def process_video(video_path):
detector = initialize_detector()
input_shape = (48, 48, 3)
num_classes = 4
cnn_weights_path = 'model/weights.h5'
DELTA = 15
dataset = Dataset()
cnn = Model(input_shape, num_classes, cnn_weights_path)
cap = cv2.VideoCapture(video_path)
while (cap.isOpened()):
_, frame = cap.read()
frame = resize(frame, (NEW_HEIGHT, NEW_WIDTH), mode='constant')
print(frame.shape)
predictions = hot_predict('dummy', detector, image=frame)
for bounding_box in predictions:
x1 = int(bounding_box['x1']) - DELTA
y1 = int(bounding_box['y1']) - DELTA
x2 = int(bounding_box['x2']) + DELTA
y2 = int(bounding_box['y2']) + DELTA
traffic_sign = frame[y1:y2, x1:x2]
processed_image = dataset._preprocess_image(traffic_sign,
centered=True)
cnn_input = np.expand_dims(processed_image, axis=0)
label = cnn.predict(cnn_input)
draw_rectangle(frame, (x1, y1, x2, y2), label)
cv2.imshow('frame', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
args = parser.parse_args()
predictions = detect_traffic_signs(args.image)
image = imread(args.image)
input_shape = (48, 48, 3)
num_classes = 4
cnn_weights_path = 'model/weights.h5'
DELTA = 15
dataset = Dataset()
cnn = Model(input_shape, num_classes, cnn_weights_path)
for bounding_box in predictions:
x1 = int(bounding_box['x1']) - DELTA
y1 = int(bounding_box['y1']) - DELTA
x2 = int(bounding_box['x2']) + DELTA
y2 = int(bounding_box['y2']) + DELTA
traffic_sign = image[y1:y2, x1:x2]
processed_image = dataset._preprocess_image(traffic_sign,
centered=True)
cnn_input = np.expand_dims(processed_image, axis=0)
label = cnn.predict(cnn_input)
draw_rectangle(image, (x1, y1, x2, y2), label)
plt.imshow(image)
plt.show()
