boost :: gil库的记忆功能 [英] Memory function of the boost::gil library
问题描述
我目前正在尝试使用 Boost :: GIL (具有挑战性)进行一些TensorFlow推理(C后端).我需要一些思考，我已经能够加载我的png图片( rgb8_image_t )并转换为 rgb32_f_image_t .
I am currently trying to make some TensorFlow Inference (C backend) using Boost::GIL (challenging). I need a few thinks, I have been able to load my png image (rgb8_image_t)
and did a conversion to rgb32_f_image_t.
我仍然需要3种想法，即数据的原始指针，分配的内存和尺寸.
I still need 3 thinks, the raw pointer of the data, memory allocated, and dimensions.
-
不幸的是，对于分配的内存，功能
total_allocated_size_in_bytes()是私有的，所以我这样做了:
for the memory allocated unfortunately the function
total_allocated_size_in_bytes()is private, so I did this:
boost :: gil :: view(dest).size()* boost :: gil :: view(dest).num_channels()* sizeof(value_type);
如果我没有关于对齐故事的任何额外填充，那是有效的.但这是否存在任何不错的选择?
Which is valid, if I do not have any extra padding for alignment story. But does it exist any nice alternative?
-
对于尺寸，我应该与
numpy(来自PILLOW)匹配，我希望两个库都使用相同的内存布局模式.根据我的理解，默认情况下，数据是交错和连续的，所以应该很好.
For the dimension, I should match with
numpy(from PILLOW), I hope both libraries are using the same memory layout pattern. From my understanding, by default, datas are interleaved and contiguous so, it should be good.
最后一个原始指针 _memory ，它是 Image 类的私有数据成员，没有专用功能. boost :: gil :: view(dest).row_begin(0)返回第一个像素的迭代器，但我不确定如何获取数据的指针 _memory .有什么建议吗?
Last the raw pointer _memory, it is a private data member of the Image class with no dedicated function. boost::gil::view(dest).row_begin(0) returns a iterator on the first pixel but I not sure how I could get the pointer of the data _memory. Any suggestions ?
非常感谢您，
++ t
ps:TensorFlow提出了一个C ++后端，但是，没有从任何软件包管理器中安装它，并且操纵Bazel超出了我的能力.
ps: TensorFlow proposes a C++ backend, however, it is not installed from any package managers, and manipulate Bazel is beyond my strength.
推荐答案
GIL文档非常准确地记录了各种内存布局.
GIL documentation pretty accurately documents the various memory layouts.
但是，库的要点是抽象出内存布局.如果您需要某种表示形式(平面/交错，打包或解压缩)，则说明库接口的工作很困难.
The point of the library, though, is to abstract away the memory layouts. If you require some representation (planar/interleaved, packed or unpacked) you are doing things "the hard way" for the library interface.
因此，我认为您可以一次性阅读和转换，例如对于jpeg:
So, I think you can read and convert in one go, e.g. for a jpeg:
gil::rgb32f_image_t img;
gil::image_read_settings<gil::jpeg_tag> settings;
read_and_convert_image("input.jpg", img, settings);
现在可以获取原始数据了:
Now getting the raw data is possible:
auto* raw_data = gil::interleaved_view_get_raw_data(view(img));
碰巧的情况是，首选的实现存储是交错的，这很可能是您所期望的.如果您的特定图像存储是平面的，则该调用将不会编译(您可能需要
planar_view_get_raw_data(vw，plane_index)代替).
请注意，如果需要的话，您将不得不重新解释_cast到 float [const] * ，因为没有公共接口来获取对 scoped_channel_value<> ;:的引用:value _ ，但是 BaseChannelValue 类型确实是 float ，并且您可以断言包装器不会增加额外的权重:
Note that you'll have to reinterpret_cast to float [const]* if you need that, because there is not public interface to get a reference to the scoped_channel_value<>::value_, but the BaseChannelValue type is indeed float and you can assert that the wrapper doesn't add additional weight:
static_assert(sizeof(float) == sizeof(raw_data[0]));
替代方法:
相反，您可以设置自己的原始像素缓冲区，在其中安装可变视图，然后使用该视图将初始负载读取/转换为:
Alternative Approach:
Conversely, you can setup your own raw pixel buffer, mount a mutable view into it and use that to read/convert your initial load into:
// get dimension
gil::image_read_settings<gil::jpeg_tag> settings;
auto info = gil::read_image_info("input.jpg", settings).get_info();
// setup raw pixel buffer & view
using pixel = gil::rgb32f_pixel_t;
auto data = std::make_unique<pixel[]>(info._width * info._height);
auto vw = gil::interleaved_view(info._width, info._height, data.get(),
info._width * sizeof(pixel));
// load into buffer
read_and_convert_view("input.jpg", vw, settings);
我实际上通过写出结果视图来检查它是否正常工作:
I've actually checked that it works correctly by writing out the resulting view:
//// just for test - doesn't work for 32f, so choose another pixel format
//gil::write_view("output.png", vw, gil::png_tag());
这篇关于boost :: gil库的记忆功能的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
