Function train_shape_predictor_on_images

tools/python/src/shape_predictor.h:154–213

    template <typename image_array>
    inline shape_predictor train_shape_predictor_on_images (
        image_array& images,
        std::vector<std::vector<full_object_detection> >& detections,
        const shape_predictor_training_options& options
    )
    {
        if (options.lambda_param <= 0)
            throw error("Invalid lambda_param value given to train_shape_predictor(), lambda_param must be > 0.");
        if (!(0 < options.nu && options.nu <= 1))
            throw error("Invalid nu value given to train_shape_predictor(). It is required that 0 < nu <= 1.");
        if (options.feature_pool_region_padding <= -0.5)
            throw error("Invalid feature_pool_region_padding value given to train_shape_predictor(), feature_pool_region_padding must be > -0.5.");

        if (images.size() != detections.size())
            throw error("The list of images must have the same length as the list of detections.");

        if (!impl::contains_any_detections(detections))
            throw error("Error, the training dataset does not have any labeled object detections in it.");

        shape_predictor_trainer trainer;

        trainer.set_cascade_depth(options.cascade_depth);
        trainer.set_tree_depth(options.tree_depth);
        trainer.set_num_trees_per_cascade_level(options.num_trees_per_cascade_level);
        trainer.set_nu(options.nu);
        trainer.set_random_seed(options.random_seed);
        trainer.set_oversampling_amount(options.oversampling_amount);
        trainer.set_oversampling_translation_jitter(options.oversampling_translation_jitter);
        trainer.set_feature_pool_size(options.feature_pool_size);
        trainer.set_feature_pool_region_padding(options.feature_pool_region_padding);
        trainer.set_lambda(options.lambda_param);
        trainer.set_num_test_splits(options.num_test_splits);
        trainer.set_num_threads(options.num_threads);
        if (options.landmark_relative_padding_mode)
            trainer.set_padding_mode(shape_predictor_trainer::landmark_relative);
        else
            trainer.set_padding_mode(shape_predictor_trainer::bounding_box_relative);

        if (options.be_verbose)
        {
            std::cout << "Training with cascade depth: " << options.cascade_depth << std::endl;
            std::cout << "Training with tree depth: " << options.tree_depth << std::endl;
            std::cout << "Training with " << options.num_trees_per_cascade_level << " trees per cascade level."<< std::endl;
            std::cout << "Training with nu: " << options.nu << std::endl;
            std::cout << "Training with random seed: " << options.random_seed << std::endl;
            std::cout << "Training with oversampling amount: " << options.oversampling_amount << std::endl;
            std::cout << "Training with oversampling translation jitter: " << options.oversampling_translation_jitter << std::endl;
            std::cout << "Training with landmark_relative_padding_mode: " << options.landmark_relative_padding_mode << std::endl;
            std::cout << "Training with feature pool size: " << options.feature_pool_size << std::endl;
            std::cout << "Training with feature pool region padding: " << options.feature_pool_region_padding << std::endl;
            std::cout << "Training with " << options.num_threads << " threads." << std::endl;
            std::cout << "Training with lambda_param: " << options.lambda_param << std::endl;
            std::cout << "Training with " << options.num_test_splits << " split tests."<< std::endl;
            trainer.be_verbose();
        }

        shape_predictor predictor = trainer.train(images, detections);