Add local binary pattern to image processing

Author: rishavk99

Image_Processing/src/local binary pattern/lbp.py

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+#!/usr/bin/python
+# OpenCV bindings
+import cv2
+import os
+# Local Binary Pattern function
+from skimage.feature import local_binary_pattern
+# To calculate a normalized histogram 
+from scipy.stats import itemfreq
+from sklearn.preprocessing import normalize
+# For plotting
+import matplotlib.pyplot as plt
+# For array manipulations
+import numpy as np
+# For saving histogram values
+from sklearn.externals import joblib
+# For command line input
+import argparse as ap
+# Utility Package
+import cvutils
+
+# Get the path of the input image
+parser = ap.ArgumentParser()
+parser.add_argument("-i", "--inputimage", help="Path to Input Image folder", required="True")
+args = vars(parser.parse_args())
+
+train_images = cvutils.imlist(args["inputimage"])
+X_name= []
+X_hist=[]
+
+for train_image in train_images:
+    # Read the image
+    im = cv2.imread(train_image)
+    # Convert to grayscale as LBP works on grayscale image
+    im_gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
+    cv2.imshow('',im_gray)
+    radius = 3
+    # Number of points to be considered as neighbourers 
+    no_points = 8 * radius
+    # Uniform LBP is used
+    lbp = local_binary_pattern(im_gray, no_points, radius, method='uniform')
+    # Calculate the histogram
+    x = itemfreq(lbp.ravel())
+    # Normalize the histogram
+    hist = x[:, 1]/sum(x[:, 1])
+    # print(X_hist)
+    print(hist)
+    plt.hist(hist, density=True, bins=30)
+    plt.show()
+    plt.clf()
+