Merge pull request #19322 from TolyaTalamanov:at/python-callbacks

[G-API] Introduce cv.gin/cv.descr_of for python

* Implement cv.gin/cv.descr_of

* Fix macos build

* Fix gcomputation tests

* Add test

* Add using to a void exceeded length for windows build

* Add using to a void exceeded length for windows build

* Fix comments to review

* Fix comments to review

* Update from latest master

* Avoid graph compilation to obtain in/out info

* Fix indentation

* Fix comments to review

* Avoid using default in switches

* Post output meta for giebackend

modules/gapi/misc/python/test/test_gapi_infer.py

@@ -49,8 +49,6 @@ class test_gapi_infer(NewOpenCVTests):
         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)
 
-        nets = cv.gapi.networks(pp)
-        args = cv.compile_args(nets)
         gapi_age, gapi_gender = comp.apply(cv.gin(img), args=cv.compile_args(cv.gapi.networks(pp)))
 
         # Check
@@ -58,5 +56,64 @@ class test_gapi_infer(NewOpenCVTests):
         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.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))
+
+
 if __name__ == '__main__':
     NewOpenCVTests.bootstrap()