First commit

app.py

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+import torch
+import spaces
+import gradio as gr
+import os
+import numpy as np
+import trimesh
+import mcubes
+import imageio
+from torchvision.utils import save_image
+from PIL import Image
+from transformers import AutoModel, AutoConfig
+from rembg import remove, new_session
+from functools import partial
+from kiui.op import recenter
+import kiui
+from gradio_litmodel3d import LitModel3D
+import shutil
+
+def find_cuda():
+    # 检查 CUDA_HOME 或 CUDA_PATH 环境变量是否已设置
+    cuda_home = os.environ.get('CUDA_HOME') or os.environ.get('CUDA_PATH')
+
+    if cuda_home and os.path.exists(cuda_home):
+        return cuda_home
+
+    # 在系统 PATH 中搜索 nvcc 可执行文件
+    nvcc_path = shutil.which('nvcc')
+
+    if nvcc_path:
+        # 删除“bin/nvcc”部分，获取 CUDA 安装路径
+        cuda_path = os.path.dirname(os.path.dirname(nvcc_path))
+        return cuda_path
+
+    return None
+
+cuda_path = find_cuda()
+
+if cuda_path:
+    print(f"CUDA 已安装在：{cuda_path}")
+else:
+    print("未找到已安装的 CUDA 路径")
+    
+# 从 HF 加载预训练模型
+class LRMGeneratorWrapper:
+    def __init__(self):
+        self.config = AutoConfig.from_pretrained("yanranxiaoxi/image-upscale", trust_remote_code=True)
+        self.model = AutoModel.from_pretrained("yanranxiaoxi/image-upscale", trust_remote_code=True)
+        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        self.model.to(self.device)
+        self.model.eval()
+
+    def forward(self, image, camera):
+        return self.model(image, camera)
+
+model_wrapper = LRMGeneratorWrapper()
+
+# 处理输入图像
+def preprocess_image(image, source_size):
+    session = new_session("isnet-general-use")
+    rembg_remove = partial(remove, session=session)
+    image = np.array(image)
+    image = rembg_remove(image)
+    mask = rembg_remove(image, only_mask=True)
+    image = recenter(image, mask, border_ratio=0.20)
+    image = torch.tensor(image).permute(2, 0, 1).unsqueeze(0) / 255.0
+    if image.shape[1] == 4:
+        image = image[:, :3, ...] * image[:, 3:, ...] + (1 - image[:, 3:, ...])
+    image = torch.nn.functional.interpolate(image, size=(source_size, source_size), mode='bicubic', align_corners=True)
+    image = torch.clamp(image, 0, 1)
+    return image
+
+def get_normalized_camera_intrinsics(intrinsics: torch.Tensor):
+    fx, fy = intrinsics[:, 0, 0], intrinsics[:, 0, 1]
+    cx, cy = intrinsics[:, 1, 0], intrinsics[:, 1, 1]
+    width, height = intrinsics[:, 2, 0], intrinsics[:, 2, 1]
+    fx, fy = fx / width, fy / height
+    cx, cy = cx / width, cy / height
+    return fx, fy, cx, cy
+
+def build_camera_principle(RT: torch.Tensor, intrinsics: torch.Tensor):
+    fx, fy, cx, cy = get_normalized_camera_intrinsics(intrinsics)
+    return torch.cat([
+        RT.reshape(-1, 12),
+        fx.unsqueeze(-1), fy.unsqueeze(-1), cx.unsqueeze(-1), cy.unsqueeze(-1),
+    ], dim=-1)
+
+def _default_intrinsics():
+    fx = fy = 384
+    cx = cy = 256
+    w = h = 512
+    intrinsics = torch.tensor([
+        [fx, fy],
+        [cx, cy],
+        [w, h],
+    ], dtype=torch.float32)
+    return intrinsics
+
+def _default_source_camera(batch_size: int = 1):
+    canonical_camera_extrinsics = torch.tensor([[
+        [0, 0, 1, 1],
+        [1, 0, 0, 0],
+        [0, 1, 0, 0],
+    ]], dtype=torch.float32)
+    canonical_camera_intrinsics = _default_intrinsics().unsqueeze(0)
+    source_camera = build_camera_principle(canonical_camera_extrinsics, canonical_camera_intrinsics)
+    return source_camera.repeat(batch_size, 1)
+
+def _center_looking_at_camera_pose(camera_position: torch.Tensor, look_at: torch.Tensor = None, up_world: torch.Tensor = None):
+    """
+    camera_position: (M, 3)
+    look_at: (3)
+    up_world: (3)
+    return: (M, 3, 4)
+    """
+    # 默认情况下，从原点向上为 pos-z
+    if look_at is None:
+        look_at = torch.tensor([0, 0, 0], dtype=torch.float32)
+    if up_world is None:
+        up_world = torch.tensor([0, 0, 1], dtype=torch.float32)
+    look_at = look_at.unsqueeze(0).repeat(camera_position.shape[0], 1)
+    up_world = up_world.unsqueeze(0).repeat(camera_position.shape[0], 1)
+
+    z_axis = camera_position - look_at
+    z_axis = z_axis / z_axis.norm(dim=-1, keepdim=True)
+    x_axis = torch.cross(up_world, z_axis)
+    x_axis = x_axis / x_axis.norm(dim=-1, keepdim=True)
+    y_axis = torch.cross(z_axis, x_axis)
+    y_axis = y_axis / y_axis.norm(dim=-1, keepdim=True)
+    extrinsics = torch.stack([x_axis, y_axis, z_axis, camera_position], dim=-1)
+    return extrinsics
+
+def compose_extrinsic_RT(RT: torch.Tensor):
+    """
+    从 RT 生成标准形式的外差矩阵。
+    分批输入/输出。
+    """
+    return torch.cat([
+        RT,
+        torch.tensor([[[0, 0, 0, 1]]], dtype=torch.float32).repeat(RT.shape[0], 1, 1).to(RT.device)
+        ], dim=1)
+
+def _build_camera_standard(RT: torch.Tensor, intrinsics: torch.Tensor):
+    """
+    RT: (N, 3, 4)
+    intrinsics: (N, 3, 2), [[fx, fy], [cx, cy], [width, height]]
+    """
+    E = compose_extrinsic_RT(RT)
+    fx, fy, cx, cy = get_normalized_camera_intrinsics(intrinsics)
+    I = torch.stack([
+        torch.stack([fx, torch.zeros_like(fx), cx], dim=-1),
+        torch.stack([torch.zeros_like(fy), fy, cy], dim=-1),
+        torch.tensor([[0, 0, 1]], dtype=torch.float32, device=RT.device).repeat(RT.shape[0], 1),
+    ], dim=1)
+    return torch.cat([
+        E.reshape(-1, 16),
+        I.reshape(-1, 9),
+    ], dim=-1)
+
+def _default_render_cameras(batch_size: int = 1):
+    M = 80
+    radius = 1.5
+    elevation = 0
+    camera_positions = []
+    rand_theta = np.random.uniform(0, np.pi/180)
+    elevation = np.radians(elevation)
+    for i in range(M):
+        theta = 2 * np.pi * i / M + rand_theta
+        x = radius * np.cos(theta) * np.cos(elevation)
+        y = radius * np.sin(theta) * np.cos(elevation)
+        z = radius * np.sin(elevation)
+        camera_positions.append([x, y, z])
+    camera_positions = torch.tensor(camera_positions, dtype=torch.float32)
+    extrinsics = _center_looking_at_camera_pose(camera_positions)
+
+    render_camera_intrinsics = _default_intrinsics().unsqueeze(0).repeat(extrinsics.shape[0], 1, 1)
+    render_cameras = _build_camera_standard(extrinsics, render_camera_intrinsics)
+    return render_cameras.unsqueeze(0).repeat(batch_size, 1, 1)
+    
+@spaces.GPU
+def generate_mesh(image, source_size=512, render_size=384, mesh_size=512, export_mesh=False, export_video=True, fps=30):
+    image = preprocess_image(image, source_size).to(model_wrapper.device)
+    source_camera = _default_source_camera(batch_size=1).to(model_wrapper.device)
+
+    with torch.no_grad():
+        planes = model_wrapper.forward(image, source_camera)
+
+        if export_mesh:
+            grid_out = model_wrapper.model.synthesizer.forward_grid(planes=planes, grid_size=mesh_size)
+            vtx, faces = mcubes.marching_cubes(grid_out['sigma'].float().squeeze(0).squeeze(-1).cpu().numpy(), 1.0)
+            vtx = vtx / (mesh_size - 1) * 2 - 1
+            vtx_tensor = torch.tensor(vtx, dtype=torch.float32, device=model_wrapper.device).unsqueeze(0)
+            vtx_colors = model_wrapper.model.synthesizer.forward_points(planes, vtx_tensor)['rgb'].float().squeeze(0).cpu().numpy()
+            vtx_colors = (vtx_colors * 255).astype(np.uint8)
+            mesh = trimesh.Trimesh(vertices=vtx, faces=faces, vertex_colors=vtx_colors)
+
+            mesh_path = "xiaoxis_mesh.obj"
+            mesh.export(mesh_path, 'obj')
+
+            return mesh_path, mesh_path
+
+        if export_video:
+            render_cameras = _default_render_cameras(batch_size=1).to(model_wrapper.device)
+            frames = []
+            chunk_size = 1
+            for i in range(0, render_cameras.shape[1], chunk_size):
+                frame_chunk = model_wrapper.model.synthesizer(
+                    planes,
+                    render_cameras[:, i:i + chunk_size],
+                    render_size,
+                    render_size,
+                    0,
+                    0
+                )
+                frames.append(frame_chunk['images_rgb'])
+
+            frames = torch.cat(frames, dim=1)
+            frames = frames.squeeze(0)
+            frames = (frames.permute(0, 2, 3, 1).cpu().numpy() * 255).astype(np.uint8)
+
+            video_path = "xiaoxis_video.mp4"
+            imageio.mimwrite(video_path, frames, fps=fps)
+
+            return None, video_path
+
+    return None, None
+
+def step_1_generate_obj(image):
+    mesh_path, _ = generate_mesh(image, export_mesh=True)
+    return mesh_path, mesh_path
+
+def step_2_generate_video(image):
+    _, video_path = generate_mesh(image, export_video=True)
+    return video_path
+
+def step_3_display_3d_model(mesh_file):
+    return mesh_file
+
+# 从 assets 文件夹中设置示例文件，并限制最多读取 10 个文件
+example_folder = "assets"
+examples = [os.path.join(example_folder, f) for f in os.listdir(example_folder) if f.endswith(('.png', '.jpg', '.jpeg'))][:10]
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        
+        with gr.Column():
+            gr.Markdown("""
+            # Image Upscale Demo
+
+            从单张图像生成三维点云与模型
+
+            """)
+            img_input = gr.Image(type="pil", label="输入图像")
+            examples_component = gr.Examples(examples=examples, inputs=img_input, outputs=None, examples_per_page=3)
+            generate_mesh_button = gr.Button("生成模型")
+            # generate_video_button = gr.Button("生成视频")
+            obj_file_output = gr.File(label="下载 .obj 文件")
+            # video_file_output = gr.File(label="下载视频")
+
+        with gr.Column():
+            model_output = LitModel3D(
+                clear_color=[0, 0, 0, 0],  # 可调整背景颜色，以获得更好的对比度
+                label="3D 模型可视化",
+                scale=1.0,
+                tonemapping="aces",  # 可使用 aces 色调映射，使灯光更逼真
+                exposure=1.0,        # 可调节曝光以控制亮度
+                contrast=1.1,        # 可略微增加对比度，以获得更好的深度
+                camera_position=(0, 0, 2),  # 将设置初始摄像机位置，使模型居中
+                zoom_speed=0.5,      # 将调整变焦速度，以便更好地控制
+                pan_speed=0.5,       # 将调整摇摄速度，以便更好地控制
+                interactive=True     # 这样用户就可以与模型进行交互
+            )
+            
+        
+    # clear outputs
+    def clear_model_viewer():
+        """在加载新模型前重置 Model3D 组件。"""
+        return gr.update(value=None)
+    
+    def generate_and_visualize(image):
+        mesh_path = step_1_generate_obj(image)
+        return mesh_path, mesh_path
+
+    # first we clear the existing 3D model
+    img_input.change(clear_model_viewer, inputs=None, outputs=model_output)
+
+    # then, generate the mesh and video
+    generate_mesh_button.click(step_1_generate_obj, inputs=img_input, outputs=[obj_file_output, model_output])
+    generate_video_button.click(step_2_generate_video, inputs=img_input, outputs=video_file_output)
+
+demo.launch()

requirements.txt

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+torch==2.2.0
+gradio==4.31.4
+gradio-litmodel3d==0.0.1
+numpy
+trimesh==4.3.2
+PyMCubes==0.1.4
+imageio[ffmpeg]
+rembg[gpu,cli]
+kiui
+Pillow
+transformers
+torchvision