42 lines
1.6 KiB
ReStructuredText
42 lines
1.6 KiB
ReStructuredText
=====
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Usage
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=====
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To use Face Recognition in a project::
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import face_recognition
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See the examples in the /examples folder on github for how to use each function.
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You can also check the API docs for the 'face_recognition' module to see the possible parameters for each function.
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The basic idea is that first you load an image::
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import face_recognition
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image = face_recognition.load_image_file("your_file.jpg")
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That loads the image into a numpy array. If you already have an image in a numpy array, you can skip this step.
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Then you can perform operations on the image, like finding faces, identifying facial features or finding face encodings::
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# Find all the faces in the image
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face_locations = face_recognition.face_locations(image)
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# Or maybe find the facial features in the image
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face_landmarks_list = face_recognition.face_landmarks(image)
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# Or you could get face encodings for each face in the image:
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list_of_face_encodings = face_recognition.face_encodings(image)
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Face encodings can be compared against each other to see if the faces are a match. Note: Finding the encoding for a face
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is a bit slow, so you might want to save the results for each image in a database or cache if you need to refer back to
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it later.
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But once you have the encodings for faces, you can compare them like this::
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# results is an array of True/False telling if the unknown face matched anyone in the known_faces array
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results = face_recognition.compare_faces(known_face_encodings, a_single_unknown_face_encoding)
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It's that simple! Check out the examples for more details.
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