I am following OpenCV 3 Blueprints' Chapter 5 Object Detection for Industrial Applications by Steven Puttenams
https://www.packtpub.com/application-development/opencv-3-blueprints
The training was successfully completed and I computed the F1 score to be approximately 0.86 as validated on the training dataset of 523 images (just to test if the training was okay).
Ex.
[root@cobalt workspace]# opencv_traincascade -vec npimages.vec -data output_lbp2/ -numPos 444 -numNeg 888 -bg negatives.txt -numStages 20 -featureType LBP -precalcValBufSize 2048 -precalcIdxBufSize 2048 -w 24 -h 24
However, my main problem is plotting the Precision-Recall Curve (PRC) curve, as suggested in Chapter 5, we could use the ff. to output the score:
detector.detectMultiScale(equalize, objects, levels, scores, 1.05, 1, 0, Size(), Size(), true);
But the score I'm getting have very small variations:
FILENAME NUM_RECT X Y WIDTH HEIGHT SCORE
/home/cobalt/Data/IMG_20160610_170847.jpg 1 190 287 68 68 -1.08848
/home/cobalt/Data/IMG_20160610_170925.jpg 1 186 294 68 68 -1.06534
/home/cobalt/Data/IMG_20160610_170957.jpg 1 189 286 68 68 -1.06534
/home/cobalt/Data/IMG_20160610_171038.jpg 1 191 289 67 67 -0.998512
... ... .. .. .. ...
/home/cobalt/Data/IMG_20160610_205103.jpg 1 190 291 68 68 1.82761
/home/cobalt/Data/IMG_20160610_205106.jpg 1 190 291 68 68 1.82761
/home/cobalt/Data/IMG_20160610_205122.jpg 1 194 297 68 68 1.82761
And this gives rather small values for precision-recall:
Ex. coordinates.txt
0.00761905 0.00761905 -1.09
0.00763359 0.00761905 -1.08
0.00763359 0.00761905 -1.07
0.00766284 0.00761905 -1.06
0.00766284 0.00761905 -1.05
0.00766284 0.00761905 -1.04
... .. ...
0.00952381 0.00761905 1.77
0.00952381 0.00761905 1.78
0.00952381 0.00761905 1.79
0.00952381 0.00761905 1.8
0.00952381 0.00761905 1.81
0.00952381 0.00761905 1.82
With the values above, I cannot get a decent PRC curve. I'm not quite sure what's the problem here....
Though, I can compute the Precision-Recall values using the detect_simple.cpp as I modified it here:
https://gist.github.com/melvincabatuan/45a0de3624e99a5c34d308d4a0b99b45
Ex. Output:
The number of true positives (TP) are: TP = 628.
The number of false positives (FP) are: FP = 185.
The number of false negatives (FN) are: FN = 7.
Precision = 0.772448.
Recall = 0.988976.
F1 Score = 0.867403.
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