numpy数组transpose与图像使用详解

import numpy as np

# 一维数组transpose无意义
data = np.arange(4)
print(data)
data = data.transpose()
print(data)


# 二维数组，横纵坐标互换
data = np.arange(8).reshape(4, 2)
print(data)
data = data.transpose()
print(data)

# 三维数组，可以理解为2个 3行 * 4列的二维数组
data = np.arange(24).reshape(2, 3, 4)
print(data)

# transpose其实接受参数，不过对于一二维数组没有必要
# 参数可以是0,1,2，可以想象成对应x,y,z坐标
data = data.transpose(2, 1, 0)
print(data)

img = Image.open(r"G:\tmp\wbc.jpg")
img_arr = np.array(img).astype(np.uint8)
print(img_arr.shape)
print(img_arr)

img_arr = img_arr.transpose(2, 0, 1)
print(img_arr.shape)
print(img_arr)

import numpy as np
from PIL import Image


def gray_world(src_img_path, dest_img_path):
    """
    灰度世界算法
    """
    src_img = Image.open(src_img_path)
    # 图形转numpy数组
    image_arr = np.array(src_img).astype(np.uint32)
    # 分离RGB
    image_arr = image_arr.transpose(2, 0, 1)

    avg_red = np.average(image_arr[0])
    avg_green = np.average(image_arr[1])
    avg_blue = np.average(image_arr[2])

    gray = (avg_red + avg_green + avg_blue) / 3

    kred = gray / avg_red
    kgreen = gray / avg_green
    kblue = gray / avg_blue

    image_arr[0] = np.minimum(image_arr[0] * kred, 255)
    image_arr[1] = np.minimum(image_arr[1] * kgreen, 255)
    image_arr[2] = np.minimum(image_arr[2] * kblue, 255)

    # 重新合并RGB
    image_arr = image_arr.transpose(1, 2, 0)
    # numpy数组转图像
    img = Image.fromarray(image_arr.astype('uint8')).convert('RGB')
    img.save(dest_img_path)

if __name__ == '__main__':
    img_path = r"G:\tmp\2d.jpg";
    gray_world(img_path, r"G:\tmp\gray_world.jpg")