def test_sub(self):
height = 4
width = 5
channels = 3
img = np.arange(height * width * channels).reshape(
[height, width, channels])
# input size is an int
value = 10.0
seq = Sequential(
[pp.Image2Gpubuffer(),
pp.Sub(value),
pp.Gpubuffer2Image()])
result = seq(img).reshape(-1)
for i in range(0, result.size):
self.assertEqual(i - value, result[i])
# input size is a sequence
values = (9, 4, 2)
seq = Sequential(
[pp.Image2Gpubuffer(),
pp.Sub(values),
pp.Gpubuffer2Image()])
result = seq(img)
for i in range(0, result.shape[0]):
for j in range(0, result.shape[1]):
for k in range(0, result.shape[2]):
self.assertEqual(result[i][j][k], img[i][j][k] - values[k])
def test_resize(self):
height = 9
width = 5
channels = 3
img = np.arange(height * width).reshape([height, width, 1]) * np.ones(
(1, channels))
# input size is an int
for new_size in [3, 10]:
seq_gpu = Sequential([
pp.Image2Gpubuffer(),
pp.Resize(new_size),
pp.Gpubuffer2Image()
])
seq_paddle = Sequential([Resize(new_size)])
result_gpu = seq_gpu(img)
result_paddle = seq_paddle(img)
self.assertEqual(result_gpu.shape, result_paddle.shape)
for i in range(0, result_gpu.shape[0]):
for j in range(0, result_gpu.shape[1]):
for k in range(0, result_gpu.shape[2]):
self.assertAlmostEqual(result_gpu[i][j][k],
result_paddle[i][j][k], 5)
# input size is a sequence
for new_height, new_width in [(7, 3), (15, 10)]:
seq_gpu = Sequential([
pp.Image2Gpubuffer(),
pp.Resize((new_width, new_height)),
pp.Gpubuffer2Image()
])
seq_paddle = Sequential([Resize((new_width, new_height))])
result_gpu = seq_gpu(img)
result_paddle = seq_paddle(img)
self.assertEqual(result_gpu.shape, result_paddle.shape)
for i in range(0, result_gpu.shape[0]):
for j in range(0, result_gpu.shape[1]):
for k in range(0, result_gpu.shape[2]):
self.assertAlmostEqual(result_gpu[i][j][k],
result_paddle[i][j][k], 5)
def test_div(self):
height = 4
width = 5
channels = 3
value = 255.0
img = np.arange(height * width * channels).reshape(
[height, width, channels])
seq = Sequential(
[pp.Image2Gpubuffer(),
pp.Div(value),
pp.Gpubuffer2Image()])
result = seq(img).reshape(-1)
for i in range(0, result.size):
self.assertAlmostEqual(i / value, result[i], 5)
def test_resize_fixed_point(self):
new_height = 256
new_width = 256 * 4 / 3
seq = Sequential([
File2Image(),
pp.Image2Gpubuffer(),
pp.Resize((new_width, new_height), use_fixed_point=True),
pp.Gpubuffer2Image()
])
img = seq("./capture_16.bmp")
img = np.resize(img, (new_height, new_width * 3))
img_vis = np.loadtxt("./cap_resize_16.raw")
img_resize_diff = img_vis - img
self.assertEqual(np.all(img_resize_diff == 0), True)
def test_center_crop(self):
height = 9
width = 7
channels = 3
img = np.arange(height * width * channels).reshape(
[height, width, channels])
new_size = 5
seq = Sequential([
pp.Image2Gpubuffer(),
pp.CenterCrop(new_size),
pp.Gpubuffer2Image()
])
result = seq(img)
self.assertEqual(result.shape[0], new_size)
self.assertEqual(result.shape[1], new_size)
self.assertEqual(result.shape[2], channels)
def test_normalize(self):
height = 4
width = 5
channels = 3
img = np.random.rand(height, width, channels)
mean = [5.0, 5.0, 5.0]
std = [2.0, 2.0, 2.0]
seq = Sequential([
pp.Image2Gpubuffer(),
pp.Normalize(mean, std),
pp.Gpubuffer2Image()
])
result = seq(img)
for i in range(0, height):
for j in range(0, width):
for k in range(0, channels):
self.assertAlmostEqual((img[i][j][k] - mean[k]) / std[k],
result[i][j][k], 5)