# featureExtraction.py

import cv2
import numpy as np

from globalVars import HEIGHT, WIDTH


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 preprocess_frame(frame):
    # Check frame dimensions and resize if necessary
    if frame.shape[:2] != (HEIGHT, WIDTH):
        frame = cv2.resize(frame, (WIDTH, HEIGHT), interpolation=cv2.INTER_NEAREST)
    
    # Extract features
    edge_feature = extract_edge_features(frame)
    histogram_feature = extract_histogram_features(frame)
    
    histogram_feature_image = np.full((HEIGHT, WIDTH), histogram_feature.mean()) # Convert histogram feature to image-like shape
    combined_feature = np.stack([edge_feature, histogram_feature_image], axis=-1)

    compressed_frame = frame / 255.0  # Assuming the frame is uint8, scale to [0, 1]
    return compressed_frame