我正在尝试通过训练OpenCV的Fisher Face分类器来修改以下代码，用于特定的面部图像。而且我不知道为什么下面的代码显示
Traceback (most recent call last):
File "create_model.py", line 109, in <module>
update(emotions)
File "create_model.py", line 104, in update
run_recognizer(emotions)
File "create_model.py", line 101, in run_recognizer
fishface.train(np.array(training_data), npar_trainlabs)
TypeError: src is not a numpy array, neither a scalar
training_data包含 dlib 的vectorized_landmarks，我正在将它们转换为 numpy 数组，training_labels只是标签 1 或 2。
Traceback涉及的功能如下：
fishface = cv2.face.createFisherFaceRecognizer()
emotions = ["True", "Glasses"]
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
def get_landmarks(image):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
cimage = clahe.apply(gray)
detections = detector(cimage, 1)
landmarks_vectorised = []
for k, d in enumerate(detections): # For all detected face instances individually
shape = predictor(cimage, d) # Draw Facial Landmarks with the predictor class
xlist = []
ylist = []
for i in range(1, 68): # Store X and Y coordinates in two lists
xlist.append(float(shape.part(i).x))
ylist.append(float(shape.part(i).y))
xmean = np.mean(xlist) # Get the mean of both axes to determine centre of gravity
ymean = np.mean(ylist)
xcentral = [(x - xmean) for x in xlist] # get distance between each point and the central point in both axes
ycentral = [(y - ymean) for y in ylist]
if xlist[26] == xlist[
29]: # If x-coordinates of the set are the same, the angle is 0, catch to prevent 'divide by 0' error in function
anglenose = 0
else:
anglenose = int(math.atan((ylist[26] - ylist[29]) / (xlist[26] - xlist[29])) * 180 / math.pi)
if anglenose < 0:
anglenose += 90
else:
anglenose -= 90
landmarks_vectorised = []
if len(detections) < 1:
landmarks_vectorised = "error"
for x, y, w, z in zip(xcentral, ycentral, xlist, ylist):
landmarks_vectorised.append(x)
landmarks_vectorised.append(y)
meannp = np.asarray((ymean, xmean))
coornp = np.asarray((z, w))
dist = np.linalg.norm(coornp - meannp)
anglerelative = (math.atan((z - ymean) / (w - xmean)) * 180 / math.pi) - anglenose
landmarks_vectorised.append(dist)
landmarks_vectorised.append(anglerelative)
return landmarks_vectorised
def make_sets(labels):
training_data = []
training_labels = []
for label in labels:
training = glob.glob("data\%s\*" % label)
print(len(training))
for item in training:
try:
image = cv2.imread(item)
except:
continue
print(item)
landmarks_vectorised = get_landmarks(image)
if landmarks_vectorised == "error":
print("error with landmarks")
pass
else:
training_data.append(landmarks_vectorised)
if str(label) == "True":
training_labels.append(2)
elif str(label) == "Glasses":
training_labels.append(1)
print("sets created")
return training_data, training_labels
def make_sets(labels):
training_data = []
training_labels = []
for label in labels:
training = glob.glob("data\%s\*" % label)
print(len(training))
for item in training:
try:
image = cv2.imread(item)
except:
continue
print(item)
landmarks_vectorised = get_landmarks(image)
if landmarks_vectorised == "error":
print("error with landmarks")
pass
else:
training_data.append(landmarks_vectorised)
if str(label) == "True":
training_labels.append(2)
elif str(label) == "Glasses":
training_labels.append(1)
print("sets created")
return training_data, training_labels
def run_recognizer(emotions):
training_data, training_labels = make_sets(emotions)
print("training fisher face classifier")
print(type(training_data))
print(type(training_labels))
npar_train = np.array(training_data)
npar_trainlabs = np.array(training_labels)
fishface.train(np.array(training_data), npar_trainlabs)
def update(emotions):
run_recognizer(emotions)
fishface.save("glasses.xml")
update(emotions)
请帮助我理解这种错误的含义。
尝试打印您的training_data及其dtype，也许您可以将它们放在list中，然后将列表转换为np.array。对标签执行相同的操作。