smooth.py

import numpy as np

# This is from the scipy smoothing demo
# http://scipy-cookbook.readthedocs.io/items/SignalSmooth.html
# window_len must be odd

def smooth(x, window_len=25, window='blackman'):
    if x.ndim != 1:
        raise ValueError("smooth only accepts 1 dimension arrays.")

    if x.size < window_len:
        raise ValueError("Input vector needs to be bigger than window size.")


    if window_len<3:
        return x


    if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:
        raise ValueError("Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'")


    s=np.r_[x[window_len-1:0:-1],x,x[-2:-window_len-1:-1]]
    if window == 'flat': #moving average
        w=np.ones(window_len,'d')
    else:
        w=eval('np.'+window+'(window_len)')

    y=np.convolve(w/w.sum(),s,mode='valid')
    return y

# For smoothing the luminance precentile curves of video frames
def smoothen_luminance(predictions, percs):
    percs = np.array(percs, dtype='float32').transpose()
    smooth_percs = []
    for i, perc in enumerate(percs):
        smooth_percs.append(smooth(perc))
    # smooth_low, smooth_high = smooth(np.array(low)), smooth(np.array(high))
    ret = []
    smooth_percs = np.array(smooth_percs).transpose()
    percs = percs.transpose()
    for i, pred in enumerate(predictions):
        smooth_pred = np.interp(pred, percs[i], smooth_percs[i]).astype('float32')
        ret.append(smooth_pred.clip(0,1))
    return ret