face recognition by opencv python

Question

i have a code for face recognition by open cv python

import face_recognition as fr
import os
import cv2
import face_recognition
import numpy as np
from time import sleep


def get_encoded_faces():
    """
    looks through the faces folder and encodes all
    the faces

    :return: dict of (name, image encoded)
    """
    encoded = {}

    for dirpath, dnames, fnames in os.walk("./faces"):
        for f in fnames:
            if f.endswith(".jpg") or f.endswith(".png"):
                face = fr.load_image_file("faces/" + f)
                encoding = fr.face_encodings(face)[0]
                encoded[f.split(".")[0]] = encoding

    return encoded


def unknown_image_encoded(img):
    """
    encode a face given the file name
    """
    face = fr.load_image_file("faces/" + img)
    encoding = fr.face_encodings(face)[0]

    return encoding


def classify_face(im):
    """
    will find all of the faces in a given image and label
    them if it knows what they are

    :param im: str of file path
    :return: list of face names
    """
    faces = get_encoded_faces()
    faces_encoded = list(faces.values())
    known_face_names = list(faces.keys())

    img = cv2.imread(im, 1)
    #img = cv2.resize(img, (0, 0), fx=0.5, fy=0.5)
    #img = img[:,:,::-1]

    face_locations = face_recognition.face_locations(img)
    unknown_face_encodings = face_recognition.face_encodings(img, face_locations)

    face_names = []
    for face_encoding in unknown_face_encodings:
        # See if the face is a match for the known face(s)
        matches = face_recognition.compare_faces(faces_encoded, face_encoding)
        name = "Unknown"

        # use the known face with the smallest distance to the new face
        face_distances = face_recognition.face_distance(faces_encoded, face_encoding)
        best_match_index = np.argmin(face_distances)
        if matches[best_match_index]:
            name = known_face_names[best_match_index]

        face_names.append(name)

        for (top, right, bottom, left), name in zip(face_locations, face_names):
            # Draw a box around the face
            cv2.rectangle(img, (left-20, top-20), (right+20, bottom+20), (255, 0, 0), 2)

            # Draw a label with a name below the face
            cv2.rectangle(img, (left-20, bottom -15), (right+20, bottom+20), (255, 0, 0), cv2.FILLED)
            font = cv2.FONT_HERSHEY_DUPLEX
            cv2.putText(img, name, (left -20, bottom + 15), font, 1.0, (255, 255, 255), 2)


    # Display the resulting image
    while True:

        cv2.imshow('Video', img)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            return face_names 


print(classify_face("test-image");

it is taking image that we save to test but i want it to take image from camera then recognize it So any one here can please tell how i can change test image so it will take test image from camera not what we save in data base to test...........

Answer 1

If you want to check single frame from camera then simply use

cap = cv2.VideoCapture(0)
status, img = cap.read()

instead of

img = cv2.imread(im, 1)

---

If you want to check faces in stream then you need to put all in loop

def classify_face(im):

    faces = get_encoded_faces()
    faces_encoded = list(faces.values())
    known_face_names = list(faces.keys())

    cap = cv2.VideoCapture(0)

    while True:

        status, img = cap.read()

        face_locations = face_recognition.face_locations(img)
        unknown_face_encodings = face_recognition.face_encodings(img, face_locations)

        face_names = []
        for face_encoding in unknown_face_encodings:
            # See if the face is a match for the known face(s)
            matches = face_recognition.compare_faces(faces_encoded, face_encoding)
            name = "Unknown"

            # use the known face with the smallest distance to the new face
            face_distances = face_recognition.face_distance(faces_encoded, face_encoding)
            best_match_index = np.argmin(face_distances)
            if matches[best_match_index]:
                name = known_face_names[best_match_index]

            face_names.append(name)

            for (top, right, bottom, left), name in zip(face_locations, face_names):
                # Draw a box around the face
                cv2.rectangle(img, (left-20, top-20), (right+20, bottom+20), (255, 0, 0), 2)

                # Draw a label with a name below the face
                cv2.rectangle(img, (left-20, bottom -15), (right+20, bottom+20), (255, 0, 0), cv2.FILLED)
                font = cv2.FONT_HERSHEY_DUPLEX
                cv2.putText(img, name, (left -20, bottom + 15), font, 1.0, (255, 255, 255), 2)

        cv2.imshow('Video', img)

        print(face_names)

        if cv2.waitKey(1) & 0xFF == ord('q'):
            return

---

EDIT: Full code (with small changes) for work with video stream. It works for me. But if previous function did works for you then this version may not help you - function classify_face is almost the same as in previous example.

import os
import cv2
import face_recognition as fr
import numpy as np


def get_encoded_faces(folder="./faces"):
    """
    looks through the faces folder and encodes all
    the faces

    :return: dict of (name, image encoded)
    """
    encoded = {}

    for dirpath, dnames, fnames in os.walk(folder):
        for f in fnames:
            if f.lower().endswith(".jpg") or f.lower().endswith(".png"):

                fullpath = os.path.join(dirpath, f)
                face = fr.load_image_file(fullpath)

                # normally face_encodings check if face is on image - and it can get empty result
                height, width = face.shape[:2]
                encoding = fr.face_encodings(face, known_face_locations=[(0, width, height, 0)])
                if len(encoding) > 0:
                    encoding = encoding[0]
                    encoded[f.split(".")[0]] = encoding

    return encoded


def classify_face(im):
    """
    will find all of the faces in a given image and label
    them if it knows what they are

    :param im: str of file path
    :return: list of face names
    """
    faces = get_encoded_faces()
    faces_encoded = list(faces.values())
    known_face_names = list(faces.keys())

    cap = cv2.VideoCapture(0)

    while True:

        status, img = cap.read()
        #print('status:', status)

        face_locations = fr.face_locations(img)
        unknown_face_encodings = fr.face_encodings(img, face_locations)

        face_names = []
        for location, face_encoding in zip(face_locations, unknown_face_encodings): # I moved `zip()` in this place

            # See if the face is a match for the known face(s)
            matches = fr.compare_faces(faces_encoded, face_encoding)
            name = "Unknown"

            # use the known face with the smallest distance to the new face
            face_distances = fr.face_distance(faces_encoded, face_encoding)

            best_match_index = np.argmin(face_distances)
            if matches[best_match_index]:
                name = known_face_names[best_match_index]

            face_names.append(name)

            top, right, bottom, left = location
            # Draw a box around the face

            cv2.rectangle(img, (left-20, top-20), (right+20, bottom+20), (255, 0, 0), 2)

            # Draw a label with a name below the face
            cv2.rectangle(img, (left-20, bottom -15), (right+20, bottom+20), (255, 0, 0), cv2.FILLED)
            font = cv2.FONT_HERSHEY_DUPLEX
            cv2.putText(img, name, (left -20, bottom + 15), font, 1.0, (255, 255, 255), 2)

        print('face_names:', face_names)
        cv2.imshow('Video', img)

        if cv2.waitKey(1) & 0xFF == ord('q'):
            return face_names 

# --- main ---

print(classify_face("test-image"))

cv2.destroyAllWindows()