如何使用PIL使所有白色像素透明？

Question 1

我试图用Python图像库使所有的白色像素透明。 (我是一个试图学习Python的C黑客，所以请温柔一点) 我已经完成了转换（至少像素值看起来是正确的），但我不知道如何将列表转换成缓冲区来重新创建图像。以下是我的代码

img = Image.open('img.png')
imga = img.convert("RGBA")
datas = imga.getdata()
newData = list()
for item in datas:
    if item[0] == 255 and item[1] == 255 and item[2] == 255:
        newData.append([255, 255, 255, 0])
    else:
        newData.append(item)
imgb = Image.frombuffer("RGBA", imga.size, newData, "raw", "RGBA", 0, 1)
imgb.save("img2.png", "PNG")

Question 2


          
           你需要做以下修改。
          
          
           append a tuple
           
            (255, 255, 255, 0)
           
           and not a list
           
            [255, 255, 255, 0]
           
          
          
           use
           
            img.putdata(newData)
           
          
          
           This is the working code:
          
          from PIL import Image
img = Image.open('img.png')
img = img.convert("RGBA")
datas = img.getdata()
newData = []
for item in datas:
    if item[0] == 255 and item[1] == 255 and item[2] == 255:
        newData.append((255, 255, 255, 0))
    else:
        newData.append(item)
img.putdata(newData)
img.save("img2.png", "PNG")

Question 3


          
           
            你也可以使用像素访问模式来就地修改图像。
           
           from PIL import Image
img = Image.open('img.png')
img = img.convert("RGBA")
pixdata = img.load()
width, height = img.size
for y in range(height):
    for x in range(width):
        if pixdata[x, y] == (255, 255, 255, 255):
            pixdata[x, y] = (255, 255, 255, 0)
img.save("img2.png", "PNG")
如果你经常使用，你或许也可以把上述内容包成一个脚本。

Question 4


          
           
            
             由于这是目前谷歌搜索 "Pillow white to transparent "的第一个结果，我想补充的是，用numpy也可以实现同样的效果，而且在我的基准测试中（一张有很多白色背景的800万像素的图片），速度要快10倍左右（大约300ms vs 3.28s的拟议解决方案）。代码也更短一些。
            
            import numpy as np
def white_to_transparency(img):
    x = np.asarray(img.convert('RGBA')).copy()
    x[:, :, 3] = (255 * (x[:, :, :3] != 255).any(axis=2)).astype(np.uint8)
    return Image.fromarray(x)
它也很容易变成 "几乎是白色的"（例如一个通道是254而不是255）是 "几乎透明的 "版本。当然，这将使整个图片部分透明，除了纯黑色。
def white_to_transparency_gradient(img):
    x = np.asarray(img.convert('RGBA')).copy()
    x[:, :, 3] = (255 - x[:, :, :3].mean(axis=2)).astype(np.uint8)
    return Image.fromarray(x)
备注：需要.copy()是因为默认情况下，Pillow图像被转换为只读数组。

Question 5


          
           
            
             
              import Image
import ImageMath
def distance2(a, b):
    return (a[0] - b[0]) * (a[0] - b[0]) + (a[1] - b[1]) * (a[1] - b[1]) + (a[2] - b[2]) * (a[2] - b[2])
def makeColorTransparent(image, color, thresh2=0):
    image = image.convert("RGBA")
    red, green, blue, alpha = image.split()
    image.putalpha(ImageMath.eval("""convert(((((t - d(c, (r, g, b))) >> 31) + 1) ^ 1) * a, 'L')""",
        t=thresh2, d=distance2, c=color, r=red, g=green, b=blue, a=alpha))
    return image
if __name__ == '__main__':
    import sys
    makeColorTransparent(Image.open(sys.argv[1]), (255, 255, 255)).save(sys.argv[2]);

Question 6


          
           
            
             
              
               
                由于循环对一张大图片来说需要很长的时间，所以采用了一种更加pythonic的方式。
               
               from PIL import Image
img = Image.open('img.png')
img = img.convert("RGBA")
imgnp = np.array(img)
white = np.sum(imgnp[:,:,:3], axis=2)
white_mask = np.where(white == 255*3, 1, 0)
alpha = np.where(white_mask, 0, imgnp[:,:,-1])
imgnp[:,:,-1] = alpha 
img = Image.fromarray(np.uint8(imgnp))
img.save("img2.png", "PNG")

Question 7


          
           
            
             
              
               
                
                 Python 3版本，所有文件都在一个目录中
                
                import glob
from PIL import Image
def transparent(myimage):
    img = Image.open(myimage)
    img = img.convert("RGBA")
    pixdata = img.load()
    width, height = img.size
    for y in range(height):
        for x in range(width):
            if pixdata[x, y] == (255, 255, 255, 255):
                pixdata[x, y] = (255, 255, 255, 0)
    img.save(myimage, "PNG")
for image in glob.glob("*.png"):
    transparent(image)

Question 8


          
           
            
             
              
               
                
                 
                  这个函数结合了之前解决方案的所有优点：它允许任何背景，并使用numpy（这比经典的列表更快）。
                 
                 import numpy as np
from PIL import Image
def convert_png_transparent(src_file, dst_file, bg_color=(255,255,255)):
    image = Image.open(src_file).convert("RGBA")
    array = np.array(image, dtype=np.ubyte)
    mask = (array[:,:,:3] == bg_color).all(axis=2)
    alpha = np.where(mask, 0, 255)
    array[:,:,-1] = alpha
    Image.fromarray(np.ubyte(array)).save(dst_file, "PNG")

Question 9


          
           
            
             
              
               
                
                 
                  
                   我很惊讶，没有人看到不仅需要改变一个特定的颜色，而是需要改变该颜色与其他颜色的混合。这就是Gimp的 "颜色转α "功能。将cr333的代码扩展为
                   
                    https://stackoverflow.com/a/62334218/5189462
                   
                   我们得到类似于这种功能的东西。
                  
                  from PIL import Image
target_color = (255, 255, 255)
img   = Image.open('img.png')
imga  = img.convert("RGBA")
datas = imga.getdata()
newData = list()
for item in datas:
    newData.append((
        item[0], item[1], item[2],
            abs(item[0] - target_color[0]), 
            abs(item[1] - target_color[1]), 
            abs(item[2] - target_color[2]), 
imgb = Image.frombuffer("RGBA", imga.size, newData, "raw", "RGBA", 0, 1)
imgb.save("img2.png", "PNG")

Question 10


          
           
            
             
              
               
                
                 
                  
                   
                    @egeres使用到目标颜色的距离来创建一个alpha值的方法真的很整洁，并创造了一个更漂亮的结果。这里是使用numpy。
                   
                   import numpy as np
import matplotlib.pyplot as plt
def color_to_alpha(im, target_color):
    alpha = np.max(
            np.abs(im[..., 0] - target_color[0]),
            np.abs(im[..., 1] - target_color[1]),
            np.abs(im[..., 2] - target_color[2]),
        axis=0,
    ny, nx, _ = im.shape
    im_rgba = np.zeros((ny, nx, 4), dtype=im.dtype)
    for i in range(3):
        im_rgba[..., i] = im[..., i]
    im_rgba[..., 3] = alpha
    return im_rgba
target_color = (0.0, 0.0, 0.0)
im = plt.imread("img.png")
im_rgba = color_to_alpha(im, target_color)
为了完整起见，我在下面附上了与应用于matplotlib标识的基于遮罩的版本的对比。
from pathlib import Path
import matplotlib.pyplot as pl
import numpy as np
def color_to_alpha(im, alpha_color):
    alpha = np.max(
            np.abs(im[..., 0] - alpha_color[0]),
            np.abs(im[..., 1] - alpha_color[1]),
            np.abs(im[..., 2] - alpha_color[2]),
        axis=0,
    ny, nx, _ = im.shape
    im_rgba = np.zeros((ny, nx, 4), dtype=im.dtype)
    for i in range(3):
        im_rgba[..., i] = im[..., i]
    im_rgba[..., 3] = alpha
    return im_rgba
def color_to_alpha_mask(im, alpha_color):
    mask = (im[..., :3] == alpha_color).all(axis=2)
    alpha = np.where(mask, 0, 255)
    ny, nx, _ = im.shape
    im_rgba = np.zeros((ny, nx, 4), dtype=im.dtype)
    im_rgba[..., :3] = im
    im_rgba[..., -1] = alpha
    return im_rgba
# load example from images included with matplotlib
fn_img = Path(plt.__file__).parent / "mpl-data" / "images" / "matplotlib_large.png"
im = plt.imread(fn_img)[..., :3]  # get rid of alpha channel already in image
target_color = [1.0, 1.0, 1.0]
im_rgba = color_to_alpha(im, target_color)
im_rgba_masked = color_to_alpha_mask(im, target_color)
fig, axes = plt.subplots(ncols=3, figsize=(12, 4))
[ax.set_facecolor("lightblue") for ax in axes]
axes[0].imshow(im)
axes[0].set_title("original")
axes[1].imshow(im_rgba)
axes[1].set_title("using distance to color")
axes[2].imshow(im_rgba_masked)
axes[2].set_title("mask on color")

Question 11


          
           
            
             
              
               
                
                 
                  
                   
                    
                     
                      我非常喜欢Jonathan的回答。可以用NumPy和不使用
                      
                       np.where
                      
                      的另一种方式来实现。
                     
                     import numpy as np
from PIL import Image
img = Image.open('img.png') # n x m x 3
imga = img.convert("RGBA")  # n x m x 4
imga = np.asarray(imga) 
r, g, b, a = np.rollaxis(imga, axis=-1) # split into 4 n x m arrays 
r_m = r != 255 # binary mask for red channel, True for all non white values
g_m = g != 255 # binary mask for green channel, True for all non white values
b_m = b != 255 # binary mask for blue channel, True for all non white values
# combine the three masks using the binary "or" operation 
# multiply the combined binary mask with the alpha channel
a = a * ((r_m == 1) | (g_m == 1) | (b_m == 1))
# stack the img back together 
imga =  Image.fromarray(np.dstack([r, g, b, a]), 'RGBA')
我把我的方法与keithb的方法（评分最高的答案）进行了比较，我的方法要快18个百分点（102张124*124大小的图片的平均数）。