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Author: beginner-fei

chaoxi/README.md

@@ -8,6 +8,9 @@ Python技术 公众号文章代码库
 ![](http://favorites.ren/assets/images/python.jpg)
 
 ## 实例代码
+opencv_img
+
+[惊艳！利用 Python 图像处理绘制专属头像](https://github.com/JustDoPython/python-examples/tree/master/chaoxi/opencv_img) 惊艳！利用 Python 图像处理绘制专属头像
 
 [七夕不懂浪漫？Python 帮你制造惊喜！！！](https://github.com/JustDoPython/python-examples/tree/master/chaoxi/qixi) 七夕不懂浪漫？Python 帮你制造惊喜！！！
 

chaoxi/opencv_img/opencv_img.py

@@ -0,0 +1,188 @@
+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()