face_recognition_knn.py

"""
Algorithm Description:
The knn classifier is first trained on a set of labeled (known) faces and can then predict the person
in an unknown image by finding the k most similar faces (images with closet face-features under eucledian distance)
in its training set, and performing a majority vote (possibly weighted) on their label.

For example, if k=3, and the three closest face images to the given image in the training set are one image of Biden
and two images of Obama, The result would be 'Obama'.

* This implementation uses a weighted vote, such that the votes of closer-neighbors are weighted more heavily.

*** NOTE: This is the original face_recognition_knn example from the face_recognition library, 
slightly modified to work with base64 images that are parsed from flask endpoints. You can use this approach
to modify any other function from the original face_recognition library to make it work with an active stateless flask backend.

"""

import math
from sklearn import neighbors
import os
import os.path
import pickle
from PIL import Image, ImageDraw, ImageFont
import face_recognition
from face_recognition.face_recognition_cli import image_files_in_folder

ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg'}




def predict(image, knn_clf=None, model_path=None, distance_threshold=0.6):
    """
    Recognizes faces in given image using a trained KNN classifier

    :param X_img_path: path to image to be recognized
    :param knn_clf: (optional) a knn classifier object. if not specified, model_save_path must be specified.
    :param model_path: (optional) path to a pickled knn classifier. if not specified, model_save_path must be knn_clf.
    :param distance_threshold: (optional) distance threshold for face classification. the larger it is, the more chance
           of mis-classifying an unknown person as a known one.
    :return: a list of names and face locations for the recognized faces in the image: [(name, bounding box), ...].
        For faces of unrecognized persons, the name 'unknown' will be returned.
    """

    if knn_clf is None and model_path is None:
        raise Exception("Must supply knn classifier either thourgh knn_clf or model_path")

    # Load a trained KNN model (if one was passed in)
    if knn_clf is None:
        with open(model_path, 'rb') as f:
            knn_clf = pickle.load(f)

    # Load image file and find face locations
    X_img = face_recognition.load_image_file(image)
    X_face_locations = face_recognition.face_locations(X_img)

    # If no faces are found in the image, return an empty result.
    if len(X_face_locations) == 0:
        return []

    # Find encodings for faces in the test iamge
    faces_encodings = face_recognition.face_encodings(X_img, known_face_locations=X_face_locations)

    # Use the KNN model to find the best matches for the test face
    closest_distances = knn_clf.kneighbors(faces_encodings, n_neighbors=1)
    are_matches = [closest_distances[0][i][0] <= distance_threshold for i in range(len(X_face_locations))]

    # Predict classes and remove classifications that aren't within the threshold
    return [(pred, loc) if rec else ("unknown", loc) for pred, loc, rec in zip(knn_clf.predict(faces_encodings), X_face_locations, are_matches)]


def show_prediction_labels_on_image(image, predictions):
    """
    Shows the face recognition results visually.

    :param img_path: path to image to be recognized
    :param predictions: results of the predict function
    :return:
    """
    pil_image = Image.open(image).convert("RGB")
    draw = ImageDraw.Draw(pil_image)

    for name, (top, right, bottom, left) in predictions:
        # Draw a box around the face using the Pillow module
        draw.rectangle(((left, top), (right, bottom)), outline=(0, 0, 255))

        # There's a bug in Pillow where it blows up with non-UTF-8 text
        # when using the default bitmap font
        name = name.encode("UTF-8") # doesn't work with a truetype font

        # Draw a label with a name below the face
        text_width, text_height = draw.textsize(name)
        draw.rectangle(((left, bottom - text_height + 10), (right, bottom + 10)), fill=(0, 0, 255), outline=(0, 0, 255))
        draw.text((left + 6, bottom - text_height + 10), name, fill=(255, 255, 255, 255))

    # Remove the drawing library from memory as per the Pillow docs
    del draw

    # Return the resulting image
    return pil_image


#if __name__ == "__main__":