From 0753408e1032915d7a57b5fe533433a2c5962a5c Mon Sep 17 00:00:00 2001
From: Anatoliy Talamanov <anatoliy.talamanov@intel.com>
Date: Thu, 25 Mar 2021 19:55:29 +0300
Subject: [PATCH] Merge pull request #19318 from
TolyaTalamanov:at/python-generic-infer
[G-API] Python ROI generic inference
* Python generic infer overloads
* Move wrappers to appropriate file
---
modules/gapi/include/opencv2/gapi/infer.hpp | 5 -
modules/gapi/misc/python/pyopencv_gapi.hpp | 13 +-
modules/gapi/misc/python/python_bridge.hpp | 26 ++
modules/gapi/misc/python/shadow_gapi.hpp | 15 ++
.../gapi/misc/python/test/test_gapi_core.py | 5 +-
.../gapi/misc/python/test/test_gapi_infer.py | 228 +++++++++++++++++-
6 files changed, 265 insertions(+), 27 deletions(-)
diff --git a/modules/gapi/include/opencv2/gapi/infer.hpp b/modules/gapi/include/opencv2/gapi/infer.hpp
index 68678e883c..6e71f59df9 100644
--- a/modules/gapi/include/opencv2/gapi/infer.hpp
+++ b/modules/gapi/include/opencv2/gapi/infer.hpp
@@ -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
diff --git a/modules/gapi/misc/python/pyopencv_gapi.hpp b/modules/gapi/misc/python/pyopencv_gapi.hpp
index ddf5207c69..850791cb4d 100644
--- a/modules/gapi/misc/python/pyopencv_gapi.hpp
+++ b/modules/gapi/misc/python/pyopencv_gapi.hpp
@@ -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:
{
diff --git a/modules/gapi/misc/python/python_bridge.hpp b/modules/gapi/misc/python/python_bridge.hpp
index 8a5d96bfae..b92553b5dd 100644
--- a/modules/gapi/misc/python/python_bridge.hpp
+++ b/modules/gapi/misc/python/python_bridge.hpp
@@ -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 {
diff --git a/modules/gapi/misc/python/shadow_gapi.hpp b/modules/gapi/misc/python/shadow_gapi.hpp
index 877a7c4c10..038391950d 100644
--- a/modules/gapi/misc/python/shadow_gapi.hpp
+++ b/modules/gapi/misc/python/shadow_gapi.hpp
@@ -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 { };
diff --git a/modules/gapi/misc/python/test/test_gapi_core.py b/modules/gapi/misc/python/test/test_gapi_core.py
index 8227ebdffc..814d05d7cd 100644
--- a/modules/gapi/misc/python/test/test_gapi_core.py
+++ b/modules/gapi/misc/python/test/test_gapi_core.py
@@ -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
diff --git a/modules/gapi/misc/python/test/test_gapi_infer.py b/modules/gapi/misc/python/test/test_gapi_infer.py
index 28ff464632..db048f5786 100644
--- a/modules/gapi/misc/python/test/test_gapi_infer.py
+++ b/modules/gapi/misc/python/test/test_gapi_infer.py
@@ -9,13 +9,145 @@ 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 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)
+
+ blob = cv.dnn.blobFromImage(img)
+
+ net.setInput(blob)
+ 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()
+ 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)
+
+ 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_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
@@ -23,9 +155,59 @@ class test_gapi_infer(NewOpenCVTests):
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))
+
+ 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)
@@ -34,26 +216,46 @@ class test_gapi_infer(NewOpenCVTests):
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_age, dnn_gender = net.forward(net.getUnconnectedOutLayersNames())
+ 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)
- outputs = cv.gapi.infer("net", inputs)
- age_g = outputs.at("age_conv3")
- gender_g = outputs.at("prob")
+ 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(age_g, gender_g))
+ 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)))
- # 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))
+ 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):