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183 lines
7.1 KiB
Python
183 lines
7.1 KiB
Python
#!/usr/bin/env python
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import os
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import cv2 as cv
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import numpy as np
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from tests_common import NewOpenCVTests, unittest
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def normAssert(test, a, b, lInf=1e-5):
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test.assertLess(np.max(np.abs(a - b)), lInf)
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def inter_area(box1, box2):
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x_min, x_max = max(box1[0], box2[0]), min(box1[2], box2[2])
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y_min, y_max = max(box1[1], box2[1]), min(box1[3], box2[3])
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return (x_max - x_min) * (y_max - y_min)
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def area(box):
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return (box[2] - box[0]) * (box[3] - box[1])
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def box2str(box):
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left, top = box[0], box[1]
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width, height = box[2] - left, box[3] - top
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return '[%f x %f from (%f, %f)]' % (width, height, left, top)
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def normAssertDetections(test, ref, out, confThreshold=0.0, scores_diff=1e-5, boxes_iou_diff=1e-4):
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ref = np.array(ref, np.float32)
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refClassIds, testClassIds = ref[:, 1], out[:, 1]
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refScores, testScores = ref[:, 2], out[:, 2]
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refBoxes, testBoxes = ref[:, 3:], out[:, 3:]
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matchedRefBoxes = [False] * len(refBoxes)
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errMsg = ''
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for i in range(len(refBoxes)):
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testScore = testScores[i]
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if testScore < confThreshold:
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continue
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testClassId, testBox = testClassIds[i], testBoxes[i]
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matched = False
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for j in range(len(refBoxes)):
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if (not matchedRefBoxes[j]) and testClassId == refClassIds[j] and \
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abs(testScore - refScores[j]) < scores_diff:
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interArea = inter_area(testBox, refBoxes[j])
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iou = interArea / (area(testBox) + area(refBoxes[j]) - interArea)
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if abs(iou - 1.0) < boxes_iou_diff:
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matched = True
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matchedRefBoxes[j] = True
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if not matched:
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errMsg += '\nUnmatched prediction: class %d score %f box %s' % (testClassId, testScore, box2str(testBox))
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for i in range(len(refBoxes)):
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if (not matchedRefBoxes[i]) and refScores[i] > confThreshold:
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errMsg += '\nUnmatched reference: class %d score %f box %s' % (refClassIds[i], refScores[i], box2str(refBoxes[i]))
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if errMsg:
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test.fail(errMsg)
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# Returns a simple one-layer network created from Caffe's format
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def getSimpleNet():
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prototxt = """
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name: "simpleNet"
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input: "data"
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layer {
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type: "Identity"
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name: "testLayer"
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top: "testLayer"
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bottom: "data"
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}
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"""
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return cv.dnn.readNetFromCaffe(bytearray(prototxt, 'utf8'))
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def testBackendAndTarget(backend, target):
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net = getSimpleNet()
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net.setPreferableBackend(backend)
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net.setPreferableTarget(target)
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inp = np.random.standard_normal([1, 2, 3, 4]).astype(np.float32)
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try:
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net.setInput(inp)
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net.forward()
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except BaseException as e:
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return False
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return True
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haveInfEngine = testBackendAndTarget(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_CPU)
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dnnBackendsAndTargets = [
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[cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_TARGET_CPU],
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]
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if haveInfEngine:
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dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_CPU])
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if testBackendAndTarget(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_MYRIAD):
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dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_MYRIAD])
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if cv.ocl.haveOpenCL() and cv.ocl.useOpenCL():
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dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_TARGET_OPENCL])
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dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_TARGET_OPENCL_FP16])
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if haveInfEngine and cv.ocl_Device.getDefault().isIntel():
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dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_OPENCL])
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dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_OPENCL_FP16])
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def printParams(backend, target):
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backendNames = {
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cv.dnn.DNN_BACKEND_OPENCV: 'OCV',
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cv.dnn.DNN_BACKEND_INFERENCE_ENGINE: 'DLIE'
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}
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targetNames = {
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cv.dnn.DNN_TARGET_CPU: 'CPU',
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cv.dnn.DNN_TARGET_OPENCL: 'OCL',
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cv.dnn.DNN_TARGET_OPENCL_FP16: 'OCL_FP16',
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cv.dnn.DNN_TARGET_MYRIAD: 'MYRIAD'
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}
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print('%s/%s' % (backendNames[backend], targetNames[target]))
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class dnn_test(NewOpenCVTests):
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def find_dnn_file(self, filename, required=True):
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return self.find_file(filename, [os.environ.get('OPENCV_DNN_TEST_DATA_PATH', os.getcwd())], required=required)
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def test_blobFromImage(self):
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np.random.seed(324)
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width = 6
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height = 7
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scale = 1.0/127.5
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mean = (10, 20, 30)
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# Test arguments names.
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img = np.random.randint(0, 255, [4, 5, 3]).astype(np.uint8)
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blob = cv.dnn.blobFromImage(img, scale, (width, height), mean, True, False)
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blob_args = cv.dnn.blobFromImage(img, scalefactor=scale, size=(width, height),
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mean=mean, swapRB=True, crop=False)
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normAssert(self, blob, blob_args)
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# Test values.
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target = cv.resize(img, (width, height), interpolation=cv.INTER_LINEAR)
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target = target.astype(np.float32)
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target = target[:,:,[2, 1, 0]] # BGR2RGB
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target[:,:,0] -= mean[0]
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target[:,:,1] -= mean[1]
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target[:,:,2] -= mean[2]
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target *= scale
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target = target.transpose(2, 0, 1).reshape(1, 3, height, width) # to NCHW
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normAssert(self, blob, target)
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def test_face_detection(self):
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testdata_required = bool(os.environ.get('OPENCV_DNN_TEST_REQUIRE_TESTDATA', False))
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proto = self.find_dnn_file('dnn/opencv_face_detector.prototxt2', required=testdata_required)
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model = self.find_dnn_file('dnn/opencv_face_detector.caffemodel', required=testdata_required)
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if proto is None or model is None:
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raise unittest.SkipTest("Missing DNN test files (dnn/opencv_face_detector.{prototxt/caffemodel}). Verify OPENCV_DNN_TEST_DATA_PATH configuration parameter.")
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img = self.get_sample('gpu/lbpcascade/er.png')
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blob = cv.dnn.blobFromImage(img, mean=(104, 177, 123), swapRB=False, crop=False)
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ref = [[0, 1, 0.99520785, 0.80997437, 0.16379407, 0.87996572, 0.26685631],
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[0, 1, 0.9934696, 0.2831718, 0.50738752, 0.345781, 0.5985168],
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[0, 1, 0.99096733, 0.13629119, 0.24892329, 0.19756334, 0.3310290],
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[0, 1, 0.98977017, 0.23901358, 0.09084064, 0.29902688, 0.1769477],
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[0, 1, 0.97203469, 0.67965847, 0.06876482, 0.73999709, 0.1513494],
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[0, 1, 0.95097077, 0.51901293, 0.45863652, 0.5777427, 0.5347801]]
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print('\n')
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for backend, target in dnnBackendsAndTargets:
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printParams(backend, target)
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net = cv.dnn.readNet(proto, model)
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net.setPreferableBackend(backend)
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net.setPreferableTarget(target)
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net.setInput(blob)
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out = net.forward().reshape(-1, 7)
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scoresDiff = 4e-3 if target in [cv.dnn.DNN_TARGET_OPENCL_FP16, cv.dnn.DNN_TARGET_MYRIAD] else 1e-5
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iouDiff = 2e-2 if target in [cv.dnn.DNN_TARGET_OPENCL_FP16, cv.dnn.DNN_TARGET_MYRIAD] else 1e-4
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normAssertDetections(self, ref, out, 0.5, scoresDiff, iouDiff)
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if __name__ == '__main__':
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NewOpenCVTests.bootstrap()
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