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)

解释 for si, detections in enumerate(output): labels = targets[targets[:, 0] == si, 1:] seen += 1

这是一段 Python 代码，其中使用了一个 for 循环和一个 enumerate 函数，遍历名为 output 的集合。在每次遍历时，使用 si 变量存储当前遍历到的索引位置，使用 detections 变量存储当前遍历到的元素。在循环体内，...

if len(det): # Rescale boxes from img_size to im0 size这个把注释掉的那行怎么加进去

for *xyxy, conf, cls in reversed(det): c = int(cls) # integer class label = f'{names[c]} {conf:.2f}' plot_one_box(xyxy, im0, label=label, color=colors(c, True), line_thickness=3) 这样就可以将...

def button_image_open(self): print('button_image_open') name_list = [] img_name, _ = QtWidgets.QFileDialog.getOpenFileName( self, "打开图片", "", ".jpg;;.png;;All Files()") if not img_name: return img = cv2.imread(img_name) print(img_name) showimg = img with torch.no_grad(): img = letterbox(img, new_shape=self.opt.img_size)[0] # Convert # BGR to RGB, to 3x416x416 img = img[:, :, ::-1].transpose(2, 0, 1) img = np.ascontiguousarray(img) img = torch.from_numpy(img).to(self.device) img = img.half() if self.half else img.float() # uint8 to fp16/32 img /= 255.0 # 0 - 255 to 0.0 - 1.0 if img.ndimension() == 3: img = img.unsqueeze(0) # Inference pred = self.model(img, augment=self.opt.augment)[0] # Apply NMS pred = non_max_suppression(pred, self.opt.conf_thres, self.opt.iou_thres, classes=self.opt.classes, agnostic=self.opt.agnostic_nms) print(pred) # Process detections for i, det in enumerate(pred): 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], showimg.shape).round() for xyxy, conf, cls in reversed(det): label = '%s %.2f' % (self.names[int(cls)], conf) name_list.append(self.names[int(cls)]) plot_one_box(xyxy, showimg, label=label, color=self.colors[int(cls)], line_thickness=10)

1. 使用QtWidgets.QFileDialog打开一个图片选择对话框，选中需要检测的图片。 2. 使用OpenCV的cv2.imread函数读取所选图片。 3. 对读取的图片进行预处理，包括调整图片大小、转换颜色空间、归一化等操作，使其符合...

修改#检测目标 detections1 = model(image1) detections2 = model(image2) detections3 = model(image3)报错RuntimeError: expected scalar type Byte but found Float

这个错误通常是由于输入图像的像素值类型不正确...detections1 = model(image1) detections2 = model(image2) detections3 = model(image3) 这将确保您的输入图像像素值类型正确，并且可以被YOLOv7模型正确处理。

解释代码： def detect_cma(self): # pass model = self.model output_size = self.output_size # source = self.img2predict # file/dir/URL/glob, 0 for webcam imgsz = [640, 640] # inference size (pixels) conf_thres = 0.25 # confidence threshold iou_thres = 0.45 # NMS IOU threshold max_det = 1000 # maximum detections per image # device = self.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 save_crop = False # save cropped prediction boxes nosave = False # do not save images/videos classes = None # filter by class: --class 0, or --class 0 2 3 agnostic_nms = False # class-agnostic NMS augment = False # ugmented inference visualize = False # visualize features line_thickness = 3 # bounding box thickness (pixels) hide_labels = False # hide labels hide_conf = False # hide confidences half = False # use FP16 half-precision inference dnn = False # use OpenCV DNN for ONNX inference source = str(self.vid_source) device = select_device(self.device) stride, names, pt, jit, onnx = model.stride, model.names, model.pt, model.jit, model.onnx imgsz = check_img_size(imgsz, s=stride) # check image size save_img = not nosave and not source.endswith('.txt') # save inference images

这段代码用于使用 YOLOv5 模型对图像或视频进行目标检测。它首先从类的属性中获取模型、输出大小、视频源等信息。然后设置了一些参数，如推理尺寸、置信度阈值、NMS IOU 阈值等。接着根据设备类型选择使用 CPU 还是 ...

detPos(i,:) = detections{i}.Measurement(1:2)'这个语句是什么意思

这个语句是将第i个检测结果的前两个测量值（通常是在像素坐标系下的x和y坐标）存储到名为detPos的向量中。...通过detections{i}.Measurement(1:2)可以获取前两个测量值，再通过'将其转换为列向量并存储到detPos中。

img = cv2.imread(fileName) print(fileName) showimg = img with torch.no_grad(): img = letterbox(img, new_shape=self.opt.img_size)[0] # Convert # BGR to RGB, to 3x416x416 img = img[:, :, ::-1].transpose(2, 0, 1) img = np.ascontiguousarray(img) img = torch.from_numpy(img).to(self.device) img = img.half() if self.half else img.float() # uint8 to fp16/32 img /= 255.0 # 0 - 255 to 0.0 - 1.0 if img.ndimension() == 3: img = img.unsqueeze(0) # Inference pred = self.model(img, augment=self.opt.augment)[0] # Apply NMS pred = non_max_suppression(pred, self.opt.conf_thres, self.opt.iou_thres, classes=self.opt.classes, agnostic=self.opt.agnostic_nms) print(pred) # Process detections for i, det in enumerate(pred): 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], showimg.shape).round() for *xyxy, conf, cls in reversed(det): label = '%s %.2f' % (self.names[int(cls)], conf) name_list.append(self.names[int(cls)]) plot_one_box(xyxy, showimg, label=label, color=self.colors[int(cls)], line_thickness=2) cv2.imwrite('prediction.jpg', showimg) self.result = cv2.cvtColor(showimg, cv2.COLOR_BGR2BGRA) self.QtImg = QtGui.QImage( self.result.data, self.result.shape[1], self.result.shape[0], QtGui.QImage.Format_RGB32) self.label_4.setPixmap(QtGui.QPixmap.fromImage(self.QtImg))

这段代码主要是将文件加载并读入到img变量中，然后对图像进行缩放（使用letterbox函数）以适应所选的img_size。最后将变换后的图像存入img中。在PyTorch的上下文中，这些变换是不进行梯度计算的，因为没有必要对它们...

for c in det[:, -1].unique(): n = (det[:, -1] == c).sum() # detections per class s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string

这段代码是用于统计目标检测算法检测结果中每个类别的数量，并将结果以字符串的形式输出。具体来说： 1. det是一个二维数组，每一行表示一个检测结果，其中最后一列是表示该检测结果属于哪个类别的标签。 2. det...

帮我简析以下代码： for det in pred: # 每张图片的检测 if len(det): # 将框从 img 大小重新缩放为 original_image 大小 # 将预测信息映射到原图(坐标框的位置信息映射回原图，填入det中前面的4个位置信息上) det[:, :4] = scale_coords(img.shape[2:], det[:, :4], original_image.shape).round() # ====写入结果==== #打印检测到的类别名称和数量 for *xyxy, conf, cls in reversed(det): c = int(cls) # integer class label = (self.names[c] if args.hide_conf else f'{self.names[c]} {conf:.2f}') plot_one_box(xyxy, original_image, label=label, color=colors(c, True), line_thickness=2) # Save results (image with detections) # cv2.imwrite(save_path, original_image) print(f'Done. ({time.time() - t0:.3f}s)') return original_image

1. 对于每张输入图片，循环遍历其对应的预测结果pred中的每个检测框det。 2. 如果检测结果det不为空，则将框从原始输入图片img大小重新缩放为原图original_image大小，并将预测信息映射到原图，填入det中前面的4个...

把这段代码补充完整：import numpy as np import cv2 # 定义目标检测函数 def detect_objects(image, threshold): # 使用OpenCV加载预训练的目标检测模型 model = cv2.dnn.readNetFromCaffe("deploy.prototxt", "model.caffemodel") # 图像预处理 blob = cv2.dnn.blobFromImage(image, scalefactor=1.0, size=(300, 300), mean=(104.0, 177.0, 123.0)) # 输入图像到模型中进行目标检测 model.setInput(blob) detections = model.forward() # 解析检测结果 num_detections = detections.shape[2] filtered_detections = [] for i in range(num_detections): confidence = detections[0, 0, i, 2] if confidence > threshold: x1 = int(detections[0, 0, i, 3] * image.shape[1]) y1 = int(detections[0, 0, i, 4] * image.shape[0]) x2 = int(detections[0, 0, i, 5] * image.shape[1])

for i in range(num_detections): confidence = detections[0, 0, i, 2] if confidence > threshold: x1 = int(detections[0, 0, i, 3] * image.shape[1]) y1 = int(detections[0, 0, i, 4] * image.shape[0]) ...

解释以下这段代码：@torch.no_grad() def run(self, imgsz=640, # inference size (pixels) max_det=1000, # maximum detections per image device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu view_img=True, # show results save_txt=False, # save results to *.txt save_conf=False, # save confidences in --save-txt labels save_crop=False, # save cropped prediction boxes nosave=False, # do not save images/videos classes=None, # filter by class: --class 0, or --class 0 2 3 agnostic_nms=False, # class-agnostic NMS augment=False, # augmented inference visualize=False, # visualize features update=False, # update all models project='runs/detect', # save results to project/name name='exp', # save results to project/name exist_ok=False, # existing project/name ok, do not increment line_thickness=3, # bounding box thickness (pixels) hide_labels=False, # hide labels hide_conf=False, # hide confidences half=False, # use FP16 half-precision inference ):

这段代码定义了DetThread类的run()方法，该方法用于在DetThread线程中执行目标检测任务。方法前面的@torch.no_grad()表示在执行该方法时不需要计算梯度。方法中的各个参数含义如下： - imgsz：表示输入...

请帮我翻译每一句代码： weights=ROOT / 'weights/best.pt', # model path or triton URL source=ROOT / 'data/images', # file/dir/URL/glob/screen/0(webcam) data=ROOT / 'data/coco128.yaml', # dataset.yaml path imgsz=(640, 640), # inference size (height, width) conf_thres=0.25, # confidence threshold iou_thres=0.45, # NMS IOU threshold max_det=1000, # maximum detections per image device='cpu', # 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 save_crop=False, # save cropped prediction boxes nosave=False, # do not save images/videos classes=None, # filter by class: --class 0, or --class 0 2 3 agnostic_nms=False, # class-agnostic NMS augment=False, # augmented inference visualize=False, # visualize features update=False, # update all models project=ROOT / 'runs/detect', # save results to project/name name='exp', # save results to project/name exist_ok=False, # existing project/name ok, do not increment line_thickness=3, # bounding box thickness (pixels) hide_labels=False, # hide labels hide_conf=False, # hide confidences half=False, # use FP16 half-precision inference dnn=False, # use OpenCV DNN for ONNX inference vid_stride=1, # video frame-rate stride

source=ROOT / 'data/images', # 文件/目录/URL/glob/screen/0(webcam) data=ROOT / 'data/coco128.yaml', # 数据集.yaml路径 imgsz=(640, 640), # 推理尺寸（高度，宽度） conf_thres=0.25, # 置信度阈值 iou_thres...

代码解释 if 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 += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string # Write results for xyxy, conf, cls in reversed(det): if save_txt: # Write to file xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh line = (cls, xywh, conf) if opt.save_conf else (cls, xywh) # label format with open(txt_path + '.txt', 'a') as f: f.write(('%g ' len(line)).rstrip() % line + '\n') if save_img or view_img: # Add bbox to image label = f'{names[int(cls)]} {conf:.2f}' plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=1)

这段代码是用来处理检测结果的。首先，它会判断检测结果列表(det)是否非空，如果非空则进行结果处理。接着，将检测框从img_size大小重新缩放到im0大小，并将结果输出到字符串s中。之后，对于每一个检测框，如果save_...

parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')

- help='maximum detections per image' 是一个帮助信息，用于描述这个参数的作用，当用户输入 --help 时会显示这个信息。因此，这段代码的作用是在命令行中添加一个 --max-det 参数，用于指定每张图像最多...

vector<int> not_assigned_detections; vector<int>::iterator it; for(int i=0;i<detections.size();i++) { it=find(assignment.begin(), assignment.end(), i); if(it==assignment.end()) { not_assigned_detections.push_back(i); } }

这段代码的作用是将未被分配的检测结果的索引存储在一个名为 not_assigned_detections 的向量中。具体来说，这段代码首先创建一个空的向量 not_assigned_detections，然后遍历所有检测结果，查找它们是否被分配...

解释 for i, det in enumerate(pred): # detections per image 遍历一个batch中的每个图片 if webcam: # batch_size >= 1 p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count else: p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0)

n = (pred[:, -1] == c).type(torch.int).sum() # detections per class

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解释 for i, det in enumerate(pred): # detections per image 遍历一个batch中的每个图片 if webcam: # batch_size >= 1 p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count else: p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0)

n = (pred[:, -1] == c).type(torch.int).sum() # detections per class

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COCO_test-dev2017_detections_AP_H_609_person.json

Video-Based Algorithms for Accident Detections_accident_

COCO_val2017_detections_AP_H_56_person.zip

解释 for si, detections in enumerate(output): labels = targets[targets[:, 0] == si, 1:] seen += 1

if len(det): # Rescale boxes from img_size to im0 size这个把注释掉的那行怎么加进去

修改#检测目标 detections1 = model(image1) detections2 = model(image2) detections3 = model(image3)报错RuntimeError: expected scalar type Byte but found Float

detPos(i,:) = detections{i}.Measurement(1:2)'这个语句是什么意思

for c in det[:, -1].unique(): n = (det[:, -1] == c).sum() # detections per class s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string

parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')

vector<int> not_assigned_detections; vector<int>::iterator it; for(int i=0;i<detections.size();i++) { it=find(assignment.begin(), assignment.end(), i); if(it==assignment.end()) { not_assigned_detections.push_back(i); } }

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