Update app.py

app.py

@@ -1,82 +1,4 @@
-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 DepthAnything
-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'
-encoder = 'vitl' # can also be 'vitb' or 'vitl'
-model = DepthAnything.from_pretrained(f"LiheYoung/depth_anything_{encoder}14").to(DEVICE).eval()
-
-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'))
+  raw_depth = Image.fromarray(depth.cpu().numpy().astype('uint16'))
         tmp = tempfile.NamedTemporaryFile(suffix='.png', delete=False)
         raw_depth.save(tmp.name)
 
@@ -84,15 +6,4 @@ with gr.Blocks(css=css) as demo:
         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()
+        return [(original_image, colored_depth), tmp.name]