chore: import upstream snapshot with attribution
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wehub-resource-sync
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#!/usr/bin/env python
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import numpy as np
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import cv2 as cv
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import os
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import sys
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import unittest
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from tests_common import NewOpenCVTests
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try:
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if sys.version_info[:2] < (3, 0):
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raise unittest.SkipTest('Python 2.x is not supported')
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openvino_is_available = True
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try:
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from openvino.runtime import Core, Type, Layout, PartialShape
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from openvino.preprocess import ResizeAlgorithm, PrePostProcessor
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except ImportError:
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openvino_is_available = False
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def skip_if_openvino_not_available():
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if not openvino_is_available:
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raise unittest.SkipTest("OpenVINO isn't available from python.")
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class AgeGenderOV:
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def __init__(self, model_path, bin_path, device):
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self.device = device
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self.core = Core()
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self.model = self.core.read_model(model_path, bin_path)
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def reshape(self, new_shape):
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self.model.reshape(new_shape)
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def cfgPrePostProcessing(self, pp_callback):
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ppp = PrePostProcessor(self.model)
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pp_callback(ppp)
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self.model = ppp.build()
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def apply(self, in_data):
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compiled_model = self.core.compile_model(self.model, self.device)
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infer_request = compiled_model.create_infer_request()
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results = infer_request.infer(in_data)
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ov_age = results['age_conv3'].squeeze()
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ov_gender = results['prob'].squeeze()
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return ov_age, ov_gender
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class AgeGenderGAPI:
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tag = 'age-gender-net'
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def __init__(self, model_path, bin_path, device):
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g_in = cv.GMat()
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inputs = cv.GInferInputs()
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inputs.setInput('data', g_in)
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# TODO: It'd be nice to pass dict instead.
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# E.g cv.gapi.infer("net", {'data': g_in})
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outputs = cv.gapi.infer(AgeGenderGAPI.tag, inputs)
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age_g = outputs.at("age_conv3")
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gender_g = outputs.at("prob")
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self.comp = cv.GComputation(cv.GIn(g_in), cv.GOut(age_g, gender_g))
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self.pp = cv.gapi.ov.params(AgeGenderGAPI.tag, \
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model_path, bin_path, device)
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def apply(self, in_data):
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compile_args = cv.gapi.compile_args(cv.gapi.networks(self.pp))
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gapi_age, gapi_gender = self.comp.apply(cv.gin(in_data), compile_args)
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gapi_gender = gapi_gender.squeeze()
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gapi_age = gapi_age.squeeze()
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return gapi_age, gapi_gender
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class test_gapi_infer_ov(NewOpenCVTests):
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def test_age_gender_infer_image(self):
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skip_if_openvino_not_available()
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root_path = '/omz_intel_models/intel/age-gender-recognition-retail-0013/FP32/age-gender-recognition-retail-0013'
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model_path = self.find_file(root_path + '.xml', [os.environ.get('OPENCV_DNN_TEST_DATA_PATH')], required=False)
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bin_path = self.find_file(root_path + '.bin', [os.environ.get('OPENCV_DNN_TEST_DATA_PATH')], required=False)
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device_id = 'CPU'
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img_path = self.find_file('cv/face/david2.jpg', [os.environ.get('OPENCV_TEST_DATA_PATH')])
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img = cv.imread(img_path)
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# OpenVINO
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def preproc(ppp):
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ppp.input().model().set_layout(Layout("NCHW"))
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ppp.input().tensor().set_element_type(Type.u8) \
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.set_spatial_static_shape(img.shape[0], img.shape[1]) \
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.set_layout(Layout("NHWC"))
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ppp.input().preprocess().resize(ResizeAlgorithm.RESIZE_LINEAR)
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ref = AgeGenderOV(model_path, bin_path, device_id)
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ref.cfgPrePostProcessing(preproc)
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ov_age, ov_gender = ref.apply(np.expand_dims(img, 0))
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# OpenCV G-API (No preproc required)
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comp = AgeGenderGAPI(model_path, bin_path, device_id)
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gapi_age, gapi_gender = comp.apply(img)
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# Check
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self.assertEqual(0.0, cv.norm(ov_gender, gapi_gender, cv.NORM_INF))
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self.assertEqual(0.0, cv.norm(ov_age, gapi_age, cv.NORM_INF))
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def test_age_gender_infer_tensor(self):
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skip_if_openvino_not_available()
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root_path = '/omz_intel_models/intel/age-gender-recognition-retail-0013/FP32/age-gender-recognition-retail-0013'
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model_path = self.find_file(root_path + '.xml', [os.environ.get('OPENCV_DNN_TEST_DATA_PATH')], required=False)
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bin_path = self.find_file(root_path + '.bin', [os.environ.get('OPENCV_DNN_TEST_DATA_PATH')], required=False)
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device_id = 'CPU'
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img_path = self.find_file('cv/face/david2.jpg', [os.environ.get('OPENCV_TEST_DATA_PATH')])
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img = cv.imread(img_path)
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# Prepare data manually
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tensor = cv.resize(img, (62, 62)).astype(np.float32)
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tensor = np.transpose(tensor, (2, 0, 1))
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tensor = np.expand_dims(tensor, 0)
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# OpenVINO (No preproce required)
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ref = AgeGenderOV(model_path, bin_path, device_id)
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ov_age, ov_gender = ref.apply(tensor)
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# OpenCV G-API (No preproc required)
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comp = AgeGenderGAPI(model_path, bin_path, device_id)
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gapi_age, gapi_gender = comp.apply(tensor)
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# Check
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self.assertEqual(0.0, cv.norm(ov_gender, gapi_gender, cv.NORM_INF))
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self.assertEqual(0.0, cv.norm(ov_age, gapi_age, cv.NORM_INF))
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def test_age_gender_infer_batch(self):
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skip_if_openvino_not_available()
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root_path = '/omz_intel_models/intel/age-gender-recognition-retail-0013/FP32/age-gender-recognition-retail-0013'
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model_path = self.find_file(root_path + '.xml', [os.environ.get('OPENCV_DNN_TEST_DATA_PATH')], required=False)
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bin_path = self.find_file(root_path + '.bin', [os.environ.get('OPENCV_DNN_TEST_DATA_PATH')], required=False)
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device_id = 'CPU'
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img_path1 = self.find_file('cv/face/david1.jpg', [os.environ.get('OPENCV_TEST_DATA_PATH')])
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img_path2 = self.find_file('cv/face/david2.jpg', [os.environ.get('OPENCV_TEST_DATA_PATH')])
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img1 = cv.imread(img_path1)
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img2 = cv.imread(img_path2)
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# img1 and img2 have the same size
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batch_img = np.array([img1, img2])
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# OpenVINO
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def preproc(ppp):
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ppp.input().model().set_layout(Layout("NCHW"))
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ppp.input().tensor().set_element_type(Type.u8) \
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.set_spatial_static_shape(img1.shape[0], img2.shape[1]) \
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.set_layout(Layout("NHWC"))
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ppp.input().preprocess().resize(ResizeAlgorithm.RESIZE_LINEAR)
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ref = AgeGenderOV(model_path, bin_path, device_id)
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ref.reshape(PartialShape([2, 3, 62, 62]))
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ref.cfgPrePostProcessing(preproc)
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ov_age, ov_gender = ref.apply(batch_img)
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# OpenCV G-API
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comp = AgeGenderGAPI(model_path, bin_path, device_id)
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comp.pp.cfgReshape([2, 3, 62, 62]) \
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.cfgInputModelLayout("NCHW") \
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.cfgInputTensorLayout("NHWC") \
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.cfgResize(cv.INTER_LINEAR)
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gapi_age, gapi_gender = comp.apply(batch_img)
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# Check
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self.assertEqual(0.0, cv.norm(ov_gender, gapi_gender, cv.NORM_INF))
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self.assertEqual(0.0, cv.norm(ov_age, gapi_age, cv.NORM_INF))
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def test_age_gender_infer_planar(self):
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skip_if_openvino_not_available()
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root_path = '/omz_intel_models/intel/age-gender-recognition-retail-0013/FP32/age-gender-recognition-retail-0013'
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model_path = self.find_file(root_path + '.xml', [os.environ.get('OPENCV_DNN_TEST_DATA_PATH')], required=False)
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bin_path = self.find_file(root_path + '.bin', [os.environ.get('OPENCV_DNN_TEST_DATA_PATH')], required=False)
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device_id = 'CPU'
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img_path = self.find_file('cv/face/david2.jpg', [os.environ.get('OPENCV_TEST_DATA_PATH')])
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img = cv.imread(img_path)
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planar_img = np.transpose(img, (2, 0, 1))
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planar_img = np.expand_dims(planar_img, 0)
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# OpenVINO
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def preproc(ppp):
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ppp.input().tensor().set_element_type(Type.u8) \
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.set_spatial_static_shape(img.shape[0], img.shape[1])
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ppp.input().preprocess().resize(ResizeAlgorithm.RESIZE_LINEAR)
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ref = AgeGenderOV(model_path, bin_path, device_id)
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ref.cfgPrePostProcessing(preproc)
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ov_age, ov_gender = ref.apply(planar_img)
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# OpenCV G-API
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comp = AgeGenderGAPI(model_path, bin_path, device_id)
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comp.pp.cfgResize(cv.INTER_LINEAR)
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gapi_age, gapi_gender = comp.apply(planar_img)
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# Check
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self.assertEqual(0.0, cv.norm(ov_gender, gapi_gender, cv.NORM_INF))
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self.assertEqual(0.0, cv.norm(ov_age, gapi_age, cv.NORM_INF))
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except unittest.SkipTest as e:
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message = str(e)
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class TestSkip(unittest.TestCase):
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def setUp(self):
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self.skipTest('Skip tests: ' + message)
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def test_skip():
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pass
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pass
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if __name__ == '__main__':
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NewOpenCVTests.bootstrap()
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