Repository: opencv
Issue: libprotobuf FATAL, CHECK failed: (index) < (current_size_)
Labels: feature,category: dnn

##### System information (version)
<!-- Example
- OpenCV => 4.2
- Operating System / Platform => Windows 64 Bit
- Compiler => Visual Studio 2017
-->

OpenCV : 4.4.0
Operating System / Platform : Windows 10
Compiler : Visual C++ building Tools v142
Tensorflow 1.15.0

##### Detailed description

<!-- your description -->
When loading model failed, C++ code on Visual Studio 2019 , error info:
[libprotobuf FATAL C:\build\master_winpack-build-win64-vc15\opencv\3rdparty\protobuf\src\google/protobuf/repeated_field.h:1522] CHECK failed: (index) < (current_size_):

##### Steps to reproduce
Used MobileNet-SSD v1 2017_11_17, weights and config(downloaded from tensorflow.org) to load tf gragh model(.pb) succesfully in following c++ code.
But when I changed my own trained model(.pb), opencv loaded model failed. 
[pb files for test](https://gitee.com/kevin819/tensorflow)

I used the ssd_mobilenet_v1_coco_2017_11_17 to train the tensorflow ssd_mobilenet_v1 model. Modified the pipeline.config file to a larger image size (2048 x 1350), 1 class instead of 90.Trained and froze the model and generated pbtxt succesfully.
```
//main.cpp
#include
#include
#include

#include <opencv2/dnn.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>

using namespace std;
using namespace cv;
using namespace dnn;

float confThreshold, nmsThreshold;
std::vectorstd::string classes;

void postprocess(Mat& frame, const std::vector& out, Net& net);
void drawPred(int classId, float conf, int left, int top, int right, int bottom, Mat& frame);

int main()
{
confThreshold = 0.5;
nmsThreshold = 0.4;

float scale = 1.0;
Scalar mean = { 0, 0, 0 };
bool swapRB = true;
int inpWidth = 300;
int inpHeight = 300;

String modelPath = "frozen_inference_graph.pb";
String configPath = "ssd_mobilenet_v1_coco_2017_11_17.pbtxt";
String framework = "";

int backendId = cv::dnn::DNN_BACKEND_OPENCV;
int targetId = cv::dnn::DNN_TARGET_CPU;
/*
String classesFile = R"(object_detection_classes_coco.txt)";

// Open file with classes names.
if (!classesFile.empty()) {
	const std::string& file = classesFile;
	std::ifstream ifs(file.c_str());
	if (!ifs.is_open())
		CV_Error(Error::StsError, "File " + file + " not found");
	std::string line;
	while (std::getline(ifs, line)) {
		classes.push_back(line);
	}
}
*/
cout << "Load a model." << endl;
// Load a model.
Net net = readNet(modelPath, configPath, framework);
net.setPreferableBackend(backendId);
net.setPreferableTarget(targetId);

std::vector<String> outNames = net.getUnconnectedOutLayersNames();

// Create a window
static const std::string kWinName = "Deep learning object detection in OpenCV";

// Process frames.
Mat frame, blob;
frame = imread("image1.jpg");

// Create a 4D blob from a frame.
Size inpSize(inpWidth > 0 ? inpWidth : frame.cols,
	inpHeight > 0 ? inpHeight : frame.rows);
blobFromImage(frame, blob, scale, inpSize, mean, swapRB, false);

cout << "Run a model." << endl;
// Run a model.
net.setInput(blob);
cout << "Faster-RCNN or R-FCN." << endl;
if (net.getLayer(0)->outputNameToIndex("im_info") != -1)  // Faster-RCNN or R-FCN
{
	resize(frame, frame, inpSize);
	Mat imInfo = (Mat_<float>(1, 3) << inpSize.height, inpSize.width, 1.6f);
	net.setInput(imInfo, "im_info");
}

std::vector<Mat> outs;
net.forward(outs, outNames);
postprocess(frame, outs, net);

cout << "Put efficiency information.." << endl;
// Put efficiency information.
std::vector<double> layersTimes;
double freq = getTickFrequency() / 1000;
double t = net.getPerfProfile(layersTimes) / freq;
std::string label = format("Inference time: %.2f ms", t);
putText(frame, label, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));

imshow(kWinName, frame);
waitKey(0);

return 0;
}

void postprocess(Mat& frame, const std::vector& outs, Net& net)
{
static std::vector outLayers = net.getUnconnectedOutLayers();
static std::string outLayerType = net.getLayer(outLayers[0])->type;

std::vector<int> classIds;
std::vector<float> confidences;
std::vector<Rect> boxes;
if (net.getLayer(0)->outputNameToIndex("im_info") != -1)  // Faster-RCNN or R-FCN
{
	// Network produces output blob with a shape 1x1xNx7 where N is a number of
	// detections and an every detection is a vector of values
	// [batchId, classId, confidence, left, top, right, bottom]
	CV_Assert(outs.size() == 1);
	float* data = (float*)outs[0].data;
	for (size_t i = 0; i < outs[0].total(); i += 7) {
		float confidence = data[i + 2];
		if (confidence > confThreshold) {
			int left = (int)data[i + 3];
			int top = (int)data[i + 4];
			int right = (int)data[i + 5];
			int bottom = (int)data[i + 6];
			int width = right - left + 1;
			int height = bottom - top + 1;
			classIds.push_back((int)(data[i + 1]) - 1);  // Skip 0th background class id.
			boxes.push_back(Rect(left, top, width, height));
			confidences.push_back(confidence);
		}
	}
}
else if (outLayerType == "DetectionOutput") {
	// Network produces output blob with a shape 1x1xNx7 where N is a number of
	// detections and an every detection is a vector of values
	// [batchId, classId, confidence, left, top, right, bottom]
	CV_Assert(outs.size() == 1);
	float* data = (float*)outs[0].data;
	for (size_t i = 0; i < outs[0].total(); i += 7) {
		float confidence = data[i + 2];
		if (confidence > confThreshold) {
			int left = (int)(data[i + 3] * frame.cols);
			int top = (int)(data[i + 4] * frame.rows);
			int right = (int)(data[i + 5] * frame.cols);
			int bottom = (int)(data[i + 6] * frame.rows);
			int width = right - left + 1;
			int height = bottom - top + 1;
			classIds.push_back((int)(data[i + 1]) - 1);  // Skip 0th background class id.
			boxes.push_back(Rect(left, top, width, height));
			confidences.push_back(confidence);
		}
	}
}
else if (outLayerType == "Region") {
	for (size_t i = 0; i < outs.size(); ++i) {
		// Network produces output blob with a shape NxC where N is a number of
		// detected objects and C is a number of classes + 4 where the first 4
		// numbers are [center_x, center_y, width, height]
		float* data = (float*)outs[i].data;
		for (int j = 0; j < outs[i].rows; ++j, data += outs[i].cols) {
			Mat scores = outs[i].row(j).colRange(5, outs[i].cols);
			Point classIdPoint;
			double confidence;
			minMaxLoc(scores, 0, &confidence, 0, &classIdPoint);
			if (confidence > confThreshold) {
				int centerX = (int)(data[0] * frame.cols);
				int centerY = (int)(data[1] * frame.rows);
				int width = (int)(data[2] * frame.cols);
				int height = (int)(data[3] * frame.rows);
				int left = centerX - width / 2;
				int top = centerY - height / 2;

				classIds.push_back(classIdPoint.x);
				confidences.push_back((float)confidence);
				boxes.push_back(Rect(left, top, width, height));
			}
		}
	}
}
else
	CV_Error(Error::StsNotImplemented, "Unknown output layer type: " + outLayerType);

std::vector<int> indices;
NMSBoxes(boxes, confidences, confThreshold, nmsThreshold, indices);
for (size_t i = 0; i < indices.size(); ++i) {
	int idx = indices[i];
	Rect box = boxes[idx];
	drawPred(classIds[idx], confidences[idx], box.x, box.y,
		box.x + box.width, box.y + box.height, frame);
}
}

void drawPred(int classId, float conf, int left, int top, int right, int bottom, Mat& frame)
{
rectangle(frame, Point(left, top), Point(right, bottom), Scalar(0, 255, 0));

std::string label = format("%.2f", conf);
if (!classes.empty()) {
	CV_Assert(classId < (int)classes.size());
	label = classes[classId] + ": " + label;
}

int baseLine;
Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);

top = max(top, labelSize.height);
rectangle(frame, Point(left, top - labelSize.height),
	Point(left + labelSize.width, top + baseLine), Scalar::all(255), FILLED);
putText(frame, label, Point(left, top), FONT_HERSHEY_SIMPLEX, 0.5, Scalar());
}
```