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[英]OpenCV xfeatures2d_SURF -213:The function/feature is not implemented
[英]"The function/feature is not implemented" error when running opencv example
運行以下代碼時出現以下錯誤:
import cv2, sys, numpy, os
haar_file = 'haarcascade_frontalface_default.xml'
datasets = 'datasets'
print('Recognizing Face Please Be in sufficient Lights...')
(images, lables, names, id) = ([], [], {}, 0)
for (subdirs, dirs, files) in os.walk(datasets):
for subdir in dirs:
names[id] = subdir
subjectpath = os.path.join(datasets, subdir)
for filename in os.listdir(subjectpath):
path = subjectpath + '/' + filename
lable = id
images.append(cv2.imread(path))
lables.append(int(lable))
id += 1
(width, height) = (130, 100)
(images, lables) = [numpy.array(lis) for lis in [images, lables]]
model = cv2.face.LBPHFaceRecognizer_create()
model.train(images, lables) # error comes here
face_cascade = cv2.CascadeClassifier(haar_file)
webcam = cv2.VideoCapture(0)
while True:
(_, im) = webcam.read()
gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
cv2.rectangle(im, (x, y), (x + w, y + h), (255, 0, 0), 2)
face = gray[y:y + h, x:x + w]
face_resize = cv2.resize(face, (width, height))
prediction = model.predict(face_resize)
cv2.rectangle(im, (x, y), (x + w, y + h), (0, 255, 0), 3)
if prediction[1]<500:
cv2.putText(im, '% s' %
(names[prediction[0]]), (x-10, y-10),
cv2.FONT_HERSHEY_PLAIN, 1, (0, 255, 0))
else:
cv2.putText(im, 'not recognized',
(x-10, y-10), cv2.FONT_HERSHEY_PLAIN, 1, (0, 255, 0))
cv2.imshow('OpenCV', im)
key = cv2.waitKey(10)
if key == 27:
break
cv2.destroyAllWindows()
彈出的錯誤是:
Traceback (most recent call last):
File "Y:\vigyantram\AI-20200807T104521Z-001\AI\img processing1\face_recognize.py", line 19, in <module>
model.train(images, lables)
cv2.error: OpenCV(4.3.0) C:\projects\opencv-python\opencv_contrib\modules\face\src\lbph_faces.cpp:265: error: (-213:The function/feature is not implemented) Using Original Local Binary Patterns for feature extraction only works on single-channel images (given 16). Please pass the image data as a grayscale image! in function 'cv::face::elbp'?
先感謝您。
這可能會解決問題。 如果它不起作用,請使用 8 或 16 的大小(寬度和高度)倍數,讓我知道你需要將測試和訓練圖像都轉換為灰色嘗試它也可以工作。
import cv2, sys, numpy, os
haar_file = 'haarcascade_frontalface_default.xml'
datasets = 'datasets'
print('Recognizing Face Please Be in sufficient Lights...')
(images, lables, names, id) = ([], [], {}, 0)
for (subdirs, dirs, files) in os.walk(datasets):
for subdir in dirs:
names[id] = subdir
subjectpath = os.path.join(datasets, subdir)
for filename in os.listdir(subjectpath):
path = subjectpath + '/' + filename
lable = id
images.append(cv2.imread(path))
lables.append(int(lable))
id += 1
(width, height) = (200, 200) #here 200 is multiple of 8
(images, lables) = [numpy.array(lis) for lis in [images, lables]]
model = cv2.face.LBPHFaceRecognizer_create()
model.train(images, lables) #error comes here
face_cascade = cv2.CascadeClassifier(haar_file)
webcam = cv2.VideoCapture(0)
while True:
(_, im) = webcam.read()
gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
cv2.rectangle(im, (x, y), (x + w, y + h), (255, 0, 0), 2)
face = gray[y:y + h, x:x + w]
face_resize = cv2.resize(face, (width, height))
prediction = model.predict(face_resize)
cv2.rectangle(im, (x, y), (x + w, y + h), (0, 255, 0), 3)
if prediction[1]<500:
cv2.putText(im, '% s' %
(names[prediction[0]]), (x-10, y-10),
cv2.FONT_HERSHEY_PLAIN, 1, (0, 255, 0))
else:
cv2.putText(im, 'not recognized',
(x-10, y-10), cv2.FONT_HERSHEY_PLAIN, 1, (0, 255, 0))
cv2.imshow('OpenCV', im)
key = cv2.waitKey(10)
if key == 27:
break
cv2.destroyAllWindows()
我知道該怎么做。
images.append(cv2.imread(path,0))
必須添加“0”。
我對下面的代碼有同樣的錯誤
cv2.error: OpenCV(4.5.4) D:\a\opencv-python\opencv-python\opencv_contrib\modules\face\src\lbph_faces.cpp:265: error: (-213: The function/feature is not implemented ) 使用原始局部二進制模式進行特征提取僅適用於單通道圖像(給定 16)。 請將圖像數據作為灰度圖像傳遞:在 function 'cv::face::elbp'
import cv2
import os
import numpy as np
eigenface = cv2.face.EigenFaceRecognizer_create()
fisherface = cv2.face.FisherFaceRecognizer_create()
lbph = cv2.face.LBPHFaceRecognizer_create()
def getImagemComId():
caminhos = [os.path.join('fotos', f) for f in os.listdir('fotos')]
#print(caminhos)
faces = []
ids = []
for caminhoImagem in caminhos:
imagemFace = cv2.cvtColor(cv2.imread(caminhoImagem), cv2.COLOR_BGR2RGB)
id = int(os.path.split(caminhoImagem)[-1].split('.')[1])
#print(id)
ids.append(id)
faces.append(imagemFace)
#cv2.imshow('face', imagemFace)
#cv2.waitKey(10)
return np.array(ids), faces
ids, faces = getImagemComId()
#print(faces)
print("Treinando...")
eigenface.train(faces, ids)
eigenface.write('classificadorEigen.yml')
fisherface.train(faces, ids)
fisherface.write('classificadorFisher.yml')
lbph.train(faces, ids)
lbph.write('classificadorLBPH.yml')
print("Treinamento realizado")
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