import numpy as np
import cv2
from skimage.metrics import structural_similarity as ssim
from skimage.filters import sobel
from sklearn.metrics import mutual_info_score


# Helper to compute mutual information between two grayscale images
def evaluate_mutual_information(img1_gray, img2_gray):
    hist_2d, _, _ = np.histogram2d(img1_gray.ravel(), img2_gray.ravel(), bins=256)
    pxy = hist_2d / float(np.sum(hist_2d))
    px = np.sum(pxy, axis=1)
    py = np.sum(pxy, axis=0)
    px_py = np.outer(px, py)
    nzs = pxy > 0
    mi = np.sum(pxy[nzs] * np.log(pxy[nzs] / px_py[nzs]))
    return mi


# Compute SSIM between two grayscale images
def evaluate_registration_ssim(img1_gray, img2_gray):
    return ssim(img1_gray, img2_gray)


# Entropy of grayscale image (fusion quality)
def evaluate_fusion_entropy(fusion_img):
    gray = cv2.cvtColor(fusion_img, cv2.COLOR_RGB2GRAY)
    hist = cv2.calcHist([gray], [0], None, [256], [0, 256])
    hist = hist.ravel() / hist.sum()
    entropy = -np.sum(hist * np.log2(hist + 1e-9))
    return entropy


# Edge strength using Sobel (fusion quality)
def evaluate_fusion_edges(fusion_img):
    gray = cv2.cvtColor(fusion_img, cv2.COLOR_RGB2GRAY)
    edges = sobel(gray.astype(float) / 255.0)
    return np.mean(edges)


# SSIM between fused image and one of the sources
def evaluate_fusion_ssim(fusion_img, reference_img):
    fusion_gray = cv2.cvtColor(fusion_img, cv2.COLOR_RGB2GRAY)
    ref_gray = cv2.cvtColor(reference_img, cv2.COLOR_RGB2GRAY)
    return ssim(fusion_gray, ref_gray)


# Return all in one place (stub images would be required to test)
def summarize_evaluation(img1_gray, img2_gray, fusion_img, ref_img_for_ssim):
    return {
        "Registration SSIM": evaluate_registration_ssim(img1_gray, img2_gray),
        "Mutual Information": evaluate_mutual_information(img1_gray, img2_gray),
        "Fusion Entropy": evaluate_fusion_entropy(fusion_img),
        "Fusion Edge Strength": evaluate_fusion_edges(fusion_img),
        "Fusion SSIM (vs Ref)": evaluate_fusion_ssim(fusion_img, ref_img_for_ssim),
    }

# 将所有评价封装成一个高层函数 evaluate_all
def evaluate_all(img1_gray, img2_gray, fusion_img, ref_img_for_ssim, verbose=True):
    """
    评估图像配准和融合质量的通用函数
    :param img1_gray: 可见光灰度图像（原图）
    :param img2_gray: 红外灰度图像（配准后）
    :param fusion_img: 融合图像（RGB）
    :param ref_img_for_ssim: 可见光RGB图，用于对比SSIM
    :param verbose: 是否打印结果
    :return: dict 评价指标结果
    """
    results = summarize_evaluation(img1_gray, img2_gray, fusion_img, ref_img_for_ssim)
    if verbose:
        print("图像评价指标如下：")
        for k, v in results.items():
            print(f"{k}: {v:.4f}")
    return results