numpy对图像的处理演示

pip3 install pillow

 

from PIL import Image
import numpy as np
im=np.array(Image.open('timg.jpg'))

print(im.shape,im.dtype)

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

　　运算结果：

D:\python3\python.exe D:/python入门/数据可视化/numpy_demo/的mo.py
(749, 1200, 3) uint8

Process finished with exit code 0　　

 

 

---

# from PIL import Image
# import numpy as np
# im=np.array(Image.open('timg.jpg'))
# print(im.shape,im.dtype)
# b=im*(100/255)+150
# a=Image.fromarray(b.astype('uint8'))
# a.save('new-timg.jpg')


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

from PIL import Image
import numpy as np
im=np.array(Image.open('timg.jpg'))
print(im.shape,im.dtype)
b=255*(im/255)**2
a=Image.fromarray(b.astype('uint8'))
a.save('new-timg.jpg')

 手绘风格的效果

from PIL import Image
import numpy as np

a = np.asarray(Image.open('timg.jpg').convert('L')).astype('float')

depth = 8.  # (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 * depth / 100.
grad_y = grad_y * depth / 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('b.jpg')