adding python script

Author: alexattia

FaceRecognition/detect_recognize.py

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+import dlib
+from skimage import io, transform
+import cv2
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import glob
+import openface
+import pickle 
+import os
+import sys
+
+from sklearn.svm import SVC
+from sklearn.preprocessing import LabelEncoder
+
+import face_recognition_models
+
+face_detector = dlib.get_frontal_face_detector()
+face_recognition_model = face_recognition_models.face_recognition_model_location()
+face_encoder = dlib.face_recognition_model_v1(face_recognition_model)
+face_pose_predictor = dlib.shape_predictor('./model/shape_predictor_68_face_landmarks.dat')
+
+def get_detected_faces(filename):
+ image = cv2.imread(filename)
+ image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+ return image, face_detector(image, 1)
+
+def get_face_encoding(image, detected_face):
+ pose_landmarks = face_pose_predictor(image, detected_face)
+ face_encoding = face_encoder.compute_face_descriptor(image, pose_landmarks, 1)
+ return np.array(face_encoding)
+
+def training(people):
+ """
+ We need to have only one person/face per picture
+ """
+ # parsing labels and reprensations
+ df = pd.DataFrame()
+ for p in people:
+ l = []
+ for filename in glob.glob('./data/%s/*' % p):
+ image, face_detect = get_detected_faces(filename)
+ face_encoding = get_face_encoding(image, face_detect[0])
+ l.append(np.append(face_encoding, [p]))
+ temp = pd.DataFrame(np.array(l))
+ df = pd.concat([df, temp])
+ df.reset_index(drop=True, inplace=True)
+
+ # converting labels into int
+ le = LabelEncoder()
+ y = le.fit_transform(df[128])
+ print("Training for {} classes.".format(len(le.classes_)))
+ X = df.drop(128, axis=1)
+
+ # training
+ clf = SVC(C=1, kernel='linear', probability=True)
+ clf.fit(X, y)
+
+ # dumping model
+ fName = "./classifier.pkl"
+ print("Saving classifier to '{}'".format(fName))
+ with open(fName, 'wb') as f:
+ pickle.dump((le, clf), f)
+
+def predict(filename):
+ with open("./classifier.pkl", 'rb') as f:
+ (le, clf) = pickle.load(f)
+ image, detected_faces = get_detected_faces(filename)
+ img = np.copy(image)
+ font = cv2.FONT_HERSHEY_SIMPLEX
+ for face_detect in detected_faces:
+ # draw bounding boxes
+ cv2.rectangle(img, (face_detect.left(), face_detect.top()), 
+ (face_detect.right(), face_detect.bottom()), (255, 0, 0), 2)
+ p = clf.predict_proba(get_face_encoding(image, face_detect).reshape(1, 128))
+ if np.max(p) > 0.8:
+ y_pred = le.inverse_transform(np.argmax(p))
+ cv2.putText(img, y_pred, (face_detect.left(), face_detect.top()-5), font, 0.3, (255, 0, 0))
+ return img
+
+if __name__ == '__main__':
+ people = os.listdir('./data/')