def run_camera(camera_chain, video_file_loc):
# buffers for demo in batch
buffer_inp = list()
# Loop through frames
elapsed = 0
start = timer()
data_collector = DataCollector()
print('Video file to open: ' + str(video_file_loc))
while True:
frame_num = 0
while frame_num < FRAME_LIMIT:
print('Press [ESC] to quit demo')
camera = cv2.VideoCapture(video_file_loc)
cam_num = UserParameters().get_cam_num()
while camera.isOpened():
elapsed += 1
_, frame = camera.read()
frame_num = frame_num + 1
if frame is None:
print('\nEnd of Video')
break
buffer_inp.append(frame)
# Only process and imshow when queue is full
if elapsed % QUEUE == 0:
for image in buffer_inp:
collector_start_time = timer()
process_image_response = process_img_request(
image, cam_num, frame_num,
camera_chain.get_processor())
cv2.imshow('', process_image_response.boxed_image)
#cv2.imshow('', image)
duration = str(timer() - collector_start_time)
print('Processing Time: ' + duration)
data_collector.write(DATA_TYPE_PROCESSING_TIME,
duration)
# Clear Buffers
buffer_inp = list()
if elapsed % 5 == 0:
sys.stdout.write('\r')
fps = elapsed / (timer() - start)
sys.stdout.write('{0:3.3f} FPS\n'.format(fps))
data_collector.write(DATA_TYPE_FPS, str(fps))
sys.stdout.flush()
choice = cv2.waitKey(1)
if choice == 27:
break
sys.stdout.write('\n')
camera.release()
data_collector.close()
cv2.destroyAllWindows()
#run_camera()
def send_request(url, request):
print('Size of request: ' + str(request.image.size))
start_time = time.time()
# Send full image to edge device for further processing
pickle.dump(request.image, open(PICKLED_TEMP_LOC, 'wb'))
resp = requests.post(url,
files={'file': open(PICKLED_TEMP_LOC, 'rb')},
verify=False)
status_code = resp.status_code
size_of_resp = len(resp.content)
total_size = str(request.image.size + size_of_resp)
resp = resp.json()
print('Size of response: ' + str(size_of_resp))
print('Total transfer size: ' + total_size)
duration = str(time.time() - start_time)
prediction = 'Count: ' + str(len(resp['prediction'])) + ', ' + str(
resp['prediction'])
print('Data Transfer Time: ' + duration)
print('Results: ' + prediction)
data_collector = DataCollector()
data_collector.write(DATA_TYPE_TRANSFER_SIZE, total_size)
data_collector.write(DATA_TYPE_DATA_TRANSFER_TIME, duration)
data_collector.write(DATA_TYPE_DETECTION, prediction)
return NodeResponse(cc.get_status(status_code),
cc.get_image(request.image, resp['prediction']),
resp['prediction'])
def post_to_multi_view(request):
data = {
'cam':
request.cam_num,
'frame':
request.frame_num,
'prediction':
[create_box_resp_field(pred) for pred in request.predicted_box_loc]
}
size_of_request = asizeof.asizeof(data)
print('Size of request: ' + str(size_of_request))
start_time = time.time()
resp = requests.post(str(UserParameters().get_edge_ip()) +
MULTI_VIEW_RELATIVE_URL,
headers={'Content-Type': 'application/json'},
data=json.dumps(data),
verify=False)
status_code = resp.status_code
size_of_resp = asizeof.asizeof(resp.content)
total_size = str(size_of_request + size_of_resp)
resp = resp.json()
print('Size of response: ' + str(size_of_resp))
print('Total transfer size: ' + total_size)
duration = str(time.time() - start_time)
prediction = 'Count: ' + str(len(resp['prediction'])) + ', ' + str(
resp['prediction'])
print('Data Transfer Time: ' + duration)
print('Results: ' + prediction)
data_collector = DataCollector()
data_collector.write(DATA_TYPE_TRANSFER_SIZE, total_size)
data_collector.write(DATA_TYPE_DATA_TRANSFER_TIME, duration)
data_collector.write(DATA_TYPE_DETECTION, prediction)
return NodeResponse(cc.get_status(status_code),
cc.get_image(request.image, resp['prediction']),
resp['prediction'])