MakeDataset.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "MakeDataset.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "6hIKF2nMFxXa",
        "outputId": "44507183-78bc-4d82-cda6-3807598b1553",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "!pip install retry"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting retry\n",
            "  Downloading https://files.pythonhosted.org/packages/4b/0d/53aea75710af4528a25ed6837d71d117602b01946b307a3912cb3cfcbcba/retry-0.9.2-py2.py3-none-any.whl\n",
            "Requirement already satisfied: py<2.0.0,>=1.4.26 in /usr/local/lib/python3.6/dist-packages (from retry) (1.9.0)\n",
            "Requirement already satisfied: decorator>=3.4.2 in /usr/local/lib/python3.6/dist-packages (from retry) (4.4.2)\n",
            "Installing collected packages: retry\n",
            "Successfully installed retry-0.9.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pwu1pSmS25VV"
      },
      "source": [
        "import os\n",
        "import cv2\n",
        "import numpy as np\n",
        "from PIL import Image\n",
        "from collections import Counter\n",
        "from google.colab.patches import cv2_imshow\n",
        "from torchvision.transforms import Compose, RandomAffine\n",
        "from joblib import Parallel, delayed\n",
        "from random import choice\n",
        "import time\n",
        "import platform\n",
        "from tqdm import trange\n",
        "\n",
        "\n",
        "import uuid\n",
        "from retry import retry\n",
        "\n",
        "IMG_DIR = \"images_train\"\n",
        "IMG_VALID_DIR = \"images_test\"\n",
        "os.makedirs(IMG_DIR, exist_ok=True)\n",
        "os.makedirs(IMG_VALID_DIR, exist_ok=True)\n"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Df0nir1ZQ_a3"
      },
      "source": [
        "def get_token(cookie=\"./cookie\"):\n",
        "    with open(cookie) as f:\n",
        "        for line in f:\n",
        "            if \"download\" in line:\n",
        "                return line.split()[-1]\n",
        "    return \"\"\n",
        "\n",
        "def gdrive_download(id, name):\n",
        "    # Downloads a file from Google Drive. from utils.google_utils import *; gdrive_download()\n",
        "    t = time.time()\n",
        "\n",
        "    print('Downloading https://drive.google.com/uc?export=download&id=%s as %s... ' % (id, name), end='')\n",
        "    os.remove(name) if os.path.exists(name) else None  # remove existing\n",
        "    os.remove('cookie') if os.path.exists('cookie') else None\n",
        "\n",
        "    # Attempt file download\n",
        "    out = \"NUL\" if platform.system() == \"Windows\" else \"/dev/null\"\n",
        "    os.system('curl -c ./cookie -s -L \"drive.google.com/uc?export=download&id=%s\" > %s ' % (id, out))\n",
        "    if os.path.exists('cookie'):  # large file\n",
        "        s = 'curl -Lb ./cookie \"drive.google.com/uc?export=download&confirm=%s&id=%s\" -o %s' % (get_token(), id, name)\n",
        "    else:  # small file\n",
        "        s = 'curl -s -L -o %s \"drive.google.com/uc?export=download&id=%s\"' % (name, id)\n",
        "    r = os.system(s)  # execute, capture return\n",
        "    os.remove('cookie') if os.path.exists('cookie') else None\n",
        "\n",
        "    # Error check\n",
        "    if r != 0:\n",
        "        os.remove(name) if os.path.exists(name) else None  # remove partial\n",
        "        print('Download error ')  # raise Exception('Download error')\n",
        "        return r\n",
        "\n",
        "    # Unzip if archive\n",
        "    if name.endswith('.zip'):\n",
        "        print('unzipping... ', end='')\n",
        "        os.system('unzip -q %s' % name)  # unzip\n",
        "        os.remove(name)  # remove zip to free space\n",
        "\n",
        "    print('Done (%.1fs)' % (time.time() - t))\n",
        "    return r\n"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vU-yc8YXPv-e"
      },
      "source": [
        "def cut_mask(mask):\n",
        "    x_mask = mask.any(axis = -1).any(axis = 0).flatten()\n",
        "    indicies, = np.where(x_mask)\n",
        "    x_b = indicies.min()\n",
        "    x_e = indicies.max() + 1\n",
        "        \n",
        "    y_mask = mask.any(axis = -1).any(axis = 1).flatten()\n",
        "    indicies, = np.where(y_mask)\n",
        "    y_b = indicies.min()\n",
        "    y_e = indicies.max() + 1\n",
        "\n",
        "    mask = mask[y_b:y_e, x_b:x_e]\n",
        "    return mask\n",
        "\n",
        "\n",
        "class Point: \n",
        "    def __init__(self, x, y): \n",
        "        self.x = x \n",
        "        self.y = y \n",
        "\n",
        "def intersects(bb, x, y, w, h):\n",
        "    bb_x, bb_y, bb_w, bb_h = bb\n",
        "    x00 = bb_x - bb_w/2\n",
        "    x01 = bb_x + bb_w/2\n",
        "    y00 = bb_y - bb_h/2\n",
        "    y01 = bb_y + bb_h/2\n",
        "\n",
        "    l1 = Point(x00, y00)\n",
        "    r1 = Point(x01, y01)\n",
        "\n",
        "    x10 = x\n",
        "    x11 = x + w\n",
        "    y10 = y\n",
        "    y11 = y + h\n",
        "\n",
        "    l2 = Point(x10, y10)\n",
        "    r2 = Point(x11, y11)\n",
        "\n",
        "    if (l1.x >= r2.x or l2.x >= r1.x):\n",
        "        return False\n",
        "\n",
        "    if (l1.y >= r2.y or l2.y >= r1.y):\n",
        "        return False\n",
        "\n",
        "    return True\n",
        "\n",
        "    \n",
        "\n",
        "def augment(picture_path,\n",
        "            initial_bb = [],\n",
        "            mask_path = 'logo.npz', \n",
        "            out_file = 'temp.png',\n",
        "            rotate_angle_max = 22.5,\n",
        "            max_fract = 0.3,\n",
        "            min_scale = 0.01,\n",
        "            B_interval = (0, 256),\n",
        "            G_interval = (0, 256),\n",
        "            R_interval = (0, 256),\n",
        "            shear = (-20, 20, -20, 20)):\n",
        "\n",
        "    picture_to_augment = cv2.imread(picture_path)\n",
        "    input_image_shape = picture_to_augment.shape\n",
        "    mask = np.load(mask_path)['mask']\n",
        "\n",
        "    B = np.random.randint(B_interval[0], B_interval[1])\n",
        "    G = np.random.randint(G_interval[0], G_interval[1])\n",
        "    R = np.random.randint(R_interval[0], R_interval[1])\n",
        "    A = 255\n",
        "\n",
        "    color = np.expand_dims(np.array((B, G, R, A)), [0, 1])\n",
        "    img_shape = tuple(list(mask.shape)[:-1] + [4])\n",
        "\n",
        "    aug_logo = np.zeros(img_shape)\n",
        "    aug_logo = aug_logo + color * mask\n",
        "\n",
        "    max_x_scale = input_image_shape[1] * max_fract / aug_logo.shape[1]\n",
        "    max_y_scale = input_image_shape[0] * max_fract / aug_logo.shape[0]\n",
        "    max_scale = min(max_x_scale, max_y_scale)\n",
        "\n",
        "    composer = Compose([\n",
        "                    RandomAffine(22.5, scale = (min_scale, max_scale), shear = shear)\n",
        "                  ])\n",
        "    \n",
        "    pil_logo = Image.fromarray(aug_logo.astype('uint8'))\n",
        "    \n",
        "    while True:\n",
        "        wide_logo = np.array(composer(pil_logo))\n",
        "        \n",
        "        wide_logo = cut_mask(wide_logo)\n",
        "        aug_shape = wide_logo.shape\n",
        "\n",
        "        y_coord = np.random.randint(0, input_image_shape[0] - aug_shape[0])\n",
        "        x_coord = np.random.randint(0, input_image_shape[1] - aug_shape[1])\n",
        "\n",
        "        if not initial_bb:\n",
        "            break\n",
        "\n",
        "\n",
        "        for bb in initial_bb:\n",
        "            if intersects(bb, \n",
        "                          x_coord/input_image_shape[1], \n",
        "                          y_coord/input_image_shape[0], \n",
        "                          aug_shape[1]/input_image_shape[1],\n",
        "                          aug_shape[0]/input_image_shape[0]):\n",
        "                break\n",
        "        else:\n",
        "            break\n",
        "    \n",
        "    new_mask = wide_logo.any(axis=-1, keepdims=True)\n",
        "    slice_of_interest = picture_to_augment[y_coord:y_coord+aug_shape[0],\n",
        "                                            x_coord:x_coord+aug_shape[1]] \n",
        "\n",
        "    np.multiply(slice_of_interest, np.logical_not(new_mask), out=slice_of_interest)\n",
        "\n",
        "    np.add(slice_of_interest, wide_logo[:, :, :-1], out = slice_of_interest)\n",
        "\n",
        "    cv2.imwrite(out_file, picture_to_augment)\n",
        "\n",
        "    w = aug_shape[1] / input_image_shape[1]\n",
        "    h = aug_shape[0] / input_image_shape[0]\n",
        "    x = x_coord / input_image_shape[1] + w / 2\n",
        "    y = y_coord / input_image_shape[0] + h / 2\n",
        "\n",
        "    initial_bb.append((x, y, w, h))\n",
        "\n",
        "    return out_file, initial_bb"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "562j31pzP-QG"
      },
      "source": [
        "def monochrome(array):\n",
        "    channels = array.shape[-1]\n",
        "    mask = array[:,:,-1:] // 255\n",
        "    array = array * mask\n",
        "    temporal = list(array.reshape(-1, channels))\n",
        "    temporal = map(tuple, temporal)\n",
        "    count = Counter(temporal)\n",
        "    del count[(0,0,0,0)]\n",
        "    return len(count.most_common()) == 1\n",
        "\n",
        "def augment_other(picture_path,\n",
        "                  initial_bb,\n",
        "                  aug_logo_path,\n",
        "                  out_file,\n",
        "                  rotate_angle_max = 22.5,\n",
        "                  max_fract = 0.3,\n",
        "                  min_scale = 0.01,\n",
        "                  B_interval = (0, 256),\n",
        "                  G_interval = (0, 256),\n",
        "                  R_interval = (0, 256),\n",
        "                  shear = (-20, 20, -20, 20)):\n",
        "    picture_to_augment = cv2.imread(picture_path)\n",
        "    if picture_to_augment is None:\n",
        "      print(picture_path)\n",
        "    input_image_shape = picture_to_augment.shape\n",
        "    aug_logo = cv2.imread(aug_logo_path, cv2.IMREAD_UNCHANGED)\n",
        "\n",
        "    aug_logo = cut_mask(aug_logo)\n",
        "    \n",
        "    max_x_scale = input_image_shape[1] * max_fract / aug_logo.shape[1]\n",
        "    max_y_scale = input_image_shape[0] * max_fract / aug_logo.shape[0]\n",
        "    max_scale = min(max_x_scale, max_y_scale)\n",
        "\n",
        "    composer = Compose([\n",
        "                    RandomAffine(22.5, scale = (min_scale, max_scale), shear = shear)\n",
        "                  ])\n",
        "    \n",
        "\n",
        "    pil_logo = Image.fromarray(aug_logo.astype('uint8'))\n",
        "\n",
        "    while True:\n",
        "        wide_logo = np.array(composer(pil_logo))\n",
        "        wide_logo = cut_mask(wide_logo)\n",
        "\n",
        "        aug_shape = wide_logo.shape\n",
        "\n",
        "        y_coord = np.random.randint(0, input_image_shape[0] - aug_shape[0])\n",
        "        x_coord = np.random.randint(0, input_image_shape[1] - aug_shape[1])\n",
        "\n",
        "        if not initial_bb:\n",
        "            break\n",
        "\n",
        "\n",
        "        for bb in initial_bb:\n",
        "            if intersects(bb, \n",
        "                          x_coord/input_image_shape[1], \n",
        "                          y_coord/input_image_shape[0], \n",
        "                          aug_shape[1]/input_image_shape[1],\n",
        "                          aug_shape[0]/input_image_shape[0]):\n",
        "                break\n",
        "        else:\n",
        "            break\n",
        "\n",
        "    new_mask = wide_logo.any(axis=-1, keepdims=True)\n",
        "\n",
        "    if monochrome(wide_logo):\n",
        "        B = np.random.randint(B_interval[0], B_interval[1])\n",
        "        G = np.random.randint(G_interval[0], G_interval[1])\n",
        "        R = np.random.randint(R_interval[0], R_interval[1])\n",
        "        A = 255\n",
        "\n",
        "        color = np.expand_dims(np.array((B, G, R, A)), [0, 1]).astype('uint8')\n",
        "        img_shape = tuple(list(new_mask.shape)[:-1] + [4])\n",
        "\n",
        "        wide_logo = np.zeros(img_shape, dtype = 'uint8')\n",
        "        wide_logo = wide_logo + color * new_mask\n",
        "\n",
        "    slice_of_interest = picture_to_augment[y_coord:y_coord+aug_shape[0],\n",
        "                                            x_coord:x_coord+aug_shape[1]] \n",
        "\n",
        "    np.multiply(slice_of_interest, np.logical_not(new_mask), out=slice_of_interest)\n",
        "\n",
        "    np.add(slice_of_interest, wide_logo[:, :, :-1], out = slice_of_interest)\n",
        "\n",
        "    cv2.imwrite(out_file, picture_to_augment)"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qvHQ4wshUJMQ"
      },
      "source": [
        "def augment_image(picture_path,\n",
        "                  logo_path,\n",
        "                  temp_path,\n",
        "                  fake_folder,\n",
        "                  rotate_angle_max = 22.5, \n",
        "                  max_fract = 0.3,\n",
        "                  min_scale = 0.01,\n",
        "                  true_range = (1, 2),\n",
        "                  fake_range = (1, 3),\n",
        "                  B_interval = (0, 256),\n",
        "                  G_interval = (0, 256),\n",
        "                  R_interval = (0, 256),\n",
        "                  shear = (-20, 20, -20, 20)):\n",
        "    true_to_paste = np.random.randint(true_range[0], true_range[1])\n",
        "\n",
        "    bboxes = []\n",
        "\n",
        "    if true_to_paste:\n",
        "        file_path, bboxes = augment(picture_path, bboxes, out_file = temp_path)\n",
        "\n",
        "        for i in range(true_to_paste - 1):\n",
        "            file_path, bboxes = augment(file_path, bboxes)\n",
        "    \n",
        "    fake_to_paste = np.random.randint(fake_range[0], fake_range[1])\n",
        "    possible_fakes = os.listdir(fake_folder)\n",
        "\n",
        "    for i in range(fake_to_paste):\n",
        "        current_fake = np.random.choice(possible_fakes, 1)[0]\n",
        "        augment_other(file_path, bboxes, fake_folder + current_fake, out_file = temp_path)\n",
        "    \n",
        "    return temp_path, bboxes"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u1DkY7pUQVqY",
        "outputId": "f23ec9b6-9efa-427e-f20b-cce5bfad4658",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "gdrive_download(\"1j2VX2eFW90L-1YVQjj0L5gre2YjnCz69\", \"similar.zip\")\n",
        "gdrive_download(\"1G8NjtqQYTGG0SPPogGuG7tY23uMl9DmR\", \"stock.zip\")\n",
        "gdrive_download(\"1NPg4nf6ReQK2yq9QV-lVEM1i7bQN0JBw\", \"logo.npz\")"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading https://drive.google.com/uc?export=download&id=1j2VX2eFW90L-1YVQjj0L5gre2YjnCz69 as similar.zip... unzipping... Done (2.6s)\n",
            "Downloading https://drive.google.com/uc?export=download&id=1G8NjtqQYTGG0SPPogGuG7tY23uMl9DmR as stock.zip... unzipping... Done (48.8s)\n",
            "Downloading https://drive.google.com/uc?export=download&id=1NPg4nf6ReQK2yq9QV-lVEM1i7bQN0JBw as logo.npz... Done (1.6s)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HJpEcifpCPwL",
        "outputId": "b53c4b19-bfe9-4858-b381-05bb67b7ae86",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "TRAIN_SIZE = 1000\n",
        "VALID_SIZE = 250\n",
        "\n",
        "base_path = '/content/Stock'\n",
        "image_choices = os.listdir(base_path)\n",
        "\n",
        "# augment_image(\n",
        "#     os.path.join(base_path, choice(image_choices)),\n",
        "#     'logo.npz',\n",
        "#     '%s/%s.png' % (IMG_DIR, \"image_\" + str(uuid.uuid4())),\n",
        "#     'similar/')\n",
        "images = Parallel(n_jobs=4)(delayed(augment_image)(\n",
        "    os.path.join(base_path, choice(image_choices)),\n",
        "    'logo.npz',\n",
        "    '%s/%s.png' % (IMG_DIR if i < TRAIN_SIZE else IMG_VALID_DIR, \"image_\" + str(uuid.uuid4())),\n",
        "    'similar/') for i in trange(TRAIN_SIZE + VALID_SIZE))"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
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            "\n",
            "\n",
            "\n",
            "\n",
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            "  3%|▎         | 40/1250 [00:20<11:12,  1.80it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "  4%|▎         | 44/1250 [00:22<10:45,  1.87it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "  4%|▍         | 48/1250 [00:24<10:30,  1.91it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "  4%|▍         | 52/1250 [00:26<10:21,  1.93it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "  4%|▍         | 56/1250 [00:28<10:15,  1.94it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "  5%|▍         | 60/1250 [00:31<10:13,  1.94it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "  5%|▌         | 64/1250 [00:33<10:40,  1.85it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "  5%|▌         | 68/1250 [00:35<10:56,  1.80it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "  6%|▌         | 72/1250 [00:37<10:26,  1.88it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "  6%|▌         | 76/1250 [00:39<10:35,  1.85it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "  6%|▋         | 80/1250 [00:42<11:33,  1.69it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "  7%|▋         | 84/1250 [00:45<11:31,  1.69it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "  7%|▋         | 88/1250 [00:47<11:15,  1.72it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "  7%|▋         | 92/1250 [00:49<11:09,  1.73it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "  8%|▊         | 96/1250 [00:51<10:30,  1.83it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "  8%|▊         | 100/1250 [00:53<10:07,  1.89it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "  8%|▊         | 104/1250 [00:57<12:47,  1.49it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
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            "\n",
            "\n",
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            "  9%|▊         | 108/1250 [01:01<15:03,  1.26it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            "  9%|▉         | 112/1250 [01:03<13:37,  1.39it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
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            "  9%|▉         | 116/1250 [01:06<12:45,  1.48it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
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            " 10%|▉         | 120/1250 [01:08<12:13,  1.54it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
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            " 10%|▉         | 124/1250 [01:10<11:06,  1.69it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
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            " 10%|█         | 128/1250 [01:12<10:21,  1.81it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
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            " 11%|█         | 132/1250 [01:14<09:47,  1.90it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " 11%|█         | 136/1250 [01:15<09:18,  2.00it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " 11%|█         | 140/1250 [01:17<08:54,  2.08it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " 12%|█▏        | 144/1250 [01:20<09:58,  1.85it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " 12%|█▏        | 148/1250 [01:22<09:53,  1.86it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " 12%|█▏        | 152/1250 [01:25<10:28,  1.75it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " 12%|█▏        | 156/1250 [01:27<10:09,  1.80it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " 13%|█▎        | 160/1250 [01:29<09:47,  1.85it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " 13%|█▎        | 164/1250 [01:31<09:44,  1.86it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " 13%|█▎        | 168/1250 [01:33<10:11,  1.77it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " 14%|█▍        | 172/1250 [01:36<10:15,  1.75it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " 14%|█▍        | 176/1250 [01:38<10:10,  1.76it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " 14%|█▍        | 180/1250 [01:40<09:40,  1.84it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " 15%|█▍        | 184/1250 [01:42<09:36,  1.85it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " 15%|█▌        | 188/1250 [01:44<09:27,  1.87it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " 15%|█▌        | 192/1250 [01:46<09:33,  1.85it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " 16%|█▌        | 196/1250 [01:49<09:37,  1.82it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " 16%|█▌        | 200/1250 [01:51<09:57,  1.76it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " 16%|█▋        | 204/1250 [01:54<10:17,  1.69it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " 17%|█▋        | 208/1250 [01:56<10:09,  1.71it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " 17%|█▋        | 212/1250 [01:58<10:08,  1.71it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " 17%|█▋        | 216/1250 [02:00<09:42,  1.77it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " 18%|█▊        | 220/1250 [02:02<09:14,  1.86it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " 18%|█▊        | 224/1250 [02:04<09:06,  1.88it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " 18%|█▊        | 228/1250 [02:06<08:55,  1.91it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " 19%|█▊        | 232/1250 [02:08<08:34,  1.98it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " 19%|█▉        | 236/1250 [02:10<08:24,  2.01it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " 19%|█▉        | 240/1250 [02:12<08:33,  1.97it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " 20%|█▉        | 244/1250 [02:14<08:47,  1.91it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " 20%|█▉        | 248/1250 [02:17<08:50,  1.89it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " 20%|██        | 252/1250 [02:20<10:16,  1.62it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " 20%|██        | 256/1250 [02:22<10:06,  1.64it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " 21%|██        | 260/1250 [02:24<09:23,  1.76it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " 21%|██        | 264/1250 [02:26<09:15,  1.77it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " 21%|██▏       | 268/1250 [02:28<08:51,  1.85it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " 22%|██▏       | 272/1250 [02:31<09:33,  1.71it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " 22%|██▏       | 276/1250 [02:33<09:07,  1.78it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " 22%|██▏       | 280/1250 [02:35<08:50,  1.83it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " 23%|██▎       | 284/1250 [02:37<08:36,  1.87it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " 23%|██▎       | 288/1250 [02:40<08:52,  1.81it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " 23%|██▎       | 292/1250 [02:42<08:38,  1.85it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            " 24%|██▎       | 296/1250 [02:44<08:24,  1.89it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jXMPREZPJmk2"
      },
      "source": [
        "for image in images:\n",
        "  path, bboxes = image\n",
        "  annot_path = path.replace(\".png\", \".txt\")\n",
        "\n",
        "  for bbox in bboxes:\n",
        "    x, y, w, h = bbox\n",
        "    \n",
        "    with open(annot_path, \"w\") as f:\n",
        "      f.write(f\"0 {x} {y} {w} {h}\")"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WNLpyyDBkDHQ",
        "outputId": "bc0d5114-ae12-4034-84cc-0f38ff5365eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "!ls -la images_train/*.txt | head -n 1"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-rw-r--r-- 1 root root 44 Oct  5 20:35 images_train/image_0015f1b8-220a-4c90-a53e-4e32b5c9c6bc.txt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ktGyerO9SOV2",
        "outputId": "49a1f46d-4d96-42fe-d55a-69d43224a5cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "!zip -r images.zip images_train images_test"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "updating: images_train/ (stored 0%)\n",
            "updating: images_test/ (stored 0%)\n",
            "  adding: images_train/image_fb7a0d79-2b75-485c-acf6-05add8422ea4.txt (deflated 17%)\n",
            "  adding: images_train/image_20d6724b-05ee-4f32-b184-571ff4e80052.txt (deflated 50%)\n",
            "  adding: images_train/image_791fe3d0-ff90-4b38-908e-a832473cd14a.txt (deflated 35%)\n",
            "  adding: images_train/image_8a6a995d-8d9c-4865-bf2f-0a7279c8e8ed.txt (deflated 49%)\n",
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            "  adding: images_train/image_1041fd8b-80a0-498c-8bd0-8da16d0a628e.png (deflated 8%)\n",
            "  adding: images_train/image_3b9fad82-cab3-43db-ae29-38b340cfad2c.txt (deflated 26%)\n",
            "  adding: images_train/image_f810ed65-bcc5-4350-8bd3-730fdf5d9b97.txt (deflated 32%)\n",
            "  adding: images_train/image_dab5d473-b316-4068-b30b-7cc1a9c620a4.png (deflated 5%)\n",
            "  adding: images_train/image_4db2229a-7a55-4139-9304-5c5f3fa67555.txt (deflated 29%)\n",
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            "  adding: images_train/image_35b65acc-7697-431d-8274-e49d83173937.txt (deflated 33%)\n",
            "  adding: images_train/image_d55c373e-0fb7-41fd-86ab-8847edf3b900.png (deflated 1%)\n",
            "  adding: images_train/image_726a0295-b39a-43b5-a1ff-9da28fec2092.txt (deflated 35%)\n",
            "  adding: images_train/image_41437d91-982c-4091-b6ee-56a4580e3d6a.png (deflated 1%)\n",
            "  adding: images_train/image_cdeb6300-0807-4912-bdd5-704c6a6852b1.txt (deflated 34%)\n",
            "  adding: images_train/image_b9ecd454-e4f1-4aeb-a557-b5b054ff1c7f.png (deflated 5%)\n",
            "  adding: images_train/image_447e72de-3c68-4a08-8cfd-9e05266513ee.txt (deflated 18%)\n",
            "  adding: images_train/image_e1dc7a80-b2b7-4033-8a82-fe6647189603.txt (deflated 26%)\n",
            "  adding: images_train/image_87d66e60-c331-49ee-989b-e5a4b84d7950.txt (deflated 38%)\n",
            "  adding: images_train/image_0bacd5d0-4111-4fe1-ac4a-764e911e13b7.png (deflated 0%)\n",
            "  adding: images_train/image_78df845b-a0dd-4630-a5fb-14ba133ab97d.png (deflated 5%)\n",
            "  adding: images_train/image_3bf5dcca-ba47-404e-a389-19ff38d29a73.txt (deflated 26%)\n",
            "  adding: images_train/image_2a868656-fe30-471f-a262-cc2db0164b00.png (deflated 6%)\n",
            "  adding: images_train/image_ef7ddc4d-08ab-42f0-8584-2b8a53ebf4da.txt (deflated 53%)\n",
            "  adding: images_train/image_6e36291b-fdce-447a-b3c8-f883dae9f3e5.png (deflated 13%)\n",
            "  adding: images_train/image_9fec5853-88f4-4a25-b835-b7fbcd0c7747.png (deflated 0%)\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Whq3OKqXUfrG",
        "outputId": "1f215448-793a-43e0-b1db-f4566fc88306",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/gdrive')\n",
        "%cp images.zip /gdrive/My\\ Drive/"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /gdrive; to attempt to forcibly remount, call drive.mount(\"/gdrive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3c5o_emWfpHz",
        "outputId": "e2090f7d-0b2d-4a2b-cc19-ad09cd13457d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "!ls -lh images.zip"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-rw-r--r-- 1 root root 1.3G Oct  5 20:36 images.zip\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PFPMZ9_IVTUz",
        "outputId": "a6eabb83-c4bf-4cac-e90b-f3e163b02b5b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        }
      },
      "source": [
        "!ls -lh /gdrive/My\\ Drive/"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 12G\n",
            "-rw------- 1 root root 1.3G Oct  3 14:45  albums.zip\n",
            "-rw------- 1 root root 7.6M Oct  2 08:52  all_hists.npz\n",
            "drwx------ 2 root root 4.0K Oct 26  2017  Classroom\n",
            "drwx------ 2 root root 4.0K Sep 30 21:51 'Colab Notebooks'\n",
            "-rw------- 1 root root 7.5K Sep 30 22:15  crop_dataset.py\n",
            "-rw------- 1 root root 344M Oct  1 13:39  DETRAC_cropped.zip\n",
            "-rw------- 1 root root  13K Sep 30 22:15  detrac_to_yolo.py\n",
            "-rw------- 1 root root  13M Sep 30 22:14  DETRAC-Train-Annotations-XML-v3.zip\n",
            "-rw------- 1 root root 5.3G Sep 30 21:52  DETRAC-train-data.zip\n",
            "drwx------ 2 root root 4.0K Oct  2 17:55  eval_output\n",
            "drwx------ 2 root root 4.0K Oct  1 16:05  histograms\n",
            "-rw------- 1 root root 5.2G Oct  5 01:16  images.zip\n",
            "drwx------ 2 root root 4.0K Oct  2 08:53  output\n",
            "-rw------- 1 root root 3.3K Sep 30 22:15  README.md\n",
            "drwx------ 2 root root 4.0K Oct  1 07:56  Stock\n",
            "drwx------ 2 root root 4.0K Oct  1 16:05  visualisation\n",
            "-rw------- 1 root root  12K Sep 30 22:15  yolov4-obj.cfg\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}