Step 1 -
Tools Needed:
You can find these executables as part of the opencv default installation. they are located in the BIN folder
Samples:
you should have alot of positive and negative images.
Take note that compressed images such as jpeg have certain issues therefore selecting a BMP image is recommended.
Create folders for the Negative and Positive images and include an info file aswell
Basically there should be four sets of images that you work on:
- a positive sample set representing your object that you want to train
- another positive sample set for testing purpose
- a negative sample set (or so-called backgrounds) for training
- and another negative sample set for testing, too
so your 2 folders containing the images are:
/opencv/project/negative
/opencv/project/positive
as well as the info file:
-train.txt
-test.txt
To work with these images in the utilities, a list of the files is needed. Using Win32 as host OS,
you can get the list via command promt. Move to the folder where the info file will be stored and
type “dir /b /s > infofile.txt”. Open “infofile.txt” and make the paths relative to the
info file (i.e. find “C:\Temp\negatives\” and replace with “”).
Step 2 - Test Creation
Once your done preparing you need to create a vec-file in the data folder. This is done by createsamples.exe by calling:
double check if the vec file really contains the desired images.
Step 3 Training
Im assuming the following the default values of hitrate (0.995), maxfalsealarm (0.5), weighttrimming (0.95) and boosting type (GAB, “Gentle Ada Boost”) are good in most cases we use the following call:"
- 1300 positive images
- 5000 negative images
- 30 stages
- 1300 MB of RAM
Step 4 Testing
the test of hitrate and falsealarm will be done by calling
Thats it, tell me how it goes.,,,
Tools Needed:
You can find these executables as part of the opencv default installation. they are located in the BIN folder
Samples:
you should have alot of positive and negative images.
Take note that compressed images such as jpeg have certain issues therefore selecting a BMP image is recommended.
Create folders for the Negative and Positive images and include an info file aswell
Basically there should be four sets of images that you work on:
- a positive sample set representing your object that you want to train
- another positive sample set for testing purpose
- a negative sample set (or so-called backgrounds) for training
- and another negative sample set for testing, too
so your 2 folders containing the images are:
/opencv/project/negative
/opencv/project/positive
as well as the info file:
-train.txt
-test.txt
To work with these images in the utilities, a list of the files is needed. Using Win32 as host OS,
you can get the list via command promt. Move to the folder where the info file will be stored and
type “dir /b /s > infofile.txt”. Open “infofile.txt” and make the paths relative to the
info file (i.e. find “C:\Temp\negatives\” and replace with “”).
Step 2 - Test Creation
Once your done preparing you need to create a vec-file in the data folder. This is done by createsamples.exe by calling:
opencv_createsamples.exe -info positives/train.txt -vec data/positives.vec -num 1300 -w 20 –h 20the numbers show 1300 samples with a width and height of 20
double check if the vec file really contains the desired images.
Step 3 Training
Im assuming the following the default values of hitrate (0.995), maxfalsealarm (0.5), weighttrimming (0.95) and boosting type (GAB, “Gentle Ada Boost”) are good in most cases we use the following call:"
haartraining.exe -data data/cascade -vec data/positives.vec -bg negatives/train.txt
-npos 1300 -nneg 5000 -nstages 30 -mem 1300 -mode ALL -w 20 -h 20
- 5000 negative images
- 30 stages
- 1300 MB of RAM
Step 4 Testing
the test of hitrate and falsealarm will be done by calling
performance.exe -data data/cascade -info positives/testing/testing.txt -w 20 -h 20 -rs 30
Thats it, tell me how it goes.,,,
Comments
Post a Comment