diff --git a/.idea/.gitignore b/.idea/.gitignore new file mode 100644 index 0000000..10b731c --- /dev/null +++ b/.idea/.gitignore @@ -0,0 +1,5 @@ +# 默认忽略的文件 +/shelf/ +/workspace.xml +# 基于编辑器的 HTTP 客户端请求 +/httpRequests/ diff --git a/.idea/Dlib_face_recognition_from_camera.iml b/.idea/Dlib_face_recognition_from_camera.iml new file mode 100644 index 0000000..a7bda17 --- /dev/null +++ b/.idea/Dlib_face_recognition_from_camera.iml @@ -0,0 +1,10 @@ + + + + + + + + + + \ No newline at end of file diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml new file mode 100644 index 0000000..105ce2d --- /dev/null +++ b/.idea/inspectionProfiles/profiles_settings.xml @@ -0,0 +1,6 @@ + + + + \ No newline at end of file diff --git a/.idea/misc.xml b/.idea/misc.xml new file mode 100644 index 0000000..dc07e76 --- /dev/null +++ b/.idea/misc.xml @@ -0,0 +1,7 @@ + + + + + + \ No newline at end of file diff --git a/.idea/modules.xml b/.idea/modules.xml new file mode 100644 index 0000000..4490d0e --- /dev/null +++ b/.idea/modules.xml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/.idea/vcs.xml b/.idea/vcs.xml new file mode 100644 index 0000000..8306744 --- /dev/null +++ b/.idea/vcs.xml @@ -0,0 +1,7 @@ + + + + + + + \ No newline at end of file diff --git a/test_tkinter.py b/README.md similarity index 100% rename from test_tkinter.py rename to README.md diff --git a/README.rst b/README.rst deleted file mode 100755 index ce36a39..0000000 --- a/README.rst +++ /dev/null @@ -1,269 +0,0 @@ -Face recognition from camera with Dlib -###################################### - -Introduction -************ - -调用摄像头进行人脸识别, 支持多张人脸同时识别 / Detect and recognize single or multi faces from camera; - -#. Tkinter 人脸录入界面, 支持录入时设置 (中文) 姓名 / Face register GUI with Tkinter, support setting (chinese) name when registering - - .. image:: introduction/face_register_tkinter_GUI.png - :width: 1000 - :align: center - -#. 简单的 OpenCV 摄像头人脸录入界面 / Simple face register GUI with OpenCV, tkinter not needed and cannot set name - - .. image:: introduction/face_register.png - :width: 1000 - :align: center - - 离摄像头过近, 人脸超出摄像头范围时, 会有 "OUT OF RANGE" 提醒 / - Too close to the camera, or face ROI out of camera area, will have "OUT OF RANGE" warning; - - .. image:: introduction/face_register_warning.png - :width: 1000 - :align: center - -#. 提取特征建立人脸数据库 / Generate face database from images captured -#. 利用摄像头进行人脸识别 / Face recognizer - - face_reco_from_camera.py, 对于每一帧都做检测识别 / Do detection and recognition for every frame: - - .. image:: introduction/face_reco.png - :width: 1000 - :align: center - - face_reco_from_camera_single_face.py, 对于人脸<=1, 只有新人脸出现才进行再识别来提高 FPS / - Do re-reco only for new single face: - - .. image:: introduction/face_reco_single.png - :width: 1000 - :align: center - - face_reco_from_camera_ot.py, 利用 OT 来实现再识别提高 FPS / Use OT to instead of re-reco for every frame to improve FPS: - - .. image:: introduction/face_reco_ot.png - :width: 1000 - :align: center - - 定制显示名字, 可以写中文 / Show chinese name: - - .. image:: introduction/face_reco_chinese_name.png - :width: 1000 - :align: center - - -** 关于精度 / About accuracy: - -* When using a distance threshold of ``0.6``, the dlib model obtains an accuracy of ``99.38%`` on the standard LFW face recognition benchmark. - -** 关于算法 / About algorithm - -* 基于 Residual Neural Network / 残差网络的 CNN 模型; - -* This model is a ResNet network with 29 conv layers. -It's essentially a version of the ResNet-34 network from the paper Deep Residual Learning for Image Recognition -by He, Zhang, Ren, and Sun with a few layers removed and the number of filters per layer reduced by half. - -Overview -******** - -此项目中人脸识别的实现流程 (no OT, 每一帧都进行检测+识别) / -Design of this repo, do detection and recognization for every frame: - -.. image:: introduction/overview.png - :width: 1000 - :align: center - -实现流程 (with OT, 初始帧进行检测+识别, 后续帧检测+质心跟踪) / OT used: - -.. image:: introduction/overview_with_ot.png - :width: 1000 - :align: center - -如果利用 OT 来跟踪, 可以大大提高 FPS, 因为做识别时候需要提取特征描述子的耗时很多 / -Use OT can save the time for face descriptor computation to improve FPS; - -Steps -***** - -#. 下载源码 / Git clone source code - - .. code-block:: bash - - git clone https://github.com/coneypo/Dlib_face_recognition_from_camera - -#. 安装依赖库 / Install some python packages needed - - .. code-block:: bash - - pip install -r requirements.txt - -#. 进行人脸信息采集录入, Tkinter GUI / Register faces with Tkinter GUI - - .. code-block:: bash - - # Install Tkinter - sudo apt-get install python3-tk python3-pil python3-pil.imagetk - - python3 get_faces_from_camera_tkinter.py - -#. 进行人脸信息采集录入, OpenCV GUI / Register faces with OpenCV GUI, same with above step - - .. code-block:: bash - - python3 get_face_from_camera.py - -#. 提取所有录入人脸数据存入 ``features_all.csv`` / Features extraction and save into ``features_all.csv`` - - .. code-block:: bash - - python3 features_extraction_to_csv.py - -#. 调用摄像头进行实时人脸识别 / Real-time face recognition - - .. code-block:: bash - - python3 face_reco_from_camera.py - -#. 对于人脸数<=1, 调用摄像头进行实时人脸识别 / Real-time face recognition (Better FPS compared with ``face_reco_from_camera.py``) - - .. code-block:: bash - - python3 face_reco_from_camera_single_face.py - -#. 利用 OT 算法, 调用摄像头进行实时人脸识别 / Real-time face recognition with OT (Better FPS) - - .. code-block:: bash - - python3 face_reco_from_camera_ot.py - -About Source Code -***************** - -代码结构 / Code structure: - -:: - - . - ├── get_faces_from_camera.py # Step 1. Face register GUI with OpenCV - ├── get_faces_from_camera_tkinter.py # Step 1. Face register GUI with Tkinter - ├── features_extraction_to_csv.py # Step 2. Feature extraction - ├── face_reco_from_camera.py # Step 3. Face recognizer - ├── face_reco_from_camera_single_face.py # Step 3. Face recognizer for single person - ├── face_reco_from_camera_ot.py # Step 3. Face recognizer with OT - ├── face_descriptor_from_camera.py # Face descriptor computation - ├── how_to_use_camera.py # Use the default camera by opencv - ├── data - │   ├── data_dlib # Dlib's model - │   │   ├── dlib_face_recognition_resnet_model_v1.dat - │   │   └── shape_predictor_68_face_landmarks.dat - │   ├── data_faces_from_camera # Face images captured from camera (will generate after step 1) - │   │   ├── person_1 - │   │   │   ├── img_face_1.jpg - │   │   │   └── img_face_2.jpg - │   │   └── person_2 - │   │   └── img_face_1.jpg - │   │   └── img_face_2.jpg - │   └── features_all.csv # CSV to save all the features of known faces (will generate after step 2) - ├── README.rst - └── requirements.txt # Some python packages needed - -用到的 Dlib 相关模型函数 / Dlib related functions used in this repo: - -#. Dlib 正向人脸检测器 (based on HOG), output: ```` / Dlib frontal face detector - - - .. code-block:: python - - detector = dlib.get_frontal_face_detector() - faces = detector(img_gray, 0) - -#. Dlib 人脸 landmark 特征点检测器, output: ```` / Dlib face landmark predictor, will use ``shape_predictor_68_face_landmarks.dat`` - - .. code-block:: python - - # This is trained on the ibug 300-W dataset (https://ibug.doc.ic.ac.uk/resources/facial-point-annotations/) - # Also note that this model file is designed for use with dlib's HOG face detector. - # That is, it expects the bounding boxes from the face detector to be aligned a certain way, - the way dlib's HOG face detector does it. - # It won't work as well when used with a face detector that produces differently aligned boxes, - # such as the CNN based mmod_human_face_detector.dat face detector. - - predictor = dlib.shape_predictor("data/data_dlib/shape_predictor_68_face_landmarks.dat") - shape = predictor(img_rd, faces[i]) - - -#. Dlib 特征描述子 / Face recognition model, the object maps human faces into 128D vectors - - - .. code-block:: python - - face_rec = dlib.face_recognition_model_v1("data/data_dlib/dlib_face_recognition_resnet_model_v1.dat") - - -Python 源码介绍如下 / Source code: - -#. ``get_face_from_camera.py``: - - 人脸信息采集录入 / Face register with OpenCV GUI - - * 请注意存储人脸图片时, 矩形框不要超出摄像头范围, 要不然无法保存到本地; - * 超出会有 "out of range" 的提醒; - - -#. ``get_faces_from_camera_tkinter.py``: - - 进行人脸信息采集录入 Tkinter GUI / Face register with Tkinter GUI - -#. ``features_extraction_to_csv.py``: - - 从上一步存下来的图像文件中, 提取人脸数据存入 CSV / Extract features from face images saved in step 1; - - * 会生成一个存储所有特征人脸数据的 ``features_all.csv`` - * Size: ``n*129`` , n means n faces you registered and 129 means face name + 128D features of this face - -#. ``face_reco_from_camera.py``: - - 这一步将调用摄像头进行实时人脸识别; / This part will implement real-time face recognition; - - * 将捕获到的人脸数据和之前存的人脸数据进行对比计算欧式距离, 由此判断是否是同一个人; - - * Compare the faces captured from camera with the faces you have registered which are saved in ``features_all.csv``; - -#. ``face_reco_from_camera_single_face.py``: - - 针对于人脸数 <=1 的场景, 区别于 ``face_reco_from_camera.py`` (对每一帧都进行检测+识别), 只有人脸出现的时候进行识别; - -#. ``face_reco_from_camera_ot.py``: - - 只会对初始帧做检测+识别, 对后续帧做检测+质心跟踪; - -#. (optional) ``face_descriptor_from_camera.py`` - - 调用摄像头进行实时特征描述子计算; / Real-time face descriptor computation; - -More -**** - -#. 如果希望详细了解 dlib 的用法, 请参考 Dlib 官方 Python api 的网站 / You can refer to this link for more information of how to use dlib: http://dlib.net/python/index.html - -#. Modify log level to ``logging.basicConfig(level=logging.DEBUG)`` to print info for every frame if needed (Default is ``logging.INFO``) - -#. 代码最好不要有中文路径 / No chinese characters in your code directory - -#. 人脸录入的时候先建文件夹再保存图片, 先 ``N`` 再 ``S`` / Press ``N`` before ``S`` - -#. 关于 ``face_reco_from_camera.py`` 人脸识别卡顿 FPS 低问题, 原因是特征描述子提取很费时间; 光跑 ``face_descriptor_from_camera.py`` 中 ``face_reco_model.compute_face_descriptor`` 在我的机器上得到的平均 FPS 在 5 左右 (检测在 ``0.03s`` , 特征描述子提取在 ``0.158s`` , 和已知人脸进行遍历对比在 ``0.003s`` 左右); 所以主要提取特征时候耗资源, 可以用 OT 去做追踪 (使用 ``face_reco_from_camera_ot.py`` ), 而不是对每一帧都做检测+识别, 识别的性能从 20 FPS -> 200 FPS - -可以访问我的博客获取本项目的更详细介绍, 如有问题可以邮件联系我 / -For more details, please visit my blog (in chinese) or send mail to coneypo@foxmail.com: - -* Blog: https://www.cnblogs.com/AdaminXie/p/9010298.html - -* 关于 OT 部分的更新在 Blog: https://www.cnblogs.com/AdaminXie/p/13566269.html - -* Feel free to create issue or contribute PR for it:) - -Thanks for your support. diff --git a/data/data_faces/person_马斌昊/img_face_1.jpg b/data/data_faces/person_马斌昊/img_face_1.jpg new file mode 100644 index 0000000..a99be71 Binary files /dev/null and b/data/data_faces/person_马斌昊/img_face_1.jpg differ diff --git a/data/data_faces/person_马斌昊/img_face_2.jpg b/data/data_faces/person_马斌昊/img_face_2.jpg new file mode 100644 index 0000000..896317c Binary files /dev/null and b/data/data_faces/person_马斌昊/img_face_2.jpg differ diff --git a/data/data_faces/person_马斌昊/img_face_3.jpg b/data/data_faces/person_马斌昊/img_face_3.jpg new file mode 100644 index 0000000..80b257b Binary files /dev/null and b/data/data_faces/person_马斌昊/img_face_3.jpg differ diff --git a/data/features_all.csv b/data/features_all.csv new file mode 100644 index 0000000..380d34a --- /dev/null +++ b/data/features_all.csv @@ -0,0 +1 @@ +马斌昊,-0.0899050161242485,0.02384429331868887,0.10691650211811066,-0.026448555290699005,-0.12473130598664284,-0.026039691641926765,-0.10633478686213493,-0.10004803165793419,0.0756474919617176,-0.09969355911016464,0.2548818141222,-0.02838892675936222,-0.18727628886699677,-0.04292612988501787,-0.038223471492528915,0.14358164370059967,-0.13197344541549683,-0.08303523808717728,-0.04544464126229286,0.005225636065006256,0.05400150641798973,0.016614584252238274,0.04487479291856289,0.0208868607878685,-0.12604008615016937,-0.3497048169374466,-0.1073501706123352,-0.08952881768345833,-0.058808863162994385,-0.007420409703627229,-0.07389594614505768,0.027185998857021332,-0.14693009108304977,0.032583920285105705,0.002864556387066841,0.08570835366845131,-0.030903122387826443,-0.09934812411665916,0.13845199346542358,-0.052308812737464905,-0.2402488812804222,0.03694266080856323,0.07542015612125397,0.1459449827671051,0.2269286960363388,0.014682859182357788,0.05472549982368946,-0.11605571955442429,0.06804285570979118,-0.17394205182790756,0.028083041310310364,0.10427030548453331,0.05113978683948517,0.05636468715965748,0.0043844198808074,-0.14433760941028595,-0.00407144520431757,0.12244492024183273,-0.09491624310612679,-0.015221940353512764,0.024541491642594337,-0.07212522253394127,-0.001256248913705349,-0.08840961009263992,0.22439686954021454,0.11492674425244331,-0.15236739814281464,-0.1425611972808838,0.06785859912633896,-0.18730811774730682,-0.07806657627224922,0.07729200273752213,-0.1676863580942154,-0.21197973936796188,-0.32569898664951324,0.027618836611509323,0.39188095927238464,0.10446011275053024,-0.17949597537517548,0.014993381686508656,-0.06068217195570469,0.00010182708501815796,0.09084133803844452,0.1250796541571617,-0.021716125309467316,0.008610536344349384,-0.11595302447676659,-0.0045088487677276134,0.27449268102645874,-0.07868175581097603,-0.0018516536802053452,0.2252117618918419,-0.00012384727597236633,0.06658063270151615,0.010239068418741226,0.06843071430921555,-0.024587254971265793,0.015095856040716171,-0.10306384414434433,0.04500553756952286,-0.013208229094743729,-0.07549317926168442,-0.024796511977910995,0.06462816521525383,-0.1793217957019806,0.07073099911212921,-0.008851969614624977,-0.003468985203653574,0.019725533202290535,-0.03128764219582081,-0.11080201715230942,-0.05722355656325817,0.15097910165786743,-0.24694078415632248,0.1483754888176918,0.183498315513134,0.1129513792693615,0.17476002871990204,0.13292384147644043,0.095770925283432,-0.055514756590127945,-0.03940589725971222,-0.17228589206933975,0.006599367829039693,0.07312680780887604,-0.0077499039471149445,0.07860656827688217,0.03986034169793129 diff --git a/face_descriptor_from_camera.py b/face_descriptor_from_camera.py deleted file mode 100644 index e64f408..0000000 --- a/face_descriptor_from_camera.py +++ /dev/null @@ -1,92 +0,0 @@ -# Copyright (C) 2018-2021 coneypo -# SPDX-License-Identifier: MIT - -# 摄像头实时人脸特征描述子计算 / Real-time face descriptor computing - -import dlib # 人脸识别的库 Dlib -import cv2 # 图像处理的库 OpenCV -import time - -# 1. Dlib 正向人脸检测器 -detector = dlib.get_frontal_face_detector() - -# 2. Dlib 人脸 landmark 特征点检测器 -predictor = dlib.shape_predictor('data/data_dlib/shape_predictor_68_face_landmarks.dat') - -# 3. Dlib Resnet 人脸识别模型，提取 128D 的特征矢量 -face_reco_model = dlib.face_recognition_model_v1("data/data_dlib/dlib_face_recognition_resnet_model_v1.dat") - - -class Face_Descriptor: - def __init__(self): - self.frame_time = 0 - self.frame_start_time = 0 - self.fps = 0 - self.frame_cnt = 0 - - def update_fps(self): - now = time.time() - self.frame_time = now - self.frame_start_time - self.fps = 1.0 / self.frame_time - self.frame_start_time = now - - def run(self): - cap = cv2.VideoCapture(0) - cap.set(3, 480) - self.process(cap) - cap.release() - cv2.destroyAllWindows() - - def process(self, stream): - while stream.isOpened(): - flag, img_rd = stream.read() - self.frame_cnt+=1 - k = cv2.waitKey(1) - - print('- Frame ', self.frame_cnt, " starts:") - - timestamp1 = time.time() - faces = detector(img_rd, 0) - timestamp2 = time.time() - print("--- Time used to `detector`: %s seconds ---" % (timestamp2 - timestamp1)) - - font = cv2.FONT_HERSHEY_SIMPLEX - - # 检测到人脸 - if len(faces) != 0: - for face in faces: - timestamp3 = time.time() - face_shape = predictor(img_rd, face) - timestamp4 = time.time() - print("--- Time used to `predictor`: %s seconds ---" % (timestamp4 - timestamp3)) - - timestamp5 = time.time() - face_desc = face_reco_model.compute_face_descriptor(img_rd, face_shape) - timestamp6 = time.time() - print("--- Time used to `compute_face_descriptor：` %s seconds ---" % (timestamp6 - timestamp5)) - - # 添加说明 - cv2.putText(img_rd, "Face descriptor", (20, 40), font, 1, (255, 255, 255), 1, cv2.LINE_AA) - cv2.putText(img_rd, "FPS: " + str(self.fps.__round__(2)), (20, 100), font, 0.8, (0, 255, 0), 1, cv2.LINE_AA) - cv2.putText(img_rd, "Faces: " + str(len(faces)), (20, 140), font, 0.8, (0, 255, 0), 1, cv2.LINE_AA) - cv2.putText(img_rd, "S: Save current face", (20, 400), font, 0.8, (255, 255, 255), 1, cv2.LINE_AA) - cv2.putText(img_rd, "Q: Quit", (20, 450), font, 0.8, (255, 255, 255), 1, cv2.LINE_AA) - - # 按下 'q' 键退出 - if k == ord('q'): - break - - self.update_fps() - - cv2.namedWindow("camera", 1) - cv2.imshow("camera", img_rd) - print('\n') - - -def main(): - Face_Descriptor_con = Face_Descriptor() - Face_Descriptor_con.run() - - -if __name__ == '__main__': - main() \ No newline at end of file diff --git a/face_reco_from_camera.py b/face_reco_from_camera.py index 60293b5..2437d1c 100755 --- a/face_reco_from_camera.py +++ b/face_reco_from_camera.py @@ -1,13 +1,6 @@ # Copyright (C) 2018-2021 coneypo # SPDX-License-Identifier: MIT -# Author: coneypo -# Blog: http://www.cnblogs.com/AdaminXie -# GitHub: https://github.com/coneypo/Dlib_face_recognition_from_camera -# Mail: coneypo@foxmail.com - -# 摄像头实时人脸识别 / Real-time face detection and recognition - import dlib import numpy as np import cv2 @@ -17,44 +10,49 @@ import time import logging from PIL import Image, ImageDraw, ImageFont -# Dlib 正向人脸检测器 / Use frontal face detector of Dlib +# Dlib 正向人脸检测器 detector = dlib.get_frontal_face_detector() - -# Dlib 人脸 landmark 特征点检测器 / Get face landmarks predictor = dlib.shape_predictor('data/data_dlib/shape_predictor_68_face_landmarks.dat') - -# Dlib Resnet 人脸识别模型，提取 128D 的特征矢量 / Use Dlib resnet50 model to get 128D face descriptor face_reco_model = dlib.face_recognition_model_v1("data/data_dlib/dlib_face_recognition_resnet_model_v1.dat") class Face_Recognizer: def __init__(self): - self.face_feature_known_list = [] # 用来存放所有录入人脸特征的数组 / Save the features of faces in database - self.face_name_known_list = [] # 存储录入人脸名字 / Save the name of faces in database + self.face_feature_known_list = [] + self.face_name_known_list = [] - self.current_frame_face_cnt = 0 # 存储当前摄像头中捕获到的人脸数 / Counter for faces in current frame - self.current_frame_face_feature_list = [] # 存储当前摄像头中捕获到的人脸特征 / Features of faces in current frame - self.current_frame_face_name_list = [] # 存储当前摄像头中捕获到的所有人脸的名字 / Names of faces in current frame - self.current_frame_face_name_position_list = [] # 存储当前摄像头中捕获到的所有人脸的名字坐标 / Positions of faces in current frame + self.current_frame_face_cnt = 0 + self.current_frame_face_feature_list = [] + self.current_frame_face_name_list = [] + self.current_frame_face_name_position_list = [] # Update FPS - self.fps = 0 # FPS of current frame - self.fps_show = 0 # FPS per second + self.fps = 0 + self.fps_show = 0 self.frame_start_time = 0 self.frame_cnt = 0 self.start_time = time.time() self.font = cv2.FONT_ITALIC - self.font_chinese = ImageFont.truetype("simsun.ttc", 30) + # 安全加载中文字体 + try: + self.font_chinese = ImageFont.truetype("simsun.ttc", 30) + except: + print("警告: 无法加载中文字体，使用默认字体") + self.font_chinese = ImageFont.load_default() + + # 添加退出标志 + self.exit_flag = False - # 从 "features_all.csv" 读取录入人脸特征 / Read known faces from "features_all.csv" def get_face_database(self): if os.path.exists("data/features_all.csv"): path_features_known_csv = "data/features_all.csv" csv_rd = pd.read_csv(path_features_known_csv, header=None) for i in range(csv_rd.shape[0]): features_someone_arr = [] - self.face_name_known_list.append(csv_rd.iloc[i][0]) + # 修复：确保姓名为字符串 + name = str(csv_rd.iloc[i][0]) + self.face_name_known_list.append(name) for j in range(1, 129): if csv_rd.iloc[i][j] == '': features_someone_arr.append('0') @@ -65,11 +63,8 @@ class Face_Recognizer: return 1 else: logging.warning("'features_all.csv' not found!") - logging.warning("Please run 'get_faces_from_camera.py' " - "and 'features_extraction_to_csv.py' before 'face_reco_from_camera.py'") return 0 - # 计算两个128D向量间的欧式距离 / Compute the e-distance between two 128D features @staticmethod def return_euclidean_distance(feature_1, feature_2): feature_1 = np.array(feature_1) @@ -77,10 +72,8 @@ class Face_Recognizer: dist = np.sqrt(np.sum(np.square(feature_1 - feature_2))) return dist - # 更新 FPS / Update FPS of Video stream def update_fps(self): now = time.time() - # 每秒刷新 fps / Refresh fps per second if str(self.start_time).split(".")[0] != str(now).split(".")[0]: self.fps_show = self.fps self.start_time = now @@ -88,11 +81,9 @@ class Face_Recognizer: self.fps = 1.0 / self.frame_time self.frame_start_time = now - # 生成的 cv2 window 上面添加说明文字 / PutText on cv2 window def draw_note(self, img_rd): cv2.putText(img_rd, "Face Recognizer", (20, 40), self.font, 1, (255, 255, 255), 1, cv2.LINE_AA) - cv2.putText(img_rd, "Frame: " + str(self.frame_cnt), (20, 100), self.font, 0.8, (0, 255, 0), 1, - cv2.LINE_AA) + cv2.putText(img_rd, "Frame: " + str(self.frame_cnt), (20, 100), self.font, 0.8, (0, 255, 0), 1, cv2.LINE_AA) cv2.putText(img_rd, "FPS: " + str(self.fps_show.__round__(2)), (20, 130), self.font, 0.8, (0, 255, 0), 1, cv2.LINE_AA) cv2.putText(img_rd, "Faces: " + str(self.current_frame_face_cnt), (20, 160), self.font, 0.8, (0, 255, 0), 1, @@ -100,126 +91,176 @@ class Face_Recognizer: cv2.putText(img_rd, "Q: Quit", (20, 450), self.font, 0.8, (255, 255, 255), 1, cv2.LINE_AA) def draw_name(self, img_rd): - # 在人脸框下面写人脸名字 / Write names under rectangle + # 在人脸框下面写人脸名字 img = Image.fromarray(cv2.cvtColor(img_rd, cv2.COLOR_BGR2RGB)) draw = ImageDraw.Draw(img) for i in range(self.current_frame_face_cnt): - # cv2.putText(img_rd, self.current_frame_face_name_list[i], self.current_frame_face_name_position_list[i], self.font, 0.8, (0, 255, 255), 1, cv2.LINE_AA) - draw.text(xy=self.current_frame_face_name_position_list[i], text=self.current_frame_face_name_list[i], font=self.font_chinese, - fill=(255, 255, 0)) - img_rd = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR) + try: + # 安全处理姓名 + name = str(self.current_frame_face_name_list[i]) + position = tuple(map(int, self.current_frame_face_name_position_list[i])) + draw.text(xy=position, text=name, font=self.font_chinese, fill=(255, 255, 0)) + except Exception as e: + print(f"绘制姓名时出错: {e}") + continue + img_rd = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR) return img_rd - # 修改显示人名 / Show names in chinese - def show_chinese_name(self): - # Default known name: person_1, person_2, person_3 - if self.current_frame_face_cnt >= 1: - # 修改录入的人脸姓名 / Modify names in face_name_known_list to chinese name - self.face_name_known_list[0] = '张三'.encode('utf-8').decode() - # self.face_name_known_list[1] = '张四'.encode('utf-8').decode() + def check_window_closed(self, window_name="camera"): + """检查窗口是否被关闭""" + try: + # 尝试获取窗口属性，如果窗口关闭会返回 -1 + if cv2.getWindowProperty(window_name, cv2.WND_PROP_VISIBLE) < 1: + return True + return False + except: + # 如果窗口不存在，也会触发异常 + return True - # 处理获取的视频流，进行人脸识别 / Face detection and recognition from input video stream def process(self, stream): - # 1. 读取存放所有人脸特征的 csv / Read known faces from "features.all.csv" - if self.get_face_database(): - while stream.isOpened(): - self.frame_cnt += 1 - logging.debug("Frame %d starts", self.frame_cnt) - flag, img_rd = stream.read() - faces = detector(img_rd, 0) - kk = cv2.waitKey(1) - # 按下 q 键退出 / Press 'q' to quit - if kk == ord('q'): - break - else: - self.draw_note(img_rd) - self.current_frame_face_feature_list = [] - self.current_frame_face_cnt = 0 - self.current_frame_face_name_position_list = [] - self.current_frame_face_name_list = [] + # 1. 读取存放所有人脸特征的 csv + if not self.get_face_database(): + print("错误: 无法加载人脸数据库") + return - # 2. 检测到人脸 / Face detected in current frame - if len(faces) != 0: - # 3. 获取当前捕获到的图像的所有人脸的特征 / Compute the face descriptors for faces in current frame - for i in range(len(faces)): - shape = predictor(img_rd, faces[i]) - self.current_frame_face_feature_list.append(face_reco_model.compute_face_descriptor(img_rd, shape)) - # 4. 遍历捕获到的图像中所有的人脸 / Traversal all the faces in the database - for k in range(len(faces)): - logging.debug("For face %d in camera:", k+1) - # 先默认所有人不认识，是 unknown / Set the default names of faces with "unknown" - self.current_frame_face_name_list.append("unknown") + print("人脸识别系统启动成功!") + print("按 'Q' 键退出程序") + print("或点击窗口关闭按钮退出") - # 每个捕获人脸的名字坐标 / Positions of faces captured - self.current_frame_face_name_position_list.append(tuple( - [faces[k].left(), int(faces[k].bottom() + (faces[k].bottom() - faces[k].top()) / 4)])) + # 创建窗口并设置为正常模式 + cv2.namedWindow("camera", cv2.WINDOW_NORMAL) - # 5. 对于某张人脸，遍历所有存储的人脸特征 - # For every faces detected, compare the faces in the database - current_frame_e_distance_list = [] - for i in range(len(self.face_feature_known_list)): - # 如果 person_X 数据不为空 - if str(self.face_feature_known_list[i][0]) != '0.0': - e_distance_tmp = self.return_euclidean_distance(self.current_frame_face_feature_list[k], - self.face_feature_known_list[i]) - logging.debug(" With person %s, the e-distance is %f", str(i + 1), e_distance_tmp) - current_frame_e_distance_list.append(e_distance_tmp) - else: - # 空数据 person_X - current_frame_e_distance_list.append(999999999) - # 6. 寻找出最小的欧式距离匹配 / Find the one with minimum e-distance - similar_person_num = current_frame_e_distance_list.index(min(current_frame_e_distance_list)) - logging.debug("Minimum e-distance with %s: %f", self.face_name_known_list[similar_person_num], min(current_frame_e_distance_list)) + while stream.isOpened() and not self.exit_flag: + # 检查窗口是否被关闭 + if self.check_window_closed(): + print("检测到窗口关闭，退出程序") + break - if min(current_frame_e_distance_list) < 0.4: - self.current_frame_face_name_list[k] = self.face_name_known_list[similar_person_num] - logging.debug("Face recognition result: %s", self.face_name_known_list[similar_person_num]) - else: - logging.debug("Face recognition result: Unknown person") - logging.debug("\n") + self.frame_cnt += 1 + flag, img_rd = stream.read() - # 矩形框 / Draw rectangle - for kk, d in enumerate(faces): - # 绘制矩形框 - cv2.rectangle(img_rd, tuple([d.left(), d.top()]), tuple([d.right(), d.bottom()]), - (255, 255, 255), 2) + if not flag: + print("无法读取视频帧") + break - self.current_frame_face_cnt = len(faces) + # 检测按键和窗口关闭 + kk = cv2.waitKey(1) & 0xFF - # 7. 在这里更改显示的人名 / Modify name if needed - # self.show_chinese_name() + # 按下 q 键退出 + if kk == ord('q') or kk == ord('Q'): + print("接收到退出信号，退出程序") + break - # 8. 写名字 / Draw name - img_with_name = self.draw_name(img_rd) + # 检查窗口关闭 + if cv2.getWindowProperty("camera", cv2.WND_PROP_VISIBLE) < 1: + print("窗口已关闭，退出程序") + break - else: - img_with_name = img_rd + self.draw_note(img_rd) + self.current_frame_face_feature_list = [] + self.current_frame_face_cnt = 0 + self.current_frame_face_name_position_list = [] + self.current_frame_face_name_list = [] - logging.debug("Faces in camera now: %s", self.current_frame_face_name_list) + # 2. 检测到人脸 + faces = detector(img_rd, 0) + if len(faces) != 0: + # 3. 获取当前捕获到的图像的所有人脸的特征 + for i in range(len(faces)): + shape = predictor(img_rd, faces[i]) + self.current_frame_face_feature_list.append(face_reco_model.compute_face_descriptor(img_rd, shape)) - cv2.imshow("camera", img_with_name) + # 4. 遍历捕获到的图像中所有的人脸 + for k in range(len(faces)): + # 先默认所有人不认识 + self.current_frame_face_name_list.append("unknown") - # 9. 更新 FPS / Update stream FPS - self.update_fps() - logging.debug("Frame ends\n\n") + # 每个捕获人脸的名字坐标 + self.current_frame_face_name_position_list.append(tuple( + [faces[k].left(), int(faces[k].bottom() + (faces[k].bottom() - faces[k].top()) / 4)])) - # OpenCV 调用摄像头并进行 process - def run(self): - # cap = cv2.VideoCapture("video.mp4") # Get video stream from video file - cap = cv2.VideoCapture(0) # Get video stream from camera - cap.set(3, 480) # 640x480 - self.process(cap) + # 5. 对于某张人脸，遍历所有存储的人脸特征 + current_frame_e_distance_list = [] + for i in range(len(self.face_feature_known_list)): + if str(self.face_feature_known_list[i][0]) != '0.0': + e_distance_tmp = self.return_euclidean_distance( + self.current_frame_face_feature_list[k], + self.face_feature_known_list[i] + ) + current_frame_e_distance_list.append(e_distance_tmp) + else: + current_frame_e_distance_list.append(999999999) - cap.release() + # 6. 寻找出最小的欧式距离匹配 + if current_frame_e_distance_list: + similar_person_num = current_frame_e_distance_list.index(min(current_frame_e_distance_list)) + min_distance = min(current_frame_e_distance_list) + + if min_distance < 0.4: + self.current_frame_face_name_list[k] = self.face_name_known_list[similar_person_num] + + # 绘制矩形框 + cv2.rectangle(img_rd, + (faces[k].left(), faces[k].top()), + (faces[k].right(), faces[k].bottom()), + (255, 255, 255), 2) + + self.current_frame_face_cnt = len(faces) + + # 8. 写名字 + img_rd = self.draw_name(img_rd) + + # 显示图像 + cv2.imshow("camera", img_rd) + + # 9. 更新 FPS + self.update_fps() + + # 清理资源 cv2.destroyAllWindows() + print("程序正常退出") + + def run(self): + cap = cv2.VideoCapture(0) + if not cap.isOpened(): + print("错误: 无法打开摄像头") + return + + # 设置摄像头参数 + cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640) + cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480) + + try: + self.process(cap) + except KeyboardInterrupt: + print("\n接收到 Ctrl+C，退出程序") + except Exception as e: + print(f"程序异常: {e}") + finally: + # 确保资源被释放 + cap.release() + cv2.destroyAllWindows() def main(): - # logging.basicConfig(level=logging.DEBUG) # Set log level to 'logging.DEBUG' to print debug info of every frame logging.basicConfig(level=logging.INFO) + print("=== 人脸识别系统启动 ===") + + # 检查必要的文件 + required_files = [ + 'data/data_dlib/shape_predictor_68_face_landmarks.dat', + 'data/data_dlib/dlib_face_recognition_resnet_model_v1.dat', + 'data/features_all.csv' + ] + + for file in required_files: + if not os.path.exists(file): + print(f"错误: 缺少必要文件 {file}") + return + Face_Recognizer_con = Face_Recognizer() Face_Recognizer_con.run() if __name__ == '__main__': - main() + main() \ No newline at end of file diff --git a/face_reco_from_camera_single_face.py b/face_reco_from_camera_single_face.py deleted file mode 100644 index 245844b..0000000 --- a/face_reco_from_camera_single_face.py +++ /dev/null @@ -1,329 +0,0 @@ -# Copyright (C) 2018-2021 coneypo -# SPDX-License-Identifier: MIT - -# Author: coneypo -# Blog: http://www.cnblogs.com/AdaminXie -# GitHub: https://github.com/coneypo/Dlib_face_recognition_from_camera -# Mail: coneypo@foxmail.com - -# 单张人脸实时识别 / Real-time face detection and recognition for single face -# 检测 -> 识别人脸, 新人脸出现 -> 再识别, 不会对于每一帧都进行识别 / Do detection -> recognize face, new face -> do re-recognition -# 其实对于单张人脸, 不需要 OT 进行跟踪, 对于新出现的人脸, 再识别一次就好了 / No OT here, OT will be used only for multi faces - -import dlib -import numpy as np -import cv2 -import os -import pandas as pd -import time -from PIL import Image, ImageDraw, ImageFont -import logging - -# Dlib 正向人脸检测器 / Use frontal face detector of Dlib -detector = dlib.get_frontal_face_detector() - -# Dlib 人脸 landmark 特征点检测器 / Get face landmarks -predictor = dlib.shape_predictor('data/data_dlib/shape_predictor_68_face_landmarks.dat') - -# Dlib Resnet 人脸识别模型, 提取 128D 的特征矢量 / Use Dlib resnet50 model to get 128D face descriptor -face_reco_model = dlib.face_recognition_model_v1("data/data_dlib/dlib_face_recognition_resnet_model_v1.dat") - - -class Face_Recognizer: - def __init__(self): - self.font = cv2.FONT_ITALIC - self.font_chinese = ImageFont.truetype("simsun.ttc", 30) - - # 统计 FPS / For FPS - self.frame_time = 0 - self.frame_start_time = 0 - self.fps = 0 - self.fps_show = 0 - self.start_time = time.time() - - # 统计帧数 / cnt for frame - self.frame_cnt = 0 - - # 用来存储所有录入人脸特征的数组 / Save the features of faces in the database - self.features_known_list = [] - # 用来存储录入人脸名字 / Save the name of faces in the database - self.face_name_known_list = [] - - # 用来存储上一帧和当前帧 ROI 的质心坐标 / List to save centroid positions of ROI in frame N-1 and N - self.last_frame_centroid_list = [] - self.current_frame_centroid_list = [] - - # 用来存储当前帧检测出目标的名字 / List to save names of objects in current frame - self.current_frame_name_list = [] - - # 上一帧和当前帧中人脸数的计数器 / cnt for faces in frame N-1 and N - self.last_frame_faces_cnt = 0 - self.current_frame_face_cnt = 0 - - # 用来存放进行识别时候对比的欧氏距离 / Save the e-distance for faceX when recognizing - self.current_frame_face_X_e_distance_list = [] - - # 存储当前摄像头中捕获到的所有人脸的坐标名字 / Save the positions and names of current faces captured - self.current_frame_face_position_list = [] - # 存储当前摄像头中捕获到的人脸特征 / Save the features of people in current frame - self.current_frame_face_feature_list = [] - - # 控制再识别的后续帧数 / Reclassify after 'reclassify_interval' frames - # 如果识别出 "unknown" 的脸, 将在 reclassify_interval_cnt 计数到 reclassify_interval 后, 对于人脸进行重新识别 - self.reclassify_interval_cnt = 0 - self.reclassify_interval = 10 - - # 从 "features_all.csv" 读取录入人脸特征 / Get known faces from "features_all.csv" - def get_face_database(self): - if os.path.exists("data/features_all.csv"): - path_features_known_csv = "data/features_all.csv" - csv_rd = pd.read_csv(path_features_known_csv, header=None) - for i in range(csv_rd.shape[0]): - features_someone_arr = [] - self.face_name_known_list.append(csv_rd.iloc[i][0]) - for j in range(1, 129): - if csv_rd.iloc[i][j] == '': - features_someone_arr.append('0') - else: - features_someone_arr.append(csv_rd.iloc[i][j]) - self.features_known_list.append(features_someone_arr) - logging.info("Faces in Database： %d", len(self.features_known_list)) - return 1 - else: - logging.warning("'features_all.csv' not found!") - logging.warning("Please run 'get_faces_from_camera.py' " - "and 'features_extraction_to_csv.py' before 'face_reco_from_camera.py'") - return 0 - - # 获取处理之后 stream 的帧数 / Update FPS of video stream - def update_fps(self): - now = time.time() - # 每秒刷新 fps / Refresh fps per second - if str(self.start_time).split(".")[0] != str(now).split(".")[0]: - self.fps_show = self.fps - self.start_time = now - self.frame_time = now - self.frame_start_time - self.fps = 1.0 / self.frame_time - self.frame_start_time = now - - # 计算两个128D向量间的欧式距离 / Compute the e-distance between two 128D features - @staticmethod - def return_euclidean_distance(feature_1, feature_2): - feature_1 = np.array(feature_1) - feature_2 = np.array(feature_2) - dist = np.sqrt(np.sum(np.square(feature_1 - feature_2))) - return dist - - # 生成的 cv2 window 上面添加说明文字 / putText on cv2 window - def draw_note(self, img_rd): - # 添加说明 (Add some statements - cv2.putText(img_rd, "Face Recognizer for single face", (20, 40), self.font, 1, (255, 255, 255), 1, - cv2.LINE_AA) - cv2.putText(img_rd, "Frame: " + str(self.frame_cnt), (20, 100), self.font, 0.8, (0, 255, 0), 1, - cv2.LINE_AA) - cv2.putText(img_rd, "FPS: " + str(self.fps_show.__round__(2)), (20, 130), self.font, 0.8, (0, 255, 0), 1, - cv2.LINE_AA) - cv2.putText(img_rd, "Faces: " + str(self.current_frame_face_cnt), (20, 160), self.font, 0.8, (0, 255, 0), 1, - cv2.LINE_AA) - cv2.putText(img_rd, "Q: Quit", (20, 450), self.font, 0.8, (255, 255, 255), 1, cv2.LINE_AA) - - def draw_name(self, img_rd): - # 在人脸框下面写人脸名字 / Write names under ROI - logging.debug(self.current_frame_name_list) - img = Image.fromarray(cv2.cvtColor(img_rd, cv2.COLOR_BGR2RGB)) - draw = ImageDraw.Draw(img) - draw.text(xy=self.current_frame_face_position_list[0], text=self.current_frame_name_list[0], font=self.font_chinese, - fill=(255, 255, 0)) - img_rd = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR) - return img_rd - - def show_chinese_name(self): - if self.current_frame_face_cnt >= 1: - logging.debug(self.face_name_known_list) - # 修改录入的人脸姓名 / Modify names in face_name_known_list to chinese name - self.face_name_known_list[0] = '张三'.encode('utf-8').decode() - # self.face_name_known_list[1] = '张四'.encode('utf-8').decode() - - # 处理获取的视频流, 进行人脸识别 / Face detection and recognition wit OT from input video stream - def process(self, stream): - # 1. 读取存放所有人脸特征的 csv / Get faces known from "features.all.csv" - if self.get_face_database(): - while stream.isOpened(): - self.frame_cnt += 1 - logging.debug("Frame " + str(self.frame_cnt) + " starts") - flag, img_rd = stream.read() - kk = cv2.waitKey(1) - - # 2. 检测人脸 / Detect faces for frame X - faces = detector(img_rd, 0) - - # 3. 更新帧中的人脸数 / Update cnt for faces in frames - self.last_frame_faces_cnt = self.current_frame_face_cnt - self.current_frame_face_cnt = len(faces) - - # 4.1 当前帧和上一帧相比没有发生人脸数变化 / If cnt not changes, 1->1 or 0->0 - if self.current_frame_face_cnt == self.last_frame_faces_cnt: - logging.debug("scene 1: 当前帧和上一帧相比没有发生人脸数变化 / No face cnt changes in this frame!!!") - - if "unknown" in self.current_frame_name_list: - logging.debug(" >>> 有未知人脸, 开始进行 reclassify_interval_cnt 计数") - self.reclassify_interval_cnt += 1 - - # 4.1.1 当前帧一张人脸 / One face in this frame - if self.current_frame_face_cnt == 1: - if self.reclassify_interval_cnt == self.reclassify_interval: - logging.debug(" scene 1.1 需要对于当前帧重新进行人脸识别 / Re-classify for current frame") - - self.reclassify_interval_cnt = 0 - self.current_frame_face_feature_list = [] - self.current_frame_face_X_e_distance_list = [] - self.current_frame_name_list = [] - - for i in range(len(faces)): - shape = predictor(img_rd, faces[i]) - self.current_frame_face_feature_list.append( - face_reco_model.compute_face_descriptor(img_rd, shape)) - - # a. 遍历捕获到的图像中所有的人脸 / Traversal all the faces in the database - for k in range(len(faces)): - self.current_frame_name_list.append("unknown") - - # b. 每个捕获人脸的名字坐标 / Positions of faces captured - self.current_frame_face_position_list.append(tuple( - [faces[k].left(), - int(faces[k].bottom() + (faces[k].bottom() - faces[k].top()) / 4)])) - - # c. 对于某张人脸, 遍历所有存储的人脸特征 / For every face detected, compare it with all the faces in the database - for i in range(len(self.features_known_list)): - # 如果 person_X 数据不为空 / If the data of person_X is not empty - if str(self.features_known_list[i][0]) != '0.0': - e_distance_tmp = self.return_euclidean_distance( - self.current_frame_face_feature_list[k], - self.features_known_list[i]) - logging.debug(" with person %d, the e-distance: %f", i + 1, e_distance_tmp) - self.current_frame_face_X_e_distance_list.append(e_distance_tmp) - else: - # 空数据 person_X / For empty data - self.current_frame_face_X_e_distance_list.append(999999999) - - # d. 寻找出最小的欧式距离匹配 / Find the one with minimum e distance - similar_person_num = self.current_frame_face_X_e_distance_list.index( - min(self.current_frame_face_X_e_distance_list)) - - if min(self.current_frame_face_X_e_distance_list) < 0.4: - # 在这里更改显示的人名 / Modify name if needed - self.show_chinese_name() - self.current_frame_name_list[k] = self.face_name_known_list[similar_person_num] - logging.debug(" recognition result for face %d: %s", k + 1, - self.face_name_known_list[similar_person_num]) - else: - logging.debug(" recognition result for face %d: %s", k + 1, "unknown") - else: - logging.debug( - " scene 1.2 不需要对于当前帧重新进行人脸识别 / No re-classification needed for current frame") - # 获取特征框坐标 / Get ROI positions - for k, d in enumerate(faces): - cv2.rectangle(img_rd, - tuple([d.left(), d.top()]), - tuple([d.right(), d.bottom()]), - (255, 255, 255), 2) - - self.current_frame_face_position_list[k] = tuple( - [faces[k].left(), - int(faces[k].bottom() + (faces[k].bottom() - faces[k].top()) / 4)]) - - img_rd = self.draw_name(img_rd) - - # 4.2 当前帧和上一帧相比发生人脸数变化 / If face cnt changes, 1->0 or 0->1 - else: - logging.debug("scene 2: 当前帧和上一帧相比人脸数发生变化 / Faces cnt changes in this frame") - self.current_frame_face_position_list = [] - self.current_frame_face_X_e_distance_list = [] - self.current_frame_face_feature_list = [] - - # 4.2.1 人脸数从 0->1 / Face cnt 0->1 - if self.current_frame_face_cnt == 1: - logging.debug(" scene 2.1 出现人脸, 进行人脸识别 / Get faces in this frame and do face recognition") - self.current_frame_name_list = [] - - for i in range(len(faces)): - shape = predictor(img_rd, faces[i]) - self.current_frame_face_feature_list.append( - face_reco_model.compute_face_descriptor(img_rd, shape)) - - # a. 遍历捕获到的图像中所有的人脸 / Traversal all the faces in the database - for k in range(len(faces)): - self.current_frame_name_list.append("unknown") - - # b. 每个捕获人脸的名字坐标 / Positions of faces captured - self.current_frame_face_position_list.append(tuple( - [faces[k].left(), int(faces[k].bottom() + (faces[k].bottom() - faces[k].top()) / 4)])) - - # c. 对于某张人脸, 遍历所有存储的人脸特征 / For every face detected, compare it with all the faces in database - for i in range(len(self.features_known_list)): - # 如果 person_X 数据不为空 / If data of person_X is not empty - if str(self.features_known_list[i][0]) != '0.0': - e_distance_tmp = self.return_euclidean_distance( - self.current_frame_face_feature_list[k], - self.features_known_list[i]) - logging.debug(" with person %d, the e-distance: %f", i + 1, e_distance_tmp) - self.current_frame_face_X_e_distance_list.append(e_distance_tmp) - else: - # 空数据 person_X / Empty data for person_X - self.current_frame_face_X_e_distance_list.append(999999999) - - # d. 寻找出最小的欧式距离匹配 / Find the one with minimum e distance - similar_person_num = self.current_frame_face_X_e_distance_list.index( - min(self.current_frame_face_X_e_distance_list)) - - if min(self.current_frame_face_X_e_distance_list) < 0.4: - # 在这里更改显示的人名 / Modify name if needed - self.show_chinese_name() - self.current_frame_name_list[k] = self.face_name_known_list[similar_person_num] - logging.debug(" recognition result for face %d: %s", k + 1, - self.face_name_known_list[similar_person_num]) - else: - logging.debug(" recognition result for face %d: %s", k + 1, "unknown") - - if "unknown" in self.current_frame_name_list: - self.reclassify_interval_cnt += 1 - - # 4.2.1 人脸数从 1->0 / Face cnt 1->0 - elif self.current_frame_face_cnt == 0: - logging.debug(" scene 2.2 人脸消失, 当前帧中没有人脸 / No face in this frame!!!") - - self.reclassify_interval_cnt = 0 - self.current_frame_name_list = [] - self.current_frame_face_feature_list = [] - - # 5. 生成的窗口添加说明文字 / Add note on cv2 window - self.draw_note(img_rd) - - if kk == ord('q'): - break - - self.update_fps() - - cv2.namedWindow("camera", 1) - cv2.imshow("camera", img_rd) - - logging.debug("Frame ends\n\n") - - def run(self): - # cap = cv2.VideoCapture("video.mp4") # Get video stream from video file - cap = cv2.VideoCapture(0) # Get video stream from camera - self.process(cap) - - cap.release() - cv2.destroyAllWindows() - - -def main(): - # logging.basicConfig(level=logging.DEBUG) # Set log level to 'logging.DEBUG' to print debug info of every frame - logging.basicConfig(level=logging.INFO) - Face_Recognizer_con = Face_Recognizer() - Face_Recognizer_con.run() - - -if __name__ == '__main__': - main() diff --git a/features_extraction_to_csv.py b/features_extraction_to_csv.py index c19051b..e1694ac 100755 --- a/features_extraction_to_csv.py +++ b/features_extraction_to_csv.py @@ -1,13 +1,6 @@ # Copyright (C) 2018-2021 coneypo # SPDX-License-Identifier: MIT -# Author: coneypo -# Blog: http://www.cnblogs.com/AdaminXie -# GitHub: https://github.com/coneypo/Dlib_face_recognition_from_camera -# Mail: coneypo@foxmail.com - -# 从人脸图像文件中提取人脸特征存入 "features_all.csv" / Extract features from images and save into "features_all.csv" - import os import dlib import csv @@ -16,88 +9,128 @@ import logging import cv2 from PIL import Image -# 要读取人脸图像文件的路径 / Path of cropped faces -path_images_from_camera = "data/data_faces_from_camera/" +# 要读取人脸图像文件的路径 +path_images_from_camera = "data/data_faces" -# Dlib 正向人脸检测器 / Use frontal face detector of Dlib +# Dlib 检测器 detector = dlib.get_frontal_face_detector() - -# Dlib 人脸 landmark 特征点检测器 / Get face landmarks predictor = dlib.shape_predictor('data/data_dlib/shape_predictor_68_face_landmarks.dat') - -# Dlib Resnet 人脸识别模型，提取 128D 的特征矢量 / Use Dlib resnet50 model to get 128D face descriptor face_reco_model = dlib.face_recognition_model_v1("data/data_dlib/dlib_face_recognition_resnet_model_v1.dat") -# 返回单张图像的 128D 特征 / Return 128D features for single image -# Input: path_img -# Output: face_descriptor def return_128d_features(path_img): - img_pil = Image.open(path_img) - img_np = np.array(img_pil) - img_rd = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR) - faces = detector(img_rd, 1) + """返回单张图像的 128D 特征""" + try: + img_pil = Image.open(path_img) + img_np = np.array(img_pil) + img_rd = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR) + faces = detector(img_rd, 1) - logging.info("%-40s %-20s", "检测到人脸的图像 / Image with faces detected:", path_img) + logging.info("%-40s %-20s", "检测到人脸的图像:", path_img) - # 因为有可能截下来的人脸再去检测，检测不出来人脸了, 所以要确保是 检测到人脸的人脸图像拿去算特征 - # For photos of faces saved, we need to make sure that we can detect faces from the cropped images - if len(faces) != 0: - shape = predictor(img_rd, faces[0]) - face_descriptor = face_reco_model.compute_face_descriptor(img_rd, shape) - else: - face_descriptor = 0 - logging.warning("no face") - return face_descriptor + if len(faces) != 0: + shape = predictor(img_rd, faces[0]) + face_descriptor = face_reco_model.compute_face_descriptor(img_rd, shape) + return face_descriptor + else: + logging.warning("未检测到人脸: %s", path_img) + return None + except Exception as e: + logging.error("处理图像时出错 %s: %s", path_img, e) + return None -# 返回 personX 的 128D 特征均值 / Return the mean value of 128D face descriptor for person X -# Input: path_face_personX -# Output: features_mean_personX def return_features_mean_personX(path_face_personX): + """返回 personX 的 128D 特征均值""" features_list_personX = [] photos_list = os.listdir(path_face_personX) + if photos_list: - for i in range(len(photos_list)): - # 调用 return_128d_features() 得到 128D 特征 / Get 128D features for single image of personX - logging.info("%-40s %-20s", "正在读的人脸图像 / Reading image:", path_face_personX + "/" + photos_list[i]) - features_128d = return_128d_features(path_face_personX + "/" + photos_list[i]) - # 遇到没有检测出人脸的图片跳过 / Jump if no face detected from image - if features_128d == 0: - i += 1 - else: + for photo in photos_list: + photo_path = os.path.join(path_face_personX, photo) + logging.info("正在读取图像: %s", photo_path) + + features_128d = return_128d_features(photo_path) + if features_128d is not None: features_list_personX.append(features_128d) else: - logging.warning("文件夹内图像文件为空 / Warning: No images in%s/", path_face_personX) + logging.warning("文件夹为空: %s", path_face_personX) - # 计算 128D 特征的均值 / Compute the mean - # personX 的 N 张图像 x 128D -> 1 x 128D + # 计算 128D 特征的均值 if features_list_personX: - features_mean_personX = np.array(features_list_personX, dtype=object).mean(axis=0) + features_mean_personX = np.array(features_list_personX).mean(axis=0) else: - features_mean_personX = np.zeros(128, dtype=object, order='C') + features_mean_personX = np.zeros(128, dtype=np.float64) + return features_mean_personX +def get_person_name_from_folder(folder_name): + """从文件夹名称获取有意义的姓名""" + # 常见的文件夹前缀 + prefixes = ['person_', 'face_', 'user_'] + + for prefix in prefixes: + if folder_name.startswith(prefix): + name_part = folder_name[len(prefix):] + # 如果剩下的部分是纯数字，使用完整文件夹名 + if name_part.isdigit(): + return folder_name + else: + return name_part + + return folder_name + + def main(): logging.basicConfig(level=logging.INFO) - # 获取已录入的最后一个人脸序号 / Get the order of latest person - person_list = os.listdir("data/data_faces_from_camera/") + + # 检查源文件夹是否存在 + if not os.path.exists(path_images_from_camera): + logging.error("人脸图像文件夹不存在: %s", path_images_from_camera) + return + + # 获取人脸文件夹列表 + person_list = os.listdir(path_images_from_camera) person_list.sort() + if not person_list: + logging.error("没有人脸文件夹可处理") + return + + logging.info("找到 %d 个人脸文件夹: %s", len(person_list), person_list) + + # 创建CSV文件 with open("data/features_all.csv", "w", newline="", encoding="utf-8") as csvfile: writer = csv.writer(csvfile) - for person in person_list: - # Get the mean/average features of face/personX, it will be a list with a length of 128D - logging.info("%sperson_%s", path_images_from_camera, person) - features_mean_personX = return_features_mean_personX(path_images_from_camera + person) - person_name = person.split('_', 2)[-1] - features_mean_personX = np.insert(features_mean_personX, 0, person_name, axis=0) - # features_mean_personX will be 129D, person name + 128 features - writer.writerow(features_mean_personX) - logging.info('\n') - logging.info("所有录入人脸数据存入 / Save all the features of faces registered into: data/features_all.csv") + + successful_count = 0 + for person_folder in person_list: + folder_path = os.path.join(path_images_from_camera, person_folder) + + if not os.path.isdir(folder_path): + continue + + logging.info("处理文件夹: %s", person_folder) + + # 提取特征 + features_mean = return_features_mean_personX(folder_path) + + # 获取有意义的姓名 + person_name = get_person_name_from_folder(person_folder) + logging.info("使用姓名: %s", person_name) + + # 构建行数据：姓名 + 128维特征 + row_data = [person_name] + features_mean.tolist() + writer.writerow(row_data) + + successful_count += 1 + logging.info("完成: %s", person_name) + logging.info("-" * 50) + + logging.info("成功处理 %d/%d 个人脸文件夹", successful_count, len(person_list)) + logging.info("特征数据已保存到: data/features_all.csv") if __name__ == '__main__': - main() + main() \ No newline at end of file diff --git a/get_faces.py b/get_faces.py new file mode 100755 index 0000000..ca8a62c --- /dev/null +++ b/get_faces.py @@ -0,0 +1,232 @@ +# Copyright (C) 2018-2021 coneypo +# SPDX-License-Identifier: MIT + +import dlib +import numpy as np +import cv2 +import os +import shutil +import time +import logging + +# Dlib 正向人脸检测器 +detector = dlib.get_frontal_face_detector() + + +class Face_Register: + def __init__(self): + self.path_photos_from_camera = "data/data_faces/" + self.font = cv2.FONT_ITALIC + + self.existing_faces_cnt = 0 + self.ss_cnt = 0 + self.current_frame_faces_cnt = 0 + + self.save_flag = 1 + self.press_n_flag = 0 + self.current_person_name = "" # 新增：当前录入人姓名 + + # FPS + self.frame_time = 0 + self.frame_start_time = 0 + self.fps = 0 + self.fps_show = 0 + self.start_time = time.time() + + def pre_work_mkdir(self): + if os.path.isdir(self.path_photos_from_camera): + pass + else: + os.makedirs(self.path_photos_from_camera, exist_ok=True) + + def pre_work_del_old_face_folders(self): + folders_rd = os.listdir(self.path_photos_from_camera) + for folder in folders_rd: + shutil.rmtree(os.path.join(self.path_photos_from_camera, folder)) + if os.path.isfile("data/features_all.csv"): + os.remove("data/features_all.csv") + + def check_existing_faces_cnt(self): + if os.listdir(self.path_photos_from_camera): + person_list = os.listdir(self.path_photos_from_camera) + # 修改：不再提取数字序号，而是统计数量 + self.existing_faces_cnt = len(person_list) + else: + self.existing_faces_cnt = 0 + + def update_fps(self): + now = time.time() + if str(self.start_time).split(".")[0] != str(now).split(".")[0]: + self.fps_show = self.fps + self.start_time = now + self.frame_time = now - self.frame_start_time + self.fps = 1.0 / self.frame_time + self.frame_start_time = now + + def draw_note(self, img_rd): + # 添加说明 + cv2.putText(img_rd, "Face Register", (20, 40), self.font, 1, (255, 255, 255), 1, cv2.LINE_AA) + cv2.putText(img_rd, "FPS: " + str(self.fps_show.__round__(2)), (20, 100), self.font, 0.8, (0, 255, 0), 1, + cv2.LINE_AA) + cv2.putText(img_rd, "Faces: " + str(self.current_frame_faces_cnt), (20, 140), self.font, 0.8, (0, 255, 0), 1, + cv2.LINE_AA) + + # 修改：显示当前录入人姓名 + if self.current_person_name: + cv2.putText(img_rd, f"Name: {self.current_person_name}", (20, 180), self.font, 0.8, (255, 255, 0), 1, + cv2.LINE_AA) + + cv2.putText(img_rd, "N: Input Name & Create folder", (20, 320), self.font, 0.8, (255, 255, 255), 1, cv2.LINE_AA) + cv2.putText(img_rd, "S: Save current face", (20, 350), self.font, 0.8, (255, 255, 255), 1, cv2.LINE_AA) + cv2.putText(img_rd, "Q: Quit", (20, 380), self.font, 0.8, (255, 255, 255), 1, cv2.LINE_AA) + cv2.putText(img_rd, "D: Delete all data", (20, 410), self.font, 0.8, (255, 255, 255), 1, cv2.LINE_AA) + + def get_person_name_from_input(self): + """从用户输入获取姓名""" + print("\n请输入姓名（中文或英文）:") + name = input().strip() + return name if name else f"person_{self.existing_faces_cnt + 1}" + + def create_person_folder(self, person_name): + """创建人员文件夹""" + # 清理文件名中的非法字符 + safe_name = "".join(c for c in person_name if c.isalnum() or c in (' ', '-', '_')).rstrip() + if not safe_name: + safe_name = f"person_{self.existing_faces_cnt + 1}" + + # 创建文件夹路径 + folder_name = f"person_{safe_name}" + current_face_dir = os.path.join(self.path_photos_from_camera, folder_name) + + os.makedirs(current_face_dir, exist_ok=True) + logging.info("新建人脸文件夹: %s", current_face_dir) + + return current_face_dir, safe_name + + def process(self, stream): + # 1. 新建储存人脸图像文件目录 + self.pre_work_mkdir() + + # 2. 检查已有人脸文件 + self.check_existing_faces_cnt() + + current_face_dir = "" + print("人脸录入说明:") + print("- 按 'N': 输入姓名并创建新人员文件夹") + print("- 按 'S': 保存当前检测到的人脸") + print("- 按 'D': 删除所有已录入数据") + print("- 按 'Q': 退出程序") + + while stream.isOpened(): + flag, img_rd = stream.read() + if not flag: + break + + kk = cv2.waitKey(1) + faces = detector(img_rd, 0) + + # 4. 按下 'n' 输入姓名并新建文件夹 + if kk == ord('n'): + person_name = self.get_person_name_from_input() + current_face_dir, self.current_person_name = self.create_person_folder(person_name) + self.ss_cnt = 0 + self.press_n_flag = 1 + print(f"已创建文件夹: {current_face_dir}") + print("请调整位置并按 'S' 保存人脸") + + # 5. 按下 'd' 删除所有数据 + elif kk == ord('d'): + confirm = input("确定要删除所有数据吗？(y/n): ") + if confirm.lower() == 'y': + self.pre_work_del_old_face_folders() + self.existing_faces_cnt = 0 + self.current_person_name = "" + self.press_n_flag = 0 + print("所有数据已删除") + continue + + # 6. 检测到人脸 + if len(faces) != 0: + for k, d in enumerate(faces): + # 计算矩形框大小 + height = d.bottom() - d.top() + width = d.right() - d.left() + hh = int(height / 2) + ww = int(width / 2) + + # 判断人脸是否在范围内 + if (d.right() + ww > 640 or d.bottom() + hh > 480 or + d.left() - ww < 0 or d.top() - hh < 0): + cv2.putText(img_rd, "OUT OF RANGE", (20, 300), self.font, 0.8, (0, 0, 255), 1, cv2.LINE_AA) + color_rectangle = (0, 0, 255) + save_flag = 0 + else: + color_rectangle = (255, 255, 255) + save_flag = 1 + + # 绘制人脸框 + cv2.rectangle(img_rd, + (d.left() - ww, d.top() - hh), + (d.right() + ww, d.bottom() + hh), + color_rectangle, 2) + + # 创建空白图像用于保存人脸 + img_blank = np.zeros((height * 2, width * 2, 3), np.uint8) + + if save_flag and kk == ord('s'): + # 检查是否已创建文件夹 + if self.press_n_flag: + self.ss_cnt += 1 + # 提取人脸区域 + for ii in range(height * 2): + for jj in range(width * 2): + img_blank[ii][jj] = img_rd[d.top() - hh + ii][d.left() - ww + jj] + + # 保存人脸图像 + filename = f"img_face_{self.ss_cnt}.jpg" + filepath = os.path.join(current_face_dir, filename) + cv2.imwrite(filepath, img_blank) + + logging.info("保存人脸: %s", filepath) + print(f"已保存第 {self.ss_cnt} 张人脸图片") + else: + logging.warning("请先按 'N' 输入姓名创建文件夹") + + self.current_frame_faces_cnt = len(faces) + + # 绘制说明文字 + self.draw_note(img_rd) + + # 按下 'q' 退出 + if kk == ord('q'): + break + + # 更新 FPS + self.update_fps() + + cv2.imshow("Face Register", img_rd) + + def run(self): + cap = cv2.VideoCapture(0) + if not cap.isOpened(): + print("错误: 无法打开摄像头") + return + + # 设置摄像头参数 + cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640) + cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480) + + self.process(cap) + cap.release() + cv2.destroyAllWindows() + print("程序结束") + + +def main(): + logging.basicConfig(level=logging.INFO) + Face_Register_con = Face_Register() + Face_Register_con.run() + + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/get_faces_from_camera_tkinter.py b/get_faces_UI.py similarity index 100% rename from get_faces_from_camera_tkinter.py rename to get_faces_UI.py diff --git a/get_faces_from_camera.py b/get_faces_from_camera.py deleted file mode 100755 index 0763d79..0000000 --- a/get_faces_from_camera.py +++ /dev/null @@ -1,198 +0,0 @@ -# Copyright (C) 2018-2021 coneypo -# SPDX-License-Identifier: MIT - -# Author: coneypo -# Blog: http://www.cnblogs.com/AdaminXie -# GitHub: https://github.com/coneypo/Dlib_face_recognition_from_camera -# Mail: coneypo@foxmail.com - -# 进行人脸录入 / Face register - -import dlib -import numpy as np -import cv2 -import os -import shutil -import time -import logging - -# Dlib 正向人脸检测器 / Use frontal face detector of Dlib -detector = dlib.get_frontal_face_detector() - - -class Face_Register: - def __init__(self): - self.path_photos_from_camera = "data/data_faces_from_camera/" - self.font = cv2.FONT_ITALIC - - self.existing_faces_cnt = 0 # 已录入的人脸计数器 / cnt for counting saved faces - self.ss_cnt = 0 # 录入 personX 人脸时图片计数器 / cnt for screen shots - self.current_frame_faces_cnt = 0 # 录入人脸计数器 / cnt for counting faces in current frame - - self.save_flag = 1 # 之后用来控制是否保存图像的 flag / The flag to control if save - self.press_n_flag = 0 # 之后用来检查是否先按 'n' 再按 's' / The flag to check if press 'n' before 's' - - # FPS - self.frame_time = 0 - self.frame_start_time = 0 - self.fps = 0 - self.fps_show = 0 - self.start_time = time.time() - - # 新建保存人脸图像文件和数据 CSV 文件夹 / Mkdir for saving photos and csv - def pre_work_mkdir(self): - # 新建文件夹 / Create folders to save face images and csv - if os.path.isdir(self.path_photos_from_camera): - pass - else: - os.mkdir(self.path_photos_from_camera) - - # 删除之前存的人脸数据文件夹 / Delete old face folders - def pre_work_del_old_face_folders(self): - # 删除之前存的人脸数据文件夹, 删除 "/data_faces_from_camera/person_x/"... - folders_rd = os.listdir(self.path_photos_from_camera) - for i in range(len(folders_rd)): - shutil.rmtree(self.path_photos_from_camera+folders_rd[i]) - if os.path.isfile("data/features_all.csv"): - os.remove("data/features_all.csv") - - # 如果有之前录入的人脸, 在之前 person_x 的序号按照 person_x+1 开始录入 / Start from person_x+1 - def check_existing_faces_cnt(self): - if os.listdir("data/data_faces_from_camera/"): - # 获取已录入的最后一个人脸序号 / Get the order of latest person - person_list = os.listdir("data/data_faces_from_camera/") - person_num_list = [] - for person in person_list: - person_num_list.append(int(person.split('_')[-1])) - self.existing_faces_cnt = max(person_num_list) - - # 如果第一次存储或者没有之前录入的人脸, 按照 person_1 开始录入 / Start from person_1 - else: - self.existing_faces_cnt = 0 - - # 更新 FPS / Update FPS of Video stream - def update_fps(self): - now = time.time() - # 每秒刷新 fps / Refresh fps per second - if str(self.start_time).split(".")[0] != str(now).split(".")[0]: - self.fps_show = self.fps - self.start_time = now - self.frame_time = now - self.frame_start_time - self.fps = 1.0 / self.frame_time - self.frame_start_time = now - - # 生成的 cv2 window 上面添加说明文字 / PutText on cv2 window - def draw_note(self, img_rd): - # 添加说明 / Add some notes - cv2.putText(img_rd, "Face Register", (20, 40), self.font, 1, (255, 255, 255), 1, cv2.LINE_AA) - cv2.putText(img_rd, "FPS: " + str(self.fps_show.__round__(2)), (20, 100), self.font, 0.8, (0, 255, 0), 1, - cv2.LINE_AA) - cv2.putText(img_rd, "Faces: " + str(self.current_frame_faces_cnt), (20, 140), self.font, 0.8, (0, 255, 0), 1, cv2.LINE_AA) - cv2.putText(img_rd, "N: Create face folder", (20, 350), self.font, 0.8, (255, 255, 255), 1, cv2.LINE_AA) - cv2.putText(img_rd, "S: Save current face", (20, 400), self.font, 0.8, (255, 255, 255), 1, cv2.LINE_AA) - cv2.putText(img_rd, "Q: Quit", (20, 450), self.font, 0.8, (255, 255, 255), 1, cv2.LINE_AA) - - # 获取人脸 / Main process of face detection and saving - def process(self, stream): - # 1. 新建储存人脸图像文件目录 / Create folders to save photos - self.pre_work_mkdir() - - # 2. 删除 "/data/data_faces_from_camera" 中已有人脸图像文件 - # / Uncomment if want to delete the saved faces and start from person_1 - # if os.path.isdir(self.path_photos_from_camera): - # self.pre_work_del_old_face_folders() - - # 3. 检查 "/data/data_faces_from_camera" 中已有人脸文件 - self.check_existing_faces_cnt() - - while stream.isOpened(): - flag, img_rd = stream.read() # Get camera video stream - kk = cv2.waitKey(1) - faces = detector(img_rd, 0) # Use Dlib face detector - - # 4. 按下 'n' 新建存储人脸的文件夹 / Press 'n' to create the folders for saving faces - if kk == ord('n'): - self.existing_faces_cnt += 1 - current_face_dir = self.path_photos_from_camera + "person_" + str(self.existing_faces_cnt) - os.makedirs(current_face_dir) - logging.info("\n%-40s %s", "新建的人脸文件夹 / Create folders:", current_face_dir) - - self.ss_cnt = 0 # 将人脸计数器清零 / Clear the cnt of screen shots - self.press_n_flag = 1 # 已经按下 'n' / Pressed 'n' already - - # 5. 检测到人脸 / Face detected - if len(faces) != 0: - # 矩形框 / Show the ROI of faces - for k, d in enumerate(faces): - # 计算矩形框大小 / Compute the size of rectangle box - height = (d.bottom() - d.top()) - width = (d.right() - d.left()) - hh = int(height/2) - ww = int(width/2) - - # 6. 判断人脸矩形框是否超出 480x640 / If the size of ROI > 480x640 - if (d.right()+ww) > 640 or (d.bottom()+hh > 480) or (d.left()-ww < 0) or (d.top()-hh < 0): - cv2.putText(img_rd, "OUT OF RANGE", (20, 300), self.font, 0.8, (0, 0, 255), 1, cv2.LINE_AA) - color_rectangle = (0, 0, 255) - save_flag = 0 - if kk == ord('s'): - logging.warning("请调整位置 / Please adjust your position") - else: - color_rectangle = (255, 255, 255) - save_flag = 1 - - cv2.rectangle(img_rd, - tuple([d.left() - ww, d.top() - hh]), - tuple([d.right() + ww, d.bottom() + hh]), - color_rectangle, 2) - - # 7. 根据人脸大小生成空的图像 / Create blank image according to the size of face detected - img_blank = np.zeros((int(height*2), width*2, 3), np.uint8) - - if save_flag: - # 8. 按下 's' 保存摄像头中的人脸到本地 / Press 's' to save faces into local images - if kk == ord('s'): - # 检查有没有先按'n'新建文件夹 / Check if you have pressed 'n' - if self.press_n_flag: - self.ss_cnt += 1 - for ii in range(height*2): - for jj in range(width*2): - img_blank[ii][jj] = img_rd[d.top()-hh + ii][d.left()-ww + jj] - cv2.imwrite(current_face_dir + "/img_face_" + str(self.ss_cnt) + ".jpg", img_blank) - logging.info("%-40s %s/img_face_%s.jpg", "写入本地 / Save into：", - str(current_face_dir), str(self.ss_cnt)) - else: - logging.warning("请先按 'N' 来建文件夹, 按 'S' / Please press 'N' and press 'S'") - - self.current_frame_faces_cnt = len(faces) - - # 9. 生成的窗口添加说明文字 / Add note on cv2 window - self.draw_note(img_rd) - - # 10. 按下 'q' 键退出 / Press 'q' to exit - if kk == ord('q'): - break - - # 11. Update FPS - self.update_fps() - - cv2.namedWindow("camera", 1) - cv2.imshow("camera", img_rd) - - def run(self): - # cap = cv2.VideoCapture("video.mp4") # Get video stream from video file - cap = cv2.VideoCapture(0) # Get video stream from camera - self.process(cap) - - cap.release() - cv2.destroyAllWindows() - - -def main(): - logging.basicConfig(level=logging.INFO) - Face_Register_con = Face_Register() - Face_Register_con.run() - - -if __name__ == '__main__': - main() \ No newline at end of file diff --git a/how_to_use_camera.py b/how_to_use_camera.py deleted file mode 100755 index c3af029..0000000 --- a/how_to_use_camera.py +++ /dev/null @@ -1,84 +0,0 @@ -# Author: coneypo -# Blog: http://www.cnblogs.com/AdaminXie -# GitHub: https://github.com/coneypo/Dlib_face_recognition_from_camera -# Mail: coneypo@foxmail.com - -import cv2 - -cap = cv2.VideoCapture(0) - -# cap.set(propId, value) -# 设置视频参数: propId - 设置的视频参数, value - 设置的参数值 -""" -0. cv2.CAP_PROP_POS_MSEC Current position of the video file in milliseconds. -1. cv2.CAP_PROP_POS_FRAMES 0-based index of the frame to be decoded/captured next. -2. cv2.CAP_PROP_POS_AVI_RATIO Relative position of the video file -3. cv2.CAP_PROP_FRAME_WIDTH Width of the frames in the video stream. -4. cv2.CAP_PROP_FRAME_HEIGHT Height of the frames in the video stream. -5. cv2.CAP_PROP_FPS Frame rate. -6. cv2.CAP_PROP_FOURCC 4-character code of codec. -7. cv2.CAP_PROP_FRAME_COUNT Number of frames in the video file. -8. cv2.CAP_PROP_FORMAT Format of the Mat objects returned by retrieve() . -9. cv2.CAP_PROP_MODE Backend-specific value indicating the current capture mode. -10. cv2.CAP_PROP_BRIGHTNESS Brightness of the image (only for cameras). -11. cv2.CAP_PROP_CONTRAST Contrast of the image (only for cameras). -12. cv2.CAP_PROP_SATURATION Saturation of the image (only for cameras). -13. cv2.CAP_PROP_HUE Hue of the image (only for cameras). -14. cv2.CAP_PROP_GAIN Gain of the image (only for cameras). -15. cv2.CAP_PROP_EXPOSURE Exposure (only for cameras). -16. cv2.CAP_PROP_CONVERT_RGB Boolean flags indicating whether images should be converted to RGB. -17. cv2.CAP_PROP_WHITE_BALANCE Currently unsupported -18. cv2.CAP_PROP_RECTIFICATION Rectification flag for stereo cameras (note: only supported by DC1394 v 2.x backend currently) -""" - -# The default size of frame from camera will be 640x480 in Windows or Ubuntu -# So we will not set "cap.set" here, it doesn't work -# cap.set(propId=cv2.CAP_PROP_FRAME_WIDTH, value=cap.get(cv2.CAP_PROP_FRAME_WIDTH)) - -# cap.isOpened() 返回 true/false, 检查摄像头初始化是否成功 -print(cap.isOpened()) - -# cap.read() -""" -返回两个值 - 先返回一个布尔值, 如果视频读取正确, 则为 True, 如果错误, 则为 False; - 也可用来判断是否到视频末尾; - - 再返回一个值, 为每一帧的图像, 该值是一个三维矩阵; - - 通用接收方法为: - ret,frame = cap.read(); - ret: 布尔值; - frame: 图像的三维矩阵; - 这样 ret 存储布尔值, frame 存储图像; - - 若使用一个变量来接收两个值, 如: - frame = cap.read() - 则 frame 为一个元组, 原来使用 frame 处需更改为 frame[1] -""" - -while cap.isOpened(): - ret_flag, img_camera = cap.read() - - print("height: ", img_camera.shape[0]) - print("width: ", img_camera.shape[1]) - print('\n') - - cv2.imshow("camera", img_camera) - - # 每帧数据延时 1ms, 延时为0, 读取的是静态帧 - k = cv2.waitKey(1) - - # 按下 's' 保存截图 - if k == ord('s'): - cv2.imwrite("test.jpg", img_camera) - - # 按下 'q' 退出 - if k == ord('q'): - break - -# 释放所有摄像头 -cap.release() - -# 删除建立的所有窗口 -cv2.destroyAllWindows() diff --git a/introduction/Dlib_Face_recognition_by_coneypo.pptx b/introduction/Dlib_Face_recognition_by_coneypo.pptx deleted file mode 100755 index 4df6ec6..0000000 Binary files a/introduction/Dlib_Face_recognition_by_coneypo.pptx and /dev/null differ diff --git a/introduction/face_reco.png b/introduction/face_reco.png deleted file mode 100644 index cf7e435..0000000 Binary files a/introduction/face_reco.png and /dev/null differ diff --git a/introduction/face_reco_chinese_name.png b/introduction/face_reco_chinese_name.png deleted file mode 100644 index 81b815c..0000000 Binary files a/introduction/face_reco_chinese_name.png and /dev/null differ diff --git a/introduction/face_reco_ot.png b/introduction/face_reco_ot.png deleted file mode 100644 index 475224b..0000000 Binary files a/introduction/face_reco_ot.png and /dev/null differ diff --git a/introduction/face_reco_single.png b/introduction/face_reco_single.png deleted file mode 100644 index 197f09a..0000000 Binary files a/introduction/face_reco_single.png and /dev/null differ diff --git a/introduction/face_register.png b/introduction/face_register.png deleted file mode 100644 index 2c71f7a..0000000 Binary files a/introduction/face_register.png and /dev/null differ diff --git a/introduction/face_register_tkinter_GUI.png b/introduction/face_register_tkinter_GUI.png deleted file mode 100644 index 726efec..0000000 Binary files a/introduction/face_register_tkinter_GUI.png and /dev/null differ diff --git a/introduction/face_register_warning.png b/introduction/face_register_warning.png deleted file mode 100644 index a39788b..0000000 Binary files a/introduction/face_register_warning.png and /dev/null differ diff --git a/introduction/overview.png b/introduction/overview.png deleted file mode 100755 index 1af5cb8..0000000 Binary files a/introduction/overview.png and /dev/null differ diff --git a/introduction/overview_with_ot.png b/introduction/overview_with_ot.png deleted file mode 100644 index acf2f59..0000000 Binary files a/introduction/overview_with_ot.png and /dev/null differ