python版opencv人脸训练与人脸识别

1.人脸识别准备

使用的两个opencv包

D:\python2023>pip list |findstr opencv opencv-contrib-python 4.8.1.78 opencv-python 4.8.1.78

数据集使用前一篇Javacv的数据集,网上随便找的60张图片,只是都挪到了D:\face目录下方便遍历

D:\face\1 30张刘德华图片 D:\face\2 30张刘亦菲图片

2.人脸识别模型训练 # -*- coding: utf-8 -*- import os import cv2 import numpy as np recognizer = cv2.face.LBPHFaceRecognizer().create() # Fisher需要reshape classifier = cv2.CascadeClassifier('E:\opencv\sources\data\haarcascades\haarcascade_frontalface_default.xml') def load_dataset(dataset_path): images=[] labels=[] for root,dirs,files in os.walk(dataset_path): for file in files: images.append(cv2.imread(os.path.join(root, file),cv2.IMREAD_GRAYSCALE)) labels.append(int(os.path.basename(root))) return images,labels if __name__ == '__main__': images,labels = load_dataset('D:\\face') recognizer.train(images,np.array(labels)) recognizer.save('face_model.xml') 3.人脸识别推理预测 # -*- coding: utf-8 -*- import os import cv2 def face_detect(image): gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) classifier = cv2.CascadeClassifier('E:\opencv\sources\data\haarcascades\haarcascade_frontalface_default.xml') faces = classifier.detectMultiScale(gray, 1.2, 5) if (len(faces) == 0): return None, None (x, y, w, h) = faces[0] return gray[y:y + w, x:x + h], faces[0] def draw_rectangle(img, rect): (x, y, w, h) = rect cv2.rectangle(img, (x, y), (x + w, y + h), (255, 255, 0), 2) def draw_text(img, text, x, y): cv2.putText(img, text, (x, y), cv2.FONT_HERSHEY_COMPLEX, 1, (128, 128, 0), 2) def predict(image): image_copy = image.copy() face, rect = face_detect(image_copy) tuple = recognizer.predict(face) print(tuple) draw_rectangle(image_copy, rect) draw_text(image_copy, str(tuple[0]), rect[0], rect[1]) return image_copy if __name__ == '__main__': recognizer = cv2.face.LBPHFaceRecognizer().create() # Fisher需要reshape recognizer.read("face_model.xml") for root, dirs, files in os.walk('D:\\face\\2'): for file in files: file_path = os.path.join(root, file) predict_image = predict(cv2.imread(file_path)) cv2.imshow('result', predict_image) cv2.waitKey(1000) 总结

代码逻辑基本同Javacv,但更简洁，这里训练出来模型准确度也高于Javacv (可能是参数不一致导致的)