Python PIL.Image 模块，eval() 实例源码
我们从Python开源项目中，提取了以下8个代码示例，用于说明如何使用PIL.Image.eval()。
def save_target_image(self, source, name, x_offset, y_offset, x_size, y_size, flip, convert):
m_img = Image.open(source)
if x_size!=0 and y_size!=0:
m_img = m_img.crop((x_offset, x_offset + x_size, y_offset + y_size))
if flip is True:
m_img = m_img.transpose(Image.FLIP_LEFT_RIGHT)
if convert is True:
m_img.load()
alpha = m_img.split()[- 1]
m_img = m_img.convert('RGB').convert('P', palette=Image.ADAPTIVE, colors=255)
mask = Image.eval(alpha, lambda a: 255 if a <= 128 else 0)
m_img.paste(255, mask)
m_img.save(join(self.emotedb_path, name) + ".png", transparency=255, optimize=True)
else:
m_img.save(join(self.emotedb_path, optimize=True)
def save_target_image(self, mask)
m_img.save(join(self.emotes_path, optimize=True)
else:
m_img.save(join(self.emotes_path, optimize=True)
def test1():
filename = '../ETL8G/ETL8G_01'
id_record = 0
with open(filename, 'rb') as f:
f.seek(id_record * sz_record)
r = read_record_ETL8G(f)
print(r[0:-2], hex(r[1]))
iE = Image.eval(r[-1], lambda x: 255 - x * 16)
fn = '../tmp/ETL8G_{:d}_{:s}.png'.format((r[0] - 1) % 20 + 1, hex(r[1])[-4:])
iE.save(fn, 'PNG')
def test2():
filename = '../ETL8G/ETL8G_01'
id_dataset = 0
new_img = Image.new('L', (128 * 32, 128 * 30))
with open(filename, 'rb') as f:
f.seek(id_dataset * 956 * sz_record)
for i in range(956):
r = read_record_ETL8G(f)
new_img.paste(r[-1], (128 * (i % 32), 128 * (i // 32)))
iE = Image.eval(new_img, lambda x: 255 - x * 16)
fn = '../tmp/ETL8G_ds{:03d}.png'.format(id_dataset)
iE.save(fn, 'PNG')
def dump_all():
for j in range(1, 33):
for id_dataset in range(5):
new_img = Image.new('L', 128 * 30))
filename = '../ETL8G/ETL8G_{:02d}'.format(j)
with open(filename, 'rb') as f:
f.seek(id_dataset * 956 * sz_record)
for i in range(956):
r = read_record_ETL8G(f)
new_img.paste(r[-1], 128 * (i // 32)))
iE = Image.eval(new_img, lambda x: 255 - x * 16)
fn = '../tmp/ETL8G_ds{:02d}_{:01d}.png'.format(j, id_dataset)
iE.save(fn, 'PNG')
def combine_pixels(self,p1,p2,alpha=0.5):
return tuple([round(alpha*p1[i] + (1 - alpha)*p2[i]) for i in range(3)])
# The use of Image.eval applies the func to each BAND,independently,if image pixels are RGB tuples.
def map_image(self,func,image=False):
# "Apply func to each pixel of the image,returning a new image"
image = image if image else self.image
return Imager(image=Image.eval(image,func)) # Eval creates a new image,so no need for me to do a copy.
# This applies the function to each RGB TUPLE,returning a new tuple to appear in the new image. So func
# must return a 3-tuple if the image has RGB pixels.
def convertimagesToPIL(self, images, dither, nq=0, images_info=None):
""" convertimagesToPIL(images,nq=0)
Convert images to Paletted PIL images,which can then be
written to a single animated GIF.
"""
# Convert to PIL images
images2 = []
for im in images:
if isinstance(im, Image.Image):
images2.append(im)
elif np and isinstance(im, np.ndarray):
if im.ndim == 3 and im.shape[2] == 3:
im = Image.fromarray(im, 'RGB')
elif im.ndim == 3 and im.shape[2] == 4:
# im = Image.fromarray(im[:,:,:3],'RGB')
self.transparency = True
im = Image.fromarray(im[:, :, :4], 'RGBA')
elif im.ndim == 2:
im = Image.fromarray(im, 'L')
images2.append(im)
# Convert to paletted PIL images
images, images2 = images2, []
if nq >= 1:
# NeuQuant algorithm
for im in images:
im = im.convert("RGBA") # NQ assumes RGBA
nqInstance = NeuQuant(im, int(nq)) # Learn colors from image
if dither:
im = im.convert("RGB").quantize(palette=nqInstance.paletteImage(), colors=255)
else:
im = nqInstance.quantize(im, colors=255) # Use to quantize the image itself
self.transparency = True # since NQ assumes transparency
if self.transparency:
alpha = im.split()[3]
mask = Image.eval(alpha, lambda a: 255 if a <= 128 else 0)
im.paste(255, mask=mask)
images2.append(im)
else:
# Adaptive PIL algorithm
AD = Image.ADAPTIVE
# for index,im in enumerate(images):
for i in range(len(images)):
im = images[i].convert('RGB').convert('P', palette=AD, dither=dither, colors=255)
if self.transparency:
alpha = images[i].split()[3]
mask = Image.eval(alpha, mask=mask)
images2.append(im)
# Done
return images2
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