Create 2_image_smoothing.py

Author: worklifesg

2_image_smoothing.py

@@ -0,0 +1,108 @@
+#******************************************#
+'''
+This program can be used to apply smoothing (blurring) techniques on brain MRI scan image
+All image processing functions are performed on grayscale image (need for opencv tool)
+'''
+#******************************************#
+
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+
+kernel = np.ones((5,5),np.float32)/25 #for cv2.filter2D()
+
+class smoothing(object):
+
+    def read_image(self,image):
+        image_read = cv2.imread(image)
+        image_gray = cv2.cvtColor(image_read,cv2.COLOR_BGR2GRAY)
+
+        return image_gray
+    '''
+    cv2.filter2D() - to convolve the kernel with an image
+    '''
+    def filter2D(self,image_gray):
+        image_result = cv2.filter2D(image_gray,-1,kernel)
+        return image_result
+    '''
+    Blurring - Low pass filter kernel 
+        - removal of noise
+        - removes high frequency content (noise, edges)
+        - 4 types - averaging, median, gaussian, bilateral
+    '''
+    '''
+    cv2.blur() or cv2.boxFilter() - convolving image with normalized box filter
+    - takes avg of pixels under kernel area and replaces central element with this average
+    '''
+    def averaging(self,image_gray):
+        image_result = cv2.blur(image_gray,(5,5))
+        return image_result
+    '''
+    cv2.GaussianBlur() - remove gaussian noise from the noise
+    Instead of box filter with equal coefficients, we specify the width and height of kernel (positive and odd)
+    SigmaX Gaussian kernel standard deviation in X direction and SigmaY in Y-direction
+    Gaussian filter takes the a neighborhood around the pixel and finds its Gaussian weighted average
+    '''
+    def gaussian(self,image_gray):
+        image_result = cv2.GaussianBlur(image_gray,(5,5),sigmaX=0)
+        return image_result
+    '''
+    cv2.meadianBlur() - computes median of all pixels under the kernel and the central pixel
+    is replaced with this median value.
+    Effective in removing salt and pepper noise - reduces noise effectively.
+    '''
+    def median(self,image_gray):
+        image_result = cv2.medianBlur(image_gray,5)
+        return image_result
+    
+    '''
+    cv2.bilateralFilter() - noise removal while preserving edges
+    bilateral filter = Gaussian filter + multiplicative gaussian filter component which is a function of pixel intensity differences.
+    - sptail neigbors + intensity domain
+    '''
+    def bilateral(self,image_gray):
+        image_result = cv2.bilateralFilter(image_gray,9,75,75)
+        return image_result
+
+_inst = smoothing()
+read_image = _inst.read_image
+filter2D = _inst.filter2D
+averaging = _inst.averaging
+gaussian = _inst.gaussian
+median = _inst.median
+bilateral = _inst.bilateral
+
+image_path = 'image_tumor.jpg'
+tumor = read_image(image_path)
+
+filter2D = filter2D(tumor)
+averaging = averaging(tumor)
+gaussian = gaussian(tumor)
+median = median(tumor)
+bilateral = bilateral(tumor)
+
+#####################################
+## Plotting all stages of image blurring ##
+#####################################
+fig, axs = plt.subplots(2,3, figsize=(15, 9), facecolor='w', edgecolor='k')
+fig.subplots_adjust(hspace = .25, wspace=.5)
+
+axs = axs.ravel()
+
+axs[0].imshow(tumor,'gray')
+axs[0].set_title('1. Orginial Gray Image')
+axs[1].imshow(filter2D,'gray')
+axs[1].set_title('2. Using cv2.filter2D()')
+axs[2].imshow(averaging,'gray')
+axs[2].set_title('3. Using cv2.blur()')
+axs[3].imshow(gaussian,'gray')
+axs[3].set_title('4. Using cv2.GaussianBlur()')
+axs[4].imshow(median,'gray')
+axs[4].set_title('5. Using cv2.medianBlur()')
+axs[5].imshow(bilateral,'gray')
+axs[5].set_title('6. Using cv2.bilateralFilter()')
+
+plt.show()
+
+    
+