65 lines
2.0 KiB
Python
65 lines
2.0 KiB
Python
from flask import Flask, request
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import json
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import cv2
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import base64
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import numpy as np
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from deepface import DeepFace
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import face_recognition as face
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import os
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app = Flask(__name__)
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face_encodings: list = []
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face_names: list = []
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def init_face() -> None:
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for file in os.scandir("faces"):
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face_name = file.name.split('.')
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face_name = '.'.join(face_name[0:len(face_name)-1])
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face_names.append(face_name)
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face_image = face.load_image_file(file.path)
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face_encodings.append(face.face_encodings(face_image)[0])
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init_face()
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@app.route('/')
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def home() -> str:
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return '<h1>Ching Chong Bing Bong Ding Dong!!</h1>'
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@app.route('/process_image', methods=['POST'])
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def process_image() -> str:
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print(request.data)
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request_data = json.loads(request.data.decode("utf-8"))
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img_nparr = np.frombuffer(base64.b64decode(request_data['image']), np.uint8)
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img = cv2.imdecode(img_nparr,cv2.IMREAD_COLOR)
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try:
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racist_detector = DeepFace.analyze(img)
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return racist_detector
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except:
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return []
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@app.route('/identify_face', methods=['POST'])
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def identify_face() -> str:
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request_data = json.loads(request.data.decode("utf-8"))
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target_confidence: float = request_data['target_confidence']
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img_nparr = np.frombuffer(base64.b64decode(request_data['image']), np.uint8)
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img = cv2.imdecode(img_nparr,cv2.IMREAD_COLOR)
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img = cv2.resize(img, (0,0), fx=0.5,fy=0.5)
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img = np.ascontiguousarray(img[:, :, ::-1])
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face_locations = face.face_locations(img)
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face_encodings_img = face.face_encodings(img, face_locations)
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response: list = []
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for face_encoding in face_encodings_img:
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face_distances = face.face_distance(face_encodings, face_encoding)
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index = np.argmin(face_distances)
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confidence = 1-face_distances[index]
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if confidence >= target_confidence:
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response.append({'name':face_names[index],'confidence': confidence})
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return response
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if __name__ == '__main__':
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app.run()
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