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ANSCORE/tests/ANSOCR-UnitTest/ANSOCR-UnitTest.cpp

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Add unit tests
2026-03-29 08:45:38 +11:00
#include <iostream>
#include <opencv2/highgui.hpp>
#include "boost/property_tree/ptree.hpp"
#include "boost/property_tree/json_parser.hpp"
#include "boost/foreach.hpp"
#include "boost/optional.hpp"
#include <opencv2/core.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/dnn/dnn.hpp>
#include <iostream>
#include <vector>
#include <numeric>
#include <string>
#include <functional>
#include <filesystem>
#include <algorithm>
#include <chrono>
#include <ANSOCRBase.h>
#ifdef WIN32
const char sep = '\\';
#else
const char sep = '/';
#endif
using namespace cv;
template <typename T>
T GetData(const boost::property_tree::ptree& pt, const std::string& key)
{
T ret;
if (boost::optional<T> data = pt.get_optional<T>(key))
{
ret = data.get();
}
return ret;
}
unsigned char* CVMatToBytes(cv::Mat image, unsigned int& bufferLengh)
{
int size = int(image.total() * image.elemSize());
std::cout << "size:" << size << std::endl;
unsigned char* bytes = new unsigned char[size]; // you will have to delete[] that later
std::memcpy(bytes, image.data, size * sizeof(unsigned char));
bufferLengh = size * sizeof(unsigned char);
return bytes;
}
int TestOCRImage() {
ANSCENTER::ANSOCRBase* infHandle = nullptr;
boost::property_tree::ptree root;
boost::property_tree::ptree detectionObjects;
boost::property_tree::ptree pt;
std::filesystem::path currentPath = std::filesystem::current_path();
std::cout << "Current working directory: " << currentPath << std::endl;
std::string licenseKey = "";
std::string modelFilePath = currentPath.string() + "\\ansocrmodels.zip";
std::string imagePath = currentPath.string() + "\\ocrsample.png";
std::string defaultDir = "C:\\Programs\\DemoAssets\\ANSAIModels";
if (!std::filesystem::exists(modelFilePath)) modelFilePath = defaultDir + "\\ANS_GenericOCR_v1.0.zip";
if (!std::filesystem::exists(imagePath)) imagePath = defaultDir + "\\ocrsample.png";
imagePath = "C:\\Projects\\ANSVIS\\Documentation\\TestImages\\OCR\\ocrsample.png";
int language = 0; // CUSTOM
int engine = 0;
int createResult = CreateANSOCRHandle(&infHandle, licenseKey.c_str(), modelFilePath.c_str(), "", language, engine);
std::cout << "ANSOCR Engine Creation:" << createResult << std::endl;
cv::Mat input = cv::imread(imagePath, cv::IMREAD_COLOR);
cv::Mat frame = input.clone();
int height = frame.rows;
int width = frame.cols;
unsigned int bufferLength = 0;
unsigned char* jpeg_string = CVMatToBytes(frame, bufferLength);
std::string detectionResult = RunInferenceBinary(&infHandle, jpeg_string, width, height);
std::cout << "Result:" << detectionResult;
delete jpeg_string;
if (!detectionResult.empty()) {
pt.clear();
std::stringstream ss;
ss.clear();
ss << detectionResult;
boost::property_tree::read_json(ss, pt);
BOOST_FOREACH(const boost::property_tree::ptree::value_type & child, pt.get_child("results"))
{
const boost::property_tree::ptree& result = child.second;
const auto class_id = GetData<int>(result, "class_id");
const auto class_name = GetData<std::string>(result, "class_name");
const auto x = GetData<float>(result, "x");
const auto y = GetData<float>(result, "y");
const auto width = GetData<float>(result, "width");
const auto height = GetData<float>(result, "height");
cv::rectangle(frame, cv::Rect(x, y, width, height), 123, 2);
cv::putText(frame, cv::format("%s", class_name), cv::Point(x, y - 5),
0, 2.0, cv::Scalar(0, 0, 255), 3, cv::LINE_AA);
}
}
cv::resize(frame, frame, cv::Size(frame.cols / 2, frame.rows / 2)); // to half size or even smaller
frame.release();
ReleaseANSOCRHandle(&infHandle);
return 0;
}
int ANSOCR_VideoTest() {
// Get the current working directory
std::filesystem::path currentPath = std::filesystem::current_path();
// Print the current working directory
std::cout << "Current working directory: " << currentPath << std::endl;
boost::property_tree::ptree root;
boost::property_tree::ptree detectionObjects;
boost::property_tree::ptree pt;
ANSCENTER::ANSOCRBase* infHandle;
std::string licenseKey = "";
std::string modelFilePath = "C:\\ProgramData\\ANSCENTER\\Shared\\ANS_GenericOCR_v1.0.zip";
std::string videoFilePath = "C:\\Programs\\DemoAssets\\Videos\\ALRP\\ALPR1.mp4";
cv::VideoCapture capture(videoFilePath);
if (!capture.isOpened()) {
printf("could not read this video file...\n");
return -1;
}
int language = 0;// CUSTOM
int engine = 0;
int createResult = CreateANSOCRHandle(&infHandle, licenseKey.c_str(), modelFilePath.c_str(), "", language, engine);
while (true)
{
cv::Mat frame;
if (!capture.read(frame)) // if not success, break loop
{
std::cout << "\n Cannot read the video file. please check your video.\n";
break;
}
auto start = std::chrono::system_clock::now();
unsigned int bufferLength = 0;
unsigned char* jpeg_string = CVMatToBytes(frame, bufferLength);
int height = frame.rows;
int width = frame.cols;
std::string detectionResult = RunInferenceBinary(&infHandle, jpeg_string, width, height);
if (!detectionResult.empty()) {
pt.clear();
std::stringstream ss;
ss.clear();
ss << detectionResult;
boost::property_tree::read_json(ss, pt);
BOOST_FOREACH(const boost::property_tree::ptree::value_type & child, pt.get_child("results"))
{
const boost::property_tree::ptree& result = child.second;
const auto class_id = GetData<int>(result, "class_id");
const auto class_name = GetData<std::string>(result, "class_name");
const auto x = GetData<float>(result, "x");
const auto y = GetData<float>(result, "y");
const auto width = GetData<float>(result, "width");
const auto height = GetData<float>(result, "height");
cv::rectangle(frame, cv::Rect(x, y, width, height), 123, 2);
cv::putText(frame, cv::format("%s", class_name), cv::Point(x, y - 5),
0, 2.0, cv::Scalar(0, 0, 255), 3, cv::LINE_AA);
}
}
auto end = std::chrono::system_clock::now();
auto elapsed = std::chrono::duration_cast<std::chrono::milliseconds>(end - start);
printf("Time = %lld ms\n", static_cast<long long int>(elapsed.count()));
// cv::resize(frame, frame, cv::Size(frame.cols / 2, frame.rows / 2)); // to half size or even smaller
cv::imshow("ANSOCR", frame);
if (cv::waitKey(30) == 27) // Wait for 'esc' key press to exit
{
std::cout << "End of inserting faces.\n";
}
frame.release();
delete jpeg_string;
}
capture.release();
cv::destroyAllWindows();
ReleaseANSOCRHandle(&infHandle);
}
// Viewer state for zoom/pan
struct ImageViewerState {
cv::Mat image; // Full-resolution annotated image
double zoom = 1.0; // 1.0 = fit-to-screen
double panX = 0.0; // Top-left corner in original image coords
double panY = 0.0;
int dispW, dispH; // Display window size (pixels)
double fitScale; // Base scale to fit image into window
bool dragging = false;
int dragX0, dragY0;
double panX0, panY0;
bool dirty = true;
};
static void onViewerMouse(int event, int x, int y, int flags, void* userdata) {
ImageViewerState& s = *(ImageViewerState*)userdata;
if (event == cv::EVENT_MOUSEWHEEL) {
double factor = (cv::getMouseWheelDelta(flags) > 0) ? 1.25 : 0.8;
// Zoom centered on mouse cursor position
double sc = s.fitScale * s.zoom;
double imgX = s.panX + x / sc;
double imgY = s.panY + y / sc;
s.zoom = std::clamp(s.zoom * factor, 0.2, 50.0);
double newSc = s.fitScale * s.zoom;
s.panX = imgX - x / newSc;
s.panY = imgY - y / newSc;
s.dirty = true;
}
else if (event == cv::EVENT_LBUTTONDOWN) {
s.dragging = true;
s.dragX0 = x; s.dragY0 = y;
s.panX0 = s.panX; s.panY0 = s.panY;
}
else if (event == cv::EVENT_MOUSEMOVE && s.dragging) {
double sc = s.fitScale * s.zoom;
s.panX = s.panX0 - (x - s.dragX0) / sc;
s.panY = s.panY0 - (y - s.dragY0) / sc;
s.dirty = true;
}
else if (event == cv::EVENT_LBUTTONUP) {
s.dragging = false;
}
}
int TestOCRv5mage() {
ANSCENTER::ANSOCRBase* infHandle = nullptr;
boost::property_tree::ptree root;
boost::property_tree::ptree detectionObjects;
boost::property_tree::ptree pt;
std::filesystem::path currentPath = std::filesystem::current_path();
std::cout << "Current working directory: " << currentPath << std::endl;
std::string licenseKey = "";
std::string modelFilePath = "C:\\Projects\\ANSVIS\\Models\\ANS_GenericOCR_v2.0.zip";
std::string imagePath = "E:\\Programs\\DemoAssets\\Images\\OCR\\ref3_000.bmp";
int language = 0; // CUSTOM
int engine = 1;// GPU
// For high-resolution images with PP-OCRv5 server models, use higher limitSideLen
// (default 960 downscales large images too aggressively, missing small text)
int gpuId = 0;
double detDBThresh = 0.3, detBoxThresh = 0.6, detUnclipRatio = 1.5;
double clsThresh = 0.9;
int useDilation = 0;
int limitSideLen = 2560; // 2560 Higher resolution for server-grade detection
int createResult = CreateANSOCRHandleEx(&infHandle, licenseKey.c_str(), modelFilePath.c_str(), "",
language, engine, gpuId, detDBThresh, detBoxThresh, detUnclipRatio, clsThresh, useDilation, limitSideLen);
std::cout << "ANSOCR Engine Creation:" << createResult << std::endl;
cv::Mat input = cv::imread(imagePath, cv::IMREAD_COLOR);
if (input.empty()) {
std::cerr << "Failed to load image: " << imagePath << std::endl;
ReleaseANSOCRHandle(&infHandle);
return -1;
}
cv::Mat frame = input.clone();
int height = frame.rows;
int width = frame.cols;
unsigned int bufferLength = 0;
unsigned char* jpeg_string = CVMatToBytes(frame, bufferLength);
// --- Warmup run (first run includes GPU kernel compilation / cache warmup) ---
auto warmupStart = std::chrono::high_resolution_clock::now();
std::string detectionResult = RunInferenceBinary(&infHandle, jpeg_string, width, height);
auto warmupEnd = std::chrono::high_resolution_clock::now();
double warmupMs = std::chrono::duration<double, std::milli>(warmupEnd - warmupStart).count();
std::cout << "Warmup inference: " << warmupMs << " ms" << std::endl;
std::cout << "Result:" << detectionResult << std::endl;
// --- Benchmark: run N iterations and report stats ---
const int benchmarkIterations = 10;
std::vector<double> times;
times.reserve(benchmarkIterations);
for (int i = 0; i < benchmarkIterations; ++i) {
auto t0 = std::chrono::high_resolution_clock::now();
std::string result = RunInferenceBinary(&infHandle, jpeg_string, width, height);
auto t1 = std::chrono::high_resolution_clock::now();
double ms = std::chrono::duration<double, std::milli>(t1 - t0).count();
times.push_back(ms);
std::cout << " Run " << (i + 1) << "/" << benchmarkIterations << ": " << ms << " ms" << std::endl;
}
std::sort(times.begin(), times.end());
double sum = std::accumulate(times.begin(), times.end(), 0.0);
double avg = sum / benchmarkIterations;
double median = (benchmarkIterations % 2 == 0)
? (times[benchmarkIterations / 2 - 1] + times[benchmarkIterations / 2]) / 2.0
: times[benchmarkIterations / 2];
std::cout << "\n=== Benchmark (" << benchmarkIterations << " runs) ===" << std::endl;
std::cout << " Avg: " << avg << " ms" << std::endl;
std::cout << " Median: " << median << " ms" << std::endl;
std::cout << " Min: " << times.front() << " ms" << std::endl;
std::cout << " Max: " << times.back() << " ms" << std::endl;
std::cout << " FPS: " << (1000.0 / avg) << std::endl;
delete[] jpeg_string;
// Draw OCR results on frame — 1.5x of original (was fontScale=1.5, thickness=3, offset=5)
double fontScale = 2.25; // 1.5 * 1.5
int boxThickness = 3;
int fontThickness = 5; // ceil(3 * 1.5)
int textOffset = 8;
if (!detectionResult.empty()) {
pt.clear();
std::stringstream ss;
ss.clear();
ss << detectionResult;
boost::property_tree::read_json(ss, pt);
BOOST_FOREACH(const boost::property_tree::ptree::value_type & child, pt.get_child("results"))
{
const boost::property_tree::ptree& result = child.second;
const auto class_id = GetData<int>(result, "class_id");
const auto class_name = GetData<std::string>(result, "class_name");
const auto x = GetData<float>(result, "x");
const auto y = GetData<float>(result, "y");
const auto w = GetData<float>(result, "width");
const auto h = GetData<float>(result, "height");
cv::rectangle(frame, cv::Rect((int)x, (int)y, (int)w, (int)h),
cv::Scalar(0, 255, 0), boxThickness);
cv::putText(frame, class_name, cv::Point((int)x, (int)y - textOffset),
cv::FONT_HERSHEY_SIMPLEX, fontScale, cv::Scalar(0, 0, 255), fontThickness, cv::LINE_AA);
}
}
// === Interactive Image Viewer (zoom/pan) ===
ImageViewerState vs;
vs.image = frame;
// Calculate scale to fit image into ~80% of a 1920x1080 screen
const int maxWinW = 1600, maxWinH = 900;
double scaleX = (double)maxWinW / frame.cols;
double scaleY = (double)maxWinH / frame.rows;
vs.fitScale = std::min(scaleX, scaleY);
if (vs.fitScale > 1.0) vs.fitScale = 1.0; // Don't upscale small images
vs.dispW = (int)(frame.cols * vs.fitScale);
vs.dispH = (int)(frame.rows * vs.fitScale);
const std::string winName = "ANSOCR [Scroll=Zoom | Drag=Pan | R=Reset | ESC=Quit]";
cv::namedWindow(winName, cv::WINDOW_AUTOSIZE);
cv::setMouseCallback(winName, onViewerMouse, &vs);
while (true) {
if (vs.dirty) {
double sc = vs.fitScale * vs.zoom;
int srcW = std::min((int)(vs.dispW / sc), vs.image.cols);
int srcH = std::min((int)(vs.dispH / sc), vs.image.rows);
if (srcW <= 0) srcW = 1;
if (srcH <= 0) srcH = 1;
int sx = std::clamp((int)vs.panX, 0, std::max(0, vs.image.cols - srcW));
int sy = std::clamp((int)vs.panY, 0, std::max(0, vs.image.rows - srcH));
vs.panX = sx;
vs.panY = sy;
cv::Mat roi = vs.image(cv::Rect(sx, sy, srcW, srcH));
cv::Mat display;
cv::resize(roi, display, cv::Size(vs.dispW, vs.dispH), 0, 0,
(sc >= 1.0) ? cv::INTER_LINEAR : cv::INTER_AREA);
// Overlay zoom info
cv::putText(display, cv::format("Zoom: %.1fx (%dx%d)", vs.zoom, vs.image.cols, vs.image.rows),
cv::Point(10, 25), cv::FONT_HERSHEY_SIMPLEX, 0.6, cv::Scalar(0, 255, 0), 2);
cv::imshow(winName, display);
vs.dirty = false;
}
int key = cv::waitKey(30) & 0xFF;
if (key == 27) break; // ESC to quit
if (key == 'r' || key == 'R') {
vs.zoom = 1.0; vs.panX = 0; vs.panY = 0; vs.dirty = true; // Reset view
}
if (key == '+' || key == '=') {
vs.zoom = std::min(vs.zoom * 1.25, 50.0); vs.dirty = true; // Keyboard zoom in
}
if (key == '-' || key == '_') {
vs.zoom = std::max(vs.zoom * 0.8, 0.2); vs.dirty = true; // Keyboard zoom out
}
// Quit when user closes the window (clicks X button)
if (cv::getWindowProperty(winName, cv::WND_PROP_VISIBLE) < 1) break;
}
cv::destroyAllWindows();
frame.release();
input.release();
ReleaseANSOCRHandle(&infHandle);
return 0;
}
int main()
{
TestOCRv5mage();
//ANSOCR_VideoTest();
// TestOCRImage();
/* for (int i = 0; i < 20; i++) {
TestOCRImage();
}*/
return 0;
}
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