def main():
'''
Parse command line arguments and execute the code
'''
######### TENSORBOX PARAMETERS
start = time.time()
parser = argparse.ArgumentParser()
# parser.add_argument('--result_folder', default='summary_result/', type=str)
# parser.add_argument('--summary_file', default='results.txt', type=str)
parser.add_argument('--output_name', default='output.mp4', type=str)
parser.add_argument('--hypes', default='./TENSORBOX/hypes/overfeat_rezoom.json', type=str)
parser.add_argument('--weights', default='./TENSORBOX/data/save.ckpt-1250000', type=str)
parser.add_argument('--perc', default=100, type=int)
parser.add_argument('--path_video', default='ILSVRC2015_val_00004000.mp4', type=str)# required=True, type=str)
args = parser.parse_args()
# hypes_file = './hypes/overfeat_rezoom.json'
# weights_file= './output/save.ckpt-1090000'
path_video_folder = os.path.splitext(os.path.basename(args.path_video))[0]
pred_idl = './%s/%s_val.idl' % (path_video_folder, path_video_folder)
idl_filename=path_video_folder+'/'+path_video_folder+'.idl'
frame_tensorbox=[]
frame_inception=[]
frame_tensorbox, frame_inception = utils_video.extract_frames_incten(args.path_video, args.perc, path_video_folder, idl_filename )
progress = progressbar.ProgressBar(widgets=[progressbar.Bar('=', '[', ']'), ' ',progressbar.Percentage(), ' ',progressbar.ETA()])
for image_path in progress(frame_tensorbox):
utils_image.resizeImage(image_path)
utils_image.resizeImage(-1)
video_info=Utils_Tensorbox.bbox_det_TENSORBOX_multiclass(frame_tensorbox, path_video_folder, args.hypes, args.weights, pred_idl)
tracked_video=utils_video.recurrent_track_objects(video_info)
# tracked_video=utils_video.track_objects(video_info)
# labeled_video=Utils_Imagenet.label_video(tracked_video, frame_inception)
labeled_video=Utils_Imagenet.recurrent_label_video(tracked_video, frame_inception)
# tracked_video=utils_video.track_objects(video_info)
# tracked_video=utils_video.track_and_label_objects(video_info)
labeled_frames=utils_video.draw_rectangles(path_video_folder, labeled_video)
utils_video.make_tracked_video(args.output_name, labeled_frames)
frame.saveVideoResults(idl_filename,labeled_video)
# utils_video.make_tracked_video(args.output_name, labeled_video)
end = time.time()
print("Elapsed Time:%d Seconds"%(end-start))
print("Running Completed with Success!!!")
def main():
'''
Parse command line arguments and execute the code
'''
######### TENSORBOX PARAMETERS
start = time.time()
parser = argparse.ArgumentParser()
# parser.add_argument('--result_folder', default='summary_result/', type=str)
# parser.add_argument('--summary_file', default='results.txt', type=str)
parser.add_argument('--output_name', default='output.mp4', type=str)
parser.add_argument('--hypes',
default='./TENSORBOX/hypes/overfeat_rezoom.json',
type=str)
parser.add_argument('--weights',
default='./TENSORBOX/data/save.ckpt-1090000',
type=str)
parser.add_argument('--perc', default=2, type=int)
parser.add_argument('--path_video', required=True, type=str)
args = parser.parse_args()
# hypes_file = './hypes/overfeat_rezoom.json'
# weights_file= './output/save.ckpt-1090000'
path_video_folder = os.path.splitext(os.path.basename(args.path_video))[0]
pred_idl = './%s/%s_val.idl' % (path_video_folder, path_video_folder)
idl_filename = path_video_folder + '/' + path_video_folder + '.idl'
frame_list = []
frame_list = Utils_Video.extract_idl_from_frames(args.path_video,
args.perc,
path_video_folder,
'frames/', idl_filename)
progress = progressbar.ProgressBar(widgets=[
progressbar.Bar('=', '[', ']'), ' ',
progressbar.Percentage(), ' ',
progressbar.ETA()
])
for image_path in progress(frame_list):
Utils_Image.resizeImage(image_path)
Utils_Image.resizeImage(-1)
det_frame_list = Utils_Tensorbox.still_image_TENSORBOX_singleclass(
frame_list, path_video_folder, args.hypes, args.weights, pred_idl)
Utils_Video.make_video_from_list(args.output_name, det_frame_list)
end = time.time()
print("Elapsed Time:%d Seconds" % (end - start))
print("Running Completed with Success!!!")
def main():
'''
Parse command line arguments and execute the code
'''
######### TENSORBOX PARAMETERS
start = time.time()
parser = argparse.ArgumentParser()
# parser.add_argument('--result_folder', default='summary_result/', type=str)
# parser.add_argument('--summary_file', default='results.txt', type=str)
parser.add_argument('--output_name', default='output.mp4', type=str)
parser.add_argument('--hypes', default='./TENSORBOX/hypes/overfeat_rezoom.json', type=str)
parser.add_argument('--weights', default='./TENSORBOX/data/save.ckpt-1090000', type=str)
parser.add_argument('--perc', default=2, type=int)
parser.add_argument('--path_video', required=True, type=str)
args = parser.parse_args()
# hypes_file = './hypes/overfeat_rezoom.json'
# weights_file= './output/save.ckpt-1090000'
path_video_folder = os.path.splitext(os.path.basename(args.path_video))[0]
pred_idl = './%s/%s_val.idl' % (path_video_folder, path_video_folder)
idl_filename=path_video_folder+'/'+path_video_folder+'.idl'
frame_list=[]
frame_list = Utils_Video.extract_idl_from_frames(args.path_video, args.perc, path_video_folder, 'frames/', idl_filename )
progress = progressbar.ProgressBar(widgets=[progressbar.Bar('=', '[', ']'), ' ',progressbar.Percentage(), ' ',progressbar.ETA()])
for image_path in progress(frame_list):
Utils_Image.resizeImage(image_path)
Utils_Image.resizeImage(-1)
det_frame_list=Utils_Tensorbox.still_image_TENSORBOX_singleclass( frame_list, path_video_folder, args.hypes, args.weights, pred_idl)
Utils_Video.make_video_from_list(args.output_name, det_frame_list)
end = time.time()
print("Elapsed Time:%d Seconds"%(end-start))
print("Running Completed with Success!!!")
def track_objects(video_info):
previous_frame = None
previous_num_obj = -1
tracked_video = []
for frame_info in video_info:
print("Tracking Frame Nr: %d" % frame_info.frame)
print("Len Rects Frame: %d" % len(frame_info.rects))
current_frame = frame.Frame_Info()
current_frame = frame_info.duplicate()
current_frame.rects = []
if previous_frame is not None:
print("Len Previous Rects Frame: %d" % len(previous_frame.rects))
for rect in previous_frame.rects:
print(len(current_frame.rects))
current_rect = multiclass_rectangle.pop_max_iou(
frame_info.rects, rect)
current_rect.load_trackID(rect.trackID)
current_frame.append_labeled_rect(current_rect)
else:
trackID = 1
picked_rect = Utils_Tensorbox.NMS(frame_info.rects)
for rect in picked_rect:
current_rect = rect.duplicate()
current_rect.load_trackID(trackID)
current_frame.append_labeled_rect(current_rect)
trackID = trackID + 1
previous_frame = current_frame.duplicate()
previous_frame.rects = multiclass_rectangle.duplicate_rects(
current_frame.rects)
print("Current Frame obj:%d" % len(current_frame.rects))
tracked_video.insert(len(tracked_video), current_frame)
return tracked_video
def recurrent_track_objects(video_info):
previous_frame = None
previous_num_obj = -1
tracked_video = []
deltas_video = []
deltas_frame = []
dx1, dx2, dy1, dy2 = 0, 0, 0, 0
for frame_info in video_info:
print("Tracking Frame Nr: %d" % frame_info.frame)
print("Len Rects Frame: %d" % len(frame_info.rects))
current_frame = frame.Frame_Info()
current_frame = frame_info.duplicate()
current_frame.rects = []
if previous_frame is not None:
deltas_frame = []
if frame_info.frame > 1:
print("Len Previous Rects Frame: %d" %
len(previous_frame.rects))
rect_idx = 0
for rect in previous_frame.rects:
print(len(current_frame.rects))
rect.add_delta(
deltas_video[frame_info.frame - 2][rect_idx][0],
deltas_video[frame_info.frame - 2][rect_idx][1],
deltas_video[frame_info.frame - 2][rect_idx][2],
deltas_video[frame_info.frame - 2][rect_idx][3])
current_rect = multiclass_rectangle.pop_max_iou(
frame_info.rects, rect)
if current_rect is not None:
current_rect.load_trackID(rect.trackID)
current_rect.check_rects_motion(
frame_info.filename, rect,
deltas_video[frame_info.frame - 2][rect_idx][0],
deltas_video[frame_info.frame - 2][rect_idx][1],
deltas_video[frame_info.frame - 2][rect_idx][2],
deltas_video[frame_info.frame - 2][rect_idx][3])
current_frame.append_labeled_rect(current_rect)
dx1 = current_rect.x1 - rect.x1
dx2 = current_rect.x2 - rect.x2
dy1 = current_rect.y1 - rect.y1
dy2 = current_rect.y2 - rect.y2
deltas_frame.append((dx1, dx2, dy1, dy2))
else:
break
else:
print("Len Previous Rects Frame: %d" %
len(previous_frame.rects))
for rect in previous_frame.rects:
print(len(current_frame.rects))
current_rect = multiclass_rectangle.pop_max_iou(
frame_info.rects, rect)
if current_rect is not None:
dx1 = current_rect.x1 - rect.x1
dx2 = current_rect.x2 - rect.x2
dy1 = current_rect.y1 - rect.y1
dy2 = current_rect.y2 - rect.y2
deltas_frame.append((dx1, dx2, dy1, dy2))
current_rect.load_trackID(rect.trackID)
current_frame.append_labeled_rect(current_rect)
else:
break
deltas_video.append(deltas_frame)
else:
trackID = 1
picked_rect = Utils_Tensorbox.NMS(frame_info.rects)
for rect in picked_rect:
current_rect = rect.duplicate()
current_rect.load_trackID(trackID)
current_frame.append_labeled_rect(current_rect)
trackID = trackID + 1
previous_frame = current_frame.duplicate()
previous_frame.rects = multiclass_rectangle.duplicate_rects(
current_frame.rects)
print("Current Frame obj:%d" % len(current_frame.rects))
tracked_video.insert(len(tracked_video), current_frame)
return tracked_video