app.py

import gradio as gr
import cv2
import matplotlib
import numpy as np
import os
from PIL import Image
import spaces
import torch
import torch.nn.functional as F
import open3d as o3d
import tempfile
from gradio_imageslider import ImageSlider
from huggingface_hub import hf_hub_download

from ppd.utils.set_seed import set_seed
from ppd.utils.align_depth_func import recover_metric_depth_ransac
from ppd.utils.depth2pcd import depth2pcd
from moge.model.v2 import MoGeModel 
from ppd.models.ppd import PixelPerfectDepth

css = """
#img-display-container {
    max-height: 100vh;
}
#img-display-input {
    max-height: 100vh;
}
#img-display-output {
    max-height: 100vh;
}
#download {
    height: 62px;
}

#img-display-output .image-slider-image {
    object-fit: contain !important;
    width: 100% !important;
    height: 100% !important;
}
"""

set_seed(666)

def main(share=True):
    print("Initializing Pixel-Perfect Depth Demo...")
    
    # DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
    default_steps = 10
    model = PixelPerfectDepth(sampling_steps=default_steps)
    ckpt_path = hf_hub_download(
        repo_id="gangweix/Pixel-Perfect-Depth",
        filename="ppd.pth",
        repo_type="model"
    )
    state_dict = torch.load(ckpt_path, map_location="cpu")
    model.load_state_dict(state_dict, strict=False)
    model = model.to(DEVICE).eval()

    moge_model = MoGeModel.from_pretrained("Ruicheng/moge-2-vitl-normal").to(DEVICE).eval()

    cmap = matplotlib.colormaps.get_cmap('Spectral')

    title = "# Pixel-Perfect Depth"
    description = """Official demo for **Pixel-Perfect Depth**.
    Please refer to our [paper](), [project page](https://pixel-perfect-depth.github.io), and [github](https://github.com/gangweix/pixel-perfect-depth) for more details."""

    @spaces.GPU
    def predict_depth(image, denoise_steps):
        depth, resize_image = model.infer_image(image, sampling_steps=denoise_steps)
        return depth, resize_image

    @spaces.GPU
    def predict_moge_depth(image):
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        image = torch.tensor(image / 255, dtype=torch.float32, device=DEVICE).permute(2, 0, 1)
        metric_depth, mask, intrinsics = moge_model.infer(image)
        metric_depth[~mask] = metric_depth[mask].max()
        return metric_depth, mask, intrinsics

    def on_submit(image, denoise_steps, apply_filter):

        H, W = image.shape[:2]

        ppd_depth, resize_image = predict_depth(image[:, :, ::-1], denoise_steps)

        resize_H, resize_W = resize_image.shape[:2]

        # moge provide metric depth and intrinsics
        moge_depth, mask, intrinsics = predict_moge_depth(resize_image)

        # relative depth -> metric depth
        metric_depth = recover_metric_depth_ransac(ppd_depth.squeeze().cpu().numpy(), moge_depth, mask)
        intrinsics[0, 0] *= resize_W 
        intrinsics[1, 1] *= resize_H
        intrinsics[0, 2] *= resize_W
        intrinsics[1, 2] *= resize_H

        # metric depth -> point cloud
        pcd = depth2pcd(metric_depth, intrinsics, color=cv2.cvtColor(resize_image, cv2.COLOR_BGR2RGB), input_mask=mask, ret_pcd=True)
        if apply_filter:
            cl, ind = pcd.remove_statistical_outlier(nb_neighbors=20, std_ratio=3.0)
            pcd = pcd.select_by_index(ind)

        # save pcd to temporary .ply for Model3D
        tmp_ply = tempfile.NamedTemporaryFile(suffix='.ply', delete=False)
        o3d.io.write_point_cloud(tmp_ply.name, pcd)

        depth = cv2.resize(ppd_depth, (W, H), interpolation=cv2.INTER_LINEAR)

        # save raw depth (npy)
        tmp_raw_depth = tempfile.NamedTemporaryFile(suffix='.npy', delete=False)
        np.save(tmp_raw_depth.name, depth)

        depth_vis = (depth - depth.min()) / (depth.max() - depth.min()+1e-5) * 255.0
        depth_vis = depth_vis.astype(np.uint8)
        colored_depth = (cmap(depth_vis)[:, :, :3] * 255).astype(np.uint8)

        split_region = np.ones((image.shape[0], 50, 3), dtype=np.uint8) * 255
        combined_result = cv2.hconcat([image[:, :, ::-1], split_region, colored_depth[:, :, ::-1]])
        tmp_concat = tempfile.NamedTemporaryFile(suffix='.png', delete=False)
        cv2.imwrite(tmp_concat.name, combined_result)

        return [(image, colored_depth), tmp_ply.name, tmp_concat.name, tmp_raw_depth.name, tmp_ply.name]

    with gr.Blocks(css=css) as demo:
        gr.Markdown(title)
        gr.Markdown(description)
        gr.Markdown("### Depth Prediction demo")

        with gr.Row():
            # Left: input image + settings
            with gr.Column():
                input_image = gr.Image(label="Input Image", image_mode="RGB", type='numpy', elem_id='img-display-input')
                with gr.Accordion(label="Settings", open=False):
                    denoise_steps = gr.Slider(label="Denoising Steps", minimum=1, maximum=50, value=10, step=1)
                    apply_filter = gr.Checkbox(label="Apply filter points", value=True)
                submit_btn = gr.Button(value="Predict Depth")

            # Right: 3D point cloud + depth
            with gr.Column():
                with gr.Tabs():
                    with gr.Tab("3D View"):
                        model_3d = gr.Model3D(display_mode="solid", label="3D Point Map", clear_color=[1,1,1,1], height="60vh")
                    with gr.Tab("Depth"):
                        depth_map = ImageSlider(label="Depth Map with Slider View", elem_id='img-display-output', position=0.5)
    
        concat_file = gr.File(label="Concatenated visualization (image+depth)", elem_id="image-depth-download")
        raw_depth_file = gr.File(label="Raw depth output (saved as .npy)", elem_id="download")
        pcd_file = gr.File(label="Point Cloud (.ply)", elem_id="download-ply")

        submit_btn.click(
            on_submit, 
            inputs=[input_image, denoise_steps, apply_filter], 
            outputs=[depth_map, model_3d, concat_file, raw_depth_file, pcd_file]
        )

        example_files = os.listdir('assets/examples')
        example_files.sort()
        example_files = [os.path.join('assets/examples', filename) for filename in example_files]
        examples = gr.Examples(
            examples=example_files, 
            inputs=[input_image], 
            outputs=[depth_map, model_3d, concat_file, raw_depth_file, pcd_file], 
            fn=on_submit
        )

    demo.queue().launch(share=share) 

if __name__ == '__main__':
    main(share=True)