用服务器做网站,wordpress文章代码插件,海口今天最新通知,公司网页制作费用大概要多少钱?1.人脸识别准备
使用的两个opencv包
D:\python2023pip 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…1.人脸识别准备
使用的两个opencv包
D:\python2023pip 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 osimport cv2
import numpy as nprecognizer 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 osimport cv2def 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) rectcv2.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_copyif __name__ __main__:recognizer cv2.face.LBPHFaceRecognizer().create() # Fisher需要reshaperecognizer.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)
