ImageProcess，官方文档中的示例应用于 灰度图，从Tensor.print()出来的数值无法用matplotlib绘制出图像 about mnn HOT 10 CLOSED

Matrix trans1;
trans1.setIdentity();

ImageProcess::Config iprscfg1;
iprscfg1.filterType = BILINEAR;

iprscfg1.sourceFormat = GRAY;
iprscfg1.destFormat = GRAY;

auto process1 = ImageProcess::create(iprscfg1);
process1->setMatrix(trans1);

vector<int> shape{1,1,1920,1080};
auto nchwTensor = Tensor::create(shape, halide_type_of<uint8_t>(), static_cast<void*>(pimg), Tensor::CAFFE);

auto code = process1->convert(pimg, width, height, width, nchwTensor);
assert(code==0);
nchwTensor->print();

auto nchwTensor = Tensor::create(shape, halide_type_of<uint8_t>(), pimg, Tensor::CAFFE);
auto code = process1->convert(pimg, width, height, width, nchwTensor);

auto nchwTensor = Tensor::create(shape, halide_type_of<uint8_t>(), nullptr, Tensor::CAFFE);
auto code = process1->convert(pimg, width, height, width, nchwTensor);

    vector<int> shape{1,1,height,width};
    
    std::shared_ptr<Tensor> wrapTensor(Tensor::create(shape, halide_type_of<int8_t>(), nullptr, Tensor::CAFFE));

    auto code = process1->convert(pimg, width, height, width, wrapTensor.get());
    assert(code==0);
    for(int i=0; i<width*height; i++){
      cout << static_cast<int>(pimg[i]) << ", ";
      cout << static_cast<int>(wrapTensor->host<uint8_t>()[i]) << ", ";
    }

    CV::Matrix trans1;
    trans1.setIdentity();
    // //Dst -> [0, 1]
    // trans.postScale(1.0/size_w, 1.0/size_h);
    // //Flip Y  （因为 FreeImage 解出来的图像排列是Y方向相反的）
    // trans.postScale(1.0,-1.0, 0.0, 0.5);
    //[0, 1] -> Src
    // trans1.postScale(320, 320);

    CV::ImageProcess::Config iprscfg1;
    iprscfg1.filterType = CV::NEAREST;
    // float mean[1] = {128.94f};
    // float normals[1] = {0.227f};
    // ::memcpy(iprscfg1.mean, mean, sizeof(mean));
    // ::memcpy(iprscfg1.normal, normals, sizeof(normals));
    iprscfg1.sourceFormat = CV::GRAY;
    iprscfg1.destFormat = CV::GRAY;
    iprscfg1.wrap       = CV::ZERO;

    process1 = CV::ImageProcess::create(iprscfg1);
    process1->setMatrix(trans1);

int main(int argc, const char* argv[]) {
    if (argc < 4) {
        printf("Usage: ./pictureRotate.out input.jpg angle output.jpg\n");
        return 0;
    }
    auto inputPatch = argv[1];
    auto angle      = ::atof(argv[2]);
    auto destPath   = argv[3];
    int width, height, channel;
    auto inputImage = stbi_load(inputPatch, &width, &height, &channel, 4);

    uint8_t p[width*height];
    
    for(int i=0; i<width*height; i+=1){
        // std::cout << (int)inputImage[i*4] << ",";
        p[i] = inputImage[i*4];
    }
    std::cout << std::endl;

    MNN_PRINT("size: %d, %d\n", width, height);
    Matrix trans;
    trans.setScale(1.0 / (width - 1), 1.0 / (height - 1));
    trans.postRotate(-angle, 0.5, 0.5);
    trans.postScale((width/4), (height/4));
    ImageProcess::Config config;
    config.filterType   = BILINEAR;
    config.sourceFormat = GRAY;
    config.destFormat   = GRAY;
    config.wrap         = ZERO;

    std::shared_ptr<ImageProcess> pretreat(ImageProcess::create(config), ImageProcess::destroy);
    pretreat->setMatrix(trans);
    {
        std::shared_ptr<Tensor> wrapTensor(ImageProcess::createImageTensor<float>(width/4, height/4, 1, nullptr), MNN::Tensor::destroy);
        // std::vector<int> shape{1, 1, height/4, width/4};
        // std::shared_ptr<Tensor> wrapTensor(Tensor::create(shape, halide_type_of<float>(), nullptr, Tensor::CAFFE));
        pretreat->convert((uint8_t*)p, width/4, height/4, width/4, wrapTensor.get());
        uint8_t pp[width*height/4/4];
        for(int i=0; i<width*height/4/4; ++i){
            pp[i] = wrapTensor->host<float>()[i];
        //     std::cout << p[i] << ", ";
            if (wrapTensor->host<float>()[i] != 0)
            std::cout << wrapTensor->host<float>()[i] << ", ";
        }
        stbi_write_jpg(argv[3], width/4, height/4, 1, pp, 100);
        
    }
    stbi_image_free(inputImage);

    return 0;
}