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normalising.py
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normalising.py
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import numpy as np
import logging
logger = logging.getLogger("pose_match")
#Cut pose out of image
def feature_scaling(input):
#logger.info("inn: %s" , str(input))
# We accept the presence of (0,0) points in the input poses (undetected body-parts)
# But we don't want them to influence our normalisation
# Here it's assumed that (0,y) and (x,0) don't occur
# Is a acceptable assumption because the chance is sooooo small
# that a feature is positioned just right on the x or y axis
xmax = max(input[:, 0])
ymax = max(input[:, 1])
xmin = np.min(input[np.nonzero(input[:,0])]) #np.nanmin(input[:, 0])
ymin = np.min(input[np.nonzero(input[:,1])]) #np.nanmin(input[:, 1])
sec_x = (input[:, 0]-xmin)/(xmax-xmin)
sec_y = (input[:, 1]-ymin)/(ymax-ymin)
output = np.vstack([sec_x, sec_y]).T
output[output<0] = 0
#logger.info("out: %s", str(output))
return output
def feature_scaling_multi_person():
return
def divide_by_max(input):
xmax = max(input[:, 0])
ymax = max(input[:, 1])
xmin = min(input[:, 0])
ymin = min(input[:, 1])
#sec_x = (input[:, 0]-xmin)/(xmax-xmin)
#sec_y = (input[:, 1]-ymin)/(ymax-ymin)
sec_x = (input[:, 0]) / (xmax)
sec_y = (input[:, 1]) / (ymax)
output = np.vstack([sec_x, sec_y]).T
return output
def normalise_rescaling(input):
xmax = max(input[:, 0])
xmin = min(input[:, 0])
ymax = max(input[:, 1])
ymin = min(input[:, 1])
sec_x = (input[:, 0] - xmin) / (xmax - xmin)
sec_y = (input[:, 1] - ymin) / (ymax - ymin)
output = np.vstack([sec_x, sec_y]).T
return output
def normalise_standardization(input):
xmean = input[:,0].mean(axis=0)
ymean = input[:,1].mean(axis=0)
xstd = np.std(input[:,0])
ystd = np.std(input[:, 1])
sec_x = (input[:, 0] - xmean) / xstd
sec_y = (input[:, 1] - ymean) / ystd
output = np.vstack([sec_x, sec_y]).T
return output