Update app.py

app.py

@@ -6,7 +6,7 @@ import shutil
 import time
 import ffmpeg
 import numpy as np
-from PIL import Image, ImageOps
+from PIL import Image
 import moviepy.editor as mp
 from infer import lotus, load_models
 import logging
@@ -33,21 +33,8 @@ def preprocess_video(video_path, target_fps=24):
 
     return video
 
-# Resize image while preserving aspect ratio and adding padding
-def resize_and_pad(image, target_size):
-    """Resize and pad an image to the target size while preserving aspect ratio."""
-    # Calculate the new size preserving aspect ratio
-    image.thumbnail(target_size, Image.LANCZOS)
-
-    # Create a new image with the target size and black background
-    new_image = Image.new("RGB", target_size)
-    new_image.paste(
-        image, ((target_size[0] - image.width) // 2, (target_size[1] - image.height) // 2)
-    )
-    return new_image
-
 # Process a single frame through the depth model
-def process_frame(frame, seed=0, target_size=(1024, 1024)):
+def process_frame(frame, seed=0):
     """Process a single frame and return depth map."""
     try:
         torch.cuda.empty_cache()  # Clear GPU cache
@@ -55,21 +42,10 @@ def process_frame(frame, seed=0, target_size=(1024, 1024)):
         # Convert frame to PIL Image
         image = Image.fromarray(frame).convert('RGB')
 
-        # Resize and pad image
-        input_image = resize_and_pad(image, target_size)
-
-        # Run inference
-        depth_map = lotus(input_image, 'depth', seed, device, pipe_g, pipe_d)
-
-        # Crop the output depth map back to original image size
-        width, height = image.size
-        left = (target_size[0] - width) // 2
-        top = (target_size[1] - height) // 2
-        right = left + width
-        bottom = top + height
-        depth_map_cropped = depth_map.crop((left, top, right, bottom))
+        # Run inference without resizing
+        depth_map = lotus(image, 'depth', seed, device, pipe_g, pipe_d)
 
-        return depth_map_cropped
+        return depth_map
 
     except Exception as e:
         logger.error(f"Error processing frame: {e}")