def IOU(boxes, classes, scores, controlRectangle, controlInvalid, url): # numpy转list boxes = boxes.tolist() classes = classes.tolist() scores = scores.tolist() # 读取配置文件 if os.path.exists('stream_dict.txt'): with open('stream_dict.txt', 'r') as f: stream_dict = json.load(f) else: stream_dict = {} if os.path.exists('cameraId_dict.txt'): with open('cameraId_dict.txt', 'r') as f: cameraId_dict = json.load(f) else: cameraId_dict = {} cameraId = cameraId_dict[url] try: w = int(stream_dict[cameraId][1].split(',')[0]) h = int(stream_dict[cameraId][1].split(',')[1]) except: w = 1920 h = 1080 h1 = (w - h) * 0.5 / w * 640 img_h = 640 - h1 * 2 if controlRectangle == [] and controlInvalid == []: boxes1 = boxes classes1 = classes scores1 = scores if controlRectangle != []: boxes1 = [] classes1 = [] scores1 = [] for i in range(len(boxes)): for j in range(len(controlRectangle)): a = boxes[i] c = classes[i] s = scores[i] x1 = a[0] y1 = a[1] x2 = a[2] y2 = a[3] d = [x1, y1, x2, y2, x2, y1, x1, y2] b = [controlRectangle[j][0]*640, h1 + controlRectangle[j][1]*img_h, controlRectangle[j][2]*640, h1 + controlRectangle[j][3]*img_h, controlRectangle[j][4]*640, h1 + controlRectangle[j][5]*img_h, controlRectangle[j][6]*640, h1 + controlRectangle[j][7]*img_h] iou = calculate_iou(d, b) if iou > 0: boxes1.append(a) classes1.append(c) scores1.append(s) break else: boxes1 = boxes classes1 = classes scores1 = scores if controlInvalid != []: boxes2 = copy.deepcopy(boxes1) classes2 = copy.deepcopy(classes1) scores2 = copy.deepcopy(scores1) for i in range(len(boxes2)): for j in range(len(controlInvalid)): a = boxes2[i] c = classes2[i] s = scores2[i] x1 = a[0] y1 = a[1] x2 = a[2] y2 = a[3] d = [x1, y1, x2, y2, x2, y1, x1, y2] b = [controlInvalid[j][0]*640, h1 + controlInvalid[j][1]*img_h, controlInvalid[j][2]*640, h1 + controlInvalid[j][3]*img_h, controlInvalid[j][4]*640, h1 + controlInvalid[j][5]*img_h, controlInvalid[j][6]*640, h1 + controlInvalid[j][7]*img_h] iou = calculate_iou(d, b) if iou > 0: del boxes1[i] del classes1[i] del scores1[i] break boxes1 = np.array(boxes1) classes1 = np.array(classes1) scores1 = np.array(scores1) return boxes1, classes1, scores1

IOU.rar_IOU_女朋友小程序_表白_表白c语言

这是一个用于表白的小程序，有兴趣的朋友可以拿给自己的男朋友或女朋友看看，用c语言写的

GNS3.IOU.VM.1.3.7.ova

IoU-Uniform-R-CNN:纸张代码

IoU统一R-CNN 该存储库提供了纸质的代码该项目基于框架。设置请遵循官方和指南。训练 ./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [--validate] [other_optional_args]注意： IoU统一R-CNN的配置文件： ...

dataset = iter(dataset)，未使用局部变量 'dataset' 的值， def init(self): super(DetThread, self).init() path, img, im0s, self.vid_cap = next(dataset) self.weights = './yolov5s.pt' # 设置权重 self.current_weight = './yolov5s.pt' # 当前权重 self.source = '0' # 视频源 self.conf_thres = 0.25 # 置信度 self.iou_thres = 0.45 # iou self.jump_out = False # 跳出循环 self.is_continue = True # 继续/暂停 self.percent_length = 1000 # 进度条 self.rate_check = True # 是否启用延时 self.rate = 100 # 延时HZ怎么解决

这段代码中，未使用局部变量 'dataset' 的值是一个警告，并不会导致代码出错，如果你不需要使用 'dataset' 变量的值，可以忽略该警告。至于最后一行的问题，我不清楚你的具体意思。如果你是想问如何解决延时的问题...

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

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 GPU 进行推理。最后，它检查图像的大小是否符合模型要求，设置是否保存推理结果图片，然后返回检测结果。

iou = d2l.box_iou(boxes[i,:].reshape(-1,4), boxes[B[1:],:].reshape(-1,4)).reshape(-1)

def box_iou(boxes1, boxes2): box_area = lambda boxes: ((boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])) area1 = box_area(boxes1) area2 = box_area(boxes2) inter_upperlefts = np.maximum...

def box_iou(boxes1, boxes2): area1 = box_area(boxes1) area2 = box_area(boxes2) lt = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2] rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2] wh = (rb - lt).clamp(min=0) # [N,M,2] inter = wh[:, :, 0] * wh[:, :, 1] # [N,M] union = area1[:, None] + area2 - inter iou = inter / union return iou, union

这段代码是一个计算两个框（boxes）之间的IoU（Intersection over Union）的函数。IoU是用来衡量两个框的重叠程度的指标，范围在0到1之间，值越接近1表示两个框的重叠程度越高。代码中的boxes1和boxes2分别...

Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] =

The precision is the ratio of true positives to the total number of predicted boxes, and the recall is the ratio of true positives to the total number of ground truth boxes. 3. Average Precision ...

torch.where(iou == highest_quality[:, None])[1]

在torch.where(iou == highest_quality[:, None])[1]中，1表示在torch.where()函数的返回值中选择索引的维度。这段代码的目的是找到iou张量中与highest_quality[:, None]相等的元素所在的列索引。具体来...

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

weights=ROOT / 'weights/best.pt', # 模型路径或Triton URL 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...

def predict(im0s): # 进行推理 img = torch.zeros((1, 3, imgsz, imgsz), device=device) # 初始化img _ = model(img.half() if half else img) if device.type != 'cpu' else None # 运行一次模型 # 设置数据加载器并进行推理 img = letterbox(im0s, new_shape=imgsz)[0] # 对输入图像进行resize img = img[:, :, ::-1].transpose(2, 0, 1) # BGR转RGB, 3x416x416 img = np.ascontiguousarray(img) # 返回具有相同数据和顺序的相同形状数组 img = torch.from_numpy(img).to(device) # 将numpy数组转换为张量并传递到设备上 img = img.half() if half else img.float() # 数据类型转换为float16或float32 img /= 255.0 # 将像素值从0-255映射到0.0-1.0 if img.ndimension() == 3: img = img.unsqueeze(0) # 给张量添加一个额外的纬度，输出新的张量 # 进行推理 pred = model(img)[0] # 应用非极大值抑制 pred = non_max_suppression(pred, opt_conf_thres, opt_iou_thres) # 处理检测结果 ret = [] for i, det in enumerate(pred): # 每张图片有多个检测结果 if len(det): # 将检测框位置从img_size调整到原始图像大小 det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0s.shape).round() # 输出结果 for xyxy, conf, cls in reversed(det): label = f'{names[int(cls)]}' # 输出结果的标签信息 prob = round(float(conf) 100, 2) # 置信度转换 ret_i = [label, prob, xyxy] # 将结果存入list ret.append(ret_i) # 返回信息：标签信息 'face' 'smoke' 'drink' 'phone'，对应的置信度和位置信息（检测框） return ret

这段代码看起来像是对一个目标检测模型进行推理的代码。具体来说，输入一张图片，经过一系列处理（包括resize、数据类型转换等），然后通过模型得到预测结果。这些预测结果经过非极大值抑制后，再进行位置调整和输出...

class ComputeLoss: sort_obj_iou = False # Compute losses def init(self, model, autobalance=False): device = next(model.parameters()).device # get model device h = model.hyp # hyperparameters # Define criteria BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device)) BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device)) # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0)) # positive, negative BCE targets # Focal loss g = h['fl_gamma'] # focal loss gamma if g > 0: BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) m = de_parallel(model).model[-1] # Detect() module self.balance = {3: [4.0, 1.0, 0.4]}.get(m.nl, [4.0, 1.0, 0.25, 0.06, 0.02]) # P3-P7 self.ssi = list(m.stride).index(16) if autobalance else 0 # stride 16 index self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, 1.0, h, autobalance self.na = m.na # number of anchors self.nc = m.nc # number of classes self.nl = m.nl # number of layers self.anchors = m.anchors self.device = device这个代码什么意思

这段代码是定义了一个 ComputeLoss 类，用于计算 YOLOv5 模型的损失值。在初始化时，会根据模型的超参数定义不同的损失函数，包括分类损失 BCEcls、目标检测损失 BCEobj 和 Focal Loss。同时还对正负样本进行了平滑...

@smart_inference_mode() def run( weights=ROOT / 'yolov5s.pt', # model.pt path(s) source=ROOT / 'data/images', # file/dir/URL/glob, 0 for 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='', # 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这个代码什么意思

- iou_thres：非最大值抑制时使用的 IoU 阈值。 - max_det：每张图像中最多检测到的目标数量。 - device：使用的设备类型，如 'cpu' 或 'cuda:0'。 - view_img：是否显示检测结果。 - save_txt：是否将检测结果保存...

代码解释：weights=None, batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6,

这段代码是用于调用一个物体检测算法的函数，其中各个参数的含义如下： - weights：预训练模型的权重参数，默认为None，...- iou_thres：IoU阈值，指检测结果中重叠度大于该阈值的目标将被视为同一目标，默认为0.6。

上述211行附近的代码如下，请具体指出问题 def build_targets(self, p, targets): # Build targets for compute_loss(), input targets(image,class,x,y,w,h) na, nt = self.na, targets.shape[0] # number of anchors, targets tcls, tbox, indices, anch = [], [], [], [] gain = torch.ones(7, device=targets.device) # normalized to gridspace gain ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt) targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices g = 0.5 # bias off = torch.tensor([[0, 0], [1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm ], device=targets.device).float() * g # offsets for i in range(self.nl): anchors = self.anchors[i] gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain # Match targets to anchors t = targets * gain if nt: # Matches r = t[:, :, 4:6] / anchors[:, None] # wh ratio j = torch.max(r, 1. / r).max(2)[0] < self.hyp['anchor_t'] # compare # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) t = t[j] # filter # Offsets gxy = t[:, 2:4] # grid xy gxi = gain[[2, 3]] - gxy # inverse j, k = ((gxy % 1. < g) & (gxy > 1.)).T l, m = ((gxi % 1. < g) & (gxi > 1.)).T j = torch.stack((torch.ones_like(j), j, k, l, m)) t = t.repeat((5, 1, 1))[j] offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] else: t = targets[0] offsets = 0 # Define b, c = t[:, :2].long().T # image, class gxy = t[:, 2:4] # grid xy gwh = t[:, 4:6] # grid wh gij = (gxy - offsets).long() gi, gj = gij.T # grid xy indices # Append a = t[:, 6].long() # anchor indices indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices tbox.append(torch.cat((gxy - gij, gwh), 1)) # box anch.append(anchors[a]) # anchors tcls.append(c) # class return tcls, tbox, indices, anch

根据你提供的代码，第 211 行出现错误的地方是在 indices.append((b, a, gj.clamp_(0, ...这样修改后，应该就能解决 RuntimeError: result type Float can't be cast to the desired output type __int64 的问题了。

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)

这是一个用于打开图片并进行目标检测的函数，使用了OpenCV和PyTorch进行图像处理和模型推理。具体流程如下： 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....

解释一下下面一段代码 def detect(self, name_list, img): showimg = img with torch.no_grad(): img = letterbox(img, new_shape=self.opt.img_size)[0] img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 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) pred = self.model(img, augment=self.opt.augment)[0] pred = non_max_suppression(pred, self.opt.conf_thres, self.opt.iou_thres, classes=self.opt.classes, agnostic=self.opt.agnostic_nms) info_show = "" for i, det in enumerate(pred): if det is not None and len(det): 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)]) single_info = plot_one_box2(xyxy, showimg, label=label, color=self.colors[int(cls)], line_thickness=2) # print(single_info) info_show = info_show + single_info + "\n" return info_show

这是一个函数，名称为 detect，其输入参数有 name_list 和 img，其中 name_list 是一个列表，img 是一个图片。该函数的作用是使用 YOLOv5 检测算法对输入的图片进行目标检测，并返回检测结果的字符串形式。具体...

i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS AttributeError: module 'torchvision' has no attribute 'ops'

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i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS AttributeError: module 'torchvision' has no attribute 'ops'

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IOU.rar_IOU_女朋友小程序_表白_表白c语言

GNS3.IOU.VM.1.3.7.ova

IoU-Uniform-R-CNN:纸张代码

iou = d2l.box_iou(boxes[i,:].reshape(-1,4), boxes[B[1:],:].reshape(-1,4)).reshape(-1)

Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] =

torch.where(iou == highest_quality[:, None])[1]

代码解释：weights=None, batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6,

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