#!/usr/bin/env python import os import cv2 as cv import numpy as np from tests_common import NewOpenCVTests, unittest def normAssert(test, a, b, msg=None, lInf=1e-5): test.assertLess(np.max(np.abs(a - b)), lInf, msg) def inter_area(box1, box2): x_min, x_max = max(box1[0], box2[0]), min(box1[2], box2[2]) y_min, y_max = max(box1[1], box2[1]), min(box1[3], box2[3]) return (x_max - x_min) * (y_max - y_min) def area(box): return (box[2] - box[0]) * (box[3] - box[1]) def box2str(box): left, top = box[0], box[1] width, height = box[2] - left, box[3] - top return '[%f x %f from (%f, %f)]' % (width, height, left, top) def normAssertDetections(test, refClassIds, refScores, refBoxes, testClassIds, testScores, testBoxes, confThreshold=0.0, scores_diff=1e-5, boxes_iou_diff=1e-4): matchedRefBoxes = [False] * len(refBoxes) errMsg = '' for i in range(len(testBoxes)): testScore = testScores[i] if testScore < confThreshold: continue testClassId, testBox = testClassIds[i], testBoxes[i] matched = False for j in range(len(refBoxes)): if (not matchedRefBoxes[j]) and testClassId == refClassIds[j] and \ abs(testScore - refScores[j]) < scores_diff: interArea = inter_area(testBox, refBoxes[j]) iou = interArea / (area(testBox) + area(refBoxes[j]) - interArea) if abs(iou - 1.0) < boxes_iou_diff: matched = True matchedRefBoxes[j] = True if not matched: errMsg += '\nUnmatched prediction: class %d score %f box %s' % (testClassId, testScore, box2str(testBox)) for i in range(len(refBoxes)): if (not matchedRefBoxes[i]) and refScores[i] > confThreshold: errMsg += '\nUnmatched reference: class %d score %f box %s' % (refClassIds[i], refScores[i], box2str(refBoxes[i])) if errMsg: test.fail(errMsg) def printParams(backend, target): backendNames = { cv.dnn.DNN_BACKEND_OPENCV: 'OCV', cv.dnn.DNN_BACKEND_INFERENCE_ENGINE: 'DLIE' } targetNames = { cv.dnn.DNN_TARGET_CPU: 'CPU', cv.dnn.DNN_TARGET_OPENCL: 'OCL', cv.dnn.DNN_TARGET_OPENCL_FP16: 'OCL_FP16', cv.dnn.DNN_TARGET_MYRIAD: 'MYRIAD' } print('%s/%s' % (backendNames[backend], targetNames[target])) def getDefaultThreshold(target): if target == cv.dnn.DNN_TARGET_OPENCL_FP16 or target == cv.dnn.DNN_TARGET_MYRIAD: return 4e-3 else: return 1e-5 testdata_required = bool(os.environ.get('OPENCV_DNN_TEST_REQUIRE_TESTDATA', False)) g_dnnBackendsAndTargets = None class dnn_test(NewOpenCVTests): def setUp(self): super(dnn_test, self).setUp() global g_dnnBackendsAndTargets if g_dnnBackendsAndTargets is None: g_dnnBackendsAndTargets = self.initBackendsAndTargets() self.dnnBackendsAndTargets = g_dnnBackendsAndTargets def initBackendsAndTargets(self): self.dnnBackendsAndTargets = [ [cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_TARGET_CPU], ] if self.checkIETarget(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_CPU): self.dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_CPU]) if self.checkIETarget(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_MYRIAD): self.dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_MYRIAD]) if cv.ocl.haveOpenCL() and cv.ocl.useOpenCL(): self.dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_TARGET_OPENCL]) self.dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_TARGET_OPENCL_FP16]) if cv.ocl_Device.getDefault().isIntel(): if self.checkIETarget(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_OPENCL): self.dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_OPENCL]) if self.checkIETarget(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_OPENCL_FP16): self.dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_OPENCL_FP16]) return self.dnnBackendsAndTargets def find_dnn_file(self, filename, required=True): if not required: required = testdata_required return self.find_file(filename, [os.environ.get('OPENCV_DNN_TEST_DATA_PATH', os.getcwd()), os.environ['OPENCV_TEST_DATA_PATH']], required=required) def checkIETarget(self, backend, target): proto = self.find_dnn_file('dnn/layers/layer_convolution.prototxt') model = self.find_dnn_file('dnn/layers/layer_convolution.caffemodel') net = cv.dnn.readNet(proto, model) net.setPreferableBackend(backend) net.setPreferableTarget(target) inp = np.random.standard_normal([1, 2, 10, 11]).astype(np.float32) try: net.setInput(inp) net.forward() except BaseException as e: return False return True def test_getAvailableTargets(self): targets = cv.dnn.getAvailableTargets(cv.dnn.DNN_BACKEND_OPENCV) self.assertTrue(cv.dnn.DNN_TARGET_CPU in targets) def test_blobFromImage(self): np.random.seed(324) width = 6 height = 7 scale = 1.0/127.5 mean = (10, 20, 30) # Test arguments names. img = np.random.randint(0, 255, [4, 5, 3]).astype(np.uint8) blob = cv.dnn.blobFromImage(img, scale, (width, height), mean, True, False) blob_args = cv.dnn.blobFromImage(img, scalefactor=scale, size=(width, height), mean=mean, swapRB=True, crop=False) normAssert(self, blob, blob_args) # Test values. target = cv.resize(img, (width, height), interpolation=cv.INTER_LINEAR) target = target.astype(np.float32) target = target[:,:,[2, 1, 0]] # BGR2RGB target[:,:,0] -= mean[0] target[:,:,1] -= mean[1] target[:,:,2] -= mean[2] target *= scale target = target.transpose(2, 0, 1).reshape(1, 3, height, width) # to NCHW normAssert(self, blob, target) def test_model(self): img_path = self.find_dnn_file("dnn/street.png") weights = self.find_dnn_file("dnn/MobileNetSSD_deploy.caffemodel", required=False) config = self.find_dnn_file("dnn/MobileNetSSD_deploy.prototxt", required=False) if weights is None or config is None: raise unittest.SkipTest("Missing DNN test files (dnn/MobileNetSSD_deploy.{prototxt/caffemodel}). Verify OPENCV_DNN_TEST_DATA_PATH configuration parameter.") frame = cv.imread(img_path) model = cv.dnn_DetectionModel(weights, config) model.setInputParams(size=(300, 300), mean=(127.5, 127.5, 127.5), scale=1.0/127.5) iouDiff = 0.05 confThreshold = 0.0001 nmsThreshold = 0 scoreDiff = 1e-3 classIds, confidences, boxes = model.detect(frame, confThreshold, nmsThreshold) refClassIds = (7, 15) refConfidences = (0.9998, 0.8793) refBoxes = ((328, 238, 85, 102), (101, 188, 34, 138)) normAssertDetections(self, refClassIds, refConfidences, refBoxes, classIds, confidences, boxes,confThreshold, scoreDiff, iouDiff) for box in boxes: cv.rectangle(frame, box, (0, 255, 0)) cv.rectangle(frame, np.array(box), (0, 255, 0)) cv.rectangle(frame, tuple(box), (0, 255, 0)) cv.rectangle(frame, list(box), (0, 255, 0)) def test_classification_model(self): img_path = self.find_dnn_file("dnn/googlenet_0.png") weights = self.find_dnn_file("dnn/squeezenet_v1.1.caffemodel", required=False) config = self.find_dnn_file("dnn/squeezenet_v1.1.prototxt") ref = np.load(self.find_dnn_file("dnn/squeezenet_v1.1_prob.npy")) if weights is None or config is None: raise unittest.SkipTest("Missing DNN test files (dnn/squeezenet_v1.1.{prototxt/caffemodel}). Verify OPENCV_DNN_TEST_DATA_PATH configuration parameter.") frame = cv.imread(img_path) model = cv.dnn_ClassificationModel(config, weights) model.setInputSize(227, 227) model.setInputCrop(True) out = model.predict(frame) normAssert(self, out, ref) def test_textdetection_model(self): img_path = self.find_dnn_file("dnn/text_det_test1.png") weights = self.find_dnn_file("dnn/onnx/models/DB_TD500_resnet50.onnx", required=False) if weights is None: raise unittest.SkipTest("Missing DNN test files (onnx/models/DB_TD500_resnet50.onnx). Verify OPENCV_DNN_TEST_DATA_PATH configuration parameter.") frame = cv.imread(img_path) scale = 1.0 / 255.0 size = (736, 736) mean = (122.67891434, 116.66876762, 104.00698793) model = cv.dnn_TextDetectionModel_DB(weights) model.setInputParams(scale, size, mean) out, _ = model.detect(frame) self.assertTrue(type(out) == tuple, msg='actual type {}'.format(str(type(out)))) self.assertTrue(np.array(out).shape == (2, 4, 2)) 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', required=False) 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) ref = [[0, 1, 0.99520785, 0.80997437, 0.16379407, 0.87996572, 0.26685631], [0, 1, 0.9934696, 0.2831718, 0.50738752, 0.345781, 0.5985168], [0, 1, 0.99096733, 0.13629119, 0.24892329, 0.19756334, 0.3310290], [0, 1, 0.98977017, 0.23901358, 0.09084064, 0.29902688, 0.1769477], [0, 1, 0.97203469, 0.67965847, 0.06876482, 0.73999709, 0.1513494], [0, 1, 0.95097077, 0.51901293, 0.45863652, 0.5777427, 0.5347801]] print('\n') for backend, target in self.dnnBackendsAndTargets: printParams(backend, target) net = cv.dnn.readNet(proto, model) net.setPreferableBackend(backend) net.setPreferableTarget(target) net.setInput(blob) out = net.forward().reshape(-1, 7) scoresDiff = 4e-3 if target in [cv.dnn.DNN_TARGET_OPENCL_FP16, cv.dnn.DNN_TARGET_MYRIAD] else 1e-5 iouDiff = 2e-2 if target in [cv.dnn.DNN_TARGET_OPENCL_FP16, cv.dnn.DNN_TARGET_MYRIAD] else 1e-4 ref = np.array(ref, np.float32) refClassIds, testClassIds = ref[:, 1], out[:, 1] refScores, testScores = ref[:, 2], out[:, 2] refBoxes, testBoxes = ref[:, 3:], out[:, 3:] normAssertDetections(self, refClassIds, refScores, refBoxes, testClassIds, testScores, testBoxes, 0.5, scoresDiff, iouDiff) def test_async(self): timeout = 10*1000*10**6 # in nanoseconds (10 sec) proto = self.find_dnn_file('dnn/layers/layer_convolution.prototxt') model = self.find_dnn_file('dnn/layers/layer_convolution.caffemodel') if proto is None or model is None: raise unittest.SkipTest("Missing DNN test files (dnn/layers/layer_convolution.{prototxt/caffemodel}). Verify OPENCV_DNN_TEST_DATA_PATH configuration parameter.") print('\n') for backend, target in self.dnnBackendsAndTargets: if backend != cv.dnn.DNN_BACKEND_INFERENCE_ENGINE: continue printParams(backend, target) netSync = cv.dnn.readNet(proto, model) netSync.setPreferableBackend(backend) netSync.setPreferableTarget(target) netAsync = cv.dnn.readNet(proto, model) netAsync.setPreferableBackend(backend) netAsync.setPreferableTarget(target) # Generate inputs numInputs = 10 inputs = [] for _ in range(numInputs): inputs.append(np.random.standard_normal([2, 6, 75, 113]).astype(np.float32)) # Run synchronously refs = [] for i in range(numInputs): netSync.setInput(inputs[i]) refs.append(netSync.forward()) # Run asynchronously. To make test more robust, process inputs in the reversed order. outs = [] for i in reversed(range(numInputs)): netAsync.setInput(inputs[i]) outs.insert(0, netAsync.forwardAsync()) for i in reversed(range(numInputs)): ret, result = outs[i].get(timeoutNs=float(timeout)) self.assertTrue(ret) normAssert(self, refs[i], result, 'Index: %d' % i, 1e-10) def test_nms(self): confs = (1, 1) rects = ((0, 0, 0.4, 0.4), (0, 0, 0.2, 0.4)) # 0.5 overlap self.assertTrue(all(cv.dnn.NMSBoxes(rects, confs, 0, 0.6).ravel() == (0, 1))) def test_custom_layer(self): class CropLayer(object): def __init__(self, params, blobs): self.xstart = 0 self.xend = 0 self.ystart = 0 self.yend = 0 # Our layer receives two inputs. We need to crop the first input blob # to match a shape of the second one (keeping batch size and number of channels) def getMemoryShapes(self, inputs): inputShape, targetShape = inputs[0], inputs[1] batchSize, numChannels = inputShape[0], inputShape[1] height, width = targetShape[2], targetShape[3] self.ystart = (inputShape[2] - targetShape[2]) // 2 self.xstart = (inputShape[3] - targetShape[3]) // 2 self.yend = self.ystart + height self.xend = self.xstart + width return [[batchSize, numChannels, height, width]] def forward(self, inputs): return [inputs[0][:,:,self.ystart:self.yend,self.xstart:self.xend]] cv.dnn_registerLayer('CropCaffe', CropLayer) proto = ''' name: "TestCrop" input: "input" input_shape { dim: 1 dim: 2 dim: 5 dim: 5 } input: "roi" input_shape { dim: 1 dim: 2 dim: 3 dim: 3 } layer { name: "Crop" type: "CropCaffe" bottom: "input" bottom: "roi" top: "Crop" }''' net = cv.dnn.readNetFromCaffe(bytearray(proto.encode())) for backend, target in self.dnnBackendsAndTargets: if backend != cv.dnn.DNN_BACKEND_OPENCV: continue printParams(backend, target) net.setPreferableBackend(backend) net.setPreferableTarget(target) src_shape = [1, 2, 5, 5] dst_shape = [1, 2, 3, 3] inp = np.arange(0, np.prod(src_shape), dtype=np.float32).reshape(src_shape) roi = np.empty(dst_shape, dtype=np.float32) net.setInput(inp, "input") net.setInput(roi, "roi") out = net.forward() ref = inp[:, :, 1:4, 1:4] normAssert(self, out, ref) cv.dnn_unregisterLayer('CropCaffe') # check that dnn module can work with 3D tensor as input for network def test_input_3d(self): model = self.find_dnn_file('dnn/onnx/models/hidden_lstm.onnx') input_file = self.find_dnn_file('dnn/onnx/data/input_hidden_lstm.npy') output_file = self.find_dnn_file('dnn/onnx/data/output_hidden_lstm.npy') if model is None: raise unittest.SkipTest("Missing DNN test files (dnn/onnx/models/hidden_lstm.onnx). " "Verify OPENCV_DNN_TEST_DATA_PATH configuration parameter.") if input_file is None or output_file is None: raise unittest.SkipTest("Missing DNN test files (dnn/onnx/data/{input/output}_hidden_lstm.npy). " "Verify OPENCV_DNN_TEST_DATA_PATH configuration parameter.") net = cv.dnn.readNet(model) input = np.load(input_file) # we have to expand the shape of input tensor because Python bindings cut 3D tensors to 2D # it should be fixed in future. see : https://github.com/opencv/opencv/issues/19091 # please remove `expand_dims` after that input = np.expand_dims(input, axis=3) gold_output = np.load(output_file) net.setInput(input) for backend, target in self.dnnBackendsAndTargets: printParams(backend, target) net.setPreferableBackend(backend) net.setPreferableTarget(target) real_output = net.forward() normAssert(self, real_output, gold_output, "", getDefaultThreshold(target)) if __name__ == '__main__': NewOpenCVTests.bootstrap()