def test_sanity():
tar = TarIO.TarIO(tarfile, 'lena.png')
im = Image.open(tar)
im.load()
assert_equal(im.mode, "RGB")
assert_equal(im.size, (128, 128))
assert_equal(im.format, "PNG")
tar = TarIO.TarIO(tarfile, 'lena.jpg')
im = Image.open(tar)
im.load()
assert_equal(im.mode, "RGB")
assert_equal(im.size, (128, 128))
assert_equal(im.format, "JPEG")
def test_sanity(self):
if "zip_decoder" in codecs:
tar = TarIO.TarIO(tarfile, 'lena.png')
im = Image.open(tar)
im.load()
self.assertEqual(im.mode, "RGB")
self.assertEqual(im.size, (128, 128))
self.assertEqual(im.format, "PNG")
if "jpeg_decoder" in codecs:
tar = TarIO.TarIO(tarfile, 'lena.jpg')
im = Image.open(tar)
im.load()
self.assertEqual(im.mode, "RGB")
self.assertEqual(im.size, (128, 128))
self.assertEqual(im.format, "JPEG")
def test_sanity(self):
for codec, test_path, format in [
["zip_decoder", "hopper.png", "PNG"],
["jpeg_decoder", "hopper.jpg", "JPEG"],
]:
if codec in codecs:
tar = TarIO.TarIO(TEST_TAR_FILE, test_path)
im = Image.open(tar)
im.load()
self.assertEqual(im.mode, "RGB")
self.assertEqual(im.size, (128, 128))
self.assertEqual(im.format, format)
def test_sanity():
for codec, test_path, format in [
["zlib", "hopper.png", "PNG"],
["jpg", "hopper.jpg", "JPEG"],
]:
if features.check(codec):
with TarIO.TarIO(TEST_TAR_FILE, test_path) as tar:
with Image.open(tar) as im:
im.load()
assert im.mode == "RGB"
assert im.size == (128, 128)
assert im.format == format
def test_sanity(self):
for codec, test_path, format in [
['zip_decoder', 'hopper.png', 'PNG'],
['jpeg_decoder', 'hopper.jpg', 'JPEG']
]:
if codec in codecs:
tar = TarIO.TarIO(TEST_TAR_FILE, test_path)
im = Image.open(tar)
im.load()
self.assertEqual(im.mode, "RGB")
self.assertEqual(im.size, (128, 128))
self.assertEqual(im.format, format)
def test_close(self):
tar = TarIO.TarIO(TEST_TAR_FILE, "hopper.jpg")
tar.close()
def open():
with TarIO.TarIO(TEST_TAR_FILE, "hopper.jpg"):
pass
def open():
tar = TarIO.TarIO(TEST_TAR_FILE, "hopper.jpg")
tar.close()
def open():
TarIO.TarIO(TEST_TAR_FILE, "hopper.jpg")
# ====================================================
# more on reading images
# As described earlier, the open() function of the Image module is used to open an image file. In most cases,
# you simply pass it the filename as an argument.
img = Image.open('sample.jpg')
with open('sample.jpg', 'rb') as fp:
img = Image.open(fp)
# img.show()
# you can use ContaineIO or TarIO module to access image file embedded in a larger file.
# Reading from a tar archive.
img = TarIO.TarIO('sample.tar', 'sample.jpg')
img = Image.open(img)
# img.show()
# ====================================================
# controlling the decoder.
# Some decoders allow you to manipulate the image while reading it from a file. This can often be used to speed up
# decoding when creating thumbnails (when speed is usually more important thatn quality) and printing to a monochrome
# laser printer.
# The draft() method manipulates an opened but not yet loaded image so it as closely as possible matches the given mode and size.
# Reading in draft mode.
from PIL import Image
img = Image.open('sample.jpg')
print('original = ', img.mode, im.size)
def test_contextmanager():
with warnings.catch_warnings():
with TarIO.TarIO(TEST_TAR_FILE, "hopper.jpg"):
pass
ImageEnhance.Sharpness
'''
enh = ImageEnhance.Sharpness(im)
enh.enhance(30)
# Image sequences
'''
im = Image.open("animation.gif")
im.seek(1) # skip to the second frame
try:
while 1:
im.seek(im.tell()+1)
# do something to im
except EOFError:
pass # end of sequence
'''
# More on reading images
## Reading from a tar archive
from PIL import TarIO
fp = TarIO.TarIO('data.tar', 'data/test.png')
im_tar = Image.open(fp)
# PIL to numpy
## PIL to numpy array
im_ndarray = np.array(im_tar)
## numpy array to PIL
im = Image.fromarray(im_ndarray)
im.show()
def test_contextmanager():
with pytest.warns(None) as record:
with TarIO.TarIO(TEST_TAR_FILE, "hopper.jpg"):
pass
assert not record
def test_close():
with pytest.warns(None) as record:
tar = TarIO.TarIO(TEST_TAR_FILE, "hopper.jpg")
tar.close()
assert not record
# open_image_from_tar.py
from PIL import Image, TarIO
fobj = TarIO.TarIO("flowers.tar", "flowers.jpg")
image = Image.open(fobj)
image.show()
def test_contextmanager(self):
with TarIO.TarIO(TEST_TAR_FILE, "hopper.jpg"):
pass
for main_name in os.listdir(data_dir):
# 将tar文件加入到地址中，进而遍历，获取类名，每一个类似于 ‘n01440764.tar’,
# 进入训练集，里面还会有1000个压缩包，分别代表一类，其中包含很多图像
sub_dir = os.path.join(data_dir, main_name)
print("The dir of sub class", sub_dir)
class_name = main_name.split('.')[0]
trainname_class.append(class_name)
file = tarfile.open(sub_dir, "r")
file_list = []
# 遍历每一个 tar 文件， 进而学习特征
for i in file.getmembers():
# 获取文件名，每一个类似于 ‘n01440764.tar’
file_name = i.name
print("name of pictures ", file_name)
file_list.append(file_name)
fp = TarIO.TarIO(sub_dir, file_name)
# 打开图片
im = Image.open(fp)
# 对所有的图片进行裁剪，重新标定尺寸
img = im.resize((227, 227), Image.ANTIALIAS)
#从Image类转化为 numpy array, 事实上此时的图片已经被转换成数组，227*227*3。
img = np.asarray(img)
if img.size == 227 * 227:
img = np.concatenate((img, img, img), axis=0)
img = np.array(img)
# reshape 图片，方便放入dicts
img = img.reshape([227 * 227 * 3])
if traindata == [[]]:
traindata = [img]
else:
def test_close():
with warnings.catch_warnings():
tar = TarIO.TarIO(TEST_TAR_FILE, "hopper.jpg")
tar.close()