def main(): # parse arguments args = cli() # setup processor and visualizer processor = Processor(model=args['model']) visualizer = Visualizer() # fetch input print('image arg', args['image']) img = cv2.imread('inputs/{}'.format(args['image'])) # inference output = processor.detect(img) img = cv2.resize(img, (640, 640)) # object visualization object_grids = processor.extract_object_grids(output) visualizer.draw_object_grid(img, object_grids, 0.1) # class visualization class_grids = processor.extract_class_grids(output) visualizer.draw_class_grid(img, class_grids, 0.01) # bounding box visualization boxes = processor.extract_boxes(output) visualizer.draw_boxes(img, boxes) # final results boxes, confs, classes = processor.post_process(output) visualizer.draw_results(img, boxes, confs, classes)
def main(): # parse arguments args = cli() # setup processor and visualizer processor = Processor(model=args['model']) visualizer = Visualizer() # fetch input print('image arg', args['image']) #img = cv2.imread('inputs/{}'.format(args['image'])) img = cv2.imread("/home/jiqing/jq/bottle/33/3 (3).jpg") cap = cv2.VideoCapture(0) while 1: ret, frame = cap.read() #print(type(img)) # inference #output = processor.detect(img) #img = cv2.resize(img, (640, 640)) output = processor.detect(frame) img = cv2.resize(frame, (640, 640)) # object visualization object_grids = processor.extract_object_grids(output) #visualizer.draw_object_grid(img, object_grids, 0.1) # class visualization class_grids = processor.extract_class_grids(output) #visualizer.draw_class_grid(img, class_grids, 0.01) # bounding box visualization boxes = processor.extract_boxes(output) #visualizer.draw_boxes(img, boxes) # final results boxes, confs, classes = processor.post_process(output) #print(classes) #label = f'{names[int(classes)]} {confs:.2f}' visualizer.draw_results(img, boxes, confs, classes)