#!/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, lInf=1e-5):
test.assertLess(np.max(np.abs(a - b)), lInf)
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, ref, out, confThreshold=0.0, scores_diff=1e-5, boxes_iou_diff=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:]
matchedRefBoxes = [False] * len(refBoxes)
errMsg = ''
for i in range(len(refBoxes)):
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)
# Returns a simple one-layer network created from Caffe's format
def getSimpleNet():
prototxt = """
name: "simpleNet"
input: "data"
layer {
type: "Identity"
name: "testLayer"
top: "testLayer"
bottom: "data"
}
"""
return cv.dnn.readNetFromCaffe(bytearray(prototxt, 'utf8'))
def testBackendAndTarget(backend, target):
net = getSimpleNet()
net.setPreferableBackend(backend)
net.setPreferableTarget(target)
inp = np.random.standard_normal([1, 2, 3, 4]).astype(np.float32)
try:
net.setInput(inp)
net.forward()
except BaseException as e:
return False
return True
haveInfEngine = testBackendAndTarget(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_CPU)
dnnBackendsAndTargets = [
[cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_TARGET_CPU],
]
if haveInfEngine:
dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_CPU])
if testBackendAndTarget(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_MYRIAD):
dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_MYRIAD])
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 and cv.ocl_Device.getDefault().isIntel():
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])
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]))
class dnn_test(NewOpenCVTests):
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)
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_face_detection(self):
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)
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 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
normAssertDetections(self, ref, out, 0.5, scoresDiff, iouDiff)
if __name__ == '__main__':
NewOpenCVTests.bootstrap()