Python实现图像八大特效（毛玻璃、浮雕、油画、马赛克等）

Python实现图像八大特效：毛玻璃、浮雕、油画、马赛克、素描、怀旧、流年、卡通。可自由切换调用。程序运行截图（毛玻璃特效）：
![在这里插入图片描述](https://img-blog.csdnimg.cn/95ef4754827941ac91f6ddc79d064fee.png#pic_center)

import math
import cv2
import numpy as np

class ImgProcess:
    def __init__(self, img) -> None:
        self.src = cv2.imread(img)
        self.gray = cv2.cvtColor(self.src, cv2.COLOR_BGR2GRAY)
        self.h, self.w = self.src.shape[:2]
# 毛玻璃特效
    def glass(self):
        glassImg = np.zeros((self.h, self.w, 3), np.uint8)
        for i in range(self.h - 6):
            for j in range(self.w - 6):
                index = int(np.random.random() * 6)
                glassImg[i, j] = self.src[i + index, j + index]
        return glassImg

    # 浮雕特效
    def relief(self):
        reliefImg = np.zeros((self.h, self.w, 1), np.uint8)
        for i in range(self.h):
            for j in range(self.w - 1):
                edge = int(self.gray[i, j]) - int(self.gray[i, j + 1])    # 得到边缘
                val = edge + 120                                # 产生立体感
                if val > 255:
                    val = 255
                if val < 0:
                    val = 0
                reliefImg[i, j] = val
        return reliefImg

    #油画特效
    def oil(self):
        oilImg = np.zeros((self.h, self.w, 3), np.uint8)
        for i in range(2, self.h - 2):
            for j in range(2, self.w - 2):
                # 量化向量
                quant = np.zeros(8, np.uint8)
                # 4x4卷积核
                for k in range(-2, 2):
                    for t in range(-2, 2):
                        level = int(self.gray[i + k, j + t] / 32)
                        # 量化计数
                        quant[level] = quant[level] + 1

                # 求最大量化值及其索引
                valMax = max(quant)
                valIndex = list(quant).index(valMax)

                # 像素平均
                for k in range(-2, 2):
                    for t in range(-2, 2):
                        if self.gray[i + k, j + t] >= (valIndex * 32) 
                            and self.gray[i + k, j + t] <= ((valIndex + 1) * 32):
                            (b, g, r) = self.src[i + k, j + t]
                oilImg[i, j] = (b, g, r)
        return oilImg

    # 马赛克特效
    def mask(self):
        maskImg = np.zeros((self.h, self.w, 3), np.uint8)
        for i in range(self.h - 5):
            for j in range(self.w - 5):
                if i%5==0 and j%5==0 :
                    for k in range(5):
                        for t in range(5):
                            (b, g, r) = self.src[i, j]
                            maskImg[i + k, j + t] = (b, g, r)
        return maskImg

    # 素描特效
    def sketch(self):
        temp = 255 - self.gray
        gauss = cv2.GaussianBlur(temp, (21, 21), 0)
        inverGauss = 255 - gauss
        return cv2.divide(self.gray, inverGauss, scale = 127.0)

    # 怀旧特效
    def old(self):
        oldImg = np.zeros((self.h, self.w, 3), np.uint8)
        for i in range(self.h):
            for j in range(self.w):
                b = 0.272 * self.src[i, j][2] + 0.534 * self.src[i, j][1] + 0.131 * self.src[i, j][0]
                g = 0.349 * self.src[i, j][2] + 0.686 * self.src[i, j][1] + 0.168 * self.src[i, j][0]
                r = 0.393 * self.src[i, j][2] + 0.769 * self.src[i, j][1] + 0.189 * self.src[i, j][0]
                if b > 255:
                    b = 255
                if g > 255:
                    g = 255
                if r > 255:
                    r = 255
                oldImg[i, j] = np.uint8((b, g, r))
        return oldImg

    # 流年特效
    def fleet(self):
        fleetImg = np.zeros((self.h, self.w, 3), np.uint8)
        for i in range(self.h):
            for j in range(0, self.w):
                b = math.sqrt(self.src[i, j][0]) * 14
                g = self.src[i, j][1]
                r = self.src[i, j][2]
                if b > 255:
                    b = 255
                fleetImg[i, j] = np.uint8((b, g, r))
        return fleetImg

    # 卡通特效
    def cartoon(self):
        num = 7   # 双边滤波数目
        for i in range(num):
            cv2.bilateralFilter(self.src, d = 9, sigmaColor = 5, sigmaSpace = 3)
        median = cv2.medianBlur(self.gray, 7)
        edge = cv2.adaptiveThreshold(median, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, blockSize = 5, C = 2)
        edge = cv2.cvtColor(edge, cv2.COLOR_GRAY2RGB)
        return cv2.bitwise_and(self.src, edge)

process = ImgProcess('1.png')   #导入图片
glassImg = process.glass()
cv2.imshow("glass", glassImg)
cv2.waitKey(delay = 0)

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