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Merge pull request #11104 from asciian:reading_from_stream

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pull/11996/head^2
Alexander Alekhin 6 years ago
parent f3ee07ca11 8b5f061dae
commit 6c4f618db5
9 changed files with 292 additions and 94 deletions
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  1. 46
      modules/dnn/include/opencv2/dnn/dnn.hpp
  2. 71
      modules/dnn/misc/java/test/DnnTensorFlowTest.java
  3. 9
      modules/dnn/src/caffe/caffe_importer.cpp
  4. 78
      modules/dnn/src/darknet/darknet_importer.cpp
  5. 121
      modules/dnn/src/darknet/darknet_io.cpp
  6. 7
      modules/dnn/src/darknet/darknet_io.hpp
  7. 17
      modules/dnn/src/dnn.cpp
  8. 9
      modules/dnn/src/tensorflow/tf_importer.cpp
  9. 28
      modules/dnn/test/test_darknet_importer.cpp

46
modules/dnn/include/opencv2/dnn/dnn.hpp
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@ -644,6 +644,24 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
*/
CV_EXPORTS_W Net readNetFromDarknet(const String &cfgFile, const String &darknetModel = String());
/** @brief Reads a network model stored in <a href="https://pjreddie.com/darknet/">Darknet</a> model files.
* @param bufferCfg A buffer contains a content of .cfg file with text description of the network architecture.
* @param bufferModel A buffer contains a content of .weights file with learned network.
* @returns Net object.
*/
CV_EXPORTS_W Net readNetFromDarknet(const std::vector<uchar>& bufferCfg,
const std::vector<uchar>& bufferModel = std::vector<uchar>());
/** @brief Reads a network model stored in <a href="https://pjreddie.com/darknet/">Darknet</a> model files.
* @param bufferCfg A buffer contains a content of .cfg file with text description of the network architecture.
* @param lenCfg Number of bytes to read from bufferCfg
* @param bufferModel A buffer contains a content of .weights file with learned network.
* @param lenModel Number of bytes to read from bufferModel
* @returns Net object.
*/
CV_EXPORTS Net readNetFromDarknet(const char *bufferCfg, size_t lenCfg,
const char *bufferModel = NULL, size_t lenModel = 0);
/** @brief Reads a network model stored in <a href="http://caffe.berkeleyvision.org">Caffe</a> framework's format.
* @param prototxt path to the .prototxt file with text description of the network architecture.
* @param caffeModel path to the .caffemodel file with learned network.
@ -651,6 +669,14 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
*/
CV_EXPORTS_W Net readNetFromCaffe(const String &prototxt, const String &caffeModel = String());
/** @brief Reads a network model stored in Caffe model in memory.
* @param bufferProto buffer containing the content of the .prototxt file
* @param bufferModel buffer containing the content of the .caffemodel file
* @returns Net object.
*/
CV_EXPORTS_W Net readNetFromCaffe(const std::vector<uchar>& bufferProto,
const std::vector<uchar>& bufferModel = std::vector<uchar>());
/** @brief Reads a network model stored in Caffe model in memory.
* @details This is an overloaded member function, provided for convenience.
* It differs from the above function only in what argument(s) it accepts.
@ -672,6 +698,14 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
*/
CV_EXPORTS_W Net readNetFromTensorflow(const String &model, const String &config = String());
/** @brief Reads a network model stored in <a href="https://www.tensorflow.org/">TensorFlow</a> framework's format.
* @param bufferModel buffer containing the content of the pb file
* @param bufferConfig buffer containing the content of the pbtxt file
* @returns Net object.
*/
CV_EXPORTS_W Net readNetFromTensorflow(const std::vector<uchar>& bufferModel,
const std::vector<uchar>& bufferConfig = std::vector<uchar>());
/** @brief Reads a network model stored in <a href="https://www.tensorflow.org/">TensorFlow</a> framework's format.
* @details This is an overloaded member function, provided for convenience.
* It differs from the above function only in what argument(s) it accepts.
@ -735,6 +769,18 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
*/
CV_EXPORTS_W Net readNet(const String& model, const String& config = "", const String& framework = "");
/**
* @brief Read deep learning network represented in one of the supported formats.
* @details This is an overloaded member function, provided for convenience.
* It differs from the above function only in what argument(s) it accepts.
* @param[in] framework Name of origin framework.
* @param[in] bufferModel A buffer with a content of binary file with weights
* @param[in] bufferConfig A buffer with a content of text file contains network configuration.
* @returns Net object.
*/
CV_EXPORTS_W Net readNet(const String& framework, const std::vector<uchar>& bufferModel,
const std::vector<uchar>& bufferConfig = std::vector<uchar>());
/** @brief Loads blob which was serialized as torch.Tensor object of Torch7 framework.
* @warning This function has the same limitations as readNetFromTorch().
*/

71
modules/dnn/misc/java/test/DnnTensorFlowTest.java
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@ -1,10 +1,14 @@
package org.opencv.test.dnn;
import java.io.File;
import java.io.FileInputStream;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
import org.opencv.core.Core;
import org.opencv.core.Mat;
import org.opencv.core.MatOfFloat;
import org.opencv.core.MatOfByte;
import org.opencv.core.Scalar;
import org.opencv.core.Size;
import org.opencv.dnn.DictValue;
@ -26,6 +30,15 @@ public class DnnTensorFlowTest extends OpenCVTestCase {
Net net;
private static void normAssert(Mat ref, Mat test) {
final double l1 = 1e-5;
final double lInf = 1e-4;
double normL1 = Core.norm(ref, test, Core.NORM_L1) / ref.total();
double normLInf = Core.norm(ref, test, Core.NORM_INF) / ref.total();
assertTrue(normL1 < l1);
assertTrue(normLInf < lInf);
}
@Override
protected void setUp() throws Exception {
super.setUp();
@ -46,7 +59,7 @@ public class DnnTensorFlowTest extends OpenCVTestCase {
File testDataPath = new File(envTestDataPath);
File f = new File(testDataPath, "dnn/space_shuttle.jpg");
File f = new File(testDataPath, "dnn/grace_hopper_227.png");
sourceImageFile = f.toString();
if(!f.exists()) throw new Exception("Test image is missing: " + sourceImageFile);
@ -77,31 +90,55 @@ public class DnnTensorFlowTest extends OpenCVTestCase {
}
public void testTestNetForward() {
Mat rawImage = Imgcodecs.imread(sourceImageFile);
assertNotNull("Loading image from file failed!", rawImage);
public void checkInceptionNet(Net net)
{
Mat image = Imgcodecs.imread(sourceImageFile);
assertNotNull("Loading image from file failed!", image);
Mat image = new Mat();
Imgproc.resize(rawImage, image, new Size(224,224));
Mat inputBlob = Dnn.blobFromImage(image);
Mat inputBlob = Dnn.blobFromImage(image, 1.0, new Size(224, 224), new Scalar(0), true, true);
assertNotNull("Converting image to blob failed!", inputBlob);
Mat inputBlobP = new Mat();
Core.subtract(inputBlob, new Scalar(117.0), inputBlobP);
net.setInput(inputBlobP, "input" );
Mat result = net.forward();
net.setInput(inputBlob, "input");
Mat result = new Mat();
try {
net.setPreferableBackend(Dnn.DNN_BACKEND_OPENCV);
result = net.forward("softmax2");
}
catch (Exception e) {
fail("DNN forward failed: " + e.getMessage());
}
assertNotNull("Net returned no result!", result);
Core.MinMaxLocResult minmax = Core.minMaxLoc(result.reshape(1, 1));
result = result.reshape(1, 1);
Core.MinMaxLocResult minmax = Core.minMaxLoc(result);
assertEquals("Wrong prediction", (int)minmax.maxLoc.x, 866);
Mat top5RefScores = new MatOfFloat(new float[] {
0.63032645f, 0.2561979f, 0.032181446f, 0.015721032f, 0.014785315f
}).reshape(1, 1);
assertTrue("No image recognized!", minmax.maxVal > 0.9);
Core.sort(result, result, Core.SORT_DESCENDING);
normAssert(result.colRange(0, 5), top5RefScores);
}
public void testTestNetForward() {
checkInceptionNet(net);
}
public void testReadFromBuffer() {
File modelFile = new File(modelFileName);
byte[] modelBuffer = new byte[ (int)modelFile.length() ];
try {
FileInputStream fis = new FileInputStream(modelFile);
fis.read(modelBuffer);
fis.close();
} catch (IOException e) {
fail("Failed to read a model: " + e.getMessage());
}
net = Dnn.readNetFromTensorflow(new MatOfByte(modelBuffer));
checkInceptionNet(net);
}
}

9
modules/dnn/src/caffe/caffe_importer.cpp
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@ -453,6 +453,15 @@ Net readNetFromCaffe(const char *bufferProto, size_t lenProto,
return net;
}
Net readNetFromCaffe(const std::vector<uchar>& bufferProto, const std::vector<uchar>& bufferModel)
{
const char* bufferProtoPtr = reinterpret_cast<const char*>(&bufferProto[0]);
const char* bufferModelPtr = bufferModel.empty() ? NULL :
reinterpret_cast<const char*>(&bufferModel[0]);
return readNetFromCaffe(bufferProtoPtr, bufferProto.size(),
bufferModelPtr, bufferModel.size());
}
#endif //HAVE_PROTOBUF
CV__DNN_EXPERIMENTAL_NS_END

78
modules/dnn/src/darknet/darknet_importer.cpp
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@ -44,6 +44,7 @@
#include "../precomp.hpp"
#include <iostream>
#include <fstream>
#include <algorithm>
#include <vector>
#include <map>
@ -66,14 +67,19 @@ public:
DarknetImporter() {}
DarknetImporter(const char *cfgFile, const char *darknetModel)
DarknetImporter(std::istream &cfgStream, std::istream &darknetModelStream)
{
CV_TRACE_FUNCTION();
ReadNetParamsFromCfgFileOrDie(cfgFile, &net);
ReadNetParamsFromCfgStreamOrDie(cfgStream, &net);
ReadNetParamsFromBinaryStreamOrDie(darknetModelStream, &net);
}
if (darknetModel && darknetModel[0])
ReadNetParamsFromBinaryFileOrDie(darknetModel, &net);
DarknetImporter(std::istream &cfgStream)
{
CV_TRACE_FUNCTION();
ReadNetParamsFromCfgStreamOrDie(cfgStream, &net);
}
struct BlobNote
@ -175,15 +181,75 @@ public:
}
};
static Net readNetFromDarknet(std::istream &cfgFile, std::istream &darknetModel)
{
Net net;
DarknetImporter darknetImporter(cfgFile, darknetModel);
darknetImporter.populateNet(net);
return net;
}
Net readNetFromDarknet(const String &cfgFile, const String &darknetModel /*= String()*/)
static Net readNetFromDarknet(std::istream &cfgFile)
{
DarknetImporter darknetImporter(cfgFile.c_str(), darknetModel.c_str());
Net net;
DarknetImporter darknetImporter(cfgFile);
darknetImporter.populateNet(net);
return net;
}
}
Net readNetFromDarknet(const String &cfgFile, const String &darknetModel /*= String()*/)
{
std::ifstream cfgStream(cfgFile.c_str());
if (!cfgStream.is_open())
{
CV_Error(cv::Error::StsParseError, "Failed to parse NetParameter file: " + std::string(cfgFile));
}
if (darknetModel != String())
{
std::ifstream darknetModelStream(darknetModel.c_str(), std::ios::binary);
if (!darknetModelStream.is_open())
{
CV_Error(cv::Error::StsParseError, "Failed to parse NetParameter file: " + std::string(darknetModel));
}
return readNetFromDarknet(cfgStream, darknetModelStream);
}
else
return readNetFromDarknet(cfgStream);
}
struct BufferStream : public std::streambuf
{
BufferStream(const char* s, std::size_t n)
{
char* ptr = const_cast<char*>(s);
setg(ptr, ptr, ptr + n);
}
};
Net readNetFromDarknet(const char *bufferCfg, size_t lenCfg, const char *bufferModel, size_t lenModel)
{
BufferStream cfgBufferStream(bufferCfg, lenCfg);
std::istream cfgStream(&cfgBufferStream);
if (lenModel)
{
BufferStream weightsBufferStream(bufferModel, lenModel);
std::istream weightsStream(&weightsBufferStream);
return readNetFromDarknet(cfgStream, weightsStream);
}
else
return readNetFromDarknet(cfgStream);
}
Net readNetFromDarknet(const std::vector<uchar>& bufferCfg, const std::vector<uchar>& bufferModel)
{
const char* bufferCfgPtr = reinterpret_cast<const char*>(&bufferCfg[0]);
const char* bufferModelPtr = bufferModel.empty() ? NULL :
reinterpret_cast<const char*>(&bufferModel[0]);
return readNetFromDarknet(bufferCfgPtr, bufferCfg.size(),
bufferModelPtr, bufferModel.size());
}
CV__DNN_EXPERIMENTAL_NS_END
}} // namespace

121
modules/dnn/src/darknet/darknet_io.cpp
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@ -476,68 +476,61 @@ namespace cv {
return dst;
}
bool ReadDarknetFromCfgFile(const char *cfgFile, NetParameter *net)
bool ReadDarknetFromCfgStream(std::istream &ifile, NetParameter *net)
{
std::ifstream ifile;
ifile.open(cfgFile);
if (ifile.is_open())
{
bool read_net = false;
int layers_counter = -1;
for (std::string line; std::getline(ifile, line);) {
line = escapeString(line);
if (line.empty()) continue;
switch (line[0]) {
case '\0': break;
case '#': break;
case ';': break;
case '[':
if (line == "[net]") {
read_net = true;
}
else {
// read section
read_net = false;
++layers_counter;
const size_t layer_type_size = line.find("]") - 1;
CV_Assert(layer_type_size < line.size());
std::string layer_type = line.substr(1, layer_type_size);
net->layers_cfg[layers_counter]["type"] = layer_type;
}
break;
default:
// read entry
const size_t separator_index = line.find('=');
CV_Assert(separator_index < line.size());
if (separator_index != std::string::npos) {
std::string name = line.substr(0, separator_index);
std::string value = line.substr(separator_index + 1, line.size() - (separator_index + 1));
name = escapeString(name);
value = escapeString(value);
if (name.empty() || value.empty()) continue;
if (read_net)
net->net_cfg[name] = value;
else
net->layers_cfg[layers_counter][name] = value;
}
bool read_net = false;
int layers_counter = -1;
for (std::string line; std::getline(ifile, line);) {
line = escapeString(line);
if (line.empty()) continue;
switch (line[0]) {
case '\0': break;
case '#': break;
case ';': break;
case '[':
if (line == "[net]") {
read_net = true;
}
else {
// read section
read_net = false;
++layers_counter;
const size_t layer_type_size = line.find("]") - 1;
CV_Assert(layer_type_size < line.size());
std::string layer_type = line.substr(1, layer_type_size);
net->layers_cfg[layers_counter]["type"] = layer_type;
}
break;
default:
// read entry
const size_t separator_index = line.find('=');
CV_Assert(separator_index < line.size());
if (separator_index != std::string::npos) {
std::string name = line.substr(0, separator_index);
std::string value = line.substr(separator_index + 1, line.size() - (separator_index + 1));
name = escapeString(name);
value = escapeString(value);
if (name.empty() || value.empty()) continue;
if (read_net)
net->net_cfg[name] = value;
else
net->layers_cfg[layers_counter][name] = value;
}
}
std::string anchors = net->layers_cfg[net->layers_cfg.size() - 1]["anchors"];
std::vector<float> vec = getNumbers<float>(anchors);
std::map<std::string, std::string> &net_params = net->net_cfg;
net->width = getParam(net_params, "width", 416);
net->height = getParam(net_params, "height", 416);
net->channels = getParam(net_params, "channels", 3);
CV_Assert(net->width > 0 && net->height > 0 && net->channels > 0);
}
else
return false;
std::string anchors = net->layers_cfg[net->layers_cfg.size() - 1]["anchors"];
std::vector<float> vec = getNumbers<float>(anchors);
std::map<std::string, std::string> &net_params = net->net_cfg;
net->width = getParam(net_params, "width", 416);
net->height = getParam(net_params, "height", 416);
net->channels = getParam(net_params, "channels", 3);
CV_Assert(net->width > 0 && net->height > 0 && net->channels > 0);
int current_channels = net->channels;
net->out_channels_vec.resize(net->layers_cfg.size());
int layers_counter = -1;
layers_counter = -1;
setLayersParams setParams(net);
@ -676,13 +669,8 @@ namespace cv {
return true;
}
bool ReadDarknetFromWeightsFile(const char *darknetModel, NetParameter *net)
bool ReadDarknetFromWeightsStream(std::istream &ifile, NetParameter *net)
{
std::ifstream ifile;
ifile.open(darknetModel, std::ios::binary);
CV_Assert(ifile.is_open());
int32_t major_ver, minor_ver, revision;
ifile.read(reinterpret_cast<char *>(&major_ver), sizeof(int32_t));
ifile.read(reinterpret_cast<char *>(&minor_ver), sizeof(int32_t));
@ -778,19 +766,18 @@ namespace cv {
}
void ReadNetParamsFromCfgFileOrDie(const char *cfgFile, darknet::NetParameter *net)
void ReadNetParamsFromCfgStreamOrDie(std::istream &ifile, darknet::NetParameter *net)
{
if (!darknet::ReadDarknetFromCfgFile(cfgFile, net)) {
CV_Error(cv::Error::StsParseError, "Failed to parse NetParameter file: " + std::string(cfgFile));
if (!darknet::ReadDarknetFromCfgStream(ifile, net)) {
CV_Error(cv::Error::StsParseError, "Failed to parse NetParameter stream");
}
}
void ReadNetParamsFromBinaryFileOrDie(const char *darknetModel, darknet::NetParameter *net)
void ReadNetParamsFromBinaryStreamOrDie(std::istream &ifile, darknet::NetParameter *net)
{
if (!darknet::ReadDarknetFromWeightsFile(darknetModel, net)) {
CV_Error(cv::Error::StsParseError, "Failed to parse NetParameter file: " + std::string(darknetModel));
if (!darknet::ReadDarknetFromWeightsStream(ifile, net)) {
CV_Error(cv::Error::StsParseError, "Failed to parse NetParameter stream");
}
}
}
}

7
modules/dnn/src/darknet/darknet_io.hpp
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@ -109,10 +109,9 @@ namespace cv {
};
}
// Read parameters from a file into a NetParameter message.
void ReadNetParamsFromCfgFileOrDie(const char *cfgFile, darknet::NetParameter *net);
void ReadNetParamsFromBinaryFileOrDie(const char *darknetModel, darknet::NetParameter *net);
// Read parameters from a stream into a NetParameter message.
void ReadNetParamsFromCfgStreamOrDie(std::istream &ifile, darknet::NetParameter *net);
void ReadNetParamsFromBinaryStreamOrDie(std::istream &ifile, darknet::NetParameter *net);
}
}
#endif

17
modules/dnn/src/dnn.cpp
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@ -3126,6 +3126,23 @@ Net readNet(const String& _model, const String& _config, const String& _framewor
model + (config.empty() ? "" : ", " + config));
}
Net readNet(const String& _framework, const std::vector<uchar>& bufferModel,
const std::vector<uchar>& bufferConfig)
{
String framework = _framework.toLowerCase();
if (framework == "caffe")
return readNetFromCaffe(bufferConfig, bufferModel);
else if (framework == "tensorflow")
return readNetFromTensorflow(bufferModel, bufferConfig);
else if (framework == "darknet")
return readNetFromDarknet(bufferConfig, bufferModel);
else if (framework == "torch")
CV_Error(Error::StsNotImplemented, "Reading Torch models from buffers");
else if (framework == "dldt")
CV_Error(Error::StsNotImplemented, "Reading Intel's Model Optimizer models from buffers");
CV_Error(Error::StsError, "Cannot determine an origin framework with a name " + framework);
}
Net readNetFromModelOptimizer(const String &xml, const String &bin)
{
return Net::readFromModelOptimizer(xml, bin);

9
modules/dnn/src/tensorflow/tf_importer.cpp
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@ -1856,5 +1856,14 @@ Net readNetFromTensorflow(const char* bufferModel, size_t lenModel,
return net;
}
Net readNetFromTensorflow(const std::vector<uchar>& bufferModel, const std::vector<uchar>& bufferConfig)
{
const char* bufferModelPtr = reinterpret_cast<const char*>(&bufferModel[0]);
const char* bufferConfigPtr = bufferConfig.empty() ? NULL :
reinterpret_cast<const char*>(&bufferConfig[0]);
return readNetFromTensorflow(bufferModelPtr, bufferModel.size(),
bufferConfigPtr, bufferConfig.size());
}
CV__DNN_EXPERIMENTAL_NS_END
}} // namespace

28
modules/dnn/test/test_darknet_importer.cpp
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@ -65,6 +65,34 @@ TEST(Test_Darknet, read_yolo_voc)
ASSERT_FALSE(net.empty());
}
TEST(Test_Darknet, read_yolo_voc_stream)
{
Mat ref;
Mat sample = imread(_tf("dog416.png"));
Mat inp = blobFromImage(sample, 1.0/255, Size(416, 416), Scalar(), true, false);
const std::string cfgFile = findDataFile("dnn/yolo-voc.cfg", false);
const std::string weightsFile = findDataFile("dnn/yolo-voc.weights", false);
// Import by paths.
{
Net net = readNetFromDarknet(cfgFile, weightsFile);
net.setInput(inp);
net.setPreferableBackend(DNN_BACKEND_OPENCV);
ref = net.forward();
}
// Import from bytes array.
{
std::string cfg, weights;
readFileInMemory(cfgFile, cfg);
readFileInMemory(weightsFile, weights);
Net net = readNetFromDarknet(&cfg[0], cfg.size(), &weights[0], weights.size());
net.setInput(inp);
net.setPreferableBackend(DNN_BACKEND_OPENCV);
Mat out = net.forward();
normAssert(ref, out);
}
}
class Test_Darknet_layers : public DNNTestLayer
{
public:

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