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#!/usr/bin/env python
import numpy as np
import cv2 as cv
import os
from tests_common import NewOpenCVTests
# Plaidml is an optional backend
pkgs = [
('ocl' , cv.gapi.core.ocl.kernels()),
('cpu' , cv.gapi.core.cpu.kernels()),
('fluid' , cv.gapi.core.fluid.kernels())
# ('plaidml', cv.gapi.core.plaidml.kernels())
]
class gapi_core_test(NewOpenCVTests):
def test_add(self):
# TODO: Extend to use any type and size here
sz = (720, 1280)
in1 = np.full(sz, 100)
in2 = np.full(sz, 50)
# OpenCV
expected = cv.add(in1, in2)
# 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))
for pkg_name, pkg in pkgs:
actual = comp.apply(cv.gin(in1, in2), args=cv.compile_args(pkg))
# Comparison
self.assertEqual(0.0, cv.norm(expected, actual, cv.NORM_INF),
'Failed on ' + pkg_name + ' backend')
self.assertEqual(expected.dtype, actual.dtype, 'Failed on ' + pkg_name + ' backend')
def test_add_uint8(self):
sz = (720, 1280)
in1 = np.full(sz, 100, dtype=np.uint8)
in2 = np.full(sz, 50 , dtype=np.uint8)
# OpenCV
expected = cv.add(in1, in2)
# 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))
for pkg_name, pkg in pkgs:
actual = comp.apply(cv.gin(in1, in2), args=cv.compile_args(pkg))
# Comparison
self.assertEqual(0.0, cv.norm(expected, actual, cv.NORM_INF),
'Failed on ' + pkg_name + ' backend')
self.assertEqual(expected.dtype, actual.dtype, 'Failed on ' + pkg_name + ' backend')
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):
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.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_name, 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),
'Failed on ' + pkg_name + ' backend')
self.assertEqual(e.dtype, a.dtype, 'Failed on ' + pkg_name + ' backend')
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')
if __name__ == '__main__':
NewOpenCVTests.bootstrap()
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