Update src/LBP.py

src/LBP.py

@@ -2,6 +2,7 @@ import cv2
 from skimage import feature
 import matplotlib.pyplot as plt
 
+
 class LBPImageEncoder:
     def __init__(self, numPoints, radius):
         self.numPoints = numPoints
@@ -9,10 +10,12 @@ class LBPImageEncoder:
 
     def describe(self, image, eps=1e-7):
         lbp = feature.local_binary_pattern(image, self.numPoints, self.radius)
-        hist = plt.hist(lbp.ravel())
-        return lbp, hist
+        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(image):
+    def face_detection(self, image):
         cascadePath = "haarcascade_frontalface_default.xml"
         detector = cv2.CascadeClassifier(cascadePath)
 
@@ -23,14 +26,24 @@ class LBPImageEncoder:
     def preprocess_img(self, imagePath):
         img = cv2.imread(imagePath)
         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)
-        # plt.imshow(face , cmap="gray")
-        # print(face.shape)
-        # face = np.array(face)
 
-        lbp, hist = self.describe(face)
+            lbp = self.describe(face)
+            feature_vector[(self.numPoints + 2) * 0: (self.numPoints + 2) * 1] += lbp
 
-        return hist
+            # 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