/
eval_casia1.py
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/
eval_casia1.py
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# ------------------------------------------------------------------------------
# Libraries
# ------------------------------------------------------------------------------
import os
import numpy as np
from glob import glob
from tqdm import tqdm
from random import shuffle
from itertools import repeat
from collections import defaultdict
from multiprocessing import Pool, cpu_count
from fnc.extractFeature import extractFeature
from fnc.matching import calHammingDist
# ------------------------------------------------------------------------------
# Parameters
# ------------------------------------------------------------------------------
CASIA1_DIR = "../CASIA1"
N_IMAGES = 4
eyelashes_thresholds = np.linspace(start=10, stop=250, num=25)
thresholds = np.linspace(start=0.0, stop=1.0, num=100)
# ------------------------------------------------------------------------------
# Pool function of extracting feature
# ------------------------------------------------------------------------------
def pool_func_extract_feature(args):
im_filename, eyelashes_thres, use_multiprocess = args
template, mask, im_filename = extractFeature(
im_filename=im_filename,
eyelashes_thres=eyelashes_thres,
use_multiprocess=use_multiprocess,
)
return template, mask, im_filename
# ------------------------------------------------------------------------------
# Pool function of calculating Hamming distance
# ------------------------------------------------------------------------------
def pool_func_calHammingDist(args):
template1, mask1, template2, mask2 = args
dist = calHammingDist(template1, mask1, template2, mask2)
return dist
# ------------------------------------------------------------------------------
# Main execution
# ------------------------------------------------------------------------------
# Get identities of MMU2 dataset
identities = glob(os.path.join(CASIA1_DIR, "**"))
identities = sorted([os.path.basename(identity) for identity in identities])
# Edited to reduce number of sample sets to 10. For simplicity and testing
n_identities = len(identities)
print("Number of identities:", n_identities)
# Construct a dictionary of files
files_dict = {}
image_files = []
# Edited to reduce number of sample sets to 10. For simplicity and testing
for identity in identities:
files = glob(os.path.join(CASIA1_DIR, identity, "*.*"))
shuffle(files)
files_dict[identity] = files[:N_IMAGES]
image_files += files[:N_IMAGES]
n_image_files = len(image_files)
print("Number of image files:", n_image_files)
# Ground truth
ground_truth = np.zeros([n_image_files, n_image_files], dtype=int)
for i in range(ground_truth.shape[0]):
for j in range(ground_truth.shape[1]):
if i//N_IMAGES == j//N_IMAGES:
ground_truth[i, j] = 1
# Evaluate parameters
pools = Pool(processes=cpu_count())
best_results = []
for eye_threshold in tqdm(eyelashes_thresholds, total=len(eyelashes_thresholds)):
# Extract features
args = zip(image_files, repeat(eye_threshold), repeat(False))
features = list(pools.map(pool_func_extract_feature, args))
# Calculate the distances
args = []
for i in range(n_image_files):
for j in range(n_image_files):
if i >= j:
continue
arg = (features[i][0], features[i][1],
features[j][0], features[j][1])
args.append(arg)
distances = pools.map(pool_func_calHammingDist, args)
# Construct a distance matrix
k = 0
dist_mat = np.zeros([n_image_files, n_image_files])
for i in range(n_image_files):
for j in range(n_image_files):
if i < j:
dist_mat[i, j] = distances[k]
k += 1
elif i > j:
dist_mat[i, j] = dist_mat[j, i]
# Metrics
accuracies, precisions, recalls, fscores = [], [], [], []
for threshold in thresholds:
decision_map = (dist_mat <= threshold).astype(int)
accuracy = (decision_map == ground_truth).sum() / ground_truth.size
precision = (ground_truth*decision_map).sum() / decision_map.sum()
recall = (ground_truth*decision_map).sum() / ground_truth.sum()
fscore = 2*precision*recall / (precision+recall)
accuracies.append(accuracy)
precisions.append(precision)
recalls.append(recall)
fscores.append(fscore)
# Save the best result
best_fscore = max(fscores)
best_threshold = thresholds[fscores.index(best_fscore)]
best_results.append((eye_threshold, best_threshold, best_fscore))
# Show the final best result
eye_thresholds = [item[0] for item in best_results]
thresholds = [item[1] for item in best_results]
fscores = [item[2] for item in best_results]
print("Maximum fscore:", max(fscores))
print("Best eye_threshold:", eye_thresholds[fscores.index(max(fscores))])
print("Best threshold:", thresholds[fscores.index(max(fscores))])