372 lines
12 KiB
Python
Executable File
372 lines
12 KiB
Python
Executable File
#!/usr/bin/env python2
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#
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# Copyright 2015-2016 Carnegie Mellon University
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import sys
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fileDir = os.path.dirname(os.path.realpath(__file__))
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sys.path.append(os.path.join(fileDir, "..", ".."))
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import txaio
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txaio.use_twisted()
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from autobahn.twisted.websocket import WebSocketServerProtocol, \
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WebSocketServerFactory
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from twisted.internet import task, defer
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from twisted.internet.ssl import DefaultOpenSSLContextFactory
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from twisted.python import log
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import argparse
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import cv2
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import imagehash
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import json
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from PIL import Image
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import numpy as np
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import os
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import StringIO
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import urllib
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import base64
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from sklearn.decomposition import PCA
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from sklearn.grid_search import GridSearchCV
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from sklearn.manifold import TSNE
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from sklearn.svm import SVC
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import matplotlib as mpl
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mpl.use('Agg')
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import matplotlib.pyplot as plt
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import matplotlib.cm as cm
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import openface
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modelDir = os.path.join(fileDir, '..', '..', 'models')
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dlibModelDir = os.path.join(modelDir, 'dlib')
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openfaceModelDir = os.path.join(modelDir, 'openface')
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# For TLS connections
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tls_crt = os.path.join(fileDir, 'tls', 'server.crt')
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tls_key = os.path.join(fileDir, 'tls', 'server.key')
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parser = argparse.ArgumentParser()
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parser.add_argument('--dlibFacePredictor', type=str, help="Path to dlib's face predictor.",
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default=os.path.join(dlibModelDir, "shape_predictor_68_face_landmarks.dat"))
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parser.add_argument('--networkModel', type=str, help="Path to Torch network model.",
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default=os.path.join(openfaceModelDir, 'nn4.small2.v1.t7'))
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parser.add_argument('--imgDim', type=int,
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help="Default image dimension.", default=96)
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parser.add_argument('--cuda', action='store_true')
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parser.add_argument('--unknown', type=bool, default=False,
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help='Try to predict unknown people')
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parser.add_argument('--port', type=int, default=9000,
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help='WebSocket Port')
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args = parser.parse_args()
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align = openface.AlignDlib(args.dlibFacePredictor)
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net = openface.TorchNeuralNet(args.networkModel, imgDim=args.imgDim,
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cuda=args.cuda)
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class Face:
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def __init__(self, rep, identity):
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self.rep = rep
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self.identity = identity
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def __repr__(self):
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return "{{id: {}, rep[0:5]: {}}}".format(
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str(self.identity),
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self.rep[0:5]
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)
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class OpenFaceServerProtocol(WebSocketServerProtocol):
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def __init__(self):
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super(OpenFaceServerProtocol, self).__init__()
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self.images = {}
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self.training = True
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self.people = []
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self.svm = None
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if args.unknown:
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self.unknownImgs = np.load("./examples/web/unknown.npy")
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def onConnect(self, request):
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print("Client connecting: {0}".format(request.peer))
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self.training = True
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def onOpen(self):
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print("WebSocket connection open.")
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def onMessage(self, payload, isBinary):
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raw = payload.decode('utf8')
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msg = json.loads(raw)
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print("Received {} message of length {}.".format(
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msg['type'], len(raw)))
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if msg['type'] == "ALL_STATE":
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self.loadState(msg['images'], msg['training'], msg['people'])
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elif msg['type'] == "NULL":
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self.sendMessage('{"type": "NULL"}')
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elif msg['type'] == "FRAME":
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self.processFrame(msg['dataURL'], msg['identity'])
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self.sendMessage('{"type": "PROCESSED"}')
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elif msg['type'] == "TRAINING":
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self.training = msg['val']
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if not self.training:
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self.trainSVM()
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elif msg['type'] == "ADD_PERSON":
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self.people.append(msg['val'].encode('ascii', 'ignore'))
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print(self.people)
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elif msg['type'] == "UPDATE_IDENTITY":
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h = msg['hash'].encode('ascii', 'ignore')
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if h in self.images:
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self.images[h].identity = msg['idx']
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if not self.training:
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self.trainSVM()
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else:
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print("Image not found.")
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elif msg['type'] == "REMOVE_IMAGE":
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h = msg['hash'].encode('ascii', 'ignore')
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if h in self.images:
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del self.images[h]
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if not self.training:
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self.trainSVM()
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else:
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print("Image not found.")
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elif msg['type'] == 'REQ_TSNE':
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self.sendTSNE(msg['people'])
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else:
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print("Warning: Unknown message type: {}".format(msg['type']))
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def onClose(self, wasClean, code, reason):
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print("WebSocket connection closed: {0}".format(reason))
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def loadState(self, jsImages, training, jsPeople):
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self.training = training
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for jsImage in jsImages:
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h = jsImage['hash'].encode('ascii', 'ignore')
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self.images[h] = Face(np.array(jsImage['representation']),
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jsImage['identity'])
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for jsPerson in jsPeople:
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self.people.append(jsPerson.encode('ascii', 'ignore'))
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if not training:
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self.trainSVM()
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def getData(self):
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X = []
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y = []
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for img in self.images.values():
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X.append(img.rep)
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y.append(img.identity)
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numIdentities = len(set(y + [-1])) - 1
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if numIdentities == 0:
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return None
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if args.unknown:
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numUnknown = y.count(-1)
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numIdentified = len(y) - numUnknown
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numUnknownAdd = (numIdentified / numIdentities) - numUnknown
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if numUnknownAdd > 0:
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print("+ Augmenting with {} unknown images.".format(numUnknownAdd))
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for rep in self.unknownImgs[:numUnknownAdd]:
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# print(rep)
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X.append(rep)
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y.append(-1)
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X = np.vstack(X)
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y = np.array(y)
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return (X, y)
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def sendTSNE(self, people):
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d = self.getData()
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if d is None:
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return
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else:
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(X, y) = d
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X_pca = PCA(n_components=50).fit_transform(X, X)
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tsne = TSNE(n_components=2, init='random', random_state=0)
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X_r = tsne.fit_transform(X_pca)
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yVals = list(np.unique(y))
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colors = cm.rainbow(np.linspace(0, 1, len(yVals)))
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# print(yVals)
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plt.figure()
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for c, i in zip(colors, yVals):
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name = "Unknown" if i == -1 else people[i]
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plt.scatter(X_r[y == i, 0], X_r[y == i, 1], c=c, label=name)
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plt.legend()
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imgdata = StringIO.StringIO()
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plt.savefig(imgdata, format='png')
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imgdata.seek(0)
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content = 'data:image/png;base64,' + \
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urllib.quote(base64.b64encode(imgdata.buf))
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msg = {
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"type": "TSNE_DATA",
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"content": content
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}
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self.sendMessage(json.dumps(msg))
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def trainSVM(self):
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print("+ Training SVM on {} labeled images.".format(len(self.images)))
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d = self.getData()
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if d is None:
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self.svm = None
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return
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else:
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(X, y) = d
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numIdentities = len(set(y + [-1]))
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if numIdentities <= 1:
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return
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param_grid = [
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{'C': [1, 10, 100, 1000],
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'kernel': ['linear']},
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{'C': [1, 10, 100, 1000],
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'gamma': [0.001, 0.0001],
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'kernel': ['rbf']}
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]
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self.svm = GridSearchCV(SVC(C=1), param_grid, cv=5).fit(X, y)
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def processFrame(self, dataURL, identity):
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head = "data:image/jpeg;base64,"
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assert(dataURL.startswith(head))
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imgdata = base64.b64decode(dataURL[len(head):])
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imgF = StringIO.StringIO()
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imgF.write(imgdata)
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imgF.seek(0)
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img = Image.open(imgF)
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buf = np.fliplr(np.asarray(img))
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rgbFrame = np.zeros((300, 400, 3), dtype=np.uint8)
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rgbFrame[:, :, 0] = buf[:, :, 2]
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rgbFrame[:, :, 1] = buf[:, :, 1]
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rgbFrame[:, :, 2] = buf[:, :, 0]
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if not self.training:
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annotatedFrame = np.copy(buf)
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# cv2.imshow('frame', rgbFrame)
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# if cv2.waitKey(1) & 0xFF == ord('q'):
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# return
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identities = []
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# bbs = align.getAllFaceBoundingBoxes(rgbFrame)
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bb = align.getLargestFaceBoundingBox(rgbFrame)
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bbs = [bb] if bb is not None else []
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for bb in bbs:
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# print(len(bbs))
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landmarks = align.findLandmarks(rgbFrame, bb)
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alignedFace = align.align(args.imgDim, rgbFrame, bb,
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landmarks=landmarks,
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landmarkIndices=openface.AlignDlib.OUTER_EYES_AND_NOSE)
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if alignedFace is None:
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continue
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phash = str(imagehash.phash(Image.fromarray(alignedFace)))
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if phash in self.images:
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identity = self.images[phash].identity
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else:
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rep = net.forward(alignedFace)
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# print(rep)
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if self.training:
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self.images[phash] = Face(rep, identity)
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# TODO: Transferring as a string is suboptimal.
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# content = [str(x) for x in cv2.resize(alignedFace, (0,0),
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# fx=0.5, fy=0.5).flatten()]
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content = [str(x) for x in alignedFace.flatten()]
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msg = {
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"type": "NEW_IMAGE",
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"hash": phash,
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"content": content,
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"identity": identity,
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"representation": rep.tolist()
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}
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self.sendMessage(json.dumps(msg))
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else:
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if len(self.people) == 0:
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identity = -1
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elif len(self.people) == 1:
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identity = 0
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elif self.svm:
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identity = self.svm.predict(rep)[0]
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else:
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print("hhh")
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identity = -1
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if identity not in identities:
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identities.append(identity)
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if not self.training:
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bl = (bb.left(), bb.bottom())
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tr = (bb.right(), bb.top())
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cv2.rectangle(annotatedFrame, bl, tr, color=(153, 255, 204),
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thickness=3)
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for p in openface.AlignDlib.OUTER_EYES_AND_NOSE:
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cv2.circle(annotatedFrame, center=landmarks[p], radius=3,
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color=(102, 204, 255), thickness=-1)
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if identity == -1:
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if len(self.people) == 1:
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name = self.people[0]
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else:
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name = "Unknown"
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else:
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name = self.people[identity]
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cv2.putText(annotatedFrame, name, (bb.left(), bb.top() - 10),
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cv2.FONT_HERSHEY_SIMPLEX, fontScale=0.75,
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color=(152, 255, 204), thickness=2)
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if not self.training:
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msg = {
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"type": "IDENTITIES",
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"identities": identities
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}
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self.sendMessage(json.dumps(msg))
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plt.figure()
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plt.imshow(annotatedFrame)
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plt.xticks([])
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plt.yticks([])
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imgdata = StringIO.StringIO()
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plt.savefig(imgdata, format='png')
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imgdata.seek(0)
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content = 'data:image/png;base64,' + \
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urllib.quote(base64.b64encode(imgdata.buf))
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msg = {
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"type": "ANNOTATED",
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"content": content
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}
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plt.close()
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self.sendMessage(json.dumps(msg))
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def main(reactor):
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log.startLogging(sys.stdout)
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factory = WebSocketServerFactory()
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factory.protocol = OpenFaceServerProtocol
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ctx_factory = DefaultOpenSSLContextFactory(tls_key, tls_crt)
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reactor.listenSSL(args.port, factory, ctx_factory)
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return defer.Deferred()
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if __name__ == '__main__':
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task.react(main)
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