import tensorflow as tf
import numpy as np
import cv2
from video_compression_model import VideoCompressionModel

# Constants
CHUNK_SIZE = 24  # Adjust based on available memory and video resolution
COMPRESSED_VIDEO_FILE = 'compressed_video.mp4'
MAX_FRAMES = 24  # Limit the number of frames processed

# Load the trained model
model = tf.keras.models.load_model('models/model.keras', custom_objects={'VideoCompressionModel': VideoCompressionModel})

# Load the uncompressed video
UNCOMPRESSED_VIDEO_FILE = 'test_data/training_video.mkv'

def load_frame_from_video(video_file, frame_num):
    cap = cv2.VideoCapture(video_file)
    cap.set(cv2.CAP_PROP_POS_FRAMES, frame_num)
    ret, frame = cap.read()
    if not ret:
        return None
    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB).astype(np.float32) / 255.0  # Normalize and convert to float32
    cap.release()
    
    #display_frame = np.clip(frame * 255.0, 0, 255).astype(np.uint8)
    #cv2.imshow("uncomp", display_frame)
    #cv2.waitKey(0)  # Add this line to hold the display window until a key is pressed

    
    return frame

def predict_frame(uncompressed_frame, model, crf_value, preset_speed_value):
    crf_array = np.array([crf_value])
    preset_speed_array = np.array([preset_speed_value])
    
    compressed_frame = model.predict({
        "frame": np.array([uncompressed_frame]), 
        "crf": crf_array, 
        "preset_speed": preset_speed_array
    })
    return compressed_frame[0]

cap = cv2.VideoCapture(UNCOMPRESSED_VIDEO_FILE)
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
cap.release()

if MAX_FRAMES != 0 and total_frames > MAX_FRAMES:
    total_frames = MAX_FRAMES

crf_value = 25.0  # Example CRF value
preset_speed_value = 2  # Index for "fast" in our defined list

height, width = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)), int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
fourcc = cv2.VideoWriter_fourcc(*'H264')
out = cv2.VideoWriter(COMPRESSED_VIDEO_FILE, fourcc, 24.0, (width, height))

for i in range(total_frames):
    uncompressed_frame = load_frame_from_video(UNCOMPRESSED_VIDEO_FILE, frame_num=i)
    compressed_frame = predict_frame(uncompressed_frame, model, crf_value, preset_speed_value)
    
    compressed_frame = np.clip(compressed_frame * 255.0, 0, 255).astype(np.uint8)
    compressed_frame = cv2.cvtColor(compressed_frame, cv2.COLOR_RGB2BGR)
    out.write(compressed_frame)
    cv2.imshow("output", compressed_frame)

out.release()
print("Compression completed.")