self.draw.text((box[0], box[1] - h if outside else box[1]), label, fill=txt_color, font=self.font) else: # cv2 p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3])) #ocr操作 #左上角(int(box[0]), int(box[1])),右下角(int(box[2]), int(box[...
box_label(xyxy, label, color=colors(c, True)) # print(xyxy) print(f'{s}') # print(f'{s_result}') result_list.append(s_result) # 将conf对象中的数据写入到文件中 conf = configparser.ConfigParser() cfg_file = open("glovar.cfg", 'w') conf.add_section("default") # 在配置文件中...
AI代码解释 def_iou(self,box,box_label):wh=box[:,:,:,2:4]wh=nd.power(wh,2)center=box[:,:,:,0:1]predict_areas=wh[:,:,:,0]*wh[:,:,:,1]predict_bottom_right=center+0.5*wh predict_top_left=center-0.5*wh wh=box_label[:,:,:,2:4]wh=nd.power(wh,2)center=box_label[:,:...
annotator.box_label(xyxy, label, color=colors(c, True)) if save_crop: save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True) # Stream results im0 = annotator.result() if view_img: cv2.imshow(str(p), im0) cv2.waitKey(1) # 1 milliseco...
YOLOv5 检测结果取消显示标签(只显示边界框bbox) 检测结果取消显示类别class和置信度confidence 利用Yolov5进行密集小目标物体检测时,只显示bbox可视化效果更好 yolov5/models/common.py class Detections: def display(): # label = f'{self.names[int(cls)]} {conf:.2f}'...
self.transform = A.Compose(T, bbox_params=A.BboxParams(format='yolo', label_fields=['class_labels'])) LOGGER.info(colorstr('albumentations: ') + ', '.join(f'{x}' for x in self.transform.transforms if x.p)) except ImportError: # package not installed, skip pass except Exception ...
3组不同比例缩放的bbox对应3组anchors找到iou>0.3的并将网格的左上角坐标标记下来。 label格式为[np.zeros((train_output_sizes[i], train_output_sizes[i], anchor_per_scale,5 + num_classes)) for i in range(3)] 那么label i= [1,2,3]代表3种尺度: ...
if save_img or save_crop or view_img: # Add bbox to image c = int(cls) # integer class label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}') annotator.box_label(xyxy, label, color=colors(c, True)) ...
{'box': box, 'conf': conf, 'cls': cls, 'label': label, 'im': save_one_box(box, im, file=file, save=save)}) else: # all others annotator.box_label(box, label, color=colors(cls)) im = annotator.im else: s += '(no detections)' im = Image.fromarray(im.astype(np.uint8...
等式左边第一项就是每个网格预测的类别信息,第二三项就是每个bounding box预测的confidence。这个乘积即encode了预测的box属于某一类的概率,也有该box准确度的信息。 得到每个box的class-specific confidence score以后,设置阈值,滤掉得分低的boxes,对保留的boxes进行NMS处理,就得到最终的检测结果。