Minesweeper_v1/class1/demo7.py

# -*- coding: utf-8 -*-
import cv2
import os
import numpy as np
from PIL import Image
from psd_tools import PSDImage


def image_specifications(image_path, image2_path):
    # 打开图像文件
    image = Image.open(image_path)
    image2 = Image.open(image2_path)
    # 获取图像尺寸（宽度和高度）
    width, height = image2.size
    width2, height2 = image.size
    width_difference = abs(width - width2)
    height_difference = abs(height - height2)
    if width_difference > 20 or height_difference > 20:
        print('图片未按照规格修改，请修改在测试！')


def file_inspect():
    # 文件路径
    png_file = 'K:/work/mine_clearance/class1/userfiles/result1.png'
    psd_file = 'K:/work/mine_clearance/class1/userfiles/result1.psd'
    result_png = 'K:/work/mine_clearance/class1/result4.png'
    # 检查文件是否存在
    if os.path.exists(png_file) and os.path.exists(psd_file):
        if psd_to_png(psd_file, result_png):
            if image_similarity(png_file, result_png) < 0.99:
                print('result2.psd文件与result2.png不匹配')
                return 0
            return 1
        return 0
    else:
        print('缺少文件')
        return 0


def image_similarity(image1, image2):
    # 读取图片
    img1 = cv2.imread(image1)
    img2 = cv2.imread(image2)
    # 将图片调整为相同尺寸
    img1 = cv2.resize(img1, img2.shape[:2][::-1])
    # 计算直方图差异
    hist1 = cv2.calcHist([img1], [0, 1, 2], None, [8, 8, 8], [0, 256, 0, 256, 0, 256])
    hist2 = cv2.calcHist([img2], [0, 1, 2], None, [8, 8, 8], [0, 256, 0, 256, 0, 256])
    similarity = cv2.compareHist(hist1, hist2, cv2.HISTCMP_CORREL)
    return similarity


def psd_to_png(psd_path, png_path):
    try:
        psd_image = PSDImage.open(psd_path)
        image_pil = psd_image.topil()
        image_np = cv2.cvtColor(np.array(image_pil), cv2.COLOR_RGB2BGR)
        cv2.imwrite(png_path, image_np)
        return 1
    except Exception as e:
        print("psd文件与png文件相同大小")
        return 0


def split_image(image_path, split_path):
    # 读取图片
    image = cv2.imread(image_path)
    # 获取图片的宽度和高度
    height, width, _ = image.shape
    # 计算每个小块的宽度和高度
    block_width = width // 2
    block_height = height // 2

    # 切分图片并保存
    upper_left = image[0:block_height, 0:block_width]
    cv2.imwrite(split_path[0], upper_left)
    upper_right = image[0:block_height, block_width:width]
    cv2.imwrite(split_path[1], upper_right)
    lower_left = image[block_height:height, 0:block_width]
    cv2.imwrite(split_path[2], lower_left)
    lower_right = image[block_height:height, block_width:width]
    cv2.imwrite(split_path[3], lower_right)


def calculate_similarity(img1_path, img2_path):
    # 加载图像
    img1 = cv2.imread(img1_path, cv2.IMREAD_GRAYSCALE)
    img2 = cv2.imread(img2_path, cv2.IMREAD_GRAYSCALE)

    # 创建ORB对象
    orb = cv2.ORB_create()

    # 检测和计算图像特征
    kp1, des1 = orb.detectAndCompute(img1, None)
    kp2, des2 = orb.detectAndCompute(img2, None)

    # 创建BFMatcher匹配器
    bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)

    # 使用KNN算法进行特征匹配
    matches = bf.match(des1, des2)

    # 对匹配结果进行排序
    matches = sorted(matches, key=lambda x: x.distance)

    # 计算相似度
    similarity = len(matches) / max(len(kp1), len(kp2)) * 100

    return similarity


if __name__ == '__main__':
    # 图像文件路径
    image1 = 'K:/work/mine_clearance/class1/result2.png'
    image2 = 'K:/work/mine_clearance/class1/userfiles/result1.png'
    if file_inspect():
        image_specifications(image1, image2)
        data = []
        data_path = []
        data_path.append(
            ['K:/work/mine_clearance/class1/userfiles/upper_left1.png', 'K:/work/mine_clearance/class1/userfiles/upper_right1.png', 'K:/work/mine_clearance/class1/userfiles/lower_left1.png', 'K:/work/mine_clearance/class1/userfiles/lower_right1.png'])
        data_path.append(
            ['K:/work/mine_clearance/class1/userfiles/upper_left2.png', 'K:/work/mine_clearance/class1/userfiles/upper_right2.png', 'K:/work/mine_clearance/class1/userfiles/lower_left2.png', 'K:/work/mine_clearance/class1/userfiles/lower_right2.png'])
        # 调用函数对图片进行切分
        split_image(image1, data_path[0])
        split_image(image2, data_path[1])
        flage = 1
        # 计算图像相似度
        for i in range(4):
            data.append(calculate_similarity(data_path[0][i], data_path[1][i]))
            if data[i] < 60 or data[i] > 99:
                flage = 0
        # # 显示相似度

        if flage == 1:
            print('文字特效制作成功')
        else:
            print('还需要进一步修改')
            print("图像左上相似度: {:.2f}%".format(data[0]))
            print("图像右上度: {:.2f}%".format(data[1]))
            print("图像左下相似度: {:.2f}%".format(data[2]))
            print("图像右下相似度: {:.2f}%".format(data[3]))