def test_stateful_kernel(self):
@cv.gapi.op('custom.sum', in_types=[cv.GArray.Int], out_types=[cv.GOpaque.Int])
class GSum:
@staticmethod
def outMeta(arr_desc):
return cv.empty_gopaque_desc()
@cv.gapi.kernel(GSum)
class GSumImpl:
last_result = 0
@staticmethod
def run(arr):
GSumImpl.last_result = sum(arr)
return GSumImpl.last_result
g_in = cv.GArray.Int()
comp = cv.GComputation(cv.GIn(g_in), cv.GOut(GSum.on(g_in)))
s = comp.apply(cv.gin([1, 2, 3, 4]), args=cv.gapi.compile_args(cv.gapi.kernels(GSumImpl)))
self.assertEqual(10, s)
s = comp.apply(cv.gin([1, 2, 8, 7]), args=cv.gapi.compile_args(cv.gapi.kernels(GSumImpl)))
self.assertEqual(18, s)
self.assertEqual(18, GSumImpl.last_result)
def test_opaq_with_custom_type(self):
@cv.gapi.op('custom.op', in_types=[cv.GOpaque.Any, cv.GOpaque.String], out_types=[cv.GOpaque.Any])
class GLookUp:
@staticmethod
def outMeta(opaq_desc0, opaq_desc1):
return cv.empty_gopaque_desc()
@cv.gapi.kernel(GLookUp)
class GLookUpImpl:
@staticmethod
def run(table, key):
return table[key]
g_table = cv.GOpaque.Any()
g_key = cv.GOpaque.String()
g_out = GLookUp.on(g_table, g_key)
comp = cv.GComputation(cv.GIn(g_table, g_key), cv.GOut(g_out))
table = {
'int': 42,
'str': 'hello, world!',
'tuple': (42, 42)
}
out = comp.apply(cv.gin(table, 'int'), args=cv.gapi.compile_args(cv.gapi.kernels(GLookUpImpl)))
self.assertEqual(42, out)
out = comp.apply(cv.gin(table, 'str'), args=cv.gapi.compile_args(cv.gapi.kernels(GLookUpImpl)))
self.assertEqual('hello, world!', out)
out = comp.apply(cv.gin(table, 'tuple'), args=cv.gapi.compile_args(cv.gapi.kernels(GLookUpImpl)))
self.assertEqual((42, 42), out)
def test_video_add(self):
sz = (576, 768, 3)
in_mat = np.random.randint(0, 100, sz).astype(np.uint8)
path = self.find_file('cv/video/768x576.avi',
[os.environ['OPENCV_TEST_DATA_PATH']])
# OpenCV
cap = cv.VideoCapture(path)
# G-API
g_in1 = cv.GMat()
g_in2 = cv.GMat()
out = cv.gapi.add(g_in1, g_in2)
c = cv.GComputation(cv.GIn(g_in1, g_in2), cv.GOut(out))
ccomp = c.compileStreaming()
source = cv.gapi.wip.make_capture_src(path)
ccomp.setSource(cv.gin(source, in_mat))
ccomp.start()
# Assert
while cap.isOpened():
has_expected, frame = cap.read()
has_actual, actual = ccomp.pull()
self.assertEqual(has_expected, has_actual)
if not has_actual:
break
expected = cv.add(frame, in_mat)
self.assertEqual(0.0, cv.norm(expected, actual, cv.NORM_INF))
def test_multiple_custom_kernels(self):
sz = (3, 3, 3)
in_mat1 = np.full(sz, 45, dtype=np.uint8)
in_mat2 = np.full(sz, 50, dtype=np.uint8)
# OpenCV
expected = cv.mean(cv.split(cv.add(in_mat1, in_mat2))[1])
# G-API
g_in1 = cv.GMat()
g_in2 = cv.GMat()
g_sum = cv.gapi.add(g_in1, g_in2)
g_b, g_r, g_g = cv.gapi.split3(g_sum)
g_mean = cv.gapi.mean(g_b)
comp = cv.GComputation(cv.GIn(g_in1, g_in2), cv.GOut(g_mean))
pkg = cv.gapi_wip_kernels(
(custom_add, 'org.opencv.core.math.add'),
(custom_mean, 'org.opencv.core.math.mean'),
(custom_split3, 'org.opencv.core.transform.split3'))
actual = comp.apply(cv.gin(in_mat1, in_mat2),
args=cv.compile_args(pkg))
self.assertEqual(0.0, cv.norm(expected, actual, cv.NORM_INF))
def test_gst_multiple_sources(self):
if not cv.videoio_registry.hasBackend(cv.CAP_GSTREAMER):
raise unittest.SkipTest(
"Backend is not available/disabled: GSTREAMER")
gstpipeline = """videotestsrc is-live=true pattern=colors num-buffers=10 !
videorate ! videoscale !
video/x-raw,width=1920,height=1080,framerate=30/1 !
appsink name=sink1
videotestsrc is-live=true pattern=colors num-buffers=10 !
videorate ! videoscale !
video/x-raw,width=1920,height=1080,framerate=30/1 !
appsink name=sink2"""
g_in1 = cv.GMat()
g_in2 = cv.GMat()
g_out = cv.gapi.add(g_in1, g_in2)
c = cv.GComputation(cv.GIn(g_in1, g_in2), cv.GOut(g_out))
ccomp = c.compileStreaming()
pp = self.get_gst_pipeline(gstpipeline)
src1 = cv.gapi.wip.get_streaming_source(pp, "sink1")
src2 = cv.gapi.wip.get_streaming_source(pp, "sink2")
ccomp.setSource(cv.gin(src1, src2))
ccomp.start()
has_frame, out = ccomp.pull()
while has_frame:
self.assertTrue(out.size != 0)
has_frame, out = ccomp.pull()
def test_custom_op_goodFeaturesToTrack(self):
# G-API
img_path = self.find_file('cv/face/david2.jpg', [os.environ.get('OPENCV_TEST_DATA_PATH')])
in_mat = cv.cvtColor(cv.imread(img_path), cv.COLOR_RGB2GRAY)
# NB: goodFeaturesToTrack configuration
max_corners = 50
quality_lvl = 0.01
min_distance = 10.0
block_sz = 3
use_harris_detector = True
k = 0.04
# OpenCV
expected = cv.goodFeaturesToTrack(in_mat, max_corners, quality_lvl,
min_distance, mask=None,
blockSize=block_sz, useHarrisDetector=use_harris_detector, k=k)
# G-API
g_in = cv.GMat()
g_out = GGoodFeatures.on(g_in, max_corners, quality_lvl,
min_distance, block_sz, use_harris_detector, k)
comp = cv.GComputation(cv.GIn(g_in), cv.GOut(g_out))
pkg = cv.gapi.kernels(GGoodFeaturesImpl)
actual = comp.apply(cv.gin(in_mat), args=cv.gapi.compile_args(pkg))
# NB: OpenCV & G-API have different output types.
# OpenCV - numpy array with shape (num_points, 1, 2)
# G-API - list of tuples with size - num_points
# Comparison
self.assertEqual(0.0, cv.norm(expected.flatten(),
np.array(actual, dtype=np.float32).flatten(), cv.NORM_INF))
def test_render_primitives_on_nv12_graph(self):
y_expected = np.zeros((self.size[0], self.size[1], 1),
dtype=np.uint8)
uv_expected = np.zeros((self.size[0] // 2, self.size[1] // 2, 2),
dtype=np.uint8)
y_actual = np.array(y_expected, copy=True)
uv_actual = np.array(uv_expected, copy=True)
# OpenCV
self.render_primitives_nv12_ref(y_expected, uv_expected)
# G-API
g_y = cv.GMat()
g_uv = cv.GMat()
g_prims = cv.GArray.Prim()
g_out_y, g_out_uv = cv.gapi.wip.draw.renderNV12(g_y, g_uv, g_prims)
comp = cv.GComputation(cv.GIn(g_y, g_uv, g_prims),
cv.GOut(g_out_y, g_out_uv))
y_actual, uv_actual = comp.apply(
cv.gin(y_actual, uv_actual, self.prims))
self.assertEqual(0.0, cv.norm(y_expected, y_actual, cv.NORM_INF))
self.assertEqual(0.0, cv.norm(uv_expected, uv_actual, cv.NORM_INF))
def test_raise_in_outMeta(self):
@cv.gapi.op('custom.op', in_types=[cv.GMat, cv.GMat], out_types=[cv.GMat])
class GAdd:
@staticmethod
def outMeta(desc0, desc1):
raise NotImplementedError("outMeta isn't implemented")
@cv.gapi.kernel(GAdd)
class GAddImpl:
@staticmethod
def run(img0, img1):
return img0 + img1
g_in0 = cv.GMat()
g_in1 = cv.GMat()
g_out = GAdd.on(g_in0, g_in1)
comp = cv.GComputation(cv.GIn(g_in0, g_in1), cv.GOut(g_out))
img0 = np.array([1, 2, 3])
img1 = np.array([1, 2, 3])
with self.assertRaises(Exception): comp.apply(cv.gin(img0, img1),
args=cv.gapi.compile_args(
cv.gapi.kernels(GAddImpl)))
def test_threshold(self):
img_path = self.find_file('cv/face/david2.jpg',
[os.environ.get('OPENCV_TEST_DATA_PATH')])
in_mat = cv.cvtColor(cv.imread(img_path), cv.COLOR_RGB2GRAY)
maxv = (30, 30)
# OpenCV
expected_thresh, expected_mat = cv.threshold(in_mat, maxv[0], maxv[0],
cv.THRESH_TRIANGLE)
# G-API
g_in = cv.GMat()
g_sc = cv.GScalar()
mat, threshold = cv.gapi.threshold(g_in, g_sc, cv.THRESH_TRIANGLE)
comp = cv.GComputation(cv.GIn(g_in, g_sc), cv.GOut(mat, threshold))
for pkg_name, pkg in pkgs:
actual_mat, actual_thresh = comp.apply(cv.gin(in_mat, maxv),
args=cv.compile_args(pkg))
# Comparison
self.assertEqual(0.0, cv.norm(expected_mat, actual_mat,
cv.NORM_INF),
'Failed on ' + pkg_name + ' backend')
self.assertEqual(expected_mat.dtype, actual_mat.dtype,
'Failed on ' + pkg_name + ' backend')
self.assertEqual(expected_thresh, actual_thresh[0],
'Failed on ' + pkg_name + ' backend')
def test_invalid_outMeta(self):
@cv.gapi.op('custom.op', in_types=[cv.GMat, cv.GMat], out_types=[cv.GMat])
class GAdd:
@staticmethod
def outMeta(desc0, desc1):
# Invalid outMeta
return cv.empty_gopaque_desc()
@cv.gapi.kernel(GAdd)
class GAddImpl:
@staticmethod
def run(img0, img1):
return img0 + img1
g_in0 = cv.GMat()
g_in1 = cv.GMat()
g_out = GAdd.on(g_in0, g_in1)
comp = cv.GComputation(cv.GIn(g_in0, g_in1), cv.GOut(g_out))
img0 = np.array([1, 2, 3])
img1 = np.array([1, 2, 3])
# FIXME: Cause Bad variant access.
# Need to provide more descriptive error message.
with self.assertRaises(Exception): comp.apply(cv.gin(img0, img1),
args=cv.gapi.compile_args(
cv.gapi.kernels(GAddImpl)))
def test_kmeans(self):
# K-means params
count = 100
sz = (count, 2)
in_mat = np.random.random(sz).astype(np.float32)
K = 5
flags = cv.KMEANS_RANDOM_CENTERS
attempts = 1
criteria = (cv.TERM_CRITERIA_MAX_ITER + cv.TERM_CRITERIA_EPS, 30, 0)
# G-API
g_in = cv.GMat()
compactness, out_labels, centers = cv.gapi.kmeans(
g_in, K, criteria, attempts, flags)
comp = cv.GComputation(cv.GIn(g_in),
cv.GOut(compactness, out_labels, centers))
compact, labels, centers = comp.apply(cv.gin(in_mat))
# Assert
self.assertTrue(compact >= 0)
self.assertEqual(sz[0], labels.shape[0])
self.assertEqual(1, labels.shape[1])
self.assertTrue(labels.size != 0)
self.assertEqual(centers.shape[1], sz[1])
self.assertEqual(centers.shape[0], K)
self.assertTrue(centers.size != 0)
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_gapi_streaming_meta(self):
ksize = 3
path = self.find_file('cv/video/768x576.avi',
[os.environ['OPENCV_TEST_DATA_PATH']])
# G-API
g_in = cv.GMat()
g_ts = cv.gapi.streaming.timestamp(g_in)
g_seqno = cv.gapi.streaming.seqNo(g_in)
g_seqid = cv.gapi.streaming.seq_id(g_in)
c = cv.GComputation(cv.GIn(g_in), cv.GOut(g_ts, g_seqno, g_seqid))
ccomp = c.compileStreaming()
source = cv.gapi.wip.make_capture_src(path)
ccomp.setSource(cv.gin(source))
ccomp.start()
# Assert
max_num_frames = 10
curr_frame_number = 0
while True:
has_frame, (ts, seqno, seqid) = ccomp.pull()
if not has_frame:
break
self.assertEqual(curr_frame_number, seqno)
self.assertEqual(curr_frame_number, seqid)
curr_frame_number += 1
if curr_frame_number == max_num_frames:
break
def test_array_with_custom_type(self):
@cv.gapi.op('custom.op', in_types=[cv.GArray.Any, cv.GArray.Any], out_types=[cv.GArray.Any])
class GConcat:
@staticmethod
def outMeta(arr_desc0, arr_desc1):
return cv.empty_array_desc()
@cv.gapi.kernel(GConcat)
class GConcatImpl:
@staticmethod
def run(arr0, arr1):
return arr0 + arr1
g_arr0 = cv.GArray.Any()
g_arr1 = cv.GArray.Any()
g_out = GConcat.on(g_arr0, g_arr1)
comp = cv.GComputation(cv.GIn(g_arr0, g_arr1), cv.GOut(g_out))
arr0 = ((2, 2), 2.0)
arr1 = (3, 'str')
out = comp.apply(cv.gin(arr0, arr1),
args=cv.gapi.compile_args(cv.gapi.kernels(GConcatImpl)))
self.assertEqual(arr0 + arr1, out)
def test_pipeline_with_custom_kernels(self):
@cv.gapi.op('custom.resize',
in_types=[cv.GMat, tuple],
out_types=[cv.GMat])
class GResize:
@staticmethod
def outMeta(desc, size):
return desc.withSize(size)
@cv.gapi.kernel(GResize)
class GResizeImpl:
@staticmethod
def run(img, size):
return cv.resize(img, size)
@cv.gapi.op('custom.transpose',
in_types=[cv.GMat, tuple],
out_types=[cv.GMat])
class GTranspose:
@staticmethod
def outMeta(desc, order):
return desc
@cv.gapi.kernel(GTranspose)
class GTransposeImpl:
@staticmethod
def run(img, order):
return np.transpose(img, order)
img_path = self.find_file(
'cv/face/david2.jpg',
[os.environ.get('OPENCV_TEST_DATA_PATH')])
img = cv.imread(img_path)
size = (32, 32)
order = (1, 0, 2)
# Dummy pipeline just to validate this case:
# gapi -> custom -> custom -> gapi
# OpenCV
expected = cv.cvtColor(img, cv.COLOR_BGR2RGB)
expected = cv.resize(expected, size)
expected = np.transpose(expected, order)
expected = cv.mean(expected)
# G-API
g_bgr = cv.GMat()
g_rgb = cv.gapi.BGR2RGB(g_bgr)
g_resized = GResize.on(g_rgb, size)
g_transposed = GTranspose.on(g_resized, order)
g_mean = cv.gapi.mean(g_transposed)
comp = cv.GComputation(cv.GIn(g_bgr), cv.GOut(g_mean))
actual = comp.apply(cv.gin(img),
args=cv.compile_args(
cv.gapi.kernels(GResizeImpl,
GTransposeImpl)))
self.assertEqual(0.0, cv.norm(expected, actual, cv.NORM_INF))
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_image_input(self):
sz = (1280, 720)
in_mat = np.random.randint(0, 100, sz).astype(np.uint8)
# OpenCV
expected = cv.medianBlur(in_mat, 3)
# G-API
g_in = cv.GMat()
g_out = cv.gapi.medianBlur(g_in, 3)
c = cv.GComputation(g_in, g_out)
ccomp = c.compileStreaming(cv.descr_of(cv.gin(in_mat)))
ccomp.setSource(cv.gin(in_mat))
ccomp.start()
_, actual = ccomp.pull()
# Assert
self.assertEqual(0.0, cv.norm(expected, actual, cv.NORM_INF))
def test_gst_multiple_sources_accuracy(self):
if not cv.videoio_registry.hasBackend(cv.CAP_GSTREAMER):
raise unittest.SkipTest(
"Backend is not available/disabled: GSTREAMER")
path = self.find_file('highgui/video/big_buck_bunny.avi',
[os.environ['OPENCV_TEST_DATA_PATH']])
gstpipeline1 = """filesrc location=""" + path + """ ! decodebin ! videoconvert !
videoscale ! video/x-raw,format=NV12 ! appsink"""
gstpipeline2 = """filesrc location=""" + path + """ ! decodebin !
videoflip method=clockwise ! videoconvert ! videoscale !
video/x-raw,format=NV12 ! appsink"""
gstpipeline_gapi = gstpipeline1 + ' name=sink1 ' + gstpipeline2 + ' name=sink2'
# G-API pipeline
g_in1 = cv.GMat()
g_in2 = cv.GMat()
g_out1 = cv.gapi.copy(g_in1)
g_out2 = cv.gapi.copy(g_in2)
c = cv.GComputation(cv.GIn(g_in1, g_in2), cv.GOut(g_out1, g_out2))
ccomp = c.compileStreaming()
# G-API Gst-source
pp = self.get_gst_pipeline(gstpipeline_gapi)
src1 = cv.gapi.wip.get_streaming_source(pp, "sink1")
src2 = cv.gapi.wip.get_streaming_source(pp, "sink2")
ccomp.setSource(cv.gin(src1, src2))
ccomp.start()
# OpenCV Gst-source
cap1 = self.open_VideoCapture_gstreamer(gstpipeline1)
cap2 = self.open_VideoCapture_gstreamer(gstpipeline2)
# Assert
max_num_frames = 10
for _ in range(max_num_frames):
has_expected1, expected1 = cap1.read()
has_expected2, expected2 = cap2.read()
has_actual, (actual1, actual2) = ccomp.pull()
self.assertEqual(has_expected1, has_expected2)
has_expected = has_expected1 and has_expected2
self.assertEqual(has_expected, has_actual)
if not has_expected:
break
self.assertEqual(
0.0,
cv.norm(convertNV12p2BGR(expected1), actual1, cv.NORM_INF))
self.assertEqual(
0.0,
cv.norm(convertNV12p2BGR(expected2), actual2, cv.NORM_INF))
def test_age_gender_infer(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')])
img_path = self.find_file('cv/face/david2.jpg',
[os.environ.get('OPENCV_TEST_DATA_PATH')])
device_id = 'CPU'
img = cv.resize(cv.imread(img_path), (62, 62))
# OpenCV DNN
net = cv.dnn.readNetFromModelOptimizer(model_path, weights_path)
net.setPreferableBackend(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE)
net.setPreferableTarget(cv.dnn.DNN_TARGET_CPU)
blob = cv.dnn.blobFromImage(img)
net.setInput(blob)
dnn_age, dnn_gender = net.forward(net.getUnconnectedOutLayersNames())
# OpenCV G-API
g_in = cv.GMat()
inputs = cv.GInferInputs()
inputs.setInput('data', g_in)
outputs = cv.gapi.infer("net", inputs)
age_g = outputs.at("age_conv3")
gender_g = outputs.at("prob")
comp = cv.GComputation(cv.GIn(g_in), cv.GOut(age_g, gender_g))
pp = cv.gapi.ie.params("net", model_path, weights_path, device_id)
nets = cv.gapi.networks(pp)
args = cv.compile_args(nets)
gapi_age, gapi_gender = comp.apply(cv.gin(img),
args=cv.compile_args(
cv.gapi.networks(pp)))
# Check
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_render_primitives_on_bgr_graph(self):
expected = np.zeros(self.size, dtype=np.uint8)
actual = np.array(expected, copy=True)
# OpenCV
self.render_primitives_bgr_ref(expected)
# G-API
g_in = cv.GMat()
g_prims = cv.GArray.Prim()
g_out = cv.gapi.wip.draw.render3ch(g_in, g_prims)
comp = cv.GComputation(cv.GIn(g_in, g_prims), cv.GOut(g_out))
actual = comp.apply(cv.gin(actual, self.prims))
self.assertEqual(0.0, cv.norm(expected, actual, cv.NORM_INF))
def test_custom_op_size(self):
sz = (100, 150, 3)
in_mat = np.full(sz, 45, dtype=np.uint8)
# Open_cV
expected = (100, 150)
# G-API
g_in = cv.GMat()
g_sz = GSize.on(g_in)
comp = cv.GComputation(cv.GIn(g_in), cv.GOut(g_sz))
pkg = cv.gapi.kernels(GSizeImpl)
actual = comp.apply(cv.gin(in_mat), args=cv.gapi.compile_args(pkg))
self.assertEqual(0.0, cv.norm(expected, actual, cv.NORM_INF))
def test_mean(self):
sz = (1280, 720, 3)
in_mat = np.random.randint(0, 100, sz).astype(np.uint8)
# OpenCV
expected = cv.mean(in_mat)
# G-API
g_in = cv.GMat()
g_out = cv.gapi.mean(g_in)
comp = cv.GComputation(g_in, g_out)
for pkg in pkgs:
actual = comp.apply(cv.gin(in_mat), args=cv.compile_args(pkg))
# Comparison
self.assertEqual(0.0, cv.norm(expected, actual, cv.NORM_INF))
def test_custom_op_sizeR(self):
# x, y, h, w
roi = (10, 15, 100, 150)
expected = (100, 150)
# G-API
g_r = cv.GOpaque.Rect()
g_sz = GSizeR.on(g_r)
comp = cv.GComputation(cv.GIn(g_r), cv.GOut(g_sz))
pkg = cv.gapi.kernels(GSizeRImpl)
actual = comp.apply(cv.gin(roi), args=cv.gapi.compile_args(pkg))
# cv.norm works with tuples ?
self.assertEqual(0.0, cv.norm(expected, actual, cv.NORM_INF))
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.GArray.Point()
g_br = GBoundingRect.on(g_pts)
comp = cv.GComputation(cv.GIn(g_pts), cv.GOut(g_br))
pkg = cv.gapi.kernels(GBoundingRectImpl)
actual = comp.apply(cv.gin(points), args=cv.gapi.compile_args(pkg))
# cv.norm works with tuples ?
self.assertEqual(0.0, cv.norm(expected, actual, cv.NORM_INF))
def test_custom_sizeR(self):
# x, y, h, w
roi = (10, 15, 100, 150)
expected = (100, 150)
# G-API
g_r = cv.GOpaqueT(cv.gapi.CV_RECT)
g_sz = cv.gapi.streaming.size(g_r)
comp = cv.GComputation(cv.GIn(g_r), cv.GOut(g_sz))
pkg = cv.gapi_wip_kernels((custom_sizeR, 'org.opencv.streaming.sizeR'))
actual = comp.apply(cv.gin(roi), args=cv.compile_args(pkg))
# cv.norm works with tuples ?
self.assertEqual(0.0, cv.norm(expected, actual, cv.NORM_INF))
def test_custom_size(self):
sz = (100, 150, 3)
in_mat = np.full(sz, 45, dtype=np.uint8)
# OpenCV
expected = (100, 150)
# G-API
g_in = cv.GMat()
g_sz = cv.gapi.streaming.size(g_in)
comp = cv.GComputation(cv.GIn(g_in), cv.GOut(g_sz))
pkg = cv.gapi_wip_kernels((custom_size, 'org.opencv.streaming.size'))
actual = comp.apply(cv.gin(in_mat), args=cv.compile_args(pkg))
self.assertEqual(0.0, cv.norm(expected, actual, cv.NORM_INF))
def test_mean(self):
img_path = self.find_file('cv/face/david2.jpg', [os.environ.get('OPENCV_TEST_DATA_PATH')])
in_mat = cv.imread(img_path)
# OpenCV
expected = cv.mean(in_mat)
# G-API
g_in = cv.GMat()
g_out = cv.gapi.mean(g_in)
comp = cv.GComputation(g_in, g_out)
for pkg_name, pkg in pkgs:
actual = comp.apply(cv.gin(in_mat), args=cv.compile_args(pkg))
# Comparison
self.assertEqual(0.0, cv.norm(expected, actual, cv.NORM_INF),
'Failed on ' + pkg_name + ' backend')
def test_split3(self):
sz = (1280, 720, 3)
in_mat = np.random.randint(0, 100, sz).astype(np.uint8)
# OpenCV
expected = cv.split(in_mat)
# G-API
g_in = cv.GMat()
b, g, r = cv.gapi.split3(g_in)
comp = cv.GComputation(cv.GIn(g_in), cv.GOut(b, g, r))
for pkg in pkgs:
actual = comp.apply(cv.gin(in_mat), args=cv.compile_args(pkg))
# Comparison
for e, a in zip(expected, actual):
self.assertEqual(0.0, cv.norm(e, a, cv.NORM_INF))
def test_desync(self):
path = self.find_file('cv/video/768x576.avi',
[os.environ['OPENCV_TEST_DATA_PATH']])
# G-API
g_in = cv.GMat()
g_out1 = cv.gapi.copy(g_in)
des = cv.gapi.streaming.desync(g_in)
g_out2 = GDelay.on(des)
c = cv.GComputation(cv.GIn(g_in), cv.GOut(g_out1, g_out2))
kernels = cv.gapi.kernels(GDelayImpl)
ccomp = c.compileStreaming(args=cv.gapi.compile_args(kernels))
source = cv.gapi.wip.make_capture_src(path)
ccomp.setSource(cv.gin(source))
ccomp.start()
# Assert
max_num_frames = 10
proc_num_frames = 0
out_counter = 0
desync_out_counter = 0
none_counter = 0
while True:
has_frame, (out1, out2) = ccomp.pull()
if not has_frame:
break
if not out1 is None:
out_counter += 1
if not out2 is None:
desync_out_counter += 1
else:
none_counter += 1
proc_num_frames += 1
if proc_num_frames == max_num_frames:
ccomp.stop()
break
self.assertLess(0, proc_num_frames)
self.assertLess(desync_out_counter, out_counter)
self.assertLess(0, none_counter)
def test_custom_op_addC(self):
sz = (3, 3, 3)
in_mat = np.full(sz, 45, dtype=np.uint8)
sc = (50, 10, 20)
# Numpy reference, make array from sc to keep uint8 dtype.
expected = in_mat + np.array(sc, dtype=np.uint8)
# G-API
g_in = cv.GMat()
g_sc = cv.GScalar()
g_out = GAddC.on(g_in, g_sc, cv.CV_8UC1)
comp = cv.GComputation(cv.GIn(g_in, g_sc), cv.GOut(g_out))
pkg = cv.gapi.kernels(GAddCImpl)
actual = comp.apply(cv.gin(in_mat, sc), args=cv.gapi.compile_args(pkg))
self.assertEqual(0.0, cv.norm(expected, actual, cv.NORM_INF))