app.py

###########################################################################################
# Code based on the Hugging Face Space of Depth Anything v2
# https://huggingface.co/spaces/depth-anything/Depth-Anything-V2/blob/main/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 tempfile
from gradio_imageslider import ImageSlider
from huggingface_hub import hf_hub_download

from GeoWizard.geowizard.models.geowizard_pipeline import DepthNormalEstimationPipeline
from GeoWizard.geowizard.models.unet_2d_condition import UNet2DConditionModel
from diffusers import DDIMScheduler, AutoencoderKL
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection


css = """
#img-display-container {
    max-height: 100vh;
}
#img-display-input {
    max-height: 80vh;
}
#img-display-output {
    max-height: 80vh;
}
#download {
    height: 62px;
}
"""
DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
checkpoint_path = "GonzaloMG/geowizard-e2e-ft"
vae = AutoencoderKL.from_pretrained(checkpoint_path, subfolder='vae')
scheduler = DDIMScheduler.from_pretrained(checkpoint_path, timestep_spacing="trailing", subfolder='scheduler')
image_encoder = CLIPVisionModelWithProjection.from_pretrained(checkpoint_path, subfolder="image_encoder")
feature_extractor = CLIPImageProcessor.from_pretrained(checkpoint_path, subfolder="feature_extractor")
unet = UNet2DConditionModel.from_pretrained(checkpoint_path, subfolder="unet")
pipe = DepthNormalEstimationPipeline(vae=vae,
                            image_encoder=image_encoder,
                            feature_extractor=feature_extractor,
                            unet=unet,
                            scheduler=scheduler)
pipe = pipe.to(DEVICE)
pipe.unet.eval()

title = "# End-to-End Fine-Tuned GeoWizard"
description = """ Please refer to our [paper](https://arxiv.org/abs/2409.11355) and [GitHub](https://vision.rwth-aachen.de/diffusion-e2e-ft) for more details."""
    
@spaces.GPU
def predict(image, processing_res_choice):
    with torch.no_grad():
        pipe_out = pipe(image, denoising_steps=1, ensemble_size=1, noise="zeros", processing_res=processing_res_choice, match_input_res=True)
    # depth
    depth_pred = pipe_out.depth_np
    depth_colored = pipe_out.depth_colored
    # normals
    normal_pred = pipe_out.normal_np
    normal_colored = pipe_out.normal_colored
    return depth_pred, depth_colored, normal_pred, normal_colored

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

    with gr.Row():
        input_image = gr.Image(label="Input Image", type='numpy', elem_id='img-display-input')
        processing_res_choice = gr.Radio(
                [
                    ("Native", 0),
                    ("Recommended", 768),
                ],
                label="Processing resolution",
                value=0,
            )
        model_choice = gr.Dropdown(
            list(models.keys()), label="Select Model", value=list(models.keys())[0]
        )

    submit = gr.Button(value="Compute Depth and Normals")
        
    with gr.Row():
        depth_image_slider = ImageSlider(label="Depth Map with Slider View", elem_id='img-display-output', position=0.5)
        normal_image_slider = ImageSlider(label="Normal Map with Slider View", elem_id='normal-display-output', position=0.5)


    colored_depth_file  = gr.File(label="Colored Depth Image", elem_id="download")
    gray_depth_file     = gr.File(label="Grayscale Depth Map", elem_id="download")
    raw_depth_file      = gr.File(label="Raw Depth Data (.npy)", elem_id="download")
    colored_normal_file = gr.File(label="Colored Normal Image", elem_id="download")
    raw_normal_file     = gr.File(label="Raw Normal Data (.npy)", elem_id="download")

    
    # with gr.Row():
    #     input_image = gr.Image(label="Input Image", type='numpy', elem_id='img-display-input')
    #     depth_image_slider = ImageSlider(label="Depth Map with Slider View", elem_id='img-display-output', position=0.5)

    # with gr.Row():
    #     submit = gr.Button(value="Compute Depth")
    #     processing_res_choice = gr.Radio(
    #             [
    #                 ("Recommended (768)", 768),
    #                 ("Native", 0),
    #             ],
    #             label="Processing resolution",
    #             value=768,
    #         )

    # gray_depth_file = gr.File(label="Grayscale depth map", elem_id="download",)
    # raw_file = gr.File(label="Raw Depth Data (.npy)", elem_id="download")

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

    def on_submit(image, processing_res_choice):

        if image is None:
            print("No image uploaded.")
            return None

        pil_image = Image.fromarray(image.astype('uint8'))
        depth_pred, depth_colored, normal_pred, normal_colored = predict(pil_image, processing_res, model_choice, current_model)
    
        # Save depth and normals npy data
        tmp_npy_depth = tempfile.NamedTemporaryFile(suffix='.npy', delete=False)
        np.save(tmp_npy_depth.name, depth_pred)
        tmp_npy_normal = tempfile.NamedTemporaryFile(suffix='.npy', delete=False)
        np.save(tmp_npy_normal.name, normal_pred)
    
        # Save the grayscale depth map
        depth_gray = (depth_pred * 65535.0).astype(np.uint16)
        tmp_gray_depth = tempfile.NamedTemporaryFile(suffix='.png', delete=False)
        Image.fromarray(depth_gray).save(tmp_gray_depth.name, mode="I;16")
    
        # Save the colored depth and normals maps
        tmp_colored_depth = tempfile.NamedTemporaryFile(suffix='.png', delete=False)
        depth_colored.save(tmp_colored_depth.name)
        tmp_colored_normal = tempfile.NamedTemporaryFile(suffix='.png', delete=False)
        normal_colored.save(tmp_colored_normal.name)
    
        return (
            (pil_image, depth_colored),  # For ImageSlider: (base image, overlay image)
            (pil_image, normal_colored), # For gr.Image
            tmp_colored_depth.name,      # File outputs
            tmp_gray_depth.name,
            tmp_npy_depth.name,
            tmp_colored_normal.name,
            tmp_npy_normal.name
        )

        # pil_image = Image.fromarray(image.astype('uint8'))
        # depth_npy, depth_colored = predict_depth(pil_image, processing_res_choice)
    
        # # Save the npy data (raw depth map)
        # tmp_npy_depth = tempfile.NamedTemporaryFile(suffix='.npy', delete=False)
        # np.save(tmp_npy_depth.name, depth_npy)
    
        # # Save the grayscale depth map
        # depth_gray = (depth_npy * 65535.0).astype(np.uint16)
        # tmp_gray_depth = tempfile.NamedTemporaryFile(suffix='.png', delete=False)
        # Image.fromarray(depth_gray).save(tmp_gray_depth.name, mode="I;16")
    
        # # Save the colored depth map
        # tmp_colored_depth = tempfile.NamedTemporaryFile(suffix='.png', delete=False)
        # depth_colored.save(tmp_colored_depth.name)
   
        # return [(image, depth_colored),  tmp_gray_depth.name, tmp_npy_depth.name]

    submit.click(on_submit, inputs=[input_image, processing_res_choice], outputs=[depth_image_slider,normal_image_slider,colored_depth_file,gray_depth_file,raw_depth_file,colored_normal_file,raw_normal_file])

    example_files = os.listdir('assets/examples')
    example_files.sort()
    example_files = [os.path.join('assets/examples', filename) for filename in example_files]
    example_files = [[image, 768] for image in example_files]
    examples = gr.Examples(examples=example_files, inputs=[input_image, processing_res_choice], outputs=[depth_image_slider,normal_image_slider,colored_depth_file,gray_depth_file,raw_depth_file,colored_normal_file,raw_normal_file], fn=on_submit)


if __name__ == '__main__':
    demo.queue().launch(share=True)