Update app.py

app.py

@@ -63,54 +63,30 @@ def write_flo(flow, filename):
     flow.tofile(f)
     f.close()
 
-def warpImage(im, vx, vy, cast_uint8=True):
-    '''
-    function to warp images with different dimensions
-    '''
-    print(f"WARP IM INPUT SHAPE: {im.shape}")
-    
-    height2, width2, nChannels = im.shape
-    print(f"WARP vx INPUT SHAPE: {vx.shape}")
-    height1, width1 = vx.shape
-
-    x = np.linspace(1, width2, width2)
-    y = np.linspace(1, height2, height2)
-    X = np.linspace(1, width1, width1)
-    Y = np.linspace(1, height1, height1)
-    xx, yy = np.meshgrid(x, y)
-    XX, YY = np.meshgrid(X, Y)
-    #XX = XX + vx
-    XX = np.concatenate([XX, vx])
-    #YY = YY + vy
-    YY = np.concatenate([YY, vy])
-    mask = (XX < 1) | (XX > width2) | (YY < 1) | (YY > height2)
-    XX = np.clip(XX, 1, width2)
-    YY = np.clip(XX, 1, height2)
-
-    warpI2 = np.zeros((height1, width1, nChannels))
-    for i in range(nChannels):
-        f = LinearNDInterpolator(x, y, im[:, :, i], 'linear')
-        foo = f(X, Y)
-        foo[mask] = 0.6
-        warpI2[:, :, i] = foo
-
-    mask = 1 - mask
-
-    if cast_uint8:
-        warpI2 = warpI2.astype(np.uint8)
-
-    return warpI2, mask
 
+def warp_flow(img, flow, mul=1.):
+    img = np.array(img.convert('RGB'))  
+    #flow = np.load(flow)
+    h, w = flow.shape[:2]
+    flow = flow.copy()
+    flow[:, :, 0] += np.arange(w)
+    flow[:, :, 1] += np.arange(h)[:, np.newaxis]
+    # print('flow stats', flow.max(), flow.min(), flow.mean())
+    # print(flow)
+    flow*=mul
+    # print('flow stats mul', flow.max(), flow.min(), flow.mean())
+    # res = cv2.remap(img, flow, None, cv2.INTER_LINEAR)
+    res = cv2.remap(img, flow, None, cv2.INTER_LANCZOS4)
+    
+return res
 
 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
     print(f"FNAME IMAGE READED: {im2.shape}")
     flow = fname_flow.cpu().detach().numpy()
     print(f"FNAME FLOW READED: {flow.shape}")
-    im_warped, _ = warpImage(im2, flow[:, :, 0], flow[:, :, 1])
-    #imwrite(fname_output, im_warped)
+    res = warp_flow(im2, flow, 1.)
     
 def infer():
     video_url = "https://download.pytorch.org/tutorial/pexelscom_pavel_danilyuk_basketball_hd.mp4"