Update app.py

app.py

@@ -557,32 +557,6 @@ def draw_mask(o, b, v, d, evt: gr.EventData):
     return gr.ImageEditor(value=d)
 
 
-def findNormals(format):
-    global depths
-    d_im = cv2.cvtColor(cv2.imread(depths[frame_selected]).astype(np.uint8), cv2.COLOR_BGR2GRAY)
-    zy, zx = np.gradient(d_im)  
-    # You may also consider using Sobel to get a joint Gaussian smoothing and differentation
-    # to reduce noise
-    #zx = cv2.Sobel(d_im, cv2.CV_64F, 1, 0, ksize=5)     
-    #zy = cv2.Sobel(d_im, cv2.CV_64F, 0, 1, ksize=5)
-
-    if format == "opengl":
-        zy = -zy
-        
-    normal = np.dstack((np.ones_like(d_im), -zy, -zx))
-    n = np.linalg.norm(normal, axis=2)
-    normal[:, :, 0] /= n
-    normal[:, :, 1] /= n
-    normal[:, :, 2] /= n
-
-    # offset and rescale values to be in 0-255
-    normal += 1
-    normal /= 2
-    normal *= 255
-
-    return (normal[:, :, ::-1]).astype(np.uint8)
-
-
 load_model="""
 async(c, o, p, d, n, m)=>{
   var intv = setInterval(function(){
@@ -891,11 +865,6 @@ with gr.Blocks(css=css, js=js) as demo:
               mouse.input(fn=draw_mask, show_progress="minimal", inputs=[boffset, bsize, mouse, output_mask], outputs=[output_mask])
               reset.click(fn=reset_mask, inputs=[output_mask], outputs=[output_mask])
 
-            normals_out = gr.Image(label="Normal map", interactive=False)
-            format_normals = gr.Radio(choices=["directx", "opengl"])
-            find_normals = gr.Button("Find normals")
-            find_normals.click(fn=findNormals, inputs=[format_normals], outputs=[normals_out])
-
         with gr.Column():
             model_type = gr.Dropdown([("small", "vits"), ("base", "vitb"), ("large", "vitl"), ("giant", "vitg")], type="value", value="vits", label='Model Type')
             processed_video = gr.Video(label="Output Video", format="mp4", interactive=False)