def detect(self, npimg, rois=None, faces=None):
probs, feats = self.extract_features(npimg=npimg,
rois=rois,
faces=faces)
if self.db is None:
names = [[(self.class_names[idx], prop[idx]) for idx in
prop.argsort()[-DeepFaceConfs.get()['recognizer']['topk']:][::-1]] for prop in probs]
else:
# TODO
names = []
for feat in feats:
scores = []
for db_name, db_feature in self.db.items():
similarity = feat_distance_cosine(feat, db_feature)
scores.append((db_name, similarity))
scores.sort(key=lambda x: x[1], reverse=True)
names.append(scores)
return {
'output': probs,
'feature': feats,
'name': names
}
def test_lfw(self, set='test', model='ssdm_resnet', visualize=True):
if set is 'train':
pairfile = 'pairsDevTrain.txt'
else:
pairfile = 'pairsDevTest.txt'
lfw_path = DeepFaceConfs.get()['dataset']['lfw']
path = os.path.join(lfw_path, pairfile)
with open(path, 'r') as f:
lines = f.readlines()[1:]
pairs = []
for line in lines:
elms = line.split()
if len(elms) == 3:
pairs.append((elms[0], int(elms[1]), elms[0], int(elms[2])))
elif len(elms) == 4:
pairs.append((elms[0], int(elms[1]), elms[2], int(elms[3])))
else:
logger.warning('line should have 3 or 4 elements, line=%s' % line)
detec = FaceDetectorDlib.NAME
if model == 'baseline':
recog = FaceRecognizerVGG.NAME
just_name = 'vgg'
elif model == 'baseline_resnet':
recog = FaceRecognizerResnet.NAME
just_name = 'resnet'
elif model == 'ssdm_resnet':
recog = FaceRecognizerResnet.NAME
just_name = 'resnet'
detec = 'detector_ssd_mobilenet_v2'
else:
raise Exception('invalid model name=%s' % model)
logger.info('pair length=%d' % len(pairs))
test_result = [] # score, label(1=same)
for name1, idx1, name2, idx2 in tqdm(pairs):
img1_path = os.path.join(lfw_path, name1, '%s_%04d.jpg' % (name1, idx1))
img2_path = os.path.join(lfw_path, name2, '%s_%04d.jpg' % (name2, idx2))
img1 = cv2.imread(img1_path, cv2.IMREAD_COLOR)
img2 = cv2.imread(img2_path, cv2.IMREAD_COLOR)
if img1 is None:
logger.warning('image not read, path=%s' % img1_path)
if img2 is None:
logger.warning('image not read, path=%s' % img2_path)
result1 = self.run(image=img1, detector=detec, recognizer=recog, visualize=False)
result2 = self.run(image=img2, detector=detec, recognizer=recog, visualize=False)
if len(result1) == 0:
logger.warning('face not detected, name=%s(%d)! %s(%d)' % (name1, idx1, name2, idx2))
test_result.append((0.0, name1 == name2))
continue
if len(result2) == 0:
logger.warning('face not detected, name=%s(%d) %s(%d)!' % (name1, idx1, name2, idx2))
test_result.append((0.0, name1 == name2))
continue
feat1 = result1[0].face_feature
feat2 = result2[0].face_feature
similarity = feat_distance_cosine(feat1, feat2)
test_result.append((similarity, name1 == name2))
# calculate accuracy TODO
accuracy = sum([label == (score > DeepFaceConfs.get()['recognizer'][just_name]['score_th']) for score, label in test_result]) / float(len(test_result))
logger.info('accuracy=%.8f' % accuracy)
# ROC Curve, AUC
tps = []
fps = []
accuracy0 = []
accuracy1 = []
acc_th = []
for th in range(0, 100, 5):
th = th / 100.0
tp = 0
tn = 0
fp = 0
fn = 0
for score, label in test_result:
if score >= th and label == 1:
tp += 1
elif score >= th and label == 0:
fp += 1
elif score < th and label == 0:
tn += 1
elif score < th and label == 1:
fn += 1
tpr = tp / (tp + fn + 1e-12)
fpr = fp / (fp + tn + 1e-12)
tps.append(tpr)
fps.append(fpr)
accuracy0.append(tn / (tn + fp + 1e-12))
accuracy1.append(tp / (tp + fn + 1e-12))
acc_th.append(th)
fpr, tpr, thresh = roc_curve([x[1] for x in test_result], [x[0] for x in test_result])
fnr = 1 - tpr
eer = fnr[np.nanargmin(np.absolute((fnr - fpr)))]
logger.info('1-eer=%.4f' % (1.0 - eer))
with open('./etc/test_lfw.pkl', 'rb') as f:
results = pickle.load(f)
if visualize in [True, 'True', 'true', 1, '1']:
fig = plt.figure()
a = fig.add_subplot(1, 2, 1)
plt.title('Experiment on LFW')
plt.plot(fpr, tpr, label='%s(%.4f)' % (model, 1 - eer)) # TODO : label
for model_name in results:
if model_name == model:
continue
fpr_prev = results[model_name]['fpr']
tpr_prev = results[model_name]['tpr']
eer_prev = results[model_name]['eer']
plt.plot(fpr_prev, tpr_prev, label='%s(%.4f)' % (model_name, 1 - eer_prev))
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
a.legend()
a.set_title('Receiver operating characteristic')
a = fig.add_subplot(1, 2, 2)
plt.plot(accuracy0, acc_th, label='Accuracy_diff')
plt.plot(accuracy1, acc_th, label='Accuracy_same')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
a.legend()
a.set_title('%s : TP, TN' % model)
fig.savefig('./etc/roc.png', dpi=300)
plt.show()
plt.draw()
with open('./etc/test_lfw.pkl', 'wb') as f:
results[model] = {
'fpr': fpr,
'tpr': tpr,
'acc_th': acc_th,
'accuracy0': accuracy0,
'accuracy1': accuracy1,
'eer': eer
}
pickle.dump(results, f, pickle.HIGHEST_PROTOCOL)
return 1.0 - eer