python识别分类器_python3.6+opencv+face_recognition+knn分类器实现人脸识别
搭环境啥的就不说了, 里面的各种库的安装可以参考我的博客：
https://blog.csdn.net/weixin_39121325/article/details/85247546
注意：在安装face_recognition之前，需要先将dlib安装了，ps(在安装dlib时版本19.7的最好安，可能是兼容性比较好~~O(∩_∩)O哈哈~~)
其他的自行百度~~~
看一下代码结构
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faceRecognition_knn
knn_example下分了三个图片集合：
2.model
test : 测试图片
train ：训练集图片(图片集合是在网上下载的)
train1：也是训练集图片(将train训练集图片拆分了的，集合比较小
trained_knn_model.clf (保存的是knn分类器训练之后的模型，主要的是图片集合中图片的编码特征)
直接上代码
# -*- coding: utf-8 -*-# !/usr/bin/env python# @Time : 2019/1/10 15:50# @Author : xhh# @Desc : 利用knn分类器来进行人脸识别# @File : face_recognition_knn1.py# @Software: PyCharmimport mathfrom sklearn import neighborsimport osimport os.pathimport picklefrom PIL import Image, ImageDrawimport face_recognitionfrom face_recognition.face_recognition_cli import image_files_in_folderALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg'}def train(train_dir, model_save_path=None, n_neighbors=None, knn_algo='ball_tree', verbose=False):"""Structure:/├── /│ ├── .jpeg│ ├── .jpeg│ ├── ...├── /│ ├── .jpeg│ └── .jpeg└── ...:param train_dir: 训练集地址:param model_save_path: 模型所保存的地址:param n_neighbors: 在训练时如果没有指定权重，则自动选择权重:param knn_algo::param verbose::return: knn_clf返回训练后的模型"""X = []y = []# 循环遍历训练集中的每一个人for class_dir in os.listdir(train_dir):if not os.path.isdir(os.path.join(train_dir, class_dir)):continue# 循环遍历当前训练集中的每个人for img_path in image_files_in_folder(os.path.join(train_dir, class_dir)):image = face_recognition.load_image_file(img_path)face_bounding_boxes = face_recognition.face_locations(image)if len(face_bounding_boxes) != 1:# 如果该训练集中没有人或者有很多人，则跳过该图像if verbose:print("Image {} not suitable for training: {}".format(img_path,"Didn't find a face" if len(face_bounding_boxes) < 1 else "Found more than one face"))else:# 将图片中的人脸的编码加入到训练集中X.append(face_recognition.face_encodings(image, known_face_locations=face_bounding_boxes)[0])y.append(class_dir)# 确定KNN分类器中的权重if n_neighbors is None:n_neighbors = int(round(math.sqrt(len(X))))if verbose:print("Chose n_neighbors automatically:", n_neighbors)# 建立并训练KNN训练集knn_clf = neighbors.KNeighborsClassifier(n_neighbors=n_neighbors, algorithm=knn_algo, weights='distance')knn_clf.fit(X, y)# 保存KNN分类器if model_save_path is not None:with open(model_save_path, 'wb') as f:pickle.dump(knn_clf, f)return knn_clfdef predict(X_img_path, knn_clf=None, model_path=None, distance_threshold=0.6):""":param X_img_path: 测试集的图片地址:param knn_clf: 训练好的模型:param model_path: 模型地址:param distance_threshold: 给出当前测试图片中的人脸向量与模型中的距离:return:"""if not os.path.isfile(X_img_path) or os.path.splitext(X_img_path)[1][1:] not in ALLOWED_EXTENSIONS:raise Exception("Invalid image path: {}".format(X_img_path))if knn_clf is None and model_path is None:raise Exception("Must supply knn classifier either thourgh knn_clf or model_path")# 加载KNN模型if knn_clf is None:with open(model_path, 'rb') as f:knn_clf = pickle.load(f)# 加载图片文件夹以及人脸X_img = face_recognition.load_image_file(X_img_path)X_face_locations = face_recognition.face_locations(X_img)# 如过图片中没有人脸，则返回空的结果集if len(X_face_locations) == 0:return []# 找出测试集中的人脸编码faces_encodings = face_recognition.face_encodings(X_img, known_face_locations=X_face_locations)# 利用KNN模型找出测试集中最匹配的人脸closest_distances = knn_clf.kneighbors(faces_encodings, n_neighbors=1)are_matches = []for i in range(len(X_face_locations)):are_matches.append(closest_distances[0][i][0] <= distance_threshold)# 预测类并删除不在阈值范围内的分类return [(pred, loc) if rec else ("unknown", loc) for pred, loc, rec inzip(knn_clf.predict(faces_encodings), X_face_locations, are_matches)]def show_prediction_labels_on_image(img_path, predictions):"""在图片给出标签并展示人脸:param img_path: path to image to be recognized:param predictions: results of the predict function:return:"""pil_image = Image.open(img_path).convert("RGB")draw = ImageDraw.Draw(pil_image)for name, (top, right, bottom, left) in predictions:# 将人脸框出来利用pillow进行标注draw.rectangle(((left, top), (right, bottom)), outline=(0, 0, 255))name = name.encode("UTF-8")# 在人脸下面进行标配注text_width, text_height = draw.textsize(name)draw.rectangle(((left, bottom - text_height - 10), (right, bottom)), fill=(0, 0, 255), outline=(0, 0, 255))draw.text((left + 6, bottom - text_height - 5), name, fill=(255, 255, 255, 255))# 根据文档删除标注del draw# 显示图片结果pil_image.show()if __name__ == "__main__":# 1:训练KNN分类器，并保存# Once the model is trained and saved, you can skip this step next time.# print("Training KNN classifier...")# classifier = train("knn_examples/train1", model_save_path="model/trained_knn_model.clf", n_neighbors=2)# print("Training complete!")# STEP 2: 利用分类器，来预测该图片是否为已知的for image_file in os.listdir("knn_examples/test"):full_file_path = os.path.join("knn_examples/test", image_file)print("Looking for faces in {}".format(image_file))# Find all people in the image using a trained classifier model# Note: You can pass in either a classifier file name or a classifier model instancepredictions = predict(full_file_path, model_path="model/trained_knn_model.clf")# 在控制台打印结果for name, (top, right, bottom, left) in predictions:print("- Found {} at ({}, {})".format(name, left, top))# 在结果集中进行显示show_prediction_labels_on_image(os.path.join("knn_examples/test", image_file), predictions)
运行结果：
Training KNN classifier...
Training complete!
Looking for faces in Aaron_Eckhart_0001.jpg
- Found Aaron_Eckhart at (67, 80)
Looking for faces in Aaron_Peirsol_0003.jpg
- Found Aaron_Peirsol at (76, 86)
Looking for faces in Abba_Eban_0001.jpg
- Found Abba_Eban at (67, 80)
看下截图：
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部分图片的识别之后的标注：
训练集中没得，在这里回显示unknow的，比如下图的小女孩。。。
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github地址：https://github.com/XHHz/faceRecognition_knn
大家可以关注我和我小伙伴的公众号~~~这里有我和我的小伙伴不定时的更新一些python技术资料哦！！大家也可以留言，讨论一下技术问题，希望大家多多支持，关注一下啦，谢谢大家啦~~
【python识别分类器_python3.6+opencv+face_recognition+knn分类器实现人脸识别】
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