Update app.py

app.py

@@ -62,51 +62,46 @@ Please refer to our [paper](https://arxiv.org/abs/2406.09414), [project page](ht
 
 @spaces.GPU
 def predict_depth(image):
-    #return model.infer_image(image)
     return pipe(Image.fromarray(image))["depth"]
 
-with gr.Blocks(css=css) as demo:
+with gr.Blocks(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)
+        depth_image_slider = gr.Image(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",)
+    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()
+        depth = predict_depth(image)
 
-        h, w = image.shape[:2]
-
-        depth = predict_depth(image[:, :, ::-1])
-
-        raw_depth = Image.fromarray(depth)
+        # Convert depth to images and save
+        raw_depth = Image.fromarray(depth.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.astype(np.uint8)
-        colored_depth = (cmap(depth)[:, :, :3] * 255).astype(np.uint8)
+        # Normalize depth for color mapping
+        normalized_depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
+        normalized_depth = normalized_depth.astype(np.uint8)
+        colored_depth = (cmap(normalized_depth)[:, :, :3] * 255).astype(np.uint8)
 
-        gray_depth = Image.fromarray(depth)
+        gray_depth = Image.fromarray(normalized_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]
+        return [(Image.fromarray(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 = sorted(os.listdir('assets/examples'))
     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)