Python OpenCV像素操作

环境声明 ： Python3.6 + OpenCV3.3 + PyCharm IDE

首先要引入OpenCV和Numpy支持，添加代码如下：

1. import cv2 as cv;

2. import numpy as np;

读写像素

对RGB图像来说，在Python中第一个维度表示高度、第二个维度表示宽度、第三个维度是通道数目，可以通过下面的代码获取图像三个维度的大小

1. print(image.shape)

2. print(image.size)

3. print(image.dtype)

循环读取图像方法一：

直接从图像中读取，缺点是每次都需要访问imread之后的Mat对象，进行native操作，速度是个问题， 代码实现如下：

1. height = image.shape[0]

2. width = image.shape[1]

3. channels = image.shape[2]

4. print(image.shape)

5. for row in range(height):

6.    for col in range(width):

7.        for c in range(channels):

8.            level = image[row, col, c]

9.            pv = level + 30

10.            image[row, col, c] = (255 if pv>255 else pv)

循环读取图像方法二：

首先通过Numpy把像素数据读到内存中，在内存中进行高效循环访问每个像素，修改之后，在赋值回去即可，代码如下：

1. # read once

2. pixel_data = np.array(image, dtype = np.uint8);

3. # loop pixel by pixel

4. for row in range(height):

5.    for col in range(width):

6.        for c in range(channels):

7.            level = pixel_data[row, col, c]

8.            pixel_data[row, col, c] = 255 - level

9. # write once

10. image[ : : ] = pixel_data

案例演示 在Python语言中完成图像的属性读取、像素读取与操作、实现了图像的颜色取反、亮度提升、灰度化、梯度化、操作。首先看一下效果：

完整的Python代码实现如下：

1. import cv2 as cv;

2. import numpy as np;

3. def inverse(image):

4.    print("read and write pixel by pixel")

5.    print(image.shape)

6.    print(image.size)

7.    print(image.dtype)

8.    height = image.shape[0]

9.    width = image.shape[1]

10.    channels = image.shape[2]

11.    # read once

12.    pixel_data = np.array(image, dtype = np.uint8);

13.    # loop pixel by pixel

14.    for row in range(height):

15.        for col in range(width):

16.            for c in range(channels):

17.                level = pixel_data[row, col, c]

18.                pixel_data[row, col, c] = 255 - level

19.    # write once

20.    image[ : : ] = pixel_data

21.    cv.imshow("inverse image", image)

22. def brightness(image):

23.    print("read and write pixel by pixel")

24.    height = image.shape[0]

25.    width = image.shape[1]

26.    channels = image.shape[2]

27.    print(image.shape)

28.    for row in range(height):

29.        for col in range(width):

30.            for c in range(channels):

31.                level = image[row, col, c]

32.                pv = level + 30

33.                image[row, col, c] = (255 if pv>255 else pv)

34.    cv.imshow("inverse image", image);

35. def to_gray(image):

36.    print("RGB to Gray Image")

37.    height = image.shape[0]

38.    width = image.shape[1]

39.    channels = image.shape[2]

40.    print("channels : ", channels);

41.    print(image.shape)

42.    for row in range(height):

43.        for col in range(width):

44.            blue = image[row, col, 0]

45.            green = image[row, col, 1];

46.            red = image[row, col, 2]

47.            gray = (0.2989*red + 0.5870*green + 0.1140*blue);

48.            image[row, col, 0] = gray;

49.            image[row, col, 1] = gray;

50.            image[row, col, 2] = gray;

51.    cv.imshow("gray image", image)

52. def gradient_image(image):

53.    gx = cv.Sobel(image, cv.CV_32F, 1, 0)

54.    gy = cv.Sobel(image, cv.CV_32F, 0, 1)

55.    dst = cv.addWeighted(gx, 0.5, gy, 0.5, 50)

56.    sobel_abs = np.absolute(dst)

57.    sobel_8u = np.uint8(sobel_abs)

58.    cv.imshow("gradient image", sobel_8u)

59. def clam(pv):

60.    if pv > 255:

61.        return 255

62.    if pv < 0:

63.        return 0;

64.    return pv;

65. print("Image Pixel Operation Demo")

66. src = cv.imread("D:/vcprojects/images/demo.png")

67. cv.namedWindow("input image", cv.WINDOW_AUTOSIZE)

68. cv.imshow("input image", src)

69. gradient_image(src)

70. cv.waitKey(0)

71. cv.destroyAllWindows()


云厚者，雨必猛，

弓劲者，箭必远！

关注【OpenCV学堂】

长按或者扫码下面二维码即可关注

+OpenCV识别群 657875553

进群暗号：识别