def recognizeFace(image, faceCascade, eyeCascade, faceSize, threshold,
recognizer):
found_faces = []
gray, faces = detect.detectFaces(image,
faceCascade,
eyeCascade,
returnGray=1)
# If faces are found, try to recognize them
for ((x, y, w, h), eyedim) in faces:
# confidence formula: distance = 1.0f - sqrt( distSq / (float)(nTrainFaces * nEigens) ) / 255.0f
label, confidence = recognizer.predict(
cv2.resize(detect.levelFace(gray, ((x, y, w, h), eyedim)),
faceSize))
print "label"
print label
# note that for some distributions of python-opencv, the predict function
# returns the label only.
#label = recognizer.predict(cv2.resize(detect.levelFace(gray, ((x, y, w, h), eyedim)), faceSize))
#confidence = -1
# if confidence < threshold:
found_faces.append((label, confidence, (x, y, w, h)))
return found_faces
def RecognizeFace(image, faceCascade, eyeCascade, faceSize, threshold):
found_faces = []
recognizer = train.trainRecognizer("train", faceSize, showFaces=True)
gray, faces = detect.detectFaces(image, faceCascade, eyeCascade, returnGray=1)
for ((x, y, w, h), eyedim) in faces:
label, confidence = recognizer.predict(cv2.resize(detect.levelFace(gray, ((x, y, w, h), eyedim)), faceSize))
if confidence < threshold:
found_faces.append((label, confidence, (x, y, w, h)))
return found_faces
def extractFaces(self,image,levelFace=False):
faceCascade = cv2.CascadeClassifier(config.FACE_CASCADE_FILE)
eyeCascade = cv2.CascadeClassifier(config.EYE_CASCADE_FILE)
result = []
img, faces = detect.detectFaces(image, faceCascade, eyeCascade, True)
for ((x, y, w, h), eyedim) in faces:
if not levelFace:
result.append(img[y:y+h, x:x+w])
else:
result.append(detect.levelFace(img, ((x, y, w, h), eyedim)))
return result
def extractFaces(self, image, levelFace=False):
faceCascade = cv2.CascadeClassifier(config.FACE_CASCADE_FILE)
eyeCascade = cv2.CascadeClassifier(config.EYE_CASCADE_FILE)
result = []
img, faces = detect.detectFaces(image, faceCascade, eyeCascade, True)
for ((x, y, w, h), eyedim) in faces:
if not levelFace:
result.append(img[y:y + h, x:x + w])
else:
result.append(detect.levelFace(img, ((x, y, w, h), eyedim)))
return result
def RecognizeFace(image, faceCascade, eyeCascade, faceSize, threshold):
found_faces = []
gray, faces = detect.detectFaces(image, faceCascade, eyeCascade, returnGray=1)
# If faces are found, try to recognize them
for ((x, y, w, h), eyedim) in faces:
label, confidence = recognizer.predict(cv2.resize(detect.levelFace(gray, ((x, y, w, h), eyedim)), faceSize))
# note that for some distributions of python-opencv, the predict function
# returns the label only.
#label = recognizer.predict(cv2.resize(detect.levelFace(gray, ((x, y, w, h), eyedim)), faceSize))
#confidence = -1
if confidence < threshold:
found_faces.append((label, confidence, (x, y, w, h)))
return found_faces
def extractFaces(a_dir, folder, levelFace=False):
faceCascade = cv2.CascadeClassifier(config.FACE_CASCADE_FILE)
eyeCascade = cv2.CascadeClassifier(config.EYE_CASCADE_FILE)
the_path = join(a_dir, folder)
result = []
for img in [f for f in os.listdir(the_path) if supportedImg(f)]:
img_path = join(the_path, img)
image, faces = detect.detectFaces(cv2.imread(img_path), faceCascade, eyeCascade, True)
if len(faces) == 0:
print("No face found in " + img_path)
for ((x, y, w, h), eyedim) in faces:
if not levelFace:
result.append(image[y:y+h, x:x+w])
else:
result.append(detect.levelFace(image, ((x, y, w, h), eyedim)))
#result.append(image[y:y+h, x:x+w])
return result
def extractFaces(a_dir, folder, levelFace=False):
faceCascade = cv2.CascadeClassifier('cascades/haarcascade_frontalface_alt2.xml')
eyeCascade = cv2.CascadeClassifier('cascades/haarcascade_eye.xml')
the_path = join(a_dir, folder)
result = []
for img in [f for f in os.listdir(the_path) if supportedImg(f)]:
img_path = join(the_path, img)
image, faces = detect.detectFaces(cv2.imread(img_path), faceCascade, eyeCascade, True)
print(len(faces))
if len(faces) == 0:
print("No face found in " + img_path)
for ((x, y, w, h), eyedim) in faces:
if not levelFace:
result.append(image[y:y+h, x:x+w])
else:
result.append(detect.levelFace(image, ((x, y, w, h), eyedim)))
#result.append(image[y:y+h, x:x+w])
return result