void randomly_crop_images (
    const matrix<rgb_pixel>& img,
    dlib::array<matrix<rgb_pixel>>& crops,
    dlib::rand& rnd,
    long num_crops
)
{
    std::vector<chip_details> dets;
    for (long i = 0; i < num_crops; ++i)
    {
        auto rect = make_random_cropping_rect_resnet(img, rnd);
        dets.push_back(chip_details(rect, chip_dims(227,227)));
    }

    extract_image_chips(img, dets, crops);

    for (auto&& img : crops)
    {
        // Also randomly flip the image
        if (rnd.get_random_double() > 0.5)
            img = fliplr(img);

        // And then randomly adjust the colors.
        apply_random_color_offset(img, rnd);
    }
}
示例#2
0
void sample_hmm (
    dlib::rand& rnd,
    const matrix<double>& transition_probabilities,
    const matrix<double>& emission_probabilities,
    unsigned long previous_label,
    unsigned long& next_label,
    unsigned long& next_sample
)
/*!
    requires
        - previous_label < transition_probabilities.nr()
        - transition_probabilities.nr() == transition_probabilities.nc()
        - transition_probabilities.nr() == emission_probabilities.nr()
        - The rows of transition_probabilities and emission_probabilities must sum to 1.
          (i.e. sum_cols(transition_probabilities) and sum_cols(emission_probabilities)
          must evaluate to vectors of all 1s.)
    ensures
        - This function randomly samples the HMM defined by transition_probabilities
          and emission_probabilities assuming that the previous hidden state
          was previous_label. 
        - The HMM is defined by:
            - P(next_label |previous_label) == transition_probabilities(previous_label, next_label)
            - P(next_sample|next_label)     == emission_probabilities  (next_label,     next_sample)
        - #next_label == the sampled value of the hidden state
        - #next_sample == the sampled value of the observed state
!*/
{
    // sample next_label
    double p = rnd.get_random_double();
    for (long c = 0; p >= 0 && c < transition_probabilities.nc(); ++c)
    {
        next_label = c;
        p -= transition_probabilities(previous_label, c);
    }

    // now sample next_sample
    p = rnd.get_random_double();
    for (long c = 0; p >= 0 && c < emission_probabilities.nc(); ++c)
    {
        next_sample = c;
        p -= emission_probabilities(next_label, c);
    }
}
rectangle make_random_cropping_rect_resnet(
    const matrix<rgb_pixel>& img,
    dlib::rand& rnd
)
{
    // figure out what rectangle we want to crop from the image
    double mins = 0.466666666, maxs = 0.875;
    auto scale = mins + rnd.get_random_double()*(maxs-mins);
    auto size = scale*std::min(img.nr(), img.nc());
    rectangle rect(size, size);
    // randomly shift the box around
    point offset(rnd.get_random_32bit_number()%(img.nc()-rect.width()),
                 rnd.get_random_32bit_number()%(img.nr()-rect.height()));
    return move_rect(rect, offset);
}
void randomly_crop_image (
    const matrix<rgb_pixel>& img,
    matrix<rgb_pixel>& crop,
    dlib::rand& rnd
)
{
    auto rect = make_random_cropping_rect_resnet(img, rnd);

    // now crop it out as a 227x227 image.
    extract_image_chip(img, chip_details(rect, chip_dims(227,227)), crop);

    // Also randomly flip the image
    if (rnd.get_random_double() > 0.5)
        crop = fliplr(crop);

    // And then randomly adjust the colors.
    apply_random_color_offset(crop, rnd);
}