def proc_img(target_size, squarecrop, is_string=False, imgfile=None):
imgfile = StringIO(imgfile) if is_string else imgfile
im = PILImage.open(imgfile)
# This part does the processing
scale_factor = target_size / np.float32(min(im.size))
(wnew, hnew) = map(lambda x: int(round(scale_factor * x)), im.size)
if scale_factor != 1:
filt = PILImage.BICUBIC if scale_factor > 1 else PILImage.ANTIALIAS
im = im.resize((wnew, hnew), filt)
if squarecrop is True:
(cx, cy) = map(lambda x: (x - target_size) // 2, (wnew, hnew))
im = im.crop((cx, cy, cx+target_size, cy+target_size))
buf = StringIO()
im.save(buf, format='JPEG', subsampling=0, quality=95)
return buf.getvalue()
def proc_img(imgfile, is_string=False):
from PIL import Image
if is_string:
imgfile = StringIO(imgfile)
im = Image.open(imgfile)
# This part does the processing
scale_factor = TARGET_SIZE / np.float32(min(im.size))
(wnew, hnew) = map(lambda x: int(round(scale_factor * x)), im.size)
if scale_factor != 1:
filt = Image.BICUBIC if scale_factor > 1 else Image.ANTIALIAS
im = im.resize((wnew, hnew), filt)
if SQUARE_CROP is True:
(cx, cy) = map(lambda x: (x - TARGET_SIZE) / 2, (wnew, hnew))
im = im.crop((cx, cy, cx + TARGET_SIZE, cy + TARGET_SIZE))
buf = StringIO()
im.save(buf, format='JPEG')
return buf.getvalue()
def parse_dev_meta(self, ilsvrc_devkit_tar):
tf = self.open_tar(ilsvrc_devkit_tar, 'devkit tar')
fmeta = tf.extractfile(
tf.getmember('ILSVRC2012_devkit_t12/data/meta.mat'))
import scipy.io
meta_mat = scipy.io.loadmat(StringIO(fmeta.read()))
labels_dic = dict(
(m[0][1][0], m[0][0][0][0] - 1) for m in meta_mat['synsets']
if m[0][0][0][0] >= 1 and m[0][0][0][0] <= 1000)
label_names_dic = dict(
(m[0][1][0], m[0][2][0]) for m in meta_mat['synsets']
if (m[0][0][0][0] >= 1 and m[0][0][0][0] <= 1000))
label_names = [tup[1] for tup in sorted(
[(v, label_names_dic[k]) for k, v in labels_dic.items()],
key=lambda x:x[0])]
fvgtruth = tf.extractfile(tf.getmember(
'ILSVRC2012_devkit_t12/data/' +
'ILSVRC2012_validation_ground_truth.txt'))
vgtruth = [[int(line.strip()) - 1] for line in fvgtruth.readlines()]
tf.close()
return labels_dic, label_names, vgtruth