updated demo for longer sequences

demo.py

@@ -20,21 +20,9 @@
 def load_image(imfile):
     img = np.array(Image.open(imfile)).astype(np.uint8)
     img = torch.from_numpy(img).permute(2, 0, 1).float()
-    return img
+    return img[None].to(DEVICE)
 
 
-def load_image_list(image_files):
-    images = []
-    for imfile in sorted(image_files):
-        images.append(load_image(imfile))
-
-    images = torch.stack(images, dim=0)
-    images = images.to(DEVICE)
-
-    padder = InputPadder(images.shape)
-    return padder.pad(images)[0]
-
-
 def viz(img, flo):
     img = img[0].permute(1,2,0).cpu().numpy()
     flo = flo[0].permute(1,2,0).cpu().numpy()
@@ -43,6 +31,10 @@ def viz(img, flo):
     flo = flow_viz.flow_to_image(flo)
     img_flo = np.concatenate([img, flo], axis=0)
 
+    # import matplotlib.pyplot as plt
+    # plt.imshow(img_flo / 255.0)
+    # plt.show()
+
     cv2.imshow('image', img_flo[:, :, [2,1,0]]/255.0)
     cv2.waitKey()
 
@@ -58,11 +50,14 @@ def demo(args):
     with torch.no_grad():
         images = glob.glob(os.path.join(args.path, '*.png')) + \
                  glob.glob(os.path.join(args.path, '*.jpg'))
+
+        images = sorted(images)
+        for imfile1, imfile2 in zip(images[:-1], images[1:]):
+            image1 = load_image(imfile1)
+            image2 = load_image(imfile2)
 
-        images = load_image_list(images)
-        for i in range(images.shape[0]-1):
-            image1 = images[i,None]
-            image2 = images[i+1,None]
+            padder = InputPadder(image1.shape)
+            image1, image2 = padder.pad(image1, image2)
 
             flow_low, flow_up = model(image1, image2, iters=20, test_mode=True)
             viz(image1, flow_up)