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Merge pull request #20196 from TolyaTalamanov:at/support-vaargs-compile-args

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G-API: Support vaargs for cv.compile_args

* Support cv.compile_args to work with variadic number of inputs

* Disable python2.x G-API

* Move compile_args to gapi pkg
pull/20281/head
Anatoliy Talamanov 3 years ago committed by GitHub
parent f30f1afd47
commit 53eca2ff5b
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8 changed files with 802 additions and 689 deletions
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  1. 5
      modules/gapi/misc/python/package/gapi/__init__.py
  2. 9
      modules/gapi/misc/python/shadow_gapi.hpp
  3. 332
      modules/gapi/misc/python/test/test_gapi_core.py
  4. 163
      modules/gapi/misc/python/test/test_gapi_imgproc.py
  5. 580
      modules/gapi/misc/python/test/test_gapi_infer.py
  6. 36
      modules/gapi/misc/python/test/test_gapi_sample_pipelines.py
  7. 314
      modules/gapi/misc/python/test/test_gapi_streaming.py
  8. 52
      modules/gapi/misc/python/test/test_gapi_types.py

5
modules/gapi/misc/python/package/gapi/__init__.py
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@ -11,6 +11,11 @@ def register(mname):
return parameterized
@register('cv2.gapi')
def compile_args(*args):
return list(map(cv.GCompileArg, args))
@register('cv2')
class GOpaque():
# NB: Inheritance from c++ class cause segfault.

9
modules/gapi/misc/python/shadow_gapi.hpp
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@ -3,11 +3,10 @@
namespace cv
{
struct GAPI_EXPORTS_W_SIMPLE GCompileArg { };
GAPI_EXPORTS_W GCompileArgs compile_args(gapi::GKernelPackage pkg);
GAPI_EXPORTS_W GCompileArgs compile_args(gapi::GNetPackage pkg);
GAPI_EXPORTS_W GCompileArgs compile_args(gapi::GKernelPackage kernels, gapi::GNetPackage nets);
struct GAPI_EXPORTS_W_SIMPLE GCompileArg {
GAPI_WRAP GCompileArg(gapi::GKernelPackage pkg);
GAPI_WRAP GCompileArg(gapi::GNetPackage pkg);
};
// NB: This classes doesn't exist in *.so
// HACK: Mark them as a class to force python wrapper generate code for this entities

332
modules/gapi/misc/python/test/test_gapi_core.py
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@ -3,187 +3,209 @@
import numpy as np
import cv2 as cv
import os
import sys
import unittest
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())
]
try:
if sys.version_info[:2] < (3, 0):
raise unittest.SkipTest('Python 2.x is not supported')
class gapi_core_test(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())
]
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)
class gapi_core_test(NewOpenCVTests):
# 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))
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)
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')
# 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.gapi.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.gapi.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)
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)
# 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))
# OpenCV
expected = cv.mean(in_mat)
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')
# 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.gapi.compile_args(pkg))
# Comparison
self.assertEqual(0.0, cv.norm(expected, actual, cv.NORM_INF),
'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)
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.mean(in_mat)
# OpenCV
expected = cv.split(in_mat)
# G-API
g_in = cv.GMat()
g_out = cv.gapi.mean(g_in)
comp = cv.GComputation(g_in, g_out)
# 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
self.assertEqual(0.0, cv.norm(expected, actual, cv.NORM_INF),
'Failed on ' + pkg_name + ' backend')
for pkg_name, pkg in pkgs:
actual = comp.apply(cv.gin(in_mat), args=cv.gapi.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_split3(self):
img_path = self.find_file('cv/face/david2.jpg', [os.environ.get('OPENCV_TEST_DATA_PATH')])
in_mat = cv.imread(img_path)
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 = cv.split(in_mat)
# OpenCV
expected_thresh, expected_mat = cv.threshold(in_mat, maxv[0], maxv[0], cv.THRESH_TRIANGLE)
# 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))
# 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 = 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),
for pkg_name, pkg in pkgs:
actual_mat, actual_thresh = comp.apply(cv.gin(in_mat, maxv), args=cv.gapi.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')
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')
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 generate_random_points(self, sz):
arr = np.random.random(sz).astype(np.float32).T
return list(zip(arr[0], arr[1]))
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_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 generate_random_points(self, sz):
arr = np.random.random(sz).astype(np.float32).T
return list(zip(arr[0], arr[1]))
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))
except unittest.SkipTest as e:
message = str(e)
class TestSkip(unittest.TestCase):
def setUp(self):
self.skipTest('Skip tests: ' + message)
def test_skip():
pass
pass
if __name__ == '__main__':

163
modules/gapi/misc/python/test/test_gapi_imgproc.py
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@ -3,103 +3,124 @@
import numpy as np
import cv2 as cv
import os
import sys
import unittest
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())
]
try:
if sys.version_info[:2] < (3, 0):
raise unittest.SkipTest('Python 2.x is not supported')
class gapi_imgproc_test(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())
]
def test_good_features_to_track(self):
# TODO: Extend to use any type and size here
img_path = self.find_file('cv/face/david2.jpg', [os.environ.get('OPENCV_TEST_DATA_PATH')])
in1 = 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
class gapi_imgproc_test(NewOpenCVTests):
# OpenCV
expected = cv.goodFeaturesToTrack(in1, max_corners, quality_lvl,
min_distance, mask=mask,
blockSize=block_sz, useHarrisDetector=use_harris_detector, k=k)
def test_good_features_to_track(self):
# TODO: Extend to use any type and size here
img_path = self.find_file('cv/face/david2.jpg', [os.environ.get('OPENCV_TEST_DATA_PATH')])
in1 = cv.cvtColor(cv.imread(img_path), cv.COLOR_RGB2GRAY)
# 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)
# 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
comp = cv.GComputation(cv.GIn(g_in), cv.GOut(g_out))
# OpenCV
expected = cv.goodFeaturesToTrack(in1, max_corners, quality_lvl,
min_distance, mask=mask,
blockSize=block_sz, useHarrisDetector=use_harris_detector, k=k)
for pkg_name, pkg in pkgs:
actual = comp.apply(cv.gin(in1), args=cv.compile_args(pkg))
# NB: OpenCV & G-API have different output shapes:
# OpenCV - (num_points, 1, 2)
# G-API - (num_points, 2)
# Comparison
self.assertEqual(0.0, cv.norm(expected.flatten(),
np.array(actual, dtype=np.float32).flatten(),
cv.NORM_INF),
'Failed on ' + pkg_name + ' backend')
# 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))
def test_rgb2gray(self):
# TODO: Extend to use any type and size here
img_path = self.find_file('cv/face/david2.jpg', [os.environ.get('OPENCV_TEST_DATA_PATH')])
in1 = cv.imread(img_path)
for pkg_name, pkg in pkgs:
actual = comp.apply(cv.gin(in1), args=cv.gapi.compile_args(pkg))
# NB: OpenCV & G-API have different output shapes:
# OpenCV - (num_points, 1, 2)
# G-API - (num_points, 2)
# Comparison
self.assertEqual(0.0, cv.norm(expected.flatten(),
np.array(actual, dtype=np.float32).flatten(),
cv.NORM_INF),
'Failed on ' + pkg_name + ' backend')
# OpenCV
expected = cv.cvtColor(in1, cv.COLOR_RGB2GRAY)
# G-API
g_in = cv.GMat()
g_out = cv.gapi.RGB2Gray(g_in)
def test_rgb2gray(self):
# TODO: Extend to use any type and size here
img_path = self.find_file('cv/face/david2.jpg', [os.environ.get('OPENCV_TEST_DATA_PATH')])
in1 = cv.imread(img_path)
comp = cv.GComputation(cv.GIn(g_in), cv.GOut(g_out))
# OpenCV
expected = cv.cvtColor(in1, cv.COLOR_RGB2GRAY)
for pkg_name, pkg in pkgs:
actual = comp.apply(cv.gin(in1), args=cv.compile_args(pkg))
# Comparison
self.assertEqual(0.0, cv.norm(expected, actual, cv.NORM_INF),
'Failed on ' + pkg_name + ' backend')
# G-API
g_in = cv.GMat()
g_out = cv.gapi.RGB2Gray(g_in)
comp = cv.GComputation(cv.GIn(g_in), cv.GOut(g_out))
def test_bounding_rect(self):
sz = 1280
fscale = 256
for pkg_name, pkg in pkgs:
actual = comp.apply(cv.gin(in1), args=cv.gapi.compile_args(pkg))
# Comparison
self.assertEqual(0.0, cv.norm(expected, actual, cv.NORM_INF),
'Failed on ' + pkg_name + ' backend')
def sample_value(fscale):
return np.random.uniform(0, 255 * fscale) / fscale
points = np.array([(sample_value(fscale), sample_value(fscale)) for _ in range(1280)], np.float32)
def test_bounding_rect(self):
sz = 1280
fscale = 256
# OpenCV
expected = cv.boundingRect(points)
def sample_value(fscale):
return np.random.uniform(0, 255 * fscale) / fscale
# G-API
g_in = cv.GMat()
g_out = cv.gapi.boundingRect(g_in)
points = np.array([(sample_value(fscale), sample_value(fscale)) for _ in range(1280)], np.float32)
comp = cv.GComputation(cv.GIn(g_in), cv.GOut(g_out))
# OpenCV
expected = cv.boundingRect(points)
for pkg_name, pkg in pkgs:
actual = comp.apply(cv.gin(points), args=cv.compile_args(pkg))
# Comparison
self.assertEqual(0.0, cv.norm(expected, actual, cv.NORM_INF),
'Failed on ' + pkg_name + ' backend')
# G-API
g_in = cv.GMat()
g_out = cv.gapi.boundingRect(g_in)
comp = cv.GComputation(cv.GIn(g_in), cv.GOut(g_out))
for pkg_name, pkg in pkgs:
actual = comp.apply(cv.gin(points), args=cv.gapi.compile_args(pkg))
# Comparison
self.assertEqual(0.0, cv.norm(expected, actual, cv.NORM_INF),
'Failed on ' + pkg_name + ' backend')
except unittest.SkipTest as e:
message = str(e)
class TestSkip(unittest.TestCase):
def setUp(self):
self.skipTest('Skip tests: ' + message)
def test_skip():
pass
pass
if __name__ == '__main__':

580
modules/gapi/misc/python/test/test_gapi_infer.py
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@ -3,318 +3,338 @@
import numpy as np
import cv2 as cv
import os
import sys
import unittest
from tests_common import NewOpenCVTests
class test_gapi_infer(NewOpenCVTests):
try:
def infer_reference_network(self, model_path, weights_path, img):
net = cv.dnn.readNetFromModelOptimizer(model_path, weights_path)
net.setPreferableBackend(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE)
net.setPreferableTarget(cv.dnn.DNN_TARGET_CPU)
if sys.version_info[:2] < (3, 0):
raise unittest.SkipTest('Python 2.x is not supported')
blob = cv.dnn.blobFromImage(img)
net.setInput(blob)
return net.forward(net.getUnconnectedOutLayersNames())
class test_gapi_infer(NewOpenCVTests):
def infer_reference_network(self, model_path, weights_path, img):
net = cv.dnn.readNetFromModelOptimizer(model_path, weights_path)
net.setPreferableBackend(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE)
net.setPreferableTarget(cv.dnn.DNN_TARGET_CPU)
def make_roi(self, img, roi):
return img[roi[1]:roi[1] + roi[3], roi[0]:roi[0] + roi[2], ...]
blob = cv.dnn.blobFromImage(img)
net.setInput(blob)
return net.forward(net.getUnconnectedOutLayersNames())
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')])
device_id = 'CPU'
def make_roi(self, img, roi):
return img[roi[1]:roi[1] + roi[3], roi[0]:roi[0] + roi[2], ...]
img_path = self.find_file('cv/face/david2.jpg', [os.environ.get('OPENCV_TEST_DATA_PATH')])
img = cv.resize(cv.imread(img_path), (62,62))
# OpenCV DNN
dnn_age, dnn_gender = self.infer_reference_network(model_path, weights_path, img)
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
# OpenCV G-API
g_in = cv.GMat()
inputs = cv.GInferInputs()
inputs.setInput('data', g_in)
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'
outputs = cv.gapi.infer("net", inputs)
age_g = outputs.at("age_conv3")
gender_g = outputs.at("prob")
img_path = self.find_file('cv/face/david2.jpg', [os.environ.get('OPENCV_TEST_DATA_PATH')])
img = cv.resize(cv.imread(img_path), (62,62))
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)
# OpenCV DNN
dnn_age, dnn_gender = self.infer_reference_network(model_path, weights_path, img)
gapi_age, gapi_gender = comp.apply(cv.gin(img), args=cv.compile_args(cv.gapi.networks(pp)))
# OpenCV G-API
g_in = cv.GMat()
inputs = cv.GInferInputs()
inputs.setInput('data', g_in)
# 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))
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)
def test_age_gender_infer_roi(self):
# NB: Check IE
if not cv.dnn.DNN_TARGET_CPU in cv.dnn.getAvailableTargets(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE):
return
gapi_age, gapi_gender = comp.apply(cv.gin(img), args=cv.gapi.compile_args(cv.gapi.networks(pp)))
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'
# 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))
img_path = self.find_file('cv/face/david2.jpg', [os.environ.get('OPENCV_TEST_DATA_PATH')])
img = cv.imread(img_path)
roi = (10, 10, 62, 62)
# OpenCV DNN
dnn_age, dnn_gender = self.infer_reference_network(model_path,
weights_path,
self.make_roi(img, roi))
def test_age_gender_infer_roi(self):
# NB: Check IE
if not cv.dnn.DNN_TARGET_CPU in cv.dnn.getAvailableTargets(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE):
return
# OpenCV G-API
g_in = cv.GMat()
g_roi = cv.GOpaqueT(cv.gapi.CV_RECT)
inputs = cv.GInferInputs()
inputs.setInput('data', g_in)
outputs = cv.gapi.infer("net", g_roi, inputs)
age_g = outputs.at("age_conv3")
gender_g = outputs.at("prob")
comp = cv.GComputation(cv.GIn(g_in, g_roi), cv.GOut(age_g, gender_g))
pp = cv.gapi.ie.params("net", model_path, weights_path, device_id)
gapi_age, gapi_gender = comp.apply(cv.gin(img, roi), 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_age_gender_infer_roi_list(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.GInferInputs()
inputs.setInput('data', g_in)
outputs = cv.gapi.infer("net", g_rois, 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.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))
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'
img_path = self.find_file('cv/face/david2.jpg', [os.environ.get('OPENCV_TEST_DATA_PATH')])
img = cv.imread(img_path)
roi = (10, 10, 62, 62)
# OpenCV DNN
dnn_age, dnn_gender = self.infer_reference_network(model_path,
weights_path,
self.make_roi(img, roi))
# OpenCV G-API
g_in = cv.GMat()
g_roi = cv.GOpaqueT(cv.gapi.CV_RECT)
inputs = cv.GInferInputs()
inputs.setInput('data', g_in)
outputs = cv.gapi.infer("net", g_roi, inputs)
age_g = outputs.at("age_conv3")
gender_g = outputs.at("prob")
comp = cv.GComputation(cv.GIn(g_in, g_roi), cv.GOut(age_g, gender_g))
pp = cv.gapi.ie.params("net", model_path, weights_path, device_id)
gapi_age, gapi_gender = comp.apply(cv.gin(img, roi), args=cv.gapi.compile_args(cv.gapi.networks(pp)))
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.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):
# 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_age_gender_infer_roi_list(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.GInferInputs()
inputs.setInput('data', g_in)
outputs = cv.gapi.infer("net", g_rois, 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_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_person_detection_retail_0013(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/person-detection-retail-0013/FP32/person-detection-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('gpu/lbpcascade/er.png', [os.environ.get('OPENCV_TEST_DATA_PATH')])
device_id = 'CPU'
img = cv.resize(cv.imread(img_path), (544, 320))
# 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)
def parseSSD(detections, size):
h, w = size
bboxes = []
detections = detections.reshape(-1, 7)
for sample_id, class_id, confidence, xmin, ymin, xmax, ymax in detections:
if confidence >= 0.5:
x = int(xmin * w)
y = int(ymin * h)
width = int(xmax * w - x)
height = int(ymax * h - y)
bboxes.append((x, y, width, height))
return bboxes
net.setInput(blob)
dnn_detections = net.forward()
dnn_boxes = parseSSD(np.array(dnn_detections), img.shape[:2])
# OpenCV G-API
g_in = cv.GMat()
inputs = cv.GInferInputs()
inputs.setInput('data', g_in)
g_sz = cv.gapi.streaming.size(g_in)
outputs = cv.gapi.infer("net", inputs)
detections = outputs.at("detection_out")
bboxes = cv.gapi.parseSSD(detections, g_sz, 0.5, False, False)
comp = cv.GComputation(cv.GIn(g_in), cv.GOut(bboxes))
pp = cv.gapi.ie.params("net", model_path, weights_path, device_id)
gapi_boxes = comp.apply(cv.gin(img.astype(np.float32)),
args=cv.gapi.compile_args(cv.gapi.networks(pp)))
# Comparison
self.assertEqual(0.0, cv.norm(np.array(dnn_boxes).flatten(),
np.array(gapi_boxes).flatten(),
cv.NORM_INF))
def test_person_detection_retail_0013(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/person-detection-retail-0013/FP32/person-detection-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('gpu/lbpcascade/er.png', [os.environ.get('OPENCV_TEST_DATA_PATH')])
device_id = 'CPU'
img = cv.resize(cv.imread(img_path), (544, 320))
# 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)
def parseSSD(detections, size):
h, w = size
bboxes = []
detections = detections.reshape(-1, 7)
for sample_id, class_id, confidence, xmin, ymin, xmax, ymax in detections:
if confidence >= 0.5:
x = int(xmin * w)
y = int(ymin * h)
width = int(xmax * w - x)
height = int(ymax * h - y)
bboxes.append((x, y, width, height))
return bboxes
net.setInput(blob)
dnn_detections = net.forward()
dnn_boxes = parseSSD(np.array(dnn_detections), img.shape[:2])
# OpenCV G-API
g_in = cv.GMat()
inputs = cv.GInferInputs()
inputs.setInput('data', g_in)
g_sz = cv.gapi.streaming.size(g_in)
outputs = cv.gapi.infer("net", inputs)
detections = outputs.at("detection_out")
bboxes = cv.gapi.parseSSD(detections, g_sz, 0.5, False, False)
comp = cv.GComputation(cv.GIn(g_in), cv.GOut(bboxes))
pp = cv.gapi.ie.params("net", model_path, weights_path, device_id)
gapi_boxes = comp.apply(cv.gin(img.astype(np.float32)),
args=cv.gapi.compile_args(cv.gapi.networks(pp)))
# Comparison
self.assertEqual(0.0, cv.norm(np.array(dnn_boxes).flatten(),
np.array(gapi_boxes).flatten(),
cv.NORM_INF))
except unittest.SkipTest as e:
message = str(e)
class TestSkip(unittest.TestCase):
def setUp(self):
self.skipTest('Skip tests: ' + message)
def test_skip():
pass
def test_person_detection_retail_0013(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/person-detection-retail-0013/FP32/person-detection-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('gpu/lbpcascade/er.png', [os.environ.get('OPENCV_TEST_DATA_PATH')])
device_id = 'CPU'
img = cv.resize(cv.imread(img_path), (544, 320))
# 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)
def parseSSD(detections, size):
h, w = size
bboxes = []
detections = detections.reshape(-1, 7)
for sample_id, class_id, confidence, xmin, ymin, xmax, ymax in detections:
if confidence >= 0.5:
x = int(xmin * w)
y = int(ymin * h)
width = int(xmax * w - x)
height = int(ymax * h - y)
bboxes.append((x, y, width, height))
return bboxes
net.setInput(blob)
dnn_detections = net.forward()
dnn_boxes = parseSSD(np.array(dnn_detections), img.shape[:2])
# OpenCV G-API
g_in = cv.GMat()
inputs = cv.GInferInputs()
inputs.setInput('data', g_in)
g_sz = cv.gapi.streaming.size(g_in)
outputs = cv.gapi.infer("net", inputs)
detections = outputs.at("detection_out")
bboxes = cv.gapi.parseSSD(detections, g_sz, 0.5, False, False)
comp = cv.GComputation(cv.GIn(g_in), cv.GOut(bboxes))
pp = cv.gapi.ie.params("net", model_path, weights_path, device_id)
gapi_age, gapi_gender = comp.apply(cv.gin(img), args=cv.compile_args(cv.gapi.networks(pp)))
gapi_boxes = comp.apply(cv.gin(img.astype(np.float32)),
args=cv.compile_args(cv.gapi.networks(pp)))
# Comparison
self.assertEqual(0.0, cv.norm(np.array(dnn_boxes).flatten(),
np.array(gapi_boxes).flatten(),
cv.NORM_INF))
def test_person_detection_retail_0013(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/person-detection-retail-0013/FP32/person-detection-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('gpu/lbpcascade/er.png', [os.environ.get('OPENCV_TEST_DATA_PATH')])
device_id = 'CPU'
img = cv.resize(cv.imread(img_path), (544, 320))
# 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)
def parseSSD(detections, size):
h, w = size
bboxes = []
detections = detections.reshape(-1, 7)
for sample_id, class_id, confidence, xmin, ymin, xmax, ymax in detections:
if confidence >= 0.5:
x = int(xmin * w)
y = int(ymin * h)
width = int(xmax * w - x)
height = int(ymax * h - y)
bboxes.append((x, y, width, height))
return bboxes
net.setInput(blob)
dnn_detections = net.forward()
dnn_boxes = parseSSD(np.array(dnn_detections), img.shape[:2])
# OpenCV G-API
g_in = cv.GMat()
inputs = cv.GInferInputs()
inputs.setInput('data', g_in)
g_sz = cv.gapi.streaming.size(g_in)
outputs = cv.gapi.infer("net", inputs)
detections = outputs.at("detection_out")
bboxes = cv.gapi.parseSSD(detections, g_sz, 0.5, False, False)
comp = cv.GComputation(cv.GIn(g_in), cv.GOut(bboxes))
pp = cv.gapi.ie.params("net", model_path, weights_path, device_id)
gapi_boxes = comp.apply(cv.gin(img.astype(np.float32)),
args=cv.compile_args(cv.gapi.networks(pp)))
# Comparison
self.assertEqual(0.0, cv.norm(np.array(dnn_boxes).flatten(),
np.array(gapi_boxes).flatten(),
cv.NORM_INF))
pass
if __name__ == '__main__':

36
modules/gapi/misc/python/test/test_gapi_sample_pipelines.py
Unescape View File

@ -225,7 +225,7 @@ try:
comp = cv.GComputation(cv.GIn(g_in1, g_in2), cv.GOut(g_out))
pkg = cv.gapi.kernels(GAddImpl)
actual = comp.apply(cv.gin(in_mat1, in_mat2), args=cv.compile_args(pkg))
actual = comp.apply(cv.gin(in_mat1, in_mat2), args=cv.gapi.compile_args(pkg))
self.assertEqual(0.0, cv.norm(expected, actual, cv.NORM_INF))
@ -245,7 +245,7 @@ try:
comp = cv.GComputation(cv.GIn(g_in), cv.GOut(g_ch1, g_ch2, g_ch3))
pkg = cv.gapi.kernels(GSplit3Impl)
ch1, ch2, ch3 = comp.apply(cv.gin(in_mat), args=cv.compile_args(pkg))
ch1, ch2, ch3 = comp.apply(cv.gin(in_mat), args=cv.gapi.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))
@ -266,7 +266,7 @@ try:
comp = cv.GComputation(g_in, g_out)
pkg = cv.gapi.kernels(GMeanImpl)
actual = comp.apply(cv.gin(in_mat), args=cv.compile_args(pkg))
actual = comp.apply(cv.gin(in_mat), args=cv.gapi.compile_args(pkg))
# Comparison
self.assertEqual(expected, actual)
@ -287,7 +287,7 @@ try:
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.compile_args(pkg))
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))
@ -305,7 +305,7 @@ try:
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.compile_args(pkg))
actual = comp.apply(cv.gin(in_mat), args=cv.gapi.compile_args(pkg))
self.assertEqual(0.0, cv.norm(expected, actual, cv.NORM_INF))
@ -322,7 +322,7 @@ try:
comp = cv.GComputation(cv.GIn(g_r), cv.GOut(g_sz))
pkg = cv.gapi.kernels(GSizeRImpl)
actual = comp.apply(cv.gin(roi), args=cv.compile_args(pkg))
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))
@ -340,7 +340,7 @@ try:
comp = cv.GComputation(cv.GIn(g_pts), cv.GOut(g_br))
pkg = cv.gapi.kernels(GBoundingRectImpl)
actual = comp.apply(cv.gin(points), args=cv.compile_args(pkg))
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))
@ -371,7 +371,7 @@ try:
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.compile_args(pkg))
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)
@ -453,10 +453,10 @@ try:
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.compile_args(cv.gapi.kernels(GSumImpl)))
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.compile_args(cv.gapi.kernels(GSumImpl)))
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)
@ -488,13 +488,13 @@ try:
'tuple': (42, 42)
}
out = comp.apply(cv.gin(table, 'int'), args=cv.compile_args(cv.gapi.kernels(GLookUpImpl)))
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.compile_args(cv.gapi.kernels(GLookUpImpl)))
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.compile_args(cv.gapi.kernels(GLookUpImpl)))
out = comp.apply(cv.gin(table, 'tuple'), args=cv.gapi.compile_args(cv.gapi.kernels(GLookUpImpl)))
self.assertEqual((42, 42), out)
@ -521,7 +521,7 @@ try:
arr1 = [3, 'str']
out = comp.apply(cv.gin(arr0, arr1),
args=cv.compile_args(cv.gapi.kernels(GConcatImpl)))
args=cv.gapi.compile_args(cv.gapi.kernels(GConcatImpl)))
self.assertEqual(arr0 + arr1, out)
@ -550,7 +550,7 @@ try:
img1 = np.array([1, 2, 3])
with self.assertRaises(Exception): comp.apply(cv.gin(img0, img1),
args=cv.compile_args(
args=cv.gapi.compile_args(
cv.gapi.kernels(GAddImpl)))
@ -577,7 +577,7 @@ try:
img1 = np.array([1, 2, 3])
with self.assertRaises(Exception): comp.apply(cv.gin(img0, img1),
args=cv.compile_args(
args=cv.gapi.compile_args(
cv.gapi.kernels(GAddImpl)))
@ -607,7 +607,7 @@ try:
# FIXME: Cause Bad variant access.
# Need to provide more descriptive error messsage.
with self.assertRaises(Exception): comp.apply(cv.gin(img0, img1),
args=cv.compile_args(
args=cv.gapi.compile_args(
cv.gapi.kernels(GAddImpl)))
def test_pipeline_with_custom_kernels(self):
@ -657,7 +657,7 @@ try:
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(
actual = comp.apply(cv.gin(img), args=cv.gapi.compile_args(
cv.gapi.kernels(GResizeImpl, GTransposeImpl)))
self.assertEqual(0.0, cv.norm(expected, actual, cv.NORM_INF))

314
modules/gapi/misc/python/test/test_gapi_streaming.py
Unescape View File

@ -3,201 +3,225 @@
import numpy as np
import cv2 as cv
import os
import sys
import unittest
from tests_common import NewOpenCVTests
class test_gapi_streaming(NewOpenCVTests):
def test_image_input(self):
sz = (1280, 720)
in_mat = np.random.randint(0, 100, sz).astype(np.uint8)
try:
# OpenCV
expected = cv.medianBlur(in_mat, 3)
if sys.version_info[:2] < (3, 0):
raise unittest.SkipTest('Python 2.x is not supported')
# 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(in_mat))
ccomp.setSource(cv.gin(in_mat))
ccomp.start()
_, actual = ccomp.pull()
class test_gapi_streaming(NewOpenCVTests):
# Assert
self.assertEqual(0.0, cv.norm(expected, actual, 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(in_mat))
ccomp.setSource(cv.gin(in_mat))
ccomp.start()
def test_video_input(self):
ksize = 3
path = self.find_file('cv/video/768x576.avi', [os.environ['OPENCV_TEST_DATA_PATH']])
_, actual = ccomp.pull()
# OpenCV
cap = cv.VideoCapture(path)
# Assert
self.assertEqual(0.0, cv.norm(expected, actual, cv.NORM_INF))
# G-API
g_in = cv.GMat()
g_out = cv.gapi.medianBlur(g_in, ksize)
c = cv.GComputation(g_in, g_out)
ccomp = c.compileStreaming()
source = cv.gapi.wip.make_capture_src(path)
ccomp.setSource(source)
ccomp.start()
def test_video_input(self):
ksize = 3
path = self.find_file('cv/video/768x576.avi', [os.environ['OPENCV_TEST_DATA_PATH']])
# OpenCV
cap = cv.VideoCapture(path)
# Assert
max_num_frames = 10
proc_num_frames = 0
while cap.isOpened():
has_expected, expected = cap.read()
has_actual, actual = ccomp.pull()
# G-API
g_in = cv.GMat()
g_out = cv.gapi.medianBlur(g_in, ksize)
c = cv.GComputation(g_in, g_out)
self.assertEqual(has_expected, has_actual)
ccomp = c.compileStreaming()
source = cv.gapi.wip.make_capture_src(path)
ccomp.setSource(source)
ccomp.start()
if not has_actual:
break
# Assert
max_num_frames = 10
proc_num_frames = 0
while cap.isOpened():
has_expected, expected = cap.read()
has_actual, actual = ccomp.pull()
self.assertEqual(0.0, cv.norm(cv.medianBlur(expected, ksize), actual, cv.NORM_INF))
self.assertEqual(has_expected, has_actual)
proc_num_frames += 1
if proc_num_frames == max_num_frames:
break;
if not has_actual:
break
self.assertEqual(0.0, cv.norm(cv.medianBlur(expected, ksize), actual, cv.NORM_INF))
def test_video_split3(self):
path = self.find_file('cv/video/768x576.avi', [os.environ['OPENCV_TEST_DATA_PATH']])
proc_num_frames += 1
if proc_num_frames == max_num_frames:
break
# OpenCV
cap = cv.VideoCapture(path)
# G-API
g_in = cv.GMat()
b, g, r = cv.gapi.split3(g_in)
c = cv.GComputation(cv.GIn(g_in), cv.GOut(b, g, r))
def test_video_split3(self):
path = self.find_file('cv/video/768x576.avi', [os.environ['OPENCV_TEST_DATA_PATH']])
ccomp = c.compileStreaming()
source = cv.gapi.wip.make_capture_src(path)
ccomp.setSource(source)
ccomp.start()
# OpenCV
cap = cv.VideoCapture(path)
# Assert
max_num_frames = 10
proc_num_frames = 0
while cap.isOpened():
has_expected, frame = cap.read()
has_actual, actual = ccomp.pull()
# G-API
g_in = cv.GMat()
b, g, r = cv.gapi.split3(g_in)
c = cv.GComputation(cv.GIn(g_in), cv.GOut(b, g, r))
self.assertEqual(has_expected, has_actual)
ccomp = c.compileStreaming()
source = cv.gapi.wip.make_capture_src(path)
ccomp.setSource(source)
ccomp.start()
if not has_actual:
break
# Assert
max_num_frames = 10
proc_num_frames = 0
while cap.isOpened():
has_expected, frame = cap.read()
has_actual, actual = ccomp.pull()
expected = cv.split(frame)
for e, a in zip(expected, actual):
self.assertEqual(0.0, cv.norm(e, a, cv.NORM_INF))
self.assertEqual(has_expected, has_actual)
proc_num_frames += 1
if proc_num_frames == max_num_frames:
break;
if not has_actual:
break
expected = cv.split(frame)
for e, a in zip(expected, actual):
self.assertEqual(0.0, cv.norm(e, a, cv.NORM_INF))
def test_video_add(self):
sz = (576, 768, 3)
in_mat = np.random.randint(0, 100, sz).astype(np.uint8)
proc_num_frames += 1
if proc_num_frames == max_num_frames:
break
path = self.find_file('cv/video/768x576.avi', [os.environ['OPENCV_TEST_DATA_PATH']])
# OpenCV
cap = cv.VideoCapture(path)
def test_video_add(self):
sz = (576, 768, 3)
in_mat = np.random.randint(0, 100, sz).astype(np.uint8)
# 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))
path = self.find_file('cv/video/768x576.avi', [os.environ['OPENCV_TEST_DATA_PATH']])
ccomp = c.compileStreaming()
source = cv.gapi.wip.make_capture_src(path)
ccomp.setSource(cv.gin(source, in_mat))
ccomp.start()
# OpenCV
cap = cv.VideoCapture(path)
# Assert
max_num_frames = 10
proc_num_frames = 0
while cap.isOpened():
has_expected, frame = cap.read()
has_actual, actual = ccomp.pull()
# 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))
self.assertEqual(has_expected, has_actual)
ccomp = c.compileStreaming()
source = cv.gapi.wip.make_capture_src(path)
ccomp.setSource(cv.gin(source, in_mat))
ccomp.start()
if not has_actual:
break
# Assert
max_num_frames = 10
proc_num_frames = 0
while cap.isOpened():
has_expected, frame = cap.read()
has_actual, actual = ccomp.pull()
expected = cv.add(frame, in_mat)
self.assertEqual(0.0, cv.norm(expected, actual, cv.NORM_INF))
self.assertEqual(has_expected, has_actual)
proc_num_frames += 1
if proc_num_frames == max_num_frames:
break;
if not has_actual:
break
expected = cv.add(frame, in_mat)
self.assertEqual(0.0, cv.norm(expected, actual, cv.NORM_INF))
def test_video_good_features_to_track(self):
path = self.find_file('cv/video/768x576.avi', [os.environ['OPENCV_TEST_DATA_PATH']])
proc_num_frames += 1
if proc_num_frames == max_num_frames:
break;
# 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
cap = cv.VideoCapture(path)
def test_video_good_features_to_track(self):
path = self.find_file('cv/video/768x576.avi', [os.environ['OPENCV_TEST_DATA_PATH']])
# G-API
g_in = cv.GMat()
g_gray = cv.gapi.RGB2Gray(g_in)
g_out = cv.gapi.goodFeaturesToTrack(g_gray, max_corners, quality_lvl,
min_distance, mask, block_sz, use_harris_detector, k)
# 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
c = cv.GComputation(cv.GIn(g_in), cv.GOut(g_out))
# OpenCV
cap = cv.VideoCapture(path)
ccomp = c.compileStreaming()
source = cv.gapi.wip.make_capture_src(path)
ccomp.setSource(source)
ccomp.start()
# G-API
g_in = cv.GMat()
g_gray = cv.gapi.RGB2Gray(g_in)
g_out = cv.gapi.goodFeaturesToTrack(g_gray, max_corners, quality_lvl,
min_distance, mask, block_sz, use_harris_detector, k)
# Assert
max_num_frames = 10
proc_num_frames = 0
while cap.isOpened():
has_expected, frame = cap.read()
has_actual, actual = ccomp.pull()
c = cv.GComputation(cv.GIn(g_in), cv.GOut(g_out))
self.assertEqual(has_expected, has_actual)
ccomp = c.compileStreaming()
source = cv.gapi.wip.make_capture_src(path)
ccomp.setSource(source)
ccomp.start()
if not has_actual:
break
# Assert
max_num_frames = 10
proc_num_frames = 0
while cap.isOpened():
has_expected, frame = cap.read()
has_actual, actual = ccomp.pull()
self.assertEqual(has_expected, has_actual)
if not has_actual:
break
# OpenCV
frame = cv.cvtColor(frame, cv.COLOR_RGB2GRAY)
expected = cv.goodFeaturesToTrack(frame, max_corners, quality_lvl,
min_distance, mask=mask,
blockSize=block_sz, useHarrisDetector=use_harris_detector, k=k)
for e, a in zip(expected, actual):
# NB: OpenCV & G-API have different output shapes:
# OpenCV - (num_points, 1, 2)
# G-API - (num_points, 2)
self.assertEqual(0.0, cv.norm(e.flatten(),
np.array(a, np.float32).flatten(),
cv.NORM_INF))
proc_num_frames += 1
if proc_num_frames == max_num_frames:
break
except unittest.SkipTest as e:
message = str(e)
class TestSkip(unittest.TestCase):
def setUp(self):
self.skipTest('Skip tests: ' + message)
def test_skip():
pass
pass
# OpenCV
frame = cv.cvtColor(frame, cv.COLOR_RGB2GRAY)
expected = cv.goodFeaturesToTrack(frame, max_corners, quality_lvl,
min_distance, mask=mask,
blockSize=block_sz, useHarrisDetector=use_harris_detector, k=k)
for e, a in zip(expected, actual):
# NB: OpenCV & G-API have different output shapes:
# OpenCV - (num_points, 1, 2)
# G-API - (num_points, 2)
self.assertEqual(0.0, cv.norm(e.flatten(),
np.array(a, np.float32).flatten(),
cv.NORM_INF))
proc_num_frames += 1
if proc_num_frames == max_num_frames:
break;
if __name__ == '__main__':
NewOpenCVTests.bootstrap()

52
modules/gapi/misc/python/test/test_gapi_types.py
Unescape View File

@ -3,29 +3,51 @@
import numpy as np
import cv2 as cv
import os
import sys
import unittest
from tests_common import NewOpenCVTests
class gapi_types_test(NewOpenCVTests):
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]
try:
for t in types:
g_array = cv.GArrayT(t)
self.assertEqual(t, g_array.type())
if sys.version_info[:2] < (3, 0):
raise unittest.SkipTest('Python 2.x is not supported')
class gapi_types_test(NewOpenCVTests):
def test_gopaque_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]
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_opaque = cv.GOpaqueT(t)
self.assertEqual(t, g_opaque.type())
for t in types:
g_array = cv.GArrayT(t)
self.assertEqual(t, g_array.type())
def test_gopaque_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]
for t in types:
g_opaque = cv.GOpaqueT(t)
self.assertEqual(t, g_opaque.type())
except unittest.SkipTest as e:
message = str(e)
class TestSkip(unittest.TestCase):
def setUp(self):
self.skipTest('Skip tests: ' + message)
def test_skip():
pass
pass
if __name__ == '__main__':

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