使用PIL和NumPy将图像转换为Lab数组，修改值，然后转换回 [英] Using PIL and NumPy to convert an image to Lab array, modify the values and then convert back

问题描述

我试图使用NumPy将PIL图像转换为数组。然后，我想将该数组转换为Lab值，修改值，然后将数组转换回图像并保存图像。我有以下代码：

  import Image，color，numpy 
 
＃打开图像文件
 src = Image.open（face-him.jpg）
 
＃尝试确保图像是RGB 
 src = src.convert（mode =RGB）
 
＃使用numpy创建图像数组
 srcArray = numpy.asarray（src）
 
＃将RGB数组转换为Lab 
 srcArray = color.rgb2lab srcArray）
 
＃修改此处的数组
 
＃将数组转换回Lab 
 end = color.lab2rgb（srcArray）
 
＃Create image from array 
 final = Image.fromarray（end，RGB）
 
＃保存
 final.save（out.jpg）
  

此代码依赖于PIL，NumPy和颜色。颜色可以在SciPy树干中找到

测试所需的所有源文件 - colour-test.zip

解决方案

没有尝试过，缩放错误在转换颜色时很常见：

RGB是字节0 .. 255，例如黄色[255,255,0]，
，而 rgb2xyz（）等工作在三个浮点数，黄色[1.，1.，0]
（ color.py 没有范围检查： lab2rgb（rgb2lab（[255,255,0]））是

在IPython中，％run main.py ，然后打印srcArray的两个角， / p>

添加13July：对于记录/ google，这里是NumPy成语，打包，解压缩和转换RGB图像数组：

 ＃unpack image array，10 x 5 x 3  - > rgb  -  
 img = np.arange（10 * 5 * 3）.reshape（（10,5,3））
 printimg.shape：，img.shape 
r， g，b = img.transpose（2,0,1）＃3 10 5 
 printr.shape：，r.shape 
 
＃pack 10 x 5 rgb  - 10 x 5 x 3再次
 rgb = np.array（（r，g，b））。transpose（1,2,0）＃10 5 3再次
 printrgb.shape： ，rgb.shape 
 assert（rgb == img）.all（）
 
＃rgb 0 .. 255& float 0 .. 1  -  
 imgfloat = img.astype（np.float32）/ 255. 
 img8 =（imgfloat * 255）.round（）。astype（np.uint8）
 assert（img == img8）.all（）
  

I am trying to convert a PIL image into an array using NumPy. I then want to convert that array into Lab values, modify the values and then convert the array back in to an image and save the image. I have the following code:

import Image, color, numpy

# Open the image file
src = Image.open("face-him.jpg")

# Attempt to ensure image is RGB
src = src.convert(mode="RGB")

# Create array of image using numpy
srcArray = numpy.asarray(src)

# Convert array from RGB into Lab
srcArray = color.rgb2lab(srcArray)

# Modify array here

# Convert array back into Lab
end = color.lab2rgb(srcArray)

# Create image from array
final = Image.fromarray(end, "RGB")

# Save
final.save("out.jpg")

This code is dependent on PIL, NumPy and color. color can be found in the SciPy trunk here. I downloaded the color.py file along with certain colordata .txt files. I modified the color.py so that it can run independently from the SciPy source and it all seems to work fine - values in the array are changed when I run conversions.

My problem is that when I run the above code which simply converts an image to Lab, then back to RGB and saves it I get the following image back:

What is going wrong? Is it the fact I am using the functions from color.py?

For reference:
Source Image - face-him.jpg
All source files required to test - colour-test.zip

解决方案

Without having tried it, scaling errors are common in converting colors:
RGB is bytes 0 .. 255, e.g. yellow [255,255,0], whereas rgb2xyz() etc. work on triples of floats, yellow [1.,1.,0].
(color.py has no range checks: lab2rgb( rgb2lab([255,255,0]) ) is junk.)

In IPython, %run main.py, then print corners of srcArray and end ?

Added 13July: for the record / for google, here are NumPy idioms to pack, unpack and convert RGB image arrays:

    # unpack image array, 10 x 5 x 3 -> r g b --
img = np.arange( 10*5*3 ).reshape(( 10,5,3 ))
print "img.shape:", img.shape
r,g,b = img.transpose( 2,0,1 )  # 3 10 5
print "r.shape:", r.shape

    # pack 10 x 5 r g b -> 10 x 5 x 3 again --
rgb = np.array(( r, g, b )).transpose( 1,2,0 )  # 10 5 3 again
print "rgb.shape:", rgb.shape
assert (rgb == img).all()

    # rgb 0 .. 255 <-> float 0 .. 1 --
imgfloat = img.astype(np.float32) / 255.
img8 = (imgfloat * 255).round().astype(np.uint8)  
assert (img == img8).all()

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