def main():
print("Prepare videos and frames")
# video = Video.objects.filter(path__contains='men_single_final_gold')[0]
# movie_path = video.path
# movie_name = video.item_name()
sc = Client()
stride = 1
input_stream = NamedVideoStream(sc, movie_name, path=movie_path)
frame = sc.io.Input([input_stream])
strided_frame = sc.streams.Stride(frame, [stride])
print('Running Scanner MaskRCNN op')
maskrcnn_frame = sc.ops.MaskRCNNDetectObjects(
frame=strided_frame,
device=DeviceType.GPU, #if sc.has_gpu() else DeviceType.CPU,
batch=8,
confidence_threshold=0.5,
min_image_size=800)
maskrcnn_stream = NamedStream(sc, movie_name + '_maskrcnn')
output_op = sc.io.Output(maskrcnn_frame, [maskrcnn_stream])
sc.run(output_op,
scannerpy.common.PerfParams.estimate(),
cache_mode=scannerpy.CacheMode.Ignore)
print('Writing MaskRCNN metadata into frames')
drawn_frame = sc.ops.DrawMaskRCNN(frame=strided_frame,
bundled_data=sc.io.Input(
[maskrcnn_stream]),
min_score_thresh=0.5)
drawn_stream = NamedVideoStream(sc, movie_name + '_maskrcnn_draw')
output_op = sc.io.Output(drawn_frame, [drawn_stream])
sc.run(output_op,
scannerpy.common.PerfParams.estimate(),
cache_mode=scannerpy.CacheMode.Overwrite)
drawn_stream.save_mp4('/app/result/' + movie_name + '_maskrcnn')
def main():
print("Prepare videos and frames")
video = Video.objects.filter(path__contains='wim')[0]
video_path = video.path
video_name = video.item_name()
sc = Client()
stride = 1
input_stream = NamedVideoStream(sc, video_name, path=video_path)
frame = sc.io.Input([input_stream])
# run on all frames
# running_frame = sc.streams.Stride(frame, [stride])
hit_annotation = pickle.load(
open('/app/data/pkl/hit_annotation_tennis.pkl',
'rb'))[video_name + '.mp4']
# run on selected frames
# hit_dict = []
# for h in hit_annotation.values():
# hit_dict += h
hit_dict = hit_annotation
# frame_ids = [i for point in hit_dict for i in range(point[0]['fid']-25, point[-1]['fid']+25) ]
frame_ids = [
i for point in hit_dict for i in range(point[0] - 25, point[-1] + 25)
]
frame_ids.sort()
running_frame = sc.streams.Gather(frame, [frame_ids])
print('Running Scanner DensePose op on %d frames' % (len(frame_ids)))
densepose_frame = sc.ops.DensePoseDetectPerson(frame=running_frame,
device=DeviceType.GPU,
batch=1,
confidence_threshold=0.5,
nms_threshold=0.2)
densepose_stream = NamedStream(sc, video_name + '_densepose')
output_op = sc.io.Output(densepose_frame, [densepose_stream])
sc.run(output_op,
scannerpy.common.PerfParams.estimate(),
cache_mode=scannerpy.CacheMode.Overwrite)
exit()
print('Writing DensePose metadata into frames')
drawn_frame = sc.ops.DrawDensePose(frame=running_frame,
bundled_data=sc.io.Input(
[densepose_stream]),
min_score_thresh=0.5,
show_body=True)
drawn_stream = NamedVideoStream(sc, video_name + '_densepose_draw_uvbody')
output_op = sc.io.Output(drawn_frame, [drawn_stream])
sc.run(output_op,
scannerpy.common.PerfParams.estimate(),
cache_mode=scannerpy.CacheMode.Overwrite)
drawn_stream.save_mp4('/app/result/' + video_name + '_densepose_uvbody')
drawn_frame = sc.ops.DrawDensePose(frame=running_frame,
bundled_data=sc.io.Input(
[densepose_stream]),
min_score_thresh=0.5,
show_body=False)
drawn_stream = NamedVideoStream(sc, video_name + '_densepose_draw_full')
output_op = sc.io.Output(drawn_frame, [drawn_stream])
sc.run(output_op,
scannerpy.common.PerfParams.estimate(),
cache_mode=scannerpy.CacheMode.Overwrite)
drawn_stream.save_mp4('/app/result/' + video_name + '_densepose_full')