Python+OpenCV3.3图像处理视频教程 贾志刚 代码笔记2

11 边缘保留滤波(EPF)

import cv2 as cv
import numpy as np

def bi_demo(image):
    dst = cv.bilateralFilter(image, 0, 100, 15)#双边滤波
    #参数分别为：输入图像、像素领域的直径、颜色领域的标准差（一般尽可能大）、坐标空间的标准方差（一般尽可能小）
    cv.imshow("bi_demo", dst)

def shift_demo(image):
    dst = cv.pyrMeanShiftFiltering(image, 10, 50)#均值滤波
    #输入参数分别为：原图像、空间窗的半径、色彩窗的半径
    cv.imshow("shift_demo", dst)


print("--------- Hello Python ---------")
src = cv.imread("D:/vcprojects/images/example.png")
cv.namedWindow("input image", cv.WINDOW_AUTOSIZE)
cv.imshow("input image", src)

bi_demo(src)
shift_demo(src)

cv.waitKey(0)

cv.destroyAllWindows()

12 图像直方图(histogram)

import cv2 as cv
import numpy as np
from matplotlib import pyplot as plt

def plot_demo(image):
    plt.hist(image.ravel(), 256, [0, 256])#统计频次
    plt.show("直方图")

def image_hist(image):
    color = ('blue', 'green', 'red')
    for i, color in enumerate(color):
        hist1 = cv.calcHist([image], [i], None, [256], [0, 256])
        plt.plot(hist1, color=color)
        plt.xlim([0, 256])
    plt.show()


print("--------- Hello Python ---------")
src = cv.imread("D:/vcprojects/images/test.png")
cv.namedWindow("input image", cv.WINDOW_AUTOSIZE)
cv.imshow("input image", src)

plot_demo(src)
image_hist(src)

cv.waitKey(0)
cv.destroyAllWindows()

13 直方图应用

import cv2 as cv
import numpy as np

def equalHist_demo(image):#直方图均衡化
    gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
    dst = cv.equalizeHist(gray)
    cv.imshow("equalHist_demo", dst)

def clahe_demo(image):#局部自适应直方图均衡化
    gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
    clahe = cv.createCLAHE(clipLimit=5.0, tileGridSize=(8, 8))
    dst = clahe.apply(gray)
    cv.imshow("clahe_demo", dst)

def create_rgb_hist(image):
    h, w, c = image.shape
    rgbHist = np.zeros([16*16*16, 1], np.float32)
    bsize = 256 / 16
    for row in range(h):
        for col in range(w):
            b = image[row, col, 0]
            g = image[row, col, 1]
            r = image[row, col, 2]
            index = np.int(b/bsize)*16*16 + np.int(g/bsize)*16 + np.int(r/bsize)
            rgbHist[np.int(index), 0] = rgbHist[np.int(index), 0] + 1
    return rgbHist

def hist_compare(image1, image2):#直方图比较
    hist1 = create_rgb_hist(image1)
    hist2 = create_rgb_hist(image2)
    match1 = cv.compareHist(hist1, hist2, cv.HISTCMP_BHATTACHARYYA)#巴氏距离
    match2 = cv.compareHist(hist1, hist2, cv.HISTCMP_CORREL)#相关性
    match3 = cv.compareHist(hist1, hist2, cv.HISTCMP_CHISQR)#卡方，越大越不相似
    print("巴氏距离: %s, 相关性: %s, 卡方: %s"%(match1, match2, match3))


print("--------- Hello Python ---------")
src = cv.imread("D:/vcprojects/images/rise.png")
cv.namedWindow("input image", cv.WINDOW_AUTOSIZE)
cv.imshow("input image", src)

equalHist_demo(src)
clahe_demo(src)

image1 = cv.imread("D:/vcprojects/images/lena.png")
image2 = cv.imread("D:/vcprojects/images/lenanoise.png")
cv.imshow("image1", image1)
cv.imshow("image2", image2)
hist_compare(image1, image2)

cv.waitKey(0)

cv.destroyAllWindows()

--------- Hello Python ---------
巴氏距离: 0.8569864945854435, 相关性: 0.07862638379496767, 卡方: 46585983.69502137

14 直方图反向投影

import cv2 as cv
import numpy as np
from matplotlib import pyplot as plt

def back_projection_demo():#直方图反向投影
    sample = cv.imread("D:/vcprojects/images/sample.png")
    target = cv.imread("D:/vcprojects/images/target.png")
    roi_hsv = cv.cvtColor(sample, cv.COLOR_BGR2HSV)
    target_hsv = cv.cvtColor(target, cv.COLOR_BGR2HSV)

    # show images
    cv.imshow("sample", sample)
    cv.imshow("target", target)

    roiHist = cv.calcHist([roi_hsv], [0, 1], None, [32, 32], [0, 180, 0, 256])
    cv.normalize(roiHist, roiHist, 0, 255, cv.NORM_MINMAX)
    dst = cv.calcBackProject([target_hsv], [0, 1], roiHist, [0, 180, 0, 256], 1)
    cv.imshow("backProjectionDemo", dst)

def hist2d_demo(image):#2维直方图
    hsv = cv.cvtColor(image, cv.COLOR_BGR2HSV)
    #hist = cv.calcHist([image], [0, 1], None, [32, 32], [0, 180, 0, 256])
    hist = cv.calcHist([image], [0, 1], None, [180, 256], [0, 180, 0, 256])

    #cv.imshow("hist2d", hist)
    plt.imshow(hist, interpolation='nearest')
    plt.title("2D Histogram")
    plt.show()


print("--------- Hello Python ---------")
src = cv.imread("D:/vcprojects/images/demo.png")
#hist2d_demo(src)
cv.namedWindow("input image", cv.WINDOW_AUTOSIZE)
cv.imshow("input image", src)

hist2d_demo(src)

back_projection_demo()
cv.waitKey(0)

cv.destroyAllWindows()

15模板匹配

import cv2 as cv
import numpy as np

def template_demo():
    tpl = cv.imread("D:/vcprojects/images/person_head.png")
    target = cv.imread("D:/vcprojects/images/pedestrian.png")
    cv.imshow("template image", tpl)
    cv.imshow("target image", target)
    methods = [cv.TM_SQDIFF_NORMED, cv.TM_CCORR_NORMED, cv.TM_CCOEFF_NORMED]
    th, tw = tpl.shape[:2]
    for md in methods:
        print(md)
        result = cv.matchTemplate(target, tpl, md)
        min_val, max_val, min_loc, max_loc = cv.minMaxLoc(result)
        if md == cv.TM_SQDIFF_NORMED:
            tl = min_loc
        else:
            tl = max_loc
        br = (tl[0]+tw, tl[1]+th)

        cv.rectangle(target, tl, br, (0, 0, 255), 2)
        cv.imshow("match-" + np.str(md), target)
        #cv.imshow("match-" + np.str(md), result)


print("--------- Python OpenCV Tutorial ---------")
template_demo()
cv.waitKey(0)

cv.destroyAllWindows()

--------- Python OpenCV Tutorial ---------
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16图像二值化

import cv2 as cv
import numpy as np

def threshold_demo(image):
    gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
    ret, binary = cv.threshold(gray, 127, 255, cv.THRESH_BINARY|cv.THRESH_OTSU)
    print("threshold value %s"%ret)
    cv.imshow("threshold_binary", binary)

def local_threshold(image):#自适应阈值二值化
    gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
    binary = cv.adaptiveThreshold(gray, 255, cv.ADAPTIVE_THRESH_GAUSSIAN_C, cv.THRESH_BINARY, 25, 10)
    cv.imshow("local_threshold_binary", binary)

def custom_threshold(image):#自定义二值化
    gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
    h, w = gray.shape[:2]
    m = np.reshape(gray, [1, w*h])
    mean = m.sum() / (w*h)
    print("mean : ", mean)
    ret, binary = cv.threshold(gray, mean, 255, cv.THRESH_BINARY)
    cv.imshow("custom_threshold_binary", binary)


print("--------- Python OpenCV Tutorial ---------")
#src = cv.imread("D:/vcprojects/images/demo.png")
src = cv.imread("D:/vcprojects/images/case2.png")

cv.namedWindow("input image", cv.WINDOW_AUTOSIZE)
cv.imshow("input image", src)

threshold_demo(src)
local_threshold(src)
custom_threshold(src)
cv.waitKey(0)

cv.destroyAllWindows()

--------- Python OpenCV Tutorial ---------
threshold value 119.0
mean : 148.16271116251838

17 超大图像二值化

import cv2 as cv
import numpy as np

def big_image_binary(image):
    print(image.shape)
    cw = 256
    ch = 256
    h, w = image.shape[:2]
    gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
    for row in range(0, h, ch):
        for col in range(0, w, cw):
            roi = gray[row:row+ch, col:cw+col]
            dst=cv.adaptiveThreshold(roi, 255, cv.ADAPTIVE_THRESH_GAUSSIAN_C, cv.THRESH_BINARY, 127,20)
            gray[row:row+ch, col:cw+col]=dst

            print(np.std(dst), np.mean(dst))

    cv.imwrite("D:/result_binary.png", gray)


print("--------- Python OpenCV Tutorial ---------")
src = cv.imread("D:/vcprojects/images/red_text2.png")
big_image_binary(src)
cv.waitKey(0)

cv.destroyAllWindows()

--------- Python OpenCV Tutorial ---------
(7749, 5477, 3)

import cv2 as cv
import numpy as np

def big_image_binary(image):
    print(image.shape)
    cw = 256
    ch = 256
    h, w = image.shape[:2]
    gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
    for row in range(0, h, ch):
        for col in range(0, w, cw):
            roi = gray[row:row+ch, col:cw+col]
            print(np.std(roi), np.mean(roi))
            dev = np.std(roi)
            if dev < 15:
                gray[row:row + ch, col:cw + col] = 255
            else:
                ret, dst = cv.threshold(roi, 0, 255, cv.THRESH_BINARY | cv.THRESH_OTSU)
                gray[row:row + ch, col:cw + col] = dst
    cv.imwrite("D:/vcprojects/result_binary.png", gray)


print("--------- Python OpenCV Tutorial ---------")
src = cv.imread("D:/vcprojects/images/red_text2.png")
big_image_binary(src)
cv.waitKey(0)

cv.destroyAllWindows()

--------- Python OpenCV Tutorial ---------
(7749, 5477, 3)

18 图像金字塔

import cv2 as cv
import numpy as np

def pyramid_demo(image):#图像金字塔
    level = 3
    temp = image.copy()
    pyramid_images = []
    for i in range(level):
        dst = cv.pyrDown(temp)
        pyramid_images.append(dst)
        cv.imshow("pyramid_down_"+str(i), dst)
        temp = dst.copy()#把dst复制给temp
    return pyramid_images

def lapalian_demo(image):
    pyramid_images = pyramid_demo(image)
    level = len(pyramid_images)
    for i in range(level-1, -1, -1):
        if (i-1) < 0 :
            expand = cv.pyrUp(pyramid_images[i], dstsize=image.shape[:2])
            lpls = cv.subtract(image, expand)
            cv.imshow("lapalian_down_" + str(i), lpls)
        else:
            expand = cv.pyrUp(pyramid_images[i], dstsize=pyramid_images[i-1].shape[:2])
            lpls = cv.subtract(pyramid_images[i-1], expand)
            cv.imshow("lapalian_down_"+str(i), lpls)

print("--------- Python OpenCV Tutorial ---------")
src = cv.imread("D:/vcprojects/images/lena.png")
cv.namedWindow("input image", cv.WINDOW_AUTOSIZE)
cv.imshow("input image", src)

pyramid_demo(src)
lapalian_demo(src)
cv.waitKey(0)

cv.destroyAllWindows()

19 图像梯度

import cv2 as cv
import numpy as np

def lapalian_demo(image):
    #dst = cv.Laplacian(image, cv.CV_32F)
    #lpls = cv.convertScaleAbs(dst)

    kernel = np.array([[1, 1, 1], [1, -8, 1], [1, 1, 1]])
    dst = cv.filter2D(image, cv.CV_32F, kernel=kernel)
    lpls = cv.convertScaleAbs(dst)
    cv.imshow("lapalian_demo", lpls)

def sobel_demo(image):
    grad_x = cv.Scharr(image, cv.CV_32F, 1, 0)
    grad_y = cv.Scharr(image, cv.CV_32F, 0, 1)
    gradx = cv.convertScaleAbs(grad_x)
    grady = cv.convertScaleAbs(grad_y)
    cv.imshow("gradient-x", gradx)
    cv.imshow("gradient-y", grady)

    gradxy = cv.addWeighted(gradx, 0.5, grady, 0.5, 0)
    cv.imshow("gradient", gradxy)


print("--------- Python OpenCV Tutorial ---------")
src = cv.imread("D:/vcprojects/images/shou.png")
cv.namedWindow("input image", cv.WINDOW_AUTOSIZE)
cv.imshow("input image", src)

lapalian_demo(src)
sobel_demo(src)
cv.waitKey(0)

cv.destroyAllWindows()

20 canny边缘提取

import cv2 as cv
import numpy as np

def edge_demo(image):
    blurred = cv.GaussianBlur(image, (3, 3), 0)#降噪
    gray = cv.cvtColor(blurred, cv.COLOR_BGR2GRAY)
    # X Gradient
    xgrad = cv.Sobel(gray, cv.CV_16SC1, 1, 0)
    # Y Gradient
    ygrad = cv.Sobel(gray, cv.CV_16SC1, 0, 1)
    #edge
    #edge_output = cv.Canny(xgrad, ygrad, 50, 150)
    edge_output = cv.Canny(gray, 50, 150)
    cv.imshow("Canny Edge", edge_output)

    dst = cv.bitwise_and(image, image, mask=edge_output)
    cv.imshow("Color Edge", dst)


print("--------- Python OpenCV Tutorial ---------")
src = cv.imread("D:/vcprojects/images/test.png")
cv.namedWindow("input image", cv.WINDOW_AUTOSIZE)
cv.imshow("input image", src)
edge_demo(src)
cv.waitKey(0)

cv.destroyAllWindows()