def run_visualization(
cap: cv2.VideoCapture,
out: cv2.VideoWriter,
detection_pipeline: pipeline.DetectionPipeline,
) -> List[Dict[ID, detect.ObjectDetection]]:
ret, frame = True, None
frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
i = 1
pbar = tqdm(total=frame_count)
objects = dict()
while cap.grab():
pbar.update(1)
ret, frame = cap.retrieve(frame)
# run detections on passed in frames
detections = detection_pipeline(frame)
for det in detections:
objects[det.id] = det.obj_class
visualization.draw_all_detections(img=frame,
detections=detections,
color=[255, 0, 0],
font_face=cv2.FONT_HERSHEY_PLAIN,
font_scale=5.0,
thickness=3)
#print('objects = ' + str(objects))
out.write(frame)
return objects.values()
def construct_scene_graph(
cap: cv2.VideoCapture,
detection_pipeline: pipeline.DetectionPipeline,
scene_reconstructor: reconstruction.SceneReconstructor,
):
'''
return:void
parameters: cap, detection_pipeline, scene_reconstructor
definition: method calls scene_reconstructor functions
'''
frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
frame = None
i = 1
pbar = tqdm(total=frame_count)
while cap.grab():
pbar.update(1)
frame = cap.retrieve(frame)
detections = detection_pipeline(frame)
scene_reconstructor.add_frame(detections)
return scene_reconstructor.finalize()