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主题：opencv_python从zero到hero————之图像基本操作01

1、版本：opencv3.4.1 numpy

图像基本操作

环境配置地址：

• Anaconda:https://www.anaconda.com/download/

• Python_whl:https://www.lfd.uci.edu/~gohlke/pythonlibs/#opencv

• pycharm:按照自己的喜好，选择一个能debug就好

数据读取-图像

• cv2.IMREAD_COLOR：彩色图像

• cv2.IMREAD_GRAYSCALE：灰度图像

import cv2 #opencv读取的格式是BGRimport matplotlib.pyplot as pltimport numpy as np %matplotlib inline 

img=cv2.imread('cat.jpg')

img

array([[[142, 151, 160],        [146, 155, 164],        [151, 160, 169],        ...,        [156, 172, 185],        [155, 171, 184],        [154, 170, 183]],

       [[107, 118, 126],        [112, 123, 131],        [117, 128, 136],        ...,        [155, 171, 184],        [154, 170, 183],        [153, 169, 182]],

       [[108, 119, 127],        [112, 123, 131],        [118, 129, 137],        ...,        [154, 170, 183],        [153, 169, 182],        [152, 168, 181]],

       ...,

       [[162, 186, 198],        [157, 181, 193],        [142, 166, 178],        ...,        [181, 204, 206],        [170, 193, 195],        [149, 172, 174]],

       [[140, 164, 176],        [147, 171, 183],        [139, 163, 175],        ...,        [167, 187, 188],        [123, 143, 144],        [104, 124, 125]],

       [[154, 178, 190],        [154, 178, 190],        [121, 145, 157],        ...,        [185, 198, 200],        [130, 143, 145],        [129, 142, 144]]], dtype=uint8)

#图像的显示,也可以创建多个窗口cv2.imshow('image',img) # 等待时间，毫秒级，0表示任意键终止cv2.waitKey(0) cv2.destroyAllWindows()

def cv_show(name,img):    cv2.imshow(name,img)     cv2.waitKey(0)     cv2.destroyAllWindows()

img.shape

(414, 500, 3)

img=cv2.imread('cat.jpg',cv2.IMREAD_GRAYSCALE)img

array([[153, 157, 162, ..., 174, 173, 172],       [119, 124, 129, ..., 173, 172, 171],       [120, 124, 130, ..., 172, 171, 170],       ...,       [187, 182, 167, ..., 202, 191, 170],       [165, 172, 164, ..., 185, 141, 122],       [179, 179, 146, ..., 197, 142, 141]], dtype=uint8)

img.shape

(414, 500)

#图像的显示,也可以创建多个窗口cv2.imshow('image',img) # 等待时间，毫秒级，0表示任意键终止cv2.waitKey(10000) cv2.destroyAllWindows()

#保存cv2.imwrite('mycat.png',img)

True

type(img)

numpy.ndarray

img.size

207000

img.dtype

dtype('uint8')

数据读取-视频

• cv2.VideoCapture可以捕获摄像头，用数字来控制不同的设备，例如0,1。

• 如果是视频文件，直接指定好路径即可。

vc = cv2.VideoCapture('test.mp4')

# 检查是否打开正确if vc.isOpened():     oepn, frame = vc.read()else:    open = False

while open:    ret, frame = vc.read()    if frame is None:        break    if ret == True:        gray = cv2.cvtColor(frame,  cv2.COLOR_BGR2GRAY)        cv2.imshow('result', gray)        if cv2.waitKey(100) & 0xFF == 27:            breakvc.release()cv2.destroyAllWindows()

截取部分图像数据

img=cv2.imread('cat.jpg')cat=img[0:50,0:200] cv_show('cat',cat)

颜色通道提取

b,g,r=cv2.split(img)

r

array([[160, 164, 169, ..., 185, 184, 183],       [126, 131, 136, ..., 184, 183, 182],       [127, 131, 137, ..., 183, 182, 181],       ...,       [198, 193, 178, ..., 206, 195, 174],       [176, 183, 175, ..., 188, 144, 125],       [190, 190, 157, ..., 200, 145, 144]], dtype=uint8)

r.shape

(414, 500)

img=cv2.merge((b,g,r))img.shape

(414, 500, 3)

# 只保留Rcur_img = img.copy()cur_img[:,:,0] = 0cur_img[:,:,1] = 0cv_show('R',cur_img)

# 只保留Gcur_img = img.copy()cur_img[:,:,0] = 0cur_img[:,:,2] = 0cv_show('G',cur_img)

# 只保留Bcur_img = img.copy()cur_img[:,:,1] = 0cur_img[:,:,2] = 0cv_show('B',cur_img)

边界填充

top_size,bottom_size,left_size,right_size = (50,50,50,50)

replicate = cv2.copyMakeBorder(img, top_size, bottom_size, left_size, right_size, borderType=cv2.BORDER_REPLICATE)reflect = cv2.copyMakeBorder(img, top_size, bottom_size, left_size, right_size,cv2.BORDER_REFLECT)reflect101 = cv2.copyMakeBorder(img, top_size, bottom_size, left_size, right_size, cv2.BORDER_REFLECT_101)wrap = cv2.copyMakeBorder(img, top_size, bottom_size, left_size, right_size, cv2.BORDER_WRAP)constant = cv2.copyMakeBorder(img, top_size, bottom_size, left_size, right_size,cv2.BORDER_CONSTANT, value=0)

import matplotlib.pyplot as pltplt.subplot(231), plt.imshow(img, 'gray'), plt.title('ORIGINAL')plt.subplot(232), plt.imshow(replicate, 'gray'), plt.title('REPLICATE')plt.subplot(233), plt.imshow(reflect, 'gray'), plt.title('REFLECT')plt.subplot(234), plt.imshow(reflect101, 'gray'), plt.title('REFLECT_101')plt.subplot(235), plt.imshow(wrap, 'gray'), plt.title('WRAP')plt.subplot(236), plt.imshow(constant, 'gray'), plt.title('CONSTANT')

plt.show()

• BORDER_REPLICATE：复制法，也就是复制最边缘像素。

• BORDER_REFLECT：反射法，对感兴趣的图像中的像素在两边进行复制例如：fedcba|abcdefgh|hgfedcb

• BORDER_REFLECT_101：反射法，也就是以最边缘像素为轴，对称，gfedcb|abcdefgh|gfedcba

• BORDER_WRAP：外包装法cdefgh|abcdefgh|abcdefg

• BORDER_CONSTANT：常量法，常数值填充。

数值计算

img_cat=cv2.imread('cat.jpg')img_dog=cv2.imread('dog.jpg')

img_cat2= img_cat +10 img_cat[:5,:,0]

array([[142, 146, 151, ..., 156, 155, 154],       [107, 112, 117, ..., 155, 154, 153],       [108, 112, 118, ..., 154, 153, 152],       [139, 143, 148, ..., 156, 155, 154],       [153, 158, 163, ..., 160, 159, 158]], dtype=uint8)

img_cat2[:5,:,0]

array([[152, 156, 161, ..., 166, 165, 164],       [117, 122, 127, ..., 165, 164, 163],       [118, 122, 128, ..., 164, 163, 162],       [149, 153, 158, ..., 166, 165, 164],       [163, 168, 173, ..., 170, 169, 168]], dtype=uint8)

#相当于% 256(img_cat + img_cat2)[:5,:,0] 

array([[ 38,  46,  56, ...,  66,  64,  62],       [224, 234, 244, ...,  64,  62,  60],       [226, 234, 246, ...,  62,  60,  58],       [ 32,  40,  50, ...,  66,  64,  62],       [ 60,  70,  80, ...,  74,  72,  70]], dtype=uint8)

cv2.add(img_cat,img_cat2)[:5,:,0]

array([[255, 255, 255, ..., 255, 255, 255],       [224, 234, 244, ..., 255, 255, 255],       [226, 234, 246, ..., 255, 255, 255],       [255, 255, 255, ..., 255, 255, 255],       [255, 255, 255, ..., 255, 255, 255]], dtype=uint8)

图像融合

img_cat + img_dog

---------------------------------------------------------------------------

ValueError                                Traceback (most recent call last)

input-33-e8802e660946> in <module>----> 1 img_cat + img_dogValueError: operands could not be broadcast together with shapes (414,500,3) (429,499,3) 

img_cat.shape

(414, 500, 3)

img_dog = cv2.resize(img_dog, (500, 414))img_dog.shape

(414, 500, 3)

融合算法 y = a * X_1+ b* X_2  + B

res = cv2.addWeighted(img_cat, 0.4, img_dog, 0.6, 0)

plt.imshow(res)

<matplotlib.image.AxesImage at 0xfbd95c8>

res = cv2.resize(img, (0, 0), fx=4, fy=4)plt.imshow(res)

<matplotlib.image.AxesImage at 0x5381288>

res = cv2.resize(img, (0, 0), fx=1, fy=3)plt.imshow(res)

<matplotlib.image.AxesImage at 0x53e0b48>

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