def start(): global img, pre_pts try: root = tk.Tk() root.withdraw() f_path = filedialog.askopenfilename() if not f_path: messagebox.showinfo('提示', '请选取有效图片') else: img = cv.imread(f_path) pre_pts = -1, -1 cv.namedWindow('picture', cv.WINDOW_NORMAL) cv.resizeWindow('picture', 256, 256) cv.moveWindow('picture', 600, 300) cv.imshow('picture', img) cv.setMouseCallback('picture', draw) cv.waitKey(0) cv.destroyAllWindows() root.destroy() except Exception as e: print("出现错误:", e) 报错后如何修改可以使下面主界面可以重新点击执行start函数，且这个start函数和主界面不在同一个py文件中：class Faceshow(QMainWindow, Ui_MainWindow): def init(self, parent=None): super(Faceshow, self).init(parent) self.setupUi(self) self.main_button.clicked.connect(start)

def start(): global img, pre_pts while True: root = tk.Tk() root.withdraw() f_path = filedialog.askopenfilename() if f_path: img = cv.imread(f_path) break else: messagebox.showinfo("提示", "请选择文件！") 修改上述代码，使得打开本地文件夹不选文件或者关闭系统窗口后，点击主界面按钮仍能够触发start（）函数

global img, pre_pts while True: root = tk.Tk() root.withdraw() try: f_path = filedialog.askopenfilename() if f_path: img = cv.imread(f_path) break except: pass # 创建主界面 root = tk.Tk() ...

def start(): global img, pre_pts try: root = tk.Tk() root.withdraw() f_path = filedialog.askopenfilename() if not f_path: messagebox.showinfo('提示', '请选取有效图片') else: img = cv.imread(f_path) pre_pts = -1, -1 cv.namedWindow('picture', cv.WINDOW_NORMAL) cv.resizeWindow('picture', 256, 256) cv.moveWindow('picture', 600, 300) cv.imshow('picture', img) cv.setMouseCallback('picture', draw) cv.waitKey(0) cv.destroyAllWindows() root.destroy() except Exception as e: print("出现错误:", e)上述代码报错时，下列主界面会卡住并退出程序：class Faceshow(QMainWindow, Ui_MainWindow): def init(self, parent=None): super(Faceshow, self).init(parent) self.setupUi(self) self.main_button.clicked.connect(start) self.main_exit.clicked.connect(self.quit) def quit(self): self.close() if name == 'main': hxz = QApplication(sys.argv) ui = Faceshow() ui.show() sys.exit((hxz.exec_()))

global img, pre_pts try: root = tk.Tk() root.withdraw() f_path = filedialog.askopenfilename() if not f_path: messagebox.showinfo('提示', '请选取有效图片') else: img = cv.imread(f_path) pre_...

def start(): global img, pre_pts #count = 1 while True: root = tk.Tk() root.withdraw() f_path = filedialog.askopenfilename() if f_path: img = cv.imread(f_path) break else: messagebox.showinfo("提示", "请选择文件！") 修改上述代码，使得打开本地文件夹不选文件或者关闭系统窗口后，使得槽函数能够被重新触发

global img, pre_pts while True: root = tk.Tk() root.withdraw() f_path = filedialog.askopenfilename() root.destroy() # 关闭窗口 if f_path: img = cv.imread(f_path) break else: messagebox....

global img, pre_pts root = tk.Tk() root.withdraw() try: f_path = filedialog.askopenfilename() if not f_path: messagebox.showinfo('提示', '请选取有效图片') img = cv.imread(f_path) pre_pts = -1, -1 cv.namedWindow('picture', cv.WINDOW_NORMAL) cv.resizeWindow('picture', 256, 256) cv.moveWindow('picture', 600, 300) cv.imshow('picture', img) cv.setMouseCallback('picture', draw) cv.waitKey(0) cv.destroyAllWindows()上述代码中的异常处理如何续写

global img, pre_pts root = tk.Tk() root.withdraw() try: f_path = filedialog.askopenfilename() if not f_path: messagebox.showinfo('提示', '请选取有效图片') img = cv.imread(f_path) pre_pts ...

try: root = tk.Tk() root.withdraw() f_path = filedialog.askopenfilename() if not f_path: messagebox.showinfo('提示', '请选取有效图片') img = cv.imread(f_path) pre_pts = -1, -1 cv.namedWindow('picture', cv.WINDOW_NORMAL) cv.resizeWindow('picture', 256, 256) cv.moveWindow('picture', 600, 300) cv.imshow('picture', img) cv.setMouseCallback('picture', draw) cv.waitKey(0) cv.destroyAllWindows() except Exception as e: print("出现错误:", e) 上述代码怎么改才会使得选取本地文件界面关掉后picture窗口不再出现

f_path = filedialog.askopenfilename() if not f_path: messagebox.showinfo('提示', '请选取有效图片') else: img = cv.imread(f_path) pre_pts = -1, -1 cv.namedWindow('picture', cv.WINDOW_...

将下列生成器改造成能够匹配edge-connect中的InpaintingModel的预训练模型键值的结构：class Generator(nn.Module): def init(self): super(Generator, self).init() self.encoder = nn.Sequential( nn.Conv2d(3, 64, 3, stride=2, padding=1), nn.BatchNorm2d(64), nn.LeakyReLU(0.2), nn.Conv2d(64, 128, 3, stride=2, padding=1), nn.BatchNorm2d(128), nn.LeakyReLU(0.2), nn.Conv2d(128, 256, 3, stride=2, padding=1), nn.BatchNorm2d(256), nn.LeakyReLU(0.2), nn.Conv2d(256, 512, 3, stride=2, padding=1), nn.BatchNorm2d(512), nn.LeakyReLU(0.2), nn.Conv2d(512, 4000, 1), nn.BatchNorm2d(4000), nn.LeakyReLU(0.2) ) self.decoder = nn.Sequential( nn.ConvTranspose2d(4000, 512, 3, stride=2, padding=1, output_padding=1), nn.BatchNorm2d(512), nn.LeakyReLU(0.2), nn.ConvTranspose2d(512, 256, 3, stride=2, padding=1, output_padding=1), nn.BatchNorm2d(256), nn.LeakyReLU(0.2), nn.ConvTranspose2d(256, 128, 3, stride=2, padding=1, output_padding=1), nn.BatchNorm2d(128), nn.LeakyReLU(0.2), nn.ConvTranspose2d(128, 64, 3, stride=2, padding=1, output_padding=1), nn.BatchNorm2d(64), nn.LeakyReLU(0.2), nn.ConvTranspose2d(64, 3, 3, stride=1, padding=1), nn.Tanh() ) def forward(self, x): x = self.encoder(x) x = self.decoder(x) return x 另外修复部分代码定义为if name == 'main': root = tk.Tk() root.withdraw() f_path = filedialog.askopenfilename() img = cv.imread(f_path) pre_pts = -1, -1 cv.namedWindow('picture', cv.WINDOW_NORMAL) cv.resizeWindow('picture', 256, 256) cv.moveWindow('picture', 600, 300) cv.imshow('picture', img) cv.setMouseCallback('picture', draw) cv.waitKey(0) cv.destroyAllWindows() mask = cv.inRange(img, (0, 0, 0), (1, 1, 1)) image_tensor = transforms.ToTensor()(img) mask_tensor = transforms.ToTensor()(mask) image_tensor = image_tensor.unsqueeze(0) mask_tensor = mask_tensor.unsqueeze(0) generator = Generator() load_edgeconnect_weights(generator, 'E:/fin/models/gen.pth') image_tensor = image_tensor.cuda() mask_tensor = mask_tensor.cuda() generator = generator.cuda() with torch.no_grad(): output_tensor = generator(image_tensor, mask_tensor)

下面是将生成器改造成能够匹配edge-connect中的InpaintingModel的预...同时，将原来的nn.LeakyReLU(0.2)修改为F.leaky_relu，并且将所有的nn.BatchNorm2d替换为F.BatchNorm2d。最后，将nn.Tanh()修改为torch.tanh()。

这段代码是啥意思：list_pts_blue = [[0, h / 2], [0, h / 2 + 20], [w, h / 2 + 20], [w, h / 2]] ndarray_pts_blue = np.array(list_pts_blue, np.int32) polygon_blue_value_1 = cv2.fillPoly(mask_image_temp, [ndarray_pts_blue], color=1) polygon_blue_value_1 = polygon_blue_value_1[:, :, np.newaxis] # 填充第二个撞线polygon（黄色） mask_image_temp = np.zeros((height, width), dtype=np.uint8) list_pts_yellow = [[0, h / 2 + 70], [0, h / 2 + 90], [w, h / 2 + 90], [w, h / 2 + 70]] ndarray_pts_yellow = np.array(list_pts_yellow, np.int32) polygon_yellow_value_2 = cv2.fillPoly(mask_image_temp, [ndarray_pts_yellow], color=2) polygon_yellow_value_2 = polygon_yellow_value_2[:, :, np.newaxis]

然后，定义了两个多边形的顶点坐标列表 list_pts_blue 和 list_pts_yellow。对于第一个多边形（蓝色），将顶点坐标列表转换为 NumPy 数组 ndarray_pts_blue，数据类型为 np.int32。然后使用 cv2.fillPoly...

下面这段代码的作用是什么： if not os.path.exists(f"{dump_path}/{batch['scene_id']}"): Path(f"{dump_path}/{batch['scene_id']}").mkdir(parents=True, exist_ok=True) net = utils.get_net(net_class=SemAbs3D, **vars(args))[0] net.eval() prediction_volumes = process_batch_ovssc( net=net, batch=batch, scene_bounds=scene_bounds, device=args.device, num_input_pts=args.num_input_pts, ) logging.info(f"Dumping meshes to {dump_path}/{batch['scene_id']}") for obj_class, vol in prediction_volumes.items(): try: export_obj( vol=vol, filename=f"{dump_path}/{batch['scene_id']}/{obj_class}.obj", level=0.5, ) except RuntimeError as e: print(f"{obj_class} probably empty: {e}")

这段代码的作用是将输入的批次数据进行处理，生成预测结果，并将预测结果导出为.obj格式的三维模型文件。其中，首先判断是否存在指定路径下的文件夹，如果不存在则创建该文件夹。然后获取指定的神经网络模型，对批次...

gh1, _ = cv2.findHomography(final_match_pts2[cluster_res == 0, :], final_match_pts1[cluster_res == 0, :], cv2.RANSAC, ransac_threshold, maxIters=max_iteration)

这是一个计算机视觉...其中，final_match_pts2和final_match_pts1是两个图像中匹配点的坐标，cv2.RANSAC是使用RANSAC算法进行单应性矩阵估计，ransac_threshold是RANSAC算法中的阈值，max_iteration是最大迭代次数。

pts_2d_ori = contour_info["pts_2d"] pts_3d_ori = pm[pts_2d_ori[:, 0], pts_2d_ori[:, 1], :] pts_3d = pts_3d_ori[np.where(~np.isnan(pts_3d_ori[:, 0]))] pts_2d = pts_2d_ori[np.where(~np.isnan(pts_3d_ori[:, 0]))] pts_2d_ori = contour_info["pts_2d"] pts_3d_ori = pm[pts_2d_ori[:, 0], pts_2d_ori[:, 1], :] pts_3d = pts_3d_ori[np.where(~np.isnan(pts_3d_ori[:, 0]))] pts_2d = pts_2d_ori[np.where(~np.isnan(pts_3d_ori[:, 0]))]

这段代码的功能是将一个三维点云中的 NaN 值去除，并将点云的像素坐标和三维坐标分别保存到两个数组 pts_2d 和 pts_3d 中。具体来说，首先从 contour_info 中取出轮廓的像素坐标 pts_2d_ori。然后通过 pm 数组和 ...

pts_4d = torch.cat([points, points.new_ones(size=(num_points, 1))], dim=-1) pts_2d = pts_4d @ lidar2img_rt[view_idx].t() # pts_2d = pts_4d @ lidar2img_rt.t() pts_2d[:, 2] = torch.clamp(pts_2d[:, 2], min=1e-5) pts_2d[:, 0] /= pts_2d[:, 2] pts_2d[:, 1] /= pts_2d[:, 2] # img transformation: scale -> crop -> flip # the image is resized by img_scale_factor img_coors = pts_2d[:, 0:2] * img_scale_factor img_coors -= img_crop_offset

然后通过lidar2img_rt矩阵将点云坐标变换到图像坐标系下。接着将变换后的坐标系中的z轴坐标值进行限制，避免其小于1e-5。最后将x、y坐标值除以z坐标值，得到在图像中的坐标值。最后，将图像坐标系中的坐标进行缩放和...

def Init(self, config_path = g_data_dir + "vision/config.json"): with open(config_path, "rb") as f: self.config = json.load(f) self.use_sim = self.config["use_sim"] if not self.use_sim: self.cam = RVCManager(self.config["camera"]) if not self.cam.status: self.error_msg = f"相机 {self.cam.sn} 初始化失败!!" vision_log.error(f"{self.error_msg}") return False # 读取网络 ie = IECore() net_openvino = ie.read_network(model=g_data_dir + self.config["train_model_path"]) self.net = ie.load_network(network=net_openvino, device_name="CPU") # from robot_base to camera self.T_b_c = Pose(self.config['hand_eye_transform']).Transform() self.z_direction = [0, 0, -1] # 最后位姿的z方向 self.welding_pts_3d = [] # 焊点的3d坐标 return True

该方法接受一个可选参数 config_path，默认值是 g_data_dir + "vision/config.json"。该方法首先使用 open() 函数打开指定路径的 JSON 配置文件，并使用 json.load() 函数将其读入内存中，存储到 self....

final_match_pts1, final_match_pts2 = match_pts1[status, :], match_pts2[status, :] ]

这是一个 Python 代码的语句，用于将 match_pts1 和 match_pts2 中符合条件的元素赋值给 final_match_pts1 和 final_match_pts2。具体的条件需要在代码中给出。

import cv2import numpy as np# 读取视频帧cap = cv2.VideoCapture('test.mp4')ret, frame1 = cap.read()# 定义特征点的位置feature_params = dict(maxCorners=100, qualityLevel=0.3, minDistance=7, blockSize=7)prev_pts = cv2.goodFeaturesToTrack(cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY), mask=None, feature_params)# 定义光流追踪参数lk_params = dict(winSize=(15, 15), maxLevel=2, criteria=(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))while True: ret, frame2 = cap.read() if not ret: break # 将前一帧的特征点作为输入 next_pts, status, error = cv2.calcOpticalFlowPyrLK(cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY), cv2.cvtColor(frame2, cv2.COLOR_BGR2GRAY), prev_pts, None, lk_params) # 选取跟踪成功的特征点 good_new = next_pts[status == 1] good_old = prev_pts[status == 1] # 绘制光流轨迹 for i, (new, old) in enumerate(zip(good_new, good_old)): a, b = new.ravel() c, d = old.ravel() mask = cv2.line(mask, (a, b), (c, d), (0, 255, 0), 2) frame2 = cv2.circle(frame2, (a, b), 5, (0, 0, 255), -1) img = cv2.add(frame2, mask) cv2.imshow('frame', img) # 更新前一帧特征点 prev_pts = good_new.reshape(-1, 1, 2) k = cv2.waitKey(30) if k == 27: breakcv2.destroyAllWindows()cap.release()

这段代码是用 Python 和 OpenCV 库实现的光流追踪的功能，它可以读取视频帧并对相邻帧之间的运动进行分析。具体来说，它使用了 Lucas-Kanade 光流算法来跟踪前一帧中的特征点在当前帧中的位置，并将它们的轨迹可视化...

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gh1, _ = cv2.findHomography(final_match_pts2[cluster_res == 0, :], final_match_pts1[cluster_res == 0, :], cv2.RANSAC, ransac_threshold, maxIters=max_iteration)

final_match_pts1, final_match_pts2 = match_pts1[status, :], match_pts2[status, :] ]

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