def recognise_faces_by_cam(train_dir, face_sz=(100,100)):
if os.path.exists('model.pkl'):
face_model = load_model('model.pkl')
else:
[images, labels, foldernames] = read_images(train_dir, face_sz)
list_of_labels = list(xrange(max(labels)+1))
sub_dirnames = dict(zip(list_of_labels, foldernames))
face_model = ExtendedPredictableModel(names=sub_dirnames)
face_model.compute(images, labels)
save_model('model.pkl', face_model)
face_detector = SkinFaceDetector()
cap = cv2.VideoCapture(0)
while True:
ret, frame = cap.read()
if frame is not None:
img = frame.copy()
img = cv2.resize(frame, (frame.shape[1]/2, frame.shape[0]/2), interpolation=cv2.INTER_CUBIC)
img_tmp = img.copy()
for i, [x0,y0,x1,y1] in enumerate(face_detector.detect(img)):
face = img[y0:y1, x0:x1]
face = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)
face = cv2.resize(face, face_sz, interpolation=cv2.INTER_CUBIC)
prediction = face_model.predict(face)[0]
draw_rect(img_tmp, (x0,y0), (x1,y1))
draw_text(img_tmp, (x0-20,y0-20), face_model.names[prediction])
cv2.imshow('recoginise faces by cam', img_tmp)
if cv2.waitKey(500) & 0xFF == 27:
break
cv2.destroyAllWindows()
def recognise_faces_by_img(train_dir, test_dir, face_sz=(100,100)):
if os.path.exists('model.pkl'):
face_model = load_model('model.pkl')
else:
[images, labels, foldernames] = read_images(train_dir, face_sz)
list_of_labels = list(xrange(max(labels)+1))
sub_dirnames = dict(zip(list_of_labels, foldernames))
face_model = ExtendedPredictableModel(names=sub_dirnames)
face_model.compute(images, labels)
save_model('model.pkl', face_model)
expectations, predictions = [], []
for dirname, dirnames, filenames in os.walk(test_dir):
for subdirname in dirnames:
subpath = os.path.join(dirname, subdirname)
for filename in os.listdir(subpath):
test = cv2.imread(os.path.join(subpath, filename), cv2.IMREAD_GRAYSCALE)
test = cv2.resize(test, face_sz, interpolation=cv2.INTER_CUBIC)
prediction = face_model.predict(test)[0]
expectations.append(subdirname)
predictions.append(face_model.names[prediction])
print subdirname, face_model.names[prediction]
total = len(expectations)
rate = 0
for i in range(total):
if expectations[i] == predictions[i]:
rate = rate + 1
print rate, total, rate*100.0/total
