Update app.py

app.py

@@ -1,7 +1,5 @@
 #!/usr/bin/env python
-
 import pathlib
-
 import gradio as gr
 import matplotlib as mpl
 import numpy as np
@@ -15,8 +13,8 @@ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 image_processor = DepthProImageProcessorFast.from_pretrained("apple/DepthPro-hf")
 model = DepthProForDepthEstimation.from_pretrained("apple/DepthPro-hf").to(device)
 
-cmap = mpl.colormaps.get_cmap("Spectral_r")
-
+# Changed from Spectral_r to gray colormap
+cmap = mpl.colormaps.get_cmap("gray")
 
 @spaces.GPU(duration=20)
 @torch.inference_mode()
@@ -24,27 +22,28 @@ def run(image: PIL.Image.Image) -> tuple[tuple[PIL.Image.Image, PIL.Image.Image]
     inputs = image_processor(images=image, return_tensors="pt").to(device)
     outputs = model(**inputs)
     post_processed_output = image_processor.post_process_depth_estimation(
-        outputs,
-        target_sizes=[(image.height, image.width)],
+        outputs, target_sizes=[(image.height, image.width)],
     )
+    
     depth_raw = post_processed_output[0]["predicted_depth"]
     depth_min = depth_raw.min().item()
     depth_max = depth_raw.max().item()
-
+    
     inverse_depth = 1 / depth_raw
-
     normalized_inverse_depth = (inverse_depth - inverse_depth.min()) / (inverse_depth.max() - inverse_depth.min())
     normalized_inverse_depth = normalized_inverse_depth * 255.0
     normalized_inverse_depth = normalized_inverse_depth.detach().cpu().numpy()
     normalized_inverse_depth = PIL.Image.fromarray(normalized_inverse_depth.astype("uint8"))
-
+    
+    # For grayscale, we can either use the normalized_inverse_depth directly as grayscale
+    # or apply the gray colormap - using colormap for consistency with original code
     colored_inverse_depth = PIL.Image.fromarray(
         (cmap(np.array(normalized_inverse_depth))[:, :, :3] * 255).astype(np.uint8)
     )
-
+    
     field_of_view = post_processed_output[0]["field_of_view"].item()
     focal_length = post_processed_output[0]["focal_length"].item()
-
+    
     return (
         (image, colored_inverse_depth),
         f"{field_of_view:.2f}",
@@ -53,7 +52,6 @@ def run(image: PIL.Image.Image) -> tuple[tuple[PIL.Image.Image, PIL.Image.Image]
         f"{depth_max:.2f}",
     )
 
-
 with gr.Blocks(css="style.css") as demo:
     gr.Markdown("# DepthPro")
     with gr.Row():
@@ -62,11 +60,12 @@ with gr.Blocks(css="style.css") as demo:
             run_button = gr.Button()
         with gr.Column():
             output_image = ImageSlider()
-            with gr.Row():
-                output_field_of_view = gr.Textbox(label="Field of View")
-                output_focal_length = gr.Textbox(label="Focal Length")
-                output_depth_min = gr.Textbox(label="Depth Min")
-                output_depth_max = gr.Textbox(label="Depth Max")
+    with gr.Row():
+        output_field_of_view = gr.Textbox(label="Field of View")
+        output_focal_length = gr.Textbox(label="Focal Length")
+        output_depth_min = gr.Textbox(label="Depth Min")
+        output_depth_max = gr.Textbox(label="Depth Max")
+    
     gr.Examples(
         examples=sorted(pathlib.Path("images").glob("*.jpg")),
         inputs=input_image,
@@ -79,7 +78,7 @@ with gr.Blocks(css="style.css") as demo:
             output_depth_max,
         ],
     )
-
+    
     run_button.click(
         fn=run,
         inputs=input_image,
@@ -93,4 +92,4 @@ with gr.Blocks(css="style.css") as demo:
     )
 
 if __name__ == "__main__":
-    demo.queue().launch()
+    demo.queue().launch()