#!/usr/bin/env python import numpy as np import cv2 as cv import os import sys import unittest from tests_common import NewOpenCVTests try: if sys.version_info[:2] < (3, 0): raise unittest.SkipTest('Python 2.x is not supported') openvino_is_available = True try: from openvino.runtime import Core, Type, Layout, PartialShape from openvino.preprocess import ResizeAlgorithm, PrePostProcessor except ImportError: openvino_is_available = False def skip_if_openvino_not_available(): if not openvino_is_available: raise unittest.SkipTest("OpenVINO isn't available from python.") class AgeGenderOV: def __init__(self, model_path, bin_path, device): self.device = device self.core = Core() self.model = self.core.read_model(model_path, bin_path) def reshape(self, new_shape): self.model.reshape(new_shape) def cfgPrePostProcessing(self, pp_callback): ppp = PrePostProcessor(self.model) pp_callback(ppp) self.model = ppp.build() def apply(self, in_data): compiled_model = self.core.compile_model(self.model, self.device) infer_request = compiled_model.create_infer_request() results = infer_request.infer(in_data) ov_age = results['age_conv3'].squeeze() ov_gender = results['prob'].squeeze() return ov_age, ov_gender class AgeGenderGAPI: tag = 'age-gender-net' def __init__(self, model_path, bin_path, device): g_in = cv.GMat() inputs = cv.GInferInputs() inputs.setInput('data', g_in) # TODO: It'd be nice to pass dict instead. # E.g cv.gapi.infer("net", {'data': g_in}) outputs = cv.gapi.infer(AgeGenderGAPI.tag, inputs) age_g = outputs.at("age_conv3") gender_g = outputs.at("prob") self.comp = cv.GComputation(cv.GIn(g_in), cv.GOut(age_g, gender_g)) self.pp = cv.gapi.ov.params(AgeGenderGAPI.tag, \ model_path, bin_path, device) def apply(self, in_data): compile_args = cv.gapi.compile_args(cv.gapi.networks(self.pp)) gapi_age, gapi_gender = self.comp.apply(cv.gin(in_data), compile_args) gapi_gender = gapi_gender.squeeze() gapi_age = gapi_age.squeeze() return gapi_age, gapi_gender class test_gapi_infer_ov(NewOpenCVTests): def test_age_gender_infer_image(self): skip_if_openvino_not_available() 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')], required=False) bin_path = self.find_file(root_path + '.bin', [os.environ.get('OPENCV_DNN_TEST_DATA_PATH')], required=False) device_id = 'CPU' img_path = self.find_file('cv/face/david2.jpg', [os.environ.get('OPENCV_TEST_DATA_PATH')]) img = cv.imread(img_path) # OpenVINO def preproc(ppp): ppp.input().model().set_layout(Layout("NCHW")) ppp.input().tensor().set_element_type(Type.u8) \ .set_spatial_static_shape(img.shape[0], img.shape[1]) \ .set_layout(Layout("NHWC")) ppp.input().preprocess().resize(ResizeAlgorithm.RESIZE_LINEAR) ref = AgeGenderOV(model_path, bin_path, device_id) ref.cfgPrePostProcessing(preproc) ov_age, ov_gender = ref.apply(np.expand_dims(img, 0)) # OpenCV G-API (No preproc required) comp = AgeGenderGAPI(model_path, bin_path, device_id) gapi_age, gapi_gender = comp.apply(img) # Check self.assertEqual(0.0, cv.norm(ov_gender, gapi_gender, cv.NORM_INF)) self.assertEqual(0.0, cv.norm(ov_age, gapi_age, cv.NORM_INF)) def test_age_gender_infer_tensor(self): skip_if_openvino_not_available() 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')], required=False) bin_path = self.find_file(root_path + '.bin', [os.environ.get('OPENCV_DNN_TEST_DATA_PATH')], required=False) device_id = 'CPU' img_path = self.find_file('cv/face/david2.jpg', [os.environ.get('OPENCV_TEST_DATA_PATH')]) img = cv.imread(img_path) # Prepare data manually tensor = cv.resize(img, (62, 62)).astype(np.float32) tensor = np.transpose(tensor, (2, 0, 1)) tensor = np.expand_dims(tensor, 0) # OpenVINO (No preproce required) ref = AgeGenderOV(model_path, bin_path, device_id) ov_age, ov_gender = ref.apply(tensor) # OpenCV G-API (No preproc required) comp = AgeGenderGAPI(model_path, bin_path, device_id) gapi_age, gapi_gender = comp.apply(tensor) # Check self.assertEqual(0.0, cv.norm(ov_gender, gapi_gender, cv.NORM_INF)) self.assertEqual(0.0, cv.norm(ov_age, gapi_age, cv.NORM_INF)) def test_age_gender_infer_batch(self): skip_if_openvino_not_available() 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')], required=False) bin_path = self.find_file(root_path + '.bin', [os.environ.get('OPENCV_DNN_TEST_DATA_PATH')], required=False) device_id = 'CPU' img_path1 = self.find_file('cv/face/david1.jpg', [os.environ.get('OPENCV_TEST_DATA_PATH')]) img_path2 = self.find_file('cv/face/david2.jpg', [os.environ.get('OPENCV_TEST_DATA_PATH')]) img1 = cv.imread(img_path1) img2 = cv.imread(img_path2) # img1 and img2 have the same size batch_img = np.array([img1, img2]) # OpenVINO def preproc(ppp): ppp.input().model().set_layout(Layout("NCHW")) ppp.input().tensor().set_element_type(Type.u8) \ .set_spatial_static_shape(img1.shape[0], img2.shape[1]) \ .set_layout(Layout("NHWC")) ppp.input().preprocess().resize(ResizeAlgorithm.RESIZE_LINEAR) ref = AgeGenderOV(model_path, bin_path, device_id) ref.reshape(PartialShape([2, 3, 62, 62])) ref.cfgPrePostProcessing(preproc) ov_age, ov_gender = ref.apply(batch_img) # OpenCV G-API comp = AgeGenderGAPI(model_path, bin_path, device_id) comp.pp.cfgReshape([2, 3, 62, 62]) \ .cfgInputModelLayout("NCHW") \ .cfgInputTensorLayout("NHWC") \ .cfgResize(cv.INTER_LINEAR) gapi_age, gapi_gender = comp.apply(batch_img) # Check self.assertEqual(0.0, cv.norm(ov_gender, gapi_gender, cv.NORM_INF)) self.assertEqual(0.0, cv.norm(ov_age, gapi_age, cv.NORM_INF)) def test_age_gender_infer_planar(self): skip_if_openvino_not_available() 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')], required=False) bin_path = self.find_file(root_path + '.bin', [os.environ.get('OPENCV_DNN_TEST_DATA_PATH')], required=False) device_id = 'CPU' img_path = self.find_file('cv/face/david2.jpg', [os.environ.get('OPENCV_TEST_DATA_PATH')]) img = cv.imread(img_path) planar_img = np.transpose(img, (2, 0, 1)) planar_img = np.expand_dims(planar_img, 0) # OpenVINO def preproc(ppp): ppp.input().tensor().set_element_type(Type.u8) \ .set_spatial_static_shape(img.shape[0], img.shape[1]) ppp.input().preprocess().resize(ResizeAlgorithm.RESIZE_LINEAR) ref = AgeGenderOV(model_path, bin_path, device_id) ref.cfgPrePostProcessing(preproc) ov_age, ov_gender = ref.apply(planar_img) # OpenCV G-API comp = AgeGenderGAPI(model_path, bin_path, device_id) comp.pp.cfgResize(cv.INTER_LINEAR) gapi_age, gapi_gender = comp.apply(planar_img) # Check self.assertEqual(0.0, cv.norm(ov_gender, gapi_gender, cv.NORM_INF)) self.assertEqual(0.0, cv.norm(ov_age, gapi_age, 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 pass if __name__ == '__main__': NewOpenCVTests.bootstrap()