[7] Nging Server Setup - Building a Text Detection API using Drogon Server with OpenCV

Setting up text detection in RHEL and integrating it with a Drogon server to develop functionality that extracts text information as an API and displays it to clients.

Note: The purpose is Text Detection, not OCR (Optical Character Recognition). OCR will be integrated later.

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0. Search for Text Detection Frameworks

| Framework | Language | Pros | Cons | |---------------------|-----------------|------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------| | Tesseract OCR | C++ (Python, etc.) | Open source, supports multiple languages, customizable | Can have degraded performance without preprocessing, requires integration with OpenCV for detection | | OpenCV + EAST model | C++, Python | Fast text detection, supports both CPU and GPU, easy integration with OpenCV | Only supports text detection, so OCR is needed separately; performance limitations with high-resolution images | | EasyOCR | Python | PyTorch-based, supports multiple languages and styles, easy to install and use | May be slow on CPU, requires GPU for high performance | | Amazon Textract (API) | Multi-language | Serverless deployment on AWS, advanced features (table and form recognition), stable performance | Potentially high cost for large datasets, restricted to cloud use, security considerations | | Google Vision API (API) | Multi-language | Comprehensive text detection and image analysis, serverless deployment on GCP | High costs for large datasets, restricted to cloud use, data security concerns | | PaddleOCR | Python | Excellent performance for Asian languages, lightweight model with fast CPU processing | Based on PaddlePaddle, which may be a barrier for PyTorch/TensorFlow users, requires Python for integration |

- External API calls are excluded. - Since the objective is detection, proceed with OpenCV + EAST model. - The API will provide text coordinate data within the image and generate a new image with frames around detected text (`$filename_detection.jpg`).

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1. OpenCV Setup and Required Library Installation

Proceed with the following steps (initially implemented via shell):

``` #!/bin/sh sudo yum update -y sudo yum groupinstall -y "Development Tools" sudo yum install -y epel-release sudo yum install -y cmake git gtk2-devel boost-devel sudo yum install -y libjpeg-turbo-devel libpng-devel libtiff-devel sudo yum install -y libdc1394-devel gstreamer1-devel gstreamer1-plugins-base-devel sudo yum install -y tbb-devel

# OpenCV 4.10.0 wget https://github.com/opencv/opencv/archive/refs/tags/4.10.0.tar.gz mv 4.10.0.tar.gz opencv.tar tar -xvf opencv.tar mv opencv-4.10.0 opencv

# OpenCV Contrib 4.10.0 wget https://github.com/opencv/opencv_contrib/archive/refs/tags/4.10.0.tar.gz mv 4.10.0.tar.gz opencv_contrib.tar tar -xvf opencv_contrib.tar mv opencv_contrib-4.10.0 opencv_contrib

# OpenCV Build cd ./opencv mkdir build cd build

# CMake cmake -DOPENCV_EXTRA_MODULES_PATH=../../opencv_contrib/modules \ -DCMAKE_BUILD_TYPE=Release \ -DBUILD_SHARED_LIBS=ON \ -DWITH_IPP=ON \ -DWITH_TBB=ON \ -DWITH_OPENMP=ON \ -DENABLE_FAST_MATH=ON \ -DCMAKE_INSTALL_PREFIX=/usr/local \ -DBUILD_EXAMPLES=OFF \ -DBUILD_TESTS=OFF \ -DBUILD_PERF_TESTS=OFF \ ..

# Build and Install make -j$(nproc) # Builds using all CPU cores sudo make install sudo ldconfig # Updates the library cache ```

This setup installs in the system path by default.

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2. API Development

Continue using the previously set up server.

2-1. Connect with Drogon Ctl

``` cd /root/drogon2/drogon/build/drogon_ctl drogon_ctl create controller TextDetectionController ```

2-2. Implement TextDetection Source location ( /root/drogon2/drogon/build/drogon_ctl/testAPI/controllers )

TextDetectionController.h

 #pragma once #include  #include  #include 

using namespace drogon;
void initializeModel();
class TextDetectionController : public HttpController { public: std:: string _storagePath = "/root/storage/"; METHOD_LIST_BEGIN // Process text detection for images located in src and save to dst ADD_METHOD_TO(TextDetectionController::handleTextDetection, "/text-detection", Get); METHOD_LIST_END
// Method declaration void handleTextDetection(const HttpRequestPtr& req, std::function&& callback); std::vector decodeBoundingBoxes(const cv::Mat& scores, const cv::Mat& geometry, float scoreThresh); }; 

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TextDetectionController.cc

 #include "TextDetectionController.h" #include  #include 

using namespace cv; using namespace cv::dnn; namespace { cv::dnn::Net eastNet; const std::string eastModelPath = "./frozen_east_text_detection.pb"; }
// OpenCV Initialization // Since model initialization takes time, set globally for efficiency // Note: this is thread-unsafe and would need improvement for production server logic void initializeModel() { if (eastNet.empty()) { eastNet = cv::dnn::readNet(eastModelPath); if (eastNet.empty()) { throw std::runtime_error("Failed to load EAST model"); } } }
std::vector TextDetectionController::decodeBoundingBoxes(const cv::Mat& scores, const cv::Mat& geometry, float scoreThresh) { std::vector detections; const int numRows = scores.size[2]; const int numCols = scores.size[3];
for (int y = 0; y < numRows; y++) { const float* scoresData = scores.ptr(0, 0, y); const float* x0_data = geometry.ptr(0, 0, y); const float* x1_data = geometry.ptr(0, 1, y); const float* x2_data = geometry.ptr(0, 2, y); const float* x3_data = geometry.ptr(0, 3, y); const float* anglesData = geometry.ptr(0, 4, y);
for (int x = 0; x < numCols; x++) { float score = scoresData[x]; if (score < scoreThresh) continue;
float offsetX = x * 4.0; float offsetY = y * 4.0; float angle = anglesData[x]; float cosA = cos(angle); float sinA = sin(angle); float h = x0_data[x] + x2_data[x]; float w = x1_data[x] + x3_data[x];
Point2f offset(offsetX + cosA * x1_data[x] + sinA * x2_data[x], offsetY - sinA * x1_data[x] + cosA * x2_data[x]); Point2f p1 = Point2f(-sinA * h, -cosA * h) + offset; Point2f p3 = Point2f(-cosA * w, sinA * w) + offset; RotatedRect rrect(0.5f * (p1 + p3), Size2f(w, h), -angle * 180.0f / CV_PI); detections.push_back(rrect); } }
return detections; }
void TextDetectionController::handleTextDetection(const HttpRequestPtr& req, std::function&& callback) { Json::Value respStr; HttpStatusCode code = k200OK;
do { auto filename = req->getParameter("filename"); if (filename.empty()) { code = k404NotFound; break; }
std::string path = _storagePath.c_str() + filename; // Read Image From Server Local Mat image = imread(path); if (image.empty()) { code = k404NotFound; break ; }
// Set image dimensions and resize ratio int origH = image.rows; int origW = image.cols; int newW = 320; int newH = 320; float rW = static_cast(origW) / newW; float rH = static_cast(origH) / newH;
// Create blob and set input for the network Mat blob = blobFromImage(image, 1.0, Size(newW, newH), Scalar(123.68, 116.78, 103.94), true, false); eastNet.setInput(blob);
// Set output layers for the EAST model std::vector outputLayers = {"feature_fusion/Conv_7/Sigmoid", "feature_fusion/concat_3"}; std::vector outs; eastNet.forward(outs, outputLayers);
// Perform text detection std::vector detections = decodeBoundingBoxes(outs[0], outs[1], 0.7); if ( detections.size() <= 0 ) { // Return 404 if no text is detected code = k404NotFound; break; }
// Return results in JSON format Json::Value jsonResponse; for (const auto& detection : detections) { Rect boundingBox = detection.boundingRect(); boundingBox.x *= rW; boundingBox.y *= rH; boundingBox.width *= rW; boundingBox.height *= rH;
Json::Value box; box["x"] = boundingBox.x; box["y"] = boundingBox.y; box["width"] = boundingBox.width; box["height"] = boundingBox.height; jsonResponse["detections"].append(box); }
// Generate the response auto response = HttpResponse::newHttpJsonResponse(jsonResponse);
// Process Text Area Rectangle // Draw rectangles around detected text areas for (const auto& detection : detections) { Rect boundingBox = detection.boundingRect(); boundingBox.x *= rW; boundingBox.y *= rH; boundingBox.width *= rW; boundingBox.height *= rH;
rectangle(image, boundingBox, Scalar(0, 255, 0), 2);  // Draw green rectangle } // Save the image std::filesystem::path outputPath = _storagePath + (std::filesystem::path(path).stem().string() + "_detection.jpg"); imwrite(outputPath.string(), image);
callback(response); return ; } while(false);
auto resp = HttpResponse::newHttpResponse(); resp->setStatusCode(code); callback(resp); } 

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main.cc

 #include  #include "controllers/TextDetectionController.h"

int main() { try { // Load model during server initialization initializeModel(); } catch (const std::exception &e) { std::cerr << "Error initializing model: " << e.what() << std::endl; return 1;  // Stop server if model loading fails } drogon::app().loadConfigFile("../config.json");
LOG_INFO << "Server RUN"; drogon::app().run();
return 0; } 

2-3. Edit drogon CMake

``` cd /root/drogon2/drogon/build/drogon_ctl/testAPI vi CMakeLists.txt ```

- Modify as shown below to add TextDetectionController.cc after FileController.cc:

``` # Necessary changes: # Add find_package(OpenCV CONFIG REQUIRED) # Add ${OpenCV_LIBS} to target_link_libraries

# If OpenCV is not in the default path, specify the path (not needed if previous steps are followed) # set(OpenCV_DIR "/path/to/opencv") # Example: /usr/local/opencv

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add_executable(${PROJECT_NAME} main.cc controllers/FileController.cc controllers/TextDetectionController.cc)

find_package(Drogon CONFIG REQUIRED) find_package(OpenCV CONFIG REQUIRED) # Added target_link_libraries(${PROJECT_NAME} PRIVATE Drogon::Drogon ${OpenCV_LIBS}) # Added ```

2-4. Build drogon - Run cmake followed by make

``` cd /root/drogon2/drogon/build/drogon_ctl/testAPI/build cmake .. make ```

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3. Execution

3-1. Download Model Before Running

``` cd /root/drogon2/drogon/build/drogon_ctl/testAPI/build

# Detection model wget https://www.dropbox.com/s/r2ingd0l3zt8hxs/frozen_east_text_detection.tar.gz?dl=1

./testAPI ```

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3-2. Execution and Upload - Use a sample text image as shown below (sample image)

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- Upload the file to the server via the upload endpoint (http://127.0.0.1:10099/doUpload/). (Refer to the previous page for the upload implementation)

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- After upload, check the file list at (http://127.0.0.1:10099/list/)

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- Call the detection API (`http://127.0.0.1:10099/text-detection?filename=image_in_text.PNG`)

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- After detection, check the updated file list at (http://127.0.0.1:10099/list/)

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- Download and verify the detected image (`http://127.0.0.1:10099/download/image_in_text_detection.jpg`)

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Related Links

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Recommended Link

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