Update app.py

app.py

@@ -35,6 +35,7 @@ def get_video_dimension(filepath):
     return width, height, fps, frame_count
 
 def resize_video(input_vid, output_vid, width, height, fps):
+    print(f"RESIZING ...")
     # Open the input video file
     video = cv2.VideoCapture(input_vid)
 
@@ -61,9 +62,45 @@ def resize_video(input_vid, output_vid, width, height, fps):
     # Release the video objects
     video.release()
     output_video.release()
-
+    print(f"RESIZE VIDEO DONE!")
     return output_vid
 
+def normalize_and_save_video(input_video_path, output_video_path):
+    print(f"NORMALIZING ...")
+    cap = cv2.VideoCapture(input_video_path)
+
+    # Get video properties
+    frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    fps = cap.get(cv2.CAP_PROP_FPS)
+
+    # Create VideoWriter object to save the normalized video
+    fourcc = cv2.VideoWriter_fourcc(*'mp4v')  # Specify the codec (e.g., 'mp4v', 'XVID', 'MPEG')
+    out = cv2.VideoWriter(output_video_path, fourcc, fps, (width, height))
+
+    # Iterate through each frame in the video
+    for _ in range(frame_count):
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        # Convert frame to floating point
+        frame = frame.astype(np.float32)
+
+        # Normalize pixel values to the range [0, 1]
+        frame /= 255.0
+
+        # Write the normalized frame to the output video file
+        out.write(frame)
+
+    # Release the VideoCapture and VideoWriter objects
+    cap.release()
+    out.release()
+
+    print(f"NORMALIZE DONE!")
+    return output_video_path
+
 def chunkify(video_path, fps, nb_frames):
     chunks_array = []
 
@@ -164,6 +201,9 @@ def run_inference(prompt, video_path, condition, video_length):
     # Resize the video
     resized = resize_video(video_path, 'resized.mp4', 512, 512, target_fps)
 
+    # normalize pixels
+    normalized = normalize_and_save_video(resized, 'normalized.mp4')
+
     output_path = 'output/'
     os.makedirs(output_path, exist_ok=True)
             
@@ -171,16 +211,18 @@ def run_inference(prompt, video_path, condition, video_length):
     if os.path.exists(os.path.join(output_path, f"result.mp4")):
         # Delete the existing file
         os.remove(os.path.join(output_path, f"result.mp4"))
-    
+
+    print(f"RUNNING INFERENCE ...")
     if video_length > 12:
-        command = f"python inference.py --prompt '{prompt}' --condition '{condition}' --video_path '{resized}' --output_path '{output_path}' --temp_chunk_path 'result' --width 512 --height 512 --fps {target_fps} --video_length {video_length} --is_long_video"
+        command = f"python inference.py --prompt '{prompt}' --condition '{condition}' --video_path '{normalized}' --output_path '{output_path}' --temp_chunk_path 'result' --width 512 --height 512 --fps {target_fps} --video_length {video_length} --is_long_video"
     else:
-        command = f"python inference.py --prompt '{prompt}' --condition '{condition}' --video_path '{resized}' --output_path '{output_path}' --temp_chunk_path 'result'  --width 512 --height 512 --fps {target_fps} --video_length {video_length}"
+        command = f"python inference.py --prompt '{prompt}' --condition '{condition}' --video_path '{normalized}' --output_path '{output_path}' --temp_chunk_path 'result'  --width 512 --height 512 --fps {target_fps} --video_length {video_length}"
     subprocess.run(command, shell=True)
     
     # Construct the video path
     video_path_output = os.path.join(output_path, f"result.mp4")
 
+    print(f"FINISHED !")
     return "done", video_path_output