/demos/web/server.py
Python | 360 lines | 281 code | 53 blank | 26 comment | 55 complexity | b5ee1e018e0e7a7821c8b16a358fea1d MD5 | raw file
- #!/usr/bin/env python2
- #
- # Copyright 2015 Carnegie Mellon University
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- import os
- import sys
- fileDir = os.path.dirname(os.path.realpath(__file__))
- sys.path.append(os.path.join(fileDir, "..", ".."))
- from autobahn.twisted.websocket import WebSocketServerProtocol, \
- WebSocketServerFactory
- from twisted.python import log
- from twisted.internet import reactor
- import argparse
- import cv2
- import imagehash
- import json
- from PIL import Image
- import numpy as np
- import os
- import StringIO
- import urllib
- import base64
- from sklearn.decomposition import PCA
- from sklearn.grid_search import GridSearchCV
- from sklearn.manifold import TSNE
- from sklearn.svm import SVC
- import matplotlib.pyplot as plt
- import matplotlib.cm as cm
- import openface
- import tempfile
- modelDir = os.path.join(fileDir, '..', '..', 'models')
- dlibModelDir = os.path.join(modelDir, 'dlib')
- openfaceModelDir = os.path.join(modelDir, 'openface')
- parser = argparse.ArgumentParser()
- parser.add_argument('--dlibFaceMean', type=str, help="Path to dlib's face predictor.",
- default=os.path.join(dlibModelDir, "mean.csv"))
- parser.add_argument('--dlibFacePredictor', type=str, help="Path to dlib's face predictor.",
- default=os.path.join(dlibModelDir, "shape_predictor_68_face_landmarks.dat"))
- parser.add_argument('--dlibRoot', type=str,
- default=os.path.expanduser(
- "~/src/dlib-18.16/python_examples"),
- help="dlib directory with the dlib.so Python library.")
- parser.add_argument('--networkModel', type=str, help="Path to Torch network model.",
- default=os.path.join(openfaceModelDir, 'nn4.v1.t7'))
- parser.add_argument('--imgDim', type=int,
- help="Default image dimension.", default=96)
- parser.add_argument('--cuda', type=bool, default=False)
- parser.add_argument('--unknown', type=bool, default=False,
- help='Try to predict unknown people')
- args = parser.parse_args()
- sys.path.append(args.dlibRoot)
- import dlib
- from openface.alignment import NaiveDlib # Depends on dlib.
- align = NaiveDlib(args.dlibFaceMean, args.dlibFacePredictor)
- net = openface.TorchWrap(args.networkModel, imgDim=args.imgDim, cuda=args.cuda)
- class Face:
- def __init__(self, rep, identity):
- self.rep = rep
- self.identity = identity
- def __repr__(self):
- return "{{id: {}, rep[0:5]: {}}}".format(
- str(self.identity),
- self.rep[0:5]
- )
- class OpenFaceServerProtocol(WebSocketServerProtocol):
- def __init__(self):
- self.images = {}
- self.training = True
- self.people = []
- self.svm = None
- if args.unknown:
- self.unknownImgs = np.load("./examples/web/unknown.npy")
- def onConnect(self, request):
- print("Client connecting: {0}".format(request.peer))
- self.training = True
- def onOpen(self):
- print("WebSocket connection open.")
- def onMessage(self, payload, isBinary):
- raw = payload.decode('utf8')
- msg = json.loads(raw)
- print("Received {} message of length {}.".format(
- msg['type'], len(raw)))
- if msg['type'] == "ALL_STATE":
- self.loadState(msg['images'], msg['training'], msg['people'])
- elif msg['type'] == "NULL":
- self.sendMessage('{"type": "NULL"}')
- elif msg['type'] == "FRAME":
- self.processFrame(msg['dataURL'], msg['identity'])
- self.sendMessage('{"type": "PROCESSED"}')
- elif msg['type'] == "TRAINING":
- self.training = msg['val']
- if not self.training:
- self.trainSVM()
- elif msg['type'] == "ADD_PERSON":
- self.people.append(msg['val'].encode('ascii', 'ignore'))
- print(self.people)
- elif msg['type'] == "UPDATE_IDENTITY":
- h = msg['hash'].encode('ascii', 'ignore')
- if h in self.images:
- self.images[h].identity = msg['idx']
- if not self.training:
- self.trainSVM()
- else:
- print("Image not found.")
- elif msg['type'] == "REMOVE_IMAGE":
- h = msg['hash'].encode('ascii', 'ignore')
- if h in self.images:
- del self.images[h]
- if not self.training:
- self.trainSVM()
- else:
- print("Image not found.")
- elif msg['type'] == 'REQ_TSNE':
- self.sendTSNE(msg['people'])
- else:
- print("Warning: Unknown message type: {}".format(msg['type']))
- def onClose(self, wasClean, code, reason):
- print("WebSocket connection closed: {0}".format(reason))
- def loadState(self, jsImages, training, jsPeople):
- self.training = training
- for jsImage in jsImages:
- h = jsImage['hash'].encode('ascii', 'ignore')
- self.images[h] = Face(np.array(jsImage['representation']),
- jsImage['identity'])
- for jsPerson in jsPeople:
- self.people.append(jsPerson.encode('ascii', 'ignore'))
- if not training:
- self.trainSVM()
- def getData(self):
- X = []
- y = []
- for img in self.images.values():
- X.append(img.rep)
- y.append(img.identity)
- numIdentities = len(set(y + [-1])) - 1
- if numIdentities == 0:
- return None
- if args.unknown:
- numUnknown = y.count(-1)
- numIdentified = len(y) - numUnknown
- numUnknownAdd = (numIdentified / numIdentities) - numUnknown
- if numUnknownAdd > 0:
- print("+ Augmenting with {} unknown images.".format(numUnknownAdd))
- for rep in self.unknownImgs[:numUnknownAdd]:
- # print(rep)
- X.append(rep)
- y.append(-1)
- X = np.vstack(X)
- y = np.array(y)
- return (X, y)
- def sendTSNE(self, people):
- d = self.getData()
- if d is None:
- return
- else:
- (X, y) = d
- X_pca = PCA(n_components=50).fit_transform(X, X)
- tsne = TSNE(n_components=2, init='random', random_state=0)
- X_r = tsne.fit_transform(X_pca)
- yVals = list(np.unique(y))
- colors = cm.rainbow(np.linspace(0, 1, len(yVals)))
- # print(yVals)
- plt.figure()
- for c, i in zip(colors, yVals):
- name = "Unknown" if i == -1 else people[i]
- plt.scatter(X_r[y == i, 0], X_r[y == i, 1], c=c, label=name)
- plt.legend()
- imgdata = StringIO.StringIO()
- plt.savefig(imgdata, format='png')
- imgdata.seek(0)
- content = 'data:image/png;base64,' + \
- urllib.quote(base64.b64encode(imgdata.buf))
- msg = {
- "type": "TSNE_DATA",
- "content": content
- }
- self.sendMessage(json.dumps(msg))
- def trainSVM(self):
- print("+ Training SVM on {} labeled images.".format(len(self.images)))
- d = self.getData()
- if d is None:
- self.svm = None
- return
- else:
- (X, y) = d
- numIdentities = len(set(y + [-1]))
- if numIdentities <= 1:
- return
- param_grid = [
- {'C': [1, 10, 100, 1000],
- 'kernel': ['linear']},
- {'C': [1, 10, 100, 1000],
- 'gamma': [0.001, 0.0001],
- 'kernel': ['rbf']}
- ]
- self.svm = GridSearchCV(SVC(C=1), param_grid, cv=5).fit(X, y)
- def processFrame(self, dataURL, identity):
- head = "data:image/jpeg;base64,"
- assert(dataURL.startswith(head))
- imgdata = base64.b64decode(dataURL[len(head):])
- imgF = StringIO.StringIO()
- imgF.write(imgdata)
- imgF.seek(0)
- img = Image.open(imgF)
- buf = np.fliplr(np.asarray(img))
- rgbFrame = np.zeros((300, 400, 3), dtype=np.uint8)
- rgbFrame[:, :, 0] = buf[:, :, 2]
- rgbFrame[:, :, 1] = buf[:, :, 1]
- rgbFrame[:, :, 2] = buf[:, :, 0]
- if not self.training:
- annotatedFrame = np.copy(buf)
- # cv2.imshow('frame', rgbFrame)
- # if cv2.waitKey(1) & 0xFF == ord('q'):
- # return
- identities = []
- # bbs = align.getAllFaceBoundingBoxes(rgbFrame)
- bb = align.getLargestFaceBoundingBox(rgbFrame)
- bbs = [bb] if bb is not None else []
- for bb in bbs:
- # print(len(bbs))
- alignedFace = align.alignImg("affine", 96, rgbFrame, bb)
- if alignedFace is None:
- continue
- phash = str(imagehash.phash(Image.fromarray(alignedFace)))
- if phash in self.images:
- identity = self.images[phash].identity
- else:
- rep = net.forwardImage(alignedFace)
- # print(rep)
- if self.training:
- self.images[phash] = Face(rep, identity)
- # TODO: Transferring as a string is suboptimal.
- # content = [str(x) for x in cv2.resize(alignedFace, (0,0),
- # fx=0.5, fy=0.5).flatten()]
- content = [str(x) for x in alignedFace.flatten()]
- msg = {
- "type": "NEW_IMAGE",
- "hash": phash,
- "content": content,
- "identity": identity,
- "representation": rep.tolist()
- }
- self.sendMessage(json.dumps(msg))
- else:
- if len(self.people) == 0:
- identity = -1
- elif len(self.people) == 1:
- identity = 0
- elif self.svm:
- identity = self.svm.predict(rep)[0]
- else:
- print("hhh")
- identity = -1
- if identity not in identities:
- identities.append(identity)
- if not self.training:
- bl = (bb.left(), bb.bottom())
- tr = (bb.right(), bb.top())
- cv2.rectangle(annotatedFrame, bl, tr, color=(153, 255, 204),
- thickness=3)
- if identity == -1:
- if len(self.people) == 1:
- name = self.people[0]
- else:
- name = "Unknown"
- else:
- name = self.people[identity]
- cv2.putText(annotatedFrame, name, (bb.left(), bb.top() - 10),
- cv2.FONT_HERSHEY_SIMPLEX, fontScale=0.75,
- color=(152, 255, 204), thickness=2)
- if not self.training:
- msg = {
- "type": "IDENTITIES",
- "identities": identities
- }
- self.sendMessage(json.dumps(msg))
- plt.figure()
- plt.imshow(annotatedFrame)
- plt.xticks([])
- plt.yticks([])
- imgdata = StringIO.StringIO()
- plt.savefig(imgdata, format='png')
- imgdata.seek(0)
- content = 'data:image/png;base64,' + \
- urllib.quote(base64.b64encode(imgdata.buf))
- msg = {
- "type": "ANNOTATED",
- "content": content
- }
- self.sendMessage(json.dumps(msg))
- if __name__ == '__main__':
- log.startLogging(sys.stdout)
- factory = WebSocketServerFactory("ws://localhost:9000", debug=False)
- factory.protocol = OpenFaceServerProtocol
- reactor.listenTCP(9000, factory)
- reactor.run()