21 CLASSES = ("person", "bicycle", "car", "motorbike ", "aeroplane ", "bus ", "train", "truck ", "boat…  variable
82         classes: ndarray, classes for boxes.
95     classes = np.argmax(box_class_probs, axis=-1)
99     classes = classes[_class_pos]
102     return boxes, classes, scores
149     boxes, classes, scores = [], [], []
154         classes.append(c)
159     classes = np.concatenate(classes)
163     for c in set(classes):
164         inds = np.where(classes == c)
166         c = classes[inds]
179     classes = np.concatenate(nclasses)
182     return boxes, classes, scores
185 def draw(image, boxes, scores, classes):  argument
191         classes: ndarray, classes of objects.
193         all_classes: all classes name.
195     for box, score, cl in zip(boxes, scores, classes):
197         print('class: {}, score: {}'.format(CLASSES[cl], score))
205         cv2.putText(image, '{0} {1:.2f}'.format(CLASSES[cl], score),
307     boxes, classes, scores = yolov5_post_process(input_data)  variable
311         draw(img_1, boxes, scores, classes)