import gradio as gr import cv2 import numpy as np import os from PIL import Image import spaces import torch import torch.nn.functional as F from torchvision.transforms import Compose import tempfile from gradio_imageslider import ImageSlider from depth_anything.dpt import DPT_DINOv2 from depth_anything.util.transform import Resize, NormalizeImage, PrepareForNet css = """ #img-display-container { max-height: 100vh; } #img-display-input { max-height: 80vh; } #img-display-output { max-height: 80vh; } """ DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu' model = DPT_DINOv2(encoder='vitl', features=256, out_channels=[256, 512, 1024, 1024]).to(DEVICE).eval() model.load_state_dict(torch.load('checkpoints/depth_anything_vitl14.pth')) title = "# Depth Anything" description = """Official demo for **Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data**. Please refer to our [paper](https://arxiv.org/abs/2401.10891), [project page](https://depth-anything.github.io), or [github](https://github.com/LiheYoung/Depth-Anything) for more details.""" transform = Compose([ Resize( width=518, height=518, resize_target=False, keep_aspect_ratio=True, ensure_multiple_of=14, resize_method='lower_bound', image_interpolation_method=cv2.INTER_CUBIC, ), NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), PrepareForNet(), ]) @spaces.GPU @torch.no_grad() def predict_depth(model, image): return model(image) with gr.Blocks(css=css) as demo: gr.Markdown(title) gr.Markdown(description) gr.Markdown("### Depth Prediction demo") gr.Markdown("You can slide the output to compare the depth prediction with input image") 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,) raw_file = gr.File(label="16-bit raw depth (can be considered as disparity)") submit = gr.Button("Submit") def on_submit(image): original_image = image.copy() h, w = image.shape[:2] image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) / 255.0 image = transform({'image': image})['image'] image = torch.from_numpy(image).unsqueeze(0).to(DEVICE) depth = predict_depth(model, image) depth = F.interpolate(depth[None], (h, w), mode='bilinear', align_corners=False)[0, 0] raw_depth = Image.fromarray(depth.cpu().numpy().astype('uint16')) tmp = tempfile.NamedTemporaryFile(suffix='.png', delete=False) raw_depth.save(tmp.name) depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0 depth = depth.cpu().numpy().astype(np.uint8) colored_depth = cv2.applyColorMap(depth, cv2.COLORMAP_INFERNO)[:, :, ::-1] return [(original_image, colored_depth), tmp.name] submit.click(on_submit, inputs=[input_image], outputs=[depth_image_slider, raw_file]) example_files = os.listdir('examples') example_files.sort() example_files = [os.path.join('examples', filename) for filename in example_files] examples = gr.Examples(examples=example_files, inputs=[input_image], outputs=[depth_image_slider, raw_file], fn=on_submit, cache_examples=True) if __name__ == '__main__': demo.queue().launch()