High ISO noise (#277)

Added ISO Noise

README.md

@@ -119,6 +119,7 @@ Pixel-level transforms will change just an input image and will leave any additi
 - [IAAEmboss](https://albumentations.readthedocs.io/en/latest/api/augmentations.html#albumentations.imgaug.transforms.IAAEmboss)
 - [IAASharpen](https://albumentations.readthedocs.io/en/latest/api/augmentations.html#albumentations.imgaug.transforms.IAASharpen)
 - [IAASuperpixels](https://albumentations.readthedocs.io/en/latest/api/augmentations.html#albumentations.imgaug.transforms.IAASuperpixels)
+- [ISONoise](https://albumentations.readthedocs.io/en/latest/api/augmentations.html#albumentations.augmentations.transforms.ISONoise)
 - [InvertImg](https://albumentations.readthedocs.io/en/latest/api/augmentations.html#albumentations.augmentations.transforms.InvertImg)
 - [JpegCompression](https://albumentations.readthedocs.io/en/latest/api/augmentations.html#albumentations.augmentations.transforms.JpegCompression)
 - [MedianBlur](https://albumentations.readthedocs.io/en/latest/api/augmentations.html#albumentations.augmentations.transforms.MedianBlur)

albumentations/augmentations/functional.py

@@ -33,7 +33,10 @@ def wrapped_function(img, *args, **kwargs):
 
 
 def preserve_shape(func):
-    """Preserve shape of the image."""
+    """
+    Preserve shape of the image
+
+    """
     @wraps(func)
     def wrapped_function(img, *args, **kwargs):
         shape = img.shape
@@ -45,7 +48,10 @@ def wrapped_function(img, *args, **kwargs):
 
 
 def preserve_channel_dim(func):
-    """Preserve dummy channel dim."""
+    """
+    Preserve dummy channel dim.
+
+    """
     @wraps(func)
     def wrapped_function(img, *args, **kwargs):
         shape = img.shape
@@ -924,6 +930,48 @@ def brightness_contrast_adjust(img, alpha=1, beta=0):
         return _brightness_contrast_adjust_non_uint(img, alpha, beta)
 
 
+@clipped
+def iso_noise(image, color_shift=0.05, intensity=0.5, random_state=None, **kwargs):
+    """
+    Apply poisson noise to image to simulate camera sensor noise.
+
+    Args:
+        image: Input image, currently, only RGB, uint8 images are supported.
+        intensity: Multiplication factor for noise values. Values of ~0.5 are produce noticeable,
+                   yet acceptable level of noise.
+        random_state:
+        **kwargs:
+
+    Returns:
+        Noised image
+
+    """
+    assert image.dtype == np.uint8, 'Image must have uint8 channel type'
+    assert image.shape[2] == 3, 'Image must be RGB'
+
+    if random_state is None:
+        random_state = np.random.RandomState(42)
+
+    one_over_255 = float(1. / 255.)
+    image = np.multiply(image, one_over_255, dtype=np.float32)
+    hls = cv2.cvtColor(image, cv2.COLOR_RGB2HLS)
+    mean, stddev = cv2.meanStdDev(hls)
+
+    luminance_noise = random_state.poisson(stddev[1] * intensity * 255, size=hls.shape[:2])
+    color_noise = random_state.normal(0, color_shift * 360 * intensity, size=hls.shape[:2])
+
+    hue = hls[..., 0]
+    hue += color_noise
+    hue[hue < 0] += 360
+    hue[hue > 360] -= 360
+
+    luminance = hls[..., 1]
+    luminance += (luminance_noise / 255) * (1.0 - luminance)
+
+    image = cv2.cvtColor(hls, cv2.COLOR_HLS2RGB) * 255
+    return image.astype(np.uint8)
+
+
 def to_gray(img):
     gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
     return cv2.cvtColor(gray, cv2.COLOR_GRAY2RGB)

albumentations/augmentations/transforms.py

@@ -25,7 +25,7 @@
     'Resize', 'RandomSizedCrop', 'RandomBrightnessContrast',
     'RandomCropNearBBox', 'RandomSizedBBoxSafeCrop', 'RandomSnow',
     'RandomRain', 'RandomFog', 'RandomSunFlare', 'RandomShadow', 'Lambda',
-    'ChannelDropout',
+    'ChannelDropout', 'ISONoise'
 ]
 
 
@@ -1794,6 +1794,43 @@ def get_transform_init_args_names(self):
         return ('var_limit',)
 
 
+class ISONoise(ImageOnlyTransform):
+    """
+    Apply camera sensor noise.
+
+    Args:
+        color_shift (float, float): variance range for color hue change.
+            Measured as a fraction of 360 degree Hue angle in HLS colorspace.
+        intensity ((float, float): Multiplicative factor that control strength
+            of color and luminace noise.
+        p (float): probability of applying the transform. Default: 0.5.
+
+    Targets:
+        image
+
+    Image types:
+        uint8
+    """
+
+    def __init__(self, color_shift=(0.01, 0.05), intensity=(0.1, 0.5), always_apply=False, p=0.5):
+        super(ISONoise, self).__init__(always_apply, p)
+        self.intensity = intensity
+        self.color_shift = color_shift
+
+    def apply(self, img, color_shift=0.05, intensity=1.0, random_state=None, **params):
+        return F.iso_noise(img, color_shift, intensity, np.random.RandomState(random_state))
+
+    def get_params(self):
+        return {
+            'color_shift': random.uniform(self.color_shift[0], self.color_shift[1]),
+            'intensity': random.uniform(self.intensity[0], self.intensity[1]),
+            'random_state': random.randint(0, 65536)
+        }
+
+    def get_transform_init_args_names(self):
+        return ('intensity', 'color_shift')
+
+
 class CLAHE(ImageOnlyTransform):
     """Apply Contrast Limited Adaptive Histogram Equalization to the input image.
 
@@ -1849,7 +1886,7 @@ def __init__(self, channel_drop_range=(1, 1), fill_value=0, always_apply=False,
 
         self.fill_value = fill_value
 
-    def apply(self, img, channels_to_drop=(0, ), **params):
+    def apply(self, img, channels_to_drop=(0,), **params):
         return F.channel_dropout(img, channels_to_drop, self.fill_value)
 
     def get_params_dependent_on_targets(self, params):

tests/test_augmentations.py

@@ -14,7 +14,7 @@
     Cutout, CoarseDropout, Normalize, ToFloat, FromFloat,
     RandomBrightnessContrast, RandomSnow, RandomRain, RandomFog,
     RandomSunFlare, RandomCropNearBBox, RandomShadow, RandomSizedCrop,
-    ChannelDropout)
+    ChannelDropout, ISONoise)
 
 
 @pytest.mark.parametrize(['augmentation_cls', 'params'], [
@@ -41,6 +41,7 @@
     [RandomSunFlare, {}],
     [RandomShadow, {}],
     [ChannelDropout, {}],
+    [ISONoise, {}],
 ])
 def test_image_only_augmentations(augmentation_cls, params, image, mask):
     aug = augmentation_cls(p=1, **params)
@@ -97,6 +98,7 @@ def test_image_only_augmentations_with_float_values(augmentation_cls, params, fl
     [CenterCrop, {'height': 10, 'width': 10}],
     [RandomCrop, {'height': 10, 'width': 10}],
     [RandomSizedCrop, {'min_max_height': (4, 8), 'height': 10, 'width': 10}],
+    [ISONoise, {}],
 ])
 def test_dual_augmentations(augmentation_cls, params, image, mask):
     aug = augmentation_cls(p=1, **params)
@@ -188,6 +190,7 @@ def test_imgaug_dual_augmentations(augmentation_cls, image, mask):
     [RandomSunFlare, {}],
     [RandomShadow, {}],
     [ChannelDropout, {}],
+    [ISONoise, {}],
 ])
 def test_augmentations_wont_change_input(augmentation_cls, params, image, mask):
     image_copy = image.copy()
@@ -334,6 +337,7 @@ def test_augmentations_wont_change_shape_grayscale(augmentation_cls, params, ima
     [RandomSunFlare, {}],
     [RandomShadow, {}],
     [ChannelDropout, {}],
+    [ISONoise, {}],
 ])
 def test_augmentations_wont_change_shape_rgb(augmentation_cls, params, image, mask):
     aug = augmentation_cls(p=1, **params)