def search(classifier, img_path, feature_descriptors, c_backend):
img = Image.open(img_path)
img = img.convert("L")
print "searching image"
results = []
count = 0
for start, end in all_windows((0,0),
(img.size[1], img.size[0])):
# TODO: refactor code in faces.py so I can call that
# instead of having this mess right here.
window = img.crop((start[1], start[0], end[1], end[0]))
window_pre_resize = window
window = window.resize((16, 16))
ar = faces.f_vec(window)
faces.integrate_image(ar)
int_img = faces.IntegratedImage(ar)
# Yuck. Needs a little refactoring to make this cleaner.
if 1 == list(classifier.classify([faces.Datum(
img_path,
-1,
faces.LazyFeatureVec(int_img,
feature_descriptors,
c_backend))]))[0]:
print "match %s" % repr((start, end))
results.append((start, end))
if len(results) > 10:
break
plot_search(img, results)
def search(classifier, img_path, feature_descriptors, c_backend):
img = Image.open(img_path)
img = img.convert("L")
print "searching image"
results = []
count = 0
for start, end in all_windows((0, 0), (img.size[1], img.size[0])):
# TODO: refactor code in faces.py so I can call that
# instead of having this mess right here.
window = img.crop((start[1], start[0], end[1], end[0]))
window_pre_resize = window
window = window.resize((16, 16))
ar = faces.f_vec(window)
faces.integrate_image(ar)
int_img = faces.IntegratedImage(ar)
# Yuck. Needs a little refactoring to make this cleaner.
if 1 == list(
classifier.classify([
faces.Datum(
img_path, -1,
faces.LazyFeatureVec(int_img, feature_descriptors,
c_backend))
]))[0]:
print "match %s" % repr((start, end))
results.append((start, end))
if len(results) > 10:
break
plot_search(img, results)
def validate(self, ar, start, end, output):
ar = numpy.array(ar)
faces.integrate_image(ar)
img = faces.IntegratedImage(ar)
self.assertEqual(faces.feature_a(img, start, end), output)
def validate(self, input, output):
input = numpy.array(input, dtype=numpy.int64, ndmin=2)
output = numpy.array(output, dtype=numpy.int64, ndmin=2)
faces.integrate_image(input)
testing.assert_equal(input, output)
def validate(self, ar, start, end, output):
ar = numpy.array(ar)
faces.integrate_image(ar)
img = faces.IntegratedImage(ar)
self.assertEqual(faces.feature_a(img, start, end), output)
def validate(self, input, output):
input = numpy.array(input, dtype=numpy.int64, ndmin=2)
output = numpy.array(output, dtype=numpy.int64, ndmin=2)
faces.integrate_image(input)
testing.assert_equal(input, output)
