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import os # import for taking the imagePaths import cv2 # openCV import numpy as np # for numpy arrays from PIL import Image # pillow import openface dlibFacePredictor = 'shape_predictor_68_face_landmarks.dat' # Path to dlib's face predictor recognizer = cv2.face.LBPHFaceRecognizer_create() # Local Binary Patterns Histograms imgDim = 96 # Default image dimension align = openface.AlignDlib(dlibFacePredictor)

def getImagesWithID(path): imageFolders = [os.path.join(path, f) for f in os.listdir(path)] # Joining './dataset' and '<image names="">' faces = [] # Empty array for faces Ids = [] for imageFolder in imageFolders: imagePaths = [os.path.join(imageFolder, f) for f in os.listdir(imageFolder)] for imagePath in imagePaths: image = cv2.imread(imagePath) rgbImg = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) bb = align.getLargestFaceBoundingBox(rgbImg) alignedFace = align.align(imgDim, rgbImg, bb=None, landmarkIndices=openface.AlignDlib.OUTER_EYES_AND_NOSE) cv2.imwrite('C:/Users/ACER/Desktop/PROJECT ALL RESOURCE/Implementation/PYTHON FILES/Training Images/training.jpg', alignedFace) faceImg = Image.open('C:/Users/ACER/Desktop/PROJECT ALL RESOURCE/Implementation/PYTHON FILES/Training Images/training.jpg').convert('L') # Converting colored and GrayScale images into bilevel images using Floyd-Steinberg dither faceNp = np.array(faceImg, 'uint8') # Converting face array into numpy array ID = int(os.path.split(imagePath)[-1].split('.')[1]) # Check this again faces.append(faceNp) # adding the dilevel face into faces array Ids.append(ID) # index of ID and faceNp is same in both arrays cv2.imshow("Training", faceNp) # Showing the faces which are getting trained cv2.waitKey(10) # Empty array for Person Ids # Waiting time id 10 milisecond return Ids, faces

Ids, faces = getImagesWithID('C:/Users/ACER/Desktop/PROJECT ALL RESOURCE/Implementation/PYTHON FILES/DataSets') # Calling the function recognizer.train(faces, np.array(Ids)) # Training the faces recognizer.save('C:/Users/ACER/Desktop/PROJECT ALL RESOURCE/Implementation/PYTHON FILES/Training/trainingData.yml') # Saving the yml file cv2.destroy() # Closing all the opened windows

The error is:- Traceback (most recent call last): File "C:\Users\ACER\Desktop\PROJECT ALL RESOURCE\Implementation\PYTHON FILES\training_set.py", line 34, in <module> Ids, faces = getImagesWithID('C:/Users/ACER/Desktop/PROJECT ALL RESOURCE/Implementation/PYTHON FILES/DataSets') # Calling the function File "C:\Users\ACER\Desktop\PROJECT ALL RESOURCE\Implementation\PYTHON FILES\training_set.py", line 22, in getImagesWithID cv2.imwrite('C:/Users/ACER/Desktop/PROJECT ALL RESOURCE/Implementation/PYTHON FILES/Training Images/training.jpg', alignedFace) cv2.error: OpenCV(4.2.0) C:\projects\opencv-python\opencv\modules\imgcodecs\src\loadsave.cpp:715: error: (-215:Assertion failed) !_img.empty() in function 'cv::imwrite'

import os # import for taking the imagePaths import cv2 # openCV import numpy as np # for numpy arrays from PIL import Image # pillow import openface dlibFacePredictor = 'shape_predictor_68_face_landmarks.dat' # Path to dlib's face predictor recognizer = cv2.face.LBPHFaceRecognizer_create() # Local Binary Patterns Histograms imgDim = 96 # Default image dimension align = openface.AlignDlib(dlibFacePredictor)The error is:-

Traceback (most recent call last):
  File "C:\Users\ACER\Desktop\PROJECT ALL RESOURCE\Implementation\PYTHON FILES\training_set.py", line 34, in <module>
    Ids, faces = getImagesWithID('C:/Users/ACER/Desktop/PROJECT ALL RESOURCE/Implementation/PYTHON FILES/DataSets')                                              # Calling the function
  File "C:\Users\ACER\Desktop\PROJECT ALL RESOURCE\Implementation\PYTHON FILES\training_set.py", line 22, in getImagesWithID
    cv2.imwrite('C:/Users/ACER/Desktop/PROJECT ALL RESOURCE/Implementation/PYTHON FILES/Training Images/training.jpg', alignedFace)
cv2.error: OpenCV(4.2.0) C:\projects\opencv-python\opencv\modules\imgcodecs\src\loadsave.cpp:715: error: (-215:Assertion failed) !_img.empty() in function 'cv::imwrite''cv::imwrite'

import os                                                                       # import for taking the imagePaths
import cv2                                                                      # openCV
import numpy as np                                                              # for numpy arrays
from PIL import Image                                                           # pillow
import openface
dlibFacePredictor = 'shape_predictor_68_face_landmarks.dat'                     # Path to dlib's face predictor
recognizer = cv2.face.LBPHFaceRecognizer_create()                                     # Local Binary Patterns Histograms
imgDim = 96                                                                     # Default image dimension
align = openface.AlignDlib(dlibFacePredictor)

def getImagesWithID(path):
    imageFolders = [os.path.join(path, f) for f in os.listdir(path)]    # Joining './dataset' and '<image names>'
    faces = []                                                                  # Empty array for faces
    Ids = []
    for imageFolder in imageFolders:
        imagePaths = [os.path.join(imageFolder, f) for f in os.listdir(imageFolder)]
        for imagePath in imagePaths:
            image = cv2.imread(imagePath)
            rgbImg = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
            bb = align.getLargestFaceBoundingBox(rgbImg)
            alignedFace = align.align(imgDim, rgbImg, bb=None, landmarkIndices=openface.AlignDlib.OUTER_EYES_AND_NOSE)
            cv2.imwrite('C:/Users/ACER/Desktop/PROJECT ALL RESOURCE/Implementation/PYTHON FILES/Training Images/training.jpg', alignedFace)
            faceImg = Image.open('C:/Users/ACER/Desktop/PROJECT ALL RESOURCE/Implementation/PYTHON FILES/Training Images/training.jpg').convert('L')        # Converting colored and GrayScale images into bilevel images using Floyd-Steinberg dither
            faceNp = np.array(faceImg, 'uint8')                                     # Converting face array into numpy array
            ID = int(os.path.split(imagePath)[-1].split('.')[1])                    # Check this again
            faces.append(faceNp)                                                    # adding the dilevel face into faces array
            Ids.append(ID)                                                          # index of ID and faceNp is same in both arrays
            cv2.imshow("Training", faceNp)                                          # Showing the faces which are getting trained
            cv2.waitKey(10)
                                                                        # Empty array for Person Ids
                                                             # Waiting time id 10 milisecond
    return Ids, faces

Ids, faces = getImagesWithID('C:/Users/ACER/Desktop/PROJECT ALL RESOURCE/Implementation/PYTHON FILES/DataSets')                                              # Calling the function
recognizer.train(faces, np.array(Ids))                                          # Training the faces
recognizer.save('C:/Users/ACER/Desktop/PROJECT ALL RESOURCE/Implementation/PYTHON FILES/Training/trainingData.yml')                                # Saving the yml file
cv2.destroy()                                                         # Closing all the opened windows