局部二值模式直方图创建人脸识别器

àì夳堔傛蜴生んèń 2022-03-20 01:50 197阅读 0赞

先把代码搁这，纪录一下，可以直接运行摄像头检测：

import os
    
    import cv2
    import numpy as np
    from sklearn import preprocessing
    
    # Class to handle tasks related to label encoding
    class LabelEncoder(object):
        # Method to encode labels from words to numbers
        def encode_labels(self, label_words):
            self.le = preprocessing.LabelEncoder()
            self.le.fit(label_words)
    
        # Convert input label from word to number
        def word_to_num(self, label_word):
            return int(self.le.transform([label_word])[0])
    
        # Convert input label from number to word
        def num_to_word(self, label_num):
            return self.le.inverse_transform([label_num])[0]
    
    # Extract images and labels from input path
    def get_images_and_labels(input_path):
        label_words = []
    
        # Iterate through the input path and append files
        for root, dirs, files in os.walk(input_path):
            for filename in (x for x in files if x.endswith('.jpg')):
                filepath = os.path.join(root, filename)
                label_words.append(filepath.split('\\')[-2]) 
                
        # Initialize variables
        images = []
        le = LabelEncoder()
        le.encode_labels(label_words)
        labels = []
    
        # Parse the input directory
        for root, dirs, files in os.walk(input_path):
            for filename in (x for x in files if x.endswith('.jpg')):
                filepath = os.path.join(root, filename)
    
                # Read the image in grayscale format
                image = cv2.imread(filepath, 0) 
    
                # Extract the label
                name = filepath.split('\\')[-2]
                    
                # Perform face detection
                faces = faceCascade.detectMultiScale(image, 1.1, 2, minSize=(100,100))
    
                # Iterate through face rectangles
                for (x, y, w, h) in faces:
                    images.append(image[y:y+h, x:x+w])
                    labels.append(le.word_to_num(name))
    
        return images, labels, le
    
    if __name__=='__main__':
        cascade_path =r'C:\Users\Administrator\Desktop\face\cascade_files\haarcascade_frontalface_alt.xml'
        path_train = r'C:\Users\Administrator\Desktop\face\train'
       
        
        # Load face cascade file
        faceCascade = cv2.CascadeClassifier(cascade_path)
    
        # Initialize Local Binary Patterns Histogram face recognizer
        recognizer = cv2.face.LBPHFaceRecognizer_create()
    
        # Extract images, labels, and label encoder from training dataset
        images, labels, le = get_images_and_labels(path_train)
    
        # Train the face recognizer 
        print( "\nTraining...")
        recognizer.train(images, np.array(labels))
        recognizer.save(r'C:\Users\Administrator\Desktop\face\train\a.xml')
        # Test the recognizer on unknown images
        print( '\nPerforming prediction on test images...')
        cap = cv2.VideoCapture(0)
    
    # Define the scaling factor
    
    
    # Loop until you hit the Esc key
        while True:
        # Capture the current frame and resize it
            ret, frame = cap.read()
            frame1=cv2.resize(frame, None, fx=0.5, fy=0.5,interpolation=cv2.INTER_AREA)
            gray = cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY)
        #predict_image = cv2.resize(frame, None, fx=scaling_factor, fy=scaling_factor,interpolation=cv2.INTER_AREA)
            faces = faceCascade.detectMultiScale(gray, 1.1,2, minSize=(100,100))
        # Convert to grayscale
        #gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    
        # Run the face detector on the grayscale image
        #face_rects = face_cascade.detectMultiScale(gray, 1.3, 5)
    
        # Draw rectangles on the image
            for (x, y, w, h) in faces:
                    # Predict the output
                predicted_index, conf = recognizer.predict( gray[y:y+h, x:x+w])
    
                    # Convert to word label
                predicted_person = le.num_to_word(predicted_index)
    
                    # Overlay text on the output image and display it
                cv2.putText(frame1, predicted_person,   (10,60), cv2.FONT_HERSHEY_SIMPLEX, 2, (255,255,255), 3)
            cv2.imshow("Recognizing face", frame1)
        
            
            c = cv2.waitKey(5)
            if c == 27:
                break
        # Check if Esc key has been pressed
    
    
    # Release the video capture object and close all windows
    cap.release()
    cv2.destroyAllWindows()