图像的变换(PIL,Numpy)

from  PIL import Image
import numpy as np
a = np.array(Image.open("fcity.jpg"))
print(a.shape, a.dtype)
b = [255, 255, 255] - a
im = Image.fromarray(b.astype('uint8'))
im.save("a.jpg")

原图：
变化过的图片

from  PIL import Image
import numpy as np
a = np.array(Image.open("fcity.jpg").convert('L'))
print(a.shape, a.dtype)
b = 255 - a
im = Image.fromarray(b.astype('uint8'))
im.save("b.jpg")

变化过的图：

from  PIL import Image
import numpy as np
a = np.array(Image.open("fcity.jpg").convert('L'))
print(a.shape, a.dtype)
b = (100/255)*a + 150  #区间变换
im = Image.fromarray(b.astype('uint8'))
im.save("b.jpg")

变化结果：

from  PIL import Image
import numpy as np
a = np.array(Image.open("fcity.jpg").convert('L'))
print(a.shape, a.dtype)
b = 255*(a /255) + 150  #像素的平方
im = Image.fromarray(b.astype('uint8'))
im.save("b.jpg")

结果：

图像的手绘：

from PIL import Image
import numpy as np

a = np.asarray(Image.open('fcity.jpg').convert('L')).astype('float')
depath = 10.   # (0-100)
grad = np.gradient(a)  #取图像灰度的梯度值
grad_x,grad_y = grad  #分别取横纵图像梯度值
grad_x = grad_x * depath/100.
grad_y = grad_y * depath/100.
A = np.sqrt(grad_x ** 2 + grad_y ** 2 +1.)
uni_x = grad_x/A
uni_y = grad_y/A
uni_z = 1. /A

vec_el = np.pi/2.2  # 光源的俯视角度，弧度值
vec_az = np.pi/4  # 光源的方位角度，弧度值
dx = np.cos(vec_el)*np.cos(vec_az) #光源对x 轴的影响
dy = np.cos(vec_el)*np.sin(vec_az)   #光源对y 轴的影响
dz = np.sin(vec_el)         #光源对z 轴的影响

b = 255 * (dx*uni_x + dy*uni_y + dz *uni_z)  	#光源归一化
b = b.clip(0, 255)

im = Image.fromarray(b.astype('uint8'))   #重构图像
im.save('c.jpg')

结果：