Update app.py

app.py

@@ -68,7 +68,7 @@ def warpImage(im, vx, vy, cast_uint8=True):
     function to warp images with different dimensions
     '''
     print(f"WARP IM INPUT SHAPE: {im.shape}")
-    im.reshape([3, 520, 960]))
+    
     height2, width2, nChannels = im.shape
     print(f"WARP vx INPUT SHAPE: {vx.shape}")
     height1, width1 = vx.shape
@@ -104,8 +104,8 @@ def warpImage(im, vx, vy, cast_uint8=True):
 
 def get_warp_res(fname_image, fname_flow, fname_output='warped.png'):
     print(f"FNAME IMAGE: {fname_image}")
-    #im2 = imread(fname_image)
-    im2 = fname_image
+    im2 = imread(fname_image)
+    #im2 = fname_image
     print(f"FNAME IMAGE READED: {im2}")
     flow = fname_flow.cpu().detach().numpy()
     im_warped, _ = warpImage(im2, flow[:, :, 0], flow[:, :, 1])
@@ -212,11 +212,11 @@ def infer():
     
     # convert the tensor to PIL image using above transform
     img = transform(frames[100])
-    
+    img = img.resize((520, 960))
     # display the PIL image
     #img.show()
     img.save('frame_input.jpg')
-    res = get_warp_res(frames[100], predicted_flow, fname_output='warped.png')
+    res = get_warp_res('frame_input.jpg', predicted_flow, fname_output='warped.png')
     #print(res)
     return "done", "predicted_flow.jpg", ["flofile.flo"], 'frame_input.jpg'
 ####################################