Test YOLO Object Trackers

README.md

@@ -10,3 +10,34 @@ Then it can be run with `skellytracker`.
 Running the basic `skellytracker` will open the first webcam port on your computer and run pose estimaiton in realtime with mediapipe holistic as a tracker. You can specify the tracker with `skellytracker TRACKER_NAME`, where `TRACKER_NAME` is the name of an available tracker. To view the names of all available trackers, see `RUN_ME.py`.
 
 It will take some time to initialize the tracker the first time you run it, as it will likely need to download the model.
+
+## Using skellytracker in your project
+
+To use skellytracker in your project, import a tracker like `from skellytracker import YOLOPoseTracker`, then instantiate it with your desired parameters like `tracker = YOLOPoseTracker(model_size="medium")`, and then use `tracker.process_image(frame)` or `tracker.process_video(video_filepath)`. Processing image by image will let you access each individual annotated frame with `tracker.annotated_image`, and you can optionally record the data with `tracker.recorder.record()`. Access recorded data with `tracker.recorder.process_tracked_objects()`. The running, recording, and processing are done separately to give control over the amount of processing done at each step in the pipeline. Processing an entire video allows you to save the annotated frames as a video, and optionally saves and returns the data as a numpy array. Each tracker has an associated `ModelInfo` class to access model attributes.
+
+Skellytracker is still under development, so version updates may make breaking changes to the API. Please report any issues and pull requests to the [skellytracker repo](https://github.com/freemocap/skellytracker).
+
+### Extending the API
+To extend the API, import the `BaseTracker` and `BaseRecorder` abstract base classes from skellytracker. Then create a new tracker and recorder inheriting from the base classes and implement all of the abstract methods.
+
+## Contributing
+
+We love your input! We want to make contributing to this project as easy and transparent as possible, whether it's:
+
+- Reporting a bug
+- Discussing the current state of the code
+- Submitting a fix
+- Proposing new features
+- Becoming a maintainer
+
+Pull requests are the best way to propose changes to the codebase (we
+use [Github Flow](https://docs.github.com/en/get-started/quickstart/github-flow)). We actively welcome your pull
+requests:
+
+1. Fork the repo and create your branch from `main`.
+2. Download the development dependencies with `pip install -e '.[dev]'`.
+2. If you've added code that should be tested (including any tracker), add tests.
+3. If you've changed APIs, update the documentation.
+4. Ensure the test suite passes by running `pytest skellytracker/tests`.
+5. Make sure your code lints.
+6. Make that pull request!

skellytracker/__init__.py

@@ -27,10 +27,12 @@
 
 try:
  from skellytracker.trackers.mediapipe_tracker.mediapipe_holistic_tracker import MediapipeHolisticTracker
+ from skellytracker.trackers.mediapipe_tracker.mediapipe_model_info import MediapipeModelInfo
 except:
  print("To use mediapipe_holistic_tracker, install skellytracker[mediapipe]")
 try:
  from skellytracker.trackers.yolo_tracker.yolo_tracker import YOLOPoseTracker
+ from skellytracker.trackers.yolo_tracker.yolo_model_info import YOLOModelInfo
 except:
  print("To use yolo_tracker, install skellytracker[yolo]")
 try:

skellytracker/tests/test_mediapipe_holistic_tracker.py

@@ -1,5 +1,3 @@
-import math
-import cv2
 import pytest
 import numpy as np
 

skellytracker/tests/test_yolo_mediapipe_combo_tracker.py

@@ -0,0 +1,231 @@
+import pytest
+import numpy as np
+
+
+from skellytracker.trackers.mediapipe_tracker.mediapipe_model_info import (
+ MediapipeModelInfo,
+)
+from skellytracker.trackers.yolo_mediapipe_combo_tracker.yolo_mediapipe_combo_tracker import (
+ YOLOMediapipeComboTracker,
+)
+
+
+@pytest.mark.usefixtures("test_image")
+def test_process_image(test_image):
+ tracker = YOLOMediapipeComboTracker(
+ model_size="nano",
+ model_complexity=0,
+ )
+ tracked_objects = tracker.process_image(test_image)
+
+ assert len(tracked_objects) == 4
+ assert tracked_objects["pose_landmarks"] is not None
+ assert tracked_objects["pose_landmarks"].extra["landmarks"] is not None
+ assert tracked_objects["right_hand_landmarks"] is not None
+ assert tracked_objects["right_hand_landmarks"].extra["landmarks"] is not None
+ assert tracked_objects["left_hand_landmarks"] is not None
+ assert tracked_objects["left_hand_landmarks"].extra["landmarks"] is not None
+ assert tracked_objects["face_landmarks"] is not None
+ assert tracked_objects["face_landmarks"].extra["landmarks"] is not None
+
+
+@pytest.mark.usefixtures("test_image")
+def test_annotate_image(test_image):
+ tracker = YOLOMediapipeComboTracker(
+ model_size="nano",
+ model_complexity=0,
+ )
+ tracker.process_image(test_image)
+
+ assert tracker.annotated_image is not None
+
+
+@pytest.mark.usefixtures("test_image")
+def test_record_no_buffer(test_image):
+ tracker = YOLOMediapipeComboTracker(
+ model_size="nano",
+ model_complexity=0,
+ bounding_box_buffer_percentage=0,
+ )
+ tracked_objects = tracker.process_image(test_image)
+ tracker.recorder.record(tracked_objects=tracked_objects)
+ assert len(tracker.recorder.recorded_objects) == 1
+ assert len(tracker.recorder.recorded_objects[0]) == 4
+
+ processed_results = tracker.recorder.process_tracked_objects(
+ image_size=test_image.shape[:2]
+ )
+ assert processed_results is not None
+ assert processed_results.shape == (
+ 1,
+ MediapipeModelInfo.num_tracked_points_total,
+ 3,
+ )
+
+ expected_results = np.array(
+ [
+ [
+ [735.7643890380859, 77.78585314750671, -485.70934295654297],
+ [757.0420074462891, 73.8272774219513, -451.7356872558594],
+ [765.8688354492188, 75.42142689228058, -452.0623016357422],
+ [774.5138549804688, 77.09728181362152, -452.29907989501953],
+ [729.3473052978516, 70.99358797073364, -452.5171661376953],
+ [720.2278137207031, 70.5675083398819, -452.57137298583984],
+ [711.7780303955078, 70.23908793926239, -452.44258880615234],
+ [780.3971099853516, 86.27676665782928, -254.8146629333496],
+ [694.5964813232422, 76.93311989307404, -251.65258407592773],
+ [745.7817077636719, 89.95153248310089, -411.6321563720703],
+ [709.8857879638672, 86.26414954662323, -411.1183166503906],
+ [817.0114135742188, 149.35277938842773, -178.73090744018555],
+ [546.7483139038086, 128.01610708236694, -154.72180366516113],
+ [825.9496307373047, 219.85299110412598, -142.79363632202148],
+ [394.10709381103516, 183.29222917556763, -92.53458023071289],
+ [850.7412719726562, 284.1442108154297, -277.7159309387207],
+ [251.08980178833008, 229.20471668243408, -194.44988250732422],
+ [860.8124542236328, 301.0432004928589, -309.02509689331055],
+ [211.39860153198242, 242.8161120414734, -219.8578643798828],
+ [843.7083435058594, 300.99024295806885, -385.36643981933594],
+ [222.63912200927734, 246.6996932029724, -290.17900466918945],
+ [839.26025390625, 295.74596643447876, -312.1304130554199],
+ [240.05075454711914, 241.5028166770935, -226.83252334594727],
+ [632.852668762207, 273.6677813529968, -22.331013679504395],
+ [484.76829528808594, 259.1326332092285, 21.80124521255493],
+ [549.3550491333008, 360.4587650299072, -38.61574411392212],
+ [425.41954040527344, 343.2985496520996, -20.899696350097656],
+ [486.68445587158203, 415.20827293395996, 411.0944366455078],
+ [399.84127044677734, 408.2763719558716, 398.0601501464844],
+ [476.32495880126953, 416.31832122802734, 451.4645767211914],
+ ]
+ ]
+ )
+ assert np.allclose(
+ processed_results[:, :30, :], expected_results[:, :30, :], atol=2
+ )
+
+
+@pytest.mark.usefixtures("test_image")
+def test_record_buffer_by_image_size(test_image):
+ tracker = YOLOMediapipeComboTracker(
+ model_size="nano",
+ model_complexity=0,
+ bounding_box_buffer_percentage=10,
+ buffer_size_method="buffer_by_image_size",
+ )
+ tracked_objects = tracker.process_image(test_image)
+ tracker.recorder.record(tracked_objects=tracked_objects)
+ assert len(tracker.recorder.recorded_objects) == 1
+ assert len(tracker.recorder.recorded_objects[0]) == 4
+
+ processed_results = tracker.recorder.process_tracked_objects(
+ image_size=test_image.shape[:2]
+ )
+ assert processed_results is not None
+ assert processed_results.shape == (
+ 1,
+ MediapipeModelInfo.num_tracked_points_total,
+ 3,
+ )
+
+ expected_results = np.array(
+ [
+ [
+ [732.0687866210938, 79.0345823764801, -635.7109069824219],
+ [753.8626098632812, 75.13578772544861, -608.0960464477539],
+ [761.7510223388672, 76.88950717449188, -608.355827331543],
+ [769.5530700683594, 78.81806373596191, -608.4667587280273],
+ [729.2195892333984, 72.18442976474762, -607.5491333007812],
+ [721.5387725830078, 71.86003804206848, -607.6824188232422],
+ [714.1613006591797, 71.62245333194733, -607.6252746582031],
+ [775.9226226806641, 86.93966388702393, -409.65084075927734],
+ [698.8643646240234, 77.10046291351318, -402.1929931640625],
+ [741.6652679443359, 90.84179520606995, -557.6795196533203],
+ [709.4770050048828, 86.31318032741547, -555.5589294433594],
+ [814.6518707275391, 149.26713109016418, -293.33160400390625],
+ [544.8312759399414, 127.3474645614624, -270.33700942993164],
+ [831.3512420654297, 219.2579483985901, -218.43671798706055],
+ [392.59105682373047, 186.1086130142212, -182.6705551147461],
+ [854.2007446289062, 282.19802141189575, -344.9155044555664],
+ [248.35845947265625, 233.24616193771362, -273.1560516357422],
+ [857.7934265136719, 299.5019817352295, -382.82962799072266],
+ [202.44756698608398, 248.4213924407959, -300.88146209716797],
+ [844.1407775878906, 300.61514139175415, -460.82714080810547],
+ [212.99942016601562, 251.683087348938, -378.4083938598633],
+ [839.8464965820312, 294.21818017959595, -380.2419662475586],
+ [230.61071395874023, 246.78754091262817, -307.7861785888672],
+ [639.9309539794922, 268.61598014831543, -0.536465011537075],
+ [481.90975189208984, 254.10432815551758, 0.47791849821805954],
+ [546.6202926635742, 351.9411635398865, 89.65752601623535],
+ [416.8575668334961, 342.4706482887268, -87.49273300170898],
+ [496.87782287597656, 406.89836025238037, 635.2282333374023],
+ [393.65081787109375, 407.12096214294434, 370.8687210083008],
+ [481.2765884399414, 408.57420444488525, 681.8212890625],
+ ]
+ ]
+ )
+ assert np.allclose(
+ processed_results[:, :30, :], expected_results[:, :30, :], atol=2
+ )
+
+
+@pytest.mark.usefixtures("test_image")
+def test_record_buffer_by_box_size(test_image):
+ tracker = YOLOMediapipeComboTracker(
+ model_size="nano",
+ model_complexity=0,
+ bounding_box_buffer_percentage=10,
+ buffer_size_method="buffer_by_box_size",
+ )
+ tracked_objects = tracker.process_image(test_image)
+ tracker.recorder.record(tracked_objects=tracked_objects)
+ assert len(tracker.recorder.recorded_objects) == 1
+ assert len(tracker.recorder.recorded_objects[0]) == 4
+
+ processed_results = tracker.recorder.process_tracked_objects(
+ image_size=test_image.shape[:2]
+ )
+ assert processed_results is not None
+ assert processed_results.shape == (
+ 1,
+ MediapipeModelInfo.num_tracked_points_total,
+ 3,
+ )
+
+ expected_results = np.array(
+ [
+ [
+ [731.2718200683594, 77.88420975208282, -548.9945602416992],
+ [754.6127319335938, 74.30741965770721, -521.489372253418],
+ [762.8125762939453, 76.33775532245636, -521.7532348632812],
+ [771.2681579589844, 78.52324604988098, -521.8650436401367],
+ [730.0675964355469, 70.9510749578476, -521.0283660888672],
+ [722.1942138671875, 70.54689288139343, -521.1295318603516],
+ [714.8076629638672, 70.24498343467712, -521.0332870483398],
+ [779.2241668701172, 87.20496118068695, -340.54332733154297],
+ [700.3346252441406, 76.72817766666412, -330.6981658935547],
+ [741.3204956054688, 90.19120931625366, -479.1018295288086],
+ [708.2732391357422, 85.63711881637573, -476.2978744506836],
+ [813.9542388916016, 149.21152353286743, -249.2682647705078],
+ [545.847282409668, 128.2720971107483, -214.6741485595703],
+ [831.6414642333984, 217.95576810836792, -199.20928955078125],
+ [392.5309371948242, 185.60349941253662, -141.00683212280273],
+ [850.6895446777344, 280.559663772583, -331.45111083984375],
+ [252.97996520996094, 230.9888792037964, -229.0725326538086],
+ [856.6841125488281, 298.4851026535034, -367.7482604980469],
+ [203.86322021484375, 247.05041885375977, -250.86517333984375],
+ [841.0806274414062, 299.39956426620483, -436.93775177001953],
+ [215.9295654296875, 250.416419506073, -324.9034881591797],
+ [836.7790985107422, 292.6754379272461, -363.13419342041016],
+ [234.16423797607422, 246.12889766693115, -261.9901657104492],
+ [639.0184783935547, 271.9633984565735, -11.131852865219116],
+ [485.26206970214844, 257.6048684120178, 10.823948383331299],
+ [544.7049331665039, 353.29198837280273, 29.705591201782227],
+ [422.3688507080078, 341.57193660736084, -53.83963108062744],
+ [484.64855194091797, 412.5028896331787, 527.0954132080078],
+ [392.8578186035156, 406.18433475494385, 385.8837127685547],
+ [471.3254165649414, 411.8346977233887, 569.1376495361328],
+ ]
+ ]
+ )
+ assert np.allclose(
+ processed_results[:, :30, :], expected_results[:, :30, :], atol=2
+ )

skellytracker/tests/test_yolo_object_tracker.py

@@ -0,0 +1,42 @@
+import pytest
+import numpy as np
+
+
+from skellytracker.trackers.yolo_object_tracker.yolo_object_tracker import (
+ YOLOObjectTracker,
+)
+
+
+@pytest.mark.usefixtures("test_image")
+def test_process_image_person_only(test_image):
+ tracker = YOLOObjectTracker(model_size="nano", person_only=True)
+ tracked_objects = tracker.process_image(test_image)
+
+ assert len(tracked_objects) == 1
+ assert tracked_objects["object"] is not None
+ assert tracked_objects["object"].extra["boxes_xyxy"] is not None
+ assert np.allclose(tracked_objects["object"].extra["boxes_xyxy"], [90.676,96.981,493.54,812.03], atol=1e-2)
+ assert tracked_objects["object"].extra["original_image_shape"] == (1280, 720)
+
+@pytest.mark.usefixtures("test_image")
+def test_annotate_image(test_image):
+ tracker = YOLOObjectTracker()
+ tracker.process_image(test_image)
+
+ assert tracker.annotated_image is not None
+
+
+@pytest.mark.usefixtures("test_image")
+def test_record(test_image):
+ tracker = YOLOObjectTracker(model_size="nano", person_only=True)
+ tracked_objects = tracker.process_image(test_image)
+ tracker.recorder.record(tracked_objects=tracked_objects)
+ assert len(tracker.recorder.recorded_objects) == 1
+
+ processed_results = tracker.recorder.process_tracked_objects()
+ assert processed_results is not None
+ assert processed_results.shape == (1,4)
+
+ assert np.allclose(
+ processed_results, [90.676,96.981,493.54,812.03], atol=1e-2
+ )

skellytracker/trackers/base_tracker/base_recorder.py

@@ -53,6 +53,8 @@ def save(self, file_path: str) -> None:
  :return: None
  """
  if self.recorded_objects_array is None:
- self.process_tracked_objects()
+ recorded_objects_array = self.process_tracked_objects()
+ else:
+ recorded_objects_array = self.recorded_objects_array
  logger.info(f"Saving recorded objects to {file_path}")
- np.save(file_path, self.recorded_objects_array)
+ np.save(file_path, recorded_objects_array)