def inferOnPicture(args):
network = Network()
network.load_model(args.model, args.device, CPU_EXTENSION)
# shape = network.get_rcnn_shape()
shape = network.get_input_shape()
original = cv2.imread(args.input)
image1 = np.copy(original)
image = preprocess(image1, shape)
network.exec_rcnn(image)
# network.exec_net(image)
if (network.wait() == 0):
output = network.get_output()
print(output.shape)
new_image, i = draw_boxes(image1, output)
print(new_image.shape)
cv2.imwrite('images/new.jpg', new_image)
def infer_on_stream(args, client):
"""
Initialize the inference network, stream video to network,
and output stats and video.
:param args: Command line arguments parsed by `build_argparser()`
:param client: MQTT client
:return: None
"""
# Initialise the class
network = Network()
# Set Probability threshold for detections
threshold = args.threshold
### TODO: Load the model through `infer_network` ###
network.load_model(args.model, args.device, CPU_EXTENSION)
shape = network.get_rcnn_shape()
### TODO: Handle the input stream ###
if args.input == 'CAM':
input_im = 0
# Checks for input image
elif args.input.endswith('.jpg') or args.input.endswith('.bmp'):
single_image_mode = True
input_im = args.input
# Checks for video file
else:
input_im = args.input
assert os.path.isfile(args.input), "file doesn't exist"
cap = cv2.VideoCapture(input_im)
cap.open(input_im)
width = int(cap.get(3))
height = int(cap.get(4))
out = cv2.VideoWriter('out.mp4', 0x00000021, 30, (width, height))
last_count = 0
total = 0
detect_range = 0
average = 0
duration_count = 0
### TODO: Loop until stream is over ###
while cap.isOpened():
# Read the next frame
flag, frame = cap.read()
### TODO: Read from the video capture ###
if not flag:
break
### TODO: Pre-process the image as needed ###
image = preprocess(frame, shape)
start = time.time()
### TODO: Start asynchronous inference for specified request ###
network.exec_rcnn(image)
duration = None
### TODO: Wait for the result ###
if (network.wait() == 0):
end = time.time()
### TODO: Get the results of the inference request ###
output = network.get_output()
### TODO: Extract any desired stats from the results ###
frame, persons = draw_boxes(frame, output, threshold)
### TODO: Calculate and send relevant information on ###
diff = persons - last_count
duration_count += 1
if (diff == 0):
detect_range = 0
else:
detect_range += 1
if (detect_range > 4):
last_count = persons
if (diff > 0):
total += diff
duration_count = 0
else:
#person left based on 30 frames per seconds
duration = duration_count / 10
all_dur = average * (total - 1)
average = (all_dur + duration) / total
### current_count, total_count and duration to the MQTT server ###
### Topic "person": keys of "count" and "total" ###
client.publish("person",
json.dumps({
"count": persons,
"total": total
}))
### Topic "person/duration": key of "duration" ###
if duration is not None:
client.publish("person/duration",
json.dumps({"duration": int(average)}),
qos=0,
retain=False)
### TODO: Send the frame to the FFMPEG server ###
sys.stdout.buffer.write(frame)
sys.stdout.flush()
# Break if escape key pressed
key_pressed = cv2.waitKey(1)
if key_pressed == 27:
break
cap.release()
cv2.destroyAllWindows()
### Disconnect from MQTT
client.disconnect()
