I am writing a facial recognition program and I keep getting this error, and I am just very confused I see no other examples on the web where people include ranges when converting to UMat
Traceback (most recent call last):
File "test.py", line 48, in <module>
test_photos()
File "test.py", line 40, in test_photos
face, rect = detect_face(test_photo)
File "test.py", line 15, in detect_face
imgUMat = cv2.UMat(img)
TypeError: UMat() missing required argument 'ranges' (pos 2)
my code is
def detect_face(img):
imgUMat = cv2.UMat(img)
gray = cv2.cvtColor(imgUMat, 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]
gray = gray.get()
return gray[y:y+h,x:x+w], 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(np.array(faces), np.array(labels))
face, rect = detect_face(test_photo)
label = face_recognizer.predict(face)
if label == me:
print("it's me")
else:
print("it's not me")
test_photos()
if I do not use UMat() then I get this error:
Traceback (most recent call last):
File "test.py", line 48, in <module>
test_photos()
File "test.py", line 40, in test_photos
face, rect = detect_face(test_photo)
File "test.py", line 16, in detect_face
gray = cv2.cvtColor(imgUMat, cv2.COLOR_BGR2GRAY)
TypeError: Expected cv::UMat for argument 'src'
I am using OpenCV 4.0.0, and to be honest I just very confused because from what I have seen no one else had to use UMat to use cvtColor(), let alone use ranges inside UMat(). Any help would be greatly appreciated.
Instead of converting to a UMat
using cv2.Umat()
, just pass it in a np.float32()
. The two are identical for all intents and purposes.
Your code would look like this:
def detect_face(img):
imgUMat = np.float32(img)
gray = cv2.cvtColor(imgUMat, cv2.COLOR_BGR2GRAY)