87 lines
2.8 KiB
Python
87 lines
2.8 KiB
Python
import face_recognition
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import os
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import cv2
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from application.db import Session, Person
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import base64
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import numpy as np
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from base64 import decodestring
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import base64
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from io import StringIO
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from PIL import Image
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KNOWN_FACES_DIR = 'known_faces'
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UNKNOWN_FACES_DIR = 'unknown_faces'
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TOLERANCE = 0.6
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FRAME_THICKNESS = 3
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FONT_THICKNESS = 2
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MODEL = 'hog' # default: 'hog', other one can be 'cnn' - CUDA accelerated (if available) deep-learning pretrained model
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# Returns (R, G, B) from name
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def name_to_color(name):
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# Take 3 first letters, tolower()
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# lowercased character ord() value rage is 97 to 122, substract 97, multiply by 8
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color = [(ord(c.lower())-97)*8 for c in name[:3]]
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return color
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def readb64(base64_string):
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sbuf = StringIO()
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sbuf.write(base64.b64decode(base64_string))
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pimg = Image.open(sbuf)
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return cv2.cvtColor(np.array(pimg), cv2.COLOR_RGB2BGR)
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print('Loading known faces...')
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known_faces = []
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known_names = []
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def initFaceRec():
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session = Session()
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# We oranize known faces as subfolders of KNOWN_FACES_DIR
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# Each subfolder's name becomes our label (name)
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for face, name in session.query(Person.face, Person.person_id).all():
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# Load an image
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nparr = np.fromstring(base64.b64decode(face), np.uint8)
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image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
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# Get 128-dimension face encoding
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# Always returns a list of found faces, for this purpose we take first face only (assuming one face per image as you can't be twice on one image)
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encoding = face_recognition.face_encodings(image)[0]
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# Append encodings and name
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known_faces.append(encoding)
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known_names.append(name)
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session.close()
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def identifyFace(image):
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print('Processing unknown faces...')
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#image = face_recognition.load_image_file('C:/Users/ofjok/Desktop/1.png')
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nparr = np.fromstring(base64.b64decode(image), np.uint8)
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image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
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#image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
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locations = face_recognition.face_locations(image, model=MODEL)
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encodings = face_recognition.face_encodings(image, locations)
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res = {}
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for face_encoding, face_location in zip(encodings, locations):
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# We use compare_faces (but might use face_distance as well)
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# Returns array of True/False values in order of passed known_faces
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results = face_recognition.face_distance(known_faces, face_encoding)
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# Since order is being preserved, we check if any face was found then grab index
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# then label (name) of first matching known face withing a tolerance
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# If at least one is true, get a name of first of found labels
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res = {known_names[i]: results[i] for i in range(0, len(results)) }
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return res
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#identifyFace("") |