mirror of https://github.com/davisking/dlib.git
Python/Simple object detector trainer. Added upsample_limit option
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@ -159,7 +159,15 @@ solver more accurate but might take longer to train.")
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&type::num_threads,
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"train_simple_object_detector() will use this many threads of \n\
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execution. Set this to the number of CPU cores on your machine to \n\
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obtain the fastest training speed.");
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obtain the fastest training speed.")
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.add_property("upsample_limit", &type::upsample_limit,
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&type::upsample_limit,
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"train_simple_object_detector() will upsample images if needed \n\
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no more than upsample_limit times. Value 0 will forbid trainer to \n\
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upsample any images. If trainer is unable to fit all boxes with \n\
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required upsample_limit, exception will be thrown. Higher values \n\
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of upsample_limit exponentially increases memory requiremens. \n\
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Values higher than 2 (default) are not recommended.");
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}
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{
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typedef simple_test_results type;
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@ -33,6 +33,7 @@ namespace dlib
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detection_window_size = 80*80;
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C = 1;
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epsilon = 0.01;
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upsample_limit = 2;
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}
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bool be_verbose;
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@ -41,6 +42,7 @@ namespace dlib
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unsigned long detection_window_size;
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double C;
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double epsilon;
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unsigned long upsample_limit;
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};
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// ----------------------------------------------------------------------------------------
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@ -175,7 +177,7 @@ namespace dlib
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// upsample the images at most two times to help make the boxes obtainable.
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std::vector<std::vector<rectangle> > temp(boxes), removed;
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removed = remove_unobtainable_rectangles(trainer, images, temp);
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while (impl::contains_any_boxes(removed) && upsampling_amount < 2)
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while (impl::contains_any_boxes(removed) && upsampling_amount < options.upsample_limit)
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{
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++upsampling_amount;
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if (options.be_verbose)
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