app.py

import torch
import cv2
import numpy as np

# Load your model (assuming it is a PyTorch model)
model = torch.load('./data/model.pt', weights_only=False)

model.eval()

# Open video file (input video)
input_video = cv2.VideoCapture('input_video.mp4')

# Get the frame width, height, and frames per second (fps) from the input video
frame_width = int(input_video.get(cv2.CAP_PROP_FRAME_WIDTH))
frame_height = int(input_video.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = input_video.get(cv2.CAP_PROP_FPS)

# Define the output video writer
fourcc = cv2.VideoWriter_fourcc(*'mp4v')  # You can change this to any codec
output_video = cv2.VideoWriter('output_video.mp4', fourcc, fps, (frame_width, frame_height))

while True:
    # Read a frame from the input video
    ret, frame = input_video.read()
    if not ret:
        break  # End of video

    # Preprocess the frame if necessary (depends on your model)
    # For example, convert to tensor and normalize if required
    frame_tensor = torch.tensor(frame).float().unsqueeze(0)  # Add batch dimension

    # Pass the frame through the model
    with torch.no_grad():
        output = model(frame_tensor)  # Adjust based on your model's requirements

    # Postprocess the output if necessary (depends on your model's output format)
    output_frame = output.squeeze(0).cpu().numpy()  # Remove batch dimension and convert to NumPy

    # Convert the model output to a valid image format (if necessary)
    output_frame = np.uint8(output_frame)

    # Write the frame to the output video
    output_video.write(output_frame)

# Release resources
input_video.release()
output_video.release()

cv2.destroyAllWindows()