Spaces:
Running
Running
| import gradio as gr | |
| import cv2 | |
| import numpy as np | |
| from PIL import Image | |
| import torch | |
| import torch.nn.functional as F | |
| from torchvision.transforms import Compose | |
| import tempfile | |
| from depth_anything.dpt import DPT_DINOv2 | |
| from depth_anything.util.transform import Resize, NormalizeImage, PrepareForNet | |
| css = """ | |
| #img-display-container { | |
| max-height: 50vh; | |
| } | |
| #img-display-input { | |
| max-height: 40vh; | |
| } | |
| #img-display-output { | |
| max-height: 40vh; | |
| } | |
| """ | |
| 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](), [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(), | |
| ]) | |
| 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').style(height="auto") | |
| depth_image = gr.Image(label="Depth Map", elem_id='img-display-output') | |
| raw_file = gr.File(label="16-bit raw depth (can be considered as disparity)") | |
| submit = gr.Button("Submit") | |
| def on_submit(image): | |
| 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) | |
| with torch.no_grad(): | |
| 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 [colored_depth, tmp.name] | |
| submit.click(on_submit, inputs=[input_image], outputs=[depth_image, raw_file]) | |
| examples = gr.Examples(examples=["examples/flower.png", "examples/roller_coaster.png", "examples/hall.png", "examples/car.png", "examples/person.png"], | |
| inputs=[input_image]) | |
| if __name__ == '__main__': | |
| demo.queue().launch() | |