Cln/remove opencv dependency

This merge brings the changes from cln/remove-opencv-dependency, where the dependent functions were actually changed, to enh/remove-opencv-dependency, where the unit test were implemented.

This is the result of I mistake I made by creating 2 branches to address the same issue.

sound_classification/pre_processing.py

 import numpy as np
-import cv2
 import scipy.io.wavfile as wave
 import scipy.ndimage as ndimage
 import scipy.stats as stats
@@ -196,18 +195,23 @@ def filter_isolated_spots(img, struct):
     
     return filtered_array
 
-def blur_image(img,ksize=3,Gaussian=True):
+def blur_image(img, size=20, sigma=5, Gaussian=True):
     """ Smooth the input image using a median or Gaussian blur filter.
         Note that the input image is recasted as np.float32.
 
     Args:
         img : numpy array
             Image to be processed. 
-        ksize: int 
-            Aperture linear size. Must be odd integer greater than or equal to 1. For the median filter, the only allowed values are 1, 3, 5. 
+        size: int
+            Only used by the median filter. Describes  the shape that is taken from the input array,
+            at every element position, to define the input to the filter function
+        sigma: scalar of sequence of scalars
+            Standard deviation for Gaussian kernel (Only used if Gaussian=True). The standard deviations of the Gaussian
+            filter are given for each axis as a sequence, or as a single number, in which case it is equal for all axes.
         Gaussian: bool
             Switch between median (default) and Gaussian filter
 
+
     Returns:
         blur_img: numpy array
             Blurred image.
@@ -219,14 +223,9 @@ def blur_image(img,ksize=3,Gaussian=True):
         numpy.ndarray.astype(dtype = np.float32)    
     
     if (Gaussian):
-        img_blur = cv2.GaussianBlur(img,(ksize,ksize),0)
+        img_blur = ndimage.gaussian_filter(img,sigma=sigma)
     else:
-        try:
-            assert ksize < 6, "ksize must be 1, 3, or 5"
-        except AssertionError:
-            ksize = 5
-
-        img_blur = cv2.medianBlur(img,ksize)
+        img_blur = ndimage.median_filter(img,size=size)
 
     return img_blur
 

sound_classification/spectrogram.py

 from abc import ABC
 import numpy as np
 from scipy.fftpack import dct
+from scipy import ndimage
 from collections import namedtuple
 import matplotlib.pyplot as plt
 import datetime
-import cv2
 from sound_classification.pre_processing import make_frames
 
 
@@ -317,9 +317,9 @@ class Spectrogram():
     def blur_gaussian(self, tsigma, fsigma):
         """ Blur the spectrogram using a Gaussian filter.
 
-            This uses the GaussianBlur method from the cv2 package:
+            This uses the Gaussian filter method from the scipy.ndimage package:
             
-                https://docs.opencv.org/3.0-beta/modules/imgproc/doc/filtering.html#gaussianblur
+                https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.gaussian_filter.html
 
             Args:
                 tsigma: float
@@ -361,7 +361,7 @@ class Spectrogram():
         sigmaX = tsigma / self.tres
         sigmaY = fsigma / self.fres
         
-        self.image = cv2.GaussianBlur(src=self.image, ksize=(0,0), sigmaX=sigmaY, sigmaY=sigmaX)
+        self.image = ndimage.gaussian_filter(input=self.image, sigma=(sigmaY,sigmaX))
     
     def plot(self, decibel=False):
         """ Plot the spectrogram with proper axes ranges and labels.

setup.py

@@ -15,7 +15,6 @@ setup(name='sound_classification',
           'tables',
           'scipy',
           'pandas',
-          'opencv-python',
           'tensorflow',
           ],
       setup_requires=['pytest-runner', ],