def test_equalize_rgb_mask():
img = np.random.randint(0, 255, [256, 256, 3], dtype=np.uint8)
mask = np.zeros([256, 256], dtype=np.bool)
mask[:10, :10] = True
_img = img.copy()[:10, :10]
for i in range(3):
_img[..., i] = cv2.equalizeHist(_img[..., i])
assert np.all(_img == F.equalize(img, mask, mode="cv")[:10, :10])
_img = cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)
img_cv = _img.copy()[:10, :10]
img_cv[..., 0] = cv2.equalizeHist(img_cv[..., 0])
img_cv = cv2.cvtColor(img_cv, cv2.COLOR_YCrCb2RGB)
assert np.all(img_cv == F.equalize(
img, mask=mask, mode="cv", by_channels=False)[:10, :10])
mask = np.zeros([256, 256, 3], dtype=np.bool)
mask[:10, :10, 0] = True
mask[10:20, 10:20, 1] = True
mask[20:30, 20:30, 2] = True
img_r = img.copy()[:10, :10, 0]
img_g = img.copy()[10:20, 10:20, 1]
img_b = img.copy()[20:30, 20:30, 2]
img_r = cv2.equalizeHist(img_r)
img_g = cv2.equalizeHist(img_g)
img_b = cv2.equalizeHist(img_b)
result_img = F.equalize(img, mask=mask, mode="cv")
assert np.all(img_r == result_img[:10, :10, 0])
assert np.all(img_g == result_img[10:20, 10:20, 1])
assert np.all(img_b == result_img[20:30, 20:30, 2])
def test_equalize_grayscale():
img = np.random.randint(0, 255, [256, 256], dtype=np.uint8)
pil_img = Image.fromarray(img)
assert np.all(cv2.equalizeHist(img) == F.equalize(img, mode='cv'))
assert np.all(
np.array(ImageOps.equalize(pil_img)) == F.equalize(img, mode='pil'))
def test_equalize_rgb():
img = np.random.randint(0, 255, [256, 256, 3], dtype=np.uint8)
_img = img.copy()
for i in range(3):
_img[..., i] = cv2.equalizeHist(_img[..., i])
assert np.all(_img == F.equalize(img, mode="cv"))
_img = cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)
img_cv = _img.copy()
img_cv[..., 0] = cv2.equalizeHist(_img[..., 0])
img_cv = cv2.cvtColor(img_cv, cv2.COLOR_YCrCb2RGB)
assert np.all(img_cv == F.equalize(img, mode="cv", by_channels=False))
def test_equalize_checks():
img = np.random.randint(0, 255, [256, 256], dtype=np.uint8)
with pytest.raises(ValueError) as exc_info:
F.equalize(img, mode="other")
assert str(
exc_info.value
) == "Unsupported equalization mode. Supports: ['cv', 'pil']. Got: other"
mask = np.random.randint(0, 1, [256, 256, 3], dtype=np.bool)
with pytest.raises(ValueError) as exc_info:
F.equalize(img, mask=mask)
assert str(exc_info.value
) == "Wrong mask shape. Image shape: {}. Mask shape: {}".format(
img.shape, mask.shape)
img = np.random.randint(0, 255, [256, 256, 3], dtype=np.uint8)
with pytest.raises(ValueError) as exc_info:
F.equalize(img, mask=mask, by_channels=False)
assert str(
exc_info.value
) == "When by_channels=False only 1-channel mask supports. " "Mask shape: {}".format(
mask.shape)
img = np.random.random([256, 256, 3])
with pytest.raises(TypeError) as exc_info:
F.equalize(img, mask=mask, by_channels=False)
assert str(exc_info.value) == "Image must have uint8 channel type"
def test_equalize_grayscale_mask():
img = np.random.randint(0, 255, [256, 256], dtype=np.uint8)
pil_img = Image.fromarray(img)
mask = np.zeros([256, 256], dtype=np.bool)
mask[:10, :10] = True
pil_mask = Image.fromarray(mask)
assert np.all(
cv2.equalizeHist(img[:10, :10]) == F.equalize(
img, mask=mask, mode='cv')[:10, :10])
assert np.all(
np.array(ImageOps.equalize(pil_img, pil_mask)) == F.equalize(
img, mask=mask, mode='pil'))
def test_equalize_grayscale_mask():
img = np.random.randint(0, 255, [256, 256], dtype=np.uint8)
mask = np.zeros([256, 256], dtype=np.bool)
mask[:10, :10] = True
assert np.all(cv2.equalizeHist(img[:10, :10]) == F.equalize(img, mask=mask, mode="cv")[:10, :10])
def test_equalize():
aug = A.Equalize(p=1)
img = np.random.randint(0, 256, 256 * 256 * 3, np.uint8).reshape((256, 256, 3))
a = aug(image=img)["image"]
b = F.equalize(img)
assert np.all(a == b)
mask = np.random.randint(0, 2, 256 * 256, np.uint8).reshape((256, 256))
aug = A.Equalize(mask=mask, p=1)
a = aug(image=img)["image"]
b = F.equalize(img, mask=mask)
assert np.all(a == b)
def mask_func(image, test):
return mask
aug = A.Equalize(mask=mask_func, mask_params=["test"], p=1)
assert np.all(aug(image=img, test=mask)["image"] == F.equalize(img, mask=mask))
def test_equalize_rgb():
img = np.random.randint(0, 255, [256, 256, 3], dtype=np.uint8)
img_pil = Image.fromarray(img)
_img = img.copy()
for i in range(3):
_img[..., i] = cv2.equalizeHist(_img[..., i])
assert np.all(_img == F.equalize(img, mode='cv'))
assert np.all(
np.array(ImageOps.equalize(img_pil)) == F.equalize(img, mode='pil'))
_img = cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)
img_cv = _img.copy()
img_pil = _img.copy()
img_cv[..., 0] = cv2.equalizeHist(_img[..., 0])
img_pil[..., 0] = np.array(ImageOps.equalize(Image.fromarray(_img[...,
0])))
img_cv = cv2.cvtColor(img_cv, cv2.COLOR_YCrCb2RGB)
img_pil = cv2.cvtColor(img_pil, cv2.COLOR_YCrCb2RGB)
assert np.all(img_cv == F.equalize(img, mode='cv', by_channels=False))
assert np.all(img_pil == F.equalize(img, mode='pil', by_channels=False))
def test_equalize_rgb_mask():
img = np.random.randint(0, 255, [256, 256, 3], dtype=np.uint8)
img_pil = Image.fromarray(img)
mask = np.zeros([256, 256], dtype=np.bool)
mask[:10, :10] = True
mask_pil = Image.fromarray(mask)
_img = img.copy()[:10, :10]
for i in range(3):
_img[..., i] = cv2.equalizeHist(_img[..., i])
assert np.all(_img == F.equalize(img, mask, mode='cv')[:10, :10])
assert np.all(
np.array(ImageOps.equalize(img_pil, mask_pil)) == F.equalize(
img, mask, mode='pil'))
_img = cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)
img_cv = _img.copy()[:10, :10]
img_pil = _img.copy()
img_cv[..., 0] = cv2.equalizeHist(img_cv[..., 0])
img_pil[..., 0] = np.array(
ImageOps.equalize(Image.fromarray(_img[..., 0]), mask_pil))
img_cv = cv2.cvtColor(img_cv, cv2.COLOR_YCrCb2RGB)
img_pil = cv2.cvtColor(img_pil, cv2.COLOR_YCrCb2RGB)
assert np.all(img_cv == F.equalize(
img, mask=mask, mode='cv', by_channels=False)[:10, :10])
assert np.all(
img_pil == F.equalize(img, mask=mask, mode='pil', by_channels=False))
mask = np.zeros([256, 256, 3], dtype=np.bool)
mask[:10, :10, 0] = True
mask[10:20, 10:20, 1] = True
mask[20:30, 20:30, 2] = True
img_r = img.copy()[:10, :10, 0]
img_g = img.copy()[10:20, 10:20, 1]
img_b = img.copy()[20:30, 20:30, 2]
img_r = cv2.equalizeHist(img_r)
img_g = cv2.equalizeHist(img_g)
img_b = cv2.equalizeHist(img_b)
result_img = F.equalize(img, mask=mask, mode='cv')
assert np.all(img_r == result_img[:10, :10, 0])
assert np.all(img_g == result_img[10:20, 10:20, 1])
assert np.all(img_b == result_img[20:30, 20:30, 2])
_img = img.copy()
for i in range(3):
_img[..., i] = np.array(
ImageOps.equalize(Image.fromarray(_img[..., i]),
Image.fromarray(mask[..., i])))
assert np.all(_img == F.equalize(img, mask=mask, mode='pil'))
def albumentations(self, img):
return albumentations.equalize(img)
def test_equalize_grayscale():
img = np.random.randint(0, 255, [256, 256], dtype=np.uint8)
assert np.all(cv2.equalizeHist(img) == F.equalize(img, mode="cv"))
