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G-API: Integration branch for ONNX & Python-related changes #23597 # Changes overview ## 1. Expose ONNX backend's Normalization and Mean-value parameters in Python * Since Python G-API bindings rely on `Generic` infer to express Inference, the `Generic` specialization of `onnx::Params` was extended with new methods to control normalization (`/255`) and mean-value; these methods were exposed in the Python bindings * Found some questionable parts in the existing API which I'd like to review/discuss (see comments) UPD: 1. Thanks to @TolyaTalamanov normalization inconsistencies have been identified with `squeezenet1.0-9` ONNX model itself; tests using these model were updated to DISABLE normalization and NOT using mean/value. 2. Questionable parts were removed and tests still pass. ### Details (taken from @TolyaTalamanov's comment): `squeezenet1.0.*onnx` - doesn't require scaling to [0,1] and mean/std because the weights of the first convolution already scaled. ONNX documentation is broken. So the correct approach to use this models is: 1. ONNX: apply preprocessing from the documentation: https://github.com/onnx/models/blob/main/vision/classification/imagenet_preprocess.py#L8-L44 but without normalization step: ``` # DON'T DO IT: # mean_vec = np.array([0.485, 0.456, 0.406]) # stddev_vec = np.array([0.229, 0.224, 0.225]) # norm_img_data = np.zeros(img_data.shape).astype('float32') # for i in range(img_data.shape[0]): # norm_img_data[i,:,:] = (img_data[i,:,:]/255 - mean_vec[i]) / stddev_vec[i] # # add batch channel # norm_img_data = norm_img_data.reshape(1, 3, 224, 224).astype('float32') # return norm_img_data # INSTEAD return img_data.reshape(1, 3, 224, 224) ``` 2. G-API: Convert image from BGR to RGB and then pass to `apply` as-is with configuring parameters: ``` net = cv.gapi.onnx.params('squeezenet', model_filename) net.cfgNormalize('data_0', False) ``` **Note**: Results might be difference because `G-API` doesn't apply central crop but just do resize to model resolution. --- `squeezenet1.1.*onnx` - requires scaling to [0,1] and mean/std - onnx documentation is correct. 1. ONNX: apply preprocessing from the documentation: https://github.com/onnx/models/blob/main/vision/classification/imagenet_preprocess.py#L8-L44 2. G-API: Convert image from BGR to RGB and then pass to `apply` as-is with configuring parameters: ``` net = cv.gapi.onnx.params('squeezenet', model_filename) net.cfgNormalize('data_0', True) // default net.cfgMeanStd('data_0', [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ``` **Note**: Results might be difference because `G-API` doesn't apply central crop but just do resize to model resolution. ## 2. Expose Fluid & kernel package-related functionality in Python * `cv::gapi::combine()` * `cv::GKernelPackage::size()` (mainly for testing purposes) * `cv::gapi::imgproc::fluid::kernels()` Added a test for the above. ## 3. Fixed issues with Python stateful kernel handling Fixed error message when `outMeta()` of custom python operation fails. ## 4. Fixed various issues in Python tests 1. `test_gapi_streaming.py` - fixed behavior of Desync test to avoid sporadic issues 2. `test_gapi_infer_onnx.py` - fixed model lookup (it was still using the ONNX Zoo layout but was NOT using the proper env var we use to point to one). ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake
217 lines
6.4 KiB
Python
217 lines
6.4 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 CounterState:
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def __init__(self):
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self.counter = 0
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@cv.gapi.op('stateful_counter',
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in_types=[cv.GOpaque.Int],
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out_types=[cv.GOpaque.Int])
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class GStatefulCounter:
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"""Accumulates state counter on every call"""
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@staticmethod
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def outMeta(desc):
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return cv.empty_gopaque_desc()
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@cv.gapi.kernel(GStatefulCounter)
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class GStatefulCounterImpl:
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"""Implementation for GStatefulCounter operation."""
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@staticmethod
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def setup(desc):
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return CounterState()
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@staticmethod
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def run(value, state):
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state.counter += value
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return state.counter
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class SumState:
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def __init__(self):
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self.sum = 0
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@cv.gapi.op('stateful_sum',
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in_types=[cv.GOpaque.Int, cv.GOpaque.Int],
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out_types=[cv.GOpaque.Int])
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class GStatefulSum:
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"""Accumulates sum on every call"""
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@staticmethod
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def outMeta(lhs_desc, rhs_desc):
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return cv.empty_gopaque_desc()
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class gapi_sample_pipelines(NewOpenCVTests):
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def test_stateful_kernel_single_instance(self):
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g_in = cv.GOpaque.Int()
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g_out = GStatefulCounter.on(g_in)
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comp = cv.GComputation(cv.GIn(g_in), cv.GOut(g_out))
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pkg = cv.gapi.kernels(GStatefulCounterImpl)
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nums = [i for i in range(10)]
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acc = 0
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for v in nums:
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acc = comp.apply(cv.gin(v), args=cv.gapi.compile_args(pkg))
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self.assertEqual(sum(nums), acc)
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def test_stateful_kernel_multiple_instances(self):
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# NB: Every counter has his own independent state.
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g_in = cv.GOpaque.Int()
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g_out0 = GStatefulCounter.on(g_in)
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g_out1 = GStatefulCounter.on(g_in)
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comp = cv.GComputation(cv.GIn(g_in), cv.GOut(g_out0, g_out1))
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pkg = cv.gapi.kernels(GStatefulCounterImpl)
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nums = [i for i in range(10)]
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acc0 = acc1 = 0
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for v in nums:
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acc0, acc1 = comp.apply(cv.gin(v), args=cv.gapi.compile_args(pkg))
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ref = sum(nums)
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self.assertEqual(ref, acc0)
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self.assertEqual(ref, acc1)
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def test_stateful_throw_setup(self):
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@cv.gapi.kernel(GStatefulCounter)
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class GThrowStatefulCounterImpl:
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"""Implementation for GStatefulCounter operation
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that throw exception in setup method"""
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@staticmethod
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def setup(desc):
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raise Exception('Throw from setup method')
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@staticmethod
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def run(value, state):
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raise Exception('Unreachable')
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g_in = cv.GOpaque.Int()
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g_out = GStatefulCounter.on(g_in)
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comp = cv.GComputation(cv.GIn(g_in), cv.GOut(g_out))
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pkg = cv.gapi.kernels(GThrowStatefulCounterImpl)
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with self.assertRaises(Exception): comp.apply(cv.gin(42),
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args=cv.gapi.compile_args(pkg))
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def test_stateful_reset(self):
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g_in = cv.GOpaque.Int()
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g_out = GStatefulCounter.on(g_in)
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comp = cv.GComputation(cv.GIn(g_in), cv.GOut(g_out))
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pkg = cv.gapi.kernels(GStatefulCounterImpl)
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cc = comp.compileStreaming(args=cv.gapi.compile_args(pkg))
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cc.setSource(cv.gin(1))
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cc.start()
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for i in range(1, 10):
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_, actual = cc.pull()
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self.assertEqual(i, actual)
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cc.stop()
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cc.setSource(cv.gin(2))
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cc.start()
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for i in range(2, 10, 2):
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_, actual = cc.pull()
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self.assertEqual(i, actual)
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cc.stop()
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def test_stateful_multiple_inputs(self):
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@cv.gapi.kernel(GStatefulSum)
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class GStatefulSumImpl:
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"""Implementation for GStatefulCounter operation."""
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@staticmethod
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def setup(lhs_desc, rhs_desc):
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return SumState()
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@staticmethod
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def run(lhs, rhs, state):
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state.sum+= lhs + rhs
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return state.sum
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g_in1 = cv.GOpaque.Int()
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g_in2 = cv.GOpaque.Int()
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g_out = GStatefulSum.on(g_in1, g_in2)
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comp = cv.GComputation(cv.GIn(g_in1, g_in2), cv.GOut(g_out))
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pkg = cv.gapi.kernels(GStatefulSumImpl)
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lhs_list = [1, 10, 15]
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rhs_list = [2, 14, 32]
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ref_out = 0
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for lhs, rhs in zip(lhs_list, rhs_list):
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ref_out += lhs + rhs
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gapi_out = comp.apply(cv.gin(lhs, rhs), cv.gapi.compile_args(pkg))
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self.assertEqual(ref_out, gapi_out)
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def test_stateful_multiple_inputs_throw(self):
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@cv.gapi.kernel(GStatefulSum)
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class GStatefulSumImplIncorrect:
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"""Incorrect implementation for GStatefulCounter operation."""
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# NB: setup methods is intentionally
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# incorrect - accepts one meta arg instead of two
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@staticmethod
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def setup(desc):
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return SumState()
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@staticmethod
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def run(lhs, rhs, state):
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state.sum+= lhs + rhs
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return state.sum
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g_in1 = cv.GOpaque.Int()
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g_in2 = cv.GOpaque.Int()
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g_out = GStatefulSum.on(g_in1, g_in2)
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comp = cv.GComputation(cv.GIn(g_in1, g_in2), cv.GOut(g_out))
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pkg = cv.gapi.kernels(GStatefulSumImplIncorrect)
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with self.assertRaises(Exception): comp.apply(cv.gin(42, 42),
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args=cv.gapi.compile_args(pkg))
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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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