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FaceAlignment.py
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FaceAlignment.py
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from __future__ import print_function
import lasagne
from lasagne.layers import Conv2DLayer, batch_norm
from lasagne.init import GlorotUniform
import numpy as np
import theano
from scipy import ndimage
from AffineTransformLayer import AffineTransformLayer
from TransformParamsLayer import TransformParamsLayer
from LandmarkImageLayer import LandmarkImageLayer
from LandmarkInitLayer import LandmarkInitLayer
from LandmarkTranformLayer import LandmarkTransformLayer
import utils
class FaceAlignment(object):
def __init__(self, height, width, nChannels, nStages, confidenceLayer=False):
self.landmarkPatchSize = 16
self.data = theano.tensor.tensor4('inputs', dtype=theano.config.floatX)
self.targets = theano.tensor.tensor4('targets')
self.imageHeight = height
self.imageWidth = width
self.nChannels = nChannels
self.errors = []
self.errorsTrain = []
self.nStages = nStages
self.confidenceLayer = confidenceLayer
def initializeNetwork(self):
self.layers = self.createCNN()
self.network = self.layers['output']
self.prediction = lasagne.layers.get_output(self.network, deterministic=True)
self.generate_network_output = theano.function([self.data], [self.prediction])
def addDANStage(self, stageIdx, net):
prevStage = 's' + str(stageIdx - 1)
curStage = 's' + str(stageIdx)
#CONNNECTION LAYERS OF PREVIOUS STAGE
net[prevStage + '_transform_params'] = TransformParamsLayer(net[prevStage + '_landmarks'], self.initLandmarks)
net[prevStage + '_img_output'] = AffineTransformLayer(net['input'], net[prevStage + '_transform_params'])
net[prevStage + '_landmarks_affine'] = LandmarkTransformLayer(net[prevStage + '_landmarks'], net[prevStage + '_transform_params'])
net[prevStage + '_img_landmarks'] = LandmarkImageLayer(net[prevStage + '_landmarks_affine'], (self.imageHeight, self.imageWidth), self.landmarkPatchSize)
net[prevStage + '_img_feature'] = lasagne.layers.DenseLayer(net[prevStage + '_fc1'], num_units=56 * 56, W=GlorotUniform('relu'))
net[prevStage + '_img_feature'] = lasagne.layers.ReshapeLayer(net[prevStage + '_img_feature'], (-1, 1, 56, 56))
net[prevStage + '_img_feature'] = lasagne.layers.Upscale2DLayer(net[prevStage + '_img_feature'], 2)
#CURRENT STAGE
net[curStage + '_input'] = batch_norm(lasagne.layers.ConcatLayer([net[prevStage + '_img_output'], net[prevStage + '_img_landmarks'], net[prevStage + '_img_feature']], 1))
net[curStage + '_conv1_1'] = batch_norm(Conv2DLayer(net[curStage + '_input'], 64, 3, pad='same', W=GlorotUniform('relu')))
net[curStage + '_conv1_2'] = batch_norm(Conv2DLayer(net[curStage + '_conv1_1'], 64, 3, pad='same', W=GlorotUniform('relu')))
net[curStage + '_pool1'] = lasagne.layers.Pool2DLayer(net[curStage + '_conv1_2'], 2)
net[curStage + '_conv2_1'] = batch_norm(Conv2DLayer(net[curStage + '_pool1'], 128, 3, pad=1, W=GlorotUniform('relu')))
net[curStage + '_conv2_2'] = batch_norm(Conv2DLayer(net[curStage + '_conv2_1'], 128, 3, pad=1, W=GlorotUniform('relu')))
net[curStage + '_pool2'] = lasagne.layers.Pool2DLayer(net[curStage + '_conv2_2'], 2)
net[curStage + '_conv3_1'] = batch_norm (Conv2DLayer(net[curStage + '_pool2'], 256, 3, pad=1, W=GlorotUniform('relu')))
net[curStage + '_conv3_2'] = batch_norm (Conv2DLayer(net[curStage + '_conv3_1'], 256, 3, pad=1, W=GlorotUniform('relu')))
net[curStage + '_pool3'] = lasagne.layers.Pool2DLayer(net[curStage + '_conv3_2'], 2)
net[curStage + '_conv4_1'] = batch_norm(Conv2DLayer(net[curStage + '_pool3'], 512, 3, pad=1, W=GlorotUniform('relu')))
net[curStage + '_conv4_2'] = batch_norm (Conv2DLayer(net[curStage + '_conv4_1'], 512, 3, pad=1, W=GlorotUniform('relu')))
net[curStage + '_pool4'] = lasagne.layers.Pool2DLayer(net[curStage + '_conv4_2'], 2)
net[curStage + '_pool4'] = lasagne.layers.FlattenLayer(net[curStage + '_pool4'])
net[curStage + '_fc1_dropout'] = lasagne.layers.DropoutLayer(net[curStage + '_pool4'], p=0.5)
net[curStage + '_fc1'] = batch_norm(lasagne.layers.DenseLayer(net[curStage + '_fc1_dropout'], num_units=256, W=GlorotUniform('relu')))
net[curStage + '_output'] = lasagne.layers.DenseLayer(net[curStage + '_fc1'], num_units=136, nonlinearity=None)
net[curStage + '_landmarks'] = lasagne.layers.ElemwiseSumLayer([net[prevStage + '_landmarks_affine'], net[curStage + '_output']])
net[curStage + '_landmarks'] = LandmarkTransformLayer(net[curStage + '_landmarks'], net[prevStage + '_transform_params'], True)
def createCNN(self):
net = {}
net['input'] = lasagne.layers.InputLayer(shape=(None, self.nChannels, self.imageHeight, self.imageWidth), input_var=self.data)
print("Input shape: {0}".format(net['input'].output_shape))
#STAGE 1
net['s1_conv1_1'] = batch_norm(Conv2DLayer(net['input'], 64, 3, pad='same', W=GlorotUniform('relu')))
net['s1_conv1_2'] = batch_norm(Conv2DLayer(net['s1_conv1_1'], 64, 3, pad='same', W=GlorotUniform('relu')))
net['s1_pool1'] = lasagne.layers.Pool2DLayer(net['s1_conv1_2'], 2)
net['s1_conv2_1'] = batch_norm(Conv2DLayer(net['s1_pool1'], 128, 3, pad=1, W=GlorotUniform('relu')))
net['s1_conv2_2'] = batch_norm(Conv2DLayer(net['s1_conv2_1'], 128, 3, pad=1, W=GlorotUniform('relu')))
net['s1_pool2'] = lasagne.layers.Pool2DLayer(net['s1_conv2_2'], 2)
net['s1_conv3_1'] = batch_norm (Conv2DLayer(net['s1_pool2'], 256, 3, pad=1, W=GlorotUniform('relu')))
net['s1_conv3_2'] = batch_norm (Conv2DLayer(net['s1_conv3_1'], 256, 3, pad=1, W=GlorotUniform('relu')))
net['s1_pool3'] = lasagne.layers.Pool2DLayer(net['s1_conv3_2'], 2)
net['s1_conv4_1'] = batch_norm(Conv2DLayer(net['s1_pool3'], 512, 3, pad=1, W=GlorotUniform('relu')))
net['s1_conv4_2'] = batch_norm (Conv2DLayer(net['s1_conv4_1'], 512, 3, pad=1, W=GlorotUniform('relu')))
net['s1_pool4'] = lasagne.layers.Pool2DLayer(net['s1_conv4_2'], 2)
net['s1_fc1_dropout'] = lasagne.layers.DropoutLayer(net['s1_pool4'], p=0.5)
net['s1_fc1'] = batch_norm(lasagne.layers.DenseLayer(net['s1_fc1_dropout'], num_units=256, W=GlorotUniform('relu')))
net['s1_output'] = lasagne.layers.DenseLayer(net['s1_fc1'], num_units=136, nonlinearity=None)
net['s1_landmarks'] = LandmarkInitLayer(net['s1_output'], self.initLandmarks)
if self.confidenceLayer:
net['s1_confidence'] = lasagne.layers.DenseLayer(net['s1_fc1'], num_units=2, W=GlorotUniform('relu'), nonlinearity=lasagne.nonlinearities.softmax)
for i in range(1, self.nStages):
self.addDANStage(i + 1, net)
net['output'] = net['s' + str(self.nStages) + '_landmarks']
if self.confidenceLayer:
net['output'] = lasagne.layers.ConcatLayer([net['output'], net['s1_confidence']])
return net
def loadNetwork(self, filename):
print('Loading network...')
with np.load(filename) as f:
param_values = [f['arr_%d' % i] for i in range(len(f.files) - 5)]
self.errors = f["errors"].tolist()
self.errorsTrain = f["errorsTrain"].tolist()
self.meanImg = f["meanImg"]
self.stdDevImg = f["stdDevImg"]
self.initLandmarks = f["initLandmarks"]
self.initializeNetwork()
nParams = len(lasagne.layers.get_all_param_values(self.network))
lasagne.layers.set_all_param_values(self.network, param_values[:nParams])
def processImg(self, img, inputLandmarks):
inputImg, transform = self.CropResizeRotate(img, inputLandmarks)
inputImg = inputImg - self.meanImg
inputImg = inputImg / self.stdDevImg
output = self.generate_network_output([inputImg])[0][0]
if self.confidenceLayer:
landmarkOutput = output[:-2]
confidenceOutput = output[-2:]
landmarks = landmarkOutput.reshape((-1, 2))
confidence = confidenceOutput[1]
return np.dot(landmarks - transform[1], np.linalg.inv(transform[0])), confidence
else:
landmarks = output.reshape((-1, 2))
return np.dot(landmarks - transform[1], np.linalg.inv(transform[0]))
def processNormalizedImg(self, img):
inputImg = img.astype(np.float32)
inputImg = inputImg - self.meanImg
inputImg = inputImg / self.stdDevImg
output = self.generate_network_output([inputImg])[0][0]
if self.confidenceLayer:
landmarkOutput = output[:-2]
confidenceOutput = output[-2:]
landmarks = landmarkOutput.reshape((-1, 2))
confidence = confidenceOutput[1]
return landmarks, confidence
else:
landmarks = output.reshape((-1, 2))
return landmarks
def CropResizeRotate(self, img, inputShape):
A, t = utils.bestFit(self.initLandmarks, inputShape, True)
A2 = np.linalg.inv(A)
t2 = np.dot(-t, A2)
outImg = np.zeros((self.nChannels, self.imageHeight, self.imageWidth), dtype=np.float32)
for i in range(img.shape[0]):
outImg[i] = ndimage.interpolation.affine_transform(img[i], A2, t2[[1, 0]], output_shape=(self.imageHeight, self.imageWidth))
return outImg, [A, t]