Add local demo

app.py

@@ -0,0 +1,96 @@
+import gradio as gr
+import cv2
+import numpy as np
+import os
+from PIL import Image
+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(),
+])
+
+@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)
+    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('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, raw_file], fn=on_submit, cache_examples=False)
+
+
+if __name__ == '__main__':
+    demo.queue().launch()