Removed cv2 from lbp

src/LBP.py

@@ -1,55 +1,25 @@
-import cv2
 from skimage import feature
-import matplotlib.pyplot as plt
 import numpy as np
-
-
-class LBPImageEncoder:
-    def __init__(self, numPoints, radius):
-        self.numPoints = numPoints
-        self.radius = radius
-
-    def describe(self, image, eps=1e-7):
-        lbp = feature.local_binary_pattern(image, self.numPoints, self.radius)
-        hist, _ = np.histogram(lbp.ravel(), bins=np.arange(0, self.numPoints + 3), range=(0, self.numPoints + 2))
-        hist = hist.astype("float")
-        hist /= (hist.sum() + eps)
-        return hist
-
-    def face_detection(self, image):
-        cascadePath = "haarcascade_frontalface_default.xml"
-        detector = cv2.CascadeClassifier()
-        detector.load(cascadePath)
-
-        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-        print(gray)
-        rects = detector.detectMultiScale(gray,
-                                          scaleFactor=1.1,
-                                          minNeighbors=5,
-                                          minSize=(24, 24))
-        return rects
-
-    def preprocess_img(self, image):
-        img = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
-        rects = self.face_detection(img)
-        feature_vector = np.zeros((self.numPoints + 2) * 3)
-
-        for (x, y, w, h) in rects:
-            face = img[y:y + h, x:x + w]
-            face = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)
-
-            lbp = self.describe(face)
-            feature_vector[(self.numPoints + 2) * 0: (self.numPoints + 2) * 1] += lbp
-
-            # Process green channel
-            lbp = self.describe(face[:, :, 1])
-            feature_vector[(self.numPoints + 2) * 1: (self.numPoints + 2) * 2] += lbp
-
-            # Process blue channel
-            lbp = self.describe(face[:, :, 2])
-            feature_vector[(self.numPoints + 2) * 2: (self.numPoints + 2) * 3] += lbp
-
-        feature_vector /= len(rects)
-
-        return feature_vector
-
+from PIL import Image
+
+class LocalBinaryPatterns:
+	def __init__(self, numPoints, radius):
+		# store the number of points and radius
+		self.numPoints = numPoints
+		self.radius = radius
+        
+	def describe(self, image, eps=1e-7):
+		# compute the Local Binary Pattern representation
+		# of the image, and then use the LBP representation
+		# to build the histogram of patterns
+        gray_img = imgage.convert('L')
+		lbp = feature.local_binary_pattern(image, self.numPoints,
+			self.radius, method="uniform")
+		(hist, _) = np.histogram(lbp.ravel(),
+			bins=np.arange(0, self.numPoints + 3),
+			range=(0, self.numPoints + 2))
+		# normalize the histogram
+		hist = hist.astype("float")
+		hist /= (hist.sum() + eps)
+		# return the histogram of Local Binary Patterns
+		return hist