def test_ten_crop(self):
img = np.random.uniform(size=(3, 48, 32))
out = ten_crop(img, (32, 48))
self.assertEqual(out.shape, (10, 3, 48, 32))
for crop in out[:5]:
np.testing.assert_equal(crop, img)
for crop in out[5:]:
np.testing.assert_equal(crop[:, :, ::-1], img)
out = ten_crop(img, (12, 24))
self.assertEqual(out.shape, (10, 3, 24, 12))
def test_ten_crop(self):
img = np.random.uniform(size=(3, 48, 32))
out = ten_crop(img, (48, 32))
self.assertEqual(out.shape, (10, 3, 48, 32))
for crop in out[:5]:
np.testing.assert_equal(crop, img)
for crop in out[5:]:
np.testing.assert_equal(crop[:, :, ::-1], img)
out = ten_crop(img, (24, 12))
self.assertEqual(out.shape, (10, 3, 24, 12))
def _prepare(self, img):
"""Prepare an image for feeding it to a model.
This is a standard preprocessing scheme used by feature extraction
models.
First, the image is scaled or resized according to :math:`scale_size`.
Note that this step is optional.
Next, the image is cropped to :math:`crop_size`.
Last, the image is mean subtracted by an array :obj:`mean`.
Args:
img (~numpy.ndarray): An image. This is in CHW format.
The range of its value is :math:`[0, 255]`.
Returns:
~numpy.ndarray:
A preprocessed image. This is 4D array whose batch size is
the number of crops.
"""
if self.scale_size is not None:
if isinstance(self.scale_size, int):
img = scale(img, size=self.scale_size)
else:
img = resize(img, size=self.scale_size)
if self.crop == '10':
imgs = ten_crop(img, self.crop_size)
elif self.crop == 'center':
imgs = center_crop(img, self.crop_size)[np.newaxis]
imgs -= self.mean[np.newaxis]
return imgs
def _prepare(self, img):
"""Prepare an image for feeding it to a model.
This is a standard preprocessing scheme used by feature extraction
models.
First, the image is scaled or resized according to :math:`scale_size`.
Note that this step is optional.
Next, the image is cropped to :math:`crop_size`.
Last, the image is mean subtracted by an array :obj:`mean`.
Args:
img (~numpy.ndarray): An image. This is in CHW format.
The range of its value is :math:`[0, 255]`.
Returns:
~numpy.ndarray:
A preprocessed image. This is 4D array whose batch size is
the number of crops.
"""
if self.scale_size is not None:
if isinstance(self.scale_size, int):
img = scale(img, size=self.scale_size)
else:
img = resize(img, size=self.scale_size)
if self.crop == '10':
imgs = ten_crop(img, self.crop_size)
elif self.crop == 'center':
imgs = center_crop(img, self.crop_size)[np.newaxis]
imgs -= self.mean[np.newaxis]
return imgs