for i = 1:size(q_opt, 1)
时间: 2024-06-03 10:10:08 浏览: 15
As an AI language model, I do not have access to the entire code you are working on, but I can explain this line of code for you.
The code is written in MATLAB or Octave. "size(q_opt, 1)" returns the number of rows in the matrix "q_opt". The "for" loop runs from 1 to the number of rows in "q_opt".
The loop variable "i" takes values from 1 to the number of rows in "q_opt". This code can be used to iterate through each row of a matrix "q_opt" and perform some operation on each row.
相关问题
function [result,error,errorDim]=zjfc(s,opt) [m,n]=size(s); p=max(s(:,1)); %取水平数 q=n-1;%取列数 sum0=zeros(p,q); for i=1:q for k=1:m for j=1:p if(s(k,i)==j) sum0(j,i)=sum0(j,i)+s(k,n); %求和 end end end end totalsum=sum(s(:,n)); ss=sum0.*sum0; levelsum=m/p; %水平重复数 ss=sum(ss./levelsum)-totalsum^2/m; %每一列的S ssError=sum(ss(opt)); for i=1:q f(i)=p-1; %自由度 end fError=sum(f(opt)); %误差自由度 ssbar=ss./f; Errorbar=ssError/fError; index=find(ssbar<Errorbar); index1=find(index==opt); index(index==index(index1))=[]; %剔除重复 ssErrorNew=ssError+sum(ss(index)); %并入误差 fErrorNew=fError+sum(f(index)); %新误差自由度 F=(ss./f)/(ssErrorNew./fErrorNew); %F值 errorDim=[opt,index]; errorDim=sort(errorDim); %误差列的序号 result=[ss',f',ssbar',F']; error=[ssError,fError;ssErrorNew,fErrorNew];
根据你提供的代码,我注意到在最后一行中,你将结果errorDim进行了sort操作,以对误差列的序号进行排序。
问题可能出现在这里,如果你在调用zjfc函数时传递的opt参数不是按照升序排列的,那么对errorDim进行排序可能导致index1的值不正确。
你可以尝试在调用zjfc函数时,将opt参数按照升序排列,并确保传递给zjfc函数的opt参数与你的数据矩阵s中的列索引对应。
另外,请确保你在调用zjfc函数时传递了正确的参数,并且数据矩阵s的维度与代码中的预期一致。
如果问题仍然存在,请提供更多关于报错信息的细节,以便我能够给出更具体的解决方案。
代码解释# Process detections for i, det in enumerate(pred): # detections per image if webcam: # batch_size >= 1 p, s, im0 = path[i], '%g: ' % i, im0s[i].copy() else: p, s, im0 = path, '', im0s save_path = str(Path(out) / Path(p).name) s += '%gx%g ' % img.shape[2:] # print string gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh if det is not None and len(det): # Rescale boxes from img_size to im0 size det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round() # Print results for c in det[:, -1].unique(): n = (det[:, -1] == c).sum() # detections per class s += '%g %ss, ' % (n, names[int(c)]) # add to string # Write results for *xyxy, conf, cls in det: if save_txt: # Write to file xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh with open(save_path[:save_path.rfind('.')] + '.txt', 'a') as file: file.write(('%g ' * 5 + '\n') % (cls, *xywh)) # label format if save_img or view_img: # Add bbox to image label = '%s %.2f' % (names[int(cls)], conf) if label is not None: if (label.split())[0] == 'person': people_coords.append(xyxy) # plot_one_box(xyxy, im0, line_thickness=3) plot_dots_on_people(xyxy, im0) # Plot lines connecting people distancing(people_coords, im0, dist_thres_lim=(100, 150)) # Print time (inference + NMS) print('%sDone. (%.3fs)' % (s, t2 - t1)) # Stream results if 1: ui.showimg(im0) if cv2.waitKey(1) == ord('q'): # q to quit raise StopIteration # Save results (image with detections) if save_img: if dataset.mode == 'images': cv2.imwrite(save_path, im0) else: if vid_path != save_path: # new video vid_path = save_path if isinstance(vid_writer, cv2.VideoWriter): vid_writer.release() # release previous video writer fps = vid_cap.get(cv2.CAP_PROP_FPS) w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH)) h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*opt.fourcc), fps, (w, h)) vid_writer.write(im0)
这段代码是目标检测算法的输出结果处理部分。主要包括以下几个步骤:
1. 对每张图片的检测结果进行处理,包括将检测框从输入图像的尺寸缩放到输出图像的尺寸,并将结果写入文本文件中。
2. 对每个类别的检测结果统计数量,并将数量和类别名称添加到输出字符串中。
3. 对每个检测到的目标绘制边界框,并在边界框上标注类别和置信度。
4. 如果检测到的目标是人,则将其坐标保存在列表中,并在图像上绘制点和连线进行社交距离监测。
5. 将处理后的图像展示出来,并将图像保存到文件中。