OpenCV 4 TypeError: Expected cv::UMat for argument 'labels'
I am writing a facial recognition program and I keep getting this error when I try to train my recognizer
TypeError: Expected cv::UMat for argument 'labels'
my code is
def detect_face(img):
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
faces = face_cascade.detectMultiScale(gray, scaleFactor=1.2, minNeighbors=5);
if (len(faces)==0):
return None, None
(x, y, w, h) = faces[0]
return gray[y:y+w, x:x+h], faces[0]
def prepare_training_data():
faces =
labels =
for img in photo_name_list: #a collection of file locations as strings
image = cv2.imread(img)
face, rect = detect_face(image)
if face is not None:
faces.append(face)
labels.append("me")
return faces, labels
def test_photos():
face_recognizer = cv2.face.LBPHFaceRecognizer_create()
faces, labels = prepare_training_data()
face_recognizer.train(faces, np.ndarray(labels))
labels is list of labels for each photo in the image list returned from prepare_training_data, and I convert it to a numpy array because I read that is what train() needs it to be.
python python-3.x numpy opencv facial-identification
asked Jan 20 at 7:02
Tyler StrouthTyler Strouth
62
add a comment |
I am writing a facial recognition program and I keep getting this error when I try to train my recognizer
TypeError: Expected cv::UMat for argument 'labels'
my code is
def detect_face(img):
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
faces = face_cascade.detectMultiScale(gray, scaleFactor=1.2, minNeighbors=5);
if (len(faces)==0):
return None, None
(x, y, w, h) = faces[0]
return gray[y:y+w, x:x+h], faces[0]
def prepare_training_data():
faces =
labels =
for img in photo_name_list: #a collection of file locations as strings
image = cv2.imread(img)
face, rect = detect_face(image)
if face is not None:
faces.append(face)
labels.append("me")
return faces, labels
def test_photos():
face_recognizer = cv2.face.LBPHFaceRecognizer_create()
faces, labels = prepare_training_data()
face_recognizer.train(faces, np.ndarray(labels))
labels is list of labels for each photo in the image list returned from prepare_training_data, and I convert it to a numpy array because I read that is what train() needs it to be.
python python-3.x numpy opencv facial-identification
asked Jan 20 at 7:02
Tyler StrouthTyler Strouth
62
add a comment |
I am writing a facial recognition program and I keep getting this error when I try to train my recognizer
TypeError: Expected cv::UMat for argument 'labels'
my code is
def detect_face(img):
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
faces = face_cascade.detectMultiScale(gray, scaleFactor=1.2, minNeighbors=5);
if (len(faces)==0):
return None, None
(x, y, w, h) = faces[0]
return gray[y:y+w, x:x+h], faces[0]
def prepare_training_data():
faces =
labels =
for img in photo_name_list: #a collection of file locations as strings
image = cv2.imread(img)
face, rect = detect_face(image)
if face is not None:
faces.append(face)
labels.append("me")
return faces, labels
def test_photos():
face_recognizer = cv2.face.LBPHFaceRecognizer_create()
faces, labels = prepare_training_data()
face_recognizer.train(faces, np.ndarray(labels))
labels is list of labels for each photo in the image list returned from prepare_training_data, and I convert it to a numpy array because I read that is what train() needs it to be.
python python-3.x numpy opencv facial-identification
asked Jan 20 at 7:02
Tyler StrouthTyler Strouth
62
I am writing a facial recognition program and I keep getting this error when I try to train my recognizer
TypeError: Expected cv::UMat for argument 'labels'
my code is
def detect_face(img):
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
faces = face_cascade.detectMultiScale(gray, scaleFactor=1.2, minNeighbors=5);
if (len(faces)==0):
return None, None
(x, y, w, h) = faces[0]
return gray[y:y+w, x:x+h], faces[0]
def prepare_training_data():
faces =
labels =
for img in photo_name_list: #a collection of file locations as strings
image = cv2.imread(img)
face, rect = detect_face(image)
if face is not None:
faces.append(face)
labels.append("me")
return faces, labels
def test_photos():
face_recognizer = cv2.face.LBPHFaceRecognizer_create()
faces, labels = prepare_training_data()
face_recognizer.train(faces, np.ndarray(labels))
labels is list of labels for each photo in the image list returned from prepare_training_data, and I convert it to a numpy array because I read that is what train() needs it to be.
python python-3.x numpy opencv facial-identification
python python-3.x numpy opencv facial-identification
asked Jan 20 at 7:02
Tyler StrouthTyler Strouth
62
asked Jan 20 at 7:02
Tyler StrouthTyler Strouth
62
asked Jan 20 at 7:02
Tyler StrouthTyler Strouth
62
asked Jan 20 at 7:02
Tyler StrouthTyler Strouth
62
asked Jan 20 at 7:02
Tyler StrouthTyler Strouth
62
62
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
Solution - labels should be list of integers, and you should use numpy.array(labels) (or np.array(labels)).
Dummy example to check an error absence:
labels=[0]*len(faces)
face_recognizer.train(faces, np.array(labels))
I haven't found any documentation for openCV face recodnisers on python, so i've started to look over c++ documentation and examples. And due to documentation this library uses labels input for train as a std::vector<int>. A cpp example, provided by openCV docs, also uses vector<int> labels. And so on, library even have an error for not an integer input.
answered Jan 21 at 13:53
PolyGlotPolyGlot
215
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
Solution - labels should be list of integers, and you should use numpy.array(labels) (or np.array(labels)).
Dummy example to check an error absence:
labels=[0]*len(faces)
face_recognizer.train(faces, np.array(labels))
I haven't found any documentation for openCV face recodnisers on python, so i've started to look over c++ documentation and examples. And due to documentation this library uses labels input for train as a std::vector<int>. A cpp example, provided by openCV docs, also uses vector<int> labels. And so on, library even have an error for not an integer input.
answered Jan 21 at 13:53
PolyGlotPolyGlot
215
add a comment |
Solution - labels should be list of integers, and you should use numpy.array(labels) (or np.array(labels)).
Dummy example to check an error absence:
labels=[0]*len(faces)
face_recognizer.train(faces, np.array(labels))
I haven't found any documentation for openCV face recodnisers on python, so i've started to look over c++ documentation and examples. And due to documentation this library uses labels input for train as a std::vector<int>. A cpp example, provided by openCV docs, also uses vector<int> labels. And so on, library even have an error for not an integer input.
answered Jan 21 at 13:53
PolyGlotPolyGlot
215
add a comment |
Solution - labels should be list of integers, and you should use numpy.array(labels) (or np.array(labels)).
Dummy example to check an error absence:
labels=[0]*len(faces)
face_recognizer.train(faces, np.array(labels))
I haven't found any documentation for openCV face recodnisers on python, so i've started to look over c++ documentation and examples. And due to documentation this library uses labels input for train as a std::vector<int>. A cpp example, provided by openCV docs, also uses vector<int> labels. And so on, library even have an error for not an integer input.
answered Jan 21 at 13:53
PolyGlotPolyGlot
215
Solution - labels should be list of integers, and you should use numpy.array(labels) (or np.array(labels)).
Dummy example to check an error absence:
labels=[0]*len(faces)
face_recognizer.train(faces, np.array(labels))
I haven't found any documentation for openCV face recodnisers on python, so i've started to look over c++ documentation and examples. And due to documentation this library uses labels input for train as a std::vector<int>. A cpp example, provided by openCV docs, also uses vector<int> labels. And so on, library even have an error for not an integer input.
answered Jan 21 at 13:53
PolyGlotPolyGlot
215
answered Jan 21 at 13:53
PolyGlotPolyGlot
215
answered Jan 21 at 13:53
PolyGlotPolyGlot
215
answered Jan 21 at 13:53
PolyGlotPolyGlot
215
215
add a comment |
add a comment |
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