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 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
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)
plt.show()
# 3. Run detection
boxes, labels, probs = detector.detect(image, 0.5)
# print(list(zip(labels, probs)))
if len(labels) == 0:
print(image_path, "nothing found")
for (l, p) in zip(labels, probs):
print(image_path, class_labels[l], p)
# # 4. draw detected boxes
# visualize_boxes(image, boxes, labels, probs, config_parser.get_labels())
#
# # 5. plot
# plt.imshow(image)
# plt.show()
if __name__ == '__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)
counting += 1
if (log_progress == True):
print("Processing Frame : ", str(counting))
check_frame_interval = counting % frame_detection_interval
if (counting == 1 or check_frame_interval == 0):
try:
# detected_frame, output_objects_array = self.__detector.detectObjectsFromImage(
# input_image=frame, input_type="array", output_type="array",
# minimum_percentage_probability=minimum_percentage_probability,
# display_percentage_probability=display_percentage_probability,
# display_object_name=display_object_name)
detected_frame_pre, output_objects_array, pred = detector.detect(frame, 0.2)
visualize_boxes(frame, detected_frame_pre, output_objects_array, pred, config_parser.get_labels())
detected_frame = frame
# plt.imshow(detected_frame)
# plt.show()
except:
print('none')
None
output_frames_dict[counting] = output_objects_array
output_objects_count = {}
for eachItem in output_objects_array:
eachItemName = eachItem
try:
