opencv_img.py

import cv2
import numpy as np
import matplotlib.pyplot as plt
import math

# 读取图片
img = cv2.imread('me1.jpg')
src = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

# 新建目标图像
dst1 = np.zeros_like(img)

# 获取图像行和列
rows, cols = img.shape[:2]

# --------------毛玻璃效果--------------------
# 像素点邻域内随机像素点的颜色替代当前像素点的颜色
offsets = 5
random_num = 0
for y in range(rows - offsets):
    for x in range(cols - offsets):
        random_num = np.random.randint(0, offsets)
        dst1[y, x] = src[y + random_num, x + random_num]

# -------油漆特效------------
# 图像灰度处理
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# 自定义卷积核
kernel = np.array([[-1, -1, -1], [-1, 10, -1], [-1, -1, -1]])

# 图像浮雕效果
dst2 = cv2.filter2D(gray, -1, kernel)

# ----------素描特效-------------
# 高斯滤波降噪
gaussian = cv2.GaussianBlur(gray, (5, 5), 0)

# Canny算子
canny = cv2.Canny(gaussian, 50, 150)

# 阈值化处理
ret, dst3 = cv2.threshold(canny, 100, 255, cv2.THRESH_BINARY_INV)

# -------怀旧特效-----------------
# 新建目标图像
dst4 = np.zeros((rows, cols, 3), dtype="uint8")

# 图像怀旧特效
for i in range(rows):
    for j in range(cols):
        B = 0.272 * img[i, j][2] + 0.534 * img[i, j][1] + 0.131 * img[i, j][0]
        G = 0.349 * img[i, j][2] + 0.686 * img[i, j][1] + 0.168 * img[i, j][0]
        R = 0.393 * img[i, j][2] + 0.769 * img[i, j][1] + 0.189 * img[i, j][0]
        if B > 255:
            B = 255
        if G > 255:
            G = 255
        if R > 255:
            R = 255
        dst4[i, j] = np.uint8((B, G, R))

# ---------------光照特效--------------------
# 设置中心点
centerX = rows / 2
centerY = cols / 2
print(centerX, centerY)
radius = min(centerX, centerY)
print(radius)

# 设置光照强度
strength = 200

# 新建目标图像
dst5 = np.zeros((rows, cols, 3), dtype="uint8")

# 图像光照特效
for i in range(rows):
    for j in range(cols):
        # 计算当前点到光照中心的距离(平面坐标系中两点之间的距离)
        distance = math.pow((centerY - j), 2) + math.pow((centerX - i), 2)
        # 获取原始图像
        B = src[i, j][0]
        G = src[i, j][1]
        R = src[i, j][2]
        if (distance < radius * radius):
            # 按照距离大小计算增强的光照值
            result = (int)(strength * (1.0 - math.sqrt(distance) / radius))
            B = src[i, j][0] + result
            G = src[i, j][1] + result
            R = src[i, j][2] + result
            # 判断边界 防止越界
            B = min(255, max(0, B))
            G = min(255, max(0, G))
            R = min(255, max(0, R))
            dst5[i, j] = np.uint8((B, G, R))
        else:
            dst5[i, j] = np.uint8((B, G, R))

# --------------怀旧特效-----------------
# 新建目标图像
dst6 = np.zeros((rows, cols, 3), dtype="uint8")

# 图像流年特效
for i in range(rows):
    for j in range(cols):
        # B通道的数值开平方乘以参数12
        B = math.sqrt(src[i, j][0]) * 12
        G = src[i, j][1]
        R = src[i, j][2]
        if B > 255:
            B = 255
        dst6[i, j] = np.uint8((B, G, R))

# ------------卡通特效-------------------
# 定义双边滤波的数目
num_bilateral = 7

# 用高斯金字塔降低取样
img_color = src

# 双边滤波处理
for i in range(num_bilateral):
    img_color = cv2.bilateralFilter(img_color, d=9, sigmaColor=9, sigmaSpace=7)

# 灰度图像转换
img_gray = cv2.cvtColor(src, cv2.COLOR_RGB2GRAY)

# 中值滤波处理
img_blur = cv2.medianBlur(img_gray, 7)

# 边缘检测及自适应阈值化处理
img_edge = cv2.adaptiveThreshold(img_blur, 255,
                                 cv2.ADAPTIVE_THRESH_MEAN_C,
                                 cv2.THRESH_BINARY,
                                 blockSize=9,
                                 C=2)

# 转换回彩色图像
img_edge = cv2.cvtColor(img_edge, cv2.COLOR_GRAY2RGB)

# 与运算
dst6 = cv2.bitwise_and(img_color, img_edge)

# ------------------均衡化特效--------------------
# 新建目标图像
dst7 = np.zeros((rows, cols, 3), dtype="uint8")

# 提取三个颜色通道
(b, g, r) = cv2.split(src)

# 彩色图像均衡化
bH = cv2.equalizeHist(b)
gH = cv2.equalizeHist(g)
rH = cv2.equalizeHist(r)

# 合并通道
dst7 = cv2.merge((bH, gH, rH))

# -----------边缘特效---------------------
# 高斯滤波降噪
gaussian = cv2.GaussianBlur(gray, (3, 3), 0)

# Canny算子
# dst8 = cv2.Canny(gaussian, 50, 150)

# Scharr算子
x = cv2.Scharr(gaussian, cv2.CV_32F, 1, 0)  # X方向
y = cv2.Scharr(gaussian, cv2.CV_32F, 0, 1)  # Y方向
absX = cv2.convertScaleAbs(x)
absY = cv2.convertScaleAbs(y)
dst8 = cv2.addWeighted(absX, 0.5, absY, 0.5, 0)


# 用来正常显示中文标签
plt.rcParams['font.sans-serif'] = ['SimHei']

# 循环显示图形
titles = ['原图', '毛玻璃', '浮雕', '素描', '怀旧', '光照', '卡通', '均衡化', '边缘']
images = [src, dst1, dst2, dst3, dst4, dst5, dst6, dst7, dst8]
for i in range(9):
    plt.subplot(3, 3, i + 1), plt.imshow(images[i], 'gray')
    plt.title(titles[i])
    plt.xticks([]), plt.yticks([])

if __name__ == '__main__':

    plt.show()