图像的数组表示

from PIL import Image
import numpy as np
import numpy.core.numeric as _nx
import matplotlib.pyplot as plt

image_name = 'Lenna_(test_image).png'
image_name_2 = 'Lenna_(test_image)2.png'
image_name_3 = 'Lenna_(test_image)3.png'

def image_gradient(f, **kwargs):
    """
    图像梯度求导
    :param f: 类数组
    :return: 数组或数组列表，dtype=np.double
    可选参数：axis - 空或整数或整数列表
    """
    f = np.asanyarray(f, dtype=np.double)
    N = f.ndim  # number of dimensions

    axes = kwargs.pop('axis', None)
    if axes is None:
        axes = tuple(range(N))
    else:
        axes = _nx.normalize_axis_tuple(axes, N)
    len_axes = len(axes)

    outvals = []

    # create slice objects --- initially all are [:, :, ..., :]
    slice1 = [slice(None)] * N
    slice2 = [slice(None)] * N
    slice3 = [slice(None)] * N
    slice4 = [slice(None)] * N

    otype = f.dtype

    for axis in axes:
        if f.shape[axis] < 2:
            raise ValueError(
                "Shape of array too small to calculate a numerical gradient, "
                "at least 2 elements are required.")
        # result allocation
        out = np.empty_like(f, dtype=otype)

        # Numerical differentiation: 2nd order interior
        slice1[axis] = slice(1, -1)
        slice2[axis] = slice(None, -2)
        slice3[axis] = slice(1, -1)
        slice4[axis] = slice(2, None)
        # 1D equivalent -- out[0] = (f[1] - f[-1]) / 2
        out[tuple(slice1)] = (f[tuple(slice4)] - f[tuple(slice2)]) / 2

        slice1[axis] = 0
        slice2[axis] = 1
        slice3[axis] = 0
        # 1D equivalent -- out[0] = (f[1] - f[0]) / (x[1] - x[0])
        out[tuple(slice1)] = (f[tuple(slice2)] - f[tuple(slice3)])

        slice1[axis] = -1
        slice2[axis] = -1
        slice3[axis] = -2
        # 1D equivalent -- out[-1] = (f[-1] - f[-2]) / (x[-1] - x[-2])
        out[tuple(slice1)] = (f[tuple(slice2)] - f[tuple(slice3)])

        outvals.append(out)

        # reset the slice object in this dimension to ":"
        slice1[axis] = slice(None)
        slice2[axis] = slice(None)
        slice3[axis] = slice(None)
        slice4[axis] = slice(None)

    if len_axes == 1:
        return outvals[0]
    else:
        return outvals

def show_image(grad_x, grad_y, grad):
    plt.figure(figsize=(10, 5))  # 设置窗口大小
    plt.suptitle('image_gradient')  # 图片名称
    plt.subplot(1, 3, 1)
    plt.title('grad_x')
    plt.imshow(grad_x, cmap='gray'), plt.axis('off')
    plt.subplot(1, 3, 2)
    plt.title('gray_y')
    plt.imshow(grad_y, cmap='gray'), plt.axis('off')
    plt.subplot(1, 3, 3)
    plt.title('grad')
    plt.imshow(grad, cmap='gray'), plt.axis('off')
    plt.savefig('./grad.png') # 保存图像
    plt.show()

if __name__ == '__main__':
    '''
    # 图像的数组表示
    img = np.array(Image.open(image_name))
    #图像的变换，反色
    b = [255, 255, 255] - img
    im = Image.fromarray(b.astype(np.uint8))
    im.save(image_name_2)
    
    '''
    # 图像的数组表示
    img = np.array(Image.open(image_name).convert('L'))
    # 图像的变换，反色
    b = 255 - img
    im = Image.fromarray(b.astype(np.uint8))
    im.save(image_name_3)

    # grad_x = image_gradient(img, axis=1)
    # grad_y = image_gradient(img, axis=0)
    # grad = np.sqrt(grad_x ** 2 + grad_y ** 2)
    # abs_x = np.array(np.abs(grad_x), dtype=np.uint8)
    # abs_y = np.array(np.abs(grad_y), dtype=np.uint8)
    # abs_grad = np.array(np.abs(grad), dtype=np.uint8)
    # show_image(abs_x, abs_y, abs_grad)