import numpy as np



def averagePooling(image, block_size):
    image_height, image_width = image.shape
    block_height, block_width = block_size
    num_blocks_height = image_height // block_height
    num_blocks_width = image_width // block_width
    averaged_image = np.zeros_like(image)

    for i in range(num_blocks_height):
        for j in range(num_blocks_width):
            row_start = i * block_height
            row_end = (i + 1) * block_height
            col_start = j * block_width
            col_end = (j + 1) * block_width
            block_mean = np.mean(image[row_start:row_end, col_start:col_end])
            averaged_image[row_start:row_end, col_start:col_end] = block_mean

    return averaged_image


def maxPooling(image, block_size):
    image_height, image_width = image.shape
    block_height, block_width = block_size
    num_blocks_height = image_height // block_height
    num_blocks_width = image_width // block_width
    maxed_image = np.zeros_like(image)

    for i in range(num_blocks_height):
        for j in range(num_blocks_width):
            row_start = i * block_height
            row_end = (i + 1) * block_height
            col_start = j * block_width
            col_end = (j + 1) * block_width
            block_max = np.max(image[row_start:row_end, col_start:col_end])
            maxed_image[row_start:row_end, col_start:col_end] = block_max

    return maxed_image


def xyOverlap(targetBoundingBox, otherBoundingBox):
    x_overlap = max(0, min(targetBoundingBox[0] + targetBoundingBox[2], otherBoundingBox[0] + otherBoundingBox[2]) - max(targetBoundingBox[0], otherBoundingBox[0]))
    y_overlap = max(0, min(targetBoundingBox[1] + targetBoundingBox[3], otherBoundingBox[1] + otherBoundingBox[3]) - max(targetBoundingBox[1], otherBoundingBox[1]))
    overlap_area = x_overlap * y_overlap
    target_area = targetBoundingBox[2] * targetBoundingBox[3]
    if (overlap_area / target_area) > 0.3:
        result = 1
    else:
        result = 0
    return result