def prepare_net(self): inputs = cv2.GInferInputs() g_inputs = [] for input_name in self.inputs: if input_name in self.const_inputs: continue g_in = cv2.GMat() inputs.setInput(input_name, g_in) g_inputs.append(g_in) outputs = cv2.gapi.infer("net", inputs) g_outputs = [outputs.at(out_name) for out_name in self.output_names] self.comp = cv2.GComputation(cv2.GIn(*g_inputs), cv2.GOut(*g_outputs)) args = ['net', str(self.model)] if self.weights is not None: args.append(str(self.weights)) args.append(self.device.upper()) if self.backend == 'ie': pp = cv2.gapi.ie.params(*args) else: pp = cv2.gapi.mx.params('net', str(self.model)) for input_name, value in self._const_inputs.items(): pp.constInput(input_name, value) if self.backend == 'ie': self.network_args = compile_args(cv2.gapi.networks(pp)) else: mvcmd_file = os.environ.get('MVCMD_FILE', '') self.network_args = compile_args( cv2.gapi.networks(pp), cv2.gapi_mx_mvcmdFile(mvcmd_file) )
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_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 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) dnn_age, dnn_gender = net.forward(net.getUnconnectedOutLayersNames()) # 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) 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 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.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))
for i in range(size): for st in eyesl[i]: out_l_st += [1 if st[0] < st[1] else 0] for st in eyesr[i]: out_r_st += [1 if st[0] < st[1] else 0] return out_l_st, out_r_st if __name__ == '__main__': ARGUMENTS = build_argparser().parse_args() # ------------------------Demo's graph------------------------ g_in = cv.GMat() # Detect faces face_inputs = cv.GInferInputs() face_inputs.setInput('data', g_in) face_outputs = cv.gapi.infer('face-detection', face_inputs) faces = face_outputs.at('detection_out') # Parse faces sz = cv.gapi.streaming.size(g_in) faces_rc = cv.gapi.parseSSD(faces, sz, 0.5, False, False) # Detect poses head_inputs = cv.GInferInputs() head_inputs.setInput('data', g_in) face_outputs = cv.gapi.infer('head-pose', faces_rc, head_inputs) angles_y = face_outputs.at('angle_y_fc') angles_p = face_outputs.at('angle_p_fc') angles_r = face_outputs.at('angle_r_fc')