def main_object_detect(json_struct, video_path):
log('Main function. Starting YOLO Object Detection',
header=HEADER_SIZE - 1,
color='darkturquoise')
start = timer()
directory = os.path.dirname(video_path)
image_directory_path = os.path.join(directory, 'images', 'full')
json_struct_path = os.path.join(directory, 'metadata',
'result_struct.json')
with cd('/home/ben/Documents/darknet'):
darknet_command = "./darknet yolo test cfg/yolo-tiny.cfg yolo-tiny.weights"
# darknet_command = "./darknet yolo test cfg/yolo-small.cfg yolo-small.weights"
split_command = darknet_command.split(' ')
if os.path.isfile(json_struct_path):
with open(json_struct_path) as data_file:
json_struct = json.load(data_file)
num_images = len(json_struct['images'])
# pipe = Popen(split_command, stdin=PIPE, stdout=PIPE, stderr=PIPE)
threads = []
for idx, image in enumerate(json_struct['images']):
path = os.path.join(image_directory_path,
image['image_name']) + '\n'
t = Thread(target=run_yolo,
args=(split_command, path, idx, num_images,
json_struct))
threads.append(t)
t.start()
time.sleep(1)
# if idx > 10:
# break
time.sleep(4)
print threads
for idx, t in enumerate(threads):
t.join()
# print 'joined ', idx, ' thread'
json.dump(json_struct, open(json_struct_path, 'w'), indent=4)
end = timer()
log('YOLO Object Detection complete',
header=HEADER_SIZE,
color='green')
log('Time taken for YOLO object detection:',
round((end - start), 5),
'seconds',
color='chartreuse')
def main_object_detect(json_struct, video_path):
# log('Main function. Starting YOLO Object Detection', header=HEADER_SIZE - 1, color='darkturquoise')
log('Main function. Starting Faster R-CNN Object Detection', header=HEADER_SIZE - 1, color='darkturquoise')
start = timer()
directory = os.path.dirname(video_path)
image_directory_path = os.path.join(directory, 'images', 'full')
json_struct_path = os.path.join(directory, 'metadata', 'result_struct.json')
with cd('/home/ben/VideoUnderstanding/python_features/faster_rcnn_VOC_object_detection'):
command = 'python faster_rcnn_VOC_object_detection.py ' + json_struct_path + ' ' + video_path
split_command = command.split(' ')
if os.path.isfile(json_struct_path):
with open(json_struct_path) as data_file:
json_struct = json.load(data_file)
pipe = Popen(split_command, stdin=PIPE, stdout=PIPE, stderr=PIPE)
object_list_next = False
object_cls_score_list = []
while True:
line = pipe.stdout.readline()
if not line: break
else:
line = line[:-1]
print line
if line[:3] == '~~~':
object_list_next = False
# print 'OBJECT LIST: ', object_cls_score_list
log('Object list: ', object_cls_score_list)
object_cls_score_list = []
if object_list_next:
if line[:7] == 'NOTHING':
continue
split_line = line.split()
object_cls_score_list.append({'class': split_line[0], 'score': split_line[1]})
elif line[:9] == 'Detection':
log(line)
object_list_next = True
# json.dump(json_struct, open(json_struct_path, 'w'), indent=4)
end = timer()
log('Faster R-CNN Object Detection complete', header=HEADER_SIZE, color='green')
log('Time taken for Faster R-CNN object detection:', round((end - start), 5), 'seconds.', color='chartreuse')
# main_object_detect('/home/ben/VideoUnderstanding/example_images/Funny_Videos_Of_Funny_Animals_NEW_2015/metadata/result_struct.json', '/home/ben/VideoUnderstanding/example_images/Funny_Videos_Of_Funny_Animals_NEW_2015/Funny_Videos_Of_Funny_Animals_NEW_2015.mp4')
def main_object_detect(json_struct, video_path):
log('Main function. Starting YOLO Object Detection', header=HEADER_SIZE - 1, color='darkturquoise')
start = timer()
directory = os.path.dirname(video_path)
image_directory_path = os.path.join(directory, 'images', 'full')
json_struct_path = os.path.join(directory, 'metadata', 'result_struct.json')
with cd('/home/ben/Documents/darknet'):
darknet_command = "./darknet yolo test cfg/yolo-tiny.cfg yolo-tiny.weights"
# darknet_command = "./darknet yolo test cfg/yolo-small.cfg yolo-small.weights"
split_command = darknet_command.split(' ')
if os.path.isfile(json_struct_path):
with open(json_struct_path) as data_file:
json_struct = json.load(data_file)
num_images = len(json_struct['images'])
# pipe = Popen(split_command, stdin=PIPE, stdout=PIPE, stderr=PIPE)
threads = []
for idx, image in enumerate(json_struct['images']):
path = os.path.join(image_directory_path, image['image_name']) + '\n'
t = Thread(target=run_yolo, args=(split_command, path, idx, num_images, json_struct))
threads.append(t)
t.start()
time.sleep(1)
# if idx > 10:
# break
time.sleep(4)
print threads
for idx, t in enumerate(threads):
t.join()
# print 'joined ', idx, ' thread'
json.dump(json_struct, open(json_struct_path, 'w'), indent=4)
end = timer()
log('YOLO Object Detection complete', header=HEADER_SIZE, color='green')
log('Time taken for YOLO object detection:', round((end - start), 5), 'seconds', color='chartreuse')
def main_object_detect(json_struct, video_path):
# log('Main function. Starting YOLO Object Detection', header=HEADER_SIZE - 1, color='darkturquoise')
log('Main function. Starting Faster R-CNN Object Detection',
header=HEADER_SIZE - 1,
color='darkturquoise')
start = timer()
directory = os.path.dirname(video_path)
image_directory_path = os.path.join(directory, 'images', 'full')
json_struct_path = os.path.join(directory, 'metadata',
'result_struct.json')
with cd('/home/ben/VideoUnderstanding/python_features/faster_rcnn_VOC_object_detection'
):
command = 'python faster_rcnn_VOC_object_detection.py ' + json_struct_path + ' ' + video_path
split_command = command.split(' ')
if os.path.isfile(json_struct_path):
with open(json_struct_path) as data_file:
json_struct = json.load(data_file)
pipe = Popen(split_command, stdin=PIPE, stdout=PIPE, stderr=PIPE)
object_list_next = False
object_cls_score_list = []
while True:
line = pipe.stdout.readline()
if not line: break
else:
line = line[:-1]
print line
if line[:3] == '~~~':
object_list_next = False
# print 'OBJECT LIST: ', object_cls_score_list
log('Object list: ', object_cls_score_list)
object_cls_score_list = []
if object_list_next:
if line[:7] == 'NOTHING':
continue
split_line = line.split()
object_cls_score_list.append({
'class': split_line[0],
'score': split_line[1]
})
elif line[:9] == 'Detection':
log(line)
object_list_next = True
# json.dump(json_struct, open(json_struct_path, 'w'), indent=4)
end = timer()
log('Faster R-CNN Object Detection complete',
header=HEADER_SIZE,
color='green')
log('Time taken for Faster R-CNN object detection:',
round((end - start), 5),
'seconds.',
color='chartreuse')
# main_object_detect('/home/ben/VideoUnderstanding/example_images/Funny_Videos_Of_Funny_Animals_NEW_2015/metadata/result_struct.json', '/home/ben/VideoUnderstanding/example_images/Funny_Videos_Of_Funny_Animals_NEW_2015/Funny_Videos_Of_Funny_Animals_NEW_2015.mp4')
