Was hoping to get some guidance on a few issues...
Here is the sign I would like to be able to recognize at night: http://67.media.tumblr.com/035fa2a4d9653c8ab5cdf26dea542bc3/tumblr_mzcs4wrVxe1qc360oo1_1280.jpg
Here are my questions/issues:
To generate the images to be used for training I have used ffmpeg to create images from a video that I recorded. It created roughly 500 images, all from the left hand side of the street. I painstakingly annotated all 500 images only to have the training cease at stage 3. Should i not be using ffmpeg? As an alternative I could use the burst capability on the iphone, which will take a bunch of pictures. Should I be getting images from all angles? Should blurry images be omitted?
The negative images I used were also taken from a video using ffmpeg. The video is of the surrounding area, minus the sign of course.
I have been able to train a model successfully on a soda can (la croix) but for whatever reason I cannot get through the training for this type of object. Any help would be greatly appreciated
Here is my output:
opencv_createsamples -info annotations.txt -bg negatives.txt -vec VeniceLeft.vec -w 73 -h 10 Info file name: annotations.txt Img file name: (NULL) Vec file name: VeniceLeft.vec BG file name: negatives.txt Num: 1000 BG color: 0 BG threshold: 80 Invert: FALSE Max intensity deviation: 40 Max x angle: 1.1 Max y angle: 1.1 Max z angle: 0.5 Show samples: FALSE Original image will be scaled to: Width: $backgroundWidth / 73 Height: $backgroundHeight / 10 Create training samples from images collection... annotations.txt(553) : parse errorDone. Created 552 samples
opencv_traincascade -data cascade/ -vec VeniceLeft.vec -bg negatives.txt -numNeg 1000 -numPos 500 -minHitrate 0.995 -maxFalseAlarmRate 0.5 -mode ALL -precalcValBufSize 1024 -precalcIdxBufSize 1024 -w 73 -h 10 PARAMETERS: cascadeDirName: cascade/ vecFileName: VeniceLeft.vec bgFileName: negatives.txt numPos: 500 numNeg: 1000 numStages: 20 precalcValBufSize[Mb] : 1024 precalcIdxBufSize[Mb] : 1024 acceptanceRatioBreakValue : -1 stageType: BOOST featureType: HAAR sampleWidth: 73 sampleHeight: 10 boostType: GAB minHitRate: 0.995 maxFalseAlarmRate: 0.5 weightTrimRate: 0.95 maxDepth: 1 maxWeakCount: 100 mode: ALL Number of unique features given windowSize [73,10] : 356522
===== TRAINING 0-stage ===== <begin pos="" count="" :="" consumed="" 500="" :="" 500="" neg="" count="" :="" acceptanceratio="" 1000="" :="" 1="" precalculation="" time:="" 25="" +----+---------+---------+="" |="" n="" |="" hr="" |="" fa="" |="" +----+---------+---------+="" |="" 1|="" 0.998|="" 0.001|="" +----+---------+---------+="" end=""> Training until now has taken 0 days 0 hours 1 minutes 38 seconds.
===== TRAINING 1-stage ===== <begin pos="" count="" :="" consumed="" 500="" :="" 501="" neg="" count="" :="" acceptanceratio="" 1000="" :="" 0.00319338="" precalculation="" time:="" 22="" +----+---------+---------+="" |="" n="" |="" hr="" |="" fa="" |="" +----+---------+---------+="" |="" 1|="" 0.998|="" 0.007|="" +----+---------+---------+="" end=""> Training until now has taken 0 days 0 hours 3 minutes 12 seconds.
===== TRAINING 2-stage ===== <begin pos="" count="" :="" consumed="" 500="" :="" 502="" neg="" count="" :="" acceptanceratio="" 1000="" :="" 2.51281e-05="" precalculation="" time:="" 23="" +----+---------+---------+="" |="" n="" |="" hr="" |="" fa="" |="" +----+---------+---------+="" |="" 1|="" 1|="" 1|="" +----+---------+---------+="" |="" 2|="" 1|="" 0.019|="" +----+---------+---------+="" end=""> Training until now has taken 0 days 0 hours 10 minutes 3 seconds.
===== TRAINING 3-stage ===== <begin pos="" count="" :="" consumed="" 500="" :="" 502="" train="" dataset="" for="" temp="" stage="" can="" not="" be="" filled.="" branch="" training="" terminated.<="" p="">
