionInputs(),\n", + "]\n", + "\n", + "test_pipeline = [\n", + " DecordInit(),\n", + " ClipVideo(),\n", + " SampleFrames(clip_len=16, frame_interval=8, num_clips=3, test_mode=True),\n", + " DecordDecode(),\n", + " Resize(scale=(-1, 224)),\n", + " ThreeCrop(crop_size=224),\n", + " FormatShape(input_format=\"NCTHW\"),\n", + " PackActionInputs(),\n", "]" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "val_ann_file = \"data/Fall_Simulation_Data/annotations_val.csv\"\n", "train_ann_file = \"data/Fall_Simulation_Data/annotations_train.csv\"\n", + "test_ann_file = \"data/Fall_Simulation_Data/annotations_test.csv\"\n", "\n", "np.random.seed(42)\n", "\n", @@ -116,13 +129,22 @@ " pipeline=train_pipeline,\n", " num_classes=3,\n", " test_mode=False,\n", - " drop_ratios=[0.0, 0.0, 0.30],\n", + " # drop_ratios=[0.0, 0.0, 0.30],\n", + ")\n", + "\n", + "test_hqfd = HighQualityFallDataset(\n", + " ann_file=test_ann_file,\n", + " sampling_strategy=uniform_sampling,\n", + " label_strategy=label_strategy,\n", + " pipeline=test_pipeline,\n", + " num_classes=3,\n", + " test_mode=True,\n", ")" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -171,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -195,7 +217,7 @@ { "data": { "text/plain": [ - "10876" + "13971" ] }, "metadata": {}, @@ -214,9 +236,9 @@ "data": { "text/plain": [ "label_name\n", - "Fall 0.146837\n", - "Lying 0.194189\n", - "Other 0.658974\n", + "Fall 0.118173\n", + "Lying 0.151958\n", + "Other 0.729869\n", "Name: count, dtype: float64" ] }, @@ -236,9 +258,9 @@ "data": { "text/plain": [ "label_name\n", - "Fall 1597\n", - "Lying 2112\n", - "Other 7167\n", + "Fall 1651\n", + "Lying 2123\n", + "Other 10197\n", "Name: count, dtype: int64" ] }, @@ -247,7 +269,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -276,7 +298,7 @@ { "data": { "text/plain": [ - "5532" + "6315" ] }, "metadata": {}, @@ -295,9 +317,9 @@ "data": { "text/plain": [ "label_name\n", - "Fall 0.288684\n", - "Lying 0.381779\n", - "Other 0.329537\n", + "Fall 0.261441\n", + "Lying 0.336184\n", + "Other 0.402375\n", "Name: count, dtype: float64" ] }, @@ -317,9 +339,9 @@ "data": { "text/plain": [ "label_name\n", - "Fall 1597\n", - "Lying 2112\n", - "Other 1823\n", + "Fall 1651\n", + "Lying 2123\n", + "Other 2541\n", "Name: count, dtype: int64" ] }, @@ -328,7 +350,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -350,7 +372,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -529,7 +551,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -567,22 +589,28 @@ " idx: int | list[int],\n", " out_path: str | None = None,\n", " render: bool = True,\n", + " out_type: str = \"gif\",\n", "):\n", " if isinstance(idx, int):\n", " idx = [idx]\n", " vis = ActionVisualizer()\n", " for i in idx:\n", " # video [B, C, T, H, W] -> [T, H, W, C]\n", + "\n", " video = ds[i][\"inputs\"].squeeze(0).permute(1, 2, 3, 0).numpy().astype(np.uint8)\n", " # Convert to RGB\n", - " video = video[..., ::-1]\n", + " if out_type == \"gif\":\n", + " video = video[..., ::-1]\n", + " sample = ds[i][\"data_samples\"]\n", + " sample.set_pred_label(0)\n", + " sample.set_pred_score([0.96, 0.00, 0.04])\n", " vis.add_datasample(\n", " name=i,\n", " video=video,\n", - " data_sample=ds[i][\"data_samples\"],\n", + " data_sample=sample,\n", " draw_gt=True,\n", " show_frames=True,\n", - " out_type=\"gif\",\n", + " out_type=out_type,\n", " out_path=f\"tmp/{i}.gif\" if out_path is None else out_path + f\"/{i}.gif\",\n", " )\n", " if render:\n", @@ -606,13 +634,13 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ece263d38db14c2f996cc4766a5201d8", + "model_id": "b03d3f26d2fa47f49fa3325c97dd3511", "version_major": 2, "version_minor": 0 }, @@ -625,7 +653,8 @@ } ], "source": [ - "idx = get_random_sample_from_class(val_hqfd, 0, 20)\n", + "idx = get_random_sample_from_class(val_hqfd, 0, 8)\n", + "\n", "generate_sample_videos(val_hqfd, idx)" ] }, @@ -811,6 +840,26 @@ "\n", "display(weights)" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Other" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "dl = test_hqfd.load_data_list()\n", + "\n", + "dl = pd.DataFrame(dl)\n", + "\n", + "problem = dl.iloc[720:880]" + ] } ], "metadata": { diff --git a/notebooks/predictions.pkl.dvc b/notebooks/predictions.pkl.dvc new file mode 100644 index 0000000..4a441c1 --- /dev/null +++ b/notebooks/predictions.pkl.dvc @@ -0,0 +1,5 @@ +outs: +- md5: 0a737bd16cabb291c83aac7510441e78 + size: 6261785 + hash: md5 + path: predictions.pkl diff --git a/notebooks/result_analysis.ipynb b/notebooks/result_analysis.ipynb new file mode 100644 index 0000000..8095e39 --- /dev/null +++ b/notebooks/result_analysis.ipynb @@ -0,0 +1,320 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Result Analysis\n", + "We use the pickled predictions to run our own analysis and create visualizations." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "import pandas as pd\n", + "from mmaction.evaluation.metrics.acc_metric import ConfusionMatrix\n", + "from sklearn.metrics import ConfusionMatrixDisplay\n", + "from pathlib import Path\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "([tensor([2]), tensor([2]), tensor([2])],\n", + " [tensor([0.0289, 0.0320, 0.9390]),\n", + " tensor([0.0346, 0.0424, 0.9230]),\n", + " tensor([0.0303, 0.0563, 0.9135])])" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "PREDICTIONS_FILE = \"notebooks/predictions.pkl\"\n", + "\n", + "with open(PREDICTIONS_FILE, \"rb\") as f:\n", + " predictions = pickle.load(f)\n", + "\n", + "predictions[:3]\n", + "\n", + "gt_labels = [p[\"gt_label\"] for p in predictions]\n", + "pred_labels = [p[\"pred_label\"] for p in predictions]\n", + "pred_scores = [p[\"pred_score\"] for p in predictions]\n", + "pred_labels[:3], pred_scores[:3]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Confusion Matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cf = ConfusionMatrixDisplay.from_predictions(\n", + " gt_labels,\n", + " pred_labels,\n", + " normalize=\"true\",\n", + " display_labels=[\"Fall\", \"Lying\", \"None\"],\n", + " labels=[0, 1, 2],\n", + " cmap=\"Blues\",\n", + " colorbar=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Metrics" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " Fall 0.64 0.84 0.73 74\n", + " Lying 0.98 0.95 0.96 983\n", + " None 0.99 0.99 0.99 4544\n", + "\n", + " accuracy 0.98 5601\n", + " macro avg 0.87 0.93 0.89 5601\n", + "weighted avg 0.98 0.98 0.98 5601\n", + "\n" + ] + } + ], + "source": [ + "from sklearn.metrics import classification_report\n", + "\n", + "print(\n", + " classification_report(\n", + " gt_labels,\n", + " pred_labels,\n", + " target_names=[\"Fall\", \"Lying\", \"Other\"],\n", + " output_dict=False,\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Loss curves" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Wall timeStepValueexp_name
01.701888e+0910.095238vit-b_frame-int-8_gaussian-sampling-5s-clips-3...
11.701889e+0920.410256vit-b_frame-int-8_gaussian-sampling-5s-clips-3...
21.701890e+0930.527273vit-b_frame-int-8_gaussian-sampling-5s-clips-3...
31.701891e+0940.500000vit-b_frame-int-8_gaussian-sampling-5s-clips-3...
41.701892e+0950.529915vit-b_frame-int-8_gaussian-sampling-5s-clips-3...
\n", + "
" + ], + "text/plain": [ + " Wall time Step Value \\\n", + "0 1.701888e+09 1 0.095238 \n", + "1 1.701889e+09 2 0.410256 \n", + "2 1.701890e+09 3 0.527273 \n", + "3 1.701891e+09 4 0.500000 \n", + "4 1.701892e+09 5 0.529915 \n", + "\n", + " exp_name \n", + "0 vit-b_frame-int-8_gaussian-sampling-5s-clips-3... \n", + "1 vit-b_frame-int-8_gaussian-sampling-5s-clips-3... \n", + "2 vit-b_frame-int-8_gaussian-sampling-5s-clips-3... \n", + "3 vit-b_frame-int-8_gaussian-sampling-5s-clips-3... \n", + "4 vit-b_frame-int-8_gaussian-sampling-5s-clips-3... " + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "loss_folder = Path(\"notebooks/result_data/class_0_f1/\")\n", + "loss_files = sorted(loss_folder.glob(\"*.csv\"))\n", + "loss_dfs = []\n", + "for loss_file in loss_files:\n", + " loss_df = pd.read_csv(loss_file)\n", + " loss_df[\"exp_name\"] = loss_file.stem\n", + " loss_dfs.append(loss_df)\n", + "\n", + "losses = pd.concat(loss_dfs)\n", + "\n", + "losses.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot all experiments over steps\n", + "\n", + "fig = plt.figure(figsize=(10, 6))\n", + "\n", + "grouped_losses = losses.groupby(\"exp_name\")\n", + "\n", + "for name, group in grouped_losses:\n", + " smoothed_values = group[\"Value\"].rolling(window=5).mean()\n", + " smoothed_values = smoothed_values.fillna(group[\"Value\"]) # Apply smoothing with window size 10\n", + " plt.plot(group[\"Step\"], smoothed_values, label=name)\n", + "\n", + "plt.xlabel(\"Step\")\n", + "plt.ylabel(\"F1 Score\")\n", + "plt.title(\"Class 'Fall' F1 Scores\")\n", + "plt.legend(loc=\"lower right\", fontsize=\"small\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Merging results to video level" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "human-fall-detection", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 4fbf423733fb1f5953acd0cd98713f0855a709a4 Mon Sep 17 00:00:00 2001 From: Till Grutschus Date: Thu, 14 Dec 2023 15:56:52 +0100 Subject: [PATCH 03/13] added diagrams --- .../HQFD_Sampling_Labeling_Pipeline.drawio | 239 ++++++++++++++++++ 1 file changed, 239 insertions(+) create mode 100644 diagrams/HQFD_Sampling_Labeling_Pipeline.drawio diff --git a/diagrams/HQFD_Sampling_Labeling_Pipeline.drawio b/diagrams/HQFD_Sampling_Labeling_Pipeline.drawio new file mode 100644 index 0000000..9df65c8 --- /dev/null +++ b/diagrams/HQFD_Sampling_Labeling_Pipeline.drawio @@ -0,0 +1,239 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + From 38544e8b989598343dff0a469a8d6a212ed52d99 Mon Sep 17 00:00:00 2001 From: Till Grutschus Date: Fri, 15 Dec 2023 12:54:01 +0100 Subject: [PATCH 04/13] updated label analysis --- notebooks/dataset_label_analysis.ipynb | 35 +++++++++++++------------- 1 file changed, 17 insertions(+), 18 deletions(-) diff --git a/notebooks/dataset_label_analysis.ipynb b/notebooks/dataset_label_analysis.ipynb index dc71e98..ffd55d8 100644 --- a/notebooks/dataset_label_analysis.ipynb +++ b/notebooks/dataset_label_analysis.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -32,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -89,7 +89,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -144,7 +144,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -193,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -815,28 +815,27 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 6, "metadata": {}, "outputs": [ { - "ename": "NameError", - "evalue": "name 'df_hqfd' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m/Users/tillgrutschus/Library/CloudStorage/OneDrive-Personal/Documents/Arbeit und Beruf/Uppsala/Project in Data Science/human-fall-detection/notebooks/dataset_label_analysis.ipynb Cell 24\u001b[0m line \u001b[0;36m4\n\u001b[1;32m 2\u001b[0m num_classes \u001b[39m=\u001b[39m \u001b[39m3\u001b[39m\n\u001b[1;32m 3\u001b[0m \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(num_classes):\n\u001b[0;32m----> 4\u001b[0m total_samples \u001b[39m=\u001b[39m \u001b[39mlen\u001b[39m(df_hqfd)\n\u001b[1;32m 5\u001b[0m num_samples_in_class_i \u001b[39m=\u001b[39m \u001b[39mlen\u001b[39m(df_hqfd[df_hqfd[\u001b[39m\"\u001b[39m\u001b[39mlabel\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m==\u001b[39m i])\n\u001b[1;32m 6\u001b[0m weights\u001b[39m.\u001b[39mappend(total_samples \u001b[39m/\u001b[39m num_samples_in_class_i \u001b[39m*\u001b[39m num_classes)\n", - "\u001b[0;31mNameError\u001b[0m: name 'df_hqfd' is not defined" - ] + "data": { + "text/plain": [ + "[2.8207147183525136, 2.1935939707960435, 0.4567029518485829]" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ "weights = []\n", "num_classes = 3\n", + "train_dataframe = convert_ds_to_df(train_hqfd)\n", "for i in range(num_classes):\n", - " total_samples = len(df_hqfd)\n", - " num_samples_in_class_i = len(df_hqfd[df_hqfd[\"label\"] == i])\n", - " weights.append(total_samples / num_samples_in_class_i * num_classes)\n", + " total_samples = len(train_dataframe)\n", + " num_samples_in_class_i = len(train_dataframe[train_dataframe[\"label\"] == i])\n", + " weights.append(total_samples / (num_samples_in_class_i * num_classes))\n", "\n", "display(weights)" ] From 1952a679bfd8ad9db859a58d272a42567bc95cf3 Mon Sep 17 00:00:00 2001 From: Till Grutschus Date: Mon, 18 Dec 2023 18:47:25 +0100 Subject: [PATCH 05/13] created some diagrams --- diagrams/confusion_matrix-img0.png | Bin 0 -> 1453 bytes diagrams/confusion_matrix.pgf | 336 ++++++++++++ diagrams/label_distributions.pgf | 852 +++++++++++++++++++++++++++++ diagrams/loss_curves.pdf | Bin 0 -> 417752 bytes environment.yml | 2 + notebooks/.gitignore | 1 + notebooks/result_analysis.ipynb | 447 ++++++++++++--- notebooks/tb_event_reader.pkl.dvc | 5 + 8 files changed, 1576 insertions(+), 67 deletions(-) create mode 100644 diagrams/confusion_matrix-img0.png create mode 100644 diagrams/confusion_matrix.pgf create mode 100644 diagrams/label_distributions.pgf create mode 100644 diagrams/loss_curves.pdf create mode 100644 notebooks/tb_event_reader.pkl.dvc diff --git a/diagrams/confusion_matrix-img0.png b/diagrams/confusion_matrix-img0.png new file mode 100644 index 0000000000000000000000000000000000000000..f5699fde470957cc61f7bcaf05c2c70dc9c0f988 GIT binary patch literal 1453 zcmeAS@N?(olHy`uVBq!ia0y~yU@QV*4mP033za}l1_ss=PZ!6KiaBquZS-|YWN=NK zZ`)famVInN5wrM)GQr0(?~*(hhze;`@GboLf6k9u;XALM=*_.pgf} +%% +%% Make sure the required packages are loaded in your preamble +%% \usepackage{pgf} +%% +%% Also ensure that all the required font packages are loaded; for instance, +%% the lmodern package is sometimes necessary when using math font. +%% \usepackage{lmodern} +%% +%% Figures using additional raster images can only be included by \input if +%% they are in the same directory as the main LaTeX file. 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z9T{6h79h0`Xls6T6Re{h25V=18+1n$j0D4kK~`9-qYGL@#QvWqFvi&u0D(1~F=no& z0N#7Fm!p{l=(dTeEilTj3P9#zd25V=+`*Rd%b{Tqm?#tmMMA|zfx8d{#s`7$2^?7$ zH)nGTN(cxhMhPgvzb?SiMgX9*1?Zm`z%U=SfgJuP28ROdG;nARt|Ku73Rs3?F)=vs z_{U-gQ1M?ez|}q;LqUP#e6;VuZ?R+bfRHF)e;+jcGZ#@{e#h$p%`gaHm>=2.1 - openpyxl>=3.0 - scikit-learn>=1.3 + - seaborn >= 0.12 - pip: # code-checks @@ -30,5 +31,6 @@ dependencies: - pandas-stubs>=2.1 - ffmpeg-python>=0.2 - tensorboard>=2.15 + - tbparse >=0.0 - torch-tb-profiler>=0.4 - moviepy>=1.0 diff --git a/notebooks/.gitignore b/notebooks/.gitignore index e517473..cd53af2 100644 --- a/notebooks/.gitignore +++ b/notebooks/.gitignore @@ -1 +1,2 @@ /predictions.pkl +/tb_event_reader.pkl diff --git a/notebooks/result_analysis.ipynb b/notebooks/result_analysis.ipynb index 8095e39..7dfed7f 100644 --- a/notebooks/result_analysis.ipynb +++ b/notebooks/result_analysis.ipynb @@ -10,21 +10,33 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 94, "metadata": {}, "outputs": [], "source": [ "import pickle\n", + "from pathlib import Path\n", + "import re\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", "import pandas as pd\n", + "import seaborn as sns\n", "from mmaction.evaluation.metrics.acc_metric import ConfusionMatrix\n", "from sklearn.metrics import ConfusionMatrixDisplay\n", - "from pathlib import Path\n", - "import matplotlib.pyplot as plt" + "\n", + "plt.rcParams.update(\n", + " {\n", + " \"font.family\": \"serif\", # use serif/main font for text elements\n", + " \"text.usetex\": True, # use inline math for ticks\n", + " \"pgf.rcfonts\": False, # don't setup fonts from rc parameters\n", + " }\n", + ")" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -36,7 +48,7 @@ " tensor([0.0303, 0.0563, 0.9135])])" ] }, - "execution_count": 27, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -64,12 +76,12 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "

" ] @@ -79,14 +91,20 @@ } ], "source": [ + "# Create the confusion matrix display\n", "cf = ConfusionMatrixDisplay.from_predictions(\n", " gt_labels,\n", " pred_labels,\n", " normalize=\"true\",\n", " display_labels=[\"Fall\", \"Lying\", \"None\"],\n", " labels=[0, 1, 2],\n", - " cmap=\"Blues\",\n", " colorbar=False,\n", + " cmap=\"Blues\",\n", + ")\n", + "\n", + "# Save the confusion matrix as a LaTeX Tikzplot\n", + "cf.figure_.savefig(\n", + " \"diagrams/confusion_matrix.pgf\", format=\"pgf\", bbox_inches=\"tight\", pad_inches=0\n", ")" ] }, @@ -99,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -110,7 +128,7 @@ "\n", " Fall 0.64 0.84 0.73 74\n", " Lying 0.98 0.95 0.96 983\n", - " None 0.99 0.99 0.99 4544\n", + " Other 0.99 0.99 0.99 4544\n", "\n", " accuracy 0.98 5601\n", " macro avg 0.87 0.93 0.89 5601\n", @@ -136,12 +154,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Loss curves" + "# Accessing Tensorboard files" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -165,97 +183,162 @@ " \n", " \n", " \n", - " Wall time\n", - " Step\n", - " Value\n", - " exp_name\n", + " step\n", + " metric\n", + " value\n", + " experiment\n", + " experiment_id\n", " \n", " \n", " \n", " \n", " 0\n", - " 1.701888e+09\n", " 1\n", - " 0.095238\n", - " vit-b_frame-int-8_gaussian-sampling-5s-clips-3...\n", + " Fall_f1\n", + " 0.0000\n", + " vit-uniform-sampling_priority-labeling_k400-hy...\n", + " 1\n", " \n", " \n", " 1\n", - " 1.701889e+09\n", " 2\n", - " 0.410256\n", - " vit-b_frame-int-8_gaussian-sampling-5s-clips-3...\n", + " Fall_f1\n", + " 0.0000\n", + " vit-uniform-sampling_priority-labeling_k400-hy...\n", + " 1\n", " \n", " \n", " 2\n", - " 1.701890e+09\n", " 3\n", - " 0.527273\n", - " vit-b_frame-int-8_gaussian-sampling-5s-clips-3...\n", + " Fall_f1\n", + " 0.0000\n", + " vit-uniform-sampling_priority-labeling_k400-hy...\n", + " 1\n", " \n", " \n", " 3\n", - " 1.701891e+09\n", " 4\n", - " 0.500000\n", - " vit-b_frame-int-8_gaussian-sampling-5s-clips-3...\n", + " Fall_f1\n", + " 0.0000\n", + " vit-uniform-sampling_priority-labeling_k400-hy...\n", + " 1\n", " \n", " \n", " 4\n", - " 1.701892e+09\n", " 5\n", - " 0.529915\n", - " vit-b_frame-int-8_gaussian-sampling-5s-clips-3...\n", + " Fall_f1\n", + " 0.0625\n", + " vit-uniform-sampling_priority-labeling_k400-hy...\n", + " 1\n", " \n", " \n", "\n", "" ], "text/plain": [ - " Wall time Step Value \\\n", - "0 1.701888e+09 1 0.095238 \n", - "1 1.701889e+09 2 0.410256 \n", - "2 1.701890e+09 3 0.527273 \n", - "3 1.701891e+09 4 0.500000 \n", - "4 1.701892e+09 5 0.529915 \n", + " step metric value experiment \\\n", + "0 1 Fall_f1 0.0000 vit-uniform-sampling_priority-labeling_k400-hy... \n", + "1 2 Fall_f1 0.0000 vit-uniform-sampling_priority-labeling_k400-hy... \n", + "2 3 Fall_f1 0.0000 vit-uniform-sampling_priority-labeling_k400-hy... \n", + "3 4 Fall_f1 0.0000 vit-uniform-sampling_priority-labeling_k400-hy... \n", + "4 5 Fall_f1 0.0625 vit-uniform-sampling_priority-labeling_k400-hy... \n", "\n", - " exp_name \n", - "0 vit-b_frame-int-8_gaussian-sampling-5s-clips-3... \n", - "1 vit-b_frame-int-8_gaussian-sampling-5s-clips-3... \n", - "2 vit-b_frame-int-8_gaussian-sampling-5s-clips-3... \n", - "3 vit-b_frame-int-8_gaussian-sampling-5s-clips-3... \n", - "4 vit-b_frame-int-8_gaussian-sampling-5s-clips-3... " + " experiment_id \n", + "0 1 \n", + "1 1 \n", + "2 1 \n", + "3 1 \n", + "4 1 " ] }, - "execution_count": 22, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "loss_folder = Path(\"notebooks/result_data/class_0_f1/\")\n", - "loss_files = sorted(loss_folder.glob(\"*.csv\"))\n", - "loss_dfs = []\n", - "for loss_file in loss_files:\n", - " loss_df = pd.read_csv(loss_file)\n", - " loss_df[\"exp_name\"] = loss_file.stem\n", - " loss_dfs.append(loss_df)\n", + "from tbparse import SummaryReader\n", + "import pickle\n", + "\n", + "experiment_path = Path(\"experiments/tensorboard/\")\n", + "dump_path = Path(\"notebooks/tb_event_reader.pkl\")\n", + "\n", + "try:\n", + " reader = pickle.load(open(dump_path, \"rb\"))\n", + "except FileNotFoundError:\n", + " reader = SummaryReader(\n", + " experiment_path, event_types={\"scalars\"}, extra_columns={\"dir_name\"}\n", + " )\n", + " pickle.dump(reader, open(dump_path, \"wb\"))\n", + "\n", + "tb_data = reader.scalars\n", + "tb_data = tb_data.rename(\n", + " columns={\"tag\": \"metric\", \"dir_name\": \"experiment\"},\n", + ")\n", + "\n", + "\n", + "def process_metric_name(metric_name):\n", + " metric_name = metric_name.replace(\"acc/\", \"\")\n", + " metric_name = metric_name.replace(\"class_0_\", \"Fall_\")\n", + " metric_name = metric_name.replace(\"class_1_\", \"Lying_\")\n", + " metric_name = metric_name.replace(\"class_2_\", \"Other_\")\n", + " return metric_name\n", + "\n", + "\n", + "tb_data[\"metric\"] = tb_data[\"metric\"].apply(process_metric_name)\n", + "\n", + "METRICS = [\n", + " \"Fall_f1\",\n", + " \"Fall_precision\",\n", + " \"Fall_recall\",\n", + " \"Lying_f1\",\n", + " \"Lying_precision\",\n", + " \"Lying_recall\",\n", + " \"Other_f1\",\n", + " \"Other_precision\",\n", + " \"Other_recall\",\n", + " \"unweighted_average_f1\",\n", + " \"loss\",\n", + "]\n", + "\n", + "tb_data = tb_data[tb_data[\"metric\"].isin(METRICS)]\n", + "\n", + "EXPERIMENTS = [\n", + " \"vit-uniform-sampling_priority-labeling_k400-hyperparams\",\n", + " \"vit-uniform-sampling_priority-labeling_k400-hyperparams_weighted-ce-loss\",\n", + " \"vit-b_gaussian-sampling_priority-labeling_k400-hyperparams\",\n", + " \"vit-b_gaussian-sampling-5s-clips-30-drop_priority-labeling_k400-hyperparams\",\n", + " \"vit-b_frame-int-8_gaussian-sampling-5s-clips-30-drop_priority-labeling_k400-hyperparams\",\n", + "]\n", "\n", - "losses = pd.concat(loss_dfs)\n", + "tb_data = tb_data[tb_data[\"experiment\"].isin(EXPERIMENTS)]\n", + "tb_data[\"experiment_id\"] = tb_data[\"experiment\"].apply(\n", + " lambda x: EXPERIMENTS.index(x) + 1\n", + ")\n", "\n", - "losses.head()" + "tb_data = tb_data.sort_values(by=[\"experiment_id\", \"metric\", \"step\"])\n", + "tb_data = tb_data.reset_index(drop=True)\n", + "\n", + "tb_data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Loss curves" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 89, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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filenamelabelintervaldata_codelabel_namevideo_category
0data/Fall_Simulation_Data/videos/Fall7_Cam4.mp42(0.0, 10.0)AOtherFall
1data/Fall_Simulation_Data/videos/Fall7_Cam4.mp42(10.0, 20.0)AOtherFall
2data/Fall_Simulation_Data/videos/Fall7_Cam4.mp40(20.0, 30.0)AFallFall
3data/Fall_Simulation_Data/videos/Fall7_Cam4.mp40(30.0, 40.0)AFallFall
4data/Fall_Simulation_Data/videos/Fall7_Cam4.mp41(40.0, 50.0)ALyingFall
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" + ], + "text/plain": [ + " filename label interval \\\n", + "0 data/Fall_Simulation_Data/videos/Fall7_Cam4.mp4 2 (0.0, 10.0) \n", + "1 data/Fall_Simulation_Data/videos/Fall7_Cam4.mp4 2 (10.0, 20.0) \n", + "2 data/Fall_Simulation_Data/videos/Fall7_Cam4.mp4 0 (20.0, 30.0) \n", + "3 data/Fall_Simulation_Data/videos/Fall7_Cam4.mp4 0 (30.0, 40.0) \n", + "4 data/Fall_Simulation_Data/videos/Fall7_Cam4.mp4 1 (40.0, 50.0) \n", + "\n", + " data_code label_name video_category \n", + "0 A Other Fall \n", + "1 A Other Fall \n", + "2 A Fall Fall \n", + "3 A Fall Fall \n", + "4 A Lying Fall " + ] + }, + "execution_count": 113, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from datasets import HighQualityFallDataset\n", + "from datasets.transforms.sampling_strategy import GaussianSampling, UniformSampling\n", + "from datasets.transforms.label_strategy import PriorityLabel, HQFD_LABEL_DESCRIPTION\n", "\n", - "grouped_losses = losses.groupby(\"exp_name\")\n", + "train_ann_file = \"data/Fall_Simulation_Data/annotations_train.csv\"\n", "\n", - "for name, group in grouped_losses:\n", - " smoothed_values = group[\"Value\"].rolling(window=5).mean()\n", - " smoothed_values = smoothed_values.fillna(group[\"Value\"]) # Apply smoothing with window size 10\n", - " plt.plot(group[\"Step\"], smoothed_values, label=name)\n", + "hqfd_A = HighQualityFallDataset(\n", + " ann_file=train_ann_file,\n", + " sampling_strategy=UniformSampling(clip_len=10),\n", + " label_strategy=PriorityLabel(HQFD_LABEL_DESCRIPTION),\n", + " pipeline=[],\n", + " num_classes=3,\n", + " test_mode=False,\n", + ")\n", "\n", - "plt.xlabel(\"Step\")\n", - "plt.ylabel(\"F1 Score\")\n", - "plt.title(\"Class 'Fall' F1 Scores\")\n", - "plt.legend(loc=\"lower right\", fontsize=\"small\")\n", + "hqfd_B = HighQualityFallDataset(\n", + " ann_file=train_ann_file,\n", + " sampling_strategy=GaussianSampling(clip_len=10),\n", + " label_strategy=PriorityLabel(HQFD_LABEL_DESCRIPTION),\n", + " pipeline=[],\n", + " num_classes=3,\n", + " test_mode=False,\n", + ")\n", + "\n", + "hqfd_C = HighQualityFallDataset(\n", + " ann_file=train_ann_file,\n", + " sampling_strategy=GaussianSampling(\n", + " clip_len=5, fallback_sampler=UniformSampling(clip_len=5, stride=5)\n", + " ),\n", + " label_strategy=PriorityLabel(HQFD_LABEL_DESCRIPTION),\n", + " pipeline=[],\n", + " num_classes=3,\n", + " test_mode=False,\n", + ")\n", + "\n", + "datalist_A = pd.DataFrame(hqfd_A.load_data_list())\n", + "datalist_A[\"data_code\"] = \"A\"\n", + "datalist_B = pd.DataFrame(hqfd_B.load_data_list())\n", + "datalist_B[\"data_code\"] = \"B\"\n", + "datalist_C = pd.DataFrame(hqfd_C.load_data_list())\n", + "datalist_C[\"data_code\"] = \"C\"\n", + "datalists = pd.concat([datalist_A, datalist_B, datalist_C])\n", + "\n", + "class_names = [\"Fall\", \"Lying\", \"Other\"]\n", + "datalists[\"label_name\"] = datalists[\"label\"].apply(lambda x: class_names[x])\n", + "\n", + "\n", + "def extract_category(filename):\n", + " match = re.search(r\"(ADL|Fall)\", filename.split(\"/\")[-1])\n", + " return match.group(1) if match else None\n", + "\n", + "\n", + "datalists[\"video_category\"] = datalists[\"filename\"].apply(extract_category)\n", + "datalists.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "datalists = datalists.sort_values(by=[\"data_code\", \"label\"])\n", + "\n", + "# Calculate the counts in % of total for each bar\n", + "total_counts = (\n", + " datalists.groupby([\"label_name\", \"data_code\"]).size().reset_index(name=\"count\")\n", + ")\n", + "total_counts[\"percentage\"] = (\n", + " total_counts.groupby(\"data_code\")[\"count\"]\n", + " .apply(lambda x: x / x.sum() * 100)\n", + " .reset_index(0, drop=True)\n", + ").round(2)\n", + "\n", + "\n", + "g = sns.barplot(\n", + " data=total_counts,\n", + " x=\"label_name\",\n", + " y=\"count\",\n", + " hue=\"data_code\",\n", + " legend=True,\n", + " palette=\"tab10\",\n", + ")\n", + "\n", + "for i, container in enumerate(g.containers):\n", + " g.bar_label(container)\n", + "\n", + "\n", + "plt.xlabel(\"Label\")\n", + "plt.ylabel(\"Count\")\n", + "plt.title(\"Label Distributions\")\n", + "g.get_legend().set_title(\"Data Code\")\n", + "plt.savefig(\n", + " \"diagrams/label_distributions.pgf\", format=\"pgf\", bbox_inches=\"tight\", pad_inches=0\n", + ")\n", "plt.show()" ] }, diff --git a/notebooks/tb_event_reader.pkl.dvc b/notebooks/tb_event_reader.pkl.dvc new file mode 100644 index 0000000..3c960a7 --- /dev/null +++ b/notebooks/tb_event_reader.pkl.dvc @@ -0,0 +1,5 @@ +outs: +- md5: 948937e2cf861992f237e826454352d5 + size: 8391171 + hash: md5 + path: tb_event_reader.pkl From 32fbdb28e06a2da6effff9e553716f7bf1838f77 Mon Sep 17 00:00:00 2001 From: Till Grutschus Date: Tue, 19 Dec 2023 22:09:18 +0100 Subject: [PATCH 06/13] MOAHH VISUALS! --- notebooks/result_analysis.ipynb | 438 +++++++++++++++++++++++++++++++- 1 file changed, 435 insertions(+), 3 deletions(-) diff --git a/notebooks/result_analysis.ipynb b/notebooks/result_analysis.ipynb index 7dfed7f..96ca246 100644 --- a/notebooks/result_analysis.ipynb +++ b/notebooks/result_analysis.ipynb @@ -372,6 +372,348 @@ "plt.show()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# F1 Curves" + ] + }, + { + "cell_type": "code", + "execution_count": 184, + "metadata": {}, + "outputs": [], + "source": [ + "f1_scores = tb_data[tb_data[\"metric\"].str.contains(\"f1\")].copy()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Maximum F1 Scores table" + ] + }, + { + "cell_type": "code", + "execution_count": 185, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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stepmetricvalueexperimentexperiment_id
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1264131unweighted_average_f10.775900vit-uniform-sampling_priority-labeling_k400-hy...2
126769Fall_f10.389610vit-b_gaussian-sampling_priority-labeling_k400...3
127609Lying_f10.903371vit-b_gaussian-sampling_priority-labeling_k400...3
128449Other_f10.976346vit-b_gaussian-sampling_priority-labeling_k400...3
160439unweighted_average_f10.756442vit-b_gaussian-sampling_priority-labeling_k400...3
1607911Fall_f10.525000vit-b_gaussian-sampling-5s-clips-30-drop_prior...4
1629811Lying_f10.893434vit-b_gaussian-sampling-5s-clips-30-drop_prior...4
1651711Other_f10.975610vit-b_gaussian-sampling-5s-clips-30-drop_prior...4
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2343316Fall_f10.738095vit-b_frame-int-8_gaussian-sampling-5s-clips-3...5
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3234316unweighted_average_f10.879134vit-b_frame-int-8_gaussian-sampling-5s-clips-3...5
\n", + "
" + ], + "text/plain": [ + " step metric value \\\n", + "60 61 Fall_f1 0.627451 \n", + "252 61 Lying_f1 0.903226 \n", + "444 61 Other_f1 0.978031 \n", + "6375 61 unweighted_average_f1 0.836236 \n", + "6409 31 Fall_f1 0.452381 \n", + "6598 31 Lying_f1 0.907923 \n", + "6787 31 Other_f1 0.967396 \n", + "12641 31 unweighted_average_f1 0.775900 \n", + "12676 9 Fall_f1 0.389610 \n", + "12760 9 Lying_f1 0.903371 \n", + "12844 9 Other_f1 0.976346 \n", + "16043 9 unweighted_average_f1 0.756442 \n", + "16079 11 Fall_f1 0.525000 \n", + "16298 11 Lying_f1 0.893434 \n", + "16517 11 Other_f1 0.975610 \n", + "23355 11 unweighted_average_f1 0.798015 \n", + "23433 16 Fall_f1 0.738095 \n", + "23703 16 Lying_f1 0.916759 \n", + "23973 16 Other_f1 0.982548 \n", + "32343 16 unweighted_average_f1 0.879134 \n", + "\n", + " experiment experiment_id \n", + "60 vit-uniform-sampling_priority-labeling_k400-hy... 1 \n", + "252 vit-uniform-sampling_priority-labeling_k400-hy... 1 \n", + "444 vit-uniform-sampling_priority-labeling_k400-hy... 1 \n", + "6375 vit-uniform-sampling_priority-labeling_k400-hy... 1 \n", + "6409 vit-uniform-sampling_priority-labeling_k400-hy... 2 \n", + "6598 vit-uniform-sampling_priority-labeling_k400-hy... 2 \n", + "6787 vit-uniform-sampling_priority-labeling_k400-hy... 2 \n", + "12641 vit-uniform-sampling_priority-labeling_k400-hy... 2 \n", + "12676 vit-b_gaussian-sampling_priority-labeling_k400... 3 \n", + "12760 vit-b_gaussian-sampling_priority-labeling_k400... 3 \n", + "12844 vit-b_gaussian-sampling_priority-labeling_k400... 3 \n", + "16043 vit-b_gaussian-sampling_priority-labeling_k400... 3 \n", + "16079 vit-b_gaussian-sampling-5s-clips-30-drop_prior... 4 \n", + "16298 vit-b_gaussian-sampling-5s-clips-30-drop_prior... 4 \n", + "16517 vit-b_gaussian-sampling-5s-clips-30-drop_prior... 4 \n", + "23355 vit-b_gaussian-sampling-5s-clips-30-drop_prior... 4 \n", + "23433 vit-b_frame-int-8_gaussian-sampling-5s-clips-3... 5 \n", + "23703 vit-b_frame-int-8_gaussian-sampling-5s-clips-3... 5 \n", + "23973 vit-b_frame-int-8_gaussian-sampling-5s-clips-3... 5 \n", + "32343 vit-b_frame-int-8_gaussian-sampling-5s-clips-3... 5 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "unweighted_average_f1 = f1_scores[f1_scores[\"metric\"] == \"unweighted_average_f1\"]\n", + "max_ids = unweighted_average_f1.groupby([\"experiment_id\"])[\"value\"].idxmax()\n", + "max_f1_steps_index = (\n", + " f1_scores.loc[max_ids][[\"experiment_id\", \"step\"]]\n", + " .set_index([\"experiment_id\", \"step\"])\n", + " .index\n", + ")\n", + "f1_scores_index = f1_scores.set_index([\"experiment_id\", \"step\"]).index\n", + "max_f1_scores = f1_scores[f1_scores_index.isin(max_f1_steps_index)]\n", + "\n", + "display(max_f1_scores)" + ] + }, + { + "cell_type": "code", + "execution_count": 188, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\\begin{tabular}{lrrrr}\n", + "\\toprule\n", + "metric & Fall_f1 & Lying_f1 & Other_f1 & unweighted_average_f1 \\\\\n", + "experiment_id & & & & \\\\\n", + "\\midrule\n", + "1 & 0.63 & 0.90 & 0.98 & 0.84 \\\\\n", + "2 & 0.45 & 0.91 & 0.97 & 0.78 \\\\\n", + "3 & 0.39 & 0.90 & 0.98 & 0.76 \\\\\n", + "4 & 0.52 & 0.89 & 0.98 & 0.80 \\\\\n", + "5 & 0.74 & 0.92 & 0.98 & 0.88 \\\\\n", + "\\bottomrule\n", + "\\end{tabular}\n", + "\n" + ] + } + ], + "source": [ + "# Pivot the data\n", + "\n", + "pivot_max_f1_scores = max_f1_scores[[\"metric\", \"value\", \"experiment_id\"]].pivot(\n", + " values=\"value\", index=\"experiment_id\", columns=\"metric\"\n", + ")\n", + "print(pivot_max_f1_scores.to_latex(float_format=\"%.2f\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 181, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "unhashable type: 'DataFrame'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[181], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mf1_scores\u001b[49m\u001b[43m[\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mexperiment_id\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstep\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mmax_f1_steps\u001b[49m\n", + "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/generic.py:2013\u001b[0m, in \u001b[0;36mNDFrame.__contains__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 2010\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[1;32m 2011\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__contains__\u001b[39m(\u001b[38;5;28mself\u001b[39m, key) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m bool_t:\n\u001b[1;32m 2012\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"True if the key is in the info axis\"\"\"\u001b[39;00m\n\u001b[0;32m-> 2013\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mkey\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_info_axis\u001b[49m\n", + "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/indexes/base.py:5335\u001b[0m, in \u001b[0;36mIndex.__contains__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 5300\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__contains__\u001b[39m(\u001b[38;5;28mself\u001b[39m, key: Any) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mbool\u001b[39m:\n\u001b[1;32m 5301\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 5302\u001b[0m \u001b[38;5;124;03m Return a boolean indicating whether the provided key is in the index.\u001b[39;00m\n\u001b[1;32m 5303\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 5333\u001b[0m \u001b[38;5;124;03m False\u001b[39;00m\n\u001b[1;32m 5334\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 5335\u001b[0m \u001b[38;5;28;43mhash\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 5336\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 5337\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\n", + "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'DataFrame'" + ] + } + ], + "source": [ + "f1_scores[[\"experiment_id\", \"step\"]]" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -603,10 +945,100 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 212, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " Fall 0.95 0.96 0.95 55\n", + " ADL 0.88 0.82 0.85 17\n", + "\n", + " accuracy 0.93 72\n", + " macro avg 0.91 0.89 0.90 72\n", + "weighted avg 0.93 0.93 0.93 72\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from datasets import HighQualityFallDataset\n", + "from datasets.transforms.sampling_strategy import GaussianSampling, UniformSampling\n", + "from datasets.transforms.label_strategy import PriorityLabel, HQFD_LABEL_DESCRIPTION\n", + "\n", + "test_ann_file = \"data/Fall_Simulation_Data/annotations_test.csv\"\n", + "\n", + "hqfd_test = HighQualityFallDataset(\n", + " label_strategy=PriorityLabel(HQFD_LABEL_DESCRIPTION),\n", + " ann_file=test_ann_file,\n", + " num_classes=3,\n", + " pipeline=[],\n", + " test_mode=True,\n", + " sampling_strategy=UniformSampling(clip_len=5, overlap=False, stride=0),\n", + ")\n", + "\n", + "datalist_test = pd.DataFrame(hqfd_test.load_data_list())\n", + "datalist_test[\"prediction\"] = pd.Series(pred_labels).astype(int)\n", + "\n", + "\n", + "def extract_category(filename):\n", + " match = re.search(r\"(ADL|Fall)\", filename.split(\"/\")[-1])\n", + " return match.group(1) if match else None\n", + "\n", + "\n", + "datalist_test[\"video_category\"] = datalist_test[\"filename\"].apply(extract_category)\n", + "\n", + "class_map = {\n", + " \"Fall\": 0,\n", + " \"ADL\": 2,\n", + "}\n", + "\n", + "datalist_test[\"video_label\"] = datalist_test[\"video_category\"].replace(class_map)\n", + "\n", + "\n", + "video_level_predictions = pd.DataFrame(\n", + " datalist_test.groupby(\"filename\")[\"prediction\"].min()\n", + ")\n", + "video_level_predictions[\"prediction\"] = video_level_predictions[\"prediction\"].replace(\n", + " {1: 2}\n", + ")\n", + "\n", + "video_level_predictions[\"video_label\"] = datalist_test.groupby(\"filename\")[\n", + " \"video_label\"\n", + "].first()\n", + "\n", + "print(\n", + " classification_report(\n", + " video_level_predictions[\"video_label\"],\n", + " video_level_predictions[\"prediction\"],\n", + " labels=[0, 2],\n", + " target_names=[\"Fall\", \"ADL\"],\n", + " output_dict=False,\n", + " )\n", + ")\n", + "\n", + "cf = ConfusionMatrixDisplay.from_predictions(\n", + " video_level_predictions[\"video_label\"],\n", + " video_level_predictions[\"prediction\"],\n", + " display_labels=[\"Fall\", \"ADL\"],\n", + " labels=[0, 2],\n", + " colorbar=False,\n", + " cmap=\"Blues\",\n", + ")" + ] } ], "metadata": { From edd645974b191586d46e2c53b1b4e217079bae55 Mon Sep 17 00:00:00 2001 From: Till Grutschus Date: Wed, 3 Jan 2024 18:29:46 +0100 Subject: [PATCH 07/13] more visuals --- diagrams/label_distributions.pdf | Bin 0 -> 68043 bytes diagrams/loss_curves.pdf | Bin 417752 -> 417752 bytes notebooks/result_analysis.ipynb | 925 ++++++++++++++++++++++++++++++- 3 files changed, 900 insertions(+), 25 deletions(-) create mode 100644 diagrams/label_distributions.pdf diff --git a/diagrams/label_distributions.pdf b/diagrams/label_distributions.pdf new file mode 100644 index 0000000000000000000000000000000000000000..ab4c81bbf451a3f03eecba8d5ef0e05eff9ccb1e GIT binary patch literal 68043 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"execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -331,7 +331,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -381,7 +381,7 @@ }, { "cell_type": "code", - "execution_count": 184, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -397,7 +397,7 @@ }, { "cell_type": "code", - "execution_count": 185, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -659,7 +659,7 @@ }, { "cell_type": "code", - "execution_count": 188, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -693,21 +693,115 @@ }, { "cell_type": "code", - "execution_count": 181, + "execution_count": 10, "metadata": {}, "outputs": [ { - "ename": "TypeError", - "evalue": "unhashable type: 'DataFrame'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[181], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mf1_scores\u001b[49m\u001b[43m[\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mexperiment_id\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstep\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mmax_f1_steps\u001b[49m\n", - "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/generic.py:2013\u001b[0m, in \u001b[0;36mNDFrame.__contains__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 2010\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[1;32m 2011\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__contains__\u001b[39m(\u001b[38;5;28mself\u001b[39m, key) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m bool_t:\n\u001b[1;32m 2012\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"True if the key is in the info axis\"\"\"\u001b[39;00m\n\u001b[0;32m-> 2013\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mkey\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_info_axis\u001b[49m\n", - "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/indexes/base.py:5335\u001b[0m, in \u001b[0;36mIndex.__contains__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 5300\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__contains__\u001b[39m(\u001b[38;5;28mself\u001b[39m, key: Any) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mbool\u001b[39m:\n\u001b[1;32m 5301\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 5302\u001b[0m \u001b[38;5;124;03m Return a boolean indicating whether the provided key is in the index.\u001b[39;00m\n\u001b[1;32m 5303\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 5333\u001b[0m \u001b[38;5;124;03m False\u001b[39;00m\n\u001b[1;32m 5334\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 5335\u001b[0m \u001b[38;5;28;43mhash\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 5336\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 5337\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\n", - "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'DataFrame'" - ] + "data": { + "text/html": [ + "

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" + ], + "text/plain": [ + " experiment_id step\n", + "0 1 1\n", + "1 1 2\n", + "2 1 3\n", + "3 1 4\n", + "4 1 5\n", + "... ... ...\n", + "32413 5 86\n", + "32414 5 87\n", + "32415 5 88\n", + "32416 5 89\n", + "32417 5 90\n", + "\n", + "[1272 rows x 2 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -723,7 +817,7 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -821,7 +915,7 @@ "4 A Lying Fall " ] }, - "execution_count": 113, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -885,12 +979,12 @@ }, { "cell_type": "code", - "execution_count": 156, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -931,11 +1025,86 @@ "plt.title(\"Label Distributions\")\n", "g.get_legend().set_title(\"Data Code\")\n", "plt.savefig(\n", - " \"diagrams/label_distributions.pgf\", format=\"pgf\", bbox_inches=\"tight\", pad_inches=0\n", + " \"diagrams/label_distributions.pdf\", format=\"pdf\", bbox_inches=\"tight\", pad_inches=0\n", ")\n", "plt.show()" ] }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\\begin{tabular}{llll}\n", + "\\toprule\n", + "data_code & A & B & C \\\\\n", + "label_name & & & \\\\\n", + "\\midrule\n", + "Fall & 231 (2.1%) & 1,221 (11.3%) & 1,590 (11.4%) \\\\\n", + "Lying & 1,769 (16.4%) & 835 (7.8%) & 2,085 (14.9%) \\\\\n", + "Other & 8,764 (81.4%) & 8,708 (80.9%) & 10,296 (73.7%) \\\\\n", + "\\bottomrule\n", + "\\end{tabular}\n", + "\n" + ] + }, + { + "ename": "TypeError", + "evalue": "agg function failed [how->mean,dtype->object]", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/groupby/groupby.py:1874\u001b[0m, in \u001b[0;36mGroupBy._agg_py_fallback\u001b[0;34m(self, how, values, ndim, alt)\u001b[0m\n\u001b[1;32m 1873\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1874\u001b[0m res_values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgrouper\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43magg_series\u001b[49m\u001b[43m(\u001b[49m\u001b[43mser\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43malt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpreserve_dtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 1875\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", + "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/groupby/ops.py:849\u001b[0m, in \u001b[0;36mBaseGrouper.agg_series\u001b[0;34m(self, obj, func, preserve_dtype)\u001b[0m\n\u001b[1;32m 847\u001b[0m preserve_dtype \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m--> 849\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_aggregate_series_pure_python\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 851\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(obj) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(result) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(obj\u001b[38;5;241m.\u001b[39mdtype, ExtensionDtype):\n", + "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/groupby/ops.py:877\u001b[0m, in \u001b[0;36mBaseGrouper._aggregate_series_pure_python\u001b[0;34m(self, obj, func)\u001b[0m\n\u001b[1;32m 876\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, group \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(splitter):\n\u001b[0;32m--> 877\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 878\u001b[0m res \u001b[38;5;241m=\u001b[39m extract_result(res)\n", + "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/groupby/groupby.py:2380\u001b[0m, in \u001b[0;36mGroupBy.mean..\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 2377\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 2378\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cython_agg_general(\n\u001b[1;32m 2379\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmean\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m-> 2380\u001b[0m alt\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mlambda\u001b[39;00m x: \u001b[43mSeries\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmean\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnumeric_only\u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m 2381\u001b[0m numeric_only\u001b[38;5;241m=\u001b[39mnumeric_only,\n\u001b[1;32m 2382\u001b[0m )\n\u001b[1;32m 2383\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgroupby\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/series.py:6225\u001b[0m, in \u001b[0;36mSeries.mean\u001b[0;34m(self, axis, skipna, numeric_only, **kwargs)\u001b[0m\n\u001b[1;32m 6217\u001b[0m \u001b[38;5;129m@doc\u001b[39m(make_doc(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmean\u001b[39m\u001b[38;5;124m\"\u001b[39m, ndim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m))\n\u001b[1;32m 6218\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmean\u001b[39m(\n\u001b[1;32m 6219\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 6223\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 6224\u001b[0m ):\n\u001b[0;32m-> 6225\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mNDFrame\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmean\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/generic.py:11992\u001b[0m, in \u001b[0;36mNDFrame.mean\u001b[0;34m(self, axis, skipna, numeric_only, **kwargs)\u001b[0m\n\u001b[1;32m 11985\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmean\u001b[39m(\n\u001b[1;32m 11986\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 11987\u001b[0m axis: Axis \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 11990\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 11991\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Series \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mfloat\u001b[39m:\n\u001b[0;32m> 11992\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_stat_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 11993\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmean\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnanops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnanmean\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m 11994\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/generic.py:11949\u001b[0m, in \u001b[0;36mNDFrame._stat_function\u001b[0;34m(self, name, func, axis, skipna, numeric_only, **kwargs)\u001b[0m\n\u001b[1;32m 11947\u001b[0m validate_bool_kwarg(skipna, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mskipna\u001b[39m\u001b[38;5;124m\"\u001b[39m, none_allowed\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m> 11949\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_reduce\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 11950\u001b[0m \u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnumeric_only\u001b[49m\n\u001b[1;32m 11951\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/series.py:6133\u001b[0m, in \u001b[0;36mSeries._reduce\u001b[0;34m(self, op, name, axis, skipna, numeric_only, filter_type, **kwds)\u001b[0m\n\u001b[1;32m 6129\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m 6130\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSeries.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m does not allow \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkwd_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnumeric_only\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 6131\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwith non-numeric dtypes.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 6132\u001b[0m )\n\u001b[0;32m-> 6133\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdelegate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/nanops.py:147\u001b[0m, in \u001b[0;36mbottleneck_switch.__call__..f\u001b[0;34m(values, axis, skipna, **kwds)\u001b[0m\n\u001b[1;32m 146\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 147\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43malt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", + "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/nanops.py:404\u001b[0m, in \u001b[0;36m_datetimelike_compat..new_func\u001b[0;34m(values, axis, skipna, mask, **kwargs)\u001b[0m\n\u001b[1;32m 402\u001b[0m mask \u001b[38;5;241m=\u001b[39m isna(values)\n\u001b[0;32m--> 404\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m 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\u001b[38;5;241m=\u001b[39m \u001b[43m_ensure_numeric\u001b[49m\u001b[43m(\u001b[49m\u001b[43mthe_sum\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 722\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m axis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(the_sum, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mndim\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", + "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/nanops.py:1693\u001b[0m, in \u001b[0;36m_ensure_numeric\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 1691\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m 1692\u001b[0m \u001b[38;5;66;03m# GH#44008, GH#36703 avoid casting e.g. strings to numeric\u001b[39;00m\n\u001b[0;32m-> 1693\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCould not convert string \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m to numeric\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 1694\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n", + "\u001b[0;31mTypeError\u001b[0m: Could not convert string '231 (2.1%)' to numeric", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[34], line 9\u001b[0m\n\u001b[1;32m 3\u001b[0m total_counts[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m total_counts\u001b[38;5;241m.\u001b[39mapply(\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m x: 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columns\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata_code\u001b[39m\u001b[38;5;124m\"\u001b[39m, values\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m\"\u001b[39m)\u001b[38;5;241m.\u001b[39mto_latex())\n\u001b[0;32m----> 9\u001b[0m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpivot_table\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 10\u001b[0m \u001b[43m \u001b[49m\u001b[43mtotal_counts\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 11\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalues\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mvalue\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 12\u001b[0m \u001b[43m 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\u001b[38;5;241m=\u001b[39m concat(pieces, keys\u001b[38;5;241m=\u001b[39mkeys, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m table\u001b[38;5;241m.\u001b[39m__finalize__(data, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpivot_table\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 99\u001b[0m table \u001b[38;5;241m=\u001b[39m \u001b[43m__internal_pivot_table\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 101\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 102\u001b[0m \u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 103\u001b[0m \u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 104\u001b[0m \u001b[43m \u001b[49m\u001b[43maggfunc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 105\u001b[0m \u001b[43m \u001b[49m\u001b[43mfill_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 106\u001b[0m \u001b[43m \u001b[49m\u001b[43mmargins\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 107\u001b[0m \u001b[43m \u001b[49m\u001b[43mdropna\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 108\u001b[0m \u001b[43m \u001b[49m\u001b[43mmargins_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 109\u001b[0m \u001b[43m \u001b[49m\u001b[43mobserved\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 110\u001b[0m \u001b[43m \u001b[49m\u001b[43msort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 111\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 112\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m table\u001b[38;5;241m.\u001b[39m__finalize__(data, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpivot_table\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/reshape/pivot.py:169\u001b[0m, in \u001b[0;36m__internal_pivot_table\u001b[0;34m(data, values, index, columns, aggfunc, fill_value, margins, dropna, margins_name, observed, sort)\u001b[0m\n\u001b[1;32m 166\u001b[0m values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(values)\n\u001b[1;32m 168\u001b[0m grouped \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mgroupby(keys, observed\u001b[38;5;241m=\u001b[39mobserved, sort\u001b[38;5;241m=\u001b[39msort, dropna\u001b[38;5;241m=\u001b[39mdropna)\n\u001b[0;32m--> 169\u001b[0m agged \u001b[38;5;241m=\u001b[39m \u001b[43mgrouped\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43magg\u001b[49m\u001b[43m(\u001b[49m\u001b[43maggfunc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 171\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dropna \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(agged, ABCDataFrame) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(agged\u001b[38;5;241m.\u001b[39mcolumns):\n\u001b[1;32m 172\u001b[0m agged \u001b[38;5;241m=\u001b[39m agged\u001b[38;5;241m.\u001b[39mdropna(how\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mall\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/groupby/generic.py:1445\u001b[0m, in \u001b[0;36mDataFrameGroupBy.aggregate\u001b[0;34m(self, func, engine, engine_kwargs, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1442\u001b[0m kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mengine_kwargs\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m engine_kwargs\n\u001b[1;32m 1444\u001b[0m op \u001b[38;5;241m=\u001b[39m GroupByApply(\u001b[38;5;28mself\u001b[39m, func, args\u001b[38;5;241m=\u001b[39margs, kwargs\u001b[38;5;241m=\u001b[39mkwargs)\n\u001b[0;32m-> 1445\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43magg\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1446\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_dict_like(func) \u001b[38;5;129;01mand\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1447\u001b[0m \u001b[38;5;66;03m# GH #52849\u001b[39;00m\n\u001b[1;32m 1448\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mas_index \u001b[38;5;129;01mand\u001b[39;00m is_list_like(func):\n", + "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/apply.py:172\u001b[0m, in \u001b[0;36mApply.agg\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 169\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m 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\u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/apply.py:669\u001b[0m, in \u001b[0;36mApply._apply_str\u001b[0;34m(self, obj, func, *args, **kwargs)\u001b[0m\n\u001b[1;32m 667\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(obj, func)\n\u001b[1;32m 668\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcallable\u001b[39m(f):\n\u001b[0;32m--> 669\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 671\u001b[0m \u001b[38;5;66;03m# people may aggregate on a non-callable attribute\u001b[39;00m\n\u001b[1;32m 672\u001b[0m \u001b[38;5;66;03m# but don't let them think they can pass args to it\u001b[39;00m\n\u001b[1;32m 673\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m\n", + "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/groupby/groupby.py:2378\u001b[0m, in \u001b[0;36mGroupBy.mean\u001b[0;34m(self, numeric_only, engine, engine_kwargs)\u001b[0m\n\u001b[1;32m 2371\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_numba_agg_general(\n\u001b[1;32m 2372\u001b[0m grouped_mean,\n\u001b[1;32m 2373\u001b[0m executor\u001b[38;5;241m.\u001b[39mfloat_dtype_mapping,\n\u001b[1;32m 2374\u001b[0m engine_kwargs,\n\u001b[1;32m 2375\u001b[0m min_periods\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m,\n\u001b[1;32m 2376\u001b[0m )\n\u001b[1;32m 2377\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 2378\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cython_agg_general\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2379\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmean\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2380\u001b[0m \u001b[43m \u001b[49m\u001b[43malt\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mSeries\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmean\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnumeric_only\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2381\u001b[0m \u001b[43m \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnumeric_only\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2382\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2383\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgroupby\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/groupby/groupby.py:1929\u001b[0m, in \u001b[0;36mGroupBy._cython_agg_general\u001b[0;34m(self, how, alt, numeric_only, min_count, **kwargs)\u001b[0m\n\u001b[1;32m 1926\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_agg_py_fallback(how, values, ndim\u001b[38;5;241m=\u001b[39mdata\u001b[38;5;241m.\u001b[39mndim, alt\u001b[38;5;241m=\u001b[39malt)\n\u001b[1;32m 1927\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n\u001b[0;32m-> 1929\u001b[0m new_mgr \u001b[38;5;241m=\u001b[39m \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgrouped_reduce\u001b[49m\u001b[43m(\u001b[49m\u001b[43marray_func\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1930\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_wrap_agged_manager(new_mgr)\n\u001b[1;32m 1931\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_wrap_aggregated_output(res)\n", + "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/internals/managers.py:1428\u001b[0m, in \u001b[0;36mBlockManager.grouped_reduce\u001b[0;34m(self, func)\u001b[0m\n\u001b[1;32m 1424\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m blk\u001b[38;5;241m.\u001b[39mis_object:\n\u001b[1;32m 1425\u001b[0m \u001b[38;5;66;03m# split on object-dtype blocks bc some columns may raise\u001b[39;00m\n\u001b[1;32m 1426\u001b[0m \u001b[38;5;66;03m# while others do not.\u001b[39;00m\n\u001b[1;32m 1427\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sb \u001b[38;5;129;01min\u001b[39;00m blk\u001b[38;5;241m.\u001b[39m_split():\n\u001b[0;32m-> 1428\u001b[0m applied \u001b[38;5;241m=\u001b[39m \u001b[43msb\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1429\u001b[0m result_blocks \u001b[38;5;241m=\u001b[39m extend_blocks(applied, result_blocks)\n\u001b[1;32m 1430\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", + "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/internals/blocks.py:366\u001b[0m, in \u001b[0;36mBlock.apply\u001b[0;34m(self, func, **kwargs)\u001b[0m\n\u001b[1;32m 360\u001b[0m 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\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 368\u001b[0m result \u001b[38;5;241m=\u001b[39m maybe_coerce_values(result)\n\u001b[1;32m 369\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_split_op_result(result)\n", + "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/groupby/groupby.py:1926\u001b[0m, in \u001b[0;36mGroupBy._cython_agg_general..array_func\u001b[0;34m(values)\u001b[0m\n\u001b[1;32m 1923\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1924\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n\u001b[0;32m-> 1926\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_agg_py_fallback\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhow\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mndim\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mndim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43malt\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43malt\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1927\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", + "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/groupby/groupby.py:1878\u001b[0m, in \u001b[0;36mGroupBy._agg_py_fallback\u001b[0;34m(self, how, values, ndim, alt)\u001b[0m\n\u001b[1;32m 1876\u001b[0m msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124magg function failed [how->\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mhow\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m,dtype->\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mser\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1877\u001b[0m \u001b[38;5;66;03m# preserve the kind of exception that raised\u001b[39;00m\n\u001b[0;32m-> 1878\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(err)(msg) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m 1880\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ser\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mobject\u001b[39m:\n\u001b[1;32m 1881\u001b[0m res_values \u001b[38;5;241m=\u001b[39m res_values\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;28mobject\u001b[39m, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n", + "\u001b[0;31mTypeError\u001b[0m: agg function failed [how->mean,dtype->object]" + ] + } + ], + "source": [ + "# Make it a table\n", + "\n", + "total_counts[\"value\"] = total_counts.apply(\n", + " lambda x: f\"{x['count']:,} ({(x['percentage']/100):.1%})\", axis=1\n", + ")\n", + "\n", + "print(\n", + " total_counts.pivot(\n", + " index=\"label_name\", columns=\"data_code\", values=\"value\"\n", + " ).to_latex()\n", + ")" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -945,7 +1114,7 @@ }, { "cell_type": "code", - "execution_count": 212, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -1039,6 +1208,712 @@ " cmap=\"Blues\",\n", ")" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Video-level IOU calculation" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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video_pathfall_interval
0data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4(128.0, 131.0)
1data/Fall_Simulation_Data/videos/Fall13_Cam3.mp4(24.0, 27.0)
2data/Fall_Simulation_Data/videos/Fall1_Cam1.mp4(47.0, 50.0)
3data/Fall_Simulation_Data/videos/Fall45_Cam2.mp4(20.0, 22.0)
4data/Fall_Simulation_Data/videos/ADL10_Cam3.mp4(nan, nan)
.........
67data/Fall_Simulation_Data/videos/ADL5_Cam2.mp4(nan, nan)
68data/Fall_Simulation_Data/videos/Fall47_Cam4.mp4(25.0, 28.0)
69data/Fall_Simulation_Data/videos/Fall38_Cam4.mp4(24.0, 26.0)
70data/Fall_Simulation_Data/videos/ADL16_Cam1.mp4(nan, nan)
71data/Fall_Simulation_Data/videos/Fall48_Cam3.mp4(32.0, 34.0)
\n", + "

72 rows × 2 columns

\n", + "
" + ], + "text/plain": [ + " video_path fall_interval\n", + "0 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 (128.0, 131.0)\n", + "1 data/Fall_Simulation_Data/videos/Fall13_Cam3.mp4 (24.0, 27.0)\n", + "2 data/Fall_Simulation_Data/videos/Fall1_Cam1.mp4 (47.0, 50.0)\n", + "3 data/Fall_Simulation_Data/videos/Fall45_Cam2.mp4 (20.0, 22.0)\n", + "4 data/Fall_Simulation_Data/videos/ADL10_Cam3.mp4 (nan, nan)\n", + ".. ... ...\n", + "67 data/Fall_Simulation_Data/videos/ADL5_Cam2.mp4 (nan, nan)\n", + "68 data/Fall_Simulation_Data/videos/Fall47_Cam4.mp4 (25.0, 28.0)\n", + "69 data/Fall_Simulation_Data/videos/Fall38_Cam4.mp4 (24.0, 26.0)\n", + "70 data/Fall_Simulation_Data/videos/ADL16_Cam1.mp4 (nan, nan)\n", + "71 data/Fall_Simulation_Data/videos/Fall48_Cam3.mp4 (32.0, 34.0)\n", + "\n", + "[72 rows x 2 columns]" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "annotations = pd.read_csv(\"data/Fall_Simulation_Data/annotations_test.csv\")\n", + "annotations[\"fall_interval\"] = annotations.apply(\n", + " lambda x: (x[\"fall_start\"], x[\"fall_end\"]), axis=1\n", + ")\n", + "\n", + "annotations = annotations[[\"video_path\", \"fall_interval\"]]\n", + "\n", + "annotations" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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filenamelabelintervalpredictionvideo_categoryvideo_label
0data/Fall_Simulation_Data/videos/Fall3_Cam2.mp42(0.0, 5.0)2Fall0
1data/Fall_Simulation_Data/videos/Fall3_Cam2.mp42(5.0, 10.0)2Fall0
2data/Fall_Simulation_Data/videos/Fall3_Cam2.mp42(10.0, 15.0)2Fall0
3data/Fall_Simulation_Data/videos/Fall3_Cam2.mp42(15.0, 20.0)2Fall0
4data/Fall_Simulation_Data/videos/Fall3_Cam2.mp42(20.0, 25.0)2Fall0
5data/Fall_Simulation_Data/videos/Fall3_Cam2.mp42(25.0, 30.0)2Fall0
6data/Fall_Simulation_Data/videos/Fall3_Cam2.mp42(30.0, 35.0)2Fall0
7data/Fall_Simulation_Data/videos/Fall3_Cam2.mp42(35.0, 40.0)2Fall0
8data/Fall_Simulation_Data/videos/Fall3_Cam2.mp42(40.0, 45.0)2Fall0
9data/Fall_Simulation_Data/videos/Fall3_Cam2.mp42(45.0, 50.0)2Fall0
10data/Fall_Simulation_Data/videos/Fall3_Cam2.mp42(50.0, 55.0)2Fall0
11data/Fall_Simulation_Data/videos/Fall3_Cam2.mp42(55.0, 60.0)2Fall0
12data/Fall_Simulation_Data/videos/Fall3_Cam2.mp42(60.0, 65.0)2Fall0
13data/Fall_Simulation_Data/videos/Fall3_Cam2.mp42(65.0, 70.0)2Fall0
14data/Fall_Simulation_Data/videos/Fall3_Cam2.mp42(70.0, 75.0)2Fall0
15data/Fall_Simulation_Data/videos/Fall3_Cam2.mp42(75.0, 80.0)2Fall0
16data/Fall_Simulation_Data/videos/Fall3_Cam2.mp42(80.0, 85.0)2Fall0
17data/Fall_Simulation_Data/videos/Fall3_Cam2.mp42(85.0, 90.0)2Fall0
18data/Fall_Simulation_Data/videos/Fall3_Cam2.mp42(90.0, 95.0)2Fall0
19data/Fall_Simulation_Data/videos/Fall3_Cam2.mp42(95.0, 100.0)2Fall0
20data/Fall_Simulation_Data/videos/Fall3_Cam2.mp42(100.0, 105.0)2Fall0
21data/Fall_Simulation_Data/videos/Fall3_Cam2.mp42(105.0, 110.0)2Fall0
22data/Fall_Simulation_Data/videos/Fall3_Cam2.mp42(110.0, 115.0)2Fall0
23data/Fall_Simulation_Data/videos/Fall3_Cam2.mp42(115.0, 120.0)2Fall0
24data/Fall_Simulation_Data/videos/Fall3_Cam2.mp42(120.0, 125.0)2Fall0
25data/Fall_Simulation_Data/videos/Fall3_Cam2.mp40(125.0, 130.0)0Fall0
26data/Fall_Simulation_Data/videos/Fall3_Cam2.mp40(130.0, 135.0)0Fall0
27data/Fall_Simulation_Data/videos/Fall3_Cam2.mp41(135.0, 140.0)1Fall0
28data/Fall_Simulation_Data/videos/Fall3_Cam2.mp41(140.0, 145.0)1Fall0
29data/Fall_Simulation_Data/videos/Fall3_Cam2.mp41(145.0, 150.0)1Fall0
30data/Fall_Simulation_Data/videos/Fall3_Cam2.mp41(150.0, 155.0)1Fall0
31data/Fall_Simulation_Data/videos/Fall3_Cam2.mp41(155.0, 160.0)1Fall0
32data/Fall_Simulation_Data/videos/Fall3_Cam2.mp41(160.0, 165.0)1Fall0
33data/Fall_Simulation_Data/videos/Fall3_Cam2.mp41(165.0, 170.0)1Fall0
34data/Fall_Simulation_Data/videos/Fall3_Cam2.mp41(170.0, 175.0)1Fall0
35data/Fall_Simulation_Data/videos/Fall3_Cam2.mp41(175.0, 180.0)1Fall0
36data/Fall_Simulation_Data/videos/Fall3_Cam2.mp41(180.0, 185.0)1Fall0
37data/Fall_Simulation_Data/videos/Fall3_Cam2.mp41(185.0, 190.0)1Fall0
38data/Fall_Simulation_Data/videos/Fall3_Cam2.mp41(190.0, 195.0)1Fall0
39data/Fall_Simulation_Data/videos/Fall3_Cam2.mp41(195.0, 200.0)1Fall0
40data/Fall_Simulation_Data/videos/Fall3_Cam2.mp41(200.0, 205.0)1Fall0
41data/Fall_Simulation_Data/videos/Fall3_Cam2.mp41(205.0, 210.0)1Fall0
42data/Fall_Simulation_Data/videos/Fall3_Cam2.mp41(210.0, 215.0)1Fall0
43data/Fall_Simulation_Data/videos/Fall3_Cam2.mp41(215.0, 220.0)1Fall0
44data/Fall_Simulation_Data/videos/Fall3_Cam2.mp41(220.0, 225.0)1Fall0
45data/Fall_Simulation_Data/videos/Fall3_Cam2.mp42(225.0, 230.0)2Fall0
46data/Fall_Simulation_Data/videos/Fall3_Cam2.mp42(230.0, 235.0)2Fall0
\n", + "
" + ], + "text/plain": [ + " filename label interval \\\n", + "0 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 2 (0.0, 5.0) \n", + "1 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 2 (5.0, 10.0) \n", + "2 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 2 (10.0, 15.0) \n", + "3 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 2 (15.0, 20.0) \n", + "4 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 2 (20.0, 25.0) \n", + "5 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 2 (25.0, 30.0) \n", + "6 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 2 (30.0, 35.0) \n", + "7 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 2 (35.0, 40.0) \n", + "8 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 2 (40.0, 45.0) \n", + "9 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 2 (45.0, 50.0) \n", + "10 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 2 (50.0, 55.0) \n", + "11 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 2 (55.0, 60.0) \n", + "12 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 2 (60.0, 65.0) \n", + "13 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 2 (65.0, 70.0) \n", + "14 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 2 (70.0, 75.0) \n", + "15 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 2 (75.0, 80.0) \n", + "16 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 2 (80.0, 85.0) \n", + "17 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 2 (85.0, 90.0) \n", + "18 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 2 (90.0, 95.0) \n", + "19 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 2 (95.0, 100.0) \n", + "20 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 2 (100.0, 105.0) \n", + "21 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 2 (105.0, 110.0) \n", + "22 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 2 (110.0, 115.0) \n", + "23 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 2 (115.0, 120.0) \n", + "24 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 2 (120.0, 125.0) \n", + "25 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 0 (125.0, 130.0) \n", + "26 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 0 (130.0, 135.0) \n", + "27 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 1 (135.0, 140.0) \n", + "28 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 1 (140.0, 145.0) \n", + "29 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 1 (145.0, 150.0) \n", + "30 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 1 (150.0, 155.0) \n", + "31 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 1 (155.0, 160.0) \n", + "32 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 1 (160.0, 165.0) \n", + "33 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 1 (165.0, 170.0) \n", + "34 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 1 (170.0, 175.0) \n", + "35 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 1 (175.0, 180.0) \n", + "36 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 1 (180.0, 185.0) \n", + "37 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 1 (185.0, 190.0) \n", + "38 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 1 (190.0, 195.0) \n", + "39 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 1 (195.0, 200.0) \n", + "40 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 1 (200.0, 205.0) \n", + "41 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 1 (205.0, 210.0) \n", + "42 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 1 (210.0, 215.0) \n", + "43 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 1 (215.0, 220.0) \n", + "44 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 1 (220.0, 225.0) \n", + "45 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 2 (225.0, 230.0) \n", + "46 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 2 (230.0, 235.0) \n", + "\n", + " prediction video_category video_label \n", + "0 2 Fall 0 \n", + "1 2 Fall 0 \n", + "2 2 Fall 0 \n", + "3 2 Fall 0 \n", + "4 2 Fall 0 \n", + "5 2 Fall 0 \n", + "6 2 Fall 0 \n", + "7 2 Fall 0 \n", + "8 2 Fall 0 \n", + "9 2 Fall 0 \n", + "10 2 Fall 0 \n", + "11 2 Fall 0 \n", + "12 2 Fall 0 \n", + "13 2 Fall 0 \n", + "14 2 Fall 0 \n", + "15 2 Fall 0 \n", + "16 2 Fall 0 \n", + "17 2 Fall 0 \n", + "18 2 Fall 0 \n", + "19 2 Fall 0 \n", + "20 2 Fall 0 \n", + "21 2 Fall 0 \n", + "22 2 Fall 0 \n", + "23 2 Fall 0 \n", + "24 2 Fall 0 \n", + "25 0 Fall 0 \n", + "26 0 Fall 0 \n", + "27 1 Fall 0 \n", + "28 1 Fall 0 \n", + "29 1 Fall 0 \n", + "30 1 Fall 0 \n", + "31 1 Fall 0 \n", + "32 1 Fall 0 \n", + "33 1 Fall 0 \n", + "34 1 Fall 0 \n", + "35 1 Fall 0 \n", + "36 1 Fall 0 \n", + "37 1 Fall 0 \n", + "38 1 Fall 0 \n", + "39 1 Fall 0 \n", + "40 1 Fall 0 \n", + "41 1 Fall 0 \n", + "42 1 Fall 0 \n", + "43 1 Fall 0 \n", + "44 1 Fall 0 \n", + "45 2 Fall 0 \n", + "46 2 Fall 0 " + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "group_test = datalist_test.groupby(\"filename\").get_group(\n", + " \"data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4\"\n", + ")\n", + "\n", + "group_test" + ] } ], "metadata": { From 72b0e3cb3c95656384d2206b4e9e9bc91366af5f Mon Sep 17 00:00:00 2001 From: Till Grutschus Date: Mon, 8 Jan 2024 18:48:02 +0100 Subject: [PATCH 08/13] diagrams --- .../HQFD_Sampling_Labeling_Pipeline.drawio | 284 +++++++++++++----- diagrams/desmos-graph.svg | 19 ++ diagrams/label_distributions.pdf | Bin 68043 -> 68039 bytes notebooks/dataset_label_analysis.ipynb | 55 ++-- notebooks/result_analysis.ipynb | 15 +- 5 files changed, 270 insertions(+), 103 deletions(-) create mode 100644 diagrams/desmos-graph.svg diff --git a/diagrams/HQFD_Sampling_Labeling_Pipeline.drawio b/diagrams/HQFD_Sampling_Labeling_Pipeline.drawio index 9df65c8..790498e 100644 --- a/diagrams/HQFD_Sampling_Labeling_Pipeline.drawio +++ b/diagrams/HQFD_Sampling_Labeling_Pipeline.drawio @@ -1,238 +1,378 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + + + + + + + + + + + + + + + + + + + + + + + + + + + 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\u001b[49m\u001b[43midx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpresentation_gifs_good/\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", + "Cell \u001b[0;32mIn[29], line 43\u001b[0m, in \u001b[0;36mgenerate_sample_videos\u001b[0;34m(ds, idx, out_path, render, out_type)\u001b[0m\n\u001b[1;32m 39\u001b[0m vis \u001b[38;5;241m=\u001b[39m ActionVisualizer()\n\u001b[1;32m 40\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m idx:\n\u001b[1;32m 41\u001b[0m \u001b[38;5;66;03m# video [B, C, T, H, W] -> [T, H, W, C]\u001b[39;00m\n\u001b[0;32m---> 43\u001b[0m video \u001b[38;5;241m=\u001b[39m \u001b[43mds\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minputs\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39msqueeze(\u001b[38;5;241m0\u001b[39m)\u001b[38;5;241m.\u001b[39mpermute(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m0\u001b[39m)\u001b[38;5;241m.\u001b[39mnumpy()\u001b[38;5;241m.\u001b[39mastype(np\u001b[38;5;241m.\u001b[39muint8)\n\u001b[1;32m 44\u001b[0m \u001b[38;5;66;03m# Convert to RGB\u001b[39;00m\n\u001b[1;32m 45\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m out_type \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgif\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n", + "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/mmengine/dataset/base_dataset.py:410\u001b[0m, in \u001b[0;36mBaseDataset.__getitem__\u001b[0;34m(self, idx)\u001b[0m\n\u001b[1;32m 407\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m data\n\u001b[1;32m 409\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m _ \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmax_refetch \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m):\n\u001b[0;32m--> 410\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprepare_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43midx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 411\u001b[0m \u001b[38;5;66;03m# Broken images or random augmentations may cause the returned data\u001b[39;00m\n\u001b[1;32m 412\u001b[0m \u001b[38;5;66;03m# to be None\u001b[39;00m\n\u001b[1;32m 413\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/mmengine/dataset/base_dataset.py:793\u001b[0m, in \u001b[0;36mBaseDataset.prepare_data\u001b[0;34m(self, idx)\u001b[0m\n\u001b[1;32m 784\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Get data processed by ``self.pipeline``.\u001b[39;00m\n\u001b[1;32m 785\u001b[0m \n\u001b[1;32m 786\u001b[0m \u001b[38;5;124;03mArgs:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 790\u001b[0m \u001b[38;5;124;03m Any: Depends on ``self.pipeline``.\u001b[39;00m\n\u001b[1;32m 791\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 792\u001b[0m data_info \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_data_info(idx)\n\u001b[0;32m--> 793\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpipeline\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_info\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/mmengine/dataset/base_dataset.py:60\u001b[0m, in \u001b[0;36mCompose.__call__\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 51\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Call function to apply transforms sequentially.\u001b[39;00m\n\u001b[1;32m 52\u001b[0m \n\u001b[1;32m 53\u001b[0m \u001b[38;5;124;03mArgs:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[38;5;124;03m dict: Transformed data.\u001b[39;00m\n\u001b[1;32m 58\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 59\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m t \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransforms:\n\u001b[0;32m---> 60\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[43mt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 61\u001b[0m \u001b[38;5;66;03m# The transform will return None when it failed to load images or\u001b[39;00m\n\u001b[1;32m 62\u001b[0m \u001b[38;5;66;03m# cannot find suitable augmentation parameters to augment the data.\u001b[39;00m\n\u001b[1;32m 63\u001b[0m \u001b[38;5;66;03m# Here we simply return None if the transform returns None and the\u001b[39;00m\n\u001b[1;32m 64\u001b[0m \u001b[38;5;66;03m# dataset will handle it by randomly selecting another data sample.\u001b[39;00m\n\u001b[1;32m 65\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/mmcv/transforms/base.py:12\u001b[0m, in \u001b[0;36mBaseTransform.__call__\u001b[0;34m(self, results)\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 10\u001b[0m results: Dict) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Optional[Union[Dict, Tuple[List, List]]]:\n\u001b[0;32m---> 12\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtransform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresults\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Library/CloudStorage/OneDrive-Personal/Documents/Arbeit und Beruf/Uppsala/Project in Data Science/human-fall-detection/mmaction2/mmaction/datasets/transforms/loading.py:1155\u001b[0m, in \u001b[0;36mDecordInit.transform\u001b[0;34m(self, results)\u001b[0m\n\u001b[1;32m 1146\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mtransform\u001b[39m(\u001b[38;5;28mself\u001b[39m, results: Dict) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Dict:\n\u001b[1;32m 1147\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Perform the Decord initialization.\u001b[39;00m\n\u001b[1;32m 1148\u001b[0m \n\u001b[1;32m 1149\u001b[0m \u001b[38;5;124;03m Args:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1153\u001b[0m \u001b[38;5;124;03m dict: The result dict.\u001b[39;00m\n\u001b[1;32m 1154\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1155\u001b[0m container \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_video_reader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresults\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mfilename\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1156\u001b[0m results[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtotal_frames\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(container)\n\u001b[1;32m 1158\u001b[0m results[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvideo_reader\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m container\n", + "File \u001b[0;32m~/Library/CloudStorage/OneDrive-Personal/Documents/Arbeit und Beruf/Uppsala/Project in Data Science/human-fall-detection/mmaction2/mmaction/datasets/transforms/loading.py:1142\u001b[0m, in \u001b[0;36mDecordInit._get_video_reader\u001b[0;34m(self, filename)\u001b[0m\n\u001b[1;32m 1140\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfile_client \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1141\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfile_client \u001b[38;5;241m=\u001b[39m FileClient(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mio_backend, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkwargs)\n\u001b[0;32m-> 1142\u001b[0m file_obj \u001b[38;5;241m=\u001b[39m io\u001b[38;5;241m.\u001b[39mBytesIO(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfile_client\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 1143\u001b[0m container \u001b[38;5;241m=\u001b[39m decord\u001b[38;5;241m.\u001b[39mVideoReader(file_obj, num_threads\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_threads)\n\u001b[1;32m 1144\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m container\n", + "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/mmengine/fileio/file_client.py:301\u001b[0m, in \u001b[0;36mFileClient.get\u001b[0;34m(self, filepath)\u001b[0m\n\u001b[1;32m 285\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget\u001b[39m(\u001b[38;5;28mself\u001b[39m, filepath: Union[\u001b[38;5;28mstr\u001b[39m, Path]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Union[\u001b[38;5;28mbytes\u001b[39m, \u001b[38;5;28mmemoryview\u001b[39m]:\n\u001b[1;32m 286\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Read data from a given ``filepath`` with 'rb' mode.\u001b[39;00m\n\u001b[1;32m 287\u001b[0m \n\u001b[1;32m 288\u001b[0m \u001b[38;5;124;03m Note:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 299\u001b[0m \u001b[38;5;124;03m bytes object.\u001b[39;00m\n\u001b[1;32m 300\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 301\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclient\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/mmengine/fileio/backends/local_backend.py:33\u001b[0m, in \u001b[0;36mLocalBackend.get\u001b[0;34m(self, filepath)\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget\u001b[39m(\u001b[38;5;28mself\u001b[39m, filepath: Union[\u001b[38;5;28mstr\u001b[39m, Path]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mbytes\u001b[39m:\n\u001b[1;32m 19\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Read bytes from a given ``filepath`` with 'rb' mode.\u001b[39;00m\n\u001b[1;32m 20\u001b[0m \n\u001b[1;32m 21\u001b[0m \u001b[38;5;124;03m Args:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[38;5;124;03m b'hello world'\u001b[39;00m\n\u001b[1;32m 32\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m---> 33\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mfilepath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mrb\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m 34\u001b[0m value \u001b[38;5;241m=\u001b[39m f\u001b[38;5;241m.\u001b[39mread()\n\u001b[1;32m 35\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m value\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'data/Fall_Simulation_Data/videos/Fall55_Cam4.mp4'" + ] } ], "source": [ - "idx = get_random_sample_from_class(train_hqfd, 0, 20)\n", - "generate_sample_videos(train_hqfd, idx)" + "idx = get_random_sample_from_class(train_hqfd, 0, 16)\n", + "generate_sample_videos(train_hqfd, idx, out_path=\"presentation_gifs_good/\")" ] }, { diff --git a/notebooks/result_analysis.ipynb b/notebooks/result_analysis.ipynb index b5fe80e..501fd2c 100644 --- a/notebooks/result_analysis.ipynb +++ b/notebooks/result_analysis.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -817,7 +817,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -915,7 +915,7 @@ "4 A Lying Fall " ] }, - "execution_count": 11, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -979,12 +979,12 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1013,9 +1013,10 @@ " y=\"count\",\n", " hue=\"data_code\",\n", " legend=True,\n", - " palette=\"tab10\",\n", + " palette=[\"#F5C342\", \"#DE8344\", \"#7EAB55\"],\n", ")\n", "\n", + "\n", "for i, container in enumerate(g.containers):\n", " g.bar_label(container)\n", "\n", @@ -1023,7 +1024,7 @@ "plt.xlabel(\"Label\")\n", "plt.ylabel(\"Count\")\n", "plt.title(\"Label Distributions\")\n", - "g.get_legend().set_title(\"Data Code\")\n", + "g.get_legend().set_title(\"Experiment\")\n", "plt.savefig(\n", " \"diagrams/label_distributions.pdf\", format=\"pdf\", bbox_inches=\"tight\", pad_inches=0\n", ")\n", From de8b4edeaa84b0d6ae14e8f3c7eed4c1c12a8a49 Mon Sep 17 00:00:00 2001 From: Till Grutschus Date: Mon, 8 Jan 2024 20:28:59 +0100 Subject: [PATCH 09/13] added tIOU and AP calculation --- notebooks/result_analysis.ipynb | 1233 +++++++++++++++++-------------- 1 file changed, 669 insertions(+), 564 deletions(-) diff --git a/notebooks/result_analysis.ipynb b/notebooks/result_analysis.ipynb index 501fd2c..b79f094 100644 --- a/notebooks/result_analysis.ipynb +++ b/notebooks/result_analysis.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -36,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -48,7 +48,7 @@ " tensor([0.0303, 0.0563, 0.9135])])" ] }, - "execution_count": 2, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -76,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -117,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -159,7 +159,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -251,7 +251,7 @@ "4 1 " ] }, - "execution_count": 5, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -331,7 +331,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -381,7 +381,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -397,7 +397,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -659,7 +659,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -693,7 +693,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -799,7 +799,7 @@ "[1272 rows x 2 columns]" ] }, - "execution_count": 10, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -817,7 +817,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -915,7 +915,7 @@ "4 A Lying Fall " ] }, - "execution_count": 3, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -979,12 +979,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { - "image/png": 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26hoEAMAdIQhhLlEUyRijYrG46qYASCFjjFqtlnzfV6PRkDEm2ef7vqIoUhRFE88NgkCtVktBECgIgonHeJ439XxsJobGcC3f92VZlowx6nQ6+vrrr+U4zqqbBSCFfN9XpVJJbpfLZTWbTUma+AWtXq+rUqkoCAK12201m00ZY5TP59Xv98eODcNQjUZD1Wp1uU8Ca4UghGuNFkbbti3P89RsNmXb9gpbBSCNjo+Px4JQLIoitdvtsb9XjUYjObZcLqvX60m6+DvW6XSu3Icxhr9rKcTQGObiuq4sy1K5XF51UwCkUCaTUS6XkzFGQRAon88n+0ZDkO/7yW1jjAaDgSzLUhiGiqLoSuAZPR7pQhDC3Pb393V6errqZgBIofhCjWw2O9YDNHpRRxRFGgwGSdgJw1CZTEa+78u27aTGaPT40fPv0qyaJml63VIYhgrDMLmP+Pf4tud5arVa1DTdAYbGMLdOp6Nnz56tuhkAUigIAtXrdRljkp7puEYo5nme6vV6cnswGMgYk/Rol0ol7e7uajgcSpJOTk5UKpWW0t5ZNU2z6pbiedyki5740St18/m8er1e0sMVlytgMQShFZpngsP7Fk+oKF38URmdUNG2bf7RAbh3xhh1u90k5Liuq1wuJ8/zkt6fKIoUBMFYD49t27IsK9kW/zcMQw0Gg6V+sZtW0yTNrlvK5XJ6+/btWHslJb1G8TbHcdRqtfibfAsEIUzkOI6azSb/uACsjTAMtb+/n9y2bVvVanVsaOj09PTKMNd1BdAnJyfJ78YY1Wo1HR4e3snVsXFNU7vdTnp94scZrVuybftKOycN100bBgvDkKt5F0QQAgA8CPEXtNGi5jdv3owFgLgeaJRt29rb20tqgeKrwyYFh3K5rHK5fGdXj7XbbR0cHCibzapUKiVfLkfrllzXVavVkm3byXOLoiipY+p2u0mbHMcZqzOKa4cGg8GdtDeNCEIAgAfBtm3l83k1Go2kt2TSFayTQky73Zbnecrlcur1elcun4+iKKnJqdfrKpfLd9LDMq2m6bq6pVKplDzH+Hn3+33Ztp0sf/Ts2bMkFF0Of7i5rWH8quOK8/Nz7ezs6OzsTNvb22P7/vjHP+rly5d6+vSpfvSjH62ohQ8fryOATWWMUbPZTGqajDFJEItn6I/rgCRpa2tLvV5PjuOMDXXFazzGQSi+r3gagN3dXb19+/baK98+/4+/X84TXZLPP/vVwufO+vy+jB4hAACWYFZN06yhtzAMdXBwMBaSpB96fUYnfowD07Iu/08D5hECAGAJHMdRt9sd2xbXNI3WLUkaq1uKh79iQRCoUCgkYSeXyyXnjfY4YTH0CAEAVu7Vi1+suglz+eTT/7z2mOtqmqbVLVmWpb29veS8fr8/No9QvV5XEAQaDAYqFotyXfdun1zKEIQAAFgS13WnBhXLsqZOUeI4ztRi7WVN/phWDI0BAIDUokdohe67gn/RCvxWq6V+v69sNqtMJpPMw+G6Lis1A0il09rfrboJ8/v4g1W3YC0RhDBTPp9XsVgcK8YLw3BsTRwAAB4qhsYwVaPRkHR1PNpxHMaoAQAbgR4hTFWr1fT8+fOJ+4rF4j23BgCAu0cQwkSjs5ZOwuWaAIBNwNAYAABILYIQJhpdz2aSuMcIAICHjCCEqSqVytTJvsIwZG0bAMCDRxDCVPEl861Wa2w7PUEAgE1BsTRm6nQ6ajQa8jwvmVBRkgqFwopbBgDA7RGEVmjRmZ7vW6VSWXUTAABYCobGAABAahGEAABAahGEAABAahGEAABAahGEbmk4HK66CQ8arx8AYJUIQgt6//33JUnffffdilvysMWvX/x6AgBwn7h8fkHvvfeeLMvSN998I0n68MMPtbW1teJWPRzD4VDfffedvvnmG1mWpffee2/VTQIApNDaBSFjjIIgUCaTkTFGhUJhbN0r3/dl27aMMSqVSskyD4vuu42f/OQnkpSEIczPsqzkdQQA4L6tXRDyfX9sAr9yuZysd1UsFtXr9SRdhJujoyO12+1b7buNra0t/fSnP9VHH32k77///tb3lzbvv/8+PUEAgJVauyB0fHw8cSbjy6ug27atIAhute+uvPfee3ygAwDwAK1dsXQmk1Eul0uGyPL5vCQlw2WXjw3DcOF9AAAg3dYuCMVDVtlsVu12O1ncc9qK54PBYOF9l717907n5+djPwAAYHOt3dBYEASq1+syxqhcLktSUiM0ybSgs8i+Wq2mL7744qZNBQAAD9xa9QgZY9TtduW6rkqlkvr9vk5OTmSMkWVZV3pxBoOBLMtaeN9l1WpVZ2dnyc/r16/v/DkCAID1sVZBKAxD7e/vJ7dt21a1WlUURXJdd+I5e3t7C++77NGjR9re3h77AQAAm2utgpDjOOp2u2Pb3rx5I8dxkrmEYsYY7e3tybKshfcBAIB0W6saIdu2lc/n1Wg0kqAS1wlJF4XUnudpf39f3W53bC6gRfcBAID02hqy6uVU5+fn2tnZ0dnZGcNkALBEr178YtVNmMsfeg/vo/M3H3+w6ibM5fPPfrXwufN8fq/V0BgAAMB9IggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDUIggBAIDU+stVN2CSIAhkjJFt25Ik13UlScYY+b4v27ZljFGpVJJlWbfaBwAA0mvtglAQBGq322o2mzLGKJ/Pq9/vS5KKxaJ6vZ6ki3BzdHSkdrt9q30AACC91i4IlcvlJLTYtq1OpyPpIsCMsm1bQRDcah8AAEi3taoRMsZoMBjIsiyFYagoipLhsSAIlMlkxo7PZDIKw3DhfQAAIN3WKgiFYahMJpPU87RaLfm+L0mKomjiOYPBYOF9l717907n5+djPwAAYHOt1dDYYDCQMUau68qyLJVKJe3u7mo4HE49Z1rQWWRfrVbTF198MUeLAQDAQ7ZWPUK2bcuyrOSKrvi/YRjKsqwrvTjxMNqi+y6rVqs6OztLfl6/fn1nzw0AAKyftQtC08SX0F+2t7e38L7LHj16pO3t7bEfAACwudZqaMy2be3t7SmKIlmWlcwl5DjOlWONMdrb2xvrQZp3HwAASLeFg9CrV6/0ySefSJLOzs4UBIFyuVyybVHtdlue5ymXy6nX6yWXz4/u29/fV7fbHZsLaNF9AAAgvbaGsyqRZ/jyyy/1j//4j9due8jOz8+1s7Ojs7MzhskAYIlevfjFqpswlz/0FvroXKnffPzBqpswl88/+9XC587z+T1Xj9DZ2ZlOTk60tbU11lMT6/V6GxWEAADAZpsrCO3s7Mh1XdXrdfX7fT19+nRsf6VSudPGAQAALNPcNUJPnz7Vv//7v+vrr7/WwcHB2L5Xr17dVbsAAACWbuFi6YODA/3ud78bm6On2Wzq+Pj4ThoGAACwbAsHoWfPniWXucf+53/+5y7aBAAAcC8WDkL5fF5HR0dj27766qtbNwgAAOC+LDyzdDabvdE2AACAdbVwj1C/31ez2dT+/r4kaTgc6uTkRN1u984aBwAAsEwL9wg1m009ffpUw+EwWR1+wbkZAQAAVmLhHqF6vX7l8vlpC5wCAACso4V7hC6HIEna3d29VWMAAADu08I9Qv/93/89djuKIjWbTf32t7+9daMAAADuw8JBqFQqKZfLJXVBQRAon8/fWcMAAACW7VY1Qn/7t387tu3rr7++dYMAAADuy8I1QpdDkCRtbW3dqjEAAAD3aeEeoX/9138du/3mzRtFUaSf//znt24UAADAfVi4R+i//uu/kjmEhsOhbNvWv/zLv9xl2wAAAJbqTucRAgAAeEgWDkIHBwc6Pz/XycmJpIvV6Le3t++sYQAAAMu28NDYy5cv9fOf/1wvXrzQixcvlMvl9Lvf/e4OmwYAALBcC/cIffXVVzo9PR3bVq1W9dd//de3bRMAAMC9WLhH6OnTp1e27e3t3aoxAAAA92nhIGSMubLt5cuXt2oMAADAfVp4aMx1XX366afK5XKSLpbYqNfrd9YwAACAZVu4R+hnP/uZms1mMo9Qq9ViMkUAAPCg3LhH6NWrV2O3P/nkEz19+lTValU7Ozt33S4AAIClu3GPUKfTkW3bqtfrY/VBg8FAX331lX79618vpYEAAADLcuMeIdu21ev19LOf/Wxs+9OnT/X06VOdnZ3p17/+tf7mb/7mzhsJAACwDDfuETo7O7sSgkbt7OxoOBzeSaMAAADuw42D0GAwuPaYt2/f3qoxAAAA9+nGQajf79/JMQAAAOvixkHItm19+eWXU/d/+eWXymQyd9IoAACA+3DjYumjoyN9+umnarfb+qd/+qdkiQ1jjJrNpiTpt7/97XJaCQAAsARzTaj44sULHRwc6B/+4R+Uy+XkOI4KhYIcxyEEAQCAB2fuJTYqlYoqlUqyrtikxVcBAAAegoXXGiMAAQCAh27htcYAAAAeOoIQAABILYIQAABILYIQAABILYIQAABILYIQAABILYIQAABILYIQAABILYIQAABILYIQAABILYIQAABILYIQAABILYIQAABILYIQAABILYIQAABILYIQAABILYIQAABILYIQAABILYIQAABILYIQAABILYIQAABILYIQAABIrb9cdQNm8TxP1WpVlmVJkowx8n1ftm3LGKNSqXTrfQAAIL3WNgiFYahGo6FqtZpsKxaL6vV6ki7CzdHRkdrt9q32AQCA9FrboTFjjGzbHrs9yrZtBUFwq30AACDd1jII+b6vQqEwti0IAmUymbFtmUxGYRguvA8AAKTb2g2NRVE0sX4niqKJxw8Gg4X3Xfbu3Tu9e/cuuX1+fn5dcwEAwAO2dj1CJycncl33xsdPCzqL7KvVatrZ2Ul+njx5cuN2AACAh2etglAQBHr27NnEfZZlXenFGQwGsixr4X2XVatVnZ2dJT+vX7++3RMCAABrba2CkHTRI9RqtdRqtWSMUa1WUxiGU3uJ9vb2Ft532aNHj7S9vT32AwAANtda1QhdDi3lclnlcnns6rGYMUZ7e3tJr88i+wAAQLqtVRCKRVGkVqslSarX6yqXy3IcR+12W57naX9/X91ud2wuoEX3AQCA9NoaDofDVTdiXZ2fn2tnZ0dnZ2cMkwHAEr168YtVN2Euf+g9vI/O33z8waqbMJfPP/vVwufO8/m9djVCAAAA94UgBAAAUosgBAAAUosgBAAAUosgBAAAUosgBAAAUosgBAAAUosgBAAAUosgBAAAUosgBAAAUosgBAAAUosgBAAAUosgBAAAUosgBAAAUosgBAAAUosgBAAAUosgBAAAUosgBAAAUosgBAAAUosgBAAAUosgBAAAUosgBAAAUosgBAAAUosgBAAAUosgBAAAUosgBAAAUosgBAAAUosgBAAAUosgBAAAUosgBAAAUosgBAAAUosgBAAAUosgBAAAUosgBAAAUosgBAAAUosgBAAAUosgBAAAUosgBAAAUosgBAAAUosgBAAAUosgBAAAUosgBAAAUosgBAAAUosgBAAAUosgBAAAUosgBAAAUusvV90AAEijMAwVBIEkqdvt6vnz57Isa2z/0dGRer3ejc8zxqjZbCqbzarf76tarY7dJ4CrCEIAsAJBEKhSqUiSGo2GDg4OktDj+75s21YYhnOdl8/n1ev1ZFmWwjCU53lqNpv39IyAh2lrOBwOV92IdXV+fq6dnR2dnZ1pe3t71c0BsCHCMNTBwYHevn0r6aInJ+7FsW07OW5ra0ujf6JnnWeMUblcVr/fn3r+Onv14herbsJc/tB7GK/rqN98/MGqmzCXzz/71cLnzvP5TY0QANwzx3H0/Pnz5HYURZKkTCaz8Hnx75dN6lUC8AOCEACsQKFQSH4/Pj6W67o3queZdp7jODLGJPviADQYDO6u0cAGIggBwApFUSTf99Vut291nm3bqtfrarVaiqIoCUXX9TIBaUexNACskOd56nQ6c1/dNem8SqUiY4yMMXJdV5LGao4AXEUQAoAVaTQa8jxPtm0nNT43CUTTzjPGJMEnDEM5jsPl88A1GBoDgBXwfV+O4yRh5uTkZGJouVwEPeu8XC6XHN9sNlWv15f7JIANQI8QANwzY4yKxeLYNsuyVCqVJF3MFdTpdCRJtVpN+/v7KhQK155Xr9cVBIEGg4GKxWIyPAZgOuYRmoF5hADgfjCP0PIxj9BkDI0BAIDUWruhsevW0YmnnjfGqFQq3XofAABIr7ULQrPW0SkWi8nvxhgdHR0lc2gsug/AZNMW/fR9P6k9mfSFIgiCsauX4mPTtCDo5//x96tuwlxuMwQBPHRrNTQWhqFqtVpyu1AoKAzDZF6MUbZtJz1Hi+4DMJnv+5ImL89QLBa1u7ur3d1dbW1taWtrS41GQ9JFCGq32yqVSrJtW+VyOTkvn8+rWq2qVCrp8PBQnufdz5MBgBnWKgjNWkcnCIIrM6RmMplkKG2RfQAmKxQKchznyvYoitRutzUcDpOfer2e9OKWy+Xkkm3btpMrn+IvH3EPkOM4arVa9/BMAGC2tQpC0vR1dKYtKDgYDBbed9m7d+90fn4+9gNg3Oi/Ud/3k9vGGA0GA1mWpTAMFUVRMjzGgqAA1tXaBaHYTdffmfYHdpF9tVpNOzs7yc+TJ09u2FqsuzAMlcvlJu4LgkCtVktBEEwdNvU8b+w94/u+oiia+R7bRKM1PVEUaTAYjM1knMlkkgsTWq1WMsTGgqAA1tXaBqHL6+hYlnXlj2b87XPRfZdVq1WdnZ0lP69fv77T54TVmFXvMqumJRaGYVIDE5tVJ5MWnufp2bNnye3BYJCscRVP8hdP/seCoADW1VoGocvr6ERRNHWG1L29vYX3Xfbo0SNtb2+P/eDhm1bvIk2vaRk1egWUdH2dTBpEUaQgCMa+UNi2nXz5kH7oPYoDaKVSkeu6LAgKYK2sXRCato7O5T+Yxhjt7e3dah/SbVZNS2y0BmbUtDqZtDg9Pb3yb+i6UBMHyniYjAVBAayDtZpH6Lp1dNrttjzP0/7+vrrd7lj90KL7kF6jNS2u66rVasm27STURFE08YN6Vp3MJpr0OsSv3SjbtrW3t5ccPxp8pIsFQV++fCnLslgQFMDaWKsgZNu2Zi19FtcZSLryDXzRfUivSTUtu7u7yXvw5OQkCeHTeJ63kR/o0xb9HDUp/MVfOnK5nHq93thQIwuCAlhHaxWEgPs0q6ZlMBiMFQJPMqlOZlO4rivXdaeGvGn1UHFvzyTXhUoAWAWCEFLruuGsk5OT5HdjjGq1mg4PD5Ohnkl1MgCAh4UghFQZrXe5rqZlVLlcVrlcHgtPk+pkHopXL36x6ibM5ZNP/3PVTQCwoQhC2Hiz6l1m1bRIF8EpXgqiXq+rXC6PBaVNLpIGgDTYGs6qTk658/Nz7ezs6OzsjDmFsFHoEVouVp+f30N7T/6h9/A+On/z8QerbsJcbvO+nOfze+3mEQIAALgvDI3hwePbNwBgUfQIAQCA1CIIAQCA1CIIYSFhGCqXy03c3mg01Gg0VCwWFUXRjc4zxsjzPLVaLXmed+U8AACWgRohzM33fdm2nawqPioIgmTW4UajoYODA/V6vWvPy+fz6vV6yQKonudNnaEYAIC7Qo8Q5lYoFCZOOhiGoWq12thxYRjKGDPzvCAIJP2wxIXjOMncPQAALBNBCHfGcRw9f/48uR0Pb103+/K0YbBJPUcAANwlghDu1OgK5cfHx8nK7rM4jpP0Gkk/BKDBYLCUNgIAECMIYSmiKJLv+2q329cea9u26vW6Wq2WoihKQtFDXccLAPBwUCyNpfA8T51O58ars1cqFRljZIyR67qSWMcLALB8BCHcuUajIc/zZNt2Uv9zXSCKV36XLobGHMe5cYgCAGBRDI3hVi4XOvu+L8dxkhB0cnIyMdBcPi+XyyXbms2m6vX6choMAMAIeoQwtyAI1Ol0JEm1Wk37+/sqFAoyxqhYLI4da1mWSqXSzPMkqV6vKwgCDQYDFYvFZHgMAIBlIghhbq7rynXdK702tm1rOBzOfZ6kJCwBAHCfCEIANpIxRkEQKJPJyBijQqGQ1KHN2idd9F6O1q3RQwlsLoIQrjit/d2qmzCfjz9YdQuwhnzfT5Z7kaRyuZws2zJrXxAEarfbajabMsYon8+r3+/fb+MB3BuKpQFspOPj44X2lcvlZPjWtu2krg3AZiIIAdhImUxGuVwuGQbL5/PX7jPGaDAYJIv/RlHEfFbAhiMIAdhI8azm2WxW7XZ7bPmXafvCMFQmk5Hv+7JtW61WS77v33/jAdwbaoQAbKQgCFSv12WMUblclqSxOqBJ+waDQTK7eTz1w+7u7syrIQE8bPQIAdg4xhh1u125rqtSqaR+v6+Tk5NkGZdp+2zblmVZySSg8X/jhYABbB56hABsnDAMtb+/n9y2bVvVajVZ1HfaPuqBgPShRwjAxnEcR91ud2zbmzdv5DjOzH22bWtvby9Z7iXuJXIc576aDuCe0SMEYOPYtq18Pq9Go5EMb8W1QLP2SReF1J7nKZfLqdfrcfk8sOEIQgA2Uryky7z7LMtKiqoBbD6GxgAAQGoRhAAAQGoxNAZg7bH+HYBloUcIAACkFkEIAACkFkEIAACkFkEIAACkFkEIAACkFkEIAACkFkEIAACkFkEIAACkFkEIAACkFjNLr6EwDBUEgSSp2+3q+fPnySrZ8fYoitTtdnV4eCjHccbOPTo6Uq/Xu/d2AwDw0NAjtIaCIFClUlGlUtH+/r4ODg6SfcViUZlMRoVCQdlsVsViMdnn+76kizAEAACuRxBaM2EYqlarJbcLhYLCMJQxRpLUbrfHeoDinqL42NF9AABgNoLQmnEcR8+fP09uR1EkScpkMpIk13WTfe12W+Vy+V7bBwDAJqFGaA0VCoXk9+PjY7muO9bzE4ahjo+Plc/nVSqVVtBCAAA2Az1CayyKIvm+r3a7PbbdcRxVq1X1+/2kLggAAMyPILTGPM9Tp9MZ6w2KWZalYrGoYrGYDJ8BAID5EITWVKPRkOd5sm1bURQpiiIFQaDd3d3kGNu2JSkppAYAAPMhCK0h3/flOE4Sgk5OTmRZljKZzFixdBiGsixr4pVi9BIBAHA9iqXXjDFmbG4g6WIYrFQqyXEcHR4eqtVqSZI6nc7YxIlBEKjT6UiSarWa9vf3xwqvAQDAOILQmrFtW8PhcOr+0WBz+Yox13Xluq7q9frS2gcAwCZhaAwAAKQWPUL34NWLX6y6CQAAYAJ6hAAAQGoRhAAAQGqlYmjMGCPf92XbtowxKpVKEycpBAAA6ZKKIFQsFpPLzI0xOjo6urJsBQAASJ+NHxq7POuybdsKgmBFrQEAAOtk44NQEATKZDJj2zKZjMIwXFGLAADAutj4obFpS00MBoMr2969e6d3794lt8/OziRJ5+fnt2rDt///97c6/779f3+cPqHjOnr3f95bdRPmctv3013gPblcvCfnx3ty+dL0vozPnTVBcWzjg9A0kwJSrVbTF198cWX7kydP7qFFSIt/+X/+31U3ARjDexLr6C7el99++612dnZmHrPxQciyrCu9P4PBYOJVY9VqVb/85S+T23/+8581GAz0+PFjbW1tLbupG+38/FxPnjzR69evtb29vermALwnsZZ4X96N4XCob7/9Vh9//PG1x258EHJdV81m88r2vb29K9sePXqkR48ejW3jMvu7tb29zT9urBXek1hHvC9v77qeoNjGF0vbtj122xijvb09Ag4AANj8HiFJarfb8jxP+/v76na7zCEEAAAkpSQI2bater0uSSoUCituTTo9evRI//zP/3xl6BFYFd6TWEe8L+/f1vAm15YBAABsoI2vEQIAAJiGIAQAAFKLIIRbCcNQ5XJZW1tb8jxPrVZLjUYj2TZtZm/pYvmTXC6nVqs18TYwy+h7r1wuy/f9ue8jl8stdB6AzUGNEG7NGKNsNqu3b9+OTUvQarW0t7cnx3GmnttoNGRZlkql0sTbwCzT3ns3FQQB02lgKeK/ZZlMRsYY2bZ97cU6QRDI8zyVy2X+Bt6jVFw1huW6vKht7NmzZzo9Pb3n1iBNpr33bsp13TtqCfCDXC6n58+fj30J9DxP3W43uYI5FkVREsRd1+U9uQIMjeHOhWGY/OO+PKElAGwyz/Nk2/aVnvB6va5Wq6UwDJNtxhidnJyMHff48eN7aSd+QBDCnTs+Pk5+t21bvu8rCAK1Wi15nrfCliFtfN9XNptVsViUdPHtO5vNyvM8hWE4tUYtfr/G543eX6PRUKvVUrlcVhAEajQa9/68sL4ajYby+fzEfa7rqlarJbcv9w5hNRgaw50Z/UCpVqvJ9mKxqH6/L9d1k6JWJrbEfSgUChoMBur1epIu1g70PC+pvzg8PEyOjYclOp1OMvt8u91WGIZyHEdRFOno6Ehv376VpCRQMZSBmDFG0uS1LKUfvhhKF38nT09Pk0XBXddNetCjKFIQBDLGjL0f4/PCMJRt28lQWxAEKpfLyRfNZrOZvOdxPYIQ7kypVJJlWVe6hONCVmOMBoNB8scCWJbRsF0qlbS7u6tms5msNTjN48ePx4YmLMtKPqiAm7rJeyYO3tls9kphdLfbVaVSkTQexo0x8jwvCTmDwUCNRkOVSkWu66rX66nZbN66di5tCEK4c5e/IddqNT1+/FiFQoGaIdyLy2G7VCqp1Wopk8ks3BsZX80YXw1ULpd5P2NM/H6Y9mUvDjTX2d/fT34fDeNxyAmCINnf7XaT4+IQT4/7fAhCuLVZ337ibtxOpyPposv38ePHCoIgCUyX5xqaNfcQMGrSey+KInU6neQbtSSVy2Xl8/mJNRnzvN8eP348dr/AZZVKRe12e+Ll76enp1MX/R69emwWx3HGvmyOPg7BfDEEIdxKGIZqNpuSLq6WyOfzY99G4jla4m8wxWJRzWZTtm0rDMOksDr+hz16+ybfnJBeo++9Wq2mbDarfr8v3/evfCDEV/GMvjfj91/cSxRF0dj7zxiTPIZt27JtW/1+X9lsNpkfplgsMt8LxtTrdeVyubEve9JFGH/27NnUmrIgCK7tyTk8PNTR0dGV8+L7ZBh3MUyoCCAVblukH/duxj1Ccb1GuVymYBpXeJ6nbDY7c0JFY0wSnFzXTQryJen58+fJe8xxHNXrddm2rSAI1Ol0kuEz13V1enoqz/OUyWQo4F8AQQjAxiqXyyoWi8pkMree18rzPB0eHo71VPq+r8FgQK8Q8IARhABsrCAIktqLu/iWHM8ZFAcqQhDw8BGEAABAajGzNAAASC2CEAAASC2CEAAASC2CEAAASC2CEIAHKQxDlctlbW1tJYv5ziMIAmWz2bnOG12hHsBm4KoxAA+WMUbZbDZZ2Hde8dIb80y0GK81xmXzwGagRwjAg3XbVbYXCU8ANgtBCAAApBaLrgLYaL7vy7IsGWPU7/evrEDf6XRkWZaiKFK321W1Wk0WCg7DULZtq9vtTly5HsDDR48QgI1WLBZl27ZKpZKiKLpSHJ3NZuW6rgqFgvL5vIrFYrLYZaVSUaFQUDabTZbXALBZ6BECsHFGV5qPC6mNMRoMBjLGjB07Wifkuq7y+bz+7d/+TZlMRkEQJPu63e69tB3A/SIIAdg4o2GnVqvp8ePHKhQKc60+7zjO2EKtXCUGbCaGxgA8WIPB4Mq2KIrU6XQkKanzqVQqsm1bURQl20ePj4VhKNd19dlnn40dM+scAA8bPUIAHqQwDNVsNiVd9Ppks1n1+335vp/0/Ozt7SWFz9JFvVCz2RzrGbJtW8YYRVGk09NTtdttWZaler0uz/O0v78v6WLYLAxDHR8fJ7cdx7nPpwxgCZhQEQAApBZDYwAAILUIQgAAILUIQgAAILUIQgAAILUIQgAAILUIQgAAILUIQgAAILUIQgAAILUIQgAAILUIQgAAILUIQgAAILUIQgAAILX+LwVe5+WGdet6AAAAAElFTkSuQmCC", + "image/png": 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", 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" ] @@ -1033,7 +1033,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -1045,51 +1045,13 @@ "data_code & A & B & C \\\\\n", "label_name & & & \\\\\n", "\\midrule\n", - "Fall & 231 (2.1%) & 1,221 (11.3%) & 1,590 (11.4%) \\\\\n", - "Lying & 1,769 (16.4%) & 835 (7.8%) & 2,085 (14.9%) \\\\\n", - "Other & 8,764 (81.4%) & 8,708 (80.9%) & 10,296 (73.7%) \\\\\n", + "Fall & 231 (2.1%) & 1,231 (11.4%) & 1,619 (11.6%) \\\\\n", + "Lying & 1,769 (16.4%) & 903 (8.4%) & 2,138 (15.3%) \\\\\n", + "Other & 8,764 (81.4%) & 8,630 (80.2%) & 10,214 (73.1%) \\\\\n", "\\bottomrule\n", "\\end{tabular}\n", "\n" ] - }, - { - "ename": "TypeError", - "evalue": "agg function failed [how->mean,dtype->object]", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/groupby/groupby.py:1874\u001b[0m, in \u001b[0;36mGroupBy._agg_py_fallback\u001b[0;34m(self, how, values, ndim, alt)\u001b[0m\n\u001b[1;32m 1873\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1874\u001b[0m res_values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgrouper\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43magg_series\u001b[49m\u001b[43m(\u001b[49m\u001b[43mser\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43malt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpreserve_dtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 1875\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", - "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/groupby/ops.py:849\u001b[0m, in \u001b[0;36mBaseGrouper.agg_series\u001b[0;34m(self, obj, func, preserve_dtype)\u001b[0m\n\u001b[1;32m 847\u001b[0m preserve_dtype \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m--> 849\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_aggregate_series_pure_python\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 851\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(obj) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(result) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(obj\u001b[38;5;241m.\u001b[39mdtype, ExtensionDtype):\n", - "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/groupby/ops.py:877\u001b[0m, in \u001b[0;36mBaseGrouper._aggregate_series_pure_python\u001b[0;34m(self, obj, func)\u001b[0m\n\u001b[1;32m 876\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, group \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(splitter):\n\u001b[0;32m--> 877\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgroup\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 878\u001b[0m res \u001b[38;5;241m=\u001b[39m extract_result(res)\n", - "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/groupby/groupby.py:2380\u001b[0m, in \u001b[0;36mGroupBy.mean..\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 2377\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 2378\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cython_agg_general(\n\u001b[1;32m 2379\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmean\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m-> 2380\u001b[0m alt\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mlambda\u001b[39;00m x: \u001b[43mSeries\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmean\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnumeric_only\u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m 2381\u001b[0m numeric_only\u001b[38;5;241m=\u001b[39mnumeric_only,\n\u001b[1;32m 2382\u001b[0m )\n\u001b[1;32m 2383\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgroupby\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/series.py:6225\u001b[0m, in \u001b[0;36mSeries.mean\u001b[0;34m(self, axis, skipna, numeric_only, **kwargs)\u001b[0m\n\u001b[1;32m 6217\u001b[0m \u001b[38;5;129m@doc\u001b[39m(make_doc(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmean\u001b[39m\u001b[38;5;124m\"\u001b[39m, ndim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m))\n\u001b[1;32m 6218\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmean\u001b[39m(\n\u001b[1;32m 6219\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 6223\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 6224\u001b[0m ):\n\u001b[0;32m-> 6225\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mNDFrame\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmean\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/generic.py:11992\u001b[0m, in \u001b[0;36mNDFrame.mean\u001b[0;34m(self, axis, skipna, numeric_only, **kwargs)\u001b[0m\n\u001b[1;32m 11985\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmean\u001b[39m(\n\u001b[1;32m 11986\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 11987\u001b[0m axis: Axis \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 11990\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 11991\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Series \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mfloat\u001b[39m:\n\u001b[0;32m> 11992\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_stat_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 11993\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmean\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnanops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnanmean\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m 11994\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/generic.py:11949\u001b[0m, in \u001b[0;36mNDFrame._stat_function\u001b[0;34m(self, name, func, axis, skipna, numeric_only, **kwargs)\u001b[0m\n\u001b[1;32m 11947\u001b[0m validate_bool_kwarg(skipna, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mskipna\u001b[39m\u001b[38;5;124m\"\u001b[39m, none_allowed\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m> 11949\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_reduce\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 11950\u001b[0m \u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnumeric_only\u001b[49m\n\u001b[1;32m 11951\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/series.py:6133\u001b[0m, in \u001b[0;36mSeries._reduce\u001b[0;34m(self, op, name, axis, skipna, numeric_only, filter_type, **kwds)\u001b[0m\n\u001b[1;32m 6129\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m 6130\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSeries.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m does not allow \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkwd_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnumeric_only\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 6131\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwith non-numeric dtypes.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 6132\u001b[0m )\n\u001b[0;32m-> 6133\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdelegate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/nanops.py:147\u001b[0m, in \u001b[0;36mbottleneck_switch.__call__..f\u001b[0;34m(values, axis, skipna, **kwds)\u001b[0m\n\u001b[1;32m 146\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 147\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43malt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", - "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/nanops.py:404\u001b[0m, in \u001b[0;36m_datetimelike_compat..new_func\u001b[0;34m(values, axis, skipna, mask, **kwargs)\u001b[0m\n\u001b[1;32m 402\u001b[0m mask \u001b[38;5;241m=\u001b[39m isna(values)\n\u001b[0;32m--> 404\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskipna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskipna\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 406\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m datetimelike:\n", - "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/nanops.py:720\u001b[0m, in \u001b[0;36mnanmean\u001b[0;34m(values, axis, skipna, mask)\u001b[0m\n\u001b[1;32m 719\u001b[0m the_sum \u001b[38;5;241m=\u001b[39m values\u001b[38;5;241m.\u001b[39msum(axis, dtype\u001b[38;5;241m=\u001b[39mdtype_sum)\n\u001b[0;32m--> 720\u001b[0m the_sum \u001b[38;5;241m=\u001b[39m \u001b[43m_ensure_numeric\u001b[49m\u001b[43m(\u001b[49m\u001b[43mthe_sum\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 722\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m axis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(the_sum, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mndim\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", - "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/nanops.py:1693\u001b[0m, in \u001b[0;36m_ensure_numeric\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 1691\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m 1692\u001b[0m \u001b[38;5;66;03m# GH#44008, GH#36703 avoid casting e.g. strings to numeric\u001b[39;00m\n\u001b[0;32m-> 1693\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCould not convert string \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m to numeric\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 1694\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n", - "\u001b[0;31mTypeError\u001b[0m: Could not convert string '231 (2.1%)' to numeric", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[34], line 9\u001b[0m\n\u001b[1;32m 3\u001b[0m total_counts[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m total_counts\u001b[38;5;241m.\u001b[39mapply(\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m x: \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcount\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m,\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m(x[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpercentage\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m100\u001b[39m)\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.1%\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m\n\u001b[1;32m 5\u001b[0m )\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28mprint\u001b[39m(total_counts\u001b[38;5;241m.\u001b[39mpivot(index\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlabel_name\u001b[39m\u001b[38;5;124m\"\u001b[39m, columns\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata_code\u001b[39m\u001b[38;5;124m\"\u001b[39m, values\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m\"\u001b[39m)\u001b[38;5;241m.\u001b[39mto_latex())\n\u001b[0;32m----> 9\u001b[0m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpivot_table\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 10\u001b[0m \u001b[43m \u001b[49m\u001b[43mtotal_counts\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 11\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalues\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mvalue\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 12\u001b[0m \u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlabel_name\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 13\u001b[0m \u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata_code\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 14\u001b[0m \u001b[43m \u001b[49m\u001b[43mmargins\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 15\u001b[0m \u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/reshape/pivot.py:99\u001b[0m, in \u001b[0;36mpivot_table\u001b[0;34m(data, values, index, columns, aggfunc, fill_value, margins, dropna, margins_name, observed, sort)\u001b[0m\n\u001b[1;32m 96\u001b[0m table \u001b[38;5;241m=\u001b[39m concat(pieces, keys\u001b[38;5;241m=\u001b[39mkeys, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m table\u001b[38;5;241m.\u001b[39m__finalize__(data, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpivot_table\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 99\u001b[0m table \u001b[38;5;241m=\u001b[39m \u001b[43m__internal_pivot_table\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 101\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 102\u001b[0m \u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 103\u001b[0m \u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 104\u001b[0m \u001b[43m \u001b[49m\u001b[43maggfunc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 105\u001b[0m \u001b[43m \u001b[49m\u001b[43mfill_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 106\u001b[0m \u001b[43m \u001b[49m\u001b[43mmargins\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 107\u001b[0m \u001b[43m \u001b[49m\u001b[43mdropna\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 108\u001b[0m \u001b[43m \u001b[49m\u001b[43mmargins_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 109\u001b[0m \u001b[43m \u001b[49m\u001b[43mobserved\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 110\u001b[0m \u001b[43m \u001b[49m\u001b[43msort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 111\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 112\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m table\u001b[38;5;241m.\u001b[39m__finalize__(data, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpivot_table\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/reshape/pivot.py:169\u001b[0m, in \u001b[0;36m__internal_pivot_table\u001b[0;34m(data, values, index, columns, aggfunc, fill_value, margins, dropna, margins_name, observed, sort)\u001b[0m\n\u001b[1;32m 166\u001b[0m values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(values)\n\u001b[1;32m 168\u001b[0m grouped \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mgroupby(keys, observed\u001b[38;5;241m=\u001b[39mobserved, sort\u001b[38;5;241m=\u001b[39msort, dropna\u001b[38;5;241m=\u001b[39mdropna)\n\u001b[0;32m--> 169\u001b[0m agged \u001b[38;5;241m=\u001b[39m \u001b[43mgrouped\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43magg\u001b[49m\u001b[43m(\u001b[49m\u001b[43maggfunc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 171\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dropna \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(agged, ABCDataFrame) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(agged\u001b[38;5;241m.\u001b[39mcolumns):\n\u001b[1;32m 172\u001b[0m agged \u001b[38;5;241m=\u001b[39m agged\u001b[38;5;241m.\u001b[39mdropna(how\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mall\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/groupby/generic.py:1445\u001b[0m, in \u001b[0;36mDataFrameGroupBy.aggregate\u001b[0;34m(self, func, engine, engine_kwargs, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1442\u001b[0m kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mengine_kwargs\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m engine_kwargs\n\u001b[1;32m 1444\u001b[0m op \u001b[38;5;241m=\u001b[39m GroupByApply(\u001b[38;5;28mself\u001b[39m, func, args\u001b[38;5;241m=\u001b[39margs, kwargs\u001b[38;5;241m=\u001b[39mkwargs)\n\u001b[0;32m-> 1445\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43magg\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1446\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_dict_like(func) \u001b[38;5;129;01mand\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1447\u001b[0m \u001b[38;5;66;03m# GH #52849\u001b[39;00m\n\u001b[1;32m 1448\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mas_index \u001b[38;5;129;01mand\u001b[39;00m is_list_like(func):\n", - "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/apply.py:172\u001b[0m, in \u001b[0;36mApply.agg\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 169\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkwargs\n\u001b[1;32m 171\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(func, \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m--> 172\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_str\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 174\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_dict_like(func):\n\u001b[1;32m 175\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39magg_dict_like()\n", - "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/apply.py:586\u001b[0m, in \u001b[0;36mApply.apply_str\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 584\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 585\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maxis\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis\n\u001b[0;32m--> 586\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_apply_str\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/apply.py:669\u001b[0m, in \u001b[0;36mApply._apply_str\u001b[0;34m(self, obj, func, *args, **kwargs)\u001b[0m\n\u001b[1;32m 667\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(obj, func)\n\u001b[1;32m 668\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcallable\u001b[39m(f):\n\u001b[0;32m--> 669\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 671\u001b[0m \u001b[38;5;66;03m# people may aggregate on a non-callable attribute\u001b[39;00m\n\u001b[1;32m 672\u001b[0m \u001b[38;5;66;03m# but don't let them think they can pass args to it\u001b[39;00m\n\u001b[1;32m 673\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m\n", - "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/groupby/groupby.py:2378\u001b[0m, in \u001b[0;36mGroupBy.mean\u001b[0;34m(self, numeric_only, engine, engine_kwargs)\u001b[0m\n\u001b[1;32m 2371\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_numba_agg_general(\n\u001b[1;32m 2372\u001b[0m grouped_mean,\n\u001b[1;32m 2373\u001b[0m executor\u001b[38;5;241m.\u001b[39mfloat_dtype_mapping,\n\u001b[1;32m 2374\u001b[0m engine_kwargs,\n\u001b[1;32m 2375\u001b[0m min_periods\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m,\n\u001b[1;32m 2376\u001b[0m )\n\u001b[1;32m 2377\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 2378\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cython_agg_general\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2379\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmean\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2380\u001b[0m \u001b[43m \u001b[49m\u001b[43malt\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mSeries\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmean\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnumeric_only\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2381\u001b[0m \u001b[43m \u001b[49m\u001b[43mnumeric_only\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnumeric_only\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2382\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2383\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgroupby\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/groupby/groupby.py:1929\u001b[0m, in \u001b[0;36mGroupBy._cython_agg_general\u001b[0;34m(self, how, alt, numeric_only, min_count, **kwargs)\u001b[0m\n\u001b[1;32m 1926\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_agg_py_fallback(how, values, ndim\u001b[38;5;241m=\u001b[39mdata\u001b[38;5;241m.\u001b[39mndim, alt\u001b[38;5;241m=\u001b[39malt)\n\u001b[1;32m 1927\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n\u001b[0;32m-> 1929\u001b[0m new_mgr \u001b[38;5;241m=\u001b[39m \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgrouped_reduce\u001b[49m\u001b[43m(\u001b[49m\u001b[43marray_func\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1930\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_wrap_agged_manager(new_mgr)\n\u001b[1;32m 1931\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_wrap_aggregated_output(res)\n", - "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/internals/managers.py:1428\u001b[0m, in \u001b[0;36mBlockManager.grouped_reduce\u001b[0;34m(self, func)\u001b[0m\n\u001b[1;32m 1424\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m blk\u001b[38;5;241m.\u001b[39mis_object:\n\u001b[1;32m 1425\u001b[0m \u001b[38;5;66;03m# split on object-dtype blocks bc some columns may raise\u001b[39;00m\n\u001b[1;32m 1426\u001b[0m \u001b[38;5;66;03m# while others do not.\u001b[39;00m\n\u001b[1;32m 1427\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sb \u001b[38;5;129;01min\u001b[39;00m blk\u001b[38;5;241m.\u001b[39m_split():\n\u001b[0;32m-> 1428\u001b[0m applied \u001b[38;5;241m=\u001b[39m \u001b[43msb\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1429\u001b[0m result_blocks \u001b[38;5;241m=\u001b[39m extend_blocks(applied, result_blocks)\n\u001b[1;32m 1430\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", - "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/internals/blocks.py:366\u001b[0m, in \u001b[0;36mBlock.apply\u001b[0;34m(self, func, **kwargs)\u001b[0m\n\u001b[1;32m 360\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[1;32m 361\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply\u001b[39m(\u001b[38;5;28mself\u001b[39m, func, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mlist\u001b[39m[Block]:\n\u001b[1;32m 362\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 363\u001b[0m \u001b[38;5;124;03m apply the function to my values; return a block if we are not\u001b[39;00m\n\u001b[1;32m 364\u001b[0m \u001b[38;5;124;03m one\u001b[39;00m\n\u001b[1;32m 365\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 366\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 368\u001b[0m result \u001b[38;5;241m=\u001b[39m maybe_coerce_values(result)\n\u001b[1;32m 369\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_split_op_result(result)\n", - "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/groupby/groupby.py:1926\u001b[0m, in \u001b[0;36mGroupBy._cython_agg_general..array_func\u001b[0;34m(values)\u001b[0m\n\u001b[1;32m 1923\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1924\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n\u001b[0;32m-> 1926\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_agg_py_fallback\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhow\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mndim\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mndim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43malt\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43malt\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1927\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", - "File \u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/human-fall-detection/lib/python3.10/site-packages/pandas/core/groupby/groupby.py:1878\u001b[0m, in \u001b[0;36mGroupBy._agg_py_fallback\u001b[0;34m(self, how, values, ndim, alt)\u001b[0m\n\u001b[1;32m 1876\u001b[0m msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124magg function failed [how->\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mhow\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m,dtype->\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mser\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1877\u001b[0m \u001b[38;5;66;03m# preserve the kind of exception that raised\u001b[39;00m\n\u001b[0;32m-> 1878\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(err)(msg) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m 1880\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ser\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mobject\u001b[39m:\n\u001b[1;32m 1881\u001b[0m res_values \u001b[38;5;241m=\u001b[39m res_values\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;28mobject\u001b[39m, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n", - "\u001b[0;31mTypeError\u001b[0m: agg function failed [how->mean,dtype->object]" - ] } ], "source": [ @@ -1115,7 +1077,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 80, "metadata": {}, "outputs": [ { @@ -1162,6 +1124,9 @@ "\n", "datalist_test = pd.DataFrame(hqfd_test.load_data_list())\n", "datalist_test[\"prediction\"] = pd.Series(pred_labels).astype(int)\n", + "datalist_test[\"prediction_score\"] = pd.Series(\n", + " [max(score) for score in pred_scores]\n", + ").astype(float)\n", "\n", "\n", "def extract_category(filename):\n", @@ -1219,131 +1184,142 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 102, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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video_pathfall_interval
0data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4(128.0, 131.0)
1data/Fall_Simulation_Data/videos/Fall13_Cam3.mp4(24.0, 27.0)
2data/Fall_Simulation_Data/videos/Fall1_Cam1.mp4(47.0, 50.0)
3data/Fall_Simulation_Data/videos/Fall45_Cam2.mp4(20.0, 22.0)
4data/Fall_Simulation_Data/videos/ADL10_Cam3.mp4(nan, nan)
.........
67data/Fall_Simulation_Data/videos/ADL5_Cam2.mp4(nan, nan)
68data/Fall_Simulation_Data/videos/Fall47_Cam4.mp4(25.0, 28.0)
69data/Fall_Simulation_Data/videos/Fall38_Cam4.mp4(24.0, 26.0)
70data/Fall_Simulation_Data/videos/ADL16_Cam1.mp4(nan, nan)
71data/Fall_Simulation_Data/videos/Fall48_Cam3.mp4(32.0, 34.0)
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72 rows × 2 columns

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" - ], - "text/plain": [ - " video_path fall_interval\n", - "0 data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4 (128.0, 131.0)\n", - "1 data/Fall_Simulation_Data/videos/Fall13_Cam3.mp4 (24.0, 27.0)\n", - "2 data/Fall_Simulation_Data/videos/Fall1_Cam1.mp4 (47.0, 50.0)\n", - "3 data/Fall_Simulation_Data/videos/Fall45_Cam2.mp4 (20.0, 22.0)\n", - "4 data/Fall_Simulation_Data/videos/ADL10_Cam3.mp4 (nan, nan)\n", - ".. ... ...\n", - "67 data/Fall_Simulation_Data/videos/ADL5_Cam2.mp4 (nan, nan)\n", - "68 data/Fall_Simulation_Data/videos/Fall47_Cam4.mp4 (25.0, 28.0)\n", - "69 data/Fall_Simulation_Data/videos/Fall38_Cam4.mp4 (24.0, 26.0)\n", - "70 data/Fall_Simulation_Data/videos/ADL16_Cam1.mp4 (nan, nan)\n", - "71 data/Fall_Simulation_Data/videos/Fall48_Cam3.mp4 (32.0, 34.0)\n", - "\n", - "[72 rows x 2 columns]" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "annotations = pd.read_csv(\"data/Fall_Simulation_Data/annotations_test.csv\")\n", - "annotations[\"fall_interval\"] = annotations.apply(\n", - " lambda x: (x[\"fall_start\"], x[\"fall_end\"]), axis=1\n", - ")\n", + "# From https://github.com/activitynet/ActivityNet/blob/master/Evaluation/\n", + "def segment_iou(target_segment, candidate_segments):\n", + " \"\"\"Compute the temporal intersection over union between a\n", + " target segment and all the test segments.\n", "\n", - "annotations = annotations[[\"video_path\", \"fall_interval\"]]\n", + " Parameters\n", + " ----------\n", + " target_segment : 1d array\n", + " Temporal target segment containing [starting, ending] times.\n", + " candidate_segments : 2d array\n", + " Temporal candidate segments containing N x [starting, ending] times.\n", "\n", - "annotations" + " Outputs\n", + " -------\n", + " tiou : 1d array\n", + " Temporal intersection over union score of the N's candidate segments.\n", + " \"\"\"\n", + " tt1 = np.maximum(target_segment[0], candidate_segments[:, 0])\n", + " tt2 = np.minimum(target_segment[1], candidate_segments[:, 1])\n", + " # Intersection including Non-negative overlap score.\n", + " segments_intersection = (tt2 - tt1).clip(0)\n", + " # Segment union.\n", + " segments_union = (\n", + " (candidate_segments[:, 1] - candidate_segments[:, 0])\n", + " + (target_segment[1] - target_segment[0])\n", + " - segments_intersection\n", + " )\n", + " # Compute overlap as the ratio of the intersection\n", + " # over union of two segments.\n", + " tIoU = segments_intersection.astype(float) / segments_union\n", + " return tIoU\n", + "\n", + "\n", + "def interpolated_prec_rec(prec, rec):\n", + " \"\"\"Interpolated AP - VOCdevkit from VOC 2011.\"\"\"\n", + " mprec = np.hstack([[0], prec, [0]])\n", + " mrec = np.hstack([[0], rec, [1]])\n", + " for i in range(len(mprec) - 1)[::-1]:\n", + " mprec[i] = max(mprec[i], mprec[i + 1])\n", + " idx = np.where(mrec[1::] != mrec[0:-1])[0] + 1\n", + " ap = np.sum((mrec[idx] - mrec[idx - 1]) * mprec[idx])\n", + " return ap\n", + "\n", + "\n", + "def compute_average_precision_detection(\n", + " ground_truth, prediction, tiou_thresholds=np.linspace(0.5, 0.95, 10)\n", + "):\n", + " \"\"\"Compute average precision (detection task) between ground truth and\n", + " predictions data frames. If multiple predictions occurs for the same\n", + " predicted segment, only the one with highest score is matches as\n", + " true positive. This code is greatly inspired by Pascal VOC devkit.\n", + "\n", + " Parameters\n", + " ----------\n", + " ground_truth : df\n", + " Data frame containing the ground truth instances.\n", + " Required fields: ['video-id', 't-start', 't-end']\n", + " prediction : df\n", + " Data frame containing the prediction instances.\n", + " Required fields: ['video-id, 't-start', 't-end', 'score']\n", + " tiou_thresholds : 1darray, optional\n", + " Temporal intersection over union threshold.\n", + "\n", + " Outputs\n", + " -------\n", + " ap : float\n", + " Average precision score.\n", + " \"\"\"\n", + " ap = np.zeros(len(tiou_thresholds))\n", + " if prediction.empty:\n", + " return ap\n", + "\n", + " npos = float(len(ground_truth))\n", + " lock_gt = np.ones((len(tiou_thresholds), len(ground_truth))) * -1\n", + " # Sort predictions by decreasing score order.\n", + " sort_idx = prediction[\"score\"].values.argsort()[::-1]\n", + " prediction = prediction.loc[sort_idx].reset_index(drop=True)\n", + "\n", + " # Initialize true positive and false positive vectors.\n", + " tp = np.zeros((len(tiou_thresholds), len(prediction)))\n", + " fp = np.zeros((len(tiou_thresholds), len(prediction)))\n", + "\n", + " # Adaptation to query faster\n", + " ground_truth_gbvn = ground_truth.groupby(\"video-id\")\n", + "\n", + " # Assigning true positive to truly grount truth instances.\n", + " for idx, this_pred in prediction.iterrows():\n", + " try:\n", + " # Check if there is at least one ground truth in the video associated.\n", + " ground_truth_videoid = ground_truth_gbvn.get_group(this_pred[\"video-id\"])\n", + " except Exception as e:\n", + " fp[:, idx] = 1\n", + " continue\n", + "\n", + " this_gt = ground_truth_videoid.reset_index()\n", + " tiou_arr = segment_iou(\n", + " this_pred[[\"t-start\", \"t-end\"]].values, this_gt[[\"t-start\", \"t-end\"]].values\n", + " )\n", + " # We would like to retrieve the predictions with highest tiou score.\n", + " tiou_sorted_idx = tiou_arr.argsort()[::-1]\n", + " for tidx, tiou_thr in enumerate(tiou_thresholds):\n", + " for jdx in tiou_sorted_idx:\n", + " if tiou_arr[jdx] < tiou_thr:\n", + " fp[tidx, idx] = 1\n", + " break\n", + " if lock_gt[tidx, this_gt.loc[jdx][\"index\"]] >= 0:\n", + " continue\n", + " # Assign as true positive after the filters above.\n", + " tp[tidx, idx] = 1\n", + " lock_gt[tidx, this_gt.loc[jdx][\"index\"]] = idx\n", + " break\n", + "\n", + " if fp[tidx, idx] == 0 and tp[tidx, idx] == 0:\n", + " fp[tidx, idx] = 1\n", + "\n", + " tp_cumsum = np.cumsum(tp, axis=1).astype(float)\n", + " fp_cumsum = np.cumsum(fp, axis=1).astype(float)\n", + " recall_cumsum = tp_cumsum / npos\n", + "\n", + " precision_cumsum = tp_cumsum / (tp_cumsum + fp_cumsum)\n", + "\n", + " for tidx in range(len(tiou_thresholds)):\n", + " ap[tidx] = interpolated_prec_rec(\n", + " precision_cumsum[tidx, :], recall_cumsum[tidx, :]\n", + " )\n", + "\n", + " return ap" ] }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 103, "metadata": {}, "outputs": [ { @@ -1367,553 +1343,682 @@ " \n", " \n", " \n", - " filename\n", - " label\n", - " interval\n", - " prediction\n", - " video_category\n", - " video_label\n", + " video-id\n", + " t-start\n", + " t-end\n", " \n", " \n", " \n", " \n", " 0\n", " data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4\n", - " 2\n", - " (0.0, 5.0)\n", - " 2\n", - " Fall\n", - " 0\n", + " 128.0\n", + " 131.0\n", " \n", " \n", " 1\n", - " data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4\n", - " 2\n", - " (5.0, 10.0)\n", - " 2\n", - " Fall\n", - " 0\n", + " data/Fall_Simulation_Data/videos/Fall13_Cam3.mp4\n", + " 24.0\n", + " 27.0\n", " \n", " \n", " 2\n", - " data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4\n", - " 2\n", - " (10.0, 15.0)\n", - " 2\n", - " Fall\n", - " 0\n", + " data/Fall_Simulation_Data/videos/Fall1_Cam1.mp4\n", + " 47.0\n", + " 50.0\n", " \n", " \n", " 3\n", - " data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4\n", - " 2\n", - " (15.0, 20.0)\n", - " 2\n", - " Fall\n", - " 0\n", + " data/Fall_Simulation_Data/videos/Fall45_Cam2.mp4\n", + " 20.0\n", + " 22.0\n", " \n", " \n", " 4\n", - " data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4\n", - " 2\n", - " (20.0, 25.0)\n", - " 2\n", - " Fall\n", - " 0\n", + " data/Fall_Simulation_Data/videos/Fall22_Cam5.mp4\n", + " 19.0\n", + " 22.0\n", " \n", " \n", " 5\n", - " data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4\n", - " 2\n", - " (25.0, 30.0)\n", - " 2\n", - " Fall\n", - " 0\n", + " data/Fall_Simulation_Data/videos/Fall29_Cam4.mp4\n", + " 23.0\n", + " 25.0\n", " \n", " \n", " 6\n", - " data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4\n", - " 2\n", - " (30.0, 35.0)\n", - " 2\n", - " Fall\n", - " 0\n", + " data/Fall_Simulation_Data/videos/Fall27_Cam4.mp4\n", + " 67.0\n", + " 70.0\n", " \n", " \n", " 7\n", - " data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4\n", - " 2\n", - " (35.0, 40.0)\n", - " 2\n", - " Fall\n", - " 0\n", + " data/Fall_Simulation_Data/videos/Fall28_Cam2.mp4\n", + " 68.0\n", + " 70.0\n", " \n", " \n", " 8\n", 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data/Fall_Simulation_Data/videos/Fall28_Cam2.mp4 68.0 70.0\n", + "8 data/Fall_Simulation_Data/videos/Fall10_Cam5.mp4 23.0 25.0\n", + "9 data/Fall_Simulation_Data/videos/Fall31_Cam1.mp4 65.0 67.0\n", + "10 data/Fall_Simulation_Data/videos/Fall1_Cam5.mp4 47.0 50.0\n", + "11 data/Fall_Simulation_Data/videos/Fall46_Cam5.mp4 26.0 28.0\n", + "12 data/Fall_Simulation_Data/videos/Fall30_Cam1.mp4 24.0 27.0\n", + "13 data/Fall_Simulation_Data/videos/Fall49_Cam2.mp4 24.0 26.0\n", + "14 data/Fall_Simulation_Data/videos/Fall35_Cam4.mp4 29.0 32.0\n", + "15 data/Fall_Simulation_Data/videos/Fall15_Cam5.mp4 10.0 12.0\n", + "16 data/Fall_Simulation_Data/videos/Fall35_Cam2.mp4 29.0 32.0\n", + "17 data/Fall_Simulation_Data/videos/Fall51_Cam4.mp4 28.0 31.0\n", + "18 data/Fall_Simulation_Data/videos/Fall6_Cam4.mp4 44.0 47.0\n", + "19 data/Fall_Simulation_Data/videos/Fall27_Cam3.mp4 67.0 70.0\n", + "20 data/Fall_Simulation_Data/videos/Fall33_Cam5.mp4 28.0 30.0\n", + "21 data/Fall_Simulation_Data/videos/Fall41_Cam3.mp4 39.0 42.0\n", + "22 data/Fall_Simulation_Data/videos/Fall43_Cam5.mp4 64.0 66.0\n", + "23 data/Fall_Simulation_Data/videos/Fall3_Cam3.mp4 128.0 131.0\n", + "24 data/Fall_Simulation_Data/videos/Fall26_Cam1.mp4 15.0 18.0\n", + "25 data/Fall_Simulation_Data/videos/Fall30_Cam4.mp4 24.0 27.0\n", + "26 data/Fall_Simulation_Data/videos/Fall50_Cam5.mp4 15.0 18.0\n", + "27 data/Fall_Simulation_Data/videos/Fall37_Cam2.mp4 30.0 33.0\n", + "28 data/Fall_Simulation_Data/videos/Fall28_Cam4.mp4 68.0 70.0\n", + "29 data/Fall_Simulation_Data/videos/Fall26_Cam4.mp4 15.0 18.0\n", + "30 data/Fall_Simulation_Data/videos/Fall46_Cam4.mp4 26.0 28.0\n", + "31 data/Fall_Simulation_Data/videos/Fall52_Cam2.mp4 43.0 45.0\n", + "32 data/Fall_Simulation_Data/videos/Fall14_Cam1.mp4 11.0 13.0\n", + "33 data/Fall_Simulation_Data/videos/Fall24_Cam2.mp4 30.0 33.0\n", + "34 data/Fall_Simulation_Data/videos/Fall10_cam4.mp4 23.0 25.0\n", + "35 data/Fall_Simulation_Data/videos/Fall5_Cam3.mp4 32.0 34.0\n", + "36 data/Fall_Simulation_Data/videos/Fall33_Cam3.mp4 28.0 30.0\n", + "37 data/Fall_Simulation_Data/videos/Fall35_Cam3.mp4 29.0 32.0\n", + "38 data/Fall_Simulation_Data/videos/Fall14_Cam2.mp4 11.0 13.0\n", + "39 data/Fall_Simulation_Data/videos/Fall23_Cam5.mp4 16.0 19.0\n", + "40 data/Fall_Simulation_Data/videos/Fall12_Cam1.mp4 16.0 18.0\n", + "41 data/Fall_Simulation_Data/videos/Fall33_Cam2.mp4 28.0 30.0\n", + "42 data/Fall_Simulation_Data/videos/Fall39_Cam2.mp4 30.0 32.0\n", + "43 data/Fall_Simulation_Data/videos/Fall44_Cam4.mp4 26.0 28.0\n", + "44 data/Fall_Simulation_Data/videos/Fall27_Cam2.mp4 67.0 70.0\n", + "45 data/Fall_Simulation_Data/videos/Fall4_Cam5.mp4 95.0 96.0\n", + "46 data/Fall_Simulation_Data/videos/Fall30_Cam3.mp4 24.0 27.0\n", + "47 data/Fall_Simulation_Data/videos/Fall46_Cam2.mp4 26.0 28.0\n", + "48 data/Fall_Simulation_Data/videos/Fall42_Cam4.mp4 79.0 81.0\n", + "49 data/Fall_Simulation_Data/videos/Fall29_Cam2.mp4 23.0 25.0\n", + "50 data/Fall_Simulation_Data/videos/Fall7_Cam5.mp4 28.0 31.0\n", + "51 data/Fall_Simulation_Data/videos/Fall6_Cam5.mp4 44.0 47.0\n", + "52 data/Fall_Simulation_Data/videos/Fall47_Cam4.mp4 25.0 28.0\n", + "53 data/Fall_Simulation_Data/videos/Fall38_Cam4.mp4 24.0 26.0\n", + "54 data/Fall_Simulation_Data/videos/Fall48_Cam3.mp4 32.0 34.0" + ] + }, + "execution_count": 103, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Prepare ground truth for ANet evaluation\n", + "\n", + "annotations = pd.read_csv(\"data/Fall_Simulation_Data/annotations_test.csv\")\n", + "annotations[\"gt_fall_interval\"] = annotations.apply(\n", + " lambda x: (x[\"fall_start\"], x[\"fall_end\"]), axis=1\n", + ")\n", + "\n", + "annotations = annotations[[\"video_path\", \"gt_fall_interval\"]]\n", + "annotations = annotations.rename(columns={\"video_path\": \"filename\"})\n", + "\n", + "annotations[\"video-id\"] = annotations[\"filename\"]\n", + "annotations[\"t-start\"] = annotations[\"gt_fall_interval\"].apply(lambda x: x[0])\n", + "annotations[\"t-end\"] = annotations[\"gt_fall_interval\"].apply(lambda x: x[1])\n", + "\n", + "annotations = annotations.dropna()\n", + "\n", + "annotations = annotations[[\"video-id\", \"t-start\", \"t-end\"]].reset_index(drop=True)\n", + "\n", + "annotations" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " video-id t-start t-end \\\n", + "0 data/Fall_Simulation_Data/videos/ADL12_Cam3.mp4 1000.0 1005.0 \n", + "1 data/Fall_Simulation_Data/videos/ADL12_Cam3.mp4 1105.0 1110.0 \n", + "2 data/Fall_Simulation_Data/videos/ADL12_Cam4.mp4 270.0 275.0 \n", + "3 data/Fall_Simulation_Data/videos/ADL15_Cam5.mp4 690.0 695.0 \n", + "4 data/Fall_Simulation_Data/videos/Fall10_Cam5.mp4 25.0 30.0 \n", + ".. ... ... ... \n", + "67 data/Fall_Simulation_Data/videos/Fall5_Cam3.mp4 30.0 35.0 \n", + "68 data/Fall_Simulation_Data/videos/Fall5_Cam3.mp4 215.0 220.0 \n", + "69 data/Fall_Simulation_Data/videos/Fall6_Cam4.mp4 45.0 50.0 \n", + "70 data/Fall_Simulation_Data/videos/Fall6_Cam5.mp4 40.0 50.0 \n", + "71 data/Fall_Simulation_Data/videos/Fall7_Cam5.mp4 25.0 35.0 \n", "\n", - " prediction video_category video_label \n", - "0 2 Fall 0 \n", - "1 2 Fall 0 \n", - "2 2 Fall 0 \n", - "3 2 Fall 0 \n", - "4 2 Fall 0 \n", - "5 2 Fall 0 \n", - "6 2 Fall 0 \n", - "7 2 Fall 0 \n", - "8 2 Fall 0 \n", - "9 2 Fall 0 \n", - "10 2 Fall 0 \n", - "11 2 Fall 0 \n", - "12 2 Fall 0 \n", - "13 2 Fall 0 \n", - "14 2 Fall 0 \n", - "15 2 Fall 0 \n", - "16 2 Fall 0 \n", - "17 2 Fall 0 \n", - "18 2 Fall 0 \n", - "19 2 Fall 0 \n", - "20 2 Fall 0 \n", - "21 2 Fall 0 \n", - "22 2 Fall 0 \n", - "23 2 Fall 0 \n", - "24 2 Fall 0 \n", - "25 0 Fall 0 \n", - "26 0 Fall 0 \n", - "27 1 Fall 0 \n", - "28 1 Fall 0 \n", - "29 1 Fall 0 \n", - "30 1 Fall 0 \n", - "31 1 Fall 0 \n", - "32 1 Fall 0 \n", - "33 1 Fall 0 \n", - "34 1 Fall 0 \n", - "35 1 Fall 0 \n", - "36 1 Fall 0 \n", - "37 1 Fall 0 \n", - "38 1 Fall 0 \n", - "39 1 Fall 0 \n", - "40 1 Fall 0 \n", - "41 1 Fall 0 \n", - "42 1 Fall 0 \n", - "43 1 Fall 0 \n", - "44 1 Fall 0 \n", - "45 2 Fall 0 \n", - "46 2 Fall 0 " + " score \n", + "0 0.973675 \n", + "1 0.812018 \n", + "2 0.989435 \n", + "3 0.973979 \n", + "4 0.729522 \n", + ".. ... \n", + "67 0.952380 \n", + "68 0.953219 \n", + "69 0.850325 \n", + "70 0.693482 \n", + "71 0.714431 \n", + "\n", + "[72 rows x 4 columns]" ] }, - "execution_count": 49, + "execution_count": 104, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "group_test = datalist_test.groupby(\"filename\").get_group(\n", - " \"data/Fall_Simulation_Data/videos/Fall3_Cam2.mp4\"\n", + "# Prepare predictions for ANet evaluation\n", + "\n", + "\n", + "def concat_intervals(group):\n", + " concatenated_rows = []\n", + " score = 0\n", + " count = 1\n", + " for i, (_, row) in enumerate(group.iterrows()):\n", + " # First row\n", + " if not concatenated_rows:\n", + " concatenated_rows.append(row)\n", + " score = row[\"prediction_score\"]\n", + " continue\n", + "\n", + " # New block\n", + " if row[\"prediction\"] != concatenated_rows[-1][\"prediction\"]:\n", + " concatenated_rows.append(row)\n", + " concatenated_rows[-1][\"prediction_score\"] = score / count\n", + " score = row[\"prediction_score\"]\n", + " count = 1\n", + " # Same block\n", + " else:\n", + " concatenated_rows[-1][\"interval\"] = (\n", + " concatenated_rows[-1][\"interval\"][0],\n", + " row[\"interval\"][1],\n", + " )\n", + " count += 1\n", + " score += row[\"prediction_score\"]\n", + " # Last row and same block\n", + " if i == len(group) - 1:\n", + " concatenated_rows[-1][\"prediction_score\"] = score / count\n", + "\n", + " return pd.DataFrame(concatenated_rows)\n", + "\n", + "\n", + "predicted_intervals = (\n", + " datalist_test.groupby(\"filename\").apply(concat_intervals).reset_index(drop=True)\n", ")\n", "\n", - "group_test" + "fall_intervals = predicted_intervals[predicted_intervals[\"prediction\"] == 0]\n", + "\n", + "fall_intervals = fall_intervals.rename(columns={\"interval\": \"predicted_fall_interval\"})\n", + "\n", + "fall_intervals[\"video-id\"] = fall_intervals[\"filename\"]\n", + "fall_intervals[\"t-start\"] = fall_intervals[\"predicted_fall_interval\"].apply(\n", + " lambda x: x[0]\n", + ")\n", + "fall_intervals[\"t-end\"] = fall_intervals[\"predicted_fall_interval\"].apply(\n", + " lambda x: x[1]\n", + ")\n", + "fall_intervals[\"score\"] = fall_intervals[\"prediction_score\"]\n", + "\n", + "fall_intervals = fall_intervals[[\"video-id\", \"t-start\", \"t-end\", \"score\"]].reset_index(\n", + " drop=True\n", + ")\n", + "fall_intervals" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.71650474 0.71650474 0.43232323 0.154001 0.02188906 0.02188906\n", + " 0. 0. 0. 0. ]\n" + ] + }, + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 110, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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v65eHTuuPP75Gj33iDqvLAQBkQDrf35ztgjn3bs+Pfnp+AAAmIXxgzn1i7QqVlxWrP3JJL5lnrS4HAJBlCB+Yc2UlRfrcpkpJUqCHjacAgIkIH5gXyZ4fB45r4PKQxdUAALIJ4QPzYnONodXLFuny0Cg9PwAAExA+MC/Gen7USJL89PwAAFyF8IF583Btlew2ac875/SbMxesLgcAkCUIH5g3K5xl+vDtyyRJT7LxFABwBeED8+rdnh99GqHnBwBAhA/Ms4a7VshZVqzj0ct68cgZq8sBAGQBwgfmVVlJkR7YXCWJnh8AgDGED8y78aWXHx84oRg9PwCg4BE+MO82Vrt02/LFig+P6gf76PkBAIWO8IF5N9bzY2z2w7+n1+JqAABWI3wgIx7aUqUiu02hoxEdOX3e6nIAABYifCAjljvL9BF6fgAARPhABo0vvTwV6qfnBwAUMMIHMuaeu5bLWFiiE7HLeuEwPT8AoFARPpAxjuIiPbCpUhI9PwCgkBE+kFHjJ90+d/CEopfo+QEAhYjwgYxaX+XUHSvKNTg8qu/vO2Z1OQAACxA+kFETe36w9AIAhYjwgYx78ErPj729ER0+NWB1OQCADCN8IOOWlTv0sTvGen4EevotrgYAkGmED1hifOnl6Vf66PkBAAWG8AFLfPzOFapYWKKTsbh+eei01eUAADKI8AFLlBbb9eCWKklSF4fNAUBBIXzAMo1Xen4EXz+ps+fjFlcDAMgUwgcss7bSqQ1VLg2NJPTMXnp+AEChIHzAUlvrx2Y/urp7lUiw8RQACgHhA5b63KZKOYrtevPkgPb1Ra0uBwCQAYQPWMq1oESfXL9SEhtPAaBQED5guW3esaWX7+09pkuDIxZXAwCYb4QPWO59niWqrliggfiwfnzwuNXlAADmGeEDlrPbbcnHbru6OWwOAPId4QNZweetls0mvWSe1dGzF60uBwAwj9IKH6Zpqr29XYFAQO3t7YpEIil9rqWlJeWxKExVxgJ9cM1SSVKgh42nAJDP0gofjY2Nam5uls/nk8/n0/bt22/4mVAopPb29hkXiMKx9crGU38Ph80BQD5LOXyYpjnhtcfjUTAYTOlzHo8n/cpQcO5du0KuBSU6Hr2sFw6fsbocAMA8STl8BINBud3uCe+53W6FQqFpPxMIBOTz+WZeHQpKWUmRHtxcKYmeHwCQz1IOH9Pt2QiHw9OONwzjhr9vPB5XLBabcKFwjbdb333wpM5dGLS4GgDAfJj10y7ThZKuri41NDTc8PNtbW1yuVzJq6amZrYlIYetq3RpXaVTgyOjemZvv9XlAADmQcrhwzCMSbMc4XB4ytmNYDCorVu3pvT7tra2KhqNJq/eXqbbC934xtNdHDYHAHkp5fAx3SyG1+ud8v2uri51dnaqs7NTpmmqra1tyv0hDodDTqdzwoXC9sDmSpUW2/XGiQEdPMYyHADkm+JUB177xIppmvJ6vcmZj1AoJMMw5PF4JgWVHTt2aMeOHTz1gpQYC0t137qV+vdXj2lXd6/WV7msLgkAMIfS2vPh9/vV0tKiQCCgjo4O+f3+5M/a2toUCAQmjI9EIskeHzt37rzukzHA1bZ6qyVJz+7t1+UhDpsDgHxiS2TZonosFpPL5VI0GmUJpoCNjib0ofbn1R+5pH/4/GY9sLnK6pIAANeRzvc3Z7sgK9ntNvnqxmY//Hs4bA4A8gnhA1lrPHy8cPiMesMcNgcA+YLwgaxV416oD6xZIkkK9DD7AQD5gvCBrDbe8yPQ06dRDpsDgLxA+EBWu2/dSjnLitUfuaQXj5y1uhwAwBwgfCCrlZUUJZ902cVhcwCQFwgfyHrjSy/PHTyhyEUOmwOAXEf4QNZbX+XUnSvLNTg8qu+9eszqcgAAs0T4QNaz2WzaVj82+9HF0gsA5DzCB3LCg5urVFpk14H+mA4ei1pdDgBgFggfyAkVi0p179oVkuh4CgC5jvCBnLH1ytLL069w2BwA5DLCB3LGB9cs1SpXmaKXhrT7tZNWlwMAmCHCB3JG0VWHzbHxFAByF+EDOaWxbmzp5YXDZ9QfuWRxNQCAmSB8IKfctGSh3u9ZokRCepLD5gAgJxE+kHO21r+79MJhcwCQewgfyDmfXLdK5Y5i9Z27pF+bHDYHALmG8IGcs6C0SJ/dXCmJjacAkIsIH8hJ264cNvejAycUvTRkcTUAgHQQPpCTNla7dMeKcsU5bA4Acg7hAznJZrOp0Tu28dTP0gsA5BTCB3LWQ1uqVFJk076+qF4/HrO6HABAiggfyFlLFjvUcBeHzQFAriF8IKdt9Y4fNten+DCHzQFALiB8IKd96LalWuF06NzFIf309VNWlwMASAHhAzmtuMjOYXMAkGMIH8h544fN/fLQaR2PctgcAGQ7wgdy3i1LF+k9t7o1ymFzAJATCB/IC+MbT7v29HHYHABkOcIH8sKnN6zUYkexjoYv6uW3w1aXAwC4DsIH8sLC0mJ9dtMqSVJXNxtPASCbET6QNxqvLL388MBxxS5z2BwAZCvCB/LGlhpDa5Yv1uWhUX3/1eNWlwMAmAbhA3nDZrNpW3LjKUsvAJCtCB/IKw9uqVKx3aa9vREdOjlgdTkAgCkUpzPYNE0FAgF5PB6ZpqmmpiYZhjHl2GAwKEmKRCLq7u7Wtm3bVFtbO+uCgetZVu7Qx+9crp+8dlJd3b36y/vXWl0SAOAaac18NDY2qrm5WT6fTz6fT9u3b7/uWLfbLZ/Pp9WrV6uxsXHWxQKp2FY/fthcvwaHRy2uBgBwrZTDh2maE157PJ7k7MZU/H7/hJmO6WZIgLn2kduXaVm5Q2cvDOpnb3DYHABkm5TDRzAYlNvtnvCe2+1WKBSacnxDQ0Py136/Xzt27JhyXDweVywWm3ABs1FcZNcjtRw2BwDZKuXwEYlEpnw/HJ6+m2QoFFJLS4vuvfdeNTU1TTmmra1NLpcredXU1KRaEjCtRu9Y+Pj5m6d0MnbZ4moAAFeb9dMu04USSaqtrVVra6uOHDmiQCAw5ZjW1lZFo9Hk1dvL31Qxe6uXLVb9LRVjh82FOGwOALJJyuHDMIxJsxzhcPiGezkMw1BjY6MaGxunDCoOh0NOp3PCBcyF8Y6n/j19SiQ4bA4AskXK4ePqPRxX83q9k94LBoOqqKhIvvZ4PJImb1oF5tNnNqzSwtIi/ebMBXW/fc7qcgAAV6QcPsYDxDjTNOX1epMzH6FQKBku3G73hLASCoVkGAZ9PpBRixzFun/jlcPm2HgKAFkjrT0ffr9fLS0tCgQC6ujokN/vT/6sra0tua+jtrZW27ZtU2dnpzo7O7Vr1y719PTMbeVACsZ7fvxg33Gdjw9bXA0AQJJsiSxbDI/FYnK5XIpGo+z/wKwlEgnd83e/kHn6gv764Q36/HtusrokAMhL6Xx/c7YL8prNZtNWDpsDgKxC+EDee7i2SkV2m0JHIzp8isPmAMBqhA/kveXlZfrYHcsljT12CwCwFuEDBWHrlY6nT4b6NDTCYXMAYCXCBwrCx+5crqWLHTpzflDPc9gcAFiK8IGCUFJk1yO1VZKkLpZeAMBShA8UjPHD5p5/85RODXDYHABYhfCBgrFmeblqbzI0MprQU6F+q8sBgIJF+EBBubrnR5b11wOAgkH4QEG5f1OlFpQUyTx9QaGjHDYHAFYgfKCgLHYU6zNXDpvb1U3HUwCwAuEDBWd86eX7+47rAofNAUDGET5QcOpvqdCtSxfp4uCIfrD/uNXlAEDBIXyg4NhstuRjt34OmwOAjCN8oCA9Ulstu03qfvucjpw+b3U5AFBQCB8oSCucZfooh80BgCUIHyhY4xtPnwz1aZjD5gAgYwgfKFgfv3O5liwq1emBuH5x6LTV5QBAwSB8oGCVFtv10Jaxw+bo+QEAmUP4QEHbWj+29PKzN07p9EDc4moAoDAQPlDQbl9Rrs01hoZHE3rmFQ6bA4BMIHyg4HHYHABkFuEDBe/+TatUVmLXW6fO65XeiNXlAEDeI3yg4DnLSvTp9WOHzdHxFADmH+ED0LsbT//91eO6OMhhcwAwnwgfgKT33urWzUsW6nx8WD/cf8LqcgAgrxE+AF05bK5u7LC5LpZeAGBeET6AKx6pGzts7uXfhPWbMxesLgcA8hbhA7hilWuBPnz7MklSoIfZDwCYL4QP4CrjPT8CPRw2BwDzhfABXOWeu5arYmGJTsbi+tVbZ6wuBwDyEuEDuIqjuEgPbWHjKQDMJ8IHcI2t9WPhI/j6SZ09z2FzADDXCB/ANe5c6dTGapeGRhJ6msPmAGDOET6AKTRy2BwAzBvCBzCFz22qlKPYrkMnz2tfX9TqcgAgrxSnM9g0TQUCAXk8HpmmqaamJhmGMeXYUCikYDAoSeru7tbjjz8+7Vgg27gWlOhT61fqmb3H1LWnV5tqDKtLAoC8kdbMR2Njo5qbm+Xz+eTz+bR9+/ZpxwaDQTU3N6u5uVn19fW65557Zl0skEnjPT++t/eYLg2OWFwNAOSPlMOHaZoTXns8nuTMxrVCoZDa2tqSr30+n0Kh0KTfA8hm7/MsUY17gQbiw/rxweNWlwMAeSPl8BEMBuV2uye853a7FQqFJo2tra3V448/nnwdiUSS468Vj8cVi8UmXEA2sNttaqy7svG0u8/iagAgf6QcPsYDxLXC4fCU7/t8vuSvd+3apYaGhin3fLS1tcnlciWvmpqaVEsC5t0jddWy2aSXzLN65yyHzQHAXJj10y7ThZKrfx4IBOT3+6f8eWtrq6LRaPLq7aWrJLJHlbFAH1yzVNLYeS8AgNlLOXwYhjFpliMcDt/wCZaWlhbt3r172nEOh0NOp3PCBWSTqw+bGxml5wcAzFbK4aOhoWHK971e77SfaW9vV0tLizwejyKRyA1nSYBs9Il1K2QsLNHx6GW9cJjD5gBgtlIOHx6PZ8Jr0zTl9XqTMxrXPs0SCARUW1ubDB5dXV30+UBOchQX6cHNVZKkrm6WBQFgtmyJNHpHm6apjo4O1dfXq7u7W62trclA0djYqPr6ejU3N8s0Ta1evXrCZw3D0Llz527474jFYnK5XIpGoyzBIGscPBbVZ77xgkqKbPrPP2+Qe1Gp1SUBQFZJ5/s7rfCRCYQPZKv7//FXOtAf01c/u1a/94FbrS4HALJKOt/fnO0CpGh84+mubg6bA4DZIHwAKfrcpkqVFtv1xokBHeinGR4AzBThA0iRsbBU961bKUnq2sPGUwCYKcIHkIZtV5Zent3br8tDHDYHADNB+ADScPfqJaoyFih2eVjPHTxhdTkAkJMIH0Aa7HabfHXVklh6AYCZInwAafJdOWzuPw6fVW/4otXlAEDOIXwAaapxL9QHVnPYHADMFOEDmIFG79jSC4fNAUD6CB/ADNy3bqWcZcXqj1zSi0c4bA4A0kH4AGagrKRID265ctjcHpZeACAdhA9ghsbbrT938IQiFwctrgYAcgfhA5ihdZVO3bXKqcHhUT2795jV5QBAziB8ADNks9m01UvPDwBIF+EDmIUHN1eptMiug8dievEwG08BIBWED2AWKhaV6pG6sY2nf/TdV9QfuWRxRQCQ/QgfwCx95f51WrvKqbMXBrXjW3t0aZAD5wDgeggfwCwtKC1S56N1ci8q1YH+mP77U/uUSNB4DACmQ/gA5kB1xUL982/Xqshu07N7j+nxX5lWlwQAWYvwAcyR969eoq/cv1aS9Nc/ekO/OHTa4ooAIDsRPoA59Oj7b9ZWb7VGE9IffSekt89csLokAMg6hA9gDtlsNv3PB9dry02GYpeHtf2JPTofH7a6LADIKoQPYI45iov0L1+o0/Jyh946dV6P7dqrUU6+BYAkwgcwD1Y4y9TxxTqVFtn1k9dO6hs/e8vqkgAgaxA+gHmy5aYK/a+H1kuS/j74lp47eMLiigAgOxA+gHm01Vuj3737FknSY7v26tDJAWsLAoAsQPgA5tlffOYuvd+zRBcGR9T0xB5FLw5ZXRIAWIrwAcyzkiK7/vl3alVlLNDbZy/qD78b0ggbUAEUMMIHkAHuRaXqfLROZSV2/eqtM2r/8RtWlwQAliF8ABmyrtKl/+PbJEnq+KWpZ/f2W1wRAFiD8AFk0Gc3Veq/fHS1JKk5sE8H+qMWVwQAmUf4ADLsv33iDn30jmWKD4+q6Yk9OnM+bnVJAJBRhA8gw4rsNv3D57fIs3SRjkUv6w/+LaShkVGrywKAjCF8ABZwLShR56N1Wuwo1su/Ceuv/v01q0sCgIwhfAAWWbO8XH+/bbNsNulbv35H3335qNUlAUBGED4ACzWsXaHHGm6XJH3l2QPa83bY4ooAYP6lFT5M01R7e7sCgYDa29sViUSuOz4UCqmurm429QF57w8/vkafWr9SQyMJfenbIR2PXrK6JACYV2mFj8bGRjU3N8vn88nn82n79u3Tjg0EApLGAgiA6dlsNv1N4ybdubJcZ87H9aVv9ejy0IjVZQHAvEk5fJimOeG1x+NRMBicdrzP51Ntbe3MKwMKyCJHsTq/6JWxsESv9kX150/vVyJBC3YA+Snl8BEMBuV2uye853a7Zz2zEY/HFYvFJlxAIbppyUL902/Vym6Tngr161//422rSwKAeZFy+Jhuf0c4PLsNcm1tbXK5XMmrpqZmVr8fkMs+eNtS/fmn75Ikff2Hr+s/Dp+xuCIAmHuzftrlRptOb6S1tVXRaDR59fb2zrYkIKf9/gdv1cNbqjQymtCXvxPS0bMXrS4JAOZUyuHDMIxJsxzhcFiGYcyqAIfDIafTOeECCpnNZtPXH96gjdUuRS4Oqelbe3QhPmx1WQAwZ1IOHw0NDVO+7/V656wYAGPKSorU8cU6LV3s0BsnBvRngVfZgAogb6QcPjwez4TXpmnK6/UmZz5CodCkJ2LGzXZpBihEq1wL1PHFWpUU2fTD/Sf0z88ftrokAJgTae358Pv9amlpUSAQUEdHh/x+f/JnbW1tyd4e0tjTMS0tLVP+DEBq6m52668eWC9J+tvdh/TT109aXBEAzJ4tkWVzubFYTC6XS9FolP0fwBV/+cx+ffvXR7XYUaxnvvwBrVm+2OqSAGCCdL6/OdsFyAFfuX+d3nOLW+fjw2p6Yo+il4asLgkAZozwAeSA0mK7vvmFWlW6ymSeuaA//X+vaGQ0qyYtASBlhA8gRyxd7FDHF71yFNv1/Jun9bc/edPqkgBgRggfQA7ZUO1Su2+jJOmbPz+i7+87ZnFFAJA+wgeQYx7YXKWmD489+v5n/n167RjnIQHILYQPIAe1fPJOfei2pbo0NKLtT+xR+MKg1SUBQMoIH0AOKrLb9E+/VaublyxUf+SSvvxvIQ2NjFpdFgCkhPAB5CjXwhI9/qhXi0qL9JJ5Vv/7B69bXRIApITwAeSw21eU6++2bZYk/d8X31bXHk6FBpD9CB9Ajrtv3Ur9yT23SZL+8ukDeuXoOYsrAoDrI3wAeeBP7rlNn1i7QoMjo/rSt3t0KnbZ6pIAYFqEDyAP2O02/d22zbpt+WKdjMW149s9ig+PWF0WAEyJ8AHkicWOYj3+qFfOsmK9cjSi//HMAWXZuZEAIInwAeSVW5Yu0j/+dq3sNqlrT5+eeOkdq0sCgEkIH0Ce+cjty9TyyTslSX/1/df00pGzFlcEABMRPoA81PRhjz63qVIjowl9+Tsh9Z27aHVJAJBE+ADykM1m085HNmpdpVPhC4NqeqJHlwbZgAogOxA+gDy1oLRInY96tWRRqV47HtOfBV5lAyqArED4APJYlbFA3/ydWhXbbfr+vuP6l1+YVpcEAIQPIN+917NEX/3sWklS+3Nv6Pk3T1lcEYBCR/gACsAX3nezPl9fo0RC+uPvviLz9HmrSwJQwAgfQAGw2Wz62gPrVHuToYHLw2r6Vo8GLg9ZXRaAAkX4AAqEo7hI//KFOq1wOnT41Hn91117NTrKBlQAmUf4AArIcmeZOr7oVWmxXcHXT+nvg4esLglAASJ8AAVmc42hrz+0QZL0jZ8d1o8PHLe4IgCFhvABFCBfXbV+7wO3SJIe63pVb5yIWVsQgIJC+AAK1F98+i7dvXqJLg6OqOmJHkUuDlpdEoACQfgAClRxkV3/9Nu1qq5YoKPhi/rD77yi4ZFRq8sCUABsiSzrtxyLxeRyuRSNRuV0Oq0uB8h7rx+P6eFvvqhLQyP63btv0e9/8FarS5q14iKbVjrLZLPZrC4FKBjpfH8TPgDoB/uO68vfCVldxpxylhVrY7WhDdUubap2aUO1oUoXgQSYL+l8fxdnqCYAWewzG1fpePQu/ePPDmtwOPeXXgZHRhW7PKwXDp/RC4fPJN9furhUG6rGgshYIHFpeXmZhZUChYmZDwB5Z3B4VIdODmhfX1T7+yN6tTeqQycHNDxFU7VVrjJtqHJpU40xFkyqXKpYVGpB1UBuY9kFAK5xeWhErx+PaV9f9MoV0eHT5zXVfwFvci98d7mmytD6KqfKy0oyXzSQQwgfAJCCC/FhHeiPan9/VK/2RbW/L6K3z16cNM5mkzxLF2nTlT0kG6tdWrvKpQWlRRZUDWQnwgcAzFD04pD290e1rz+ifb1jwaQ/cmnSuCK7TbctX5wMJJuqDd2xslylxXQwQGEifADAHDo9ENeB/qhe7Ytof9/YLMmZ8/FJ40qL7LprVfnY7EiVoY01Lq1ZtljFRQQS5L95Cx+maSoQCMjj8cg0TTU1NckwjFmPnWnxAGCFRCKhE7HLyb0j4/tIopeGJo1dUFKkdZXO5OzIhmqXbl2ySHY7j/wiv8xb+Kirq1NPT4+ksXDR0tIiv98/67EzLR4AskUikVBv+NLY7Ej/WCg50B/T+fjwpLHljmKtrxrbO7Kx2tDGapeqKxbQgwQ5bV76fJimOeG1x+NRMBic9VgAyAc2m003LVmom5Ys1Gc3VUqSRkcTMs9cuGp2JKKDx2IaiA/rJfOsXjLPJj9fsbDk3f4jVWOhZKWLHiTITymHj2AwKLfbPeE9t9utUCik2traGY+Nx+OKx99dO43FOF0TQH6w221as3yx1ixfrIdrqyVJwyOjOnTyvPb3v7tc88aJmM5dHNIvD53WLw+dTn5+6eJSLSylFyTm3pLFpXr6Dz5g2b8/5f9XRyKRKd8Ph8OzGtvW1qavfe1rqZYBADmtuMiutZVOra10alv92Hvx4RG9cXxA+/qj2tc7tmxz6OSAzpwflMRpw5h7l4dGLP33zzpSTxc0Uh3b2tqqxx57LPk6FouppqZmtmUBQM5wFBdpU42hTTWG9L6bJUkXB4d15NQFDY3mfrt7ZJ8Su7VPYKUcPgzDmDRzEQ6Hp3yCJZ2xDodDDocj1TIAoCAsLC3WhmqX1WUA8yLl6NPQ0DDl+16vd1ZjAQBAYUk5fHg8ngmvTdOU1+tNzmaEQqHkUy43GgsAAApXWns+/H6/WlpaVF9fr+7u7gl9O9ra2lRfX6/m5uYbjgUAAIWL9uoAAGDW0vn+5sABAACQUYQPAACQUYQPAACQUYQPAACQUYQPAACQUYQPAACQUYQPAACQUYQPAACQUYQPAACQUWm1V8+E8YarsVjM4koAAECqxr+3U2mcnnXhY2BgQJJUU1NjcSUAACBdAwMDcrlc1x2TdWe7jI6O6tixYyovL5fNZrO6nIIVi8VUU1Oj3t5eztjJAtyP7MM9yS7cD+slEgkNDAyosrJSdvv1d3Vk3cyH3W5XdXW11WXgCqfTyR/kLML9yD7ck+zC/bDWjWY8xrHhFAAAZBThAwAAZBThA1NyOBz66le/KofDYXUpEPcjG3FPsgv3I7dk3YZTAACQ35j5AAAAGUX4AAAAGUX4AAAAGZV1fT6QOaZpKhAIyOPxyDRNNTU1yTCMKceGQiEFg0FJUnd3tx5//PFpx2Jm0rkfV2tpaVFrayv3Yx6ke0+CwaBM05TH45EkNTQ0ZKjSwpDO/TBNU8FgUG63W6ZpyufzJe8LskACBau2tjb56yNHjiR8Pt+0Y3fu3Dnh11d/FnMjnfsxrqenJyEpce7cuXmsrHClc092796daGpqSo71eDzzXl+hmel/sxKJRPLeIDuw7FKgTNOc8Nrj8SRnNq4VCoXU1taWfO3z+RQKhSb9Hpi5dO7HtZ/jb3PzI917smPHDu3cuTM5dvfu3fNaX6FJ937s2rVrvkvCLBA+CtT4dOTV3G63QqHQpLG1tbV6/PHHk68jkUhyPOZGOvdjXCAQkM/nm+/SClY698Q0TYXDYRmGoVAopEgkQiicY+n+GXG73aqrq0suv9x7772ZKBMpInwUqPEAca1wODzl+1d/ye3atUsNDQ3sMZhD6d6PSCTC//7zLJ17EgqF5Ha7k/sROjs7FQgE5rnCwpLunxG/3y9JWr16tfx+P0E9y7DhFBNM9wf86p8HAgH19PRkpqACN9396OrqUlNTU2aLgaSp70k4HJZpmslQ3tTUpIqKCiXo4TjvpvszEgwGtXPnTpmmqR07dkiSOjo6MlgZroeZjwJlGMakvzGMTxtfT0tLi3bv3s3fuudYOvcjGAxq69atGaqscKVzTzwejwzDSP5s/J/XWzZDetK5H6Zpqru7Ww0NDWpqatKRI0fU1dXFPrUsQvgoUNM9Auj1eqf9THt7u1paWuTxeBSJRG44S4LUpXs/urq61NnZqc7OTpmmqba2Nr7o5lg694T9HfMvnfsRCoVUX1+ffO3xeNTa2sp/s7II4aNAXfsfS9M05fV6J/yN7eq/JQQCAdXW1iaDR1dXF7Mfcyid+zH+t7nxSxp70qK2tjajNee7dO6Jx+OR1+tNfrmNP4XEPZk76dyP2tpadXd3Txh/9uxZ7kcW4WC5Amaapjo6OlRfX6/u7u4JjaoaGxtVX1+v5uZmmaap1atXT/isYRg6d+6cBVXnr1Tvx7hIJKLOzk61tLSoqamJADIP0rknkUhELS0tqqurU09PT3KWEHMnnfsRDAYVCoWSP29oaOB+ZBHCBwAAyCiWXQAAQEYRPgAAQEYRPgAAQEYRPgAAQEYRPgAAQEYRPgAAQEYRPgAAQEYRPgAAQEYRPgAAQEYRPgAAQEYRPgAAQEb9f87NVOegyTGdAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Calculate AP with ANet evaluation\n", + "\n", + "ap = compute_average_precision_detection(\n", + " annotations, fall_intervals, tiou_thresholds=np.linspace(0.05, 0.95, 10)\n", + ")\n", + "print(ap)" ] } ], From 1a57ad0fd75c1934055ce9d9b0dc87fc6096d2e6 Mon Sep 17 00:00:00 2001 From: Till Grutschus Date: Mon, 8 Jan 2024 21:43:45 +0100 Subject: [PATCH 10/13] stop tracking notebooks/result_data --- ...rop_priority-labeling_k400-hyperparams.csv | 91 -- ...rop_priority-labeling_k400-hyperparams.csv | 74 -- ...ing_priority-labeling_k400-hyperparams.csv | 29 - ...ng_priority-labeling_paper-hyperparams.csv | 36 - ...rop_priority-labeling_k400-hyperparams.csv | 1001 ----------------- ...rop_priority-labeling_k400-hyperparams.csv | 1001 ----------------- ...ing_priority-labeling_k400-hyperparams.csv | 1001 ----------------- ...ng_priority-labeling_paper-hyperparams.csv | 1001 ----------------- 8 files 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6a18de130a76ca86ebf7ca87f7d3bd3bba14c879 Mon Sep 17 00:00:00 2001 From: Till Grutschus Date: Mon, 8 Jan 2024 21:45:28 +0100 Subject: [PATCH 12/13] stop tracking diagrams --- .../HQFD_Sampling_Labeling_Pipeline.drawio | 379 -------- diagrams/confusion_matrix-img0.png | Bin 1453 -> 0 bytes diagrams/confusion_matrix.pgf | 336 ------- diagrams/desmos-graph.svg | 19 - diagrams/label_distributions.pdf | Bin 68039 -> 0 bytes diagrams/label_distributions.pgf | 852 ------------------ diagrams/loss_curves.pdf | Bin 417752 -> 0 bytes 7 files changed, 1586 deletions(-) delete mode 100644 diagrams/HQFD_Sampling_Labeling_Pipeline.drawio delete mode 100644 diagrams/confusion_matrix-img0.png delete mode 100644 diagrams/confusion_matrix.pgf delete mode 100644 diagrams/desmos-graph.svg delete mode 100644 diagrams/label_distributions.pdf delete mode 100644 diagrams/label_distributions.pgf delete mode 100644 diagrams/loss_curves.pdf diff --git a/diagrams/HQFD_Sampling_Labeling_Pipeline.drawio 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z)C2@QU}HsOVE>*51{wttA;9}#s_4I_0$}a$itv9;JE(Ld5%C`qk-wHk0!tsP{!6b$ z{63oBL9zcYcwkfil6HS*{(Z8VCYI(dAQ4q-GZ(-P1~BS_9Ru86;+F9QQ-4O*N!7yQv6&CC0f))|6|7Qq{akd0NU`=O?nX4&) z_a2?)Xl4PrZDMK*tn#Y@ka<|%8si{$aAf>)Xcz=03WdQSP#7Em6BB~K_#hBIfg}6k z=4@_32?4>xC;=t-*9)+;5di3H0s1EfFwBQzAcy~r!Jz;<4P2Uo*O3?k1#H8ym>3-R z`p04iQ1M?ez|}q;LqUP-e01)?XR%}TfRHF)e;*9}(~Bt3-|>3DFbo11=Ew8I0A&2n zm^iRq$6^R59P)P_0x+77&lLwu=09TyC<1=G9ufhEAFqdiLBxT}_eU=Xm?%JT{~1Ff z5Xisx4~{rq4+)Up$IeI;DJFWnmxHrJ9m&Hwn*da`^C9zo)!H3Es{v$M8-qEx)CYH5 jMA^Xt13J9ahd&`MSQBUL;WdIo#i4+O&BG(Fp+NaR2gA>J From c167e30e312e429f69a7aeb12ca0a77eceb09f05 Mon Sep 17 00:00:00 2001 From: Till Grutschus Date: Mon, 8 Jan 2024 21:45:48 +0100 Subject: [PATCH 13/13] added diagrams to dvc --- .gitignore | 1 + diagrams.dvc | 6 ++++++ 2 files changed, 7 insertions(+) create mode 100644 diagrams.dvc diff --git a/.gitignore b/.gitignore index 7af2069..cf4b55f 100644 --- a/.gitignore +++ b/.gitignore @@ -166,3 +166,4 @@ cython_debug/ slurm-*.out /experiments /model_tests +/diagrams diff --git a/diagrams.dvc b/diagrams.dvc new file mode 100644 index 0000000..9d93cfd --- /dev/null +++ b/diagrams.dvc @@ -0,0 +1,6 @@ +outs: +- md5: 4b5c4d731c1f713db2248c1667716ec8.dir + size: 609116 + nfiles: 8 + hash: md5 + path: diagrams