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Author:XiaoMa
date:2021/11/16
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import cv2
import numpy as np
import math
import matplotlib.pyplot as plt
img0 = cv2.imread(‘E:\From Zhihu\For the desk\cvfifteen1.jpg’)
img1 = cv2.cvtColor(img0, cv2.COLOR_BGR2GRAY)
h, w = img0.shape[:2]
print(h, w)
cv2.imshow(“W0”, img0)
cv2.imshow(“W1”, img1)
cv2.waitKey(delay = 0)

毛玻璃特效

img2 = np.zeros((h – 6, w – 6, 3), np.uint8) #生成的全零矩阵思考到了随机数范畴，变小了
for i in range(0, h – 6): #避免上面的随机数超出边缘

for j in range(0, w - 6):
    index = int(np.random.random()*6)   #0~6的随机数
    (b, g, r) = img0[i + index, j + index]
    img2[i, j] = (b, g, r)

cv2.imshow(“W2”, img2)
cv2.waitKey(delay = 0)

浮雕特效(须要对灰度图像进行操作)

img3 = np.zeros((h, w, 3), np.uint8)
for i in range(0, h):

for j in range(0, w - 2):                #减2的成果和下面一样
    grayP0 = int(img1[i, j])
    grayP1 = int(img1[i, j + 2])         #取与前一个像素点相邻的点
    newP = grayP0 - grayP1 + 150         #失去差值，加一个常数能够减少浮雕立体感
    if newP > 255:
        newP = 255
    if newP < 0:
        newP = 0
    img3[i, j] = newP

cv2.imshow(“W3”, img3)
cv2.waitKey(delay = 0)

素描特效

img4 = 255 – img1 #对原灰度图像的像素点进行反转
blurred = cv2.GaussianBlur(img4, (21, 21), 0) #进行高斯含糊
inverted_blurred = 255 – blurred #反转
img4 = cv2.divide(img1, inverted_blurred, scale = 127.0) #灰度图像除以倒置的含糊图像失去铅笔素描画
cv2.imshow(“W4”, img4)
cv2.waitKey(delay = 0)

念旧特效

img5 = np.zeros((h, w, 3), np.uint8)
for i in range(0, h):

for j in range(0, w):
    B = 0.272 * img0[i, j][2] + 0.534 * img0[i, j][1] + 0.131 * img0[i, j][0]
    G = 0.349 * img0[i, j][2] + 0.686 * img0[i, j][1] + 0.168 * img0[i, j][0]
    R = 0.393 * img0[i, j][2] + 0.769 * img0[i, j][1] + 0.189 * img0[i, j][0]
    if B > 255:
        B = 255
    if G > 255:
        G = 255
    if R > 255:
        R = 255
    img5[i, j] = np.uint8((B, G, R))

cv2.imshow(“W5”, img5)
cv2.waitKey(delay = 0)

流年特效

img6 = np.zeros((h, w, 3), np.uint8)
for i in range(0, h):

for j in range(0, w):
    B = math.sqrt(img0[i, j][0]) *14       # B通道的数值开平方乘以参数14
    G = img0[i, j][1]
    R = img0[i, j][2]
    if B > 255:
        B = 255
    img6[i, j] = np.uint8((B, G, R))

cv2.imshow(“W6”, img6)
cv2.waitKey(delay = 0)

水波特效

img7 = np.zeros((h, w, 3), np.uint8)
wavelength = 20 #定义水波特效波长
amplitude = 30 #幅度
phase = math.pi / 4 #相位
centreX = 0.5 #水波中心点X
centreY = 0.5 #水波中心点Y
radius = min(h, w) / 2
icentreX = w*centreX #水波笼罩宽度
icentreY = h*centreY #水波笼罩高度
for i in range(0, h):

for j in range(0, w):
    dx = j - icentreX
    dy = i - icentreY
    distance = dx * dx + dy * dy
    if distance > radius * radius:
        x = j
        y = i
    else:
        # 计算水波区域
        distance = math.sqrt(distance)
        amount = amplitude * math.sin(distance / wavelength * 2 * math.pi - phase)
        amount = amount * (radius - distance) / radius
        amount = amount * wavelength / (distance + 0.0001)
        x = j + dx * amount
        y = i + dy * amount
    # 边界判断
    if x < 0:
        x = 0
    if x >= w - 1:
        x = w - 2
    if y < 0:
        y = 0
    if y >= h - 1:
        y = h - 2
    p = x - int(x)
    q = y - int(y)
    # 图像水波赋值
    img7[i, j, :] = (1 - p) * (1 - q) * img0[int(y), int(x), :] + p * (1 - q) * img0[int(y), int(x), :]
    + (1 - p) * q * img0[int(y), int(x), :] + p * q * img0[int(y), int(x), :]

cv2.imshow(“W7”, img7)
cv2.waitKey(delay = 0)

卡通特效

num_bilateral = 7 #定义双边滤波的数目
for i in range(num_bilateral): #双边滤波解决

img_color = cv2.bilateralFilter(img0, d = 9, sigmaColor = 5, sigmaSpace = 3)

img_blur = cv2.medianBlur(img1, 7) # 中值滤波解决
img_edge = cv2.adaptiveThreshold(img_blur, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, blockSize = 5, C = 2) #金融期货边缘检测及自适应阈值化解决
img_edge = cv2.cvtColor(img_edge, cv2.COLOR_GRAY2RGB) #转换回彩色图像
img8 = cv2.bitwise_and(img0, img_edge) #图像的与运算
cv2.imshow(‘W8’, img8)
cv2.waitKey(delay = 0)

将所有图像保留到一张图中

plt.rcParams[‘font.family’] = ‘SimHei’
imgs = [img0, img1, img2, img3, img4, img5, img6, img7, img8]
titles = [‘原图’, ‘灰度图’, ‘毛玻璃特效’, ‘浮雕特效’, ‘素描特效’, ‘念旧特效’, ‘流年特效’, ‘水波特效’, ‘卡通特效’]
for i in range(9):

imgs[i] = cv2.cvtColor(imgs[i], cv2.COLOR_BGR2RGB)
plt.subplot(3, 3, i + 1)
plt.imshow(imgs[i])
plt.title(titles[i])
plt.xticks([])
plt.yticks([])

plt.suptitle(‘图像特效解决’)
plt.savefig(‘E:\From Zhihu\For the desk\cvfifteenresult.jpg’, dpi = 1080)
plt.show()

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