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

pts_to_best_line.hdvp

halcon根据点集拟合直线封装好的函数，不懂halcon的不要下载

PCI_PTS_POI_DTRs_v3_1.zip_PCI PTS_PCI security

Payment Card Industry (PCI) PIN Transaction Security (PTS) Point of Interaction (POI) Modular Derived Test Requirements

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]

首先，将点云数据的每个点的坐标添加一维1，变成4D张量。然后，将4D张量与激光雷达到图像的变换矩阵相乘，得到对应的2D点的坐标。这里注意到深度值可能为0或负数，因此使用 clamp 函数将深度值限制在一个较小的...

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...

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 ...

# 绘制漏斗图 from pyecharts.charts import Funnel data = pd.read_excel(r"C:\Users\xiao'lu\西安.xlsx") x_data = data['评分'].tolist() y_data = data['热度'].tolist() data = [[x_data[i], y_data[i]] for i in range(len(x_data))] funnel = (Funnel() .add('', data_pair=data,label_opts=opts.Label0pts( position='inside', formatter='{b}:{d}%'), gap=2, tooltip_opts=opts.Tooltip0pts(trigger='item'), itemstyle_opts=opts.ItemStyleOpts(border_color='#fff',border_width=1)) .set_global_opts(title_opts=opts.Title0pts(title='评分与热度的漏斗图'), legend_opts=opts.Legend0pts(pos_left='40%'))) funnel.render_notebook() 完善如上代码

itemstyle_opts=opts.ItemStyleOpts(border_color='#fff', border_width=1) ) .set_global_opts( title_opts=opts.TitleOpts(title='评分与热度的漏斗图'), legend_opts=opts.LegendOpts(pos_left='40%') ) ) ...

这段代码什么意思def run_posmap_300W_LP(bfm, image_path, mat_path, save_folder, uv_h = 256, uv_w = 256, image_h = 256, image_w = 256): # 1. load image and fitted parameters image_name = image_path.strip().split('/')[-1] image = io.imread(image_path)/255. [h, w, c] = image.shape info = sio.loadmat(mat_path) pose_para = info['Pose_Para'].T.astype(np.float32) shape_para = info['Shape_Para'].astype(np.float32) exp_para = info['Exp_Para'].astype(np.float32) # 2. generate mesh # generate shape vertices = bfm.generate_vertices(shape_para, exp_para) # transform mesh s = pose_para[-1, 0] angles = pose_para[:3, 0] t = pose_para[3:6, 0] transformed_vertices = bfm.transform_3ddfa(vertices, s, angles, t) projected_vertices = transformed_vertices.copy() # using stantard camera & orth projection as in 3DDFA image_vertices = projected_vertices.copy() image_vertices[:,1] = h - image_vertices[:,1] - 1 # 3. crop image with key points kpt = image_vertices[bfm.kpt_ind, :].astype(np.int32) left = np.min(kpt[:, 0]) right = np.max(kpt[:, 0]) top = np.min(kpt[:, 1]) bottom = np.max(kpt[:, 1]) center = np.array([right - (right - left) / 2.0, bottom - (bottom - top) / 2.0]) old_size = (right - left + bottom - top)/2 size = int(old_size1.5) # random pertube. you can change the numbers marg = old_size0.1 t_x = np.random.rand()marg2 - marg t_y = np.random.rand()marg2 - marg center[0] = center[0]+t_x; center[1] = center[1]+t_y size = size(np.random.rand()0.2 + 0.9) # crop and record the transform parameters src_pts = np.array([[center[0]-size/2, center[1]-size/2], [center[0] - size/2, center[1]+size/2], [center[0]+size/2, center[1]-size/2]]) DST_PTS = np.array([[0, 0], [0, image_h - 1], [image_w - 1, 0]]) tform = skimage.transform.estimate_transform('similarity', src_pts, DST_PTS) cropped_image = skimage.transform.warp(image, tform.inverse, output_shape=(image_h, image_w)) # transform face position(image vertices) along with 2d facial image position = image_vertices.copy() position[:, 2] = 1 position = np.dot(position, tform.params.T) position[:, 2] = image_vertices[:, 2]*tform.params[0, 0] # scale z position[:, 2] = position[:, 2] - np.min(position[:, 2]) # translate z # 4. uv position map: render position in uv space uv_position_map = mesh.render.render_colors(uv_coords, bfm.full_triangles, position, uv_h, uv_w, c = 3) # 5. save files io.imsave('{}/{}'.format(save_folder, image_name), np.squeeze(cropped_image)) np.save('{}/{}'.format(save_folder, image_name.replace('jpg', 'npy')), uv_position_map) io.imsave('{}/{}'.format(save_folder, image_name.replace('.jpg', '_posmap.jpg')), (uv_position_map)/max(image_h, image_w)) # only for show # --verify # import cv2 # uv_texture_map_rec = cv2.remap(cropped_image, uv_position_map[:,:,:2].astype(np.float32), None, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT,borderValue=(0)) # io.imsave('{}/{}'.format(save_folder, image_name.replace('.jpg', '_tex.jpg')), np.squeeze(uv_texture_map_rec))

1. 加载人脸图像和拟合参数。 2. 生成人脸三维模型，并进行变换，得到变换后的人脸模型顶点位置。 3. 根据人脸关键点进行裁剪，得到人脸区域的图像，并记录变换参数。 4. 在 UV 空间中渲染人脸模型，并生成 UV 位置...

auto pts_vec = pts_vector_queue.front()

你提到的代码片段 auto pts_vec = pts_vector_queue.front() 是C++编程中的一种用法，通常用于处理队列（queue）数据结构。在C++的标准模板库（STL）中，std::queue是一种常见的队列实现。这行代码的作用是从名...

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

这段代码定义了 VisionManager 类的 Init() 方法，用于初始化视觉管理器。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...

for k in range(cfg.RPN.SA_CONFIG.NPOINTS.len()): mlps = cfg.RPN.SA_CONFIG.MLPS[k].copy() channel_out = 0 for idx in range(mlps.len()): mlps[idx] = [channel_in] + mlps[idx] channel_out += mlps[idx][-1] self.SA_modules.append( PointnetSAModuleMSG( npoint=cfg.RPN.SA_CONFIG.NPOINTS[k], radii=cfg.RPN.SA_CONFIG.RADIUS[k], nsamples=cfg.RPN.SA_CONFIG.NSAMPLE[k], mlps=mlps, use_xyz=use_xyz, bn=cfg.RPN.USE_BN ) ) skip_channel_list.append(channel_out) channel_in = channel_out self.FP_modules = nn.ModuleList() for k in range(cfg.RPN.FP_MLPS.len()): pre_channel = cfg.RPN.FP_MLPS[k + 1][-1] if k + 1 < len(cfg.RPN.FP_MLPS) else channel_out self.FP_modules.append( PointnetFPModule(mlp=[pre_channel + skip_channel_list[k]] + cfg.RPN.FP_MLPS[k]) ) def _break_up_pc(self, pc): xyz = pc[..., 0:3].contiguous() features = ( pc[..., 3:].transpose(1, 2).contiguous() if pc.size(-1) > 3 else None ) return xyz, features def forward(self, pointcloud: torch.cuda.FloatTensor): xyz, features = self._break_up_pc(pointcloud) l_xyz, l_features = [xyz], [features] for i in range(len(self.SA_modules)): li_xyz, li_features = self.SA_modules[i](l_xyz[i], l_features[i]) l_xyz.append(li_xyz) l_features.append(li_features) for i in range(-1, -(len(self.FP_modules) + 1), -1): l_features[i - 1] = self.FP_modules[i]( l_xyz[i - 1], l_xyz[i], l_features[i - 1], l_features[i] ) return l_xyz[0], l_features[0]在forward函数中，如果我要使用channel_out变量传入SA_modules中，我该如何在forward函数中计算并得到它，再传入SA_modules中，你可以给我详细的代码吗？

new_features = torch.cat(new_features_list, dim=1) for i in range(len(self.SA_modules)): new_features[:, i:i+1] = self.SA_modules[i][0](new_features[:, i:i+1]) # 初始化 MLP 中的第一个元素 if ...

def generate(self, obs, all=False): good_pts = [] good_scores = [] pts = [] scores = [] dir_set = [(1, 0), (1, -1), (0, -1), (-1, -1), (-1, 0), (-1, 1), (0, 1), (1, 1)] if all: indices = np.where(obs) check_list = [(indices[0][i], indices[1][i]) for i in range(len(indices[0]))] else: if len(self._last_move_list) > 7: check_list = self._last_move_list[-7:] else: check_list = self._last_move_list for x0, y0 in check_list: for dir in dir_set: if x0 + dir[0] in range(0, 15) and y0 + dir[1] in range(0, 15): pos = (x0 + dir[0], y0 + dir[1]) if obs[pos[0]][pos[1]] == 0 and pos not in pts: obs[pos[0]][pos[1]] = self.color score_atk = self.evaluate_point(obs, pos) obs[pos[0]][pos[1]] = -self.color score_def = self.evaluate_point(obs, pos) score = max(score_atk, score_def) if score >= score_3_live: good_pts.append(pos) good_scores.append(score) if score_atk == score_5: break pts.append(pos) scores.append(score) obs[pos[0]][pos[1]] = 0 if len(good_pts) > 0 and max(good_scores) >= score_4: # print('good') pts = good_pts scores = good_scores lst = np.array([pts, scores]) pts = lst[:, lst[1].argsort()][0] pos_list = list(pts) pos_list.reverse() return pos_list

如果存在得分大于等于特定值 score_4 的位置，则将这些位置作为好的位置 good_pts，并将它们的得分添加到 good_scores 列表中。如果存在得分为特定值 score_5 的位置，则直接选取这些位置。最后，将 pts 和 scores...

else: mask_idx = np.where(mask_c) c_pts = np.zeros((len(mask_idx[0]), 2)).astype(np.int32) c_pts[:, 0] = mask_idx[0] c_pts[:, 1] = mask_idx[1]

1.使用np.where函数找到mask_c中值为True的像素位置，并将它们的行和列分别存储在mask_idx的第一个和第二个元素中； 2.创建一个和mask_idx第一个元素大小相同的全零数组c_pts，并将其数据类型设置为np.int32； 3.将...

left_camera_matrix = np.array([[265.904987551508, -5.21040254919627, 297.745408759514], [0, 273.368561888447, 227.072711052662], [0, 0, 1]]) right_camera_matrix = np.array([[2.596626837501199e+02, -4.907135293510722, 2.861049520202752e+02], [0, 2.666351337517550e+02, 2.225444306580323e+02], [0, 0, 1]]) left_distortion_coefficients = np.array([0.083475717394610, 0.068273456012944, 0.005387539033668, 0.009869081295152, 0]) right_distortion_coefficients = np.array([0.0925662275612297, -0.0576260134516565, 0.00342071297880541, -0.0118105228989755, 0]) rotation_matrix = np.array([[-1.43171059788113, -1.44730799253265, -1.45684791306953], [0.336990301763839, 0.222726058504058, -0.0887429454517064], [0.327509712920715, 0.199344674466685, -0.0744717520896878]]) translation_vector = np.array([[631.419361434115], [-8.76449282194532], [2296.78738698791]])以上为双目相机的相机参数，已知左相机拍摄的两个物体的二维坐标分别为（670，252）和（744，326），不要代码，请直接告诉我三维坐标和两个三维坐标之间距离

points_4d = cv2.triangulatePoints(left_projection_matrix, right_projection_matrix, left_img_pts_undistort, right_img_pts_undistort) points_3d = cv2.convertPointsFromHomogeneous(points_4d.T) # 计算两...

rows, cols = img.shape[:2] src_pts = np.float32([[0, 0], [cols-1, 0], [0, rows-1], [cols-1, rows-1]]) dst_pts = np.float32([[0, 0], [cols-1, 0], [cols0.1, rows-1], [cols0.9, rows-1]]) M = cv2.getPerspectiveTransform(src_pts, dst_pts) warped = cv2.warpPerspective(img, M, (cols, rows))经过扭曲矫正的图像边缘有一大片黑色怎么办怎么将黑色变白

可以使用 OpenCV 中的阈值处理函数 cv2.threshold() 来将黑色区域变成白色。首先将图像转换为灰度图像，然后应用阈值处理函数，将灰度值低于一定阈值的像素点设置为白色，其余的像素点设置为黑色。...

src_pts = np.float32([keypoint1[m.queryIdx].pt for m in goodMatchePoints]).reshape(-1, 1, 2) dst_pts = np.float32([keypoint2[m.trainIdx].pt for m in goodMatchePoints]).reshape(-1, 1, 2) M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)怎么输出这段代码中通过ransac算法优化的结果

(- V_wind * cos(theta_wind) * (T(ix, iy, iz) - T(ix+1, iy, iz)) / dx - ... V_wind * sin(theta_wind) * (T(ix, iy, iz) - T(ix,在使用RANSAC算法优化匹配点之后，可以通过以下步骤输出优化结果： 1. 通过 iy...

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)

x = torch.cat([x, mask], dim=1) x = F.leaky_relu(self.bn1(self.conv1(x))) x = F.leaky_relu(self.bn2(self.conv2(x))) x = F.leaky_relu(self.bn3(self.conv3(x))) x = F.leaky_relu(self.bn4(self.conv4...

# 根据每个轮廓的类型, 找到对应的焊缝轨迹 paths = [] self.welding_pts_3d = [] for k, info in enumerate(contours_info): c_type = info["type"] shape, path, pts_3d, end_point_2d = self.FindTypePath(pm, info) if path is None: vision_log.error(f"no path in {k} contour") img_show = cv2.drawContours( img_show, [info["box"].astype(int)], 0, (0, 0, 0), 5) continue

这段代码的功能是根据每个轮廓的类型，找到对应的焊缝轨迹，并将轨迹信息保存到 paths 和 self.welding_pts_3d 变量中。具体来说，首先定义一个空列表 paths 和空列表 self.welding_pts_3d，用于保存轨迹信息。...

pts_2d = pts_4d @ lidar2img_rt[view_idx].t()

这是一行 Python 代码，其中 "@" 符号表示矩阵乘法，pts_4d 是一个 4 维的点坐标矩阵，lidar2img_rt[view_idx].t() 是一个 RT 变换矩阵的转置，表示从激光雷达坐标系到图像坐标系的变换。pts_2d 是矩阵乘法后得到的 ...

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

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',...

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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]

auto pts_vec = pts_vector_queue.front()

else: mask_idx = np.where(mask_c) c_pts = np.zeros((len(mask_idx[0]), 2)).astype(np.int32) c_pts[:, 0] = mask_idx[0] c_pts[:, 1] = mask_idx[1]

pts_2d = pts_4d @ lidar2img_rt[view_idx].t()

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