def test_load_image(datadirs):
h, w = 213, 320
_, formatsdir, arraydir, _ = datadirs
pathpattern = formatsdir + '*'
for filepath in glob(pathpattern):
img = ni.load_image(filepath)
is_color = 'color' in filepath
assert img.shape == (h, w, 3) if is_color else (h, w)
assert isinstance(img, np.ndarray)
assert img.dtype == np.uint8
assert np.max(img) <= 255
assert np.min(img) >= 0
for filepath in glob(pathpattern):
img = ni.load_image(filepath, as_grey=True)
assert img.shape == (h, w)
assert np.max(img) <= 255
assert np.min(img) >= 0
for filepath in glob(pathpattern):
img = ni.load_image(filepath)
fdir, fname = op.split(filepath)
arr = np.load(arraydir + fname + '.npy')
nt.assert_allclose(img, arr)
def test_distort_elastic(datadirs):
imagedir, _, _, processeddir = datadirs
img_arr = ni.load_image(imagedir + 'nut_color.bmp')
new_img = ni.distort_elastic(img_arr, 10, 5000)
imagepath = processeddir + 'nut_color_elastic.bmp'
assert_equal_image(imagepath, new_img)
img_arr = ni.load_image(imagedir + 'nut_grayscale.bmp')
new_img = ni.distort_elastic(img_arr, 10, 5000)
imagepath = processeddir + 'nut_grayscale_elastic.bmp'
assert_equal_image(imagepath, new_img)
def test_ImageWriter():
samples = [('nut_color', 1), ('nut_grayscale', 2)]
inpath = 'tests/data/img_formats/*.bmp'
img_samples = samples >> ReadImage(0, inpath) >> Collect()
imagepath = 'tests/data/test_*.bmp'
names = samples >> Get(0) >> Collect()
img_samples >> WriteImage(0, imagepath, names) >> Consume()
for sample, name in zip(img_samples, names):
filepath = 'tests/data/test_{}.bmp'.format(name)
arr = load_image(filepath)
assert np.array_equal(arr, sample[0])
os.remove(filepath)
pathfunc = lambda sample, name: 'tests/data/test_{}.jpg'.format(name)
img_samples >> WriteImage(0, pathfunc) >> Consume()
for i, sample in enumerate(img_samples):
filepath = 'tests/data/test_{}.jpg'.format(i)
os.path.exists(filepath)
os.remove(filepath)
with pytest.raises(ValueError) as ex:
img_samples >> WriteImage(0, ()) >> Consume()
assert str(ex.value).startswith('Expect path or function')
def test_save_image(datadirs):
_, formatsdir, _, _ = datadirs
formats = ['gif', 'png', 'jpg', 'bmp', 'tif', 'npy']
for format in formats:
inpath = formatsdir + 'nut_color.' + format
outpath = formatsdir + 'temp_nut_color.' + format
image = ni.load_image(inpath)
ni.save_image(outpath, image)
loaded = ni.load_image(outpath)
os.remove(outpath)
# Saved and loaded JPG images can vary greatly (lossy format) when using
# skimage and a direct comparision fails under Windows 7, Anaconda.
# Therefore, only shape and mean value are verified for JPG images.
if format == 'jpg':
assert abs(np.mean(image) - np.mean(loaded)) < 0.1
assert loaded.shape == (213, 320, 3)
else:
nt.assert_allclose(image, loaded)
def test_rotate(datadirs):
imagedir, _, _, processeddir = datadirs
img_arr = ni.load_image(imagedir + 'nut_color.bmp')
new_img = ni.rotate(img_arr, 45, order=1)
imagepath = processeddir + 'nut_color_rotated.bmp'
assert_equal_image(imagepath, new_img, rtol=0.1, atol=0.1)
img_arr = np.eye(4, dtype=np.uint8)
exp_img = np.array([[0, 0, 0, 1], [0, 0, 1, 0], [0, 1, 0, 0], [1, 0, 0, 0]])
new_img = ni.rotate(img_arr, 90)
nt.assert_allclose(new_img, exp_img)
def DISABLED_test_plotlines():
filepath = 'tests/data/temp_plotlines.png'
xs = np.arange(0, 6.3, 1.2)
ysin, ycos = np.sin(xs), np.cos(xs)
data = zip(xs, ysin, ycos)
out = data >> pl.PlotLines(1, 0, filepath=filepath) >> Collect()
assert out == data
expected = 'tests/data/img/plotlines.png'
image = ni.load_image(filepath)
os.remove(filepath)
assert_equal_image(expected, image)
def test_normalize_histo(datadirs):
imagedir, _, arraydir, processeddir = datadirs
img_arr = ni.load_image(imagedir + 'nut_color.bmp')
new_img = ni.normalize_histo(img_arr)
imagepath = processeddir + 'nut_color_normalize_histo.bmp'
assert_equal_image(imagepath, new_img)
def assert_equal_image(imagepath, image, rtol=0.01, atol=0.01):
if CREATE_DATA:
ni.save_image(imagepath, image)
expected = ni.load_image(imagepath)
nt.assert_allclose(expected, image, rtol=rtol, atol=atol)
def test_shear(datadirs):
imagedir, _, _, processeddir = datadirs
img_arr = ni.load_image(imagedir + 'nut_color.bmp')
new_img = ni.shear(img_arr, 0.5)
imagepath = processeddir + 'nut_color_sheared.bmp'
assert_equal_image(imagepath, new_img)
def test_translate(datadirs):
imagedir, _, _, processeddir = datadirs
img_arr = ni.load_image(imagedir + 'nut_color.bmp')
new_img = ni.translate(img_arr, 50, 100)
imagepath = processeddir + 'nut_color_translated.bmp'
assert_equal_image(imagepath, new_img)
def test_rgb2gray(datadirs):
imagedir, _, _, processeddir = datadirs
img_arr = ni.load_image(imagedir + 'nut_color.bmp')
new_img = ni.rgb2gray(img_arr)
imagepath = processeddir + 'nut_color_2gray.bmp'
assert_equal_image(imagepath, new_img)
def test_gray2rgb(datadirs):
imagedir, _, _, processeddir = datadirs
img_arr = ni.load_image(imagedir + 'nut_grayscale.bmp')
new_img = ni.gray2rgb(img_arr)
imagepath = processeddir + 'nut_grayscale_2rgb.bmp'
assert_equal_image(imagepath, new_img)
def test_change_color(datadirs):
imagedir, _, _, processeddir = datadirs
img_arr = ni.load_image(imagedir + 'nut_color.bmp')
new_img = ni.change_color(img_arr, 2.0)
imagepath = processeddir + 'nut_color_color.bmp'
assert_equal_image(imagepath, new_img)
def test_sharpness(datadirs):
imagedir, _, _, processeddir = datadirs
img_arr = ni.load_image(imagedir + 'nut_color.bmp')
new_img = ni.change_sharpness(img_arr, 0.5)
imagepath = processeddir + 'nut_color_sharpness.bmp'
assert_equal_image(imagepath, new_img, rtol=0, atol=10)
def test_brightness(datadirs):
imagedir, _, _, processeddir = datadirs
img_arr = ni.load_image(imagedir + 'nut_color.bmp')
new_img = ni.change_brightness(img_arr, 0.5)
imagepath = processeddir + 'nut_color_brightness.bmp'
assert_equal_image(imagepath, new_img)
def test_rotate(datadirs):
imagedir, _, _, processeddir = datadirs
img_arr = ni.load_image(imagedir + 'nut_color.bmp')
new_img = ni.rotate(img_arr, 45)
imagepath = processeddir + 'nut_color_rotated.bmp'
assert_equal_image(imagepath, new_img, rtol=0.1, atol=0.1)
