[我的OpenCV学习日志]-2 2021/10/22

1.cv2.IMREAD_CLOLR :彩色图像  

2.cv2.IMREAD_GRAYSCALE:灰度图像

# cv2.IMREAD_CLOLR :彩色图像   cv2.IMREAD_GRAYSCALE:灰度图像
img = cv2.imread('D:/fzd2.jpg',cv2.IMREAD_GRAYSCALE)
img #转换为了灰度图，在一些对图像的处理中，有时需要先把彩色图像转换为灰度图

array([[ 17,  17,  17, ...,  21,  20,  20],
       [ 17,  17,  17, ...,  19,  20,  20],
       [ 16,  16,  16, ...,  18,  19,  19],
       ...,
       [187, 187, 187, ...,  56,  57,  57],
       [188, 188, 188, ...,  56,  54,  54],
       [189, 190, 189, ...,  57,  55,  56]], dtype=uint8)

cv2.imshow('image',img)
cv2.waitKey(0)
cv2.destroyAllWindows()

#保存
cv2.imwrite('D:/grey.png',img)
print('保存成功'

type(img)

#像素点的个数
img.size

img.dtype

numpy.ndarray

1480666

dtype('uint8')

#视频由很多帧组成。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，直接制定好路径即可。
vc = cv2.VideoCapture('D:/vedio.mp4')
#检查是否打开正确
if vc.isOpened():
    open,frame = vc.read() #从第一帧开始，一帧一帧地读，一直循环
else:
    open = False

while open:
    ret,frame = vc.read()
    if frame is None:
        break
    if ret == True:
        gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)#将彩色图像转换为灰度图像
        cv2.imshow('result',gray)
        if cv2.waitKey(10) & 0xFF == 27: #括号里的数字是指处理完一帧衔接到下一帧所间隔的时间
            break
vc.release()
cv2.destroyAllWindows()

#颜色通道提取
b,g,r = cv2.split(img)
b

array([[ 49,  49,  49, ...,  62,  61,  61],
       [ 49,  49,  49, ...,  60,  61,  61],
       [ 48,  48,  48, ...,  57,  58,  58],
       ...,
       [210, 210, 210, ..., 169, 170, 170],
       [211, 211, 211, ..., 169, 167, 167],
       [212, 213, 212, ..., 169, 167, 168]], dtype=uint8)

b.shape
r.shape

(1177, 1258)

(1177, 1258)

#只保留R通道;  BGR索引为[0,1,2]
cur_img = img.copy()
cur_img[:,:,0] = 0 #将B的索引设置为0
cur_img[:,:,1] = 0 #将G的索引设置为0
cv_show('R',cur_img)

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

img = cv2.imread('D:/flower.jpg')
img
cv_show('flower',img)

#边界填充:当对图像进行变换的过程中，想把图像的边缘扩大一圈，即上边填点东西，下边填点东西，左边填点东西，右边填点东西
top_size,bottom_size,left_size,right_size = (50,50,50,50) #上下左右分别填充的大小
replicate = cv2.copyMakeBorder(img,top_size,bottom_size,left_size,right_size,borderType = cv2.BORDER_REPLICATE)
reflet = cv2.copyMakeBorder(img,top_size,bottom_size,left_size,right_size,borderType = cv2.BORDER_REFLECT)
relfect101 = cv2.copyMakeBorder(img,top_size,bottom_size,left_size,right_size,borderType = cv2.BORDER_REFLECT_101)
wrap = cv2.copyMakeBorder(img,top_size,bottom_size,left_size,right_size,borderType = cv2.BORDER_WRAP)
constant = cv2.copyMakeBorder(img,top_size,bottom_size,left_size,right_size,borderType = cv2.BORDER_CONSTANT,value = 0)

import matplotlib.pyplot as plt
plt.subplot(231),plt.imshow(img,'gray'),plt.title('ORIGINAL')
plt.subplot(232),plt.imshow(replicate,'gray'),plt.title('REPLICATE')
plt.subplot(233),plt.imshow(reflet,'gray'),plt.title('REFLECT')
plt.subplot(234),plt.imshow(relfect101,'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，复制最边缘的像素

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

BORDER_REFLECT_101:反射法，以最边缘像素为轴，对称，比如：gfedcb|abcdefgh|gfedcba

BORDER_WRAP:外包装法，比如：cdefgh|abcdefhg|abcdefg

BORDER_CONSTANT:常量法，常数值填充