This performs anaylsis of two images and gives the value of true postive, true negative, false positive, false negative.
● True Positive (TP): TP with respect to foreground detection is identifying a foreground object as foreground. TP is identifying foreground pixels as foreground pixels.
● False Positive (FP): FP with respect to foreground detection is identifying a background object as foreground. This is a type of error.
● False Negative (FN): FN with respect to foreground detection is not identifying a foreground object as foreground. This is a type of error.
● True Negative (TN): TN with respect to foreground detection is not identifying a background object as foreground.
● Precision: The accuracy in identifying foreground pixels as foreground excluding FN and TN. Precision is defined as the ratio of TP to (TP + FP)
● Percentage of Wrong Classifications (PWC): PWC is defined as the ratio of (FN + FP) to (TP + FN + FP + TN) multiplied by 100
●Percentage of Right Classsifications (PRC): PRC is defined by 100-PWC
Note: The input images should be of type *.jpg only.
To execute:
add opencv lib while compiling and
./a.out image1.jpg image2.jpg