Python-Opencv图像处理入门(一)

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0. 配置openCV

用pip即可.
这是open CV版本的Hello World

import cv2 as cv

src = cv.imread(r"F:\JupyterNotebook\ComputerVision\lena.png")
cv.namedWindow("Lena",cv.WINDOW_AUTOSIZE)
cv.imshow("Lena",src)
cv.waitKey(0)
cv.destroyAllWindows()

1. 概论

• 什么是图像?
• 什么是数字图像?
• 图像有什么属性?
• 通道数目
• 高和宽
• 像素数据
• 图像类型
  有图有真相,一图胜千言.

2. 图像的加载和保存

import cv2 as cv

src = cv.imread(r"F:\JupyterNotebook\ComputerVision\lena.png")
cv.namedWindow("Lena",cv.WINDOW_AUTOSIZE)
cv.imshow("Lena",src)
cv.imwrite(r'F:\JupyterNotebook\ComputerVision\GrayLena.png',gray)
cv.waitKey(0)
cv.destroyAllWindows()

图像的属性获取

def get_image_info(image):
    print(type(image))
    print(image.shape)
    print(image.size)
    print(image.dtype)
    pixel_data = np.array(image)
    print(pixel_data)
#Output:
#----------Hello Python----------
#
#(512, 512, 3)
#786432
#uint8
#786432=521*521*3
#还有一幅图

调用计算机自带的摄像头

def vedio_demo():
    capture = cv.VideoCapture(0)
    while(True):
        ret ,frame = capture.read()
        frame = cv.flip(frame,1)
        cv.imshow("vedio",frame)
        c=cv.waitKey(50)
        if c==27:
            break

3. Numpy和图像处理

data\dtype\size\shape\len

属性读取

def access_pixels(image):
    print(image.shape)
    height = image.shape[0]
    width = image.shape[1]
    channels = image.shape[2]
    print("width : %s,height : %s channels : %s"%(width,height,channels))

获取像素点

def access_pixels(image):
    print(image.shape)
    height = image.shape[0]
    width = image.shape[1]
    channels = image.shape[2]
    print("width : %s,height : %s channels : %s"%(width,height,channels))
    for row in range(height):
        for col in range(width):
            for c in range(channels):
                pv = image[row,col,c]
                image[row,col,c] = 255 - pv
    cv.imshow("pixels_demo",image)
    #这里实测512*512的图像跑了1.63s

暴力创建数字图像

def create_image():    
    """
    img = np.zeros([400,400,3],np.uint8)
    img[: , : , 1]=np.ones([400,400])*255
    #Ta 绿 了
    cv.imshow("new image",img);
    """
    
    """
    img = np.zeros([400,400,1],np.uint8)
    img[: , : , 0]=np.ones([400,400])*127
    #创建灰度图
    cv.imshow("new image",img);
    """
    ml=np.ones([3,3],np.float32)
    ml.fill(122.388)
    print(ml)
    #当心Overflow
    #dst = cv.bitwise_not(image)#这里调用C跑得非常快

4. 色彩空间01

• 什么是色彩空间?
• 常见的色彩空间有哪些?
  • RGB
  • HSV
  • HIS
  • YCrCb
  • YUV
• 如何实现不同色彩空间转换?

注意HSV这里用一个uint8可以存储的180把标准的360标准化.HSV对不同色彩表现比较好
YUV:Android开发,由硬件性能所限.

色彩空间之间的相互转换

最常见的是

• HSV和RGB
• YUV和RGB

def color_space_demo(image):
    gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
    cv.imshow("gray",gray)
    hsv = cv.cvtColor(image,cv.COLOR_BGR2HSV)
    cv.imshow("hsv",hsv)
    yuv = cv.cvtColor(image,cv.COLOR_BGR2YUV)
    cv.imshow("yuv",yuv)
    Ycrcb = cv.cvtColor(image,cv.COLOR_BGR2YCrCb)
    cv.imshow("Ycrcb",Ycrcb)

5. 色彩空间02

注意到HSV的取值:

• H:0-180
• S:0-255
• V:0-255

视频获取

def extrace_object_demo():
    capture=cv.VideoCapture("F:\JupyterNotebook\ComputerVision\Ko.mp4")
    while(True):
        ret, frame = capture.read()
        if ret == False:
            break;
        cv.imshow("video",frame)
        c = cv.waitKey(40)
        if c==27:
            break

过滤颜色

def extrace_object_demo():
    capture=cv.VideoCapture("F:\JupyterNotebook\ComputerVision\Ko.mp4")
    while(True):
        ret, frame = capture.read()
        if ret == False:
            break;
        hsv = cv.cvtColor(frame, cv.COLOR_BGR2HSV)    
        lower_hsv = np.array([37,43,46])
        upper_hsv = np.array([77,255,255])
        #注意看HSV和RGB转换表
        mask = cv.inRange(hsv,lowerb=lower_hsv,upperb=upper_hsv)
        cv.imshow("video",frame)
        cv.imshow("mask",mask)#二值化并且采集绿色.
        c = cv.waitKey(40)
        if c==27:
            break

分离与合并

b,g,r =cv.split(src)
#以下为其用法
cv.imshow("blue",b)
cv.imshow("green",b)
cv.imshow("red",b)
#以下为其合并
src[:,:,2]=0
src = cv.merge([b,g,r])
cv.imshow("Lena",src)

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基于python3的Opencv图像处理教程(从零到实践)
NumPy官方网站