postprocessing.py 文件源码

def gesture_overlap_csv(csvpathgt, csvpathpred, seqlenght):
    """ Evaluate this sample against the ground truth file """
    maxGestures = 20

    # Get the list of gestures from the ground truth and frame activation
    gtGestures = []
    binvec_gt = numpy.zeros((maxGestures, seqlenght))
    with open(csvpathgt, 'rb') as csvfilegt:
        csvgt = csv.reader(csvfilegt)
        for row in csvgt:
            binvec_gt[int(row[0])-1, int(row[1])-1:int(row[2])-1] = 1
            gtGestures.append(int(row[0]))

    # Get the list of gestures from prediction and frame activation
    predGestures = []
    binvec_pred = numpy.zeros((maxGestures, seqlenght))
    with open(csvpathpred, 'rb') as csvfilepred:
        csvpred = csv.reader(csvfilepred)
        for row in csvpred:
            binvec_pred[int(row[0])-1, int(row[1])-1:int(row[2])-1] = 1
            predGestures.append(int(row[0]))

    # Get the list of gestures without repetitions for ground truth and predicton
    gtGestures = numpy.unique(gtGestures)
    predGestures = numpy.unique(predGestures)

    bgt = (numpy.argmax(binvec_gt,axis=0)+1) * (numpy.max(binvec_gt,axis=0)>0)
    bpred = (numpy.argmax(binvec_pred,axis=0)+1) * (numpy.max(binvec_pred,axis=0)>0)

    # Find false positives
    falsePos=numpy.setdiff1d(gtGestures,numpy.union1d(gtGestures,numpy.union1d(gtGestures,predGestures)))

    # Get overlaps for each gesture
    overlaps = []
    for idx in gtGestures:
        intersec = sum(binvec_gt[idx-1] * binvec_pred[idx-1])
        aux = binvec_gt[idx-1] + binvec_pred[idx-1]
        union = sum(aux > 0)
        overlaps.append(intersec/union)

    # Use real gestures and false positive gestures to calculate the final score
    return sum(overlaps)/(len(overlaps)+len(falsePos))