def main(args):
""" Show detected objects with boxes, lables and prediction scores in a vide stream
"""
# Load yolo model with pretrained weights
print("Create YoloV3 model")
config_parser = ConfigParser(args.config)
model = config_parser.create_model(skip_detect_layer=False)
detector = config_parser.create_detector(model)
# Open video stream
cap = cv2.VideoCapture(args.camera)
if (cap.isOpened() == False):
print("(Error) Could not open video stream")
exit()
# Detect objects in stream
times = []
detect = 0
while True:
# Capture every nth frame only because we are too slow
# to capture every frame...
ret, image = cap.read()
#image, _ = resize_image(image, None, config_parser.get_net_size(), keep_ratio=True)
if not ret:
print("(Error) Lost connection to video stream")
break
# Detect objects and measure timing
if detect <= 0:
t1 = time.time()
min_prob = 0.90
boxes, labels, probs = detector.detect(image, min_prob)
t2 = time.time()
times.append(t2 - t1)
times = times[-20:]
detect = 50
detect -= 1
# Display detected objects
visualize_boxes(image, boxes, labels, probs,
config_parser.get_labels())
image = cv2.putText(
image, "Time: {:.2f}ms".format(sum(times) / len(times) * 1000),
(0, 30), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 2)
cv2.imshow('Frame', image)
# Exit with 'q'
if cv2.waitKey(25) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
def __init__(self,
trained_tracknet_network,
config_path='pretrained_model/kitti.json',
bbox_size=128):
# Get the TrackNet model
self.identifier = trained_tracknet_network
self.config_path = config_path
self.bbox_size = bbox_size
# Create the YOLO-v3 model and load the weights
config_parser = ConfigParser(self.config_path)
model = config_parser.create_model(skip_detect_layer=False)
self.detector = config_parser.create_detector(model)
def main():
args = argparser.parse_args()
# 1. create yolo model & load weights
config_parser = ConfigParser(args.config)
model = config_parser.create_model(skip_detect_layer=False)
detector = config_parser.create_detector(model)
labels = config_parser.get_labels()
for image in args.images:
predictImage(image, detector, labels)
saveResults()
return 0
argparser = argparse.ArgumentParser(
description='train yolo-v3 network')
argparser.add_argument(
'-c',
'--config',
default="configs/svhn.json",
help='config file')
if __name__ == '__main__':
args = argparser.parse_args()
config_parser = ConfigParser(args.config)
# 1. create generator
train_generator, valid_generator = config_parser.create_generator()
# 2. create model
model = config_parser.create_model()
# 3. training
learning_rate, save_dname, n_epoches = config_parser.get_train_params()
train_fn(model,
train_generator,
valid_generator,
learning_rate=learning_rate,
save_dname=save_dname,
num_epoches=n_epoches)
help='config file')
argparser.add_argument(
'-i',
'--image',
default="tests/samples/sample.jpeg",
help='path to image file')
if __name__ == '__main__':
args = argparser.parse_args()
image_path = args.image
# 1. create yolo model & load weights
config_parser = ConfigParser(args.config)
model = config_parser.create_model(skip_detect_layer=False)
detector = config_parser.create_detector(model)
# 2. Load image
image = cv2.imread(image_path)
image = image[:,:,::-1]
# 3. Run detection
boxes, labels, probs = detector.detect(image, 0.5)
print(probs)
# 4. draw detected boxes
visualize_boxes(image, boxes, labels, probs, config_parser.get_labels())
# 5. plot
plt.imshow(image)
