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_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_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 test_custom_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 block_sz = 3 use_harris_detector = True k = 0.04 mask = None # OpenCV expected = cv.goodFeaturesToTrack( in_mat, max_corners, quality_lvl, min_distance, mask=mask, blockSize=block_sz, useHarrisDetector=use_harris_detector, k=k) # G-API g_in = cv.GMat() g_out = cv.gapi.goodFeaturesToTrack(g_in, max_corners, quality_lvl, min_distance, mask, block_sz, use_harris_detector, k) comp = cv.GComputation(cv.GIn(g_in), cv.GOut(g_out)) pkg = cv.gapi_wip_kernels( (custom_goodFeaturesToTrack, 'org.opencv.imgproc.feature.goodFeaturesToTrack')) actual = comp.apply(cv.gin(in_mat), args=cv.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_custom_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 = cv.gapi.addC(g_in, g_sc) comp = cv.GComputation(cv.GIn(g_in, g_sc), cv.GOut(g_out)) pkg = cv.gapi_wip_kernels((custom_addC, 'org.opencv.core.math.addC')) actual = comp.apply(cv.gin(in_mat, sc), args=cv.compile_args(pkg)) self.assertEqual(0.0, cv.norm(expected, actual, cv.NORM_INF))
def test_custom_add(self): sz = (3, 3) in_mat1 = np.full(sz, 45, dtype=np.uint8) in_mat2 = np.full(sz, 50, dtype=np.uint8) # OpenCV expected = cv.add(in_mat1, in_mat2) # G-API g_in1 = cv.GMat() g_in2 = cv.GMat() g_out = cv.gapi.add(g_in1, g_in2) comp = cv.GComputation(cv.GIn(g_in1, g_in2), cv.GOut(g_out)) pkg = cv.gapi_wip_kernels((custom_add, 'org.opencv.core.math.add')) 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_custom_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) pkg = cv.gapi_wip_kernels((custom_mean, 'org.opencv.core.math.mean')) actual = comp.apply(cv.gin(in_mat), args=cv.compile_args(pkg)) # Comparison self.assertEqual(expected, actual)
def test_custom_op_split3(self): sz = (4, 4) in_ch1 = np.full(sz, 1, dtype=np.uint8) in_ch2 = np.full(sz, 2, dtype=np.uint8) in_ch3 = np.full(sz, 3, dtype=np.uint8) # H x W x C in_mat = np.stack((in_ch1, in_ch2, in_ch3), axis=2) # G-API g_in = cv.GMat() g_ch1, g_ch2, g_ch3 = split3(g_in) comp = cv.GComputation(cv.GIn(g_in), cv.GOut(g_ch1, g_ch2, g_ch3)) pkg = cv.gapi_wip_kernels((custom_split3, 'custom.split3')) ch1, ch2, ch3 = comp.apply(cv.gin(in_mat), args=cv.compile_args(pkg)) self.assertEqual(0.0, cv.norm(in_ch1, ch1, cv.NORM_INF)) self.assertEqual(0.0, cv.norm(in_ch2, ch2, cv.NORM_INF)) self.assertEqual(0.0, cv.norm(in_ch3, ch3, cv.NORM_INF))