Testing tensorflow model with opencv dnn
I have asked the following question on StackOverflow : https://stackoverflow.com/questions/47072498/evaluation-of-tensorflow-model-with-opencv-fails.
I am adding here more details and asking opencv specific question.
In the code given in StackOverflow the dimension of inputData is 64 X 32 X 32 X 3 and this format is required by the tf.nn.conv2d operator in tensorflow.
I am reading the network with opencv dnn:
m_Net = cv::dnn::readNetFromTensorflow(m_TrainedModelPath);
To run the network I initialize with
cv::Mat netInput = cv::dnn::blobFromImages(imagesBatch);
m_Net.setInput(netInput, "input/Identity");
where images batch is as required by the opencv api a vector of cv::Mat. The cv::Mat were read with cv::imread and the image conversion BGR to RGB was performed.
But then the input of the network is 64 X 3 x 32 X 32.
The network does not work from OpenCV. My question is does opencv internally swaps the data or am I required to do so ?
As required I add the code here:
for (auto testItem : m_TestData) {
if (batchCounter > 0 && batchCounter % m_BatchSize == 0) {
//recognize and update counters
cv::Mat netInput = cv::dnn::blobFromImages(imagesBatch);
m_Net.setInput(netInput, "input/Identity"); //set the network input
cv::Mat result = m_Net.forward("output/Mul"); //compute output
///compute maximum in the fully connected layer output
for (int i = 0; i < m_BatchSize; ++i) {
int maxIdx = 0;
double maxVal = result.at<double>(i, result.at<float>(i, 0);
for (int j = 1; j < result.size[1]; ++j) {
double val = result.at<double>(i, result.at<float>(i, j);
if (val > maxVal) {
maxVal = val;
maxIdx = j;
}
}
printf("Groundtruth: %d, Recognized %d\n", labelsBatch[i], maxIdx);
if (labelsBatch[i] == maxIdx)
countCorrRecog++;
}
batchCounter = 0;
imagesBatch.clear();
labelsBatch.clear();
countTestedImages += m_BatchSize;
}
cv::Mat img = cv::imread(testItem.first.toUtf8().constData());
if (img.empty()) {
std::cerr << "Can't read image from the file: " << testItem.first.toUtf8().constData() << std::endl;
exit(-1);
}
cv::cvtColor(img, img, cv::COLOR_BGR2RGB);
if (m_InputImageSize != img.size())
cv::resize(img, img, m_InputImageSize); //Resize image to input size
imagesBatch.push_back(img);
labelsBatch.push_back(testItem.second);
batchCounter++;
}
I do not receive errors but countCorrRecog stays very small when compared to countTestedImages.
