def __init__(self, id, nsamples, dst):
self._id = id
self._nsamples = nsamples
self._dst = dst
self._kinect = Kinect()
self._body = BodyDetector()
self._hand = HandDetector()
self._contour = HandContourDetector()
self._feature = GaborDescriptors(4, 4)
class TrainPose():
def __init__(self, id, nsamples, dst):
self._id = id
self._nsamples = nsamples
self._dst = dst
self._kinect = Kinect()
self._body = BodyDetector()
self._hand = HandDetector()
self._contour = HandContourDetector()
self._feature = GaborDescriptors(4, 4)
def run(self):
warmup = True
train = []
model = pickle.load(open('svm.pck', 'rb'))
for (depth, depth8, rgb) in self._kinect.get_data():
body = self._body.run(depth8)
(hand, _) = self._hand.run(body)
(cont, box, crop) = self._contour.run(hand)
hand = hand[box[1]:box[1]+box[3], box[0]:box[0]+box[2]]
cv2.imshow('hand', hand)
key = cv2.waitKey(2)
#if warmup:
# if key == GO:
# warmup = False
# continue
#if key != 97:
# continue
feature = self._feature.run(hand)
print model.predict(feature)
#train.append(feature)
#if len(train) == self._nsamples:
# self._save(train)
# break
#cv2.waitKey(2)
def _save(self, train):
data = np.array(train)
labels = self._id * np.ones(len(train), np.int)
output = {'labels': labels, 'data': data}
pickle.dump(output, open(self._dst, 'wb'))