v0.4.6

new feature

• add more mot utils (#133)

MOT Challenge formatted ground truth dataset creation:

• import required classes:

from sahi.utils.mot import MotAnnotation, MotFrame, MotVideo

• init video:

mot_video = MotVideo(name="sequence_name")

• init first frame:

mot_frame = MotFrame()

• add annotations to frame:

mot_frame.add_annotation(
  MotAnnotation(bbox=[x_min, y_min, width, height])
)

mot_frame.add_annotation(
  MotAnnotation(bbox=[x_min, y_min, width, height])
)

• add frame to video:

mot_video.add_frame(mot_frame)

• export in MOT challenge format:

mot_video.export(export_dir="mot_gt", type="gt")

• your MOT challenge formatted ground truth files are ready under mot_gt/sequence_name/ folder.

Advanced MOT Challenge formatted ground truth dataset creation:

• you can customize tracker while initializing mot video object:

tracker_params = {
  'max_distance_between_points': 30,
  'min_detection_threshold': 0,
  'hit_inertia_min': 10,
  'hit_inertia_max': 12,
  'point_transience': 4,
}
# for details: https://github.com/tryolabs/norfair/tree/master/docs#arguments

mot_video = MotVideo(tracker_kwargs=tracker_params)

• you can omit automatic track id generation and directly provide track ids of annotations:

# create annotations with track ids:
mot_frame.add_annotation(
  MotAnnotation(bbox=[x_min, y_min, width, height], track_id=1)
)

mot_frame.add_annotation(
  MotAnnotation(bbox=[x_min, y_min, width, height], track_id=2)
)

# add frame to video:
mot_video.add_frame(mot_frame)

# export in MOT challenge format without automatic track id generation:
mot_video.export(export_dir="mot_gt", type="gt", use_tracker=False)

• you can overwrite the results into already present directory by adding exist_ok=True:

mot_video.export(export_dir="mot_gt", type="gt", exist_ok=True)

MOT Challenge formatted tracker output creation:

• import required classes:

from sahi.utils.mot import MotAnnotation, MotFrame, MotVideo

• init video by providing video name:

mot_video = MotVideo(name="sequence_name")

• init first frame:

mot_frame = MotFrame()

• add tracker outputs to frame:

mot_frame.add_annotation(
  MotAnnotation(bbox=[x_min, y_min, width, height], track_id=1)
)

mot_frame.add_annotation(
  MotAnnotation(bbox=[x_min, y_min, width, height], track_id=2)
)

• add frame to video:

mot_video.add_frame(mot_frame)

• export in MOT challenge format:

mot_video.export(export_dir="mot_test", type="test")

• your MOT challenge formatted ground truth files are ready as mot_test/sequence_name.txt.

Advanced MOT Challenge formatted tracker output creation:

• you can enable tracker and directly provide object detector output:

# add object detector outputs:
mot_frame.add_annotation(
  MotAnnotation(bbox=[x_min, y_min, width, height])
)

mot_frame.add_annotation(
  MotAnnotation(bbox=[x_min, y_min, width, height])
)

# add frame to video:
mot_video.add_frame(mot_frame)

# export in MOT challenge format by applying a kalman based tracker:
mot_video.export(export_dir="mot_gt", type="gt", use_tracker=True)

• you can customize tracker while initializing mot video object:

tracker_params = {
  'max_distance_between_points': 30,
  'min_detection_threshold': 0,
  'hit_inertia_min': 10,
  'hit_inertia_max': 12,
  'point_transience': 4,
}
# for details: https://github.com/tryolabs/norfair/tree/master/docs#arguments

mot_video = MotVideo(tracker_kwargs=tracker_params)

• you can overwrite the results into already present directory by adding exist_ok=True:

mot_video.export(export_dir="mot_gt", type="gt", exist_ok=True)

documentation

• update coco docs (#134)
• add colab links into readme (#135)

Check YOLOv5 + SAHI demo:

Check MMDetection + SAHI demo:

bug fixes

• fix demo notebooks (#136)