Update app.py

app.py

@@ -60,41 +60,10 @@ def write_flo(flow, filename):
 
 #warp using scipy
 def warp_image(im, flow):
-    """
-    Use optical flow to warp image to the next
-    :param im: image to warp
-    :param flow: optical flow
-    :return: warped image
-    """
-    from scipy import interpolate
-    image_height = im.shape[0]
-    image_width = im.shape[1]
-    flow_height = flow.shape[0]
-    flow_width = flow.shape[1]
-    n = image_height * image_width
-    (iy, ix) = np.mgrid[0:image_height, 0:image_width]
-    (fy, fx) = np.mgrid[0:flow_height, 0:flow_width]
-    fx = fx.astype(np.float64)
-    fy = fy.astype(np.float64)
-    fx += flow[:,:,0]
-    fy += flow[:,:,1]
-    mask = np.logical_or(fx <0 , fx > flow_width)
-    mask = np.logical_or(mask, fy < 0)
-    mask = np.logical_or(mask, fy > flow_height)
-    fx = np.minimum(np.maximum(fx, 0), flow_width)
-    fy = np.minimum(np.maximum(fy, 0), flow_height)
-    points = np.concatenate((ix.reshape(n,1), iy.reshape(n,1)), axis=1)
-    xi = np.concatenate((fx.reshape(n, 1), fy.reshape(n,1)), axis=1)
-    warp = np.zeros((image_height, image_width, im.shape[2]))
-    for i in range(im.shape[2]):
-        channel = im[:, :, i]
-        values = channel.reshape(n, 1)
-        new_channel = interpolate.griddata(points, values, xi, method='cubic')
-        new_channel = np.reshape(new_channel, [flow_height, flow_width])
-        new_channel[mask] = 1
-        warp[:, :, i] = new_channel.astype(np.uint8)
-
-    return warp.astype(np.uint8)
+    h, w = flow.shape[:2]
+    flow[:,:,0] += np.arange(w)
+    flow[:,:,1] += np.arange(h)[:,np.newaxis]
+    prevImg = cv2.remap(curImg, flow, None, cv.INTER_LINEAR)
 
 def infer():
     video_url = "https://download.pytorch.org/tutorial/pexelscom_pavel_danilyuk_basketball_hd.mp4"