def main(_argv):
logging.info('load cat modem')
dog_1 = time.time()
dog_model = cnn.get_inception_v2_dog()
dog_2 = time.time()
logging.info('dog model load in {:.2f}ms'.format((dog_2 - dog_1)))
logging.info('Initialization connection at {}:{}'.format(FLAGS.ip, FLAGS.port))
time_c_1 = time.time()
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.connect((FLAGS.ip, FLAGS.port))
time_c_2 = time.time()
logging.info('Connected to {}:{} in {:.3f}ms'.format(FLAGS.ip, FLAGS.port, (time_c_2 - time_c_1)))
message = "0;RASP;127.0.0.1;DOG;"
send_1 = time.time()
s.send(message.encode('utf-8'))
send_2 = time.time()
logging.info('Send identification message {} in {}ms'.format(message, (send_2 - send_1)))
while True:
try:
data = s.recv(3000000)
message_parsed = parse_message_received(data.decode('utf-8'))
logging.info(
'message {} size : {:.2f}Mb received'.format(message_parsed[0], (sys.getsizeof(data) / 1000000)))
if len(message_parsed) == 4:
logging.info('\t image {}'.format(message_parsed[0]))
img = eval('np.array(' + message_parsed[2] + ')')
process = ThreadCAT(message_parsed[0], img, dog_model, s)
process.start()
if data.decode('utf-8') == "close":
break
except (ConnectionResetError, ConnectionRefusedError):
logging.info("Server close the connexion or not online")
break
# close the connection
s.close()
def main(_argv):
physical_devices = tf.config.experimental.list_physical_devices('GPU')
for physical_device in physical_devices:
tf.config.experimental.set_memory_growth(physical_device, True)
if FLAGS.tiny:
yolo = YoloV3Tiny(classes=FLAGS.num_classes)
else:
yolo = YoloV3(classes=FLAGS.num_classes)
yolo.load_weights(FLAGS.weights).expect_partial()
logging.info('weights loaded')
class_names = [c.strip() for c in open(FLAGS.classes).readlines()]
logging.info('classes loaded')
logging.info('load cat model :')
model_cat = cnn.get_inception_v2_cat()
logging.info('\tcat model loaded')
logging.info('load dog model :')
model_dog = cnn.get_inception_v2_dog()
logging.info('\t dog model loaded')
if FLAGS.tfrecord:
dataset = load_tfrecord_dataset(
FLAGS.tfrecord, FLAGS.classes, FLAGS.size)
dataset = dataset.shuffle(512)
img_raw, _label = next(iter(dataset.take(1)))
else:
img_raw = tf.image.decode_image(
open(FLAGS.image, 'rb').read(), channels=3)
img = tf.expand_dims(img_raw, 0)
img = transform_images(img, FLAGS.size)
t1 = time.time()
boxes, scores, classes, nums = yolo(img)
t2 = time.time()
img = cv2.cvtColor(img_raw.numpy(), cv2.COLOR_RGB2BGR)
t3 = time.time()
cnn_output = cnn.get_more_data(img, model_cat, model_dog, (boxes, scores, classes, nums), class_names)
t4 = time.time()
logging.info('time: {}'.format(t2 - t1))
logging.info('primary detections:')
for i in range(nums[0]):
logging.info('\t{}, {:.2f}'.format(class_names[int(classes[0][i])],
np.array(scores[0][i])))
img = cv2.cvtColor(img_raw.numpy(), cv2.COLOR_RGB2BGR)
img = draw_outputs(img, (boxes, scores, classes, nums), class_names, cnn_output)
cv2.imwrite(FLAGS.output, img)
logging.info('secondary detections:')
for dec2 in cnn_output:
logging.info('\t{} : {}'.format(dec2[1], dec2[2]))
logging.info('output saved to: {}'.format(FLAGS.output))
cv2.imshow(FLAGS.output, img)
cv2.waitKey(0)
def main(_argv):
physical_devices = tf.config.experimental.list_physical_devices('GPU')
for physical_device in physical_devices:
tf.config.experimental.set_memory_growth(physical_device, True)
if FLAGS.tiny:
yolo = YoloV3Tiny(classes=FLAGS.num_classes)
else:
yolo = YoloV3(classes=FLAGS.num_classes)
yolo.load_weights(FLAGS.weights)
logging.info('weights loaded')
class_names = [c.strip() for c in open(FLAGS.classes).readlines()]
logging.info('classes loaded')
logging.info('load cat model')
model_cat = cnn.get_inception_v2_cat()
logging.info('cat model loaded')
logging.info('load dog model')
model_dog = cnn.get_inception_v2_dog()
logging.info('dog model loaded')
times = []
times2 = []
try:
vid = cv2.VideoCapture(int(FLAGS.video))
except:
vid = cv2.VideoCapture(FLAGS.video)
out = None
if FLAGS.output:
# by default VideoCapture returns float instead of int
width = int(vid.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(vid.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = int(vid.get(cv2.CAP_PROP_FPS))
codec = cv2.VideoWriter_fourcc(*FLAGS.output_format)
out = cv2.VideoWriter(FLAGS.output, codec, fps, (width, height))
while True:
_, img = vid.read()
if img is None:
logging.warning("Empty Frame")
time.sleep(0.1)
continue
img_in = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img_in = tf.expand_dims(img_in, 0)
img_in = transform_images(img_in, FLAGS.size)
t1 = time.time()
boxes, scores, classes, nums = yolo.predict(img_in)
t2 = time.time()
t3 = time.time()
cnn_output = cnn.get_more_data(img, model_cat, model_dog,
(boxes, scores, classes, nums),
class_names)
t4 = time.time()
times.append(t2 - t1)
times2.append(t4 - t3)
times2 = times2[-20:]
times = times[-20:]
img, a, b = draw_outputs(img, model_cat, model_dog,
(boxes, scores, classes, nums), class_names,
cnn_output)
img = cv2.putText(
img, "Time prim: {:.2f}ms".format(sum(times) / len(times) * 1000),
(0, 30), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 0), 2)
img = cv2.putText(
img, "Time sec : {:.2f}ms".format(
(sum(times2) / len(times2)) * 1000), (0, 70),
cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 0), 2)
if FLAGS.output:
out.write(img)
cv2.imshow('output', img)
if cv2.waitKey(1) == ord('q'):
break
cv2.destroyAllWindows()