def donttest_detector_train(
self): # TODO: Cascade training fails for Version OpenCV 2.0
positives = []
self.eyes = EyesFile(os.path.join(SCRAPS_FACE_DATA, "coords.txt"))
n = len(self.eyes.files())
for filename in self.eyes.files()[:n / 2]:
img = pv.Image(os.path.join(SCRAPS_FACE_DATA, filename + ".pgm"))
faces = self.eyes.getFaces(img.filename)
positives.append(
[os.path.join(SCRAPS_FACE_DATA, img.filename), faces])
neg_files = []
for im_name in os.listdir(NONFACE_DATA):
if im_name[-4:] != ".jpg": continue
neg_files.append(os.path.join(NONFACE_DATA, im_name))
fd = trainHaarClassifier(positives,
neg_files,
nstages=6,
maxtreesplits=0,
max_run_time=300)
fdt = FaceDetectionTest(name='scraps')
self.eyes = EyesFile(os.path.join(SCRAPS_FACE_DATA, "coords.txt"))
for filename in self.eyes.files():
img = pv.Image(os.path.join(SCRAPS_FACE_DATA, filename + ".pgm"))
rects = fd(img)
truth = self.eyes.getFaces(img.filename)
fdt.addSample(truth, rects, im=img)
self.assertAlmostEqual(fdt.pos_rate, 0.9942196531791907)
def donttest_detector_train(self): # TODO: Cascade training fails for Version OpenCV 2.0
positives = []
self.eyes = EyesFile(os.path.join(SCRAPS_FACE_DATA,"coords.txt"))
n = len(self.eyes.files())
for filename in self.eyes.files()[:n/2]:
img = pv.Image(os.path.join(SCRAPS_FACE_DATA, filename + ".pgm"))
faces = self.eyes.getFaces(img.filename)
positives.append([os.path.join(SCRAPS_FACE_DATA,img.filename),faces])
neg_files = []
for im_name in os.listdir(NONFACE_DATA):
if im_name[-4:] != ".jpg": continue
neg_files.append(os.path.join(NONFACE_DATA,im_name))
fd = trainHaarClassifier(positives,neg_files,nstages=6,maxtreesplits=0,max_run_time=300)
fdt = FaceDetectionTest(name='scraps')
self.eyes = EyesFile(os.path.join(SCRAPS_FACE_DATA,"coords.txt"))
for filename in self.eyes.files():
img = pv.Image(os.path.join(SCRAPS_FACE_DATA, filename + ".pgm"))
rects = fd(img)
truth = self.eyes.getFaces(img.filename)
fdt.addSample(truth,rects,im=img)
self.assertAlmostEqual( fdt.pos_rate , 0.9942196531791907 )
def test_detect_scraps_celeb1(self):
fd = CascadeDetector(CELEB1_CASCADE)
fdt = FaceDetectionTest(name='scraps')
self.eyes = EyesFile(os.path.join(SCRAPS_FACE_DATA,"coords.txt"))
for filename in self.eyes.files():
img = pv.Image(os.path.join(SCRAPS_FACE_DATA, filename + ".pgm"))
rects = fd(img)
truth = self.eyes.getFaces(img.filename)
fdt.addSample(truth,rects,im=img)
self.assertAlmostEqual( fdt.pos_rate , 0.76878612716763006, places = 2 ) # TODO: Version 2 performance is better
def test_detect_scraps_celeb1(self):
fd = CascadeDetector(CELEB1_CASCADE)
fdt = FaceDetectionTest(name='scraps')
self.eyes = EyesFile(os.path.join(SCRAPS_FACE_DATA,"coords.txt"))
for filename in self.eyes.files():
img = pv.Image(os.path.join(SCRAPS_FACE_DATA, filename + ".pgm"))
rects = fd(img)
truth = self.eyes.getFaces(img.filename)
fdt.addSample(truth,rects,im=img)
self.assertAlmostEqual( fdt.pos_rate , 0.76878612716763006, places = 2 ) # TODO: Version 2 performance is better
def test_face_detection_pickle(self):
fd = CascadeDetector(OPENCV_CASCADE)
fdt = FaceDetectionTest(name='scraps')
buffer = pickle.dumps(fd)
fd = pickle.loads(buffer)
self.eyes = EyesFile(os.path.join(SCRAPS_FACE_DATA,"coords.txt"))
for filename in self.eyes.files():
img = pv.Image(os.path.join(SCRAPS_FACE_DATA, filename + ".pgm"))
rects = fd(img)
truth = self.eyes.getFaces(img.filename)
fdt.addSample(truth,rects,im=img)
self.assertAlmostEqual( fdt.pos_rate , 0.98265895953757221, places = 2 ) # TODO: Version 2 performance is better
def test_face_detection_pickle(self):
fd = CascadeDetector(OPENCV_CASCADE)
fdt = FaceDetectionTest(name='scraps')
data_buffer = pickle.dumps(fd)
fd = pickle.loads(data_buffer)
self.eyes = EyesFile(os.path.join(SCRAPS_FACE_DATA,"coords.txt"))
for filename in self.eyes.files():
img = pv.Image(os.path.join(SCRAPS_FACE_DATA, filename + ".pgm"))
rects = fd(img)
truth = self.eyes.getFaces(img.filename)
fdt.addSample(truth,rects,im=img)
self.assertAlmostEqual( fdt.pos_rate , 0.98265895953757221, places = 2 ) # TODO: Version 2 performance is better
