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HPLS_openset_feat.py
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HPLS_openset_feat.py
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import argparse
import cv2 as cv
import itertools
import matplotlib
import numpy as np
import pickle
matplotlib.use('Agg')
from auxiliar import generate_cmc_curve
from auxiliar import generate_pos_neg_dict
from auxiliar import generate_precision_recall, plot_precision_recall
from auxiliar import generate_roc_curve, plot_roc_curve
from auxiliar import learn_plsh_model
from auxiliar import load_txt_file
from auxiliar import split_known_unknown_sets, split_train_test_sets
from descriptor import Descriptor
from joblib import Parallel, delayed
from matplotlib import pyplot
from pls_classifier import PLSClassifier
parser = argparse.ArgumentParser(description='PLSH for Face Recognition')
parser.add_argument('-p', '--path', help='Path do dataset', required=False, default='./frgcv1/')
parser.add_argument('-f', '--file', help='Input file name', required=False, default='train_2_small.txt')
parser.add_argument('-d', '--desc', help='Descriptor [hog/df]', required=False, default='hog')
parser.add_argument('-r', '--rept', help='Number of executions', required=False, default=1)
parser.add_argument('-m', '--hash', help='Number of hash functions', required=False, default=100)
parser.add_argument('-iw', '--width', help='Default image width', required=False, default=128)
parser.add_argument('-ih', '--height', help='Default image height', required=False, default=144)
parser.add_argument('-ks', '--known_set_size', help='Default size of enrolled subjects', required=False, default=0.5)
parser.add_argument('-ts', '--train_set_size', help='Default size of training subset', required=False, default=0.5)
args = parser.parse_args()
def main():
PATH = str(args.path)
DATASET = str(args.file)
DESCRIPTOR = str(args.desc)
ITERATIONS = int(args.rept)
KNOWN_SET_SIZE = float(args.known_set_size)
TRAIN_SET_SIZE = float(args.train_set_size)
NUM_HASH = int(args.hash)
DATASET = DATASET.replace('.txt','')
OUTPUT_NAME = 'HPLS_' + DATASET + '_' + DESCRIPTOR + '_' + str(NUM_HASH) + '_' + str(KNOWN_SET_SIZE) + '_' + str(TRAIN_SET_SIZE) + '_' + str(ITERATIONS)
prs = []
rocs = []
with Parallel(n_jobs=-2, verbose=11, backend='multiprocessing') as parallel_pool:
for index in range(ITERATIONS):
print('ITERATION #%s' % str(index+1))
pr, roc = plshface(args, parallel_pool)
prs.append(pr)
rocs.append(roc)
with open('../files/' + OUTPUT_NAME + '.file', 'w') as outfile:
pickle.dump([prs, rocs], outfile)
plot_precision_recall(prs, OUTPUT_NAME)
plot_roc_curve(rocs, OUTPUT_NAME)
def plshface(args, parallel_pool):
PATH = str(args.path)
DATASET = str(args.file)
DESCRIPTOR = str(args.desc)
NUM_HASH = int(args.hash)
IMG_WIDTH = int(args.width)
IMG_HEIGHT = int(args.height)
KNOWN_SET_SIZE = float(args.known_set_size)
TRAIN_SET_SIZE = float(args.train_set_size)
matrix_x = []
matrix_y = []
splits = []
plotting_labels = []
plotting_scores = []
vgg_model = None
if DESCRIPTOR == 'df':
from vggface import VGGFace
vgg_model = VGGFace()
print('>> EXPLORING DATASET')
dataset_list = load_txt_file(PATH + DATASET)
known_tuples, unknown_tuples = split_known_unknown_sets(dataset_list, known_set_size=KNOWN_SET_SIZE)
known_train, known_test = split_train_test_sets(known_tuples, train_set_size=TRAIN_SET_SIZE)
print('>> LOADING GALLERY: {0} samples'.format(len(known_train)))
counterA = 0
for gallery_sample in known_train:
sample_path = gallery_sample[0]
sample_name = gallery_sample[1]
counterA += 1
print(counterA, sample_path, sample_name)
gallery_path = PATH + sample_path
gallery_image = cv.imread(gallery_path, cv.IMREAD_COLOR)
if DESCRIPTOR == 'hog':
gallery_image = cv.resize(gallery_image, (IMG_HEIGHT, IMG_WIDTH))
feature_vector = Descriptor.get_hog(gallery_image)
elif DESCRIPTOR == 'df':
feature_vector = Descriptor.get_deep_feature(gallery_image, vgg_model, layer_name='fc6')
del gallery_image
matrix_x.append(feature_vector)
matrix_y.append(sample_name)
print('>> SPLITTING POSITIVE/NEGATIVE SETS')
individuals = list(set(matrix_y))
cmc_score = np.zeros(len(individuals))
for index in range(0, NUM_HASH):
splits.append(generate_pos_neg_dict(individuals))
print('>> LEARNING PLS MODELS:')
input_list = itertools.izip(splits, itertools.repeat((matrix_x, matrix_y)))
numpy_x = np.array(matrix_x)
numpy_y = np.array(matrix_y)
numpy_s = np.array(splits)
models = parallel_pool(
delayed(learn_plsh_model) (numpy_x, numpy_y, split) for split in numpy_s
)
print('>> LOADING KNOWN PROBE: {0} samples'.format(len(known_test)))
counterB = 0
for probe_sample in known_test:
sample_path = probe_sample[0]
sample_name = probe_sample[1]
query_path = PATH + sample_path
query_image = cv.imread(query_path, cv.IMREAD_COLOR)
if DESCRIPTOR == 'hog':
query_image = cv.resize(query_image, (IMG_HEIGHT, IMG_WIDTH))
feature_vector = Descriptor.get_hog(query_image)
elif DESCRIPTOR == 'df':
feature_vector = Descriptor.get_deep_feature(query_image, vgg_model)
vote_dict = dict(map(lambda vote: (vote, 0), individuals))
for model in models:
pos_list = [key for key, value in model[1].iteritems() if value == 1]
response = model[0].predict_confidence(feature_vector)
for pos in pos_list:
vote_dict[pos] += response
result = vote_dict.items()
result.sort(key=lambda tup: tup[1], reverse=True)
for outer in range(len(individuals)):
for inner in range(outer + 1):
if result[inner][0] == sample_name:
cmc_score[outer] += 1
break
counterB += 1
denominator = np.absolute(np.mean([result[1][1], result[2][1]]))
if denominator > 0:
output = result[0][1] / denominator
else:
output = result[0][1]
print(counterB, sample_name, result[0][0], output)
# Getting known set plotting relevant information
plotting_labels.append([(sample_name, 1)])
plotting_scores.append([(sample_name, output)])
print('>> LOADING UNKNOWN PROBE: {0} samples'.format(len(unknown_tuples)))
counterC = 0
for probe_sample in unknown_tuples:
sample_path = probe_sample[0]
sample_name = probe_sample[1]
query_path = PATH + sample_path
query_image = cv.imread(query_path, cv.IMREAD_COLOR)
if DESCRIPTOR == 'hog':
query_image = cv.resize(query_image, (IMG_HEIGHT, IMG_WIDTH))
feature_vector = Descriptor.get_hog(query_image)
elif DESCRIPTOR == 'df':
feature_vector = Descriptor.get_deep_feature(query_image, vgg_model)
vote_dict = dict(map(lambda vote: (vote, 0), individuals))
for model in models:
pos_list = [key for key, value in model[1].iteritems() if value == 1]
response = model[0].predict_confidence(feature_vector)
for pos in pos_list:
vote_dict[pos] += response
result = vote_dict.items()
result.sort(key=lambda tup: tup[1], reverse=True)
counterC += 1
denominator = np.absolute(np.mean([result[1][1], result[2][1]]))
if denominator > 0:
output = result[0][1] / denominator
else:
output = result[0][1]
print(counterC, sample_name, result[0][0], output)
# Getting unknown set plotting relevant information
plotting_labels.append([(sample_name, -1)])
plotting_scores.append([(sample_name, output)])
# cmc_score_norm = np.divide(cmc_score, counterA)
# generate_cmc_curve(cmc_score_norm, DATASET + '_' + str(NUM_HASH) + '_' + DESCRIPTOR)
pr = generate_precision_recall(plotting_labels, plotting_scores)
roc = generate_roc_curve(plotting_labels, plotting_scores)
return pr, roc
if __name__ == "__main__":
main()