app_gys.py

# -*- coding: utf-8 -*-

import argparse
import gradio
import os
import torch
import numpy as np
import tempfile
import functools
import trimesh
import copy
from scipy.spatial.transform import Rotation

from dust3r.inference import inference, load_model
from dust3r.image_pairs import make_pairs
from dust3r.utils.image import load_images, rgb, resize_images
from dust3r.utils.device import to_numpy
from dust3r.viz import add_scene_cam, CAM_COLORS, OPENGL, pts3d_to_trimesh, cat_meshes
from dust3r.cloud_opt import global_aligner, GlobalAlignerMode
from sam2.build_sam import build_sam2_video_predictor
import matplotlib.pyplot as plt

import shutil
import json
from PIL import Image
import math
import cv2

plt.ion()

torch.backends.cuda.matmul.allow_tf32 = True  # for gpu >= Ampere and pytorch >= 1.12
batch_size = 1

########################## 引入grounding_dino #############################
from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection
def get_mask_from_grounding_dino(video_dir, ann_frame_idx, ann_obj_id, input_text):
    # init grounding dino model from huggingface
    model_id = "IDEA-Research/grounding-dino-tiny"
    device = "cuda" if torch.cuda.is_available() else "cpu"
    processor = AutoProcessor.from_pretrained(model_id)
    grounding_model = AutoModelForZeroShotObjectDetection.from_pretrained(model_id).to(device)

    # setup the input image and text prompt for SAM 2 and Grounding DINO
    # VERY important: text queries need to be lowercased + end with a dot


    """
    Step 2: Prompt Grounding DINO and SAM image predictor to get the box and mask for specific frame
    """
    # prompt grounding dino to get the box coordinates on specific frame
    frame_names = [
        p for p in os.listdir(video_dir)
        if os.path.splitext(p)[-1] in [".jpg", ".jpeg", ".JPG", ".JPEG"]
    ]
    # frame_names.sort(key=lambda p: os.path.splitext(p)[0])
    img_path = os.path.join(video_dir, frame_names[ann_frame_idx])
    image = Image.open(img_path)


    # run Grounding DINO on the image
    inputs = processor(images=image, text=input_text, return_tensors="pt").to(device)
    with torch.no_grad():
        outputs = grounding_model(**inputs)

    results = processor.post_process_grounded_object_detection(
        outputs,
        inputs.input_ids,
        box_threshold=0.25,
        text_threshold=0.3,
        target_sizes=[image.size[::-1]]
    )
    return results[0]["boxes"], results[0]["labels"]

def get_masks_from_grounded_sam2(h, w, predictor, video_dir, input_text):

    inference_state = predictor.init_state(video_path=video_dir)
    predictor.reset_state(inference_state)

    ann_frame_idx = 0  # the frame index we interact with
    ann_obj_id = 1  # give a unique id to each object we interact with (it can be any integers)
    print("Running Groundding DINO......")
    input_boxes, OBJECTS = get_mask_from_grounding_dino(video_dir, ann_frame_idx, ann_obj_id, input_text)
    print("Groundding DINO run over!")
    if(len(OBJECTS) < 1):
        raise gradio.Error("The images you input do not contain the target in '{}'".format(input_text))
    # 给第一个帧输入由grounding_dino输出的boxes作为prompts
    for object_id, (label, box) in enumerate(zip(OBJECTS, input_boxes)):
        _, out_obj_ids, out_mask_logits = predictor.add_new_points_or_box(
            inference_state=inference_state,
            frame_idx=ann_frame_idx,
            obj_id=ann_obj_id,
            box=box,
        )
        break #只加入第一个box


    # sam2获取所有帧的分割结果
    video_segments = {}  # video_segments contains the per-frame segmentation results
    for out_frame_idx, out_obj_ids, out_mask_logits in predictor.propagate_in_video(inference_state):
        video_segments[out_frame_idx] = {
            out_obj_id: (out_mask_logits[i] > 0.0).cpu().numpy()
            for i, out_obj_id in enumerate(out_obj_ids)
        }

    resize_mask = resize_mask_to_img(video_segments, w, h)
    return resize_mask


def handle_uploaded_files(uploaded_files, target_folder):
    # 创建目标文件夹
    if not os.path.exists(target_folder):
        os.makedirs(target_folder)

    # 遍历上传的文件，移动到目标文件夹
    for file in uploaded_files:
        file_path = file.name  # 文件的临时路径
        file_name = os.path.basename(file_path)  # 文件名
        target_path = os.path.join(target_folder, file_name)
        shutil.copy2(file_path, target_path)
        print("copy images from {} to {}".format(file_path, target_path))

    return target_folder
def show_mask(mask, ax, random_color=False):
    if random_color:
        color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)
    else:
        color = np.array([30 / 255, 144 / 255, 255 / 255, 0.6])
    h, w = mask.shape[-2:]
    mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)
    ax.imshow(mask_image)

def show_mask_sam2(mask, ax, obj_id=None, random_color=False):
    if random_color:
        color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)
    else:
        cmap = plt.get_cmap("tab10")
        cmap_idx = 0 if obj_id is None else obj_id
        color = np.array([*cmap(cmap_idx)[:3], 0.6])
    h, w = mask.shape[-2:]
    mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)
    ax.imshow(mask_image)
def show_points(coords, labels, ax, marker_size=375):
    pos_points = coords[labels == 1]
    neg_points = coords[labels == 0]
    ax.scatter(pos_points[:, 0], pos_points[:, 1], color='green', marker='*', s=marker_size, edgecolor='white',
               linewidth=1.25)
    ax.scatter(neg_points[:, 0], neg_points[:, 1], color='red', marker='*', s=marker_size, edgecolor='white',
               linewidth=1.25)

def show_box(box, ax):
    x0, y0 = box[0], box[1]
    w, h = box[2] - box[0], box[3] - box[1]
    ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0, 0, 0, 0), lw=2))



def get_args_parser():
    parser = argparse.ArgumentParser()
    parser_url = parser.add_mutually_exclusive_group()
    parser_url.add_argument("--local_network", action='store_true', default=False,
                            help="make app accessible on local network: address will be set to 0.0.0.0")
    parser_url.add_argument("--server_name", type=str, default=None, help="server url, default is 127.0.0.1")
    parser.add_argument("--image_size", type=int, default=512, choices=[512, 224], help="image size")
    parser.add_argument("--server_port", type=int, help=("will start gradio app on this port (if available). "
                                                         "If None, will search for an available port starting at 7860."),
                        default=None)
    parser.add_argument("--weights", type=str, required=True, help="path to the model weights")
    parser.add_argument("--device", type=str, default='cuda', help="pytorch device")
    parser.add_argument("--tmp_dir", type=str, default=None, help="value for tempfile.tempdir")
    return parser


# 将渲染的3D保存到outfile路径
def _convert_scene_output_to_glb(outdir, imgs, pts3d, mask, focals, cams2world, cam_size=0.05,
                                 cam_color=None, as_pointcloud=False, transparent_cams=False):
    assert len(pts3d) == len(mask) <= len(imgs) <= len(cams2world) == len(focals)
    pts3d = to_numpy(pts3d)
    imgs = to_numpy(imgs)
    focals = to_numpy(focals)
    cams2world = to_numpy(cams2world)

    scene = trimesh.Scene()

    # full pointcloud
    if as_pointcloud:
        pts = np.concatenate([p[m] for p, m in zip(pts3d, mask)])
        col = np.concatenate([p[m] for p, m in zip(imgs, mask)])
        pct = trimesh.PointCloud(pts.reshape(-1, 3), colors=col.reshape(-1, 3))
        scene.add_geometry(pct)
    else:
        meshes = []
        for i in range(len(imgs)):
            meshes.append(pts3d_to_trimesh(imgs[i], pts3d[i], mask[i]))
        mesh = trimesh.Trimesh(**cat_meshes(meshes))
        scene.add_geometry(mesh)

    # add each camera
    for i, pose_c2w in enumerate(cams2world):
        if isinstance(cam_color, list):
            camera_edge_color = cam_color[i]
        else:
            camera_edge_color = cam_color or CAM_COLORS[i % len(CAM_COLORS)]
        add_scene_cam(scene, pose_c2w, camera_edge_color,
                      None if transparent_cams else imgs[i], focals[i],
                      imsize=imgs[i].shape[1::-1], screen_width=cam_size)

    rot = np.eye(4)
    rot[:3, :3] = Rotation.from_euler('y', np.deg2rad(180)).as_matrix()
    scene.apply_transform(np.linalg.inv(cams2world[0] @ OPENGL @ rot))
    outfile = os.path.join(outdir, 'scene.glb')
    print('(exporting 3D scene to', outfile, ')')
    scene.export(file_obj=outfile)
    return outfile


def get_3D_model_from_scene(outdir, scene, sam2_masks, min_conf_thr=3, as_pointcloud=False, mask_sky=False,
                            clean_depth=False, transparent_cams=False, cam_size=0.05):
    """
    extract 3D_model (glb file) from a reconstructed scene
    """
    if scene is None:
        return None
    # post processes
    if clean_depth:
        scene = scene.clean_pointcloud()
    if mask_sky:
        scene = scene.mask_sky()

    # get optimized values from scene
    rgbimg = scene.imgs

    focals = scene.get_focals().cpu()
    cams2world = scene.get_im_poses().cpu()
    # 3D pointcloud from depthmap, poses and intrinsics
    pts3d = to_numpy(scene.get_pts3d())
    scene.min_conf_thr = float(scene.conf_trf(torch.tensor(min_conf_thr)))
    msk = to_numpy(scene.get_masks())

    assert len(msk) == len(sam2_masks)
    # 将sam2输出的mask 和 dust3r输出的置信度阈值筛选后的msk取交集
    for i in range(len(sam2_masks)):
        msk[i] = np.logical_and(msk[i], sam2_masks[i])

    return _convert_scene_output_to_glb(outdir, rgbimg, pts3d, msk, focals, cams2world, as_pointcloud=as_pointcloud,
                                        transparent_cams=transparent_cams, cam_size=cam_size) # 置信度和SAM2 mask的交集

# 将视频分割成固定帧数
def video_to_frames_fix(video_path, output_folder, frame_interval=10, target_fps=6):
    """
    将视频转换为图像帧，并保存为 JPEG 文件。
    frame_interval：保存帧的步长
    target_fps： 目标帧率（每秒保存的帧数）
    """

    # 确保输出文件夹存在
    if not os.path.exists(output_folder):
        os.makedirs(output_folder)
    # 打开视频文件
    cap = cv2.VideoCapture(video_path)
    # 获取视频总帧数
    frames_num = cap.get(cv2.CAP_PROP_FRAME_COUNT)

    # 计算动态帧间隔
    frame_interval = math.ceil(frames_num / target_fps)
    print(f"总帧数: {frames_num} FPS, 动态帧间隔: 每隔 {frame_interval} 帧保存一次.")
    frame_count = 0
    saved_frame_count = 0
    success, frame = cap.read()

    file_list = []
    # 逐帧读取视频
    while success:
        if frame_count % frame_interval == 0:
            # 每隔 frame_interval 帧保存一次
            frame_filename = os.path.join(output_folder, f"frame_{saved_frame_count:04d}.jpg")
            cv2.imwrite(frame_filename, frame)
            file_list.append(frame_filename)
            saved_frame_count += 1
        frame_count += 1
        success, frame = cap.read()

    # 释放视频捕获对象
    cap.release()
    print(f"视频处理完成，共保存了 {saved_frame_count} 帧到文件夹 '{output_folder}'.")
    return file_list


def video_to_frames(video_path, output_folder, frame_interval=10, target_fps = 2):
    """
    将视频转换为图像帧，并保存为 JPEG 文件。
    frame_interval：保存帧的步长
    target_fps： 目标帧率（每秒保存的帧数）
    """

    # 确保输出文件夹存在
    if not os.path.exists(output_folder):
        os.makedirs(output_folder)
    # 打开视频文件
    cap = cv2.VideoCapture(video_path)
    # 获取视频的实际帧率
    actual_fps = cap.get(cv2.CAP_PROP_FPS)

    # 获取视频总帧数
    frames_num = cap.get(cv2.CAP_PROP_FRAME_COUNT)

    # 计算动态帧间隔
    # frame_interval = math.ceil(actual_fps / target_fps)
    print(f"实际帧率: {actual_fps} FPS, 动态帧间隔: 每隔 {frame_interval} 帧保存一次.")
    frame_count = 0
    saved_frame_count = 0
    success, frame = cap.read()

    file_list = []
    # 逐帧读取视频
    while success:
        if frame_count % frame_interval == 0:
            # 每隔 frame_interval 帧保存一次
            frame_filename = os.path.join(output_folder, f"frame_{saved_frame_count:04d}.jpg")
            cv2.imwrite(frame_filename, frame)
            file_list.append(frame_filename)
            saved_frame_count += 1
        frame_count += 1
        success, frame = cap.read()

    # 释放视频捕获对象
    cap.release()
    print(f"视频处理完成，共保存了 {saved_frame_count} 帧到文件夹 '{output_folder}'.")
    return file_list

def overlay_mask_on_image(image, mask, color=[0, 1, 0], alpha=0.5):
    """
    将mask融合在image上显示。
    返回融合后的图片 (H, W, 3)
    """

    # 创建一个与image相同尺寸的全黑图像
    mask_colored = np.zeros_like(image)

    # 将mask为True的位置赋值为指定颜色
    mask_colored[mask] = color

    # 将彩色掩码与原图像叠加
    overlay = cv2.addWeighted(image, 1 - alpha, mask_colored, alpha, 0)

    return overlay
def get_reconstructed_video(sam2, outdir, model, device, image_size, image_mask, video_dir, schedule, niter, min_conf_thr,
                            as_pointcloud, mask_sky, clean_depth, transparent_cams, cam_size,
                            scenegraph_type, winsize, refid, input_text):
    target_dir = os.path.join(outdir, 'frames_video')
    file_list = video_to_frames_fix(video_dir, target_dir)
    scene, outfile, imgs = get_reconstructed_scene(sam2, outdir, model, device, image_size, image_mask, file_list, schedule, niter, min_conf_thr,
                            as_pointcloud, mask_sky, clean_depth, transparent_cams, cam_size,
                            scenegraph_type, winsize, refid, target_dir, input_text)
    return scene, outfile, imgs

def get_reconstructed_image(sam2, outdir, model, device, image_size, image_mask, filelist, schedule, niter, min_conf_thr,
                            as_pointcloud, mask_sky, clean_depth, transparent_cams, cam_size,
                            scenegraph_type, winsize, refid, input_text):
    target_folder = handle_uploaded_files(filelist, os.path.join(outdir, 'uploaded_images'))
    scene, outfile, imgs = get_reconstructed_scene(sam2, outdir, model, device, image_size, image_mask, filelist, schedule, niter, min_conf_thr,
                            as_pointcloud, mask_sky, clean_depth, transparent_cams, cam_size,
                            scenegraph_type, winsize, refid, target_folder, input_text)
    return scene, outfile, imgs
def get_reconstructed_scene(sam2, outdir, model, device, image_size, image_mask, filelist, schedule, niter, min_conf_thr,
                            as_pointcloud, mask_sky, clean_depth, transparent_cams, cam_size,
                            scenegraph_type, winsize, refid, images_folder, input_text=None):
    """
    from a list of images, run dust3rWithSam2 inference, global aligner.
    then run get_3D_model_from_scene
    """
    imgs = load_images(filelist, size=image_size)
    img_size = imgs[0]["true_shape"]
    for img in imgs[1:]:
        if not np.equal(img["true_shape"], img_size).all():
            raise gradio.Error("Please ensure that the images you enter are of the same size")

    if len(imgs) == 1:
        imgs = [imgs[0], copy.deepcopy(imgs[0])]
        imgs[1]['idx'] = 1
    if scenegraph_type == "swin":
        scenegraph_type = scenegraph_type + "-" + str(winsize)
    elif scenegraph_type == "oneref":
        scenegraph_type = scenegraph_type + "-" + str(refid)





    pairs = make_pairs(imgs, scene_graph=scenegraph_type, prefilter=None, symmetrize=True)
    output = inference(pairs, model, device, batch_size=batch_size)

    mode = GlobalAlignerMode.PointCloudOptimizer if len(imgs) > 2 else GlobalAlignerMode.PairViewer
    scene = global_aligner(output, device=device, mode=mode)
    lr = 0.01

    if mode == GlobalAlignerMode.PointCloudOptimizer:
        loss = scene.compute_global_alignment(init='mst', niter=niter, schedule=schedule, lr=lr)



    # also return rgb, depth and confidence imgs
    # depth is normalized with the max value for all images
    # we apply the jet colormap on the confidence maps
    rgbimg = scene.imgs
    depths = to_numpy(scene.get_depthmaps())
    confs = to_numpy([c for c in scene.im_conf])
    cmap = plt.get_cmap('jet')
    depths_max = max([d.max() for d in depths])
    depths = [d / depths_max for d in depths]
    confs_max = max([d.max() for d in confs])
    confs = [cmap(d / confs_max) for d in confs]

    # TODO 调用SAM2获取masks
    h, w = rgbimg[0].shape[:-1]
    masks = None
    if not input_text or input_text.isspace(): # input_text 为空串
        masks = get_masks_from_sam2(h, w, sam2, images_folder)
    else:
        masks = get_masks_from_grounded_sam2(h, w, sam2, images_folder, input_text)  # gd-sam2


    imgs = []
    for i in range(len(rgbimg)):
        imgs.append(rgbimg[i])
        imgs.append(rgb(depths[i]))
        imgs.append(rgb(confs[i]))
        imgs.append(overlay_mask_on_image(rgbimg[i], masks[i])) # mask融合原图，展示SAM2的分割效果

    # TODO 基于SAM2的mask过滤DUST3R的3D重建模型
    outfile = get_3D_model_from_scene(outdir, scene, masks, min_conf_thr, as_pointcloud, mask_sky,
                                      clean_depth, transparent_cams, cam_size)
    return scene, outfile, imgs


def resize_mask_to_img(masks, target_width, target_height):
    frame_mask = []
    origin_size = masks[0][1].shape # 1表示object id
    for frame, objects_mask in masks.items(): # 每个frame和该frame对应的分割结果
        # 每个frame可能包含多个object对应的mask
        masks = list(objects_mask.values())
        if not masks: # masks为空，即当前frame不包含object
            frame_mask.append(np.ones(origin_size, dtype=bool))
        else: # 将当前frame包含的所有object的mask取并集
            union_mask = masks[0]
            for mask in masks[1:]:
                union_mask = np.logical_or(union_mask, mask)
            frame_mask.append(union_mask)
    resized_mask = []
    for mask in frame_mask:
        mask_image = Image.fromarray(mask.squeeze(0).astype(np.uint8) * 255)
        resized_mask_image = mask_image.resize((target_width, target_height), Image.NEAREST)
        resized_mask.append(np.array(resized_mask_image) > 0)

    return resized_mask

def get_masks_from_sam2(h, w, predictor, video_dir):

    inference_state = predictor.init_state(video_path=video_dir)
    predictor.reset_state(inference_state)


    # 给一个帧添加points
    points = np.array([[360, 550], [340, 400]], dtype=np.float32)
    labels = np.array([1, 1], dtype=np.int32)


    _, out_obj_ids, out_mask_logits = predictor.add_new_points(
        inference_state=inference_state,
        frame_idx=0,
        obj_id=1,
        points=points,
        labels=labels,
    )

    # sam2获取所有帧的分割结果
    video_segments = {}  # video_segments contains the per-frame segmentation results
    for out_frame_idx, out_obj_ids, out_mask_logits in predictor.propagate_in_video(inference_state):
        video_segments[out_frame_idx] = {
            out_obj_id: (out_mask_logits[i] > 0.0).cpu().numpy()
            for i, out_obj_id in enumerate(out_obj_ids)
        }

    resize_mask = resize_mask_to_img(video_segments, w, h)
    return resize_mask

def set_scenegraph_options(inputfiles, winsize, refid, scenegraph_type):
    num_files = len(inputfiles) if inputfiles is not None else 1
    max_winsize = max(1, (num_files - 1) // 2)
    if scenegraph_type == "swin":
        winsize = gradio.Slider(label="Scene Graph: Window Size", value=max_winsize,
                                minimum=1, maximum=max_winsize, step=1, visible=True)
        refid = gradio.Slider(label="Scene Graph: Id", value=0, minimum=0,
                              maximum=num_files - 1, step=1, visible=False)
    elif scenegraph_type == "oneref":
        winsize = gradio.Slider(label="Scene Graph: Window Size", value=max_winsize,
                                minimum=1, maximum=max_winsize, step=1, visible=False)
        refid = gradio.Slider(label="Scene Graph: Id", value=0, minimum=0,
                              maximum=num_files - 1, step=1, visible=True)
    else:
        winsize = gradio.Slider(label="Scene Graph: Window Size", value=max_winsize,
                                minimum=1, maximum=max_winsize, step=1, visible=False)
        refid = gradio.Slider(label="Scene Graph: Id", value=0, minimum=0,
                              maximum=num_files - 1, step=1, visible=False)
    return winsize, refid

def process_images(imagesList):
    return None

def process_videos(video):
    return None

def upload_images_listener(image_size, file_list):
    if len(file_list) == 1:
        raise gradio.Error("Please enter images from at least two different views.")
    print("Uploading image[0] to ImageMask：")
    img_0 = load_images([file_list[0]], image_size)
    i1 = img_0[0]['img'].squeeze(0)
    rgb_img = rgb(i1)
    return rgb_img
def upload_video_listener(image_size, video_dir):
    cap = cv2.VideoCapture(video_dir)
    success, frame = cap.read() # 第一帧
    rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
    Image_frame = Image.fromarray(rgb_frame)
    resized_frame = resize_images([Image_frame], image_size)
    i1 = resized_frame[0]['img'].squeeze(0)
    rgb_img = rgb(i1)
    return rgb_img

def main_demo(sam2, tmpdirname, model, device, image_size, server_name, server_port):

    # functools.partial解析：https://blog.csdn.net/wuShiJingZuo/article/details/135018810
    recon_fun_image_demo = functools.partial(get_reconstructed_image,sam2, tmpdirname, model, device,
                                  image_size)

    recon_fun_video_demo = functools.partial(get_reconstructed_video, sam2, tmpdirname, model, device,
                                  image_size)

    upload_files_fun = functools.partial(upload_images_listener,image_size)
    upload_video_fun = functools.partial(upload_video_listener, image_size)
    with gradio.Blocks() as demo1:
        scene = gradio.State(None)
        gradio.HTML('<h1 style="text-align: center;">DUST3R With SAM2: Segmenting Everything In 3D</h1>')
        gradio.HTML("""<h2 style="text-align: center;">
          <a href='https://arxiv.org/abs/2304.03284' target='_blank' rel='noopener'>[paper]</a>
          <a href='https://github.com/baaivision/Painter' target='_blank' rel='noopener'>[code]</a>
        </h2>""")
        gradio.HTML("""
          <div style="text-align: center;">
            <h2 style="text-align: center;">DUSt3R can unify various 3D vision tasks and set new SoTAs on monocular/multi-view depth estimation as well as relative pose estimation.</h2>
          </div>
          """)
        gradio.set_static_paths(paths=["static/images/"])
        project_path = "static/images/project.gif"
        gradio.HTML(f"""
              <div align='center' >
              <img src="/file={project_path}"  width='720px'>
              </div>
              """)
        gradio.HTML("<p> \
            <strong>DUST3R+SAM2: One touch for any segmentation in a video.</strong> <br>\
            Choose an example below &#128293; &#128293;  &#128293; <br>\
            Or, upload by yourself: <br>\
            1. Upload a video to be tested to 'video'. If failed, please check the codec, we recommend h.264 by default. <br>2. Upload a prompt image to 'prompt' and draw <strong>a point or line on the target</strong>.  <br>\
            <br> \
            💎 SAM segments the target with any point or scribble, then SegGPT segments the whole video. <br>\
            💎 Examples below were never trained and are randomly selected for testing in the wild. <br>\
            💎 Current UI interface only unleashes a small part of the capabilities of SegGPT, i.e., 1-shot case. <br> \
                            Note: we only take the first 16 frames for the demo.    \
            </p>")

        with gradio.Column():
            with gradio.Row():
                inputfiles = gradio.File(file_count="multiple")
                with gradio.Column():
                    image_mask = gradio.ImageMask(image_mode="RGB", type="numpy", brush=gradio.Brush(),
                                                  label="prompt (提示图)", transforms=(), width=600, height=450)
                    input_text = gradio.Textbox(info="please enter object here", label="Text Prompt")
            with gradio.Row():
                schedule = gradio.Dropdown(["linear", "cosine"],
                                           value='linear', label="schedule", info="For global alignment!")
                niter = gradio.Number(value=300, precision=0, minimum=0, maximum=5000,
                                      label="num_iterations", info="For global alignment!")
                scenegraph_type = gradio.Dropdown(["complete", "swin", "oneref"],
                                                  value='complete', label="Scenegraph",
                                                  info="Define how to make pairs",
                                                  interactive=True)
                winsize = gradio.Slider(label="Scene Graph: Window Size", value=1,
                                        minimum=1, maximum=1, step=1, visible=False)
                refid = gradio.Slider(label="Scene Graph: Id", value=0, minimum=0, maximum=0, step=1, visible=False)

            run_btn = gradio.Button("Run")

            with gradio.Row():
                # adjust the confidence threshold
                min_conf_thr = gradio.Slider(label="min_conf_thr", value=3.0, minimum=1.0, maximum=20, step=0.1)
                # adjust the camera size in the output pointcloud
                cam_size = gradio.Slider(label="cam_size", value=0.05, minimum=0.001, maximum=0.1, step=0.001)
            with gradio.Row():
                as_pointcloud = gradio.Checkbox(value=False, label="As pointcloud")
                # two post process implemented
                mask_sky = gradio.Checkbox(value=False, label="Mask sky")
                clean_depth = gradio.Checkbox(value=True, label="Clean-up depthmaps")
                transparent_cams = gradio.Checkbox(value=False, label="Transparent cameras")

            outmodel = gradio.Model3D()
            outgallery = gradio.Gallery(label='rgb,depth,confidence,mask', columns=4, height="100%")

            inputfiles.upload(upload_files_fun, inputs=inputfiles, outputs=image_mask)

            run_btn.click(fn=recon_fun_image_demo,  # 调用get_reconstructed_image即DUST3R模型
                          inputs=[image_mask, inputfiles, schedule, niter, min_conf_thr, as_pointcloud,
                                  mask_sky, clean_depth, transparent_cams, cam_size,
                                  scenegraph_type, winsize, refid, input_text],
                          outputs=[scene, outmodel, outgallery])




        # ## ****************************  video  *******************************************************
    with gradio.Blocks() as demo2:
        gradio.HTML('<h1 style="text-align: center;">DUST3R With SAM2: Segmenting Everything In 3D</h1>')
        gradio.HTML("""<h2 style="text-align: center;">
          <a href='https://arxiv.org/abs/2304.03284' target='_blank' rel='noopener'>[paper]</a>
          <a href='https://github.com/baaivision/Painter' target='_blank' rel='noopener'>[code]</a>
        </h2>""")
        gradio.HTML("""
          <div style="text-align: center;">
            <h2 style="text-align: center;">DUSt3R can unify various 3D vision tasks and set new SoTAs on monocular/multi-view depth estimation as well as relative pose estimation.</h2>
          </div>
          """)
        gradio.set_static_paths(paths=["static/images/"])
        project_path = "static/images/project.gif"
        gradio.HTML(f"""
              <div align='center' >
              <img src="/file={project_path}"  width='720px'>
              </div>
              """)
        gradio.HTML("<p> \
            <strong>DUST3R+SAM2: One touch for any segmentation in a video.</strong> <br>\
            Choose an example below &#128293; &#128293;  &#128293; <br>\
            Or, upload by yourself: <br>\
            1. Upload a video to be tested to 'video'. If failed, please check the codec, we recommend h.264 by default. <br>2. Upload a prompt image to 'prompt' and draw <strong>a point or line on the target</strong>.  <br>\
            <br> \
            💎 SAM segments the target with any point or scribble, then SegGPT segments the whole video. <br>\
            💎 Examples below were never trained and are randomly selected for testing in the wild. <br>\
            💎 Current UI interface only unleashes a small part of the capabilities of SegGPT, i.e., 1-shot case. <br> \
                            Note: we only take the first 16 frames for the demo.    \
            </p>")

        with gradio.Column():
            with gradio.Row():
                input_video = gradio.Video(width=600, height=600)
                with gradio.Column():
                    image_mask = gradio.ImageMask(image_mode="RGB", type="numpy", brush=gradio.Brush(),
                                                  label="prompt (提示图)", transforms=(), width=600, height=450)
                    input_text = gradio.Textbox(info="please enter object here", label="Text Prompt")


            with gradio.Row():
                schedule = gradio.Dropdown(["linear", "cosine"],
                                           value='linear', label="schedule", info="For global alignment!")
                niter = gradio.Number(value=300, precision=0, minimum=0, maximum=5000,
                                      label="num_iterations", info="For global alignment!")
                scenegraph_type = gradio.Dropdown(["complete", "swin", "oneref"],
                                                  value='complete', label="Scenegraph",
                                                  info="Define how to make pairs",
                                                  interactive=True)
                winsize = gradio.Slider(label="Scene Graph: Window Size", value=1,
                                        minimum=1, maximum=1, step=1, visible=False)
                refid = gradio.Slider(label="Scene Graph: Id", value=0, minimum=0, maximum=0, step=1, visible=False)

            run_btn = gradio.Button("Run")

            with gradio.Row():
                # adjust the confidence threshold
                min_conf_thr = gradio.Slider(label="min_conf_thr", value=3.0, minimum=1.0, maximum=20, step=0.1)
                # adjust the camera size in the output pointcloud
                cam_size = gradio.Slider(label="cam_size", value=0.05, minimum=0.001, maximum=0.1, step=0.001)
            with gradio.Row():
                as_pointcloud = gradio.Checkbox(value=False, label="As pointcloud")
                # two post process implemented
                mask_sky = gradio.Checkbox(value=False, label="Mask sky")
                clean_depth = gradio.Checkbox(value=True, label="Clean-up depthmaps")
                transparent_cams = gradio.Checkbox(value=False, label="Transparent cameras")

            outmodel = gradio.Model3D()
            outgallery = gradio.Gallery(label='rgb,depth,confidence,mask', columns=4, height="100%")

            input_video.upload(upload_video_fun, inputs=input_video, outputs=image_mask)

            run_btn.click(fn=recon_fun_video_demo,  # 调用get_reconstructed_scene即DUST3R模型
                          inputs=[image_mask, input_video, schedule, niter, min_conf_thr, as_pointcloud,
                                  mask_sky, clean_depth, transparent_cams, cam_size,
                                  scenegraph_type, winsize, refid, input_text],
                          outputs=[scene, outmodel, outgallery])

    app = gradio.TabbedInterface([demo1, demo2], ["3d rebuilding by images", "3d rebuilding by video"])
    app.launch(share=False, server_name=server_name, server_port=server_port)


# TODO 修改bug：
#在项目的一次启动中，上传的多组图片在点击run后，会保存在同一个临时文件夹中，
# 这样后面再上传其他场景的图片时，不同场景下的图片会存在于一个文件夹中，
# 不同场景的图片导致分割与重建错误

## 目前构思的解决：在文件夹下再基于创建一个文件夹存放不同场景的图片，可以基于时间命名该文件夹


if __name__ == '__main__':
    parser = get_args_parser()
    args = parser.parse_args()

    if args.tmp_dir is not None:
        tmp_path = args.tmp_dir
        os.makedirs(tmp_path, exist_ok=True)
        tempfile.tempdir = tmp_path

    if args.server_name is not None:
        server_name = args.server_name
    else:
        server_name = '0.0.0.0' if args.local_network else '127.0.0.1'

    # DUST3R
    model = load_model(args.weights, args.device)
    # SAM2
    # 加载模型
    sam2_checkpoint = "D:\XMU\mac\hujie\\3D\DUST3RwithSAM2\dust3rWithSam2\SAM2\checkpoints\sam2_hiera_large.pt"
    model_cfg = "sam2_hiera_l.yaml"
    sam2 = build_sam2_video_predictor(model_cfg, sam2_checkpoint)
    # dust3rWithSam2 will write the 3D model inside tmpdirname
    with tempfile.TemporaryDirectory(suffix='dust3r_gradio_demo') as tmpdirname: # DUST3R生成的3D .glb 文件所在的文件夹名称
        print('Outputing stuff in', tmpdirname)
        main_demo(sam2, tmpdirname, model, args.device, args.image_size, server_name, args.server_port)