Update app.py

app.py

@@ -95,12 +95,15 @@ def infer():
     video_url = "https://download.pytorch.org/tutorial/pexelscom_pavel_danilyuk_basketball_hd.mp4"
     video_path = Path(tempfile.mkdtemp()) / "basketball.mp4"
     _ = urlretrieve(video_url, video_path)
-
+   
     frames, _, _ = read_video(str(video_path), output_format="TCHW")
-    print(f"FRAME BEFORE: {frames[100]}")
+    print(f"FRAME BEFORE stack: {frames[100]}")
+    
     img1_batch = torch.stack([frames[100]])
     img2_batch = torch.stack([frames[101]])
-    print(f"FRAME AFTER: {img1_batch}")
+    
+    print(f"FRAME AFTER stack: {img1_batch}")
+    
     weights = Raft_Large_Weights.DEFAULT
     transforms = weights.transforms()
 
@@ -175,6 +178,9 @@ def infer():
     #print(flow_imgs)
 
     predicted_flow = list_of_flows[-1][0]
+    print(f"predicted flow dtype = {predicted_flows.dtype}")
+    print(f"predicted flow shape = {predicted_flows.shape}")
+    
     flow_img = flow_to_image(predicted_flow).to("cpu")
 #     output_folder = "/tmp/"  # Update this to the folder of your choice
     write_jpeg(flow_img, f"predicted_flow.jpg")
@@ -196,7 +202,7 @@ def infer():
     # display the PIL image
     #img.show()
     img.save('frame_input.jpg')
-    res = get_warp_res('frame_input.jpg', "predicted_flow.jpg", 'warped.png')
+    #res = get_warp_res('frame_input.jpg', "predicted_flow.jpg", 'warped.png')
     #print(res)
     return "done", "predicted_flow.jpg", ["flofile.flo"], 'frame_input.jpg'
 ####################################