我正在使用 PIL 将使用 Django 上传的透明 PNG 图像转换为 JPG 文件。输出看起来坏了。
源文件
https://i.stack.imgur.com/I2uNe.png
代码
Image.open(object.logo.path).save('/tmp/output.jpg', 'JPEG')
或者
Image.open(object.logo.path).convert('RGB').save('/tmp/output.png')
结果
两种方式,生成的图像如下所示:
https://i.stack.imgur.com/rmlPz.png
有没有办法来解决这个问题?我想要在透明背景曾经是白色背景的地方。
解决方案
感谢出色的答案,我想出了以下函数集合:
import Image
import numpy as np
def alpha_to_color(image, color=(255, 255, 255)):
"""Set all fully transparent pixels of an RGBA image to the specified color.
This is a very simple solution that might leave over some ugly edges, due
to semi-transparent areas. You should use alpha_composite_with color instead.
Source: http://stackoverflow.com/a/9166671/284318
Keyword Arguments:
image -- PIL RGBA Image object
color -- Tuple r, g, b (default 255, 255, 255)
"""
x = np.array(image)
r, g, b, a = np.rollaxis(x, axis=-1)
r[a == 0] = color[0]
g[a == 0] = color[1]
b[a == 0] = color[2]
x = np.dstack([r, g, b, a])
return Image.fromarray(x, 'RGBA')
def alpha_composite(front, back):
"""Alpha composite two RGBA images.
Source: http://stackoverflow.com/a/9166671/284318
Keyword Arguments:
front -- PIL RGBA Image object
back -- PIL RGBA Image object
"""
front = np.asarray(front)
back = np.asarray(back)
result = np.empty(front.shape, dtype='float')
alpha = np.index_exp[:, :, 3:]
rgb = np.index_exp[:, :, :3]
falpha = front[alpha] / 255.0
balpha = back[alpha] / 255.0
result[alpha] = falpha + balpha * (1 - falpha)
old_setting = np.seterr(invalid='ignore')
result[rgb] = (front[rgb] * falpha + back[rgb] * balpha * (1 - falpha)) / result[alpha]
np.seterr(**old_setting)
result[alpha] *= 255
np.clip(result, 0, 255)
# astype('uint8') maps np.nan and np.inf to 0
result = result.astype('uint8')
result = Image.fromarray(result, 'RGBA')
return result
def alpha_composite_with_color(image, color=(255, 255, 255)):
"""Alpha composite an RGBA image with a single color image of the
specified color and the same size as the original image.
Keyword Arguments:
image -- PIL RGBA Image object
color -- Tuple r, g, b (default 255, 255, 255)
"""
back = Image.new('RGBA', size=image.size, color=color + (255,))
return alpha_composite(image, back)
def pure_pil_alpha_to_color_v1(image, color=(255, 255, 255)):
"""Alpha composite an RGBA Image with a specified color.
NOTE: This version is much slower than the
alpha_composite_with_color solution. Use it only if
numpy is not available.
Source: http://stackoverflow.com/a/9168169/284318
Keyword Arguments:
image -- PIL RGBA Image object
color -- Tuple r, g, b (default 255, 255, 255)
"""
def blend_value(back, front, a):
return (front * a + back * (255 - a)) / 255
def blend_rgba(back, front):
result = [blend_value(back[i], front[i], front[3]) for i in (0, 1, 2)]
return tuple(result + [255])
im = image.copy() # don't edit the reference directly
p = im.load() # load pixel array
for y in range(im.size[1]):
for x in range(im.size[0]):
p[x, y] = blend_rgba(color + (255,), p[x, y])
return im
def pure_pil_alpha_to_color_v2(image, color=(255, 255, 255)):
"""Alpha composite an RGBA Image with a specified color.
Simpler, faster version than the solutions above.
Source: http://stackoverflow.com/a/9459208/284318
Keyword Arguments:
image -- PIL RGBA Image object
color -- Tuple r, g, b (default 255, 255, 255)
"""
image.load() # needed for split()
background = Image.new('RGB', image.size, color)
background.paste(image, mask=image.split()[3]) # 3 is the alpha channel
return background
表现
简单的非合成 alpha_to_color
函数是最快的解决方案,但会留下难看的边框,因为它不处理半透明区域。
纯 PIL 和 numpy 合成解决方案都给出了很好的结果,但 alpha_composite_with_color
(8.93 毫秒)比 pure_pil_alpha_to_color
(79.6 毫秒)快得多。 如果您的系统上可以使用 numpy,那就可以了。(更新:新的纯 PIL 版本是所有提到的解决方案中最快的。)
$ python -m timeit "import Image; from apps.front import utils; i = Image.open(u'logo.png'); i2 = utils.alpha_to_color(i)"
10 loops, best of 3: 4.67 msec per loop
$ python -m timeit "import Image; from apps.front import utils; i = Image.open(u'logo.png'); i2 = utils.alpha_composite_with_color(i)"
10 loops, best of 3: 8.93 msec per loop
$ python -m timeit "import Image; from apps.front import utils; i = Image.open(u'logo.png'); i2 = utils.pure_pil_alpha_to_color(i)"
10 loops, best of 3: 79.6 msec per loop
$ python -m timeit "import Image; from apps.front import utils; i = Image.open(u'logo.png'); i2 = utils.pure_pil_alpha_to_color_v2(i)"
10 loops, best of 3: 1.1 msec per loop
pure_pil_alpha_to_color_v2
中删除 im = image.copy()
。 (当然,在将 im
的后续实例更改为 image
之后。)
这是一个更简单的版本 - 不确定它的性能如何。很大程度上基于我在构建对 sorl 缩略图的 RGBA -> JPG + BG
支持时发现的一些 django 片段。
from PIL import Image
png = Image.open(object.logo.path)
png.load() # required for png.split()
background = Image.new("RGB", png.size, (255, 255, 255))
background.paste(png, mask=png.split()[3]) # 3 is the alpha channel
background.save('foo.jpg', 'JPEG', quality=80)
结果@80%
https://i.stack.imgur.com/jaVG7.jpg
https://i.stack.imgur.com/9IsAI.jpg
通过使用 Image.alpha_composite
,Yuji 'Tomita' Tomita 的解决方案变得更加简单。如果 png 没有 Alpha 通道,此代码可以避免 tuple index out of range
错误。
from PIL import Image
png = Image.open(img_path).convert('RGBA')
background = Image.new('RGBA', png.size, (255, 255, 255))
alpha_composite = Image.alpha_composite(background, png)
alpha_composite.save('foo.jpg', 'JPEG', quality=80)
.convert("RGB")
透明部分大多具有 RGBA 值 (0,0,0,0)。由于 JPG 没有透明度,所以 jpeg 值设置为 (0,0,0),即黑色。
在圆形图标周围,有非零 RGB 值的像素,其中 A = 0。因此它们在 PNG 中看起来是透明的,但在 JPG 中看起来很有趣。
您可以使用 numpy 将 A == 0 的所有像素设置为 R = G = B = 255,如下所示:
import Image
import numpy as np
FNAME = 'logo.png'
img = Image.open(FNAME).convert('RGBA')
x = np.array(img)
r, g, b, a = np.rollaxis(x, axis = -1)
r[a == 0] = 255
g[a == 0] = 255
b[a == 0] = 255
x = np.dstack([r, g, b, a])
img = Image.fromarray(x, 'RGBA')
img.save('/tmp/out.jpg')
https://i.stack.imgur.com/4ciZO.jpg
请注意,徽标还有一些半透明像素,用于平滑文字和图标周围的边缘。保存到 jpeg 会忽略半透明度,使生成的 jpeg 看起来很锯齿。
使用 imagemagick 的 convert
命令可以获得更好的质量结果:
convert logo.png -background white -flatten /tmp/out.jpg
https://i.stack.imgur.com/Qi8d0.jpg
要使用 numpy 进行质量更好的混合,您可以使用 alpha compositing:
import Image
import numpy as np
def alpha_composite(src, dst):
'''
Return the alpha composite of src and dst.
Parameters:
src -- PIL RGBA Image object
dst -- PIL RGBA Image object
The algorithm comes from http://en.wikipedia.org/wiki/Alpha_compositing
'''
# http://stackoverflow.com/a/3375291/190597
# http://stackoverflow.com/a/9166671/190597
src = np.asarray(src)
dst = np.asarray(dst)
out = np.empty(src.shape, dtype = 'float')
alpha = np.index_exp[:, :, 3:]
rgb = np.index_exp[:, :, :3]
src_a = src[alpha]/255.0
dst_a = dst[alpha]/255.0
out[alpha] = src_a+dst_a*(1-src_a)
old_setting = np.seterr(invalid = 'ignore')
out[rgb] = (src[rgb]*src_a + dst[rgb]*dst_a*(1-src_a))/out[alpha]
np.seterr(**old_setting)
out[alpha] *= 255
np.clip(out,0,255)
# astype('uint8') maps np.nan (and np.inf) to 0
out = out.astype('uint8')
out = Image.fromarray(out, 'RGBA')
return out
FNAME = 'logo.png'
img = Image.open(FNAME).convert('RGBA')
white = Image.new('RGBA', size = img.size, color = (255, 255, 255, 255))
img = alpha_composite(img, white)
img.save('/tmp/out.jpg')
https://i.stack.imgur.com/RBBhD.jpg
这是纯 PIL 中的解决方案。
def blend_value(under, over, a):
return (over*a + under*(255-a)) / 255
def blend_rgba(under, over):
return tuple([blend_value(under[i], over[i], over[3]) for i in (0,1,2)] + [255])
white = (255, 255, 255, 255)
im = Image.open(object.logo.path)
p = im.load()
for y in range(im.size[1]):
for x in range(im.size[0]):
p[x,y] = blend_rgba(white, p[x,y])
im.save('/tmp/output.png')
它没有坏。它完全按照您的指示行事;这些像素是完全透明的黑色。您将需要遍历所有像素并将完全透明的像素转换为白色。
import numpy as np
import PIL
def convert_image(image_file):
image = Image.open(image_file) # this could be a 4D array PNG (RGBA)
original_width, original_height = image.size
np_image = np.array(image)
new_image = np.zeros((np_image.shape[0], np_image.shape[1], 3))
# create 3D array
for each_channel in range(3):
new_image[:,:,each_channel] = np_image[:,:,each_channel]
# only copy first 3 channels.
# flushing
np_image = []
return new_image
from PIL import Image
def fig2img ( fig ):
"""
@brief Convert a Matplotlib figure to a PIL Image in RGBA format and return it
@param fig a matplotlib figure
@return a Python Imaging Library ( PIL ) image
"""
# put the figure pixmap into a numpy array
buf = fig2data ( fig )
w, h, d = buf.shape
return Image.frombytes( "RGBA", ( w ,h ), buf.tostring( ) )
def fig2data ( fig ):
"""
@brief Convert a Matplotlib figure to a 4D numpy array with RGBA channels and return it
@param fig a matplotlib figure
@return a numpy 3D array of RGBA values
"""
# draw the renderer
fig.canvas.draw ( )
# Get the RGBA buffer from the figure
w,h = fig.canvas.get_width_height()
buf = np.fromstring ( fig.canvas.tostring_argb(), dtype=np.uint8 )
buf.shape = ( w, h, 4 )
# canvas.tostring_argb give pixmap in ARGB mode. Roll the ALPHA channel to have it in RGBA mode
buf = np.roll ( buf, 3, axis = 2 )
return buf
def rgba2rgb(img, c=(0, 0, 0), path='foo.jpg', is_already_saved=False, if_load=True):
if not is_already_saved:
background = Image.new("RGB", img.size, c)
background.paste(img, mask=img.split()[3]) # 3 is the alpha channel
background.save(path, 'JPEG', quality=100)
is_already_saved = True
if if_load:
if is_already_saved:
im = Image.open(path)
return np.array(im)
else:
raise ValueError('No image to load.')
background = Image.new("RGB", png.size, (255, 255, 255))
。paste
进行适当的混合。split
方法需要load
方法。听到它实际上很快/和/简单,真是太棒了!tuple index out of range
。我通过关注另一个问题(stackoverflow.com/questions/1962795/…)解决了这个问题。我必须先将 PNG 转换为 RGBA,然后对其进行切片:alpha = img.split()[-1]
然后在背景蒙版上使用它。background.paste(image, mask=image.getchannel('A'))
- 像素范围问题稍微好一点。并且可能适用于其他一些模式,例如LA