图像直方图

1.获取图像直方图

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

统计各种像素的数值

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

统计各颜色的直方图

2.直方图均衡化

#全局直方图均衡化（对比度增强,但有的时候会过度）   


def equalHist_demo(image):

    gray = cv.cvtColor(image,
cv.COLOR_BGR2GRAY)

    dst = cv.equalizeHist(gray)

    cv.imshow("equalHist_demo",dst)

#局部直方图均衡化（自适应增加对比度，效果很好）

def clahe_demo(image):

    gray = cv.cvtColor(image,
cv.COLOR_BGR2GRAY)

    clahe = cv.createCLAHE(clipLimit=2.0,tileGridSize=(8,8))

    dst = clahe.apply(gray)

cv.imshow("clahe_demo",dst)

提高图像对比度

3.直方图比较

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):

    hist1 = create_rgb_hist(image1)

    hist2 = create_rgb_hist(image2)

    match1 = cv.compareHist(hist1,hist2,
cv.HISTCMP_BHATTACHARYYA)

    match2 = cv.compareHist(hist1,hist2,
cv.HISTCMP_CORREL)

    match3 = cv.compareHist(hist1,hist2,
cv.HISTCMP_CHISQR)

print("巴氏距离：%s 相关性：%s 卡方：%s"%(match1,match2,match3))

查看两个直方图的相似程度

4.直方图反向投影

def hist2d_demo(image):

    hsv = cv.cvtColor(image, cv.COLOR_BGR2HSV)

    hist = cv.calcHist([hsv],[0,1],None,[180,250],[0,180,0,250])

    cv.imshow("hist2d",hist)

    plt.imshow(hist, interpolation='nearest')

    plt.title("2D Histogram")

    plt.show()

    

def back_projection_demo():

    sample = cv.imread('C:\\Users\\lieng\\Desktop\\timg.jpg')

    target = cv.imread('C:\\Users\\lieng\\Desktop\\text.png')

    roi_hsv =
cv.cvtColor(sample,cv.COLOR_BGR2HSV)

    target_hsv = cv.cvtColor(target, cv.COLOR_BGR2HSV)

    cv.imshow("sampel",sample)

    cv.imshow("target",target)

    roiHist = cv.calcHist([roi_hsv],[0, 1],None,[180,256],[0,100,0,256])

    cv.normalize(roiHist,roiHist,0,255,cv.NORM_MINMAX)

    dst = cv.calcBackProject([target_hsv],[0,1], roiHist,[0, 100, 0, 256], 1)

cv.imshow("backproject", dst)

可以理解为在案例图像上寻找目标图像