CV Python - 2

1.  边缘检测

Sobel算子

# 要使用16位有符号的数据类型，即cv2.CV_16S，dx和dy表示的是求导的阶数，0表示这个方向上没有求导，一般为0、1、2。
x = cv2.Sobel(img, cv2.CV_16S, 1, 0)
y = cv2.Sobel(img, cv2.CV_16S, 0, 1)

#  convertScaleAbs()函数将其转回原来的uint8形式
absX = cv2.convertScaleAbs(x) # 水平
absY = cv2.convertScaleAbs(y) # 垂直

# 用cv2.addWeighted(...)函数将其组合起来。
dst = cv2.addWeighted(absX, 0.5, absY, 0.5, 0)

from skimage import filters
edges = filters.sobel(img)

edges1 = filters.sobel_h(img)  # 水平
edges2 = filters.sobel_v(img)  # 垂直

Canny算子

# 上下阈值

canny= cv2.Canny(img, 50,150)

# sigma越小， 检测越细

from skimage import feature
edges1 = feature.canny(img)
edges2 = feature.canny(img, sigma=3)

2.  连通域分析&绘制外接矩形

label_image =skimage.measure.label(tmp1,connectivity=2)

for region in skimage.measure.regionprops(label_image): 
    if region.area < 500: continue
    # 绘制外包矩形
    minr, minc, maxr, maxc = region.bbox
    rect = mpatches.Rectangle((minc, minr),
                              maxc - minc, 
                              maxr - minr, 
                              fill=False, 
                              edgecolor='red',
                              linewidth=2)
    ax1.add_patch(rect) # ax1为plt图像

3.  常用操作

# 直方图均衡化
img = cv2.equalizeHist(img) 

# 二值化

ret,binary = cv2.threshold(img,127,255,cv2.THRESH_BINARY)

# 查找轮廓
image, contours, hierarchy = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

ct = contours[i]
area = cv2.contourArea(ct) # 计算该轮廓的面积

# Otsu's threshold 根据灰度作二值图像

import mahotas
T = mahotas.thresholding.otsu(Grayimg)

#最大类间方差法求阈值，T为阈值

thresh = Grayimg.copy()
thresh[thresh >T] = 255
thresh[thresh < 255] = 0

4.  滤波去噪

# 高斯滤波，sigma越大越模糊

from skimage import filters

gaussian = filters.gaussian(img,sigma=5)

# 中值滤波，用skimage.morphology模块来设置滤波器的形状

from skimage.morphology import disk
edges1 = filters.median(img,disk(5))

from PIL import ImageFilter

# 模糊滤波
blur = im.filter(ImageFilter.BLUR)

# 轮廓滤波，将图像中的轮廓信息全部提取出来

contour = im.filter(ImageFilter.CONTOUR)

# 边缘增强滤波

edge_enhance = im.filter(ImageFilter.EDGE_ENHANCE)

# 深度边缘增强滤波

edge_enhance_more = m.filter(ImageFilter.EDGE_ENHANCE_MORE)

# 浮雕滤波

emboss = im.filter(ImageFilter.EMBOSS)

# 锐化滤波

sharpen = im.filter(ImageFilter.SHARPEN)

# 从（size，size）的区域中拷贝最小的像素值存储到输出图像中

min = im.filter(ImageFilter.MinFilter(3))

# 从（size，size）的区域中拷贝中间的像素值存储到输出图像中

median = im.filter(ImageFilter.MedianFilter(5))