mirror of
https://gitlab.com/Shinobi-Systems/ShinobiCE.git
synced 2025-03-09 15:40:15 +00:00
113 lines
3.6 KiB
Python
113 lines
3.6 KiB
Python
from flask import Flask, request, jsonify, render_template
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from flask_socketio import SocketIO, emit
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from PIL import Image
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import face_recognition
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import cv2
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import os
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import json
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import numpy as np
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import sys
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dirname = sys.argv[1]
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try:
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with open("{}/conf.json".format(dirname)) as json_file:
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config = json.load(json_file)
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httpPort = config['pythonPort']
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try:
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httpPort
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except NameError:
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httpPort = 7990
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except Exception as e:
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print("conf.json not found.")
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httpPort = 7990
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# Load Flask
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app = Flask("DLIB for Shinobi (Pumpkin Pie)")
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socketio = SocketIO(app)
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# Silence Flask
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# import logging
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# log = logging.getLogger('werkzeug')
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# log.setLevel(logging.ERROR)
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#check for faces dir
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facesDir = "{}/faces/".format(dirname)
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if not os.path.exists(facesDir):
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os.makedirs(facesDir)
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# load faces
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included_extensions = ['jpg','jpeg', 'bmp', 'png', 'gif']
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file_names = [fn for fn in os.listdir(facesDir)
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if any(fn.endswith(ext) for ext in included_extensions)]
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known_faces = []
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face_locations = []
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face_encodings = []
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face_names = []
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for faceFile in file_names:
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face = face_recognition.load_image_file(facesDir+faceFile)
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face_encoding = face_recognition.face_encodings(face)[0]
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known_faces.append(face_encoding)
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# detection function
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def spark(filepath):
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try:
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filepath
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except NameError:
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return "File path not found."
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img = cv2.imread(filepath)
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returnData = []
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frame = img[:, :, ::-1]
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# Find all the faces and face encodings in the current frame of video
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face_locations = face_recognition.face_locations(frame)
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face_encodings = face_recognition.face_encodings(frame, face_locations)
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face_names = []
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for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
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# See if the face is a match for the known face(s)
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matrix = {}
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matrix["coordinates"] = [
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{"x" : left, "y" : top},
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{"x" : right, "y" : top},
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{"x" : right, "y" : bottom},
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{"x" : left, "y" : bottom}
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]
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(left, top), (right, bottom)
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match = face_recognition.compare_faces(known_faces, face_encoding, tolerance=0.50)
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if True in match:
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first_match_index = match.index(True)
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name = file_names[first_match_index]
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matrix["tag"] = name
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returnData.append(matrix)
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return returnData
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# bake the image data by a file path
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# POST body contains the "img" variable. The value should be to a local image path.
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# Example : /dev/shm/streams/[GROUP_KEY]/[MONITOR_ID]/s.jpg
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@app.route('/', methods=['GET'])
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def index():
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return "Pumpkin.py is running. This web interface should NEVER be accessible remotely. The Node.js plugin that runs this script should only be allowed accessible remotely."
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# bake the image data by a file path
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# POST body contains the "img" variable. The value should be to a local image path.
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# Example : /dev/shm/streams/[GROUP_KEY]/[MONITOR_ID]/s.jpg
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@app.route('/post', methods=['POST'])
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def post():
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filepath = request.form['img']
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return jsonify(spark(filepath))
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# bake the image data by a file path
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# GET string contains the "img" variable. The value should be to a local image path.
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# Example : /dev/shm/streams/[GROUP_KEY]/[MONITOR_ID]/s.jpg
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@app.route('/get', methods=['GET'])
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def get():
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filepath = request.args.get('img')
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return jsonify(spark(filepath))
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@socketio.on('f')
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def receiveMessage(message):
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emit('f',{'id':message.get("id"),'data':spark(message.get("path"))})
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# quick-and-dirty start
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if __name__ == '__main__':
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socketio.run(app, port=httpPort)
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