Noise filters implementation

ipfml/filters/noise.py

@@ -1,21 +1,22 @@
 import numpy as np
 from ipfml import processing
 
-def white_noise(image, n, distribution_interval=(-0.5, 0.5), k=0.2):
+def __global_noise_filter(image, n, random_function, identical=False, distribution_interval=(-0.5, 0.5), k=0.2):
     """
     @brief White noise filter to apply on image
     @param image - image used as input (2D or 3D image representation)
     @param n - used to set importance of noise [1, 999]
+    @param random_function - random function we want to use to generate random numpy array
     @param distribution_interval - set the distribution interval of uniform distribution
     @param k - variable that specifies the amount of noise to be taken into account in the output image
-    @return Image with white noise applied
+    @return Image with specified noise applied
 
     Usage :
 
-    >>> from ipfml.filters.noise import white_noise
+    >>> from ipfml.filters.noise import global_noise_filter
     >>> import numpy as np
     >>> image = np.random.uniform(0, 255, 10000).reshape((100, 100))
-    >>> noisy_image = white_noise(image, 10)
+    >>> noisy_image = global_noise_filter(image, 10, np.random.uniform)
     >>> noisy_image.shape
     (100, 100)
     """
@@ -31,6 +32,9 @@ def white_noise(image, n, distribution_interval=(-0.5, 0.5), k=0.2):
     a, b = distribution_interval
     nb_pixels = width * height
 
+    if identical:
+        noise_filter = random_function(a, b, nb_pixels)
+
     # final output numpy array
     output_array = []
 
@@ -42,10 +46,13 @@ def white_noise(image, n, distribution_interval=(-0.5, 0.5), k=0.2):
         else:
             image_array_flatten = image_array.reshape(nb_pixels)
 
-        white_noise_filter = np.random.uniform(a, b, nb_pixels)
+        # redefine noise if necessary
+        if not identical:
+            noise_filter = random_function(a, b, nb_pixels)
 
         # compute new pixel value
-        noisy_image = np.asarray([image_array_flatten[i] + n * k * white_noise_filter[i] for i in range(0, nb_pixels)])
+        # n * k * white_noise_filter[i]
+        noisy_image = np.asarray([image_array_flatten[i] + n * k * noise_filter[i] for i in range(0, nb_pixels)])
 
         # reshape and normalize new value
         noisy_image = noisy_image.reshape((width, height))
@@ -66,6 +73,72 @@ def white_noise(image, n, distribution_interval=(-0.5, 0.5), k=0.2):
     return np.asarray(output_array)
 
 
+def white_noise(image, n, identical=False, distribution_interval=(-0.5, 0.5), k=0.2):
+    """
+    @brief White noise filter to apply on image
+    @param image - image used as input (2D or 3D image representation)
+    @param n - used to set importance of noise [1, 999]
+    @param distribution_interval - set the distribution interval of normal law distribution
+    @param k - variable that specifies the amount of noise to be taken into account in the output image
+    @return Image with white noise applied
+
+    Usage :
+
+    >>> from ipfml.filters.noise import white_noise
+    >>> import numpy as np
+    >>> image = np.random.uniform(0, 255, 10000).reshape((100, 100))
+    >>> noisy_image = white_noise(image, 10)
+    >>> noisy_image.shape
+    (100, 100)
+    """
+
+    return __global_noise_filter(image, n, np.random.uniform, identical, distribution_interval, k)
+
+
+def gaussian_noise(image, n, identical=False, distribution_interval=(0, 1), k=0.1):
+    """
+    @brief Gaussian noise filter to apply on image
+    @param image - image used as input (2D or 3D image representation)
+    @param n - used to set importance of noise [1, 999]
+    @param distribution_interval - set the distribution interval of normal law distribution
+    @param k - variable that specifies the amount of noise to be taken into account in the output image
+    @return Image with gaussian noise applied
+
+    Usage :
+
+    >>> from ipfml.filters.noise import gaussian_noise
+    >>> import numpy as np
+    >>> image = np.random.uniform(0, 255, 10000).reshape((100, 100))
+    >>> noisy_image = gaussian_noise(image, 10)
+    >>> noisy_image.shape
+    (100, 100)
+    """
+
+    return __global_noise_filter(image, n, np.random.normal, identical, distribution_interval, k)
+
+
+def laplace_noise(image, n, identical=False, distribution_interval=(0, 1), k=0.1):
+    """
+    @brief Laplace noise filter to apply on image
+    @param image - image used as input (2D or 3D image representation)
+    @param n - used to set importance of noise [1, 999]
+    @param distribution_interval - set the distribution interval of normal law distribution
+    @param k - variable that specifies the amount of noise to be taken into account in the output image
+    @return Image with Laplace noise applied
+
+    Usage :
+
+    >>> from ipfml.filters.noise import gaussian_noise
+    >>> import numpy as np
+    >>> image = np.random.uniform(0, 255, 10000).reshape((100, 100))
+    >>> noisy_image = gaussian_noise(image, 10)
+    >>> noisy_image.shape
+    (100, 100)
+    """
+
+    return __global_noise_filter(image, n, np.random.laplace, identical, distribution_interval, k)
+
+