718c41634f
1.项目后端整体迁移至PaddleOCR-NCNN算法，已通过基本的兼容性测试 2.工程改为使用CMake组织，后续为了更好地兼容第三方库，不再提供QMake工程 3.重整权利声明文件，重整代码工程，确保最小化侵权风险 Log: 切换后端至PaddleOCR-NCNN，切换工程为CMake Change-Id: I4d5d2c5d37505a4a24b389b1a4c5d12f17bfa38c
140 lines
3.3 KiB
Markdown
140 lines
3.3 KiB
Markdown
### input data and extract output
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```cpp
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#include <opencv2/core/core.hpp>
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#include <opencv2/highgui/highgui.hpp>
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#include "net.h"
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int main()
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{
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cv::Mat img = cv::imread("image.ppm", CV_LOAD_IMAGE_GRAYSCALE);
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int w = img.cols;
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int h = img.rows;
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// subtract 128, norm to -1 ~ 1
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ncnn::Mat in = ncnn::Mat::from_pixels_resize(img.data, ncnn::Mat::PIXEL_GRAY, w, h, 60, 60);
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float mean[1] = { 128.f };
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float norm[1] = { 1/128.f };
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in.substract_mean_normalize(mean, norm);
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ncnn::Net net;
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net.load_param("model.param");
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net.load_model("model.bin");
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ncnn::Extractor ex = net.create_extractor();
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ex.set_light_mode(true);
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ex.set_num_threads(4);
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ex.input("data", in);
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ncnn::Mat feat;
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ex.extract("output", feat);
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return 0;
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}
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```
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### print Mat content
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```cpp
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void pretty_print(const ncnn::Mat& m)
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{
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for (int q=0; q<m.c; q++)
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{
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const float* ptr = m.channel(q);
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for (int z=0; z<m.d; z++)
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{
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for (int y=0; y<m.h; y++)
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{
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for (int x=0; x<m.w; x++)
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{
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printf("%f ", ptr[x]);
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}
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ptr += m.w;
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printf("\n");
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}
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printf("\n");
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}
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printf("------------------------\n");
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}
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}
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```
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### visualize Mat content
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```cpp
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void visualize(const char* title, const ncnn::Mat& m)
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{
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std::vector<cv::Mat> normed_feats(m.c);
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for (int i=0; i<m.c; i++)
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{
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cv::Mat tmp(m.h, m.w, CV_32FC1, (void*)(const float*)m.channel(i));
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cv::normalize(tmp, normed_feats[i], 0, 255, cv::NORM_MINMAX, CV_8U);
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cv::cvtColor(normed_feats[i], normed_feats[i], cv::COLOR_GRAY2BGR);
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// check NaN
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for (int y=0; y<m.h; y++)
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{
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const float* tp = tmp.ptr<float>(y);
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uchar* sp = normed_feats[i].ptr<uchar>(y);
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for (int x=0; x<m.w; x++)
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{
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float v = tp[x];
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if (v != v)
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{
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sp[0] = 0;
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sp[1] = 0;
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sp[2] = 255;
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}
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sp += 3;
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}
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}
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}
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int tw = m.w < 10 ? 32 : m.w < 20 ? 16 : m.w < 40 ? 8 : m.w < 80 ? 4 : m.w < 160 ? 2 : 1;
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int th = (m.c - 1) / tw + 1;
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cv::Mat show_map(m.h * th, m.w * tw, CV_8UC3);
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show_map = cv::Scalar(127);
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// tile
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for (int i=0; i<m.c; i++)
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{
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int ty = i / tw;
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int tx = i % tw;
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normed_feats[i].copyTo(show_map(cv::Rect(tx * m.w, ty * m.h, m.w, m.h)));
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}
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cv::resize(show_map, show_map, cv::Size(0,0), 2, 2, cv::INTER_NEAREST);
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cv::imshow(title, show_map);
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}
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```
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### FAQ
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Q ncnn的起源
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A 深度学习算法要在手机上落地，caffe依赖太多，手机上也没有cuda，需要个又快又小的前向网络实现
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Q ncnn名字的来历
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A cnn就是卷积神经网络的缩写，开头的n算是一语n关。比如new/next(全新的实现)，naive(ncnn是naive实现)，neon(ncnn最初为手机优化)，up主名字(←_←)
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Q 支持哪些平台
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A 跨平台，支持 android / ios / linux / windows / macos，也支持裸机跑
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Q 计算精度如何
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A armv7 neon float 不遵照 ieee754 标准，有些采用快速实现(如exp sin等)，速度快但确保精度足够高
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Q logo
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A up主是mc玩家，所以灵魂手绘像素猫，还可以找到ncnn...
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