同样得到giy^ ,gw、gh相当于tw和th,gw^,gh^相当于bw,bh,用bh/ph,两边同取ln得到 gw^, gh^。 NMS(非极大抑制) NMS算法一般是为了去掉模型预测后的多余框,其一般设有一个nms_threshold=0.5,具体的实现思路如下: 选取这类box中scores最大的哪一个,记为box_best,并保留它。 计算box_best与其余的box的IOU...
param.ap_iou_threshold, nms_threshold=self.param.nms_threshold) return [detect_metric] def update_fine_tune_param(self): for param in self.body_net.backbone.parameters(): param.requires_grad = False def update_train_param(self): for param in self.body_net.backbone.parameters(): param....
std::vector<int> picked; nms_sorted_bboxes(proposals, picked, nms_threshold); 6.4 后处理 static void nms_sorted_bboxes(const std::vector<Object>& faceobjects, std::vector<int>& picked, float nms_threshold) { picked.clear; const int n = faceobjects.size; std::vector<float> areas(n)...
复制 indices=cv2.dnn.NMSBoxes(boxes,confidences,confThreshold,nmsThreshold) 这里boxes就是上面得到的所有框,condifences就是对应的置信度值,confThreshold是置信度阈值,nmsthreshold是nms计算时的阈值。 返回的indices是boxes的下标列表,通过它就可以得到最终的所有boundingbox。 代码语言:javascript 复制 final_res=[]...
static void nms_sorted_bboxes(const std::vector<Object>&faceobjects, std::vector<int>&picked, float nms_threshold) { picked.clear(); const int n = faceobjects.size(); std::vector<float>areas(n); for (int i = 0; i<n;i++) ...
np.where(overlap > nms_threshold)[0]))) # return only the bounding boxes that were picked using the # integer data type returnclass_ids[pick], boxes[pick].astype("int") if__name__ =="__main__": boxes = [] boxes.append((163,0,27+163,41)) ...
float classThreshold = 0.25; float nmsThreshold = 0.45; float nmsScoreThreshold = boxThreshold * classThreshold; std::vector<std::string> className = { "p" }; bool readModel(Net& net, string& netPath, bool isCuda = false) { try { ...
如果你想去除重复的矩形,你可以使用 NMS,例如: cv2.dnn.NMSBoxes(boxes, confidences, score_threshold=0.5, nms_threshold=0.4) 此外,可以为每个检测到的矩形添加一个标题: cv2.putText(image, label, (x, y +30), font,2, color,3) 输出是: ...
nms_threshold=0.7 ) ) ], "metric_to_watch": 'mAP@0.50' } trainer.train(model=model, training_params=train_params, train_loader=train_data, valid_loader=val_data) best_model = models.get(config.MODEL_NAME, num_classes=config.NUM_CLASSES, ...
iou_thres=0.45, # NMS IOU threshold. max_det=1000, # Maximum detections per image. device='', # Cuda device, i.e. 0 or 0,1,2,3 or cpu. view_img=False, # Show results. save_txt=False, # Save results to *.txt. save_conf=False, # Save confidences in --save-txt labels. ...