app.py

import os
import cv2
import torch
import numpy as np
import gradio as gr

import trimesh
import sys
import os

sys.path.append('vggsfm_code/')
import shutil
from datetime import datetime

from vggsfm_code.hf_demo import demo_fn
from omegaconf import DictConfig, OmegaConf
from viz_utils.viz_fn import add_camera
import glob
# 
from scipy.spatial.transform import Rotation
import PIL


# import spaces

# @spaces.GPU
def vggsfm_demo(
    input_video,
    input_image,
    query_frame_num,
    max_query_pts=4096,
):
    torch.cuda.empty_cache()

    if input_video is not None:            
        if not isinstance(input_video, str):
            input_video = input_video["video"]["path"]
    
    cfg_file = "vggsfm_code/cfgs/demo.yaml"
    cfg = OmegaConf.load(cfg_file)

    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")

    max_input_image = 20

    target_dir = f"input_images_{timestamp}"
    if os.path.exists(target_dir): 
        shutil.rmtree(target_dir)

    os.makedirs(target_dir)
    target_dir_images = target_dir + "/images"
    os.makedirs(target_dir_images)

    if input_image is not None:
        if len(input_image)<3:
            return None, "Please input at least three frames"

        input_image = sorted(input_image)
        input_image = input_image[:max_input_image]
        
        # Copy files to the new directory
        for file_name in input_image:
            shutil.copy(file_name, target_dir_images)
    elif input_video is not None:
        vs = cv2.VideoCapture(input_video)

        fps = vs.get(cv2.CAP_PROP_FPS)


        frame_rate = 1
        frame_interval = int(fps * frame_rate)
        
        video_frame_num = 0
        count = 0 
        
        while video_frame_num<=max_input_image:
            (gotit, frame) = vs.read()
            count +=1

            if not gotit:
                break
            
            if count % frame_interval == 0:
                cv2.imwrite(target_dir_images+"/"+f"{video_frame_num:06}.png", frame)
                video_frame_num+=1
                
        if video_frame_num<3:
            return None, "Please input at least three frames"
    else:
        return None, "Input format incorrect"
        
    cfg.query_frame_num = query_frame_num
    cfg.max_query_pts = max_query_pts
    print(f"Files have been copied to {target_dir_images}")
    cfg.SCENE_DIR = target_dir
    
    # try:
    predictions = demo_fn(cfg)
    # except:
    # return None, "Something seems to be incorrect. Please verify that your inputs are formatted correctly. If the issue persists, kindly create a GitHub issue for further assistance."
    
    glbscene = vggsfm_predictions_to_glb(predictions)
    
    glbfile = target_dir + "/glbscene.glb"
    glbscene.export(file_obj=glbfile)    

    
    print(input_image)
    print(input_video)
    return glbfile, "Success"




def vggsfm_predictions_to_glb(predictions):
    # learned from https://github.com/naver/dust3r/blob/main/dust3r/viz.py
    points3D = predictions["points3D"].cpu().numpy()
    points3D_rgb = predictions["points3D_rgb"].cpu().numpy()
    points3D_rgb = (points3D_rgb*255).astype(np.uint8)
    
    extrinsics_opencv = predictions["extrinsics_opencv"].cpu().numpy()
    intrinsics_opencv = predictions["intrinsics_opencv"].cpu().numpy()
    raw_image_paths = predictions["raw_image_paths"]
    images = predictions["images"].permute(0,2,3,1).cpu().numpy()
    images = (images*255).astype(np.uint8)
    
    glbscene = trimesh.Scene()
    point_cloud = trimesh.PointCloud(points3D, colors=points3D_rgb)
    glbscene.add_geometry(point_cloud)


    camera_edge_colors = [(255, 0, 0), (0, 0, 255), (0, 255, 0), (255, 0, 255), (255, 204, 0), (0, 204, 204),
                (128, 255, 255), (255, 128, 255), (255, 255, 128), (0, 0, 0), (128, 128, 128)]

    frame_num = len(extrinsics_opencv)
    extrinsics_opencv_4x4 = np.zeros((frame_num, 4, 4))
    extrinsics_opencv_4x4[:, :3, :4] = extrinsics_opencv
    extrinsics_opencv_4x4[:, 3, 3] = 1

    for idx in range(frame_num):
        cam_from_world = extrinsics_opencv_4x4[idx]
        cam_to_world = np.linalg.inv(cam_from_world)
        cur_cam_color = camera_edge_colors[idx % len(camera_edge_colors)]
        cur_focal = intrinsics_opencv[idx, 0, 0]

        add_camera(glbscene, cam_to_world, cur_cam_color, image=None, imsize=(1024,1024), 
                   focal=None,screen_width=0.35)

    opengl_mat = np.array([[1, 0, 0, 0],
                    [0, -1, 0, 0],
                    [0, 0, -1, 0],
                    [0, 0, 0, 1]])

    rot = np.eye(4)
    rot[:3, :3] = Rotation.from_euler('y', np.deg2rad(180)).as_matrix()
    glbscene.apply_transform(np.linalg.inv(np.linalg.inv(extrinsics_opencv_4x4[0]) @ opengl_mat @ rot))

    # Calculate the bounding box center and apply the translation
    bounding_box = glbscene.bounds
    center = (bounding_box[0] + bounding_box[1]) / 2
    translation = np.eye(4)
    translation[:3, 3] = -center

    glbscene.apply_transform(translation)
    # glbfile = "glbscene.glb"
    # glbscene.export(file_obj=glbfile)    
    return glbscene

# apple_video = "vggsfm_code/examples/videos/apple_video.mp4"
# os.path.join(os.path.dirname(__file__), "apple_video.mp4")
british_museum_video = "vggsfm_code/examples/videos/british_museum_video.mp4"

# os.path.join(os.path.dirname(__file__), "british_museum_video.mp4")
cake_video = "vggsfm_code/examples/videos/cake_video.mp4"

bonsai_video = "vggsfm_code/examples/videos/bonsai_video.mp4"

# os.path.join(os.path.dirname(__file__), "cake_video.mp4")



# apple_images = glob.glob(f'vggsfm_code/examples/apple/images/*')
bonsai_images = glob.glob(f'vggsfm_code/examples/bonsai/images/*')
cake_images = glob.glob(f'vggsfm_code/examples/cake/images/*')
british_museum_images = glob.glob(f'vggsfm_code/examples/british_museum/images/*')




with gr.Blocks() as demo:
    gr.Markdown("# 🎨 VGGSfM: Visual Geometry Grounded Deep Structure From Motion")
    
    gr.Markdown("""
    <div style="text-align: left;"> 
    <p>Welcome to <a href="https://vggsfm.github.io/" target="_blank">VGGSfM</a> demo! 
    This space demonstrates 3D reconstruction from input image frames. </p> 
    <p>To get started quickly, you can click on our <strong> examples (the bottom of the page) </strong>. If you want to reconstruct your own data, simply: </p> 
    <ul style="display: inline-block; text-align: left;"> 
        <li>upload the images (.jpg, .png, etc.), or </li> 
        <li>upload a video (.mp4, .mov, etc.) </li> 
    </ul> 
    <p>The reconstruction should take <strong> up to 1 minute </strong>. If both images and videos are uploaded, the demo will only reconstruct the uploaded images. By default, we extract <strong> 1 image frame per second from the input video </strong>. To prevent crashes on the Hugging Face space, we currently limit reconstruction to the first 20 image frames. </p> 
    <p>SfM methods are designed for <strong> rigid/static reconstruction </strong>. When dealing with dynamic/moving inputs, these methods may still work by focusing on the rigid parts of the scene. However, to ensure high-quality results, it is better to minimize the presence of moving objects in the input data. </p> 
    <p>If you meet any problem, feel free to create an issue in our <a href="https://github.com/facebookresearch/vggsfm" target="_blank">GitHub Repo</a> ⭐</p> 
    <p>(Please note that running reconstruction on Hugging Face space is slower than on a local machine.) </p> 
    </div>
    """)

    with gr.Row():
        with gr.Column(scale=1):
            input_video = gr.Video(label="Input video", interactive=True)
            input_images = gr.File(file_count="multiple", label="Input Images", interactive=True)
            num_query_images = gr.Slider(minimum=1, maximum=10, step=1, value=5, label="Number of query images (key frames)",
                                         info="More query images usually lead to better reconstruction at lower speeds. If the viewpoint differences between your images are minimal, you can set this value to 1. ")
            num_query_points = gr.Slider(minimum=512, maximum=4096, step=1, value=1024, label="Number of query points",
                                         info="More query points usually lead to denser reconstruction at lower speeds.")
        
        with gr.Column(scale=3):
            reconstruction_output = gr.Model3D(label="Reconstruction", height=520)
            log_output = gr.Textbox(label="Log")

    with gr.Row():
        submit_btn = gr.Button("Reconstruct", scale=1)

        # submit_btn = gr.Button("Reconstruct", scale=1, elem_attributes={"style": "background-color: blue; color: white;"})
        clear_btn = gr.ClearButton([input_video, input_images, num_query_images, num_query_points, reconstruction_output, log_output], scale=1)
    
    
    examples = [
        [british_museum_video, british_museum_images, 2, 4096],
        [bonsai_video, bonsai_images, 3, 2048],
        [cake_video, cake_images, 3, 2048],
    ]
    
    gr.Examples(examples=examples, 
                inputs=[input_video, input_images, num_query_images, num_query_points],
                outputs=[reconstruction_output, log_output],  # Provide outputs
                fn=vggsfm_demo,  # Provide the function
                cache_examples=True,
                )

    submit_btn.click(
        vggsfm_demo,
        [input_video, input_images, num_query_images, num_query_points],
        [reconstruction_output, log_output],
        concurrency_limit=1
    )

    # demo.launch(debug=True, share=True)
    demo.queue(max_size=20).launch(show_error=True, share=True)
    # demo.queue(max_size=20, concurrency_count=1).launch(debug=True, share=True)
########################################################################################################################

# else:
#     import glob
#     files = glob.glob(f'vggsfm_code/examples/cake/images/*', recursive=True)
#     vggsfm_demo(files, None, None)

    
# demo.queue(max_size=20, concurrency_count=1).launch(debug=True, share=True)