Update app.py

app.py

@@ -12,14 +12,13 @@ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 model = DPTForDepthEstimation.from_pretrained("Intel/dpt-swinv2-tiny-256", torch_dtype=torch.float32)
 model.eval()
 
-# Apply global unstructured pruning
 parameters_to_prune = [
     (module, "weight") for module in filter(lambda m: isinstance(m, (torch.nn.Conv2d, torch.nn.Linear)), model.modules())
 ]
 prune.global_unstructured(
     parameters_to_prune,
     pruning_method=prune.L1Unstructured,
-    amount=0.4,  # Prune 40% of weights
+    amount=0.4,
 )
 
 for module, _ in parameters_to_prune:
@@ -33,48 +32,41 @@ model = model.to(device)
 
 processor = DPTImageProcessor.from_pretrained("Intel/dpt-swinv2-tiny-256")
 
-color_map = cv2.applyColorMap(np.arange(256, dtype=np.uint8), cv2.COLORMAP_INFERNO)
-color_map = torch.from_numpy(color_map).to(device)
-
 def preprocess_image(image):
-    image = cv2.resize(image, (128, 72))
+    image = cv2.resize(image, (128, 128))
     image = torch.from_numpy(image).permute(2, 0, 1).unsqueeze(0).float().to(device)
     return image / 255.0
 
-def plot_depth_map(depth_map):
-    fig = plt.figure(figsize=(16, 9))  # Set figure size to 16:9
+def plot_depth_map(depth_map, azimuth):
+    fig = plt.figure(figsize=(16, 9))
     ax = fig.add_subplot(111, projection='3d')
     x, y = np.meshgrid(range(depth_map.shape[1]), range(depth_map.shape[0]))
     ax.plot_surface(x, y, depth_map, cmap='viridis')
-    ax.view_init(azim=90, elev=0)  # Rotate the view to face forward
+    ax.view_init(elev=90, azim=azimuth)  # Look down onto the depth map
     ax.set_zlim(0, 1)
     plt.close(fig)
-    return fig
+    
+    fig.canvas.draw()
+    img = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8)
+    img = img.reshape(fig.canvas.get_width_height()[::-1] + (3,))
+    
+    return img
 
 @torch.inference_mode()
-def process_frame(image):
+def process_frame(image, azimuth):
     if image is None:
         return None
     preprocessed = preprocess_image(image)
     predicted_depth = model(preprocessed).predicted_depth
     depth_map = predicted_depth.squeeze().cpu().numpy()
     
-    # Normalize depth map
     depth_map = (depth_map - depth_map.min()) / (depth_map.max() - depth_map.min())
     
-    # Create 3D plot
-    fig = plot_depth_map(depth_map)
-    
-    # Convert plot to image
-    fig.canvas.draw()
-    img = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8)
-    img = img.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-    
-    return img
+    return plot_depth_map(depth_map, azimuth)
 
 interface = gr.Interface(
     fn=process_frame,
-    inputs=gr.Image(sources="webcam", streaming=True),
+    inputs=[gr.Image(sources="webcam", streaming=True), gr.Slider(0, 360, step=1)],
     outputs="image",
     live=True
 )