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