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| import torch | |
| import os | |
| from PIL import Image, ImageDraw, ImageFont | |
| from matplotlib import pyplot as plt | |
| plt.rcParams['font.sans-serif'] = ['Times New Roman'] | |
| import numpy as np | |
| import copy | |
| def render_training_image(scene, gaussians, viewpoints, render_func, pipe, background, stage, iteration, time_now): | |
| def render(gaussians, viewpoint, path, scaling): | |
| # scaling_copy = gaussians._scaling | |
| render_pkg = render_func(viewpoint, gaussians, pipe, background, stage=stage) | |
| label1 = f"stage:{stage},iter:{iteration}" | |
| times = time_now/60 | |
| if times < 1: | |
| end = "min" | |
| else: | |
| end = "mins" | |
| label2 = "time:%.2f" % times + end | |
| image = render_pkg["render"] | |
| depth = render_pkg["depth"] | |
| image_np = image.permute(1, 2, 0).cpu().numpy() # 转换通道顺序为 (H, W, 3) | |
| depth_np = depth.permute(1, 2, 0).cpu().numpy() | |
| depth_np /= depth_np.max() | |
| depth_np = np.repeat(depth_np, 3, axis=2) | |
| image_np = np.concatenate((image_np, depth_np), axis=1) | |
| image_with_labels = Image.fromarray((np.clip(image_np,0,1) * 255).astype('uint8')) # 转换为8位图像 | |
| # 创建PIL图像对象的副本以绘制标签 | |
| draw1 = ImageDraw.Draw(image_with_labels) | |
| # 选择字体和字体大小 | |
| font = ImageFont.truetype('./utils/TIMES.TTF', size=40) # 请将路径替换为您选择的字体文件路径 | |
| # 选择文本颜色 | |
| text_color = (255, 0, 0) # 白色 | |
| # 选择标签的位置（左上角坐标） | |
| label1_position = (10, 10) | |
| label2_position = (image_with_labels.width - 100 - len(label2) * 10, 10) # 右上角坐标 | |
| # 在图像上添加标签 | |
| draw1.text(label1_position, label1, fill=text_color, font=font) | |
| draw1.text(label2_position, label2, fill=text_color, font=font) | |
| image_with_labels.save(path) | |
| render_base_path = os.path.join(scene.model_path, f"{stage}_render") | |
| point_cloud_path = os.path.join(render_base_path,"pointclouds") | |
| image_path = os.path.join(render_base_path,"images") | |
| if not os.path.exists(os.path.join(scene.model_path, f"{stage}_render")): | |
| os.makedirs(render_base_path) | |
| if not os.path.exists(point_cloud_path): | |
| os.makedirs(point_cloud_path) | |
| if not os.path.exists(image_path): | |
| os.makedirs(image_path) | |
| # image:3,800,800 | |
| # point_save_path = os.path.join(point_cloud_path,f"{iteration}.jpg") | |
| for idx in range(len(viewpoints)): | |
| image_save_path = os.path.join(image_path,f"{iteration}_{idx}.jpg") | |
| render(gaussians,viewpoints[idx],image_save_path,scaling = 1) | |
| # render(gaussians,point_save_path,scaling = 0.1) | |
| # 保存带有标签的图像 | |
| pc_mask = gaussians.get_opacity | |
| pc_mask = pc_mask > 0.1 | |
| xyz = gaussians.get_xyz.detach()[pc_mask.squeeze()].cpu().permute(1,0).numpy() | |
| # visualize_and_save_point_cloud(xyz, viewpoint.R, viewpoint.T, point_save_path) | |
| # 如果需要，您可以将PIL图像转换回PyTorch张量 | |
| # return image | |
| # image_with_labels_tensor = torch.tensor(image_with_labels, dtype=torch.float32).permute(2, 0, 1) / 255.0 | |
| def visualize_and_save_point_cloud(point_cloud, R, T, filename): | |
| # 创建3D散点图 | |
| fig = plt.figure() | |
| ax = fig.add_subplot(111, projection='3d') | |
| R = R.T | |
| # 应用旋转和平移变换 | |
| T = -R.dot(T) | |
| transformed_point_cloud = np.dot(R, point_cloud) + T.reshape(-1, 1) | |
| # pcd = o3d.geometry.PointCloud() | |
| # pcd.points = o3d.utility.Vector3dVector(transformed_point_cloud.T) # 转置点云数据以匹配Open3D的格式 | |
| # transformed_point_cloud[2,:] = -transformed_point_cloud[2,:] | |
| # 可视化点云 | |
| ax.scatter(transformed_point_cloud[0], transformed_point_cloud[1], transformed_point_cloud[2], c='g', marker='o') | |
| ax.axis("off") | |
| # ax.set_xlabel('X Label') | |
| # ax.set_ylabel('Y Label') | |
| # ax.set_zlabel('Z Label') | |
| # 保存渲染结果为图片 | |
| plt.savefig(filename) | |