def test_kmeans_2d(self):
# K-means 2D params
count = 100
sz = (count, 2)
amount = sz[0]
K = 5
flags = cv.KMEANS_RANDOM_CENTERS
attempts = 1
criteria = (cv.TERM_CRITERIA_MAX_ITER + cv.TERM_CRITERIA_EPS, 30, 0)
in_vector = self.generate_random_points(sz)
in_labels = []
# G-API
data = cv.GArrayT(cv.gapi.CV_POINT2F)
best_labels = cv.GArrayT(cv.gapi.CV_INT)
compactness, out_labels, centers = cv.gapi.kmeans(
data, K, best_labels, criteria, attempts, flags)
comp = cv.GComputation(cv.GIn(data, best_labels),
cv.GOut(compactness, out_labels, centers))
compact, labels, centers = comp.apply(cv.gin(in_vector, in_labels))
# Assert
self.assertTrue(compact >= 0)
self.assertEqual(amount, len(labels))
self.assertEqual(K, len(centers))
def test_age_gender_infer2_roi(self):
# NB: Check IE
if not cv.dnn.DNN_TARGET_CPU in cv.dnn.getAvailableTargets(
cv.dnn.DNN_BACKEND_INFERENCE_ENGINE):
return
root_path = '/omz_intel_models/intel/age-gender-recognition-retail-0013/FP32/age-gender-recognition-retail-0013'
model_path = self.find_file(
root_path + '.xml',
[os.environ.get('OPENCV_DNN_TEST_DATA_PATH')])
weights_path = self.find_file(
root_path + '.bin',
[os.environ.get('OPENCV_DNN_TEST_DATA_PATH')])
device_id = 'CPU'
rois = [(10, 15, 62, 62), (23, 50, 62, 62), (14, 100, 62, 62),
(80, 50, 62, 62)]
img_path = self.find_file(
'cv/face/david2.jpg',
[os.environ.get('OPENCV_TEST_DATA_PATH')])
img = cv.imread(img_path)
# OpenCV DNN
dnn_age_list = []
dnn_gender_list = []
for roi in rois:
age, gender = self.infer_reference_network(
model_path, weights_path, self.make_roi(img, roi))
dnn_age_list.append(age)
dnn_gender_list.append(gender)
# OpenCV G-API
g_in = cv.GMat()
g_rois = cv.GArrayT(cv.gapi.CV_RECT)
inputs = cv.GInferListInputs()
inputs.setInput('data', g_rois)
outputs = cv.gapi.infer2("net", g_in, inputs)
age_g = outputs.at("age_conv3")
gender_g = outputs.at("prob")
comp = cv.GComputation(cv.GIn(g_in, g_rois),
cv.GOut(age_g, gender_g))
pp = cv.gapi.ie.params("net", model_path, weights_path, device_id)
gapi_age_list, gapi_gender_list = comp.apply(
cv.gin(img, rois),
args=cv.gapi.compile_args(cv.gapi.networks(pp)))
# Check
for gapi_age, gapi_gender, dnn_age, dnn_gender in zip(
gapi_age_list, gapi_gender_list, dnn_age_list,
dnn_gender_list):
self.assertEqual(0.0,
cv.norm(dnn_gender, gapi_gender, cv.NORM_INF))
self.assertEqual(0.0, cv.norm(dnn_age, gapi_age, cv.NORM_INF))
def test_garray_type(self):
types = [
cv.gapi.CV_BOOL, cv.gapi.CV_INT, cv.gapi.CV_DOUBLE,
cv.gapi.CV_FLOAT, cv.gapi.CV_STRING, cv.gapi.CV_POINT,
cv.gapi.CV_POINT2F, cv.gapi.CV_SIZE, cv.gapi.CV_RECT,
cv.gapi.CV_SCALAR, cv.gapi.CV_MAT, cv.gapi.CV_GMAT
]
for t in types:
g_array = cv.GArrayT(t)
self.assertEqual(t, g_array.type())
def test_custom_op_boundingRect(self):
points = [(0, 0), (0, 1), (1, 0), (1, 1)]
# OpenCV
expected = cv.boundingRect(np.array(points))
# G-API
g_pts = cv.GArrayT(cv.gapi.CV_POINT)
g_br = boundingRect(g_pts)
comp = cv.GComputation(cv.GIn(g_pts), cv.GOut(g_br))
pkg = cv.gapi.wip.kernels((custom_boundingRect, 'custom.boundingRect'))
actual = comp.apply(cv.gin(points), args=cv.compile_args(pkg))
# cv.norm works with tuples ?
self.assertEqual(0.0, cv.norm(expected, actual, cv.NORM_INF))
def __new__(self):
return cv.GArrayT(cv.gapi.CV_GMAT)
def __new__(self):
return cv.GArrayT(cv.gapi.CV_SCALAR)
def __new__(self):
return cv.GArrayT(cv.gapi.CV_POINT2F)
def __new__(self):
return cv.GArrayT(cv.gapi.CV_STRING)
def __new__(self):
return cv.GArrayT(cv.gapi.CV_DOUBLE)
def __new__(cls, argtype):
return cv.GArrayT(argtype)
