"""
The MIT License (MIT)
Copyright (c) 2016 Izhar Shaikh
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""
import json
from sklearn.svm import SVC
from sklearn.cross_validation import train_test_split
from sklearn.cross_validation import cross_val_score, KFold
from scipy.stats import sem
from sklearn import metrics
import cv2
import numpy as np
from scipy.ndimage import zoom
from sklearn import datasets
print "\n\n Please Wait . . . . .\n\n"
faces = datasets.fetch_olivetti_faces()
# ==========================================================================
# Traverses through the dataset by incrementing index & records the result
# ==========================================================================
class Trainer:
def __init__(self):
self.results = {}
self.imgs = faces.images
self.index = 0
def reset(self):
print "============================================"
print "Resetting Dataset & Previous Results.. Done!"
print "============================================"
self.results = {}
self.imgs = faces.images
self.index = 0
def increment_face(self):
if self.index + 1 >= len(self.imgs):
return self.index
else:
while str(self.index) in self.results:
# print self.index
self.index += 1
return self.index
def record_result(self, smile=True):
print "Image", self.index + 1, ":", "Happy" if smile is True else "Sad"
self.results[str(self.index)] = smile
# Trained classifier's performance evaluation
def evaluate_cross_validation(clf, X, y, K):
# create a k-fold cross validation iterator
cv = KFold(len(y), K, shuffle=True, random_state=0)
# by default the score used is the one returned by score method of the estimator (accuracy)
scores = cross_val_score(clf, X, y, cv=cv)
print "Scores: ", (scores)
print ("Mean score: {0:.3f} (+/-{1:.3f})".format(np.mean(scores), sem(scores)))
# Confusion Matrix and Results
def train_and_evaluate(clf, X_train, X_test, y_train, y_test):
clf.fit(X_train, y_train)
print ("Accuracy on training set:")
print (clf.score(X_train, y_train))
print ("Accuracy on testing set:")
print (clf.score(X_test, y_test))
y_pred = clf.predict(X_test)
print ("Classification Report:")
print (metrics.classification_report(y_test, y_pred))
print ("Confusion Matrix:")
print (metrics.confusion_matrix(y_test, y_pred))
# ===============================================================================
# from FaceDetectPredict.py
# ===============================================================================
def detectFaces(frame):
cascPath = "../data/haarcascade_frontalface_default.xml"
faceCascade = cv2.CascadeClassifier(cascPath)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
detected_faces = faceCascade.detectMultiScale(
gray,
scaleFactor=1.1,
minNeighbors=6,
minSize=(50, 50),
flags=cv2.CASCADE_SCALE_IMAGE)
return gray, detected_faces
def extract_face_features(gray, detected_face, offset_coefficients):
(x, y, w, h) = detected_face
horizontal_offset = int(offset_coefficients[0] * w)
vertical_offset = int(offset_coefficients[1] * h)
extracted_face = gray[y + vertical_offset:y + h,
x + horizontal_offset:x - horizontal_offset + w]
new_extracted_face = zoom(extracted_face, (64. / extracted_face.shape[0],
64. / extracted_face.shape[1]))
new_extracted_face = new_extracted_face.astype(np.float32)
new_extracted_face /= float(new_extracted_face.max())
return new_extracted_face
def predict_face_is_smiling(extracted_face):
return True if svc_1.predict(extracted_face.reshape(1, -1)) else False
gray1, face1 = detectFaces(cv2.imread("../data/Test3.jpg"))
gray2, face2 = detectFaces(cv2.imread("../data/Test2.jpg"))
def test_recognition(c1, c2):
extracted_face1 = extract_face_features(gray1, face1[0], (c1, c2))
print(predict_face_is_smiling(extracted_face1))
extracted_face2 = extract_face_features(gray2, face2[0], (c1, c2))
print(predict_face_is_smiling(extracted_face2))
cv2.imshow('gray1', extracted_face1)
cv2.imshow('gray2', extracted_face2)
cv2.waitKey(0)
cv2.destroyAllWindows()
# test_recognition(0.3, 0.05)
# ------------------- LIVE FACE RECOGNITION -----------------------------------
if __name__ == "__main__":
svc_1 = SVC(kernel='linear') # Initializing Classifier
trainer = Trainer()
results = json.load(open("../results/results.xml")) # Loading the classification result
trainer.results = results
indices = [int(i) for i in trainer.results] # Building the dataset now
data = faces.data[indices, :] # Image Data
target = [trainer.results[i] for i in trainer.results] # Target Vector
target = np.array(target).astype(np.int32)
# Train the classifier using 5 fold cross validation
X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.25, random_state=0)
# Trained classifier's performance evaluation
evaluate_cross_validation(svc_1, X_train, y_train, 5)
# Confusion Matrix and Results
train_and_evaluate(svc_1, X_train, X_test, y_train, y_test)
video_capture = cv2.VideoCapture(0)
while True:
# Capture frame-by-frame
ret, frame = video_capture.read()
# detect faces
gray, detected_faces = detectFaces(frame)
face_index = 0
cv2.putText(frame, "Press Esc to QUIT", (15, 15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,0,0), 1)
# predict output
for face in detected_faces:
(x, y, w, h) = face
if w > 100:
# draw rectangle around face
cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
# extract features
extracted_face = extract_face_features(gray, face, (0.3, 0.05)) #(0.075, 0.05)
# predict smile
prediction_result = predict_face_is_smiling(extracted_face)
# draw extracted face in the top right corner
frame[face_index * 64: (face_index + 1) * 64, -65:-1, :] = cv2.cvtColor(extracted_face * 255, cv2.COLOR_GRAY2RGB)
# annotate main image with a label
if prediction_result is True:
cv2.putText(frame, "SMILING",(x,y), cv2.FONT_HERSHEY_SIMPLEX, 2, 155, 5)
else:
cv2.putText(frame, "Not Smiling",(x,y), cv2.FONT_HERSHEY_SIMPLEX, 2, 155, 5)
# increment counter
face_index += 1
# Display the resulting frame
cv2.imshow('Video', frame)
if cv2.waitKey(10) & 0xFF == 27:
break
# When everything is done, release the capture
video_capture.release()
cv2.destroyAllWindows()
