python人脸识别代码+测试文件

# -*- coding: utf-8 -*- """ Created on Tue Feb 26 14:24:07 2019 @author: fancheyu """ #导入模块 from sklearn import neighbors import os import os.path import pickle from PIL import Image,ImageDraw import face_recognition as fr from face_recognition.face_recognition_cli import image_files_in_folder #函数定义 #训练 def train(train_dir,model_save_path='trained_knn_madel.clf',n_neighbors=3,knn_algo='ball_tree'): """ 训练一个KNN分类器 :param train_dir :param model_save_path :param n_neighbors :param knn_algo return：KNN分类器 """ #初始化训练集 X = [] y = [] #遍历训练集中的每个人 for class_dir in os.listdir(train_dir): if not os.path.isdir(os.path.join(train_dir,class_dir)): continue #加载图片 for img_path in image_files_in_folder(os.path.join(train_dir,class_dir)): image = fr.load_image_file(img_path) boxes = fr.face_locations(image) print("{}".format(img_path)) #对每张照片编码 X.append(fr.face_encodings(image,known_face_locations=boxes)[0]) y.append(class_dir) #确定k if n_neighbors is None: n_neighbors = 3 #训练出分类器 knn_clf = neighbors.KNeighborsClassifier(n_neighbors=n_neighbors) knn_clf.fit(X,y) #保存分类器 if model_save_path is not None: with open(model_save_path,'wb') as f: pickle.dump(knn_clf,f) #返回分类器 return knn_clf #预测 def predict(X_img_path,knn_clf=None,model_path=None,distance_threshold = 0.45): """ 利用KNN分离器识别 :return :[人名，边界，...] """ if knn_clf is None and model_path is None: raise Exception("请选择KNN分类器方式：knn_clf 或 model_path") #加载KNN模型 #rb读入二进制数据 if knn_clf is None: with open(model_path,'rb') as f: knn_clf = pickle.load(f) #加载图片，发现人脸位置 X_img = fr.load_image_file(X_img_path) X_face_locations = fr.face_locations(X_img) #编码 encodings = fr.face_encodings(X_img,known_face_locations=X_face_locations) #利用KNN找出匹配图片 closest_distances = knn_clf.kneighbors(encodings,n_neighbors = 3) are_matches = [closest_distances[0][i][0] <= distance_threshold for i in range(len(X_face_locations))] #判断类别 return[(pred,loc) if rec else ("unknown",loc) for pred,loc,rec in zip(knn_clf.predict(encodings),X_face_locations,are_matches)] #识别结果可视化 def show_names_on_image(img_path,predictions) : """ :param img_path:待识别图片位置 :Param prediction:预测结果 """ pil_image = Image.open(img_path).convert("RGB") draw = ImageDraw.Draw(pil_image) #画出人脸边界盒子 for name,(top,right,bottom,left) in predictions: draw.rectangle(((left,top),(right,bottom)),outline = (225,0,255)) #生成utf-8格式 name = name.encode("UTF-8") name = name.decode("ascii") #写下名字作为标签 text_width,text_height = draw.textsize(name) draw.rectangle(((left,bottom - text_height - 10),(right,bottom)),fill = (225,0,255),outline = (225,0,255)) draw.text((left+6,bottom - text_height - 5),name,fill = (255,255,255)) #名字列表 li_names.append(name) #从内存删除 del draw #显示结果图 pil_image.show() #统计分析 li_names = [] #计算总人数 def count(train_dir): path = train_dir count = 0 for fn in os.listdir(path):#fn代表文件夹 count = count + 1 return count #获取所有名字的列表 def list_all(train_dir): path = train_dir result = [] for fn in os.listdir(path): result.append(fn) return result #输出结果 def stat_output(): s_list = set(li_names) s_list_all = set(list_all("examples/train")) if "unknown" in s_list: s_list.remove("unknown") tot_num = count("examples/train") s_absent = set(s_list_all - s_list) print("\n") print("==================================================================") print("全体名单",s_list_all) print("已到名单",s_list) print("应到人数",tot_num) print("已到人数",len(s_list)) print("出勤率:{:.2f}".format(float(len(s_list))/float(tot_num))) print("未到",s_absent) print("==================================================================") #运行 if __name__ == "__main__": #1.训练分类器 print("正在训练KNN分类器请稍后...") print("训练详情：") train("examples/train",model_save_path="trained_knn_model.clf",n_neighbors=3) print("训练完成！") #2.利用训练好的模型预测新照片 for image_file in os.listdir("examples/test"): full_file_path = os.path.join("examples/test",image_file) #利用分类器找出人脸 predictions = predict(full_file_path,model_path = "trained_knn_model.clf") #3.输出结果 #打印 print("识别结果如下：") for name,(top,right,bottom,left) in predictions: print("发现{},位置:{},{}.".format(name,top,right)) #显示名字 show_names_on_image(os.path.join("examples/test",image_file),predictions) #统计数据 stat_output()