def getForDemo(networkFile, demoFile): self = Factory posdbos = self._get() posdbos.dm = DrowsinessMonitor() posdbos.nn = self.loadNeuralNetwork(networkFile) posdbos.dc = Factory.createDemoDataCollector(demoFile, posdbos.collectedQueue) posdbos.dp = Factory.createDataProcessor(posdbos.collectedQueue, posdbos.extractedQueue) return posdbos
def __init__(self, networkFile=None, demo=False, demoFile=None): '''Main class for drowsiness detection :param string networkFile: file name of the saved neural network (path: "/../../data/<networkFile>.nn") ''' self.demo = demo self.running = True self.config = ConfigProvider() self._initNeuralNetwork(networkFile) self._initFeatureExtractor(demoFile) self.dm = DrowsinessMonitor() self.fileUtil = EEGTableFileUtil()
class PoSDBoS(object): def __init__(self, networkFile=None, demo=False, demoFile=None): '''Main class for drowsiness detection :param string networkFile: file name of the saved neural network (path: "/../../data/<networkFile>.nn") ''' self.demo = demo self.running = True self.config = ConfigProvider() self._initPoSDBoS() self._initNeuralNetwork(networkFile) self._initFeatureExtractor(demoFile) self.dm = DrowsinessMonitor() self.fileUtil = EEGTableFileUtil() def _initPoSDBoS(self): posdbosConfig = self.config.getPoSDBoSConfig() self.drowsyMinCount = posdbosConfig.get("drowsyMinCount") self.awakeMinCount = posdbosConfig.get("awakeMinCount") self.classified = [0, 0] self.curClass = 0 self.classCount = 0 self.found = 0 def _initNeuralNetwork(self, networkFile): nnCreate = self.config.getNNInitConfig() self.nn = NeuralNetwork() if networkFile == None: self.nn.createNew(**nnCreate) else: self.nn.load(networkFile) def _initFeatureExtractor(self, demoFile): self.demoFile = demoFile collector = self._initDataCollector(self.demoFile) self.fe = FeatureExtractor(collector) self.inputQueue = self.fe.extractQueue def _initDataCollector(self, demoFile): collectorConfig = self.config.getCollectorConfig() if self.demo: return DummyDataCollector(demoFile, **collectorConfig) else: return EEGDataCollector(None, **collectorConfig) def close(self): self.running = False def run(self): fet = threading.Thread(target=self.fe.start) fet.start() dmt = threading.Thread(target=self.dm.run) dmt.start() features = [] total = 0 start = time.time() c = [] while self.running and dmt.is_alive(): try: #awake = 0, drowsy = 1 data = self.inputQueue.get(timeout=1) features.append(data) clazz = self.nn.activate(data, True) c.append([clazz, clazz]) self.setStatus(clazz) total += 1 except Empty: print "needed %sms for %d windows" % (time.time() - start, total) pass except KeyboardInterrupt: self.close() except Exception as e: print e.message self.close() #self.writeFeature(c) self.fe.close() self.dm.close() dmt.join() def setStatus(self, clazz): self.classified[clazz] += 1 if self.curClass == clazz: self.classCount += 1 else: self.curClass = clazz self.classCount = 0 info = "class %d row (%s)" % (clazz, str(self.classCount)) if clazz == 1 and self.classCount >= self.drowsyMinCount: self.dm.setStatus(clazz, info) self.found += 1 elif clazz == 0 and self.classCount >= self.awakeMinCount: self.dm.setStatus(clazz, info) def writeFeature(self, data): filePath = scriptPath + "/../data/" + "classes.csv" #filePath = scriptPath + "/../data/" + "drowsy_full_.csv" header = ["clazz", "clazz2"] #start = 4 #end = start + len(data[0])/6 #for field in self.config.getCollectorConfig().get("fields"): # header.extend([str(x) + "Hz" + field for x in range(start, end)]) self.fileUtil.writeFile(filePath, data, header)
class PoSDBoS(object): def __init__(self, networkFile=None, demo=False, demoFile=None): '''Main class for drowsiness detection :param string networkFile: file name of the saved neural network (path: "/../../data/<networkFile>.nn") ''' self.demo = demo self.running = True self.config = ConfigProvider() self._initNeuralNetwork(networkFile) self._initFeatureExtractor(demoFile) self.dm = DrowsinessMonitor() self.fileUtil = EEGTableFileUtil() def _initNeuralNetwork(self, networkFile): nn_conf = self.config.getNeuralNetworkConfig() self.nn = NeuralNetwork() if networkFile == None: self.nn.createNew(nn_conf["nInputs"], nn_conf["nHiddenLayers"], nn_conf["nOutput"], nn_conf["bias"]) else: self.nn.load(networkFile) def _initFeatureExtractor(self, demoFile): collector = self._initDataCollector(demoFile) self.fe = FeatureExtractor(collector) self.inputQueue = self.fe.extractQueue def _initDataCollector(self, demoFile): collectorConfig = self.config.getCollectorConfig() if self.demo: return DummyDataCollector(demoFile, **collectorConfig) else: return EEGDataCollector(None, **collectorConfig) def close(self): self.running = False def run(self): fet = threading.Thread(target=self.fe.start) fet.start() dmt = threading.Thread(target=self.dm.run) dmt.start() features = [] while self.running and dmt.is_alive(): try: data = self.inputQueue.get(timeout=1) features.append(data) x = random.randint(1, 10)%2 y = random.randint(1, 10)%2 data = (x, y) clazz = self.nn.activate(data) info = "%d XOR %d is %d; queue: %d" % (x, y, clazz, self.inputQueue.qsize()) self.dm.setStatus(clazz, info) #sleep(1) except Empty: pass #if self.demo: # self.close() except KeyboardInterrupt: self.close() except Exception as e: print e.message self.close() self.writeFeature(features) self.fe.close() self.dm.close() dmt.join() def writeFeature(self, data): filePath = scriptPath + "/../data/" + "test.csv" header = [] for field in ["F3", "F4", "F7", "F8"]: for i in range(1, 5): header.append("%s_%s" % (field ,str(i))) self.fileUtil.writeFile(filePath, data, header)