test_human.py

import os
import numpy as np
from PIL import Image


def main():
	image_paths, label_paths = init_path()
	hist = compute_hist(image_paths, label_paths)
	show_result(hist)

def init_path():
	list_file = './human/list/val_id.txt'
	file_names = []
	with open(list_file, 'rb') as f:
	    for fn in f:
	        file_names.append(fn.strip())

	image_dir = './human/features/attention/val/results/'
	label_dir = './human/data/labels/'

	image_paths = []
	label_paths = []
	for file_name in file_names:
	    image_paths.append(os.path.join(image_dir, file_name+'.png'))
	    label_paths.append(os.path.join(label_dir, file_name+'.png'))
	return image_paths, label_paths


def fast_hist(a, b, n):
    k = (a >= 0) & (a < n)
    return np.bincount(n * a[k].astype(int) + b[k], minlength=n**2).reshape(n, n)

def compute_hist(images, labels):
	n_cl = 20

	hist = np.zeros((n_cl, n_cl))
	for img_path, label_path in zip(images, labels):
		label = Image.open(label_path)
		label_array = np.array(label, dtype=np.int32)
		image = Image.open(img_path)
		image_array = np.array(image, dtype=np.int32)
		
		gtsz = label_array.shape
		imgsz = image_array.shape
		if not gtsz == imgsz:
			image = image.resize((gtsz[1], gtsz[0]), Image.ANTIALIAS)
			image_array = np.array(image, dtype=np.int32)

		hist += fast_hist(label_array, image_array, n_cl)

	return hist

def show_result(hist):

	classes = ['background', 'hat', 'hair', 'glove', 'sunglasses', 'upperclothes',
	           'dress', 'coat', 'socks', 'pants', 'jumpsuits', 'scarf', 'skirt',
	           'face', 'leftArm', 'rightArm', 'leftLeg', 'rightLeg', 'leftShoe',
	           'rightShoe']
	# num of correct pixels
	num_cor_pix = np.diag(hist)
	# num of gt pixels
	num_gt_pix = hist.sum(1)
	print '=' * 50

	# @evaluation 1: overall accuracy
	acc = num_cor_pix.sum() / hist.sum()
	print '>>>', 'overall accuracy', acc
	print '-' * 50

	# @evaluation 2: mean accuracy & per-class accuracy 
	print 'Accuracy for each class (pixel accuracy):'
	for i in xrange(20):
		print('%-15s: %f' % (classes[i], num_cor_pix[i] / num_gt_pix[i])) 
	acc = num_cor_pix / num_gt_pix
	print '>>>', 'mean accuracy', np.nanmean(acc)
	print '-' * 50
	
	# @evaluation 3: mean IU & per-class IU
	union = num_gt_pix + hist.sum(0) - num_cor_pix
	for i in xrange(20):
		print('%-15s: %f' % (classes[i], num_cor_pix[i] / union[i]))
	iu = num_cor_pix / (num_gt_pix + hist.sum(0) - num_cor_pix)
	print '>>>', 'mean IU', np.nanmean(iu)
	print '-' * 50

	# @evaluation 4: frequency weighted IU
	freq = num_gt_pix / hist.sum()
	print '>>>', 'fwavacc', (freq[freq > 0] * iu[freq > 0]).sum()
	print '=' * 50



if __name__ == '__main__':
    main()