人脸识别算法 python

# Face Recognition Recognize and manipulate faces from Python or from the command line with the world's simplest face recognition library. Built using [dlib](http://dlib.net/)'s state-of-the-art face recognition built with deep learning. The model has an accuracy of 99.38% on the [Labeled Faces in the Wild](http://vis-www.cs.umass.edu/lfw/) benchmark. This also provides a simple `face_recognition` command line tool that lets you do face recognition on a folder of images from the command line! [](https://pypi.python.org/pypi/face_recognition) [](https://travis-ci.org/ageitgey/face_recognition) [](http://face-recognition.readthedocs.io/en/latest/?badge=latest) ## Features #### Find faces in pictures Find all the faces that appear in a picture:  ```python import face_recognition image = face_recognition.load_image_file("your_file.jpg") face_locations = face_recognition.face_locations(image) ``` #### Find and manipulate facial features in pictures Get the locations and outlines of each person's eyes, nose, mouth and chin.  ```python import face_recognition image = face_recognition.load_image_file("your_file.jpg") face_landmarks_list = face_recognition.face_landmarks(image) ``` Finding facial features is super useful for lots of important stuff. But you can also use for really stupid stuff like applying [digital make-up](https://github.com/ageitgey/face_recognition/blob/master/examples/digital_makeup.py) (think 'Meitu'):  #### Identify faces in pictures Recognize who appears in each photo.  ```python import face_recognition known_image = face_recognition.load_image_file("biden.jpg") unknown_image = face_recognition.load_image_file("unknown.jpg") biden_encoding = face_recognition.face_encodings(known_image)[0] unknown_encoding = face_recognition.face_encodings(unknown_image)[0] results = face_recognition.compare_faces([biden_encoding], unknown_encoding) ``` You can even use this library with other Python libraries to do real-time face recognition:  See [this example](https://github.com/ageitgey/face_recognition/blob/master/examples/facerec_from_webcam_faster.py) for the code. ## Installation Requirements: * Python 3+ or Python 2.7 * macOS or Linux (Windows untested) * [Also can run on a Raspberry Pi 2+ (follow these specific instructions)](https://gist.github.com/ageitgey/1ac8dbe8572f3f533df6269dab35df65) * A [pre-configured VM image](https://medium.com/@ageitgey/try-deep-learning-in-python-now-with-a-fully-pre-configured-vm-1d97d4c3e9b) is also available. Install this module from pypi using `pip3` (or `pip2` for Python 2): ```bash pip3 install face_recognition ``` IMPORTANT NOTE: It's very likely that you will run into problems when pip tries to compile the `dlib` dependency. If that happens, check out this guide to installing dlib from source (instead of from pip) to fix the error: [How to install dlib from source](https://gist.github.com/ageitgey/629d75c1baac34dfa5ca2a1928a7aeaf) After manually installing `dlib`, try running `pip3 install face_recognition` again to complete your installation. If you are still having trouble installing this, you can also try out this [pre-configured VM](https://medium.com/@ageitgey/try-deep-learning-in-python-now-with-a-fully-pre-configured-vm-1d97d4c3e9b). ## Usage #### Command-Line Interface When you install `face_recognition`, you get a simple command-line program called `face_recognition` that you can use to recognize faces in a photograph or folder full for photographs. First, you need to provide a folder with one picture of each person you already know. There should be one image file for each person with the files named according to who is in the picture:  Next, you need a second folder with the files you want to identify:  Then in you simply run the command `face_recognition`, passing in the folder of known people and the folder (or single image) with unknown people and it tells you who is in each image: ```bash $ face_recognition ./pictures_of_people_i_know/ ./unknown_pictures/ /unknown_pictures/unknown.jpg,Barack Obama /face_recognition_test/unknown_pictures/unknown.jpg,unknown_person ``` There's one line in the output for each face. The data is comma-separated with the filename and the name of the person found. An `unknown_person` is a face in the image that didn't match anyone in your folder of known people. ##### Adjusting Tolerance / Sensitivity If you are getting multiple matches for the same person, it might be that the people in your photos look very similar and a lower tolerance value is needed to make face comparisons more strict. You can do that with the `--tolerance` parameter. The default tolerance value is 0.6 and lower numbers make face comparisons more strict: ```bash $ face_recognition --tolerance 0.54 ./pictures_of_people_i_know/ ./unknown_pictures/ /unknown_pictures/unknown.jpg,Barack Obama /face_recognition_test/unknown_pictures/unknown.jpg,unknown_person ``` If you want to see the face distance calculated for each match in order to adjust the tolerance setting, you can use `--show-distance true`: ```bash $ face_recognition --show-distance true ./pictures_of_people_i_know/ ./unknown_pictures/ /unknown_pictures/unknown.jpg,Barack Obama,0.378542298956785 /face_recognition_test/unknown_pictures/unknown.jpg,unknown_person,None ``` ##### More Examples If you simply want to know the names of the people in each photograph but don't care about file names, you could do this: ```bash $ face_recognition ./pictures_of_people_i_know/ ./unknown_pictures/ | cut -d ',' -f2 Barack Obama unknown_person ``` ##### Speeding up Face Recognition Face recognition can be done in parallel if you have a computer with multiple CPU cores. For example if your system has 4 CPU cores, you can process about 4 times as many images in the same amount of time by using all your CPU cores in parallel. If you are using Python 3.4 or newer, pass in a `--cpus <number_of_cpu_cores_to_use>` parameter: ```bash $ face_recognition --cpus 4 ./pictures_of_people_i_know/ ./unknown_pictures/ ``` You can also pass in `--cpus -1` to use all CPU cores in your system. #### Python Module You can import the `face_recognition` module and then easily manipulate faces with just a couple of lines of code. It's super easy! API Docs: [https://face-recognition.readthedocs.io](https://face-recognition.readthedocs.io/en/latest/face_recognition.html). ##### Automatically find all the faces in an image ```python import face_recognition image = face_recognition.load_image_file("my_picture.jpg") face_locations = face_recognition.face_locations(image) # face_locations is now an array listing the co-ordinates of each face! ``` See [this example](https://github.com/ageitgey/face_recognition/blob/master/examples/find_faces_in_picture.py) to try it out. You can also opt-in to a somewhat more accurate deep-learning-based face detection model. Note: GPU acceleration (via nvidia's CUDA library) is required for good performance with this m