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Open Source Computer Vision Library
https://opencv.org/
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62 lines
2.2 KiB
62 lines
2.2 KiB
#!/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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from tests_common import NewOpenCVTests |
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class test_gapi_infer(NewOpenCVTests): |
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def test_getAvailableTargets(self): |
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targets = cv.dnn.getAvailableTargets(cv.dnn.DNN_BACKEND_OPENCV) |
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self.assertTrue(cv.dnn.DNN_TARGET_CPU in targets) |
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def test_age_gender_infer(self): |
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# NB: Check IE |
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if not cv.dnn.DNN_TARGET_CPU in cv.dnn.getAvailableTargets(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE): |
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return |
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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')]) |
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weights_path = self.find_file(root_path + '.bin', [os.environ.get('OPENCV_DNN_TEST_DATA_PATH')]) |
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img_path = self.find_file('cv/face/david2.jpg', [os.environ.get('OPENCV_TEST_DATA_PATH')]) |
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device_id = 'CPU' |
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img = cv.resize(cv.imread(img_path), (62,62)) |
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# OpenCV DNN |
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net = cv.dnn.readNetFromModelOptimizer(model_path, weights_path) |
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net.setPreferableBackend(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE) |
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net.setPreferableTarget(cv.dnn.DNN_TARGET_CPU) |
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blob = cv.dnn.blobFromImage(img) |
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net.setInput(blob) |
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dnn_age, dnn_gender = net.forward(net.getUnconnectedOutLayersNames()) |
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# OpenCV G-API |
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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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outputs = cv.gapi.infer("net", inputs) |
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age_g = outputs.at("age_conv3") |
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gender_g = outputs.at("prob") |
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comp = cv.GComputation(cv.GIn(g_in), cv.GOut(age_g, gender_g)) |
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pp = cv.gapi.ie.params("net", model_path, weights_path, device_id) |
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nets = cv.gapi.networks(pp) |
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args = cv.compile_args(nets) |
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gapi_age, gapi_gender = comp.apply(cv.gin(img), args=cv.compile_args(cv.gapi.networks(pp))) |
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# Check |
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self.assertEqual(0.0, cv.norm(dnn_gender, gapi_gender, cv.NORM_INF)) |
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self.assertEqual(0.0, cv.norm(dnn_age, gapi_age, cv.NORM_INF)) |
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if __name__ == '__main__': |
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NewOpenCVTests.bootstrap()
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