add dll version and its usage
This commit is contained in:
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@@ -0,0 +1,389 @@
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#include "pch.h"
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#include "OCRCharset.h"
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#include "EasyOCR_Recognizer.h"
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uns::EasyOCR_Recognizer::NormalizePAD::Size3i uns::EasyOCR_Recognizer::NormalizePAD::Size3i::operator=(const Size3i& obj)
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{
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d0 = obj.d0;
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d1 = obj.d1;
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d2 = obj.d2;
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return (*this);
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}
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uns::EasyOCR_Recognizer::NormalizePAD::NormalizePAD(Size3i max_size, const std::string& PAD_type)
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{
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this->max_size = max_size;
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this->PAD_type = PAD_type;
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max_width_half = max_size.d2 / 2; // 计算宽度的一半，用于可选操作
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}
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cv::Mat uns::EasyOCR_Recognizer::NormalizePAD::operator()(const cv::Mat& input_img) const
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{
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// 将原图转换为32位浮点型并归一化到[0,1]
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cv::Mat img;
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input_img.convertTo(img, CV_32F, 1.0 / 255); // line 10: img = toTensor
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img = (img - 0.5f) / 0.5f; // line 11: img.sub_(0.5).div_(0.5)
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int h = img.rows; // 获取图像高度
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int w = img.cols; // 获取图像宽度
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int c = img.channels(); // 获取通道数，灰度图默认为1
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// 创建目标大小的全零Mat，类型为32F，尺寸为max_size.d1 x max_size.d2
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cv::Mat pad_img = cv::Mat::zeros(max_size.d1, max_size.d2, CV_32FC(c)); // line 13
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// 将原图像拷贝到pad_img的左上角区域，实现右侧填充
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img.copyTo(pad_img(cv::Rect(0, 0, w, h))); // line 14
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// 如果目标宽度大于原图宽度，则使用最后一列像素进行扩展填充
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if (max_size.d2 != w)
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{ // line 15
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cv::Mat last_col = img.col(w - 1);
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cv::Mat border;
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cv::repeat(last_col, 1, max_size.d2 - w, border); // 重复最后一列填充
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border.copyTo(pad_img(cv::Rect(w, 0, max_size.d2 - w, h)));
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}
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return pad_img; // 返回处理后的浮点张量
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}
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cv::Mat uns::EasyOCR_Recognizer::AlignCollate::AdjustContrastGrey(const cv::Mat& img_in, double target) const
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{
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double contrast;
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int high, low;
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ContrastGrey(img_in, contrast, high, low);
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cv::Mat img = img_in.clone();
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if (contrast < target)
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{
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cv::Mat img_i;
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img.convertTo(img_i, CV_32S);
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double ratio = 200.0 / std::max(10, high - low);
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img_i = (img_i - low + 25) * ratio;
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// 将像素值限制在[0,255]范围，并转换回8位
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img_i.forEach<int>([] (int& pixel, const int*)
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{
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pixel = std::clamp(pixel, 0, 255);
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});
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img_i.convertTo(img, CV_8U);
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}
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return img;
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}
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void uns::EasyOCR_Recognizer::AlignCollate::ContrastGrey(const cv::Mat& img, double& contrast, int& high, int& low) const
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{
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// 将Mat图像数据复制到一个连续的vector<int>中，以便排序
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std::vector<int> pixels;
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pixels.reserve(img.rows * img.cols); // 预分配空间以提高效率
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for (int i = 0; i < img.rows; ++i)
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{
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const uchar* row_ptr = img.ptr<uchar>(i);
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for (int j = 0; j < img.cols; ++j)
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pixels.push_back(static_cast<int>(row_ptr[j]));
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}
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// 对像素值进行排序，便于获取百分位数
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std::sort(pixels.begin(), pixels.end());
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// 计算90%的索引位置，与Python np.percentile保持一致
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int idx90 = static_cast<int>(0.9 * (pixels.size() - 1));
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int idx10 = static_cast<int>(0.1 * (pixels.size() - 1));
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high = pixels[idx90];
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low = pixels[idx10];
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// 计算contrast: (high - low) / max(10, high + low)
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contrast = double(high - low) / double(std::max(10, high + low));
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}
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uns::EasyOCR_Recognizer::AlignCollate::AlignCollate(int imgH, int imgW, bool keep_ratio_with_pad, double adjust_contrast)
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{
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this->imgH = imgH;
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this->imgW = imgW;
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this->adjust_contrast = adjust_contrast;
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this->keep_ratio_with_pad = keep_ratio_with_pad;
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}
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cv::Mat uns::EasyOCR_Recognizer::AlignCollate::operator()(const std::vector<cv::Mat>& batch) const
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{
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std::vector<cv::Mat> resized_images;
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// 创建NormalizePAD实例，用于归一化和填充
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NormalizePAD transform({ 1, imgH, imgW });
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for (const cv::Mat& image : batch)
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{
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cv::Mat working;
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if (adjust_contrast > 0)
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{
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cv::Mat grey;
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if (image.channels() > 1)
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cv::cvtColor(image, grey, cv::COLOR_BGR2GRAY);
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else
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grey = image;
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working = AdjustContrastGrey(grey, adjust_contrast);
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}
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else
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working = image;
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int w = working.cols;
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int h = working.rows;
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double ratio = double(w) / h;
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int resized_w = static_cast<int>(std::ceil(imgH * ratio));
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if (resized_w > imgW)
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resized_w = imgW;
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cv::Mat resized;
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cv::resize(working, resized, cv::Size(resized_w, imgH), 0, 0, cv::INTER_CUBIC);
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cv::Mat tensor = transform(resized);
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resized_images.push_back(tensor);
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}
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cv::Mat blob;
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cv::dnn::blobFromImages(resized_images, blob);
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return blob;
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}
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float uns::EasyOCR_Recognizer::CustomMean(const VecFloat& x)
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{
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size_t N = x.size();
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if (N == 0)
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return 0.0f;
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// 1. 计算所有元素的乘积
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double prod = 1.0;
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for (float v : x)
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if (v != 0)
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prod *= static_cast<double>(v);
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// 2. 计算指数 2.0 / sqrt(N)
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double exponent = 2.0 / std::sqrt(static_cast<double>(N));
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// 3. 返回 prod 的 exponent 次幂
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return static_cast<float>(std::pow(prod, exponent));
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}
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cv::Mat uns::EasyOCR_Recognizer::Preprocess(const cv::Mat& img) const
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{
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if (img.empty())
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return {}; //此处不适合抛出异常，使用空图像终止后级的处理即可
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cv::Mat gray;
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int ch = img.channels();
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// case 2: BGR 彩色图（3 通道）
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if (ch == 3)
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cv::cvtColor(img, gray, cv::COLOR_BGR2GRAY);
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// case 3: RGBA 彩色图（4 通道）
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else if (ch == 4)
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{
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// 去掉 alpha 通道，把 BGRA → GRAY
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cv::Mat bgr;
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cv::cvtColor(img, gray, cv::COLOR_BGRA2GRAY);
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}
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else // image 本身可能是 (h×w) 或者 (h×w×1)，对我们来说都当灰度处理
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gray = img;
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int width = gray.cols;
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int height = gray.rows;
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int model_height = 64, model_width = 0;
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float ratio = static_cast<float>(width) / static_cast<float>(height);
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cv::Mat resized;
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if (ratio < 1.0f)
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{
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// 垂直文本情况，使用 calculate_ratio 保证高度为 model_height
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float adj_ratio = CalculateRatio(width, height);
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model_width = static_cast<int>(model_height * adj_ratio);
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cv::resize(gray, resized, cv::Size(model_height, model_width), 0, 0, cv::INTER_LINEAR);
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ratio = adj_ratio;
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}
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else
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{
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// 横向文本情况，高度为 model_height
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model_width = static_cast<int>(model_height * ratio);
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cv::resize(gray, resized, cv::Size(model_width, model_height), 0, 0, cv::INTER_LINEAR);
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}
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AlignCollate alignCollate(model_height, model_width, true, 0.5);
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return alignCollate({ resized });
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}
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float uns::EasyOCR_Recognizer::CalculateRatio(int width, int height) const
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{
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float ratio = static_cast<float>(width) / static_cast<float>(height);
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if (ratio < 1.0f)
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ratio = 1.0f / ratio;
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return ratio;
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}
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uns::VecFloat uns::EasyOCR_Recognizer::SoftMAX(const float* logits, int C) const
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{
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// 找到最大值以稳定数值
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float m = logits[0];
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for (int i = 1; i < C; ++i)
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m = std::max(m, logits[i]);
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// 计算 exp(logit - m)
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std::vector<float> exps(C);
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float sum = 0.f;
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for (int i = 0; i < C; ++i)
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{
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exps[i] = std::exp(logits[i] - m);
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sum += exps[i];
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}
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// 归一化
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for (int i = 0; i < C; ++i)
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exps[i] /= (sum > 1e-6f ? sum : 1e-6f);
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return exps;
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}
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void uns::EasyOCR_Recognizer::PostprocessONNXOutput(const Ort::Value& outputs, int N, int T, int C, VecInt& out_indices, VecFloat& out_probs, const VecInt ignore_idx)
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{
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// 指针访问底层数据
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const float* data = outputs.GetTensorData<float>();
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out_indices.clear();
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out_probs.clear();
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// 临时存储每步概率
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std::vector<float> probs;
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probs.reserve(C);
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// 遍历每个样本、每个时间步
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for (int n = 0; n < N; ++n)
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{
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for (int t = 0; t < T; ++t)
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{
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// logits 起始位置: ((n * T) + t) * C
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const float* logits = data + ((size_t)n * T + t) * C;
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// 1) Softmax
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probs = SoftMAX(logits, C);
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// 2) 忽略 ignore_idx
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if (!ignore_idx.empty())
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for (const auto& idx : ignore_idx)
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probs[idx] = 0.f;
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// 3) 再次归一化
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float sum = 0.f;
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for (int c = 0; c < C; ++c)
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sum += probs[c];
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if (sum > 1e-6f)
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{
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for (int c = 0; c < C; ++c)
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probs[c] /= sum;
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}
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// 4) 取最大索引
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int best = 0;
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float best_prob = 0.0f;
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for (int c = 1; c < C; ++c)
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{
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if (probs[c] > probs[best])
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{
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best = c;
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best_prob = probs[c];
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}
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}
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out_indices.push_back(best);
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out_probs.push_back(best_prob);
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}
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}
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}
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uns::EasyOCR_Recognizer::EasyOCR_Recognizer()
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{
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ort_inited = false;
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ort_cpu_session = nullptr;
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model_path = G_OCRConfig.GetRecognizeModelPath();
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ort = OrtGetApiBase()->GetApi(ORT_API_VERSION);
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}
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bool uns::EasyOCR_Recognizer::Init()
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{
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if (ort_inited)
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return true;
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if (!RecheckModelInfo())
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return false;
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try
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{
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ort_env = Ort::Env(ORT_LOGGING_LEVEL_WARNING, "EasyOCR_Recognizer");
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bool fallback_to_cpu = false;
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if (!OCRToolBox::AutoSelectEP(ort, ort_session_options, fallback_to_cpu))
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return false;
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OCRToolBox::InitOrtSessionOptions(ort_session_options);
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if ((G_OCRConfig.GetGPUUsage() == easyocr::GPUUsage::CPUOnly) || fallback_to_cpu) //使用CPU则初始化cpu session
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{
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ort_cpu_session = new Ort::Session(ort_env, model_path.c_str(), ort_session_options);
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//通过CPU session获取输入输出名
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OCRToolBox::GetInputOutputNames(ort_cpu_session, input_names, input_ns, output_names, output_ns);
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}
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else
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{
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//通过临时session获取输入输出名（CUDA：线程不安全）
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Ort::Session ort_session(ort_env, model_path.c_str(), ort_session_options);
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OCRToolBox::GetInputOutputNames(&ort_session, input_names, input_ns, output_names, output_ns);
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}
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ort_inited = true;
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return true;
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}
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catch (...)
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{
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return false;
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}
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}
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bool uns::EasyOCR_Recognizer::UnInit()
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{
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try
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{
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if (ort_cpu_session != nullptr)
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delete ort_cpu_session;
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ort_cpu_session = nullptr;
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return true;
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}
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catch (...)
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{
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return false;
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}
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}
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bool uns::EasyOCR_Recognizer::RecheckModelInfo()
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{
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if (model_path.empty())
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model_path = G_OCRConfig.GetRecognizeModelPath();
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return OCRToolBox::CheckFile(model_path);
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}
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uns::EOCR_Result uns::EasyOCR_Recognizer::operator()(const cv::Mat& image)
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{
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try
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{
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if (!RecheckModelInfo())
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return { L"", -1.0f };
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cv::Mat input = Preprocess(image);
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if (input.empty())
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return { L"", 0.0f };
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std::array<int64_t, 4> inputShape = { 1, 1, input.size[2], input.size[3] };
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Ort::MemoryInfo memInfo = Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeDefault);
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Ort::Value inputTensor = Ort::Value::CreateTensor<float>(memInfo, input.ptr<float>(), input.total(), inputShape.data(), inputShape.size());
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auto outputs = ((ort_cpu_session != nullptr) ? ort_cpu_session->Run(Ort::RunOptions{nullptr}, input_names.data(), & inputTensor, 1, output_names.data(), 1) : Ort::Session(ort_env, model_path.c_str(), ort_session_options).Run(Ort::RunOptions{nullptr}, input_names.data(), & inputTensor, 1, output_names.data(), 1));
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// 输出 shape: [1, T, C]
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auto& outVal = outputs.front();
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auto info = outVal.GetTensorTypeAndShapeInfo();
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auto shape = info.GetShape(); // {1, T, C}
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int N = (int)shape[0], T = (int)shape[1], C = (int)shape[2];
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float* data = outVal.GetTensorMutableData<float>();
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// greedy pick & softmax
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std::vector<int> indices(T);
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std::vector<float> maxProbs(T);
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PostprocessONNXOutput(outputs[0], N, T, C, indices, maxProbs);
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// 解码
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std::wstring text = OCRCharset::GetString(indices);
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// 置信度
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float conf = CustomMean(maxProbs);
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return { text, conf };
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}
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catch (...)
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{
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return { L"", -2.0f };
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}
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}
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uns::EOCR_ResultSet uns::EasyOCR_Recognizer::operator()(const cv::Mat& full_image, const EOCRD_Rects& rects)
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{
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if (!RecheckModelInfo())
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return {};
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try
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{
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EOCR_ResultSet result_set;
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for (size_t i = 0; i < rects.size(); ++i)
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{
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// 将多边形转为最小外接矩形并裁剪
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cv::Rect rect = cv::boundingRect(rects[i]);
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rect &= cv::Rect(0, 0, full_image.cols, full_image.rows); // 裁剪到图像范围
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cv::Mat crop = full_image(rect);
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if (crop.empty())
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continue;
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auto [text, conf] = (*this)(crop);
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result_set.insert({ i, { text, conf } });
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}
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return result_set;
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}
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catch (...)
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{
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return {};
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}
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}
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