app_bf1.py

import os
import gradio as gr
import numpy as np
from enum import Enum
import db_examples
import cv2

from demo_utils1 import *

from misc_utils.train_utils import unit_test_create_model
from misc_utils.image_utils import save_tensor_to_gif, save_tensor_to_images
import os
from PIL import Image
import torch
import torchvision
from torchvision import transforms
from einops import rearrange
import imageio
import time

from torchvision.transforms import functional as F
from torch.hub import download_url_to_file

import os
import spaces

# 推理设置
from pl_trainer.inference.inference import InferenceIP2PVideo
from tqdm import tqdm


# if not os.path.exists(filename):
#     original_path = os.getcwd()
#     base_path = './models'
#     os.makedirs(base_path, exist_ok=True)

#     # 直接在代码中写入 Token（注意安全风险）
#     GIT_TOKEN = "955b8ea91095840b76fe38b90a088c200d4c813c"
#     repo_url = f"https://YeFang:{GIT_TOKEN}@code.openxlab.org.cn/YeFang/RIV_models.git"

#     try:
#         if os.system(f'git clone {repo_url} {base_path}') != 0:
#             raise RuntimeError("Git 克隆失败")
#         os.chdir(base_path)
#         if os.system('git lfs pull') != 0:
#             raise RuntimeError("Git LFS 拉取失败")
#     finally:
#         os.chdir(original_path)

def tensor_to_pil_image(x):
    """
    将 4D PyTorch 张量转换为 PIL 图像。
    """
    x = x.float()  # 确保张量类型为 float
    grid_img = torchvision.utils.make_grid(x, nrow=4).permute(1, 2, 0).detach().cpu().numpy()
    grid_img = (grid_img * 255).clip(0, 255).astype("uint8")  # 将 [0, 1] 范围转换为 [0, 255]
    return Image.fromarray(grid_img)

def frame_to_batch(x):
    """
    将帧维度转换为批次维度。
    """
    return rearrange(x, 'b f c h w -> (b f) c h w')

def clip_image(x, min=0., max=1.):
    """
    将图像张量裁剪到指定的最小和最大值。
    """
    return torch.clamp(x, min=min, max=max)

def unnormalize(x):
    """
    将张量范围从 [-1, 1] 转换到 [0, 1]。
    """
    return (x + 1) / 2


# 读取图像文件
def read_images_from_directory(directory, num_frames=16):
    images = []
    for i in range(num_frames):
        img_path = os.path.join(directory, f'{i:04d}.png')
        img = imageio.imread(img_path)
        images.append(torch.tensor(img).permute(2, 0, 1))  # Convert to Tensor (C, H, W)
    return images

def load_and_process_images(folder_path):
    """
    读取文件夹中的所有图片，将它们转换为 [-1, 1] 范围的张量并返回一个 4D 张量。
    """
    processed_images = []
    transform = transforms.Compose([
        transforms.ToTensor(),
        transforms.Lambda(lambda x: x * 2 - 1)  # 将 [0, 1] 转换为 [-1, 1]
    ])
    for filename in sorted(os.listdir(folder_path)):
        if filename.endswith(".png"):
            img_path = os.path.join(folder_path, filename)
            image = Image.open(img_path).convert("RGB")
            processed_image = transform(image)
            processed_images.append(processed_image)
    return torch.stack(processed_images)  # 返回 4D 张量

def load_and_process_video(video_path, num_frames=16, crop_size=512):
    """
    读取视频文件中的前 num_frames 帧，将每一帧转换为 [-1, 1] 范围的张量，
    并进行中心裁剪至 crop_size x crop_size，返回一个 4D 张量。
    """
    processed_frames = []
    transform = transforms.Compose([
        transforms.CenterCrop(crop_size),       # 中心裁剪
        transforms.ToTensor(),
        transforms.Lambda(lambda x: x * 2 - 1)  # 将 [0, 1] 转换为 [-1, 1]
    ])
    
    # 使用 OpenCV 读取视频
    cap = cv2.VideoCapture(video_path)
    
    if not cap.isOpened():
        raise ValueError(f"无法打开视频文件: {video_path}")

    frame_count = 0

    while frame_count < num_frames:
        ret, frame = cap.read()
        if not ret:
            break  # 视频帧读取完毕或视频帧不足
        
        # 转换为 RGB 格式
        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        image = Image.fromarray(frame)
        
        # 应用转换
        processed_frame = transform(image)
        processed_frames.append(processed_frame)
        
        frame_count += 1

    cap.release()  # 释放视频资源

    if len(processed_frames) < num_frames:
        raise ValueError(f"视频帧不足 {num_frames} 帧，仅找到 {len(processed_frames)} 帧。")

    return torch.stack(processed_frames)  # 返回 4D 张量 (帧数, 通道数, 高度, 宽度)


def clear_cache(output_path):
    if os.path.exists(output_path):
        os.remove(output_path)
    return None


#! 加载模型
# 配置路径和加载模型
config_path = 'configs/instruct_v2v_ic_gradio.yaml'
diffusion_model = unit_test_create_model(config_path)
diffusion_model = diffusion_model.to('cuda')

# 加载模型检查点
# ckpt_path = 'models/relvid_mm_sd15_fbc_unet.pth' #! change
# ckpt_path = 'tmp/pytorch_model.bin'
# 下载文件

os.makedirs('models', exist_ok=True)
model_path = "models/relvid_mm_sd15_fbc_unet.pth"

if not os.path.exists(model_path):
    download_url_to_file(url='https://huggingface.co/aleafy/RelightVid/resolve/main/relvid_mm_sd15_fbc_unet.pth', dst=model_path)


ckpt = torch.load(model_path, map_location='cpu')
diffusion_model.load_state_dict(ckpt, strict=False)


# import pdb; pdb.set_trace()

# 更改全局临时目录
new_tmp_dir = "./demo/gradio_bg"
os.makedirs(new_tmp_dir, exist_ok=True)

# import pdb; pdb.set_trace()

def save_video_from_frames(image_pred, save_pth, fps=8):
    """
    将 image_pred 中的帧保存为视频文件。
    参数:
    - image_pred: Tensor，形状为 (1, 16, 3, 512, 512)
    - save_pth: 保存视频的路径，例如 "output_video.mp4"
    - fps: 视频的帧率
    """
    # 视频参数
    num_frames = image_pred.shape[1]
    frame_height, frame_width = 512, 512  # 目标尺寸
    fourcc = cv2.VideoWriter_fourcc(*'mp4v')  # 使用 mp4 编码格式

    # 创建 VideoWriter 对象
    out = cv2.VideoWriter(save_pth, fourcc, fps, (frame_width, frame_height))

    for i in range(num_frames):
        # 反归一化 + 转换为 0-255 范围
        pred_frame = clip_image(unnormalize(image_pred[0][i].unsqueeze(0))) * 255
        pred_frame_resized = pred_frame.squeeze(0).detach().cpu()  # (3, 512, 512)
        pred_frame_resized = pred_frame_resized.permute(1, 2, 0).numpy().astype("uint8")  # (512, 512, 3)

        # Resize 到 256x256
        pred_frame_resized = cv2.resize(pred_frame_resized, (frame_width, frame_height))

        # 将 RGB 转为 BGR（因为 OpenCV 使用 BGR 格式）
        pred_frame_bgr = cv2.cvtColor(pred_frame_resized, cv2.COLOR_RGB2BGR)

        # 写入帧到视频
        out.write(pred_frame_bgr)

    # 释放 VideoWriter 资源
    out.release()
    print(f"视频已保存至 {save_pth}")


inf_pipe = InferenceIP2PVideo(
        diffusion_model.unet, 
        scheduler='ddpm',
        num_ddim_steps=20
    )


def process_example(*args):
    v_index = args[0]
    select_e = db_examples.background_conditioned_examples[int(v_index)-1]
    input_fg_path = select_e[1]
    input_bg_path = select_e[2]
    result_video_path = select_e[-1]
    # input_fg_img = args[1]  # 第 0 个参数
    # input_bg_img = args[2]  # 第 1 个参数
    # result_video_img = args[-1]  # 最后一个参数
    
    input_fg = input_fg_path.replace("frames/0000.png", "cropped_video.mp4")
    input_bg = input_bg_path.replace("frames/0000.png", "cropped_video.mp4")
    result_video = result_video_path.replace(".png", ".mp4")
    
    return input_fg, input_bg, result_video



# 伪函数占位（生成空白视频）
@spaces.GPU
def dummy_process(input_fg, input_bg, prompt):
    # import pdb; pdb.set_trace()

    diffusion_model.to(torch.float16)
    fg_tensor = load_and_process_video(input_fg).cuda().unsqueeze(0).to(dtype=torch.float16)
    bg_tensor = load_and_process_video(input_bg).cuda().unsqueeze(0).to(dtype=torch.float16) # (1, 16, 4, 64, 64)

    cond_fg_tensor = diffusion_model.encode_image_to_latent(fg_tensor)  # (1, 16, 4, 64, 64)
    cond_bg_tensor = diffusion_model.encode_image_to_latent(bg_tensor)
    cond_tensor = torch.cat((cond_fg_tensor, cond_bg_tensor), dim=2)

    # 初始化潜变量
    init_latent = torch.randn_like(cond_fg_tensor)    

    # EDIT_PROMPT = 'change the background'
    EDIT_PROMPT = prompt
    VIDEO_CFG = 1.2
    TEXT_CFG = 7.5
    text_cond = diffusion_model.encode_text([EDIT_PROMPT])  # (1, 77, 768)
    text_uncond = diffusion_model.encode_text([''])
    # to float16
    print('------------to float 16----------------')
    init_latent, text_cond, text_uncond, cond_tensor = (
        init_latent.to(dtype=torch.float16),
        text_cond.to(dtype=torch.float16),
        text_uncond.to(dtype=torch.float16),
        cond_tensor.to(dtype=torch.float16)
    )
    inf_pipe.unet.to(torch.float16)
    latent_pred = inf_pipe(
        latent=init_latent,
        text_cond=text_cond,
        text_uncond=text_uncond,
        img_cond=cond_tensor,
        text_cfg=TEXT_CFG,
        img_cfg=VIDEO_CFG,
    )['latent']
    

    image_pred = diffusion_model.decode_latent_to_image(latent_pred) # (1,16,3,512,512)
    output_path = os.path.join(new_tmp_dir, f"output_{int(time.time())}.mp4")
    # clear_cache(output_path)
    
    save_video_from_frames(image_pred, output_path)
    # import pdb; pdb.set_trace()
    # fps = 8
    # frames = []
    # for i in range(16):
    #     pred_frame = clip_image(unnormalize(image_pred[0][i].unsqueeze(0))) * 255
    #     pred_frame_resized = pred_frame.squeeze(0).detach().cpu() #(3,512,512)
    #     pred_frame_resized = pred_frame_resized.permute(1, 2, 0).detach().cpu().numpy().astype("uint8") #(512,512,3) np
    #     Image.fromarray(pred_frame_resized).save(save_pth)

    # # 生成一个简单的黑色视频作为示例
    # output_path = os.path.join(new_tmp_dir, "output.mp4")
    # fourcc = cv2.VideoWriter_fourcc(*'mp4v')
    # out = cv2.VideoWriter(output_path, fourcc, 20.0, (512, 512))

    # for _ in range(60):  # 生成 3 秒的视频（20fps）
    #     frame = np.zeros((512, 512, 3), dtype=np.uint8)
    #     out.write(frame)
    # out.release()
    torch.cuda.empty_cache()

    return output_path

# 枚举类用于背景选择
class BGSource(Enum):
    UPLOAD = "Use Background Video"
    UPLOAD_FLIP = "Use Flipped Background Video"
    UPLOAD_REVERSE = "Use Reversed Background Video"


# Quick prompts 示例
# quick_prompts = [
#     'beautiful woman, fantasy setting',
#     'beautiful woman, neon dynamic lighting',
#     'man in suit, tunel lighting',
#     'animated mouse, aesthetic lighting',
#     'robot warrior, a sunset background',
#     'yellow cat, reflective wet beach',
#     'camera, dock, calm sunset',
#     'astronaut, dim lighting',
#     'astronaut, colorful balloons',
#     'astronaut, desert landscape'
# ]

# quick_prompts = [
#     'beautiful woman',
#     'handsome man',
#     'beautiful woman, cinematic lighting',
#     'handsome man, cinematic lighting',
#     'beautiful woman, natural lighting',
#     'handsome man, natural lighting',
#     'beautiful woman, neo punk lighting, cyberpunk',
#     'handsome man, neo punk lighting, cyberpunk',
# ]


quick_prompts = [
    'beautiful woman',
    'handsome man',
    # 'beautiful woman, cinematic lighting',
    'handsome man, cinematic lighting',
    'beautiful woman, natural lighting',
    'handsome man, natural lighting',
    'beautiful woman, warm lighting',
    'handsome man, soft lighting',
    'change the background lighting',
]


quick_prompts = [[x] for x in quick_prompts]

# css = """
# #foreground-gallery {
#     width: 700 !important; /* 限制最大宽度 */
#     max-width: 700px !important; /* 避免它自动变宽 */
#     flex: none !important; /* 让它不自动扩展 */
# }
# """

# css = """
#     #prompt-box, #bg-source, #quick-list, #relight-btn {
#     width: 750px !important;
# }
# """

# Gradio UI 结构
block = gr.Blocks().queue()
with block:
    with gr.Row():
        # gr.Markdown("## RelightVid (Relighting with Foreground and Background Video Condition)")
        gr.Markdown("# 💡RelightVid  \n### Relighting with Foreground and Background Video Condition")

    with gr.Row():
        with gr.Column():
            with gr.Row():
                input_fg = gr.Video(label="Foreground Video", height=380, width=420, visible=True)
                input_bg = gr.Video(label="Background Video", height=380, width=420, visible=True)
            
            segment_button = gr.Button(value="Video Segmentation")
            with gr.Accordion("Segmentation Options", open=False):
                # 如果用户不使用 point_prompt，而是直接提供坐标，则使用 x, y
                with gr.Row():
                    x_coord = gr.Slider(label="X Coordinate (Point Prompt Ratio)", minimum=0.0, maximum=1.0, value=0.5, step=0.01)
                    y_coord = gr.Slider(label="Y Coordinate (Point Prompt Ratio)", minimum=0.0, maximum=1.0, value=0.5, step=0.01)
                        

            fg_gallery = gr.Gallery(height=150, object_fit='contain', label='Foreground Quick List', value=db_examples.fg_samples, columns=5, allow_preview=False)
            bg_gallery = gr.Gallery(height=450, object_fit='contain', label='Background Quick List', value=db_examples.bg_samples, columns=5, allow_preview=False)
            

            with gr.Group():
            #     with gr.Row():
            #         num_samples = gr.Slider(label="Videos", minimum=1, maximum=12, value=1, step=1)
            #         seed = gr.Number(label="Seed", value=12345, precision=0)
                with gr.Row():
                    video_width = gr.Slider(label="Video Width", minimum=256, maximum=1024, value=512, step=64, visible=False)
                    video_height = gr.Slider(label="Video Height", minimum=256, maximum=1024, value=512, step=64, visible=False)

            # with gr.Accordion("Advanced options", open=False):
            #     steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=20, step=1)
            #     cfg = gr.Slider(label="CFG Scale", minimum=1.0, maximum=32.0, value=7.0, step=0.01)
            #     highres_scale = gr.Slider(label="Highres Scale", minimum=1.0, maximum=3.0, value=1.5, step=0.01)
            #     highres_denoise = gr.Slider(label="Highres Denoise", minimum=0.1, maximum=0.9, value=0.5, step=0.01)
            #     a_prompt = gr.Textbox(label="Added Prompt", value='best quality')
            #     n_prompt = gr.Textbox(label="Negative Prompt", value='lowres, bad anatomy, bad hands, cropped, worst quality')
            #     normal_button = gr.Button(value="Compute Normal (4x Slower)")

        with gr.Column():
            result_video = gr.Video(label='Output Video', height=750, visible=True)

            prompt = gr.Textbox(label="Prompt")  
            bg_source = gr.Radio(choices=[e.value for e in BGSource],
                                value=BGSource.UPLOAD.value,
                                label="Background Source",
                                type='value')

            example_prompts = gr.Dataset(samples=quick_prompts, label='Prompt Quick List', components=[prompt])
            relight_button = gr.Button(value="Relight")

            # prompt = gr.Textbox(label="Prompt")
            # bg_source = gr.Radio(choices=[e.value for e in BGSource],
            #                     value=BGSource.UPLOAD.value,
            #                     label="Background Source", type='value')

            # example_prompts = gr.Dataset(samples=quick_prompts, label='Prompt Quick List', components=[prompt])
            # relight_button = gr.Button(value="Relight")
            # fg_gallery = gr.Gallery(witdth=400, object_fit='contain', label='Foreground Quick List', value=db_examples.bg_samples, columns=4, allow_preview=False)
            # fg_gallery = gr.Gallery(
            #     height=380, 
            #     object_fit='contain', 
            #     label='Foreground Quick List', 
            #     value=db_examples.fg_samples, 
            #     columns=4, 
            #     allow_preview=False,
            #     elem_id="foreground-gallery"  # 👈 添加 elem_id
            # )


    # 输入列表
    # ips = [input_fg, input_bg, prompt, video_width, video_height, num_samples, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, bg_source]
    ips = [input_fg, input_bg, prompt]

    # 按钮绑定处理函数
    # relight_button.click(fn=lambda: None, inputs=[], outputs=[result_video])

    relight_button.click(fn=dummy_process, inputs=ips, outputs=[result_video])
    
    # normal_button.click(fn=dummy_process, inputs=ips, outputs=[result_video])

    # 背景库选择
    def bg_gallery_selected(gal, evt: gr.SelectData):
        # import pdb; pdb.set_trace()
        # img_path = gal[evt.index][0]
        img_path = db_examples.bg_samples[evt.index]
        video_path = img_path.replace('frames/0000.png', 'cropped_video.mp4')
        return video_path

    bg_gallery.select(bg_gallery_selected, inputs=bg_gallery, outputs=input_bg)

    def fg_gallery_selected(gal, evt: gr.SelectData):
        # import pdb; pdb.set_trace()
        # img_path = gal[evt.index][0]
        img_path = db_examples.fg_samples[evt.index]
        video_path = img_path.replace('frames/0000.png', 'cropped_video.mp4')
        return video_path

    fg_gallery.select(fg_gallery_selected, inputs=fg_gallery, outputs=input_fg)

    input_fg_img = gr.Image(label="Foreground Video", visible=False)
    input_bg_img = gr.Image(label="Background Video", visible=False)
    result_video_img = gr.Image(label="Output Video", visible=False)

    v_index = gr.Textbox(label="ID", visible=False)
    example_prompts.click(lambda x: x[0], inputs=example_prompts, outputs=prompt, show_progress=False, queue=False)

    # 示例
    # dummy_video_for_outputs = gr.Video(visible=False, label='Result')
    gr.Examples(
        # fn=lambda *args: args[-1],
        fn=process_example,
        examples=db_examples.background_conditioned_examples,
        # inputs=[v_index, input_fg_img, input_bg_img, prompt, bg_source, video_width, video_height, result_video_img],
        inputs=[v_index, input_fg_img, input_bg_img, prompt, bg_source, result_video_img],
        outputs=[input_fg, input_bg, result_video],
        run_on_click=True, examples_per_page=1024
    )

# 启动 Gradio 应用
# block.launch(server_name='0.0.0.0', server_port=10002, share=True)
block.launch()