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)
