def test_equalize_masked(self):
img = torch.tensor(img_as_float(self.test_img))
mask = torch.zeros(self.test_img.shape, dtype=torch.bool)
mask[50:150, 50:250] = 1
img_mask_eq = equalize_hist(img, mask=mask)
img_eq = equalize_hist(img)
cdf, bin_edges = cumulative_distribution(img_mask_eq)
self.check_cdf_slope(cdf)
self.assertFalse(torch.equal(img_eq, img_mask_eq))
def test_equalize_ubyte(self):
with self.assertWarns(UserWarning):
img = torch.tensor(img_as_ubyte(self.test_img))
img_eq = equalize_hist(img)
cdf, bin_edges = cumulative_distribution(img_eq)
self.check_cdf_slope(cdf)
def equalize_hist_demo():
image = torch.tensor(data.camera())
equ_image = equalize_hist(image)
f, (ax1, ax2) = plt.subplots(1, 2)
ax1.set_title("original image")
ax1.get_xaxis().set_visible(False)
ax1.get_yaxis().set_visible(False)
ax1.imshow(image)
ax2.set_title("after equalization")
ax2.get_xaxis().set_visible(False)
ax2.get_yaxis().set_visible(False)
ax2.imshow(equ_image)
f.show()
def __call__(self, img): hsv = color.rgb2hsv(img) hsv[:,:,2] = exposure.equalize_hist(hsv[:,:,2]) img = color.hsv2rgb(hsv) min_side = min(img.shape[:-1]) centre = img.shape[0] // 2, img.shape[1] // 2 img = img[centre[0] - min_side // 2:centre[0] + min_side // 2, centre[1] - min_side // 2:centre[1] + min_side // 2, :] img = transform.resize(img, (self.img_size, self.img_size)) img = np.rollaxis(img, -1) return img
def test_equalize_float(self):
img = torch.tensor(img_as_float(self.test_img))
img_eq = equalize_hist(img)
cdf, bin_edges = cumulative_distribution(img_eq)
self.check_cdf_slope(cdf)
def test_equalize_uint8_approx(self):
"""Check integer bins used for uint8 images."""
img_eq0 = equalize_hist(self.test_img_int)
img_eq1 = equalize_hist(self.test_img_int, nbins=3)
self.assertTrue(torch.allclose(img_eq0, img_eq1))