TIEVisionCascadeClassifierTrainer.createSamples
Declaration
procedure createSamples(imagename: PAnsiChar; bgImagesDir: PAnsiChar; vecname: PAnsiChar; num: int32_t = 1000; width: int32_t = 24; height: int32_t = 24; bgcolor: int32_t = 0; bgthreshold: int32_t = 80; inv: bool32 = false; randomInvert: bool32 = false; maxintensitydev: int32_t = 40; maxxangle: double = 1.1; maxyangle: double = 1.1; maxzangle: double = 0.5; infoname: PAnsiChar = nil; maxscale = -1.0); safecall;Description
Create a file containing a set of positive samples, generated from a single positive image.The result of this function (named "vec" file) is then passed to trainCascade for the actual training.
If you have a set of real positive images please use createSamplesFromImageSet to create the file of positive samples.
| Parameter | Description |
|---|---|
| imagename | Source object image (the single positive sample) |
| bgImagesDir | Path to a directory containing a list of images which are used as a background for randomly distorted versions of the object |
| vecname | Name of the output file containing the positive samples for training |
| num | Number of positive samples to generate |
| width | Width (in pixels) of the output samples |
| height | Height (in pixels) of the output samples |
| bgcolor | Background color (0..255, as gray scale value). The background color denotes the transparent color |
| bgthreshold | Background color tolerance. All pixels withing bgcolor-bgthresh and bgcolor+bgthresh range are interpreted as transparent |
| inv | If True colors will be inverted |
| randomInvert | If True colors will be inverted randomly |
| maxintensitydev | Maximal intensity deviation of pixels in foreground samples |
| maxxangle | Maximal rotation angle towards x-axis, must be given in radians |
| maxyangle | Maximal rotation angle towards y-axis, must be given in radians |
| maxzangle | Maximal rotation angle towards z-axis, must be given in radians |
| infoname | Use an alternative way to create output file from a list of samples. "infoname" specifies a text file containing following information for each row: filepath, number of objects in this image "x y width height" for each object. All values separated by spaces. A row for each image |
| maxscale | Max sample scale |
Example
// create training samples file "samples.vec" containing 8000 samples, synthesized from the single file "company_logo.png"cascadeTrainer.createSamples('positive_images\company_logo.png', 'negative_images', 'samples.vec', 8000, 40, 40, 0, 10, false, false, 40, 1.1, 1.1, 0.5);
// train the cascade classifier with 10 stages using LBP. The output will be "trainingresult\cascade.xml", the you should use in TIEVisionObjectsFinder to find objects.
imagesCount := trunc(cascadeTrainer.getSamplesCount('samples.vec') * 0.85); // trainCascade images count must be less than 85% of the images in "vec" file
cascadeTrainer.trainCascade('trainingresult', 'samples.vec', 'negative_images', imagesCount, 40, 40, 10, ievLBP);