mirror of
https://github.com/opencv/opencv.git
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53eca2ff5b
G-API: Support vaargs for cv.compile_args * Support cv.compile_args to work with variadic number of inputs * Disable python2.x G-API * Move compile_args to gapi pkg
342 lines
14 KiB
Python
342 lines
14 KiB
Python
#!/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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class test_gapi_infer(NewOpenCVTests):
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def infer_reference_network(self, model_path, weights_path, img):
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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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return net.forward(net.getUnconnectedOutLayersNames())
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def make_roi(self, img, roi):
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return img[roi[1]:roi[1] + roi[3], roi[0]:roi[0] + roi[2], ...]
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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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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.resize(cv.imread(img_path), (62,62))
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# OpenCV DNN
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dnn_age, dnn_gender = self.infer_reference_network(model_path, weights_path, img)
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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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gapi_age, gapi_gender = comp.apply(cv.gin(img), args=cv.gapi.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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def test_age_gender_infer_roi(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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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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roi = (10, 10, 62, 62)
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# OpenCV DNN
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dnn_age, dnn_gender = self.infer_reference_network(model_path,
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weights_path,
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self.make_roi(img, roi))
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# OpenCV G-API
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g_in = cv.GMat()
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g_roi = cv.GOpaqueT(cv.gapi.CV_RECT)
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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", g_roi, 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, g_roi), 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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gapi_age, gapi_gender = comp.apply(cv.gin(img, roi), args=cv.gapi.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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def test_age_gender_infer_roi_list(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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device_id = 'CPU'
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rois = [(10, 15, 62, 62), (23, 50, 62, 62), (14, 100, 62, 62), (80, 50, 62, 62)]
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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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# OpenCV DNN
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dnn_age_list = []
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dnn_gender_list = []
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for roi in rois:
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age, gender = self.infer_reference_network(model_path,
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weights_path,
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self.make_roi(img, roi))
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dnn_age_list.append(age)
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dnn_gender_list.append(gender)
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# OpenCV G-API
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g_in = cv.GMat()
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g_rois = cv.GArrayT(cv.gapi.CV_RECT)
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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", g_rois, 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, g_rois), 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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gapi_age_list, gapi_gender_list = comp.apply(cv.gin(img, rois),
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args=cv.gapi.compile_args(cv.gapi.networks(pp)))
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# Check
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for gapi_age, gapi_gender, dnn_age, dnn_gender in zip(gapi_age_list,
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gapi_gender_list,
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dnn_age_list,
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dnn_gender_list):
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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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def test_age_gender_infer2_roi(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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device_id = 'CPU'
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rois = [(10, 15, 62, 62), (23, 50, 62, 62), (14, 100, 62, 62), (80, 50, 62, 62)]
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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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# OpenCV DNN
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dnn_age_list = []
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dnn_gender_list = []
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for roi in rois:
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age, gender = self.infer_reference_network(model_path,
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weights_path,
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self.make_roi(img, roi))
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dnn_age_list.append(age)
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dnn_gender_list.append(gender)
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# OpenCV G-API
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g_in = cv.GMat()
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g_rois = cv.GArrayT(cv.gapi.CV_RECT)
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inputs = cv.GInferListInputs()
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inputs.setInput('data', g_rois)
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outputs = cv.gapi.infer2("net", g_in, 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, g_rois), 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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gapi_age_list, gapi_gender_list = comp.apply(cv.gin(img, rois),
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args=cv.gapi.compile_args(cv.gapi.networks(pp)))
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# Check
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for gapi_age, gapi_gender, dnn_age, dnn_gender in zip(gapi_age_list,
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gapi_gender_list,
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dnn_age_list,
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dnn_gender_list):
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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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def test_person_detection_retail_0013(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/person-detection-retail-0013/FP32/person-detection-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('gpu/lbpcascade/er.png', [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), (544, 320))
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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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def parseSSD(detections, size):
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h, w = size
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bboxes = []
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detections = detections.reshape(-1, 7)
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for sample_id, class_id, confidence, xmin, ymin, xmax, ymax in detections:
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if confidence >= 0.5:
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x = int(xmin * w)
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y = int(ymin * h)
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width = int(xmax * w - x)
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height = int(ymax * h - y)
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bboxes.append((x, y, width, height))
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return bboxes
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net.setInput(blob)
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dnn_detections = net.forward()
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dnn_boxes = parseSSD(np.array(dnn_detections), img.shape[:2])
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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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g_sz = cv.gapi.streaming.size(g_in)
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outputs = cv.gapi.infer("net", inputs)
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detections = outputs.at("detection_out")
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bboxes = cv.gapi.parseSSD(detections, g_sz, 0.5, False, False)
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comp = cv.GComputation(cv.GIn(g_in), cv.GOut(bboxes))
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pp = cv.gapi.ie.params("net", model_path, weights_path, device_id)
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gapi_boxes = comp.apply(cv.gin(img.astype(np.float32)),
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args=cv.gapi.compile_args(cv.gapi.networks(pp)))
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# Comparison
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self.assertEqual(0.0, cv.norm(np.array(dnn_boxes).flatten(),
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np.array(gapi_boxes).flatten(),
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cv.NORM_INF))
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def test_person_detection_retail_0013(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/person-detection-retail-0013/FP32/person-detection-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('gpu/lbpcascade/er.png', [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), (544, 320))
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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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def parseSSD(detections, size):
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h, w = size
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bboxes = []
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detections = detections.reshape(-1, 7)
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for sample_id, class_id, confidence, xmin, ymin, xmax, ymax in detections:
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if confidence >= 0.5:
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x = int(xmin * w)
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y = int(ymin * h)
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width = int(xmax * w - x)
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height = int(ymax * h - y)
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bboxes.append((x, y, width, height))
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return bboxes
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net.setInput(blob)
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dnn_detections = net.forward()
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dnn_boxes = parseSSD(np.array(dnn_detections), img.shape[:2])
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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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g_sz = cv.gapi.streaming.size(g_in)
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outputs = cv.gapi.infer("net", inputs)
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detections = outputs.at("detection_out")
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bboxes = cv.gapi.parseSSD(detections, g_sz, 0.5, False, False)
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comp = cv.GComputation(cv.GIn(g_in), cv.GOut(bboxes))
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pp = cv.gapi.ie.params("net", model_path, weights_path, device_id)
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gapi_boxes = comp.apply(cv.gin(img.astype(np.float32)),
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args=cv.gapi.compile_args(cv.gapi.networks(pp)))
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# Comparison
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self.assertEqual(0.0, cv.norm(np.array(dnn_boxes).flatten(),
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np.array(gapi_boxes).flatten(),
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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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