def testCam(faceXMLpath):
#faceXMLpath = "/home/bolei/code/opencv-2.4.7/data/haarcascades/haarcascade_frontalface_alt.xml"
myDetector = faceDetector(faceXMLpath)
capture = myDetector.openCam()
if capture:
while 1:
face_set, img_rectangle = retrieveCam()
if face_set!=0:
cv.ShowImage("result", img_rectangle)
def testImg(faceXMLpath):
#faceXMLpath = "/home/bolei/code/opencv-2.4.7/data/haarcascades/haarcascade_frontalface_alt.xml"
myDetector = faceDetector(faceXMLpath)
for i in range(1,22):
input_name = '/home/bolei/Pictures/face/' + str(i) + '.jpg'
face_set, img_rectangle = myDetector.detectImg(input_name)
if face_set!=0:
print face_set
cv.ShowImage("result", img_rectangle)
cv.WaitKey()
def pipelineCam():
tagEmotion = {
0: 'angry',
1: 'disgust',
2: 'fear',
3: 'happy',
4: 'sad',
5: 'surprise',
6: 'neutral'
}
print 'load facedetector...'
faceXMLpath = "/data/vision/fisher/data1/face_celebrity/haarcascades/haarcascade_frontalface_alt.xml"
myDetector = faceDetector(faceXMLpath)
## reconstruct model
print "loading trained deep learning model..."
model = loadModel('train3040.pkl.cpu')
X = model.get_input_space().make_batch_theano()
Y = model.fprop(X)
from theano import tensor as T
y = T.argmax(Y, axis=1)
from theano import function
f = function([X], y)
capture = myDetector.openCam()
if capture:
while 1:
face_set, img_rectangle = myDetector.retrieveCam()
print img_rectangle
if face_set != None:
xx = np.array(face_set)
x_500 = np.concatenate(
(xx, np.zeros((499, xx.shape[1]), dtype=xx.dtype)), axis=0)
x_input = DataPylearn2([x_500, np.ones(500)], (48, 48, 1))
x_arg = x_input.X
if X.ndim > 2:
x_arg = x_input.get_topological_view(x_arg)
y_pred = f(x_arg.astype(X.dtype))
print tagEmotion[y_pred[0]]
cv.ShowImage("result", img_rectangle)
print img_rectangle.shape
cv.WaitKey()
if cv.WaitKey(10) >= 0:
break
def pipelineCam():
tagEmotion = {0:'angry',1:'disgust',2:'fear',3:'happy',4:'sad',5:'surprise',6:'neutral'}
print 'load facedetector...'
faceXMLpath = "/data/vision/fisher/data1/face_celebrity/haarcascades/haarcascade_frontalface_alt.xml"
myDetector = faceDetector(faceXMLpath)
## reconstruct model
print "loading trained deep learning model..."
model = loadModel('train3040.pkl.cpu')
X = model.get_input_space().make_batch_theano()
Y = model.fprop(X)
from theano import tensor as T
y = T.argmax(Y, axis=1)
from theano import function
f = function([X], y)
capture = myDetector.openCam()
if capture:
while 1:
face_set, img_rectangle = myDetector.retrieveCam()
print img_rectangle
if face_set!=None:
xx = np.array(face_set)
x_500 = np.concatenate((xx, np.zeros((499, xx.shape[1]),
dtype=xx.dtype)), axis=0)
x_input = DataPylearn2([x_500, np.ones(500)], (48,48,1))
x_arg = x_input.X
if X.ndim > 2:
x_arg = x_input.get_topological_view(x_arg)
y_pred = f(x_arg.astype(X.dtype))
print tagEmotion[y_pred[0]]
cv.ShowImage("result", img_rectangle)
print img_rectangle.shape
cv.WaitKey()
if cv.WaitKey(10) >= 0:
break
def pipelineImg():
tagEmotion = {0:'angry',1:'disgust',2:'fear',3:'happy',4:'sad',5:'surprise',6:'neutral'}
print 'load facedetector...'
faceXMLpath = "/data/vision/fisher/data1/face_celebrity/haarcascades/haarcascade_frontalface_alt.xml"
myDetector = faceDetector(faceXMLpath)
#img_folder = "/scratch/face/thumbnails_features_deduped_publish/thumbnails_features_deduped_publish/bill clinton/"
#img_folder = "/data/vision/billf/manifold-learning/DL/Data/pubfig/images/"
img_folder = "/data/vision/fisher/data1/face_celebrity/thumbnails_features_deduped_publish/thumbnails_features_deduped_publish/bill gates/"
imgs = os.listdir(img_folder)
print len(imgs)
## reconstruct model
print "loading trained deep learning model..."
""" old way to load model
FF = 'train.pkl'
a=cPickle.load(open(FF))
X = a.model.get_input_space().make_batch_theano()
Y = a.model.fprop(X)
from theano import tensor as T
y = T.argmax(Y, axis=1)
from theano import function
f = function([X], y)
for i in range(len(imgs)):
input_name = img_folder + imgs[i]
print imgs[i]
if input_name.endswith('jpg'):
face_set, img_rectangle = myDetector.detectImg(input_name)
if face_set[0]!=0:
print face_set
xx = np.array(face_set)
x_500 = np.concatenate((xx, np.zeros((499, xx.shape[1]),
dtype=xx.dtype)), axis=0)
from pylearn2.space import Conv2DSpace
ishape = Conv2DSpace(
shape = [48, 48],
num_channels = 1
)
from DBL_util import DataPylearn2
x_input = DataPylearn2([x_500, np.zeros(500)], (48,48,1))
x_arg = x_input.X
if X.ndim > 2:
x_arg = x_input.get_topological_view(x_arg)
y_pred = f(x_arg.astype(X.dtype))
print tagEmotion[y_pred[0]]
cv.ShowImage("result", img_rectangle)
cv.WaitKey()
"""
#model = loadModel('train3040.pkl.cpu')
model = loadModel('train322010.pkl.cpu')
X = model.get_input_space().make_batch_theano()
Y = model.fprop(X)
from theano import tensor as T
y = T.argmax(Y, axis=1)
from theano import function
f = function([X], y)
for i in range(len(imgs)):
input_name = img_folder + imgs[i]
if input_name.endswith('jpg'):
face_set, img_rectangle = myDetector.detectImg(input_name)
#face_set, img_rectangle = readWholeImg(input_name)
if face_set!=None:
xx = np.array(face_set)
x_500 = np.concatenate((xx, np.zeros((499, xx.shape[1]),
dtype=xx.dtype)), axis=0)
x_input = DataPylearn2([x_500, np.zeros(500)], (48,48,1))
x_arg = x_input.X
if X.ndim > 2:
x_arg = x_input.get_topological_view(x_arg)
y_pred = f(x_arg.astype(X.dtype))
#print y_pred.shape
#print y_pred[0]
print y_pred[0]
print imgs[i], tagEmotion[y_pred[0]]
cv.ShowImage("result", img_rectangle)
cv.WaitKey(0)
def pipelineImg():
tagEmotion = {
0: 'angry',
1: 'disgust',
2: 'fear',
3: 'happy',
4: 'sad',
5: 'surprise',
6: 'neutral'
}
print 'load facedetector...'
faceXMLpath = "/data/vision/fisher/data1/face_celebrity/haarcascades/haarcascade_frontalface_alt.xml"
myDetector = faceDetector(faceXMLpath)
#img_folder = "/scratch/face/thumbnails_features_deduped_publish/thumbnails_features_deduped_publish/bill clinton/"
#img_folder = "/data/vision/billf/manifold-learning/DL/Data/pubfig/images/"
img_folder = "/data/vision/fisher/data1/face_celebrity/thumbnails_features_deduped_publish/thumbnails_features_deduped_publish/bill gates/"
imgs = os.listdir(img_folder)
print len(imgs)
## reconstruct model
print "loading trained deep learning model..."
""" old way to load model
FF = 'train.pkl'
a=cPickle.load(open(FF))
X = a.model.get_input_space().make_batch_theano()
Y = a.model.fprop(X)
from theano import tensor as T
y = T.argmax(Y, axis=1)
from theano import function
f = function([X], y)
for i in range(len(imgs)):
input_name = img_folder + imgs[i]
print imgs[i]
if input_name.endswith('jpg'):
face_set, img_rectangle = myDetector.detectImg(input_name)
if face_set[0]!=0:
print face_set
xx = np.array(face_set)
x_500 = np.concatenate((xx, np.zeros((499, xx.shape[1]),
dtype=xx.dtype)), axis=0)
from pylearn2.space import Conv2DSpace
ishape = Conv2DSpace(
shape = [48, 48],
num_channels = 1
)
from DBL_util import DataPylearn2
x_input = DataPylearn2([x_500, np.zeros(500)], (48,48,1))
x_arg = x_input.X
if X.ndim > 2:
x_arg = x_input.get_topological_view(x_arg)
y_pred = f(x_arg.astype(X.dtype))
print tagEmotion[y_pred[0]]
cv.ShowImage("result", img_rectangle)
cv.WaitKey()
"""
#model = loadModel('train3040.pkl.cpu')
model = loadModel('train322010.pkl.cpu')
X = model.get_input_space().make_batch_theano()
Y = model.fprop(X)
from theano import tensor as T
y = T.argmax(Y, axis=1)
from theano import function
f = function([X], y)
for i in range(len(imgs)):
input_name = img_folder + imgs[i]
if input_name.endswith('jpg'):
face_set, img_rectangle = myDetector.detectImg(input_name)
#face_set, img_rectangle = readWholeImg(input_name)
if face_set != None:
xx = np.array(face_set)
x_500 = np.concatenate(
(xx, np.zeros((499, xx.shape[1]), dtype=xx.dtype)), axis=0)
x_input = DataPylearn2([x_500, np.zeros(500)], (48, 48, 1))
x_arg = x_input.X
if X.ndim > 2:
x_arg = x_input.get_topological_view(x_arg)
y_pred = f(x_arg.astype(X.dtype))
#print y_pred.shape
#print y_pred[0]
print y_pred[0]
print imgs[i], tagEmotion[y_pred[0]]
cv.ShowImage("result", img_rectangle)
cv.WaitKey(0)
input_name = '/home/bolei/Pictures/face/' + str(i) + '.jpg'
face_set, img_rectangle = myDetector.detectImg(input_name)
if face_set!=0:
print face_set
cv.ShowImage("result", img_rectangle)
cv.WaitKey()
def testCam(faceXMLpath):
#faceXMLpath = "/home/bolei/code/opencv-2.4.7/data/haarcascades/haarcascade_frontalface_alt.xml"
myDetector = faceDetector(faceXMLpath)
capture = myDetector.openCam()
if capture:
while 1:
face_set, img_rectangle = retrieveCam()
if face_set!=0:
cv.ShowImage("result", img_rectangle)
if __name__ == '__main__':
#testImg() # test input image
#testCam() # test camera image
#faceXMLpath = "/home/bolei/code/opencv-2.4.7/data/haarcascades/haarcascade_frontalface_alt.xml"
faceXMLpath = "/afs/csail.mit.edu/u/b/bzhou/code/OpenCV-2.4.2/data/haarcascades/haarcascade_frontalface_alt.xml"
myDetector = faceDetector(faceXMLpath)
capture = myDetector.openCam()
while 1:
face_set, img_rectangle = myDetector.retrieveCam()
cv.ShowImage("result", img_rectangle)
print face_set
if cv.WaitKey(10) >= 0:
break