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Merge pull request #19318 from TolyaTalamanov:at/python-generic-infer

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[G-API] Python ROI generic inference

* Python generic infer overloads

* Move wrappers to appropriate file
This commit is contained in:
Anatoliy Talamanov 2021-03-25 19:55:29 +03:00 committed by GitHub
parent b62d015285
commit 0753408e10
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GPG Key ID: 4AEE18F83AFDEB23
6 changed files with 272 additions and 34 deletions
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5
modules/gapi/include/opencv2/gapi/infer.hpp
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@ -636,11 +636,6 @@ infer2(const std::string& tag,
return cv::GInferListOutputs{std::move(call)};
}
GAPI_EXPORTS_W inline cv::GInferOutputs infer(const String& name, const cv::GInferInputs& inputs)
{
return infer<Generic>(name, inputs);
}
} // namespace gapi
} // namespace cv

13
modules/gapi/misc/python/pyopencv_gapi.hpp
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@ -34,6 +34,7 @@ using GArray_Size = cv::GArray<cv::Size>;
using GArray_Rect = cv::GArray<cv::Rect>;
using GArray_Scalar = cv::GArray<cv::Scalar>;
using GArray_Mat = cv::GArray<cv::Mat>;
using GArray_GMat = cv::GArray<cv::GMat>;
// FIXME: Python wrapper generate code without namespace std,
// so it cause error: "string wasn't declared"
@ -58,7 +59,7 @@ bool pyopencv_to(PyObject* obj, GRunArgs& value, const ArgInfo& info)
return pyopencv_to_generic_vec(obj, value, info);
}
template <>
template<>
PyObject* pyopencv_from(const cv::detail::OpaqueRef& o)
{
switch (o.getKind())
@ -201,6 +202,7 @@ static PyObject* extract_proto_args(PyObject* py_args, PyObject* kw)
GProtoArgs args;
Py_ssize_t size = PyTuple_Size(py_args);
args.reserve(size);
for (int i = 0; i < size; ++i)
{
PyObject* item = PyTuple_GetItem(py_args, i);
@ -318,12 +320,9 @@ static cv::GRunArg extract_run_arg(const cv::GTypeInfo& info, PyObject* item)
reinterpret_cast<pyopencv_gapi_wip_IStreamSource_t*>(item)->v;
return source;
}
else
{
cv::Mat obj;
pyopencv_to_with_check(item, obj, "Failed to obtain cv::Mat");
return obj;
}
cv::Mat obj;
pyopencv_to_with_check(item, obj, "Failed to obtain cv::Mat");
return obj;
}
case cv::GShape::GSCALAR:
{

26
modules/gapi/misc/python/python_bridge.hpp
View File

@ -68,6 +68,32 @@ enum ArgType {
CV_GMAT,
};
GAPI_EXPORTS_W inline cv::GInferOutputs infer(const String& name, const cv::GInferInputs& inputs)
{
return infer<Generic>(name, inputs);
}
GAPI_EXPORTS_W inline GInferOutputs infer(const std::string& name,
const cv::GOpaque<cv::Rect>& roi,
const GInferInputs& inputs)
{
return infer<Generic>(name, roi, inputs);
}
GAPI_EXPORTS_W inline GInferListOutputs infer(const std::string& name,
const cv::GArray<cv::Rect>& rois,
const GInferInputs& inputs)
{
return infer<Generic>(name, rois, inputs);
}
GAPI_EXPORTS_W inline GInferListOutputs infer2(const std::string& name,
const cv::GMat in,
const GInferListInputs& inputs)
{
return infer2<Generic>(name, in, inputs);
}
} // namespace gapi
namespace detail {

15
modules/gapi/misc/python/shadow_gapi.hpp
View File

@ -27,6 +27,14 @@ namespace cv
GAPI_WRAP void setInput(const std::string& name, const cv::GFrame& value);
};
class GAPI_EXPORTS_W_SIMPLE GInferListInputs
{
public:
GAPI_WRAP GInferListInputs();
GAPI_WRAP void setInput(const std::string& name, const cv::GArray<cv::GMat>& value);
GAPI_WRAP void setInput(const std::string& name, const cv::GArray<cv::Rect>& value);
};
class GAPI_EXPORTS_W_SIMPLE GInferOutputs
{
public:
@ -34,6 +42,13 @@ namespace cv
GAPI_WRAP cv::GMat at(const std::string& name);
};
class GAPI_EXPORTS_W_SIMPLE GInferListOutputs
{
public:
GAPI_WRAP GInferListOutputs();
GAPI_WRAP cv::GArray<cv::GMat> at(const std::string& name);
};
namespace detail
{
struct GAPI_EXPORTS_W_SIMPLE ExtractArgsCallback { };

5
modules/gapi/misc/python/test/test_gapi_core.py
View File

@ -13,7 +13,7 @@ pkgs = [
('cpu' , cv.gapi.core.cpu.kernels()),
('fluid' , cv.gapi.core.fluid.kernels())
# ('plaidml', cv.gapi.core.plaidml.kernels())
]
]
class gapi_core_test(NewOpenCVTests):
@ -127,7 +127,6 @@ class gapi_core_test(NewOpenCVTests):
self.assertEqual(expected_thresh, actual_thresh[0],
'Failed on ' + pkg_name + ' backend')
def test_kmeans(self):
# K-means params
count = 100
@ -154,10 +153,12 @@ class gapi_core_test(NewOpenCVTests):
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

242
modules/gapi/misc/python/test/test_gapi_infer.py
View File

@ -9,25 +9,7 @@ from tests_common import NewOpenCVTests
class test_gapi_infer(NewOpenCVTests):
def test_getAvailableTargets(self):
targets = cv.dnn.getAvailableTargets(cv.dnn.DNN_BACKEND_OPENCV)
self.assertTrue(cv.dnn.DNN_TARGET_CPU in targets)
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
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)
@ -35,7 +17,28 @@ class test_gapi_infer(NewOpenCVTests):
blob = cv.dnn.blobFromImage(img)
net.setInput(blob)
dnn_age, dnn_gender = net.forward(net.getUnconnectedOutLayersNames())
return net.forward(net.getUnconnectedOutLayersNames())
def make_roi(self, img, roi):
return img[roi[1]:roi[1] + roi[3], roi[0]:roi[0] + roi[2], ...]
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'
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)
# OpenCV G-API
g_in = cv.GMat()
@ -56,6 +59,205 @@ class test_gapi_infer(NewOpenCVTests):
self.assertEqual(0.0, cv.norm(dnn_age, gapi_age, cv.NORM_INF))
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
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.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))
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):
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_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):