99 lines
3.4 KiB
Python
99 lines
3.4 KiB
Python
import dlib
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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 io import StringIO
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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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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('Identifying Face')
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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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results = face_recognition.face_distance(known_faces, face_encoding)
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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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def identifyFaceVideo(url):
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video = cv2.VideoCapture(url)
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image = video.read()[1]
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ret, image = cv2.imencode(".png", image)
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nparr = np.fromstring(image, np.uint8)
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image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
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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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face_locations = {} #face locations to be drawn
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for face_encoding, face_location in zip(encodings, locations):
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face_locations.update(compareFace(face_encoding, face_location))
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for k, v in face_locations.items():
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# Paint frame
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cv2.rectangle(image, v[0], v[1], [255, 0, 0], FRAME_THICKNESS)
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# Wite a name
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cv2.putText(image, k, v[0], cv2.FONT_HERSHEY_SIMPLEX, 1.5, [255, 0, 255], FONT_THICKNESS)
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# Show image
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image = cv2.imencode(".jpg", image)[1]
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return image
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def compareFace(face_encoding, face_location):
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results = face_recognition.compare_faces(known_faces, face_encoding, TOLERANCE)
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face_locations = {}
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match = None
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if True in results: # If at least one is true, get a name of first of found labels
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match = "name"
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print(f' - {match} from {results}')
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top_left = (face_location[3], face_location[0])
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bottom_right = (face_location[1], face_location[2])
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face_locations[match] = (top_left, bottom_right)
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return face_locations |