gnash::key::code
Lirc::getKey()
{
// GNASH_REPORT_FUNCTION;
key::code key = gnash::key::INVALID;
byte_t buf[LIRC_PACKET_SIZE];
memset(buf, 0, LIRC_PACKET_SIZE);
// read the data if there is any
readNet(buf, LIRC_PACKET_SIZE, TIMEOUT);
string packet = reinterpret_cast<char *>(buf);
string::size_type space1 = packet.find(" ") +1;
string::size_type space2 = packet.find(" ", space1) + 1;
string::size_type space3 = packet.find(" ", space2) +1;
string code_str = packet.substr(0, space1);
string count_str = packet.substr(space1, space2-space1);
string button_str = packet.substr(space2,space3-space2);
string control_str = packet.substr(space3);
if (button_str[0] > 'A' && button_str[0] < 'Z') {
std::cerr << "Character: " << button_str << std::endl;
key = (gnash::key::code)button_str[0];
}
return key;
}
TEST(readNet, Regression)
{
Net net = readNet(findDataFile("dnn/squeezenet_v1.1.prototxt", false),
findDataFile("dnn/squeezenet_v1.1.caffemodel", false));
EXPECT_FALSE(net.empty());
net = readNet(findDataFile("dnn/opencv_face_detector.caffemodel", false),
findDataFile("dnn/opencv_face_detector.prototxt", false));
EXPECT_FALSE(net.empty());
net = readNet(findDataFile("dnn/openface_nn4.small2.v1.t7", false));
EXPECT_FALSE(net.empty());
net = readNet(findDataFile("dnn/tiny-yolo-voc.cfg", false),
findDataFile("dnn/tiny-yolo-voc.weights", false));
EXPECT_FALSE(net.empty());
net = readNet(findDataFile("dnn/ssd_mobilenet_v1_coco.pbtxt", false),
findDataFile("dnn/ssd_mobilenet_v1_coco.pb", false));
EXPECT_FALSE(net.empty());
}
// inp = cv.imread('opencv_extra/testdata/cv/ximgproc/sources/08.png')
// inp = inp[:,:,[2, 1, 0]].astype(np.float32).reshape(1, 512, 512, 3)
// outs = sess.run([sess.graph.get_tensor_by_name('feature_fusion/Conv_7/Sigmoid:0'),
// sess.graph.get_tensor_by_name('feature_fusion/concat_3:0')],
// feed_dict={'input_images:0': inp})
// scores = np.ascontiguousarray(outs[0].transpose(0, 3, 1, 2))
// geometry = np.ascontiguousarray(outs[1].transpose(0, 3, 1, 2))
// np.save('east_text_detection.scores.npy', scores)
// np.save('east_text_detection.geometry.npy', geometry)
TEST_P(Test_TensorFlow_nets, EAST_text_detection)
{
checkBackend();
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE < 2018030000
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("Test is enabled starts from OpenVINO 2018R3");
#endif
std::string netPath = findDataFile("dnn/frozen_east_text_detection.pb", false);
std::string imgPath = findDataFile("cv/ximgproc/sources/08.png", false);
std::string refScoresPath = findDataFile("dnn/east_text_detection.scores.npy", false);
std::string refGeometryPath = findDataFile("dnn/east_text_detection.geometry.npy", false);
Net net = readNet(findDataFile("dnn/frozen_east_text_detection.pb", false));
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat img = imread(imgPath);
Mat inp = blobFromImage(img, 1.0, Size(), Scalar(123.68, 116.78, 103.94), true, false);
net.setInput(inp);
std::vector<Mat> outs;
std::vector<String> outNames(2);
outNames[0] = "feature_fusion/Conv_7/Sigmoid";
outNames[1] = "feature_fusion/concat_3";
net.forward(outs, outNames);
Mat scores = outs[0];
Mat geometry = outs[1];
// Scores are in range [0, 1]. Geometry values are in range [-0.23, 290]
double l1_scores = default_l1, lInf_scores = default_lInf;
double l1_geometry = default_l1, lInf_geometry = default_lInf;
if (target == DNN_TARGET_OPENCL_FP16)
{
lInf_scores = 0.11;
l1_geometry = 0.28; lInf_geometry = 5.94;
}
else if (target == DNN_TARGET_MYRIAD)
{
lInf_scores = 0.214;
l1_geometry = 0.47; lInf_geometry = 15.34;
}
else
{
l1_geometry = 1e-4, lInf_geometry = 3e-3;
}
normAssert(scores, blobFromNPY(refScoresPath), "scores", l1_scores, lInf_scores);
normAssert(geometry, blobFromNPY(refGeometryPath), "geometry", l1_geometry, lInf_geometry);
}
TEST(Test_TensorFlow, two_inputs)
{
Net net = readNet(path("two_inputs_net.pbtxt"));
net.setPreferableBackend(DNN_BACKEND_OPENCV);
Mat firstInput(2, 3, CV_32FC1), secondInput(2, 3, CV_32FC1);
randu(firstInput, -1, 1);
randu(secondInput, -1, 1);
net.setInput(firstInput, "first_input");
net.setInput(secondInput, "second_input");
Mat out = net.forward();
normAssert(out, firstInput + secondInput);
}
void testDarknetLayer(const std::string& name, bool hasWeights = false)
{
std::string cfg = findDataFile("dnn/darknet/" + name + ".cfg", false);
std::string model = "";
if (hasWeights)
model = findDataFile("dnn/darknet/" + name + ".weights", false);
Mat inp = blobFromNPY(findDataFile("dnn/darknet/" + name + "_in.npy", false));
Mat ref = blobFromNPY(findDataFile("dnn/darknet/" + name + "_out.npy", false));
checkBackend(&inp, &ref);
Net net = readNet(cfg, model);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
net.setInput(inp);
Mat out = net.forward();
normAssert(out, ref, "", default_l1, default_lInf);
}
bool
MPTreeMgr::readInput( const char * node , const char * pl , const char * net )
{
Node * pNd1 , * pNd2;
int arg1 , arg2 , move;
cout << " > readInput() : start reading input data\n";
if ( !readNode ( node ) ) return false;
if ( !readPosition ( pl ) ) return false;
if ( !readNet ( net ) ) return false;
buildInitMPTree();
while ( !packMPTree() ) {
pNd1 = pNd2 = NULL;
arg1 = arg2 = -1;
move = rand() % 4;
perturbMPTree( &pNd1 , &pNd2 , &arg1 , &arg2 , move );
}
initCost();
return true;
}
const char *
Lirc::getButton()
{
// GNASH_REPORT_FUNCTION;
byte_t buf[LIRC_PACKET_SIZE];
memset(buf, 0, LIRC_PACKET_SIZE);
// read the data if there is any
readNet(buf, LIRC_PACKET_SIZE, TIMEOUT);
string packet = reinterpret_cast<char *>(buf);
string::size_type space1 = packet.find(" ") + 1;
string::size_type space2 = packet.find(" ", space1) + 1;
string::size_type space3 = packet.find(" ", space2) + 1;
string button_str = packet.substr(space2, space3-space2-1);
memset(_button, 0, BUTTONSIZE);
strncpy(_button, button_str.c_str(), BUTTONSIZE);
return _button;
}
// Test object detection network from Darknet framework.
void testDarknetModel(const std::string& cfg, const std::string& weights,
const std::vector<std::vector<int> >& refClassIds,
const std::vector<std::vector<float> >& refConfidences,
const std::vector<std::vector<Rect2d> >& refBoxes,
double scoreDiff, double iouDiff, float confThreshold = 0.24, float nmsThreshold = 0.4)
{
checkBackend();
Mat img1 = imread(_tf("dog416.png"));
Mat img2 = imread(_tf("street.png"));
std::vector<Mat> samples(2);
samples[0] = img1; samples[1] = img2;
// determine test type, whether batch or single img
int batch_size = refClassIds.size();
CV_Assert(batch_size == 1 || batch_size == 2);
samples.resize(batch_size);
Mat inp = blobFromImages(samples, 1.0/255, Size(416, 416), Scalar(), true, false);
Net net = readNet(findDataFile("dnn/" + cfg, false),
findDataFile("dnn/" + weights, false));
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
net.setInput(inp);
std::vector<Mat> outs;
net.forward(outs, getOutputsNames(net));
for (int b = 0; b < batch_size; ++b)
{
std::vector<int> classIds;
std::vector<float> confidences;
std::vector<Rect2d> boxes;
for (int i = 0; i < outs.size(); ++i)
{
Mat out;
if (batch_size > 1){
// get the sample slice from 3D matrix (batch, box, classes+5)
Range ranges[3] = {Range(b, b+1), Range::all(), Range::all()};
out = outs[i](ranges).reshape(1, outs[i].size[1]);
}else{
out = outs[i];
}
for (int j = 0; j < out.rows; ++j)
{
Mat scores = out.row(j).colRange(5, out.cols);
double confidence;
Point maxLoc;
minMaxLoc(scores, 0, &confidence, 0, &maxLoc);
if (confidence > confThreshold) {
float* detection = out.ptr<float>(j);
double centerX = detection[0];
double centerY = detection[1];
double width = detection[2];
double height = detection[3];
boxes.push_back(Rect2d(centerX - 0.5 * width, centerY - 0.5 * height,
width, height));
confidences.push_back(confidence);
classIds.push_back(maxLoc.x);
}
}
}
// here we need NMS of boxes
std::vector<int> indices;
NMSBoxes(boxes, confidences, confThreshold, nmsThreshold, indices);
std::vector<int> nms_classIds;
std::vector<float> nms_confidences;
std::vector<Rect2d> nms_boxes;
for (size_t i = 0; i < indices.size(); ++i)
{
int idx = indices[i];
Rect2d box = boxes[idx];
float conf = confidences[idx];
int class_id = classIds[idx];
nms_boxes.push_back(box);
nms_confidences.push_back(conf);
nms_classIds.push_back(class_id);
}
normAssertDetections(refClassIds[b], refConfidences[b], refBoxes[b], nms_classIds,
nms_confidences, nms_boxes, format("batch size %d, sample %d\n", batch_size, b).c_str(), confThreshold, scoreDiff, iouDiff);
}
}
