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 depth_anything_v2.dpt import DepthAnythingV2 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' model_configs = { 'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]}, 'vitb': {'encoder': 'vitb', 'features': 128, 'out_channels': [96, 192, 384, 768]}, 'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]}, 'vitg': {'encoder': 'vitg', 'features': 384, 'out_channels': [1536, 1536, 1536, 1536]} } encoder2name = { 'vits': 'Small', 'vitb': 'Base', 'vitl': 'Large', 'vitg': 'Giant', # we are undergoing company review procedures to release our giant model checkpoint } encoder = 'vitl' model_name = encoder2name[encoder] model = DepthAnythingV2(**model_configs[encoder]) filepath = hf_hub_download(repo_id=f"depth-anything/Depth-Anything-V2-{model_name}", filename=f"depth_anything_v2_{encoder}.pth", repo_type="model") state_dict = torch.load(filepath, map_location="cpu") model.load_state_dict(state_dict) model = model.to(DEVICE).eval() title = "# Depth Anything V2" description = """Official demo for **Depth Anything V2**. Please refer to our [paper](https://arxiv.org/abs/2406.09414), [project page](https://depth-anything-v2.github.io), or [github](https://github.com/DepthAnything/Depth-Anything-V2) for more details.""" @spaces.GPU def predict_depth(image): return model.infer_image(image).cpu() with gr.Blocks(css=css) as demo: gr.Markdown(title) gr.Markdown(description) gr.Markdown("### Depth Prediction demo") 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) submit = gr.Button(value="Compute Depth") gray_depth_file = gr.File(label="Grayscale depth map", elem_id="download",) raw_file = gr.File(label="16-bit raw output (can be considered as disparity)", elem_id="download",) cmap = matplotlib.colormaps.get_cmap('Spectral_r') def on_submit(image): original_image = image.copy() h, w = image.shape[:2] depth = predict_depth(image[:, :, ::-1]) raw_depth = Image.fromarray(depth.numpy().astype('uint16')) tmp_raw_depth = tempfile.NamedTemporaryFile(suffix='.png', delete=False) raw_depth.save(tmp_raw_depth.name) depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0 depth = depth.numpy().astype(np.uint8) colored_depth = (cmap(depth)[:, :, :3] * 255).astype(np.uint8) gray_depth = Image.fromarray(depth) tmp_gray_depth = tempfile.NamedTemporaryFile(suffix='.png', delete=False) gray_depth.save(tmp_gray_depth.name) return [(original_image, colored_depth), tmp_gray_depth.name, tmp_raw_depth.name] submit.click(on_submit, inputs=[input_image], outputs=[depth_image_slider, gray_depth_file, raw_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_image_slider, gray_depth_file, raw_file], fn=on_submit) if __name__ == '__main__': demo.queue().launch(share=True)