def _parse_pairs(self, txtfile):
pairs = []
for x in readcsv(os.path.join(self.lfwdir, txtfile), separator='\t'):
if len(x) == 3:
pairs.append(
(ImageCategory(category=x[0],
filename=os.path.join(
self.lfwdir, x[0],
'%s_%04d.jpg' % (x[0], int(x[1])))),
ImageCategory(category=x[0],
filename=os.path.join(
self.lfwdir, x[0],
'%s_%04d.jpg' % (x[0], int(x[2]))))))
elif len(x) == 4:
pairs.append(
(ImageCategory(category=x[0],
filename=os.path.join(
self.lfwdir, x[0],
'%s_%04d.jpg' % (x[0], int(x[1])))),
ImageCategory(category=x[2],
filename=os.path.join(
self.lfwdir, x[2],
'%s_%04d.jpg' % (x[2], int(x[3]))))))
else:
pass
return pairs
def _parse_cls(self, imageset='train'):
"""ImageNet Classification, imageset = {train, val}"""
import xmltodict
if imageset == 'train':
imagesetfile = 'train_cls.txt'
elif imageset == 'val':
imagesetfile = 'val.txt'
else:
raise ValueError('unsupported imageset')
csv = readcsv(os.path.join(self.datadir, 'ImageSets', 'CLS-LOC',
imagesetfile),
separator=' ')
for (subpath, k) in csv:
xmlfile = '%s.xml' % os.path.join(self.datadir, 'Annotations',
'CLS-LOC', imageset, subpath)
imfile = '%s.JPEG' % os.path.join(self.datadir, 'Data', 'CLS-LOC',
imageset, subpath)
if os.path.exists(xmlfile):
d = xmltodict.parse(open(xmlfile, 'r').read())
objlist = d['annotation']['object'] if islist(
d['annotation']['object']) else [
d['annotation']['object']
]
yield ImageCategory(filename=imfile,
category=objlist[0]['name'])
else:
yield ImageCategory(filename=imfile,
category=filepath(subpath))
def videos(self, subject):
videos = {}
for d in dirlist(os.path.join(self.ytfdir, 'frame_images_DB',
subject)):
k_videoindex = filetail(d)
videos[k_videoindex] = []
for f in imlist(d):
videos[k_videoindex].append(
ImageCategory(filename=f, category=subject))
videos[k_videoindex] = sorted(videos[k_videoindex],
key=lambda im: im.filename())
return videos
def dataset(self):
# Return json or CSV file containing dataset description
categorydir = os.path.join(self.datadir, '256_ObjectCategories')
imlist = []
for (idx_category, category) in enumerate(os.listdir(categorydir)):
imdir = os.path.join(categorydir, category)
for im in os.listdir(imdir):
imlist.append(
ImageCategory(filename=os.path.join(
categorydir, category, im),
category=category))
return imlist
def dataset(self):
return [
ImageCategory(category=s, filename=f) for s in self.subjects()
for f in imlist(os.path.join(self.lfwdir, s))
]
def subject_images(self, subject):
"""List of Images of a subject"""
fnames = imlist(os.path.join(self.lfwdir, subject))
return [ImageCategory(category=subject, filename=f) for f in fnames]
def _test_image_fileformat(imgfile):
# Filename object
im = ImageDetection(filename=imgfile, xmin=100, ymin=100, bbwidth=700, height=1000, category='face')
print('[test_image.image]["%s"]: Image __desc__: %s' % (im, imgfile))
im.crop()
print('[test_image.image]["%s"]: Image __desc__: %s' % (im, imgfile))
print('[test_image.image]["%s"]: Filename: PASSED' % imgfile)
# Clone
im = Image(filename=imgfile).load()
imb = im
im._array = im._array + 1 # modify array
np.testing.assert_array_equal(imb.numpy(), im.numpy()) # share buffer
imc = im.clone()
np.testing.assert_array_equal(imc.numpy(), imb.numpy()) # share buffer
imc._array = imc._array + 2 # modify array
assert np.any(imc.numpy() != imb.numpy())
imc = im.clone(flushforward=True)
assert(imc._array is None and im._array is not None)
imc = im.clone(flushbackward=True)
assert(im._array is None and imc._array is not None)
imc = im.clone(flush=True)
assert(im._array is None and imc._array is None)
print('[test_image.image]["%s"]: Image.clone: PASSED' % imgfile)
# Downgrade
im = ImageDetection(filename=imgfile, xmin=100, ymin=100, bbwidth=700, height=1000, category='face')
imd = im.detection()
assert imd.xywh() == im.boundingbox().xywh()
imd = im.image()
assert imd.shape() == im.shape()
print('[test_image.image]["%s"]: ImageDetection downgrade PASSED' % imgfile)
# Saveas
im = Image(filename=imgfile).load()
f = temppng()
assert im.saveas(f) == f and os.path.exists(f)
print('[test_image.image]["%s"]: Image.saveas: PASSED' % imgfile)
# Stats
im = Image(filename=imgfile).load().stats()
print('[test_image.image]["%s"]: Image.stats: PASSED' % imgfile)
# Resize
f = temppng()
im = Image(filename=imgfile).load().resize(cols=16,rows=8).saveas(f)
assert Image(filename=f).shape() == (8,16)
assert Image(filename=f).width() == 16
assert Image(filename=f).height() == 8
im = Image(filename=imgfile).load().resize(16,8).saveas(f)
assert Image(filename=f).shape() == (8,16)
assert Image(filename=f).width() == 16
assert Image(filename=f).height() == 8
im = Image(filename=imgfile).load()
(h,w) = im.shape()
im = im.resize(rows=16)
assert im.shape() == (16,int((w / float(h)) * 16.0))
print('[test_image.image]["%s"]: Image.resize: PASSED' % imgfile)
# Rescale
f = temppng()
im = Image(filename=imgfile).load().resize(rows=8).saveas(f)
assert Image(filename=f).height() == 8
im = Image(filename=imgfile).load().resize(cols=8).saveas(f)
assert Image(filename=f).width() == 8
im = Image(filename=imgfile).load().maxdim(256).saveas(f)
assert np.max(Image(filename=f).shape()) == 256
print('[test_image.image]["%s"]: Image.rescale: PASSED' % imgfile)
# GIF
im = Image(url=gifurl)
im.download(verbose=True)
assert im.shape() == (200,200)
print('[test_image.image]["%s"]: GIF: PASSED' % imgfile)
# Transparent PNG
im = Image(url=pngurl)
im.load(verbose=True)
assert im.colorspace() == 'rgba'
print('[test_image.image]["%s"]: PNG: PASSED' % imgfile)
# Image colorspace conversion
im = Image(filename=imgfile).resize(200,200)
print(im.rgb())
assert im.colorspace() == 'rgb'
assert(im.shape() == (200,200) and im.channels() == 3)
assert im.array().dtype == np.uint8
print(im.luminance())
assert im.colorspace() == 'lum'
assert(im.shape() == (200,200) and im.channels() == 1)
assert im.array().dtype == np.uint8
print(im.bgr())
assert im.colorspace() == 'bgr'
assert(im.shape() == (200,200) and im.channels() == 3)
assert im.array().dtype == np.uint8
print(im.rgba())
assert im.colorspace() == 'rgba'
assert(im.shape() == (200,200) and im.channels() == 4)
assert im.array().dtype == np.uint8
print(im.hsv())
assert im.colorspace() == 'hsv'
assert(im.shape() == (200,200) and im.channels() == 3)
assert im.array().dtype == np.uint8
print(im.bgra())
assert im.colorspace() == 'bgra'
assert(im.shape() == (200,200) and im.channels() == 4)
assert im.array().dtype == np.uint8
print(im.gray())
assert im.colorspace() in ['grey', 'gray']
assert(im.shape() == (200,200) and im.channels() == 1)
assert im.array().dtype == np.float32
print(im.float())
assert im.colorspace() == 'float'
assert(im.shape() == (200,200) and im.channels() == 1)
assert im.array().dtype == np.float32
print('[test_image.image]["%s"]: Image conversion: PASSED' % imgfile)
im = Image(filename=imgfile).load().grey()
assert im.colorspace() == 'grey'
assert im.max() == 1.0
print('[test_image.image]["%s"]: Greyscale image conversion: PASSED' % imgfile)
# Crops
imorig = Image(filename=imgfile).load().lum()
(H,W) = imorig.shape()
im = imorig.clone().maxsquare()
assert im.shape() == (np.maximum(W,H), np.maximum(W,H)) and imorig.array()[0,0] == im.array()[0,0]
im = imorig.clone().minsquare()
assert im.shape() == (np.minimum(W,H), np.minimum(W,H)) and imorig.array()[0,0] == im.array()[0,0]
im = imorig.clone().centersquare()
(xo,yo) = imorig.centerpixel()
(x,y) = im.centerpixel()
assert im.shape() == (np.minimum(W,H), np.minimum(W,H)) and imorig.array()[yo,xo] == im.array()[y,x]
print('[test_image.image]["%s"]: crops PASSED' % imgfile)
# Pixel operations
im = Image(filename=imgfile).load()
im.min()
im.max()
im.mean()
im.intensity()
im.saturate(0, 0.5)
im.mat2gray()
im.gain(1)
im.bias(2)
print('[test_image.image]["%s"]: greylevel transformations PASSED' % imgfile)
# Image conversion
im = Image(filename=imgfile).load()
im.pil()
im.numpy()
im.html()
im.torch()
print('[test_image.image]["%s"]: image conversions PASSED' % imgfile)
# Image colormaps
im = ImageDetection(filename=imgfile, xmin=100, ymin=100, bbwidth=200, bbheight=200, category='face').crop()
im.rgb().jet().bone().hot().rainbow()
print('[test_image.image]["%s"]: Image colormaps: PASSED' % imgfile)
# Image exporter
im.dict()
print('[test_image.image]["%s"]: dictionary: PASSED' % imgfile)
# Image category
im = ImageCategory(filename=imgfile, category='face')
assert im.load().category() == 'face'
print('[test_image.image]["%s"]: ImageCategory constructor PASSED' % imgfile)
assert ImageCategory(category='1') == ImageCategory(category='1')
assert ImageCategory(category='1') != ImageCategory(category='2')
print('[test_image.image]["%s"]: ImageCategory equivalence PASSED' % imgfile)
assert ImageCategory(category='1').category() == '1'
assert ImageCategory(label='1').label() == '1'
assert not ImageCategory(category='1').category == '2'
assert ImageCategory(category='1').category('2').label() == '2'
im.score(1.0)
im.probability(1.0)
try:
ImageCategory(category='1', label='2')
Failed()
except Failed:
raise
except:
pass
print('[test_image.image]["%s"]: ImageCategory category conversion PASSED' % imgfile)
# Random images
im = vipy.image.RandomImage(128,256)
assert im.shape() == (128, 256)
print('[test_image.image]["%s"]: RandomImage PASSED' % imgfile)
im = vipy.image.RandomImageDetection(128,256)
assert im.clone().crop().width() == im.bbox.imclipshape(W=256,H=128).width()
print('[test_image.image]["%s"]: RandomImageDetection PASSED' % imgfile)
d = vipy.image.RandomImageDetection(128,256).dict()
assert isinstance(d, dict)
print('[test_image.image]["%s"]: dict PASSED' % imgfile)
# Map
im = vipy.image.RandomImage(128,256)
im2 = im.clone().map(lambda img: np.float32(img)+1.0)
assert np.allclose(np.float32(im.array())+1.0, im2.array())
print('[test_image.image]["%s"]: map PASSED' % imgfile)
# interpolation
im = vipy.image.RandomImage(128,256)
im.resize(256,256, interp='bilinear')
im.resize(256,256, interp='bicubic')
im.resize(256,256, interp='nearest')
try:
im.resize(256,256, interp='somethingelse')
Failed()
except Failed:
raise
except:
pass
print('[test_image.image]["%s"]: interpolation PASSED' % imgfile)