function showImg(){ var xiangpianFileName = """ + ruleForm.xiangpian + """; $("#xiangpianImg").attr("src",baseUrl+ruleForm.xiangpian); }是怎么实现图片展示的？

代码1： beforeCreate: function(){ var id = window.sessionStorage.getItem("id"); if(id != null && id != "" && id != "null"){ $.ajax({ //ajax请求获取用户数据 type: "GET", url: baseUrl + "yonghu/info/" + id, //在请求头中设置Token beforeSend: function(xhr) {xhr.setRequestHeader("token", window.sessionStorage.getItem('token'));}, success: function(res){ if(res.code == 0){ vm.ruleForm = res.data; }else if(res.code == 401) { <%@ include file="../../static/toLogin.jsp"%> }else{ alert(res.msg)} }, }); } }, methods: { } }); 与代码二：function getDetails() { var id = window.sessionStorage.getItem("id"); if(id != null && id != "" && id != "null"){ $("#submitBtn").addClass("修改"); updateId = id; window.sessionStorage.removeItem('id'); $.ajax({ type: "GET", url: baseUrl + "yonghu/info/" + id, beforeSend: function(xhr) {xhr.setRequestHeader("token", window.sessionStorage.getItem('token'));}, success: function(res){ if(res.code == 0){ ruleForm = res.data showImg(); setContent(); setDownloadBtn(); }else if(res.code ==401){ <%@ include file="../../static/toLogin.jsp"%> }else{ alert(res.msg); } }, }); }else{ $("#submitBtn").addClass("新增"); } } 均是一个购物系统后台用户详细页的jsp文件中的代码？他们各自的作用是什么？我觉得这两段代码功能怎么一样？是否功能重复？

// 机器类里发起ajax请求，获取用户数据，并将数据赋值给Vue实例的ruleForm属性，以便在页面中展 class Machine { private String type; // 机器类型 private String number; // 机器编号 public示。代码2则是...

翻译为java代码 public FileContentResult ShowImg(int id) { var accessory = ProviderManager.GetProvider<AccessoryProviderBase>().FirstOrDefault<Accessory>(t => t.Id == id); var attachment = ProviderManager.GetProvider<AttachmentProviderBase>().FirstOrDefault<Attachment>(t => t.Id == accessory.AttachmentId); var path = Path.Combine(AttachmentProviderBase.FilePath, attachment.RelativePath); byte[] bytes = System.IO.File.ReadAllBytes(path); if (bytes != null) { return File(bytes, MediaTypeNames.Image.Jpeg); } return null; }

以下是 Java 代码的翻译，实现的功能与 C# 代码相同： ... import org.springframework.stereotype.Controller; import org.springframework.web.bind.annotation.GetMapping; import org.springframework.web....

解析代码：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) cv2.imwrite('prediction.jpg', showimg) self.result = cv2.cvtColor(showimg, cv2.COLOR_BGR2BGRA) self.result = cv2.resize( self.result, (640, 480), interpolation=cv2.INTER_AREA) self.QtImg = QtGui.QImage( self.result.data, self.result.shape[1], self.result.shape[0], QtGui.QImage.Format_RGB32) self.label.setPixmap(QtGui.QPixmap.fromImage(self.QtImg))

这段代码主要是进行目标检测的推理过程，并将检测结果展示在界面上。首先，使用OpenCV读取图片，然后对图片进行预处理，包括缩放、转换颜色空间、转换数据类型等。然后，将处理后的图片输入模型进行推理，得到检测...

修改成轮播图效果!DOCTYPE html> <html> <head> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1"> <title></title> <style type="text/css"> div{ height: 500px; background-color: skyblue; display: none; } </style> </head> <body> <input type="button" value="显示"> <input type="button" value="隐藏"> <input type="button" value="切换"> <script src="../jquery-3.7.0.js"></script> <script type="text/javascript"> $(function(){ $("input").eq(0).click(function(){ // sildeDown(null/speed,easing,.callback) / sildeUP $("div").slideDown("slow",function(){ console.log("111111") }) }) $("input").eq(1).click(function(){ // sildeDown(null/speed,easing,.callback) / sildeUP $("div").slideUp("slow",function(){ console.log("111111") }) }) }) </script> </body> </html>

function showImg(index) { $(".slider li").eq(index).fadeIn().siblings().fadeOut(); } function autoPlay() { timer = setInterval(function () { index++; if (index == len) { index = 0; } ...

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...

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....

以下代码发生TypeError: Expected Ptrcv::UMat for argument 'mat'，代码如下： def on_pushButton_5_clicked(self): # 读取左相机图像 left_image_path = '1_left.JPG' # 替换为实际图像的路径 left_image = cv2.imread(left_image_path) # 转换为HSV颜色空间 hsv_image = cv2.cvtColor(left_image, cv2.COLOR_BGR2HSV) # 定义红色的HSV颜色范围 lower_red = np.array([0, 100, 100]) upper_red = np.array([10, 255, 255]) # 对图像进行红色阈值处理 red_mask = cv2.inRange(hsv_image, lower_red, upper_red) # 执行形态学操作，去除噪声 kernel = np.ones((5, 5), np.uint8) red_mask = cv2.morphologyEx(red_mask, cv2.MORPH_OPEN, kernel) # 查找红色轮廓 contours, _ = cv2.findContours(red_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # 保留最大的两个轮廓 contours = sorted(contours, key=cv2.contourArea, reverse=True)[:2] # 遍历轮廓并绘制圆心和坐标 for contour in contours: # 计算轮廓的最小外接圆 (x, y), radius = cv2.minEnclosingCircle(contour) center = (int(x), int(y)) radius = int(radius) # 绘制圆心 cv2.circle(left_image, center, 3, (0, 255, 0), -1) # 绘制圆形轮廓 cv2.circle(left_image, center, radius, (0, 0, 255), 2) # 绘制坐标 text = f'({int(x)}, {int(y)})' cv2.putText(left_image, text, (int(x) + 10, int(y) - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2) cv2.imshow('Result', cv2.circle) cv2.waitKey() showImg = cv2.cvtColor('image', cv2.COLOR_BGR2RGB) qImgae = QImage(showImg, showImg.shape[1], showImg.shape[0], showImg.shape[1]*3, QImage.Format_RGB888) self.label.setPixmap(QPixmap(qImage).scaled(self.label.width(), self.label.height(), Qt.KeepAspectRatio))

qImage = QImage(showImg, showImg.shape[1], showImg.shape[0], showImg.shape[1]*3, QImage.Format_RGB888) self.label.setPixmap(QPixmap(qImage).scaled(self.label.width(), self.label.height(), Qt....

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...

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解释代码

这个代码是对输入的图片进行目标检测的，其中包含以下步骤： 1. 将输入的图片进行缩放，使其宽度和高度都等于opt.img_size（一般情况下为416），并将其转换为RGB格式。 2. 将缩放后的图片转换为PyTorch的tensor，...

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

1. 将输入的 img 赋值给 showimg 变量。 2. 使用 letterbox 函数将 img 调整为指定大小（416x416）。 3. 将 BGR 图像转换为 RGB 并转置通道顺序，同时将数据类型转换为 numpy 数组。 4. 将 numpy 数组转换为 ...

解释下这段代码<script> var _self; export default { data() { return { showImg:true, upload :'upload/', orders:[], u_id : 0, http:'' } }, onLoad:function(e){ _self=this; _self.uid = getApp().globalData.u_id; _self.http = getApp().globalData.http; this.getOrders(); }, methods: { getOrders:function(){ uni.request({ url: _self.http+'item/ItemList', method:'GET', data: { uid:_self.uid }, success: (res) => { var date=res.data; for (let i in date) { let d = new Date(date[i].o_time); let year = d.getFullYear(); let month = d.getMonth() + 1; let day = d.getDate(); let hh = d.getHours(); let mm = d.getMinutes(); let ss = d.getSeconds(); date[i].o_time = year+"-"+month+"-"+day+" "+hh+":"+mm+":"+ss; } _self.orders=res.data; } }); }, toDetails(id){ uni.navigateTo({ url: 'scroll/scroll?u_id='+id, }); } } } </script>

该组件包含了一个数据对象，其中包括了一个布尔类型的变量 showImg、一个字符串类型的变量 upload、一个数组类型的变量 orders、一个整型变量 u_id 和一个字符串类型的变量 http。除此之外，该组件还包含了一个 ...

show = cv2.resize(showimg, (640, 480))什么意思

这段代码是用于将图片 showimg 进行大小调整的，具体解释如下： 1. cv2.resize() 是 OpenCV 库中用于调整图像大小的函数，可以将图像缩小或放大到指定的大小。 2. showimg 是需要调整大小的原始图像。 3. ...

var i = 0; $(function(){ showImg(); window.setInterval("showImg()",1000); }); function showImg(){ $("li:eq("+i+")").fadeIn("slow"); //别的消失 $("li:eq("+i+")").siblings("li").fadeOut("slow"); if(i == 4){ i = 0; }else{ i++; } }改成show方法写

<li><img src="image1.jpg"> <li><img src="image2.jpg"> <li><img src="image3.jpg"> <li><img src="image4.jpg"> <li><img src="image5.jpg"> css .carousel-container { position: relative; ...

function showImg(){ $("li:eq("+i+")").show("slow"); //别的消失 $("li:eq("+i+")").siblings("li").hide("slow"); if(i == 4){ i = 0; }else{ i++; } }改对

function showImg() { $("ul.slide:eq(0) li:eq(" + i + ")").show("slow"); // 限制选择器，选择第一个 <ul> 下的第 i 个 $("ul.slide:eq(0) li:eq(" + i + ")").siblings("li").hide("slow"); // 同上，同时...

显示图片

佛挡杀佛

function showImg(){ var xiangpianFileName = """ + ruleForm.xiangpian + """; $("#xiangpianImg").attr("src",baseUrl+ruleForm.xiangpian); }是怎么实现图片展示的？

function showImg(){ var xiangpianFileName = "\"" + ruleForm.xiangpian + "\""; $("#xiangpianImg").attr("src",baseUrl+ruleForm.xiangpian); }

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function showImg(){ var xiangpianFileName = """ + ruleForm.xiangpian + """; $("#xiangpianImg").attr("src",baseUrl+ruleForm.xiangpian); }是怎么实现图片展示的？

function showImg(){ var xiangpianFileName = "\"" + ruleForm.xiangpian + "\""; $("#xiangpianImg").attr("src",baseUrl+ruleForm.xiangpian); }

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