app.py

import cv2
import numpy as np
import csv
import math
import torch
import tempfile
import os
import gradio as gr

# Set up device for torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")

# Try to load the RAFT model from torch.hub.
# If it fails (e.g. due to repository structure changes), we will fall back to OpenCV optical flow.
try:
    # The trust_repo parameter might prompt for confirmation; set it to True.
    raft_model = torch.hub.load("princeton-vl/RAFT", "raft_small", pretrained=True, trust_repo=True)
    raft_model = raft_model.to(device)
    raft_model.eval()
    print("RAFT model loaded successfully.")
except Exception as e:
    print("Error loading RAFT model:", e)
    print("Falling back to OpenCV optical flow for motion CSV generation.")
    raft_model = None

def generate_motion_csv(video_file, output_csv=None):
    """
    Generates a CSV file with motion data (columns: frame, mag, ang, zoom) from an input video.
    If the RAFT model is available, it uses it to compute optical flow; otherwise, it falls back to
    OpenCV's Farneback optical flow.
    
    Args:
        video_file (str): Path to the input video.
        output_csv (str): Optional output CSV file path. If None, a temporary file is created.
    
    Returns:
        output_csv (str): Path to the generated CSV file.
    """
    if output_csv is None:
        temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.csv')
        output_csv = temp_file.name
        temp_file.close()
    
    cap = cv2.VideoCapture(video_file)
    if not cap.isOpened():
        raise ValueError("Could not open video file for CSV generation.")
    
    with open(output_csv, 'w', newline='') as csvfile:
        fieldnames = ['frame', 'mag', 'ang', 'zoom']
        writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
        writer.writeheader()
        
        ret, first_frame = cap.read()
        if not ret:
            raise ValueError("Cannot read first frame from video.")
        
        if raft_model is not None:
            # Convert the first frame to RGB and then to a torch tensor.
            first_frame_rgb = cv2.cvtColor(first_frame, cv2.COLOR_BGR2RGB)
            prev_tensor = torch.from_numpy(first_frame_rgb).permute(2, 0, 1).float().unsqueeze(0) / 255.0
            prev_tensor = prev_tensor.to(device)
        else:
            prev_gray = cv2.cvtColor(first_frame, cv2.COLOR_BGR2GRAY)
        
        frame_idx = 1
        while True:
            ret, frame = cap.read()
            if not ret:
                break

            if raft_model is not None:
                curr_frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
                curr_tensor = torch.from_numpy(curr_frame_rgb).permute(2, 0, 1).float().unsqueeze(0) / 255.0
                curr_tensor = curr_tensor.to(device)
                with torch.no_grad():
                    flow_low, flow_up = raft_model(prev_tensor, curr_tensor, iters=20, test_mode=True)
                flow = flow_up[0].permute(1, 2, 0).cpu().numpy()
                prev_tensor = curr_tensor.clone()
            else:
                curr_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
                flow = cv2.calcOpticalFlowFarneback(prev_gray, curr_gray, None,
                                                    pyr_scale=0.5, levels=3, winsize=15,
                                                    iterations=3, poly_n=5, poly_sigma=1.2, flags=0)
                prev_gray = curr_gray

            # Compute median magnitude and angle of the optical flow.
            mag, ang = cv2.cartToPolar(flow[..., 0], flow[..., 1], angleInDegrees=True)
            median_mag = np.median(mag)
            median_ang = np.median(ang)
            
            # Compute a "zoom factor": fraction of pixels moving away from the center.
            h, w = flow.shape[:2]
            center_x, center_y = w / 2, h / 2
            x_coords, y_coords = np.meshgrid(np.arange(w), np.arange(h))
            x_offset = x_coords - center_x
            y_offset = y_coords - center_y
            dot = flow[..., 0] * x_offset + flow[..., 1] * y_offset
            zoom_factor = np.count_nonzero(dot > 0) / (w * h)
            
            writer.writerow({
                'frame': frame_idx,
                'mag': median_mag,
                'ang': median_ang,
                'zoom': zoom_factor
            })
            
            frame_idx += 1

    cap.release()
    print(f"Motion CSV generated: {output_csv}")
    return output_csv

def read_motion_csv(csv_filename):
    """
    Reads a motion CSV file (with columns: frame, mag, ang, zoom) and computes a cumulative
    offset per frame (the negative cumulative displacement) for stabilization.
    
    Returns:
        A dictionary mapping frame numbers to (dx, dy) offsets.
    """
    motion_data = {}
    cumulative_dx = 0.0
    cumulative_dy = 0.0
    with open(csv_filename, 'r') as csvfile:
        reader = csv.DictReader(csvfile)
        for row in reader:
            frame_num = int(row['frame'])
            mag = float(row['mag'])
            ang = float(row['ang'])
            rad = math.radians(ang)
            dx = mag * math.cos(rad)
            dy = mag * math.sin(rad)
            cumulative_dx += dx
            cumulative_dy += dy
            motion_data[frame_num] = (-cumulative_dx, -cumulative_dy)
    return motion_data

def stabilize_video_using_csv(video_file, csv_file, zoom=1.0, output_file=None):
    """
    Stabilizes the input video using motion data from the CSV file.
    
    Args:
        video_file (str): Path to the input video.
        csv_file (str): Path to the motion CSV file.
        zoom (float): Zoom factor to apply before stabilization (default: 1.0).
        output_file (str): Path for the output stabilized video. If None, a temporary file is created.
    
    Returns:
        output_file (str): Path to the stabilized video file.
    """
    motion_data = read_motion_csv(csv_file)
    
    cap = cv2.VideoCapture(video_file)
    if not cap.isOpened():
        raise ValueError("Could not open video file for stabilization.")
    
    fps = cap.get(cv2.CAP_PROP_FPS)
    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
    
    if output_file is None:
        temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.mp4')
        output_file = temp_file.name
        temp_file.close()
    
    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
    out = cv2.VideoWriter(output_file, fourcc, fps, (width, height))
    
    frame_idx = 1
    while True:
        ret, frame = cap.read()
        if not ret:
            break
        
        if zoom != 1.0:
            zoomed_frame = cv2.resize(frame, None, fx=zoom, fy=zoom, interpolation=cv2.INTER_LINEAR)
            zoomed_h, zoomed_w = zoomed_frame.shape[:2]
            start_x = max((zoomed_w - width) // 2, 0)
            start_y = max((zoomed_h - height) // 2, 0)
            frame = zoomed_frame[start_y:start_y+height, start_x:start_x+width]
        
        dx, dy = motion_data.get(frame_idx, (0, 0))
        transform = np.array([[1, 0, dx],
                              [0, 1, dy]], dtype=np.float32)
        stabilized_frame = cv2.warpAffine(frame, transform, (width, height))
        
        out.write(stabilized_frame)
        frame_idx += 1
    
    cap.release()
    out.release()
    print(f"Stabilized video saved to: {output_file}")
    return output_file

def process_video_ai(video_file, zoom):
    """
    Gradio interface function:
      - Generates motion data (CSV) from the input video using an AI model (RAFT, if available).
      - Stabilizes the video based on the generated motion data.
    
    Returns:
        Tuple containing the original video file path and the stabilized video file path.
    """
    if isinstance(video_file, dict):
        video_file = video_file.get("name", None)
    if video_file is None:
        raise ValueError("Please upload a video file.")
    
    # Generate motion CSV using the AI model (or fallback) for optical flow.
    csv_file = generate_motion_csv(video_file)
    # Stabilize the video using the generated CSV.
    stabilized_path = stabilize_video_using_csv(video_file, csv_file, zoom=zoom)
    return video_file, stabilized_path

# Build the Gradio UI.
with gr.Blocks() as demo:
    gr.Markdown("# AI-Powered Video Stabilization")
    gr.Markdown("Upload a video and select a zoom factor. The system will automatically generate motion data (video.flow.csv) using an AI model (RAFT, if available) and then stabilize the video.")
    
    with gr.Row():
        with gr.Column():
            video_input = gr.Video(label="Input Video")
            zoom_slider = gr.Slider(minimum=1.0, maximum=2.0, step=0.1, value=1.0, label="Zoom Factor")
            process_button = gr.Button("Process Video")
        with gr.Column():
            original_video = gr.Video(label="Original Video")
            stabilized_video = gr.Video(label="Stabilized Video")
    
    process_button.click(
        fn=process_video_ai,
        inputs=[video_input, zoom_slider],
        outputs=[original_video, stabilized_video]
    )

demo.launch()