import os
import cv2 as cv
import matplotlib.pylab as plt
import numpy as np
from console_progressbar import ProgressBar
from scipy.interpolate import interp1d
from scipy.signal import gaussian, convolve
from config import num_classes
def compute_class_prior(do_plot=False):
categories_folder = 'data/instance-level_human_parsing/Training/Category_ids'
names = [f for f in os.listdir(categories_folder) if f.lower().endswith('.png')]
num_samples = len(names)
prior_prob = np.zeros(num_classes)
pb = ProgressBar(total=num_samples, prefix='Compute class prior', suffix='', decimals=3, length=50, fill='=')
for i in range(num_samples):
name = names[i]
filename = os.path.join(categories_folder, name)
category = np.ravel(cv.imread(filename, 0))
counts = np.bincount(category)
idxs = np.nonzero(counts)[0]
prior_prob[idxs] += counts[idxs]
pb.print_progress_bar(i + 1)
prior_prob = prior_prob / (1.0 * np.sum(prior_prob))
# Save
np.save(os.path.join(data_dir, "prior_prob.npy"), prior_prob)
if do_plot:
plt.hist(prior_prob, bins=100)
plt.yscale("log")
plt.show()
def smooth_class_prior(sigma=5, do_plot=False):
prior_prob = np.load(os.path.join(data_dir, "prior_prob.npy"))
# add an epsilon to prior prob to avoid 0 vakues and possible NaN
prior_prob += 1E-3 * np.min(prior_prob)
# renormalize
prior_prob = prior_prob / (1.0 * np.sum(prior_prob))
# Smooth with gaussian
f = interp1d(np.arange(prior_prob.shape[0]), prior_prob)
xx = np.linspace(0, prior_prob.shape[0] - 1, 1000)
yy = f(xx)
window = gaussian(2000, sigma) # 2000 pts in the window, sigma=5
smoothed = convolve(yy, window / window.sum(), mode='same')
fout = interp1d(xx, smoothed)
prior_prob_smoothed = np.array([fout(i) for i in range(prior_prob.shape[0])])
prior_prob_smoothed = prior_prob_smoothed / np.sum(prior_prob_smoothed)
# Save
file_name = os.path.join(data_dir, "prior_prob_smoothed.npy")
np.save(file_name, prior_prob_smoothed)
if do_plot:
plt.plot(prior_prob)
plt.plot(prior_prob_smoothed, "g--")
plt.plot(xx, smoothed, "r-")
plt.yscale("log")
plt.show()
def compute_prior_factor(gamma=0.5, alpha=1, do_plot=False):
file_name = os.path.join(data_dir, "prior_prob_smoothed.npy")
prior_prob_smoothed = np.load(file_name)
u = np.ones_like(prior_prob_smoothed)
u = u / np.sum(1.0 * u)
prior_factor = (1 - gamma) * prior_prob_smoothed + gamma * u
prior_factor = np.power(prior_factor, -alpha)
# renormalize
prior_factor = prior_factor / (np.sum(prior_factor * prior_prob_smoothed))
file_name = os.path.join(data_dir, "prior_factor.npy")
np.save(file_name, prior_factor)
if do_plot:
plt.plot(prior_factor)
plt.yscale("log")
plt.show()
if __name__ == '__main__':
data_dir = 'data/'
do_plot = True
compute_class_prior(do_plot=True)
smooth_class_prior(do_plot=True)
compute_prior_factor(do_plot=True)
