def _initialize(self,frame,rect):
'''
A private method that initializes the filter and prepares this
instance of a MOSSETrack to receive new frames.
@param frame: A source video frame to _initialize from.
@type frame: pv.Image
@param rect: A rectangle defining the object to be tracked in "frame".
@type rect: pv.Rect
'''
start = time.time()
self.rect = copy.deepcopy(rect)
affine = pv.AffineFromRect(rect,self.tile_size)
pt = affine.transformPoint(self.rect.center())
if self.strategy == INIT_ONE_TRANSFORM:
tmp = affine.transformImage(frame)
self.filter.addTraining(tmp,pt)
self.input = tmp
elif self.strategy == INIT_NINE_TRANSFORM:
for scale in [-1,0,1]:
scale = pv.AffineRotate(scale*self.dscale,self.tile_size,center=pt)
for rotate in [-1,0,1]:
rot = pv.AffineRotate(rotate*self.drotate,self.tile_size,center=pt)
pt = (scale*rot*affine).transformPoint(self.rect.center())
tmp = (scale*rot*affine).transformImage(frame)
self.filter.addTraining(tmp,pt)
self.input = tmp
self.corr = np.zeros(self.tile_size,dtype=np.float64)
self.frame = 0
stop = time.time()
self.update_time = stop-start
self.best_estimate = rect.center()
def generateTransforms(self, detection):
# Transform the face
affine = pv.AffineFromRect(detection, self.tile_size)
w, h = self.tile_size
if self.perturbations:
# Randomly rotate, translate and scale the images
center = pv.AffineTranslate(-0.5 * w, -0.5 * h, self.tile_size)
rotate = pv.AffineRotate(random.uniform(-pi / 8, pi / 8),
self.tile_size)
scale = pv.AffineScale(random.uniform(0.9, 1.1), self.tile_size)
translate = pv.AffineTranslate(random.uniform(-0.05 * w, 0.05 * w),
random.uniform(-0.05 * h, 0.05 * h),
self.tile_size)
inv_center = pv.AffineTranslate(0.5 * w, 0.5 * h, self.tile_size)
affine = inv_center * translate * scale * rotate * center * affine
#affine = affine*center*rotate*scale*translate*inv_center
lx = self.left_center.X() - self.subtile_size[0] / 2
ly = self.left_center.Y() - self.subtile_size[1] / 2
rx = self.right_center.X() - self.subtile_size[0] / 2
ry = self.right_center.Y() - self.subtile_size[1] / 2
laffine = pv.AffineFromRect(
pv.Rect(lx, ly, self.subtile_size[0], self.subtile_size[1]),
self.subtile_size) * affine
raffine = pv.AffineFromRect(
pv.Rect(rx, ry, self.subtile_size[0], self.subtile_size[1]),
self.subtile_size) * affine
return laffine, raffine
def addTraining(self, left_eye, right_eye, im):
'''Train an eye detector givin a full image and the eye coordinates.'''
# determine the face rect
true_rect = face_from_eyes(left_eye, right_eye)
# run the face detector
rects = self.face_detector.detect(im)
# find the best detection if there is one
for pred_rect in rects:
if is_success(pred_rect, true_rect):
# Transform the face
affine = pv.AffineFromRect(pred_rect, self.tile_size)
w, h = self.tile_size
if self.perturbations:
# Randomly rotate, translate and scale the images
center = pv.AffineTranslate(-0.5 * w, -0.5 * h,
self.tile_size)
rotate = pv.AffineRotate(random.uniform(-pi / 8, pi / 8),
self.tile_size)
scale = pv.AffineScale(random.uniform(0.9, 1.1),
self.tile_size)
translate = pv.AffineTranslate(
random.uniform(-0.05 * w, 0.05 * w),
random.uniform(-0.05 * h, 0.05 * h), self.tile_size)
inv_center = pv.AffineTranslate(0.5 * w, 0.5 * h,
self.tile_size)
affine = inv_center * translate * scale * rotate * center * affine
#affine = affine*center*rotate*scale*translate*inv_center
cropped = affine.transformImage(im)
cropped = pv.meanStd(cropped)
# Mark the eyes
leye = affine.transformPoint(left_eye)
reye = affine.transformPoint(right_eye)
# Add training data to locators
self.training_labels.append((leye, reye))
self.normalize.addTraining(0.0, cropped)
#self.left_locator.addTraining(cropped,leye)
#self.right_locator.addTraining(cropped,reye)
# Just use the first success
return
# The face was not detected
self.detection_failures += 1
def test_rotation(self):
transform = pv.AffineRotate(3.14 / 8, (640, 480))
_ = transform.transformImage(self.test_image)
# im_a.show()
pt = transform.transformPoint(pv.Point(320, 240))
self.assertAlmostEqual(pt.X(), 203.86594448424472)
self.assertAlmostEqual(pt.Y(), 344.14920700118842)
pt = transform.invertPoint(pv.Point(320, 240))
self.assertAlmostEqual(pt.X(), 387.46570317672939)
self.assertAlmostEqual(pt.Y(), 99.349528744542198)
def test_prev_ref3(self):
fname = os.path.join(pv.__path__[0], 'data', 'nonface',
'NONFACE_13.jpg')
torig = tprev = im_a = pv.Image(fname)
#im_a.show()
w, h = im_a.size
# Scale
aff = pv.AffineScale(0.5, (w / 2, h / 2))
accu = aff
torig = aff.transformImage(torig)
tprev = aff.transformImage(tprev, use_orig=False)
taccu = accu.transformImage(im_a)
torig.annotateLabel(pv.Point(10, 10), "use_orig = True")
tprev.annotateLabel(pv.Point(10, 10), "use_orig = False")
taccu.annotateLabel(pv.Point(10, 10), "accumulated")
#torig.show()
#tprev.show()
#taccu.show()
# Translate
aff = pv.AffineTranslate(20, 20, (w / 2, h / 2))
accu = aff * accu
torig = aff.transformImage(torig)
tprev = aff.transformImage(tprev, use_orig=False)
taccu = accu.transformImage(im_a)
torig.annotateLabel(pv.Point(10, 10), "use_orig = True")
tprev.annotateLabel(pv.Point(10, 10), "use_orig = False")
taccu.annotateLabel(pv.Point(10, 10), "accumulated")
#torig.show()
#tprev.show()
#taccu.show()
# Rotate
aff = pv.AffineRotate(np.pi / 4, (w / 2, h / 2))
accu = aff * accu
torig = aff.transformImage(torig)
tprev = aff.transformImage(tprev, use_orig=False)
taccu = accu.transformImage(im_a)
torig.annotateLabel(pv.Point(10, 10), "use_orig = True")
tprev.annotateLabel(pv.Point(10, 10), "use_orig = False")
taccu.annotateLabel(pv.Point(10, 10), "accumulated")
#torig.show()
#tprev.show()
#taccu.show()
# Translate
aff = pv.AffineTranslate(100, -10, (w / 2, h / 2))
accu = aff * accu
torig = aff.transformImage(torig)
tprev = aff.transformImage(tprev, use_orig=False)
taccu = accu.transformImage(im_a)
torig.annotateLabel(pv.Point(10, 10), "use_orig = True")
tprev.annotateLabel(pv.Point(10, 10), "use_orig = False")
taccu.annotateLabel(pv.Point(10, 10), "accumulated")
#torig.show()
#tprev.show()
#taccu.show()
# Scale
aff = pv.AffineScale(2.0, (w, h))
accu = aff * accu
torig = aff.transformImage(torig)
tprev = aff.transformImage(tprev, use_orig=False)
taccu = accu.transformImage(im_a)
torig.annotateLabel(pv.Point(10, 10), "use_orig = True")
tprev.annotateLabel(pv.Point(10, 10), "use_orig = False")
taccu.annotateLabel(pv.Point(10, 10), "accumulated")