opencv traincascade haar and lbp my training parameters

Opencv traincascade haar and lbp training 

Opencv traincascade script parameter examples for windows. Traincascade utility is easy to use for training HAAR like and LBP like cascade for opencv detect multiscale by CascadeClassifier. On this blog you can find several example how to detect something by the HAAR a nd LBP cascade..

Opencv LBP detection

Opencv traincascade examples

I am using this parameters. You can start where you just ended before. This make sense. Train for 5 stages and test. If the results with higher treshold make sense. Train again with same script and increase numStages. After some time training is much and much slower and test before you run the training for the long time. 

This is my scripts.. If you have any

opencv_traincascade.exe -data v -vec vec.vec -bg bg.dat -numPos 300 -numNeg 300 -numStages 10  -numThreads 2 -stageType BOOST -featureType LBP -w 32 -h 64 -minHitRate 0.995 -maxFalseAlarmRate 0.42 -maxDepth 1 -maxWeakCount 100

opencv_traincascade.exe -data vv -vec vec.vec -bg bg.dat -numPos 540 -numNeg 800 -numStages 8  -numThreads 4 -stageType BOOST -featureType LBP -w 32 -h 64 -minHitRate 0.9995 -maxFalseAlarmRate 0.32 -maxDepth 5 -maxWeakCount 120

opencv_traincascade.exe -data cascade -vec vec.vec -bg bg.dat -numPos 680 -numNeg 800 -numStages 10 numThreads 4 -stageType BOOST -featureType LBP -w 32 -h 64 -minHitRate 0.999995 -maxFalseAlarmRate 0.42 -maxDepth 10 -maxWeakCount 120 -mode ALL

opencv traincascade documentation description

Later abou this.. Complex staff. Just try example if you have some datasets prepared or know the how to prepare vector_file and background file for training.

Usage: opencv_traincascade.exe
  -data <cascade_dir_name>
  -vec <vec_file_name>
  -bg <background_file_name>
  [-numPos <number_of_positive_samples = 2000>]
  [-numNeg <number_of_negative_samples = 1000>]
  [-numStages <number_of_stages = 20>]
  [-precalcValBufSize <precalculated_vals_buffer_size_in_Mb = 1024>]
  [-precalcIdxBufSize <precalculated_idxs_buffer_size_in_Mb = 1024>]
  [-numThreads <max_number_of_threads = 9>]
  [-acceptanceRatioBreakValue <value> = -1>]
  [-stageType <BOOST(default)>]
  [-featureType <{HAAR(default), LBP, HOG}>]
  [-w <sampleWidth = 24>]
  [-h <sampleHeight = 24>]
  [-bt <{DAB, RAB, LB, GAB(default)}>]
  [-minHitRate <min_hit_rate> = 0.995>]
  [-maxFalseAlarmRate <max_false_alarm_rate = 0.5>]
  [-weightTrimRate <weight_trim_rate = 0.95>]
  [-maxDepth <max_depth_of_weak_tree = 1>]
  [-maxWeakCount <max_weak_tree_count = 100>]
  [-mode <BASIC(default) | CORE | ALL


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