except: pass ui.showimg(image) QApplication.processEvents()解释
这段代码的作用是捕获异常,并在程序运行时展示一张图片,并让应用程序处理事件。具体来说,如果try语句块中出现异常,则程序将跳过该语句块并执行except语句块中的代码。其中,pass表示什么也不做,只是占位符。而ui.showimg(image)则展示指定的图片,而QApplication.processEvents()则让应用程序处理当前所有待处理的事件,保证程序流畅运行。
cv::Mat showimg
cv::Mat是OpenCV中用于表示图像的类。根据引用和引用,在C++中,可以通过将图像加载到cv::Mat对象中,然后使用相应的函数将其显示在窗口中。可以使用ShowMatImgToWnd函数将Mat对象显示到指定的窗口控件中。这种方法比CvvImage类中的DrawToHDC方法更稳定。
另外,根据引用,您还可以在C++中调用Python中的show_img函数来显示图像。首先,需要确保环境配置正确,包括安装OpenCV和Python相关的库。然后,可以使用Python的C API将图像转换为PyObject对象,并将其传递给Python函数show_img。该函数将创建一个名为'img'的窗口,并将图像显示在窗口中。您还可以通过调用cv2.waitKey函数来等待用户的按键操作。函数返回一个字符串,您可以将其转换为char并打印出来。
因此,cv::Mat showimg是指在C++中使用cv::Mat对象显示图像并调用名为show_img的Python函数的过程。123
引用[.reference_title]
- 1 在MFC中显示OpenCV的Mat图像矩阵 ShowMatImgToWnd(GetDlgItem(IDC_ShowImg) , matFrame);[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2
allinsert_cask~default-1-null.142^v93^chatsearchT3_1"}}] [.reference_item style="max-width: 50%"] - 2 3 c++调用python函数, cv::Mat类转ndarray[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2
allinsert_cask~default-1-null.142^v93^chatsearchT3_1"}}] [.reference_item style="max-width: 50%"] [ .reference_list ]
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:
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。
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。
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。
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。
将处理后的图像展示出来,并将图像保存到文件中。
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