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3.4: fixes

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This commit is contained in:
Alexander Alekhin
2018-09-17 22:13:01 +00:00
parent 70f38b4dfa
commit 3cab9e7a9c
3 changed files with 16 additions and 8 deletions
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modules/python/test/test_dnn.py
+9 -6
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@@ -3,7 +3,7 @@ import os
import cv2 as cv
import numpy as np
from tests_common import NewOpenCVTests
from tests_common import NewOpenCVTests, unittest
def normAssert(test, a, b, lInf=1e-5):
test.assertLess(np.max(np.abs(a - b)), lInf)
@@ -95,7 +95,7 @@ if haveInfEngine:
if cv.ocl.haveOpenCL() and cv.ocl.useOpenCL():
dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_TARGET_OPENCL])
dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_TARGET_OPENCL_FP16])
if haveInfEngine:
if haveInfEngine: # FIXIT Check Intel iGPU only
dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_OPENCL])
dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_OPENCL_FP16])
@@ -116,8 +116,8 @@ def printParams(backend, target):
class dnn_test(NewOpenCVTests):
def find_dnn_file(self, filename):
return self.find_file(filename, [os.environ['OPENCV_DNN_TEST_DATA_PATH']])
def find_dnn_file(self, filename, required=True):
return self.find_file(filename, [os.environ.get('OPENCV_DNN_TEST_DATA_PATH', os.getcwd())], required=required)
def test_blobFromImage(self):
np.random.seed(324)
@@ -147,8 +147,11 @@ class dnn_test(NewOpenCVTests):
def test_face_detection(self):
proto = self.find_dnn_file('dnn/opencv_face_detector.prototxt')
model = self.find_dnn_file('dnn/opencv_face_detector.caffemodel')
testdata_required = bool(os.environ.get('OPENCV_DNN_TEST_REQUIRE_TESTDATA', False))
proto = self.find_dnn_file('dnn/opencv_face_detector.prototxt2', required=testdata_required)
model = self.find_dnn_file('dnn/opencv_face_detector.caffemodel', required=testdata_required)
if proto is None or model is None:
raise unittest.SkipTest("Missing DNN test files (dnn/opencv_face_detector.{prototxt/caffemodel}). Verify OPENCV_DNN_TEST_DATA_PATH configuration parameter.")
img = self.get_sample('gpu/lbpcascade/er.png')
blob = cv.dnn.blobFromImage(img, mean=(104, 177, 123), swapRB=False, crop=False)