def test_adjust_gamma_zero(self):
"""White image should be returned for gamma equal to zero"""
image = torch.FloatTensor(8, 8).uniform_(0, 255)
result = adjust_gamma(image, 0)
dtype = image.dtype
self.assertTrue(
torch.all(result == torch.tensor(dtype_range[dtype][1],
dtype=result.dtype)))
def adjust_gamma_demo():
image = torch.tensor(data.astronaut())
shift_left_img = adjust_gamma(image, 1.5)
shift_right_img = adjust_gamma(image, 0.5)
f, (ax1, ax2, ax3) = plt.subplots(1, 3)
ax1.set_title("original image")
ax1.get_xaxis().set_visible(False)
ax1.get_yaxis().set_visible(False)
ax1.imshow(image)
ax2.set_title("adjust gamma 1.5")
ax2.get_xaxis().set_visible(False)
ax2.get_yaxis().set_visible(False)
ax2.imshow(shift_left_img)
ax3.set_title("adjust gamma 0.5")
ax3.get_xaxis().set_visible(False)
ax3.get_yaxis().set_visible(False)
ax3.imshow(shift_right_img)
f.show()
def test_adjust_gamma_greater_one(self):
"""Verifying the output with expected results for gamma
correction with gamma equal to two"""
image = torch.arange(0, 255, 4).reshape((8, 8)).type(torch.uint8)
expected = torch.tensor(
[[0, 0, 0, 0, 1, 1, 2, 3], [4, 5, 6, 7, 9, 10, 12, 14],
[16, 18, 20, 22, 25, 27, 30, 33], [
36, 39, 42, 45, 49, 52, 56, 60
], [64, 68, 72, 76, 81, 85, 90, 95],
[100, 105, 110, 116, 121, 127, 132, 138],
[144, 150, 156, 163, 169, 176, 182, 189],
[196, 203, 211, 218, 225, 233, 241, 249]],
dtype=torch.uint8)
result = adjust_gamma(image, 2)
self.assertTrue(torch.equal(result, expected))
def test_adjust_gamma_less_one(self):
"""Verifying the output with expected results for gamma
correction with gamma equal to half"""
image = torch.arange(0, 255, 4).reshape((8, 8)).type(torch.uint8)
expected = torch.tensor([[0, 31, 45, 55, 63, 71, 78, 84],
[90, 95, 100, 105, 110, 115, 119, 123],
[127, 131, 135, 139, 142, 146, 149, 153],
[156, 159, 162, 165, 168, 171, 174, 177],
[180, 183, 186, 188, 191, 194, 196, 199],
[201, 204, 206, 209, 211, 214, 216, 218],
[221, 223, 225, 228, 230, 232, 234, 236],
[238, 241, 243, 245, 247, 249, 251, 253]],
dtype=torch.uint8)
result = adjust_gamma(image, 0.5)
self.assertTrue(torch.equal(result, expected))
def test_adjust_gamma_one(self):
"""Same image should be returned for gamma equal to one"""
image = torch.FloatTensor(8, 8).uniform_(0, 255)
result = adjust_gamma(image, 1)
self.assertTrue(torch.equal(result, image))