# featureExtraction.py

import cv2
import numpy as np
import os

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1'

from tensorflow.keras import backend as K

from globalVars import HEIGHT, NUM_PRESET_SPEEDS, WIDTH

def scale_crf(crf):
    return crf / 51

def scale_speed_preset(speed):
    return speed / NUM_PRESET_SPEEDS


def extract_edge_features(frame):
    """
    Extract edge features using Canny edge detection.

    Args:
    - frame (ndarray): Image frame.

    Returns:
    - ndarray: Edge feature map.
    """
    edges = cv2.Canny(frame, threshold1=100, threshold2=200)
    return edges.astype(np.float32) / 255.0

def extract_histogram_features(frame, bins=64):
    """
    Extract histogram features from a frame with 3 channels.

    Args:
    - frame (ndarray): Image frame with shape (height, width, 3).
    - bins (int): Number of bins for the histogram.

    Returns:
    - ndarray: Normalized histogram feature vector.
    """
    feature_vector = []
    for channel in range(3):
        histogram, _ = np.histogram(frame[:,:,channel].flatten(), bins=bins, range=[0, 255])
        normalized_histogram = histogram.astype(np.float32) / frame[:,:,channel].size
        feature_vector.extend(normalized_histogram)

    return np.array(feature_vector)

def psnr(y_true, y_pred):
    max_pixel = 1.0
    return 10.0 * K.log((max_pixel ** 2) / (K.mean(K.square(y_pred - y_true)))) / K.log(10.0)


def preprocess_frame(frame):
    #Preprocesses a single frame, cropping it if needed
    
    # Check frame dimensions and resize if necessary
    if frame.shape[:2] != (HEIGHT, WIDTH):
        frame = cv2.resize(frame, (WIDTH, HEIGHT), interpolation=cv2.INTER_LINEAR)
    
    # Scale frame to [0, 1]
    compressed_frame = frame / 255.0

    return compressed_frame