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272 lines
12 KiB
Python
272 lines
12 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, msg=None, lInf=1e-5):
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test.assertLess(np.max(np.abs(a - b)), lInf, msg)
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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, refClassIds, refScores, refBoxes, testClassIds, testScores, testBoxes,
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confThreshold=0.0, scores_diff=1e-5, boxes_iou_diff=1e-4):
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matchedRefBoxes = [False] * len(refBoxes)
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errMsg = ''
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for i in range(len(testBoxes)):
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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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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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testdata_required = bool(os.environ.get('OPENCV_DNN_TEST_REQUIRE_TESTDATA', False))
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class dnn_test(NewOpenCVTests):
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def setUp(self):
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super(dnn_test, self).setUp()
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self.dnnBackendsAndTargets = [
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[cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_TARGET_CPU],
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]
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if self.checkIETarget(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_CPU):
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self.dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_CPU])
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if self.checkIETarget(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_MYRIAD):
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self.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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self.dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_TARGET_OPENCL])
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self.dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_TARGET_OPENCL_FP16])
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if cv.ocl_Device.getDefault().isIntel():
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if self.checkIETarget(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_OPENCL):
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self.dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_OPENCL])
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if self.checkIETarget(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_OPENCL_FP16):
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self.dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_OPENCL_FP16])
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def find_dnn_file(self, filename, required=True):
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if not required:
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required = testdata_required
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return self.find_file(filename, [os.environ.get('OPENCV_DNN_TEST_DATA_PATH', os.getcwd()),
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os.environ['OPENCV_TEST_DATA_PATH']],
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required=required)
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def checkIETarget(self, backend, target):
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proto = self.find_dnn_file('dnn/layers/layer_convolution.prototxt')
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model = self.find_dnn_file('dnn/layers/layer_convolution.caffemodel')
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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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inp = np.random.standard_normal([1, 2, 10, 11]).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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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_model(self):
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img_path = self.find_dnn_file("dnn/street.png")
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weights = self.find_dnn_file("dnn/MobileNetSSD_deploy.caffemodel", required=False)
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config = self.find_dnn_file("dnn/MobileNetSSD_deploy.prototxt", required=False)
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if weights is None or config is None:
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raise unittest.SkipTest("Missing DNN test files (dnn/MobileNetSSD_deploy.{prototxt/caffemodel}). Verify OPENCV_DNN_TEST_DATA_PATH configuration parameter.")
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frame = cv.imread(img_path)
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model = cv.dnn_DetectionModel(weights, config)
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model.setInputParams(size=(300, 300), mean=(127.5, 127.5, 127.5), scale=1.0/127.5)
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iouDiff = 0.05
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confThreshold = 0.0001
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nmsThreshold = 0
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scoreDiff = 1e-3
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classIds, confidences, boxes = model.detect(frame, confThreshold, nmsThreshold)
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refClassIds = (7, 15)
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refConfidences = (0.9998, 0.8793)
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refBoxes = ((328, 238, 85, 102), (101, 188, 34, 138))
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normAssertDetections(self, refClassIds, refConfidences, refBoxes,
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classIds, confidences, boxes,confThreshold, scoreDiff, iouDiff)
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for box in boxes:
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cv.rectangle(frame, box, (0, 255, 0))
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cv.rectangle(frame, np.array(box), (0, 255, 0))
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cv.rectangle(frame, tuple(box), (0, 255, 0))
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cv.rectangle(frame, list(box), (0, 255, 0))
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def test_classification_model(self):
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img_path = self.find_dnn_file("dnn/googlenet_0.png")
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weights = self.find_dnn_file("dnn/squeezenet_v1.1.caffemodel", required=False)
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config = self.find_dnn_file("dnn/squeezenet_v1.1.prototxt")
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ref = np.load(self.find_dnn_file("dnn/squeezenet_v1.1_prob.npy"))
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if weights is None or config is None:
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raise unittest.SkipTest("Missing DNN test files (dnn/squeezenet_v1.1.{prototxt/caffemodel}). Verify OPENCV_DNN_TEST_DATA_PATH configuration parameter.")
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frame = cv.imread(img_path)
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model = cv.dnn_ClassificationModel(config, weights)
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model.setInputSize(227, 227)
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model.setInputCrop(True)
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out = model.predict(frame)
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normAssert(self, out, ref)
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def test_face_detection(self):
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proto = self.find_dnn_file('dnn/opencv_face_detector.prototxt')
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model = self.find_dnn_file('dnn/opencv_face_detector.caffemodel', required=False)
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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 self.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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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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normAssertDetections(self, refClassIds, refScores, refBoxes, testClassIds,
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testScores, testBoxes, 0.5, scoresDiff, iouDiff)
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def test_async(self):
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timeout = 10*1000*10**6 # in nanoseconds (10 sec)
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proto = self.find_dnn_file('dnn/layers/layer_convolution.prototxt')
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model = self.find_dnn_file('dnn/layers/layer_convolution.caffemodel')
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if proto is None or model is None:
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raise unittest.SkipTest("Missing DNN test files (dnn/layers/layer_convolution.{prototxt/caffemodel}). Verify OPENCV_DNN_TEST_DATA_PATH configuration parameter.")
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print('\n')
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for backend, target in self.dnnBackendsAndTargets:
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if backend != cv.dnn.DNN_BACKEND_INFERENCE_ENGINE:
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continue
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printParams(backend, target)
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netSync = cv.dnn.readNet(proto, model)
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netSync.setPreferableBackend(backend)
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netSync.setPreferableTarget(target)
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netAsync = cv.dnn.readNet(proto, model)
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netAsync.setPreferableBackend(backend)
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netAsync.setPreferableTarget(target)
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# Generate inputs
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numInputs = 10
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inputs = []
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for _ in range(numInputs):
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inputs.append(np.random.standard_normal([2, 6, 75, 113]).astype(np.float32))
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# Run synchronously
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refs = []
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for i in range(numInputs):
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netSync.setInput(inputs[i])
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refs.append(netSync.forward())
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# Run asynchronously. To make test more robust, process inputs in the reversed order.
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outs = []
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for i in reversed(range(numInputs)):
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netAsync.setInput(inputs[i])
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outs.insert(0, netAsync.forwardAsync())
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for i in reversed(range(numInputs)):
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ret, result = outs[i].get(timeoutNs=float(timeout))
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self.assertTrue(ret)
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normAssert(self, refs[i], result, 'Index: %d' % i, 1e-10)
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if __name__ == '__main__':
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NewOpenCVTests.bootstrap()
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