Python 通过 .cube LUT 文件对图像加滤镜

一个好用的python给图片加滤镜的代码: 

https://github.com/CKboss/PyApplyLUT

这个是对C++代码的封装, 并用上了openmp来并行处理, 速度很快, 4k图片加滤镜在本地测试也只要不到0.2秒.

需要编译一下. 依赖pybind11和eigen. 好在这两个库都是只包含头文件就能用的那种. 到官网下好源码(pybind11 2.7.1, eigen 3.4), 在CMakeLists中指明pybind11和eigen的路径, 编译一下即可.

得到.so文件后, 需要把它放到python能找到的地方. 这里就直接把路径写死了.

用法如下:

 1 import cv2
 2 import numpy as np
 3 from pathlib2 import Path
 4 
 5 import sys
 6 # the path of .so where python can find it
 7 sys.path.append("Q:/WorkSpace/bfood/lut-master/build/Debug")
 8 from python.PyApplyLUT import PyApplyLUT
 9 from python.lut_tools import cube_to_npy
10 
11 INPUT_IMG = Path(r".\test\1.jpg")
12 LUT_FILE = Path(r".\test\1.cube")
13 
14 # normlizer the input picture to 0~1
15 img = cv2.imread(INPUT_IMG.as_posix())
16 img = img / 255
17 
18 # apply lut 
19 
20 # method 1 load lut from a .cube file
21 alut = PyApplyLUT(lut_file=LUT_FILE)
22 new_img = alut.apply_lut(img)
23 # recover to 0~255
24 new_img = new_img * 255
25 cv2.imwrite("./test/new_img_1.jpg",new_img)
26 
27 # method 2 load lut from the np array
28 cubenpy = cube_to_npy(LUT_FILE)
29 alut = PyApplyLUT(lut_dim=32, lut_cube=cubenpy)
30 new_img = alut.apply_lut(img)
31 # recover to 0~255
32 new_img = new_img * 255
33 cv2.imwrite("./test/new_img_2.jpg",new_img)

效果图:

   ----->    

有两种用法: 

1. 使用.cube文件

滤镜(.cube)文件格式如下:  里面的值是0~1之间的

# Created by Adobe Lightroom plugin Export LUT (1.17.0)
LUT_3D_SIZE 32
DOMAIN_MIN 0.0 0.0 0.0
DOMAIN_MAX 1.0 1.0 1.0
0.000000 0.000000 0.000000
0.047791 0.000000 0.000000
0.080140 0.000000 0.000000
0.118013 0.000000 0.000000
0.169955 0.000000 0.000000
...

输入的图片也要归一化到0~1之间, 最后输出的时候要重新放大到0~255

2. 使用一个numpy的数组格试的滤镜文件

格式是3,32,32,32这样的数组,  .cube转换到npy的代码如下:

def load_lut_file_to_input_cube(cube_path,dim=None):
    with open(cube_path,'r') as f:
        lines = f.readlines()
        for i in range(len(lines)):
            lines[i] = lines[i].strip()
            if dim is None:
                if 'LUT_3D_SIZE' in lines[i]:
                    dim = int(lines[i].split(' ')[-1])
    lines = lines[-dim*dim*dim:]
    cube = np.zeros((3,dim,dim,dim),dtype=np.float32)
    for i in range(0,dim):
        for j in range(0,dim):
            for k in range(0,dim):
                n = i * dim*dim + j * dim + k
                line = lines[n].split(' ')
                x = line
                try:
                    cube[0,i,j,k] = float(x[0]) # r
                    cube[1,i,j,k] = float(x[1]) # g
                    cube[2,i,j,k] = float(x[2]) # b
                except Exception:
                    print(lines[n])
    cube = np.array(cube,dtype=np.float32)
    return cube