mirror of https://github.com/CGAL/cgal
Fix warnings std::size_t -> int
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717ab4ba5c
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@ -127,15 +127,15 @@ public:
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if (ground_truth[i] != -1)
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if (ground_truth[i] != -1)
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++ nb_samples;
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++ nb_samples;
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cv::Mat training_features (nb_samples, m_features.size(), CV_32FC1);
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cv::Mat training_features (int(nb_samples), int(m_features.size()), CV_32FC1);
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cv::Mat training_labels (nb_samples, 1, CV_32FC1);
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cv::Mat training_labels (int(nb_samples), 1, CV_32FC1);
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for (std::size_t i = 0, index = 0; i < ground_truth.size(); ++ i)
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for (std::size_t i = 0, index = 0; i < ground_truth.size(); ++ i)
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if (ground_truth[i] != -1)
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if (ground_truth[i] != -1)
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{
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{
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for (std::size_t f = 0; f < m_features.size(); ++ f)
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for (std::size_t f = 0; f < m_features.size(); ++ f)
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training_features.at<float>(index, f) = m_features[f]->value(i);
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training_features.at<float>(int(index), int(f)) = m_features[f]->value(i);
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training_labels.at<float>(index, 0) = ground_truth[i];
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training_labels.at<float>(int(index), 0) = ground_truth[i];
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++ index;
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++ index;
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}
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}
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@ -194,9 +194,9 @@ public:
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{
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{
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out.resize (m_labels.size(), 0.);
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out.resize (m_labels.size(), 0.);
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cv::Mat feature (1, m_features.size(), CV_32FC1);
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cv::Mat feature (1, int(m_features.size()), CV_32FC1);
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for (std::size_t f = 0; f < m_features.size(); ++ f)
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for (std::size_t f = 0; f < m_features.size(); ++ f)
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feature.at<float>(0, f) = m_features[f]->value(item_index);
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feature.at<float>(0, int(f)) = m_features[f]->value(item_index);
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//compute the result of each tree
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//compute the result of each tree
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#if (CV_MAJOR_VERSION < 3)
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#if (CV_MAJOR_VERSION < 3)
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