This OpenCV tutorial is a very simple code example of GPU Cuda optical flow in OpenCV written in c++. The configuration of the project, code, and explanation are included for farneback Optical Flow method. Farneback algorithm is a dense method that is used to process all the pixels in the given image. The dense methods are slower but more accurate as all the pixels of the image are processed. In the following example, I am displaying just a few pixes based on a grid. I am not displaying all the pixes. In the opposite to dense method the sparse method like Lucas Kanade using just a selected subset of pixels. They are faster. Both methods have specific applications. Lucas-Kanade is widely used in tracking. The farneback can be used for the analysis of more complex movement in image scene and furder segmentation based on these changes. As dense methods are slightly slower, the GPU and Cuda implementation can lead to great performance improvements to calculate optical flow for all pixels o
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 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.
-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>]
[-baseFormatSave]
[-numThreads <max_number_of_threads = 9>]
[-acceptanceRatioBreakValue <value> = -1>]
--cascadeParams--
[-stageType <BOOST(default)>]
[-featureType <{HAAR(default), LBP, HOG}>]
[-w <sampleWidth = 24>]
[-h <sampleHeight = 24>]
--boostParams--
[-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>]
--haarFeatureParams--
[-mode <BASIC(default) | CORE | ALL
--lbpFeatureParams--
--HOGFeatureParams--
Thank you for sharing this.
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