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142 lines
5.0 KiB
142 lines
5.0 KiB
# -*- coding: utf-8 -*-
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import cv2
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import os
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import numpy as np
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from PIL import Image
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from psd_tools import PSDImage
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def image_specifications(image_path, image2_path):
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# 打开图像文件
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image = Image.open(image_path)
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image2 = Image.open(image2_path)
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# 获取图像尺寸（宽度和高度）
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width, height = image2.size
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width2, height2 = image.size
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width_difference = abs(width - width2)
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height_difference = abs(height - height2)
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if width_difference > 20 or height_difference > 20:
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print('图片未按照规格修改，请修改在测试！')
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def file_inspect():
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# 文件路径
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png_file = 'K:/work/mine_clearance/class1/userfiles/result1.png'
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psd_file = 'K:/work/mine_clearance/class1/userfiles/result1.psd'
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result_png = 'K:/work/mine_clearance/class1/result4.png'
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# 检查文件是否存在
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if os.path.exists(png_file) and os.path.exists(psd_file):
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if psd_to_png(psd_file, result_png):
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if image_similarity(png_file, result_png) < 0.99:
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print('result2.psd文件与result2.png不匹配')
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return 0
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return 1
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return 0
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else:
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print('缺少文件')
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return 0
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def image_similarity(image1, image2):
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# 读取图片
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img1 = cv2.imread(image1)
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img2 = cv2.imread(image2)
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# 将图片调整为相同尺寸
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img1 = cv2.resize(img1, img2.shape[:2][::-1])
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# 计算直方图差异
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hist1 = cv2.calcHist([img1], [0, 1, 2], None, [8, 8, 8], [0, 256, 0, 256, 0, 256])
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hist2 = cv2.calcHist([img2], [0, 1, 2], None, [8, 8, 8], [0, 256, 0, 256, 0, 256])
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similarity = cv2.compareHist(hist1, hist2, cv2.HISTCMP_CORREL)
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return similarity
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def psd_to_png(psd_path, png_path):
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try:
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psd_image = PSDImage.open(psd_path)
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image_pil = psd_image.topil()
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image_np = cv2.cvtColor(np.array(image_pil), cv2.COLOR_RGB2BGR)
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cv2.imwrite(png_path, image_np)
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return 1
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except Exception as e:
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print("psd文件与png文件相同大小")
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return 0
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def split_image(image_path, split_path):
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# 读取图片
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image = cv2.imread(image_path)
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# 获取图片的宽度和高度
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height, width, _ = image.shape
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# 计算每个小块的宽度和高度
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block_width = width // 2
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block_height = height // 2
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# 切分图片并保存
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upper_left = image[0:block_height, 0:block_width]
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cv2.imwrite(split_path[0], upper_left)
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upper_right = image[0:block_height, block_width:width]
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cv2.imwrite(split_path[1], upper_right)
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lower_left = image[block_height:height, 0:block_width]
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cv2.imwrite(split_path[2], lower_left)
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lower_right = image[block_height:height, block_width:width]
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cv2.imwrite(split_path[3], lower_right)
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def calculate_similarity(img1_path, img2_path):
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# 加载图像
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img1 = cv2.imread(img1_path, cv2.IMREAD_GRAYSCALE)
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img2 = cv2.imread(img2_path, cv2.IMREAD_GRAYSCALE)
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# 创建ORB对象
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orb = cv2.ORB_create()
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# 检测和计算图像特征
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kp1, des1 = orb.detectAndCompute(img1, None)
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kp2, des2 = orb.detectAndCompute(img2, None)
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# 创建BFMatcher匹配器
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bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
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# 使用KNN算法进行特征匹配
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matches = bf.match(des1, des2)
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# 对匹配结果进行排序
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matches = sorted(matches, key=lambda x: x.distance)
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# 计算相似度
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similarity = len(matches) / max(len(kp1), len(kp2)) * 100
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return similarity
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if __name__ == '__main__':
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# 图像文件路径
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image1 = 'K:/work/mine_clearance/class1/result2.png'
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image2 = 'K:/work/mine_clearance/class1/userfiles/result1.png'
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if file_inspect():
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image_specifications(image1, image2)
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data = []
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data_path = []
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data_path.append(
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['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'])
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data_path.append(
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['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'])
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# 调用函数对图片进行切分
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split_image(image1, data_path[0])
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split_image(image2, data_path[1])
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flage = 1
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# 计算图像相似度
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for i in range(4):
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data.append(calculate_similarity(data_path[0][i], data_path[1][i]))
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if data[i] < 60 or data[i] > 99:
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flage = 0
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# # 显示相似度
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if flage == 1:
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print('文字特效制作成功')
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else:
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print('还需要进一步修改')
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print("图像左上相似度: {:.2f}%".format(data[0]))
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print("图像右上度: {:.2f}%".format(data[1]))
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print("图像左下相似度: {:.2f}%".format(data[2]))
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print("图像右下相似度: {:.2f}%".format(data[3])) |