print '-------------------' # Creates and opens ports for interaction with speech module if yarpRunning: yarp.Network.init() inputInteractionPort = yarp.Port() inputInteractionPort.open("/sam/face/rpc:i"); inputBottle = yarp.Bottle(); outputBottle = yarp.Bottle(); imgHNew = 200 imgWNew = 200 # Creates a SAMpy object mySAMpy = SAMDriver_interaction(True, imgH = 400, imgW = 400, imgHNew = imgHNew, imgWNew = imgWNew,inputImagePort="/CLM/imageSeg/out") # Specification of the experiment number experiment_number = 1007#42 # Location of face data root_data_dir=dataPath # Image format image_suffix=".ppm" # Array of participants to be recognised participant_index=participantList # Poses used during the data collection pose_index=['Seg'] # Use a subset of the data for training
ratioData = modelPickle['percentTestData'] image_suffix = modelPickle['image_suffix'] model_type = modelPickle['model_type'] model_num_inducing = modelPickle['num_inducing'] model_init_iterations = modelPickle['model_init_iterations'] model_num_iterations = modelPickle['model_num_iterations'] kernelString = modelPickle['kernelString'] Q = modelPickle['Q'] economy_save = True pose_index = [''] pose_selection = 0 # # Creates a SAMpy object mySAMpy = SAMDriver_interaction(False, imgH=imgH, imgW=imgW, imgHNew=imgHNew, imgWNew=imgWNew) # # Reading face data, preparation of data and training of the model mySAMpy.readData(dataPath, participantList, pose_index) minImages = mySAMpy.Y.shape[1] Ntr = int(minImages * ratioData / 100) Ntest = minImages - Ntr allPersonsY = mySAMpy.Y allPersonsL = mySAMpy.L for i in range(len(participantList)): #print participantList[i]
print '-------------------' print 'Config file found: ' + pathFound[0] print dataPath print modelPath print participantList print '-------------------' # Creates and opens ports for interaction with speech module yarp.Network.init() inputInteractionPort = yarp.BufferedPortBottle() inputInteractionPort.open("/sam/face/interaction:i"); choice = yarp.Bottle(); # Creates a SAMpy object mySAMpy = SAMDriver_interaction(True, imgH = 400, imgW = 400, imgHNew = 200, imgWNew = 200,inputImagePort="/visionDriver/image:o") # Specification of the experiment number experiment_number = 1007#42 # Location of face data root_data_dir=dataPath # Image format image_suffix=".ppm" # Array of participants to be recognised participant_index=participantList # Poses used during the data collection pose_index=['Seg'] # Use a subset of the data for training
imgHNew = modelPickle['imgHNew'] imgWNew = modelPickle['imgWNew'] ratioData = modelPickle['percentTestData'] image_suffix = modelPickle['image_suffix'] model_type = modelPickle['model_type'] model_num_inducing = modelPickle['num_inducing'] model_init_iterations = modelPickle['model_init_iterations'] model_num_iterations = modelPickle['model_num_iterations'] kernelString = modelPickle['kernelString'] Q = modelPickle['Q'] economy_save = True pose_index=[''] pose_selection = 0 # # Creates a SAMpy object mySAMpy = SAMDriver_interaction(False, imgH = imgH, imgW = imgW, imgHNew = imgHNew, imgWNew = imgWNew) # # Reading face data, preparation of data and training of the model mySAMpy.readData(dataPath, participantList, pose_index) minImages = mySAMpy.Y.shape[1] Ntr = int(minImages*ratioData/100) Ntest = minImages - Ntr allPersonsY = mySAMpy.Y; allPersonsL = mySAMpy.L; for i in range(len(participantList)): #print participantList[i] mySAMpy.Y = allPersonsY[:,:,i,None] mySAMpy.L = allPersonsL[:,:,i,None]
modelPickle = pickle.load(open(modelPath ,'rb')) imgH = modelPickle['imgH'] imgW = modelPickle['imgW'] imgHNew = modelPickle['imgHNew'] imgWNew = modelPickle['imgWNew'] ratioData = modelPickle['percentTestData'] image_suffix = modelPickle['image_suffix'] model_type = modelPickle['model_type'] model_num_inducing = modelPickle['num_inducing'] model_init_iterations = modelPickle['model_init_iterations'] model_num_iterations = modelPickle['model_num_iterations'] kernelString = modelPickle['kernelString'] # Creates a SAMpy object mySAMpy = SAMDriver_interaction(yarpRunning, imgH = imgH, imgW = imgW, imgHNew = imgHNew, imgWNew = imgWNew) # Location of face data root_data_dir=dataPath # Array of participants to be recognised participant_index=participantList # Poses used during the data collection pose_index=[''] # Pose selected for training pose_selection = 0 print 'modelPath: ' + modelPath
print dataPath print modelPath print participantList print '-------------------' # Creates and opens ports for interaction with speech module if yarpRunning: yarp.Network.init() inputInteractionPort = yarp.BufferedPortBottle() inputInteractionPort.open("/sam/face/interaction:i") choice = yarp.Bottle() # Creates a SAMpy object mySAMpy = SAMDriver_interaction(True, imgH=400, imgW=400, imgHNew=200, imgWNew=200, inputImagePort="/visionDriver/image:o") # Specification of the experiment number experiment_number = 1007 #42 # Location of face data root_data_dir = dataPath # Image format image_suffix = ".ppm" # Array of participants to be recognised participant_index = participantList # Poses used during the data collection
# Creates and opens ports for interaction with speech module if yarpRunning: yarp.Network.init() inputInteractionPort = yarp.Port() inputInteractionPort.open("/sam/face/rpc:i") inputBottle = yarp.Bottle() outputBottle = yarp.Bottle() imgHNew = 200 imgWNew = 200 # Creates a SAMpy object mySAMpy = SAMDriver_interaction(True, imgH=400, imgW=400, imgHNew=imgHNew, imgWNew=imgWNew, inputImagePort="/CLM/imageSeg/out") # Specification of the experiment number experiment_number = 1007 #42 # Location of face data root_data_dir = dataPath # Image format image_suffix = ".ppm" # Array of participants to be recognised participant_index = participantList # Poses used during the data collection