Faster way to append to PyList in c++

Question

I'm new to c++ and looking for a faster way to append pixel value to python list, since currrently on loop it takes around 0.1 second to process one frame of image with resolution of 854x480, do anyone have any idea?

I tried to avoid using third party module if possible.

Here is what I've got so far:

PyObject* byte_list = PyList_New(static_cast<Py_ssize_t>(0));

AVFrame *pFrameRGB = av_frame_alloc();
av_frame_copy_props(pFrameRGB, this->pFrame);
pFrameRGB->width = this->pFrame->width;
pFrameRGB->height = this->pFrame->height;
pFrameRGB->format = AV_PIX_FMT_RGB24;
av_frame_get_buffer(pFrameRGB, 0);

sws_scale(this->swsCtx, this->pFrame->data, this->pFrame->linesize, 0, 
        this->pCodecContext->height, pFrameRGB->data, pFrameRGB->linesize);

if (this->_debug) {
    std::cout << "Frame linesize " << pFrameRGB->linesize[0] << "\n";
    std::cout << "Frame width " << pFrameRGB->width << "\n";
    std::cout << "Frame height " << pFrameRGB->height << "\n";
}

// This looping method seems slow
for(int y = 0; y < pFrameRGB->height; ++y) {
    for(int x = 0; x < pFrameRGB->width; ++x) {
        int p = x * 3 + y * pFrameRGB->linesize[0];
        int r = pFrameRGB->data[0][p];
        int g = pFrameRGB->data[0][p+1];
        int b = pFrameRGB->data[0][p+2];
        PyList_Append(byte_list, PyLong_FromLong(r));
        PyList_Append(byte_list, PyLong_FromLong(g));
        PyList_Append(byte_list, PyLong_FromLong(b));
    }
}

av_frame_free(&pFrameRGB);

Thanks!

Answer 1

After looking around, I've decided to use Python Built-in Array Library that can use memcpy instead of PyList which require to input the data one by one.

From my test, this improve the speed from 2-10 times, depending on the data.

PyObject *vec_to_array(std::vector<uint8_t>& vec) {
    static PyObject *single_array;
    if (!single_array) {
        PyObject *array_module = PyImport_ImportModule("array");
        if (!array_module)
            return NULL;
        PyObject *array_type = PyObject_GetAttrString(array_module, "array");
        Py_DECREF(array_module);
        if (!array_type)
            return NULL;
        single_array = PyObject_CallFunction(array_type, "s[B]", "B", 0);
        Py_DECREF(array_type);
        if (!single_array)
            return NULL;
    }
    // extra-fast way to create an empty array of count elements:
    //   array = single_element_array * count
    PyObject *pysize = PyLong_FromSsize_t(vec.size());
    if (!pysize)
        return NULL;
    PyObject *array = PyNumber_Multiply(single_array, pysize);
    Py_DECREF(pysize);
    if (!array)
        return NULL;

    // now, obtain the address of the array's buffer
    PyObject *buffer_info = PyObject_CallMethod(array, "buffer_info", "");
    if (!buffer_info) {
        Py_DECREF(array);
        return NULL;
    }
    PyObject *pyaddr = PyTuple_GetItem(buffer_info, 0);
    void *addr = PyLong_AsVoidPtr(pyaddr);

    // and, finally, copy the data.
    if (vec.size())
        memcpy(addr, &vec[0], vec.size() * sizeof(uint8_t));

    return array;
}

after that I passed the vector into that function

std::vector<uint8_t> rgb_arr;

// Copy data from AV Frame
uint8_t* rgb_data[4];  int rgb_linesize[4];
av_image_alloc(rgb_data, rgb_linesize, this->pFrame->width, this->pFrame->height, AV_PIX_FMT_RGB24, 32); 
sws_scale(this->swsCtx, this->pFrame->data, this->pFrame->linesize, 0, this->pFrame->height, rgb_data, rgb_linesize);

// Put the data into vector
int rgb_size = pFrame->height * rgb_linesize[0];
std::vector<uint8_t> rgb_vector(rgb_size);
memcpy(rgb_vector.data(), rgb_data[0], rgb_size);

// Transfer the data from vector to rgb_arr
for(int y = 0; y < pFrame->height; ++y) {
    rgb_arr.insert(
        rgb_arr.end(), 
        rgb_vector.begin() + y * rgb_linesize[0],
        rgb_vector.begin() + y * rgb_linesize[0] + 3 * pFrame->width
    );
}


PyObject* arr = vec_to_array(rgb_arr);

This then later can be accessed by python.

Answer 2

Use a container with a faster insertion time, such as std::vector or std::deque instead of std::list. These containers have a constant-time insertion time, whereas std::list has a linear-time insertion time.

Use a bulk insertion method, such as std::vector::insert() or std::deque::insert(), to insert multiple values at once instead of inserting them one at a time. This can reduce the overhead of inserting individual elements.

Use a memory-efficient data structure, such as std::bitset, to store the pixel values if each pixel only has a few possible values (eg 0 or 1). This can reduce the memory usage and improve the performance of inserting and accessing the values.

Use C++11's emplace_back() method, which avoids the overhead of constructing and copying elements by constructing the element in place in the container.

Preallocate the memory for the container to avoid the overhead of frequent memory reallocations as the container grows. You can use the reserve() method of std::vector or std::deque to preallocate the memory.

Consider using a faster algorithm or data structure for the image processing task itself. For example, you may be able to use optimized image processing libraries or parallelize the image processing using multi-threading or SIMD instructions.