def _check_colours_list(render_flag, colours_list, n_objects, error_str):
from menpo.visualize.viewmatplotlib import sample_colours_from_colourmap
if render_flag:
if colours_list is None:
# sample colours from jet colour map
colours_list = sample_colours_from_colourmap(n_objects, GLOBAL_CMAP)
if isinstance(colours_list, list):
if len(colours_list) == 1:
colours_list[0] = _parse_colour(colours_list[0])
colours_list *= n_objects
elif len(colours_list) != n_objects:
raise ValueError(error_str)
else:
colours_list = [_parse_colour(colours_list)] * n_objects
else:
colours_list = [None] * n_objects
return colours_list
def _check_colours_list(render_flag, colours_list, n_objects, error_str):
from menpo.visualize.viewmatplotlib import sample_colours_from_colourmap
if render_flag:
if colours_list is None:
# sample colours from jet colour map
colours_list = sample_colours_from_colourmap(
n_objects, GLOBAL_CMAP)
if isinstance(colours_list, list):
if len(colours_list) == 1:
colours_list[0] = _parse_colour(colours_list[0])
colours_list *= n_objects
elif len(colours_list) != n_objects:
raise ValueError(error_str)
else:
colours_list = [_parse_colour(colours_list)] * n_objects
else:
colours_list = [None] * n_objects
return colours_list
def FAR_TAR():
job='insightface'
target='A200'
files = [score_save_file]#人脸比对得到的相似度文件
methods = []
scores = []
for file in files:
methods.append(Path(file).stem)#模型方法名字
scores.append(np.load(file)) #相似度
methods = np.array(methods)
scores = dict(zip(methods,scores))
colours = dict(zip(methods, sample_colours_from_colourmap(methods.shape[0], 'Set2')))
#x_labels = [1/(10**x) for x in np.linspace(6, 0, 6)]
x_labels = [10**-6, 10**-5, 10**-4,10**-3, 10**-2, 10**-1]
tpr_fpr_table = PrettyTable(['Methods'] + [str(x) for x in x_labels])
fig = plt.figure()
for method in methods:
fpr, tpr, _ = roc_curve(label, scores[method])
roc_auc = auc(fpr, tpr)
fpr = np.flipud(fpr)
tpr = np.flipud(tpr) # select largest tpr at same fpr
plt.plot(fpr, tpr, color=colours[method], lw=1, label=('[%s (AUC = %0.4f %%)]' % (method.split('-')[-1], roc_auc*100)))
tpr_fpr_row = []
tpr_fpr_row.append("%s-%s"%(method, target))
for fpr_iter in np.arange(len(x_labels)):
_, min_index = min(list(zip(abs(fpr-x_labels[fpr_iter]), range(len(fpr)))))
#tpr_fpr_row.append('%.4f' % tpr[min_index])
tpr_fpr_row.append('%.2f' % (tpr[min_index]*100))
tpr_fpr_table.add_row(tpr_fpr_row)
plt.xlim([10**-6, 0.1])
plt.ylim([0.3, 1.0])
plt.grid(linestyle='--', linewidth=1)
plt.xticks(x_labels)
plt.yticks(np.linspace(0.3, 1.0, 8, endpoint=True))
plt.xscale('log')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('ROC on IJB')
plt.legend(loc="lower right")
#plt.show()
fig.savefig(os.path.join(save_path, '%s.pdf'%job))
print(tpr_fpr_table)
p1, p2, label = read_template_pair_list(
os.path.join('%s/meta' % image_path,
'%s_template_pair_label.txt' % 'ijbc'))
methods = []
scores = []
for file in files:
methods.append(file.split('/')[-2])
scores.append(np.load(file))
methods = np.array(methods)
scores = dict(zip(methods, scores))
colours = dict(
zip(methods, sample_colours_from_colourmap(methods.shape[0], 'Set2')))
x_labels = [10 ** -6, 10 ** -5, 10 ** -4, 10 ** -3, 10 ** -2, 10 ** -1]
tpr_fpr_table = PrettyTable(['Methods'] + [str(x) for x in x_labels])
fig = plt.figure()
for method in methods:
fpr, tpr, _ = roc_curve(label, scores[method])
roc_auc = auc(fpr, tpr)
fpr = np.flipud(fpr)
tpr = np.flipud(tpr) # select largest tpr at same fpr
plt.plot(fpr,
tpr,
color=colours[method],
lw=1,
label=('[%s (AUC = %0.4f %%)]' %
(method.split('-')[-1], roc_auc * 100)))
tpr_fpr_row = []
def main():
global args
args = parse_args()
print(args)
img_path = args.img_path
img_list_path = args.img_list_path
model_path = args.model_path
epoch_num = args.epoch_num
# os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id
gpus = args.gpu_id.split(",")
gpu_id = []
for gpu in gpus:
gpu_id.append(int(gpu))
print('gpu id:', gpu_id)
#time.sleep(3600*6.5)
# # Step1: Load Meta Data
# In[9]:
# =============================================================
# load image and template relationships for template feature embedding
# tid --> template id, mid --> media id
# format:
# image_name tid mid
# =============================================================
start = timeit.default_timer()
templates, medias = read_template_media_list(os.path.join('IJBC/meta', 'ijbc_face_tid_mid.txt'))
stop = timeit.default_timer()
print('Time: %.2f s. ' % (stop - start))
# In[10]:
# =============================================================
# load template pairs for template-to-template verification
# tid : template id, label : 1/0
# format:
# tid_1 tid_2 label
# =============================================================
start = timeit.default_timer()
p1, p2, label = read_template_pair_list(os.path.join('IJBC/meta', 'ijbc_template_pair_label.txt'))
stop = timeit.default_timer()
print('Time: %.2f s. ' % (stop - start))
# # Step 2: Get Image Features
# In[12]:
# =============================================================
# load image features
# format:
# img_feats: [image_num x feats_dim] (227630, 512)
# =============================================================
start = timeit.default_timer()
#img_feats = read_image_feature('./MS1MV2/IJBB_MS1MV2_r100_arcface.pkl')
img_feats, faceness_scores = get_image_feature(img_path, img_list_path, model_path, epoch_num, gpu_id)
stop = timeit.default_timer()
print('Time: %.2f s. ' % (stop - start))
print('Feature Shape: ({} , {}) .'.format(img_feats.shape[0], img_feats.shape[1]))
# # Step3: Get Template Features
# In[34]:
# =============================================================
# compute template features from image features.
# =============================================================
start = timeit.default_timer()
# ==========================================================
# Norm feature before aggregation into template feature?
# Feature norm from embedding network and faceness score are able to decrease weights for noise samples (not face).
# ==========================================================
# 1. FaceScore （Feature Norm）
# 2. FaceScore （Detector）
use_norm_score = False # if Ture, TestMode(N1)
use_detector_score = False # if Ture, TestMode(D1)
use_flip_test = False # if Ture, TestMode(F1)
if use_flip_test:
# concat --- F1
#img_input_feats = img_feats
# add --- F2
img_input_feats = img_feats[:,0:img_feats.shape[1]/2] + img_feats[:,img_feats.shape[1]/2:]
else:
img_input_feats = img_feats[:,0:img_feats.shape[1]/2]
if use_norm_score:
img_input_feats = img_input_feats
else:
# normalise features to remove norm information
img_input_feats = img_input_feats / np.sqrt(np.sum(img_input_feats ** 2, -1, keepdims=True))
if use_detector_score:
img_input_feats = img_input_feats * np.matlib.repmat(faceness_scores[:,np.newaxis], 1, img_input_feats.shape[1])
else:
img_input_feats = img_input_feats
template_norm_feats, unique_templates = image2template_feature(img_input_feats, templates, medias)
stop = timeit.default_timer()
print('Time: %.2f s. ' % (stop - start))
# # Step 4: Get Template Similarity Scores
# In[35]:
# =============================================================
# compute verification scores between template pairs.
# =============================================================
start = timeit.default_timer()
score = verification(template_norm_feats, unique_templates, p1, p2)
stop = timeit.default_timer()
print('Time: %.2f s. ' % (stop - start))
# In[36]:
score_save_name = './IJBC/result/MS1MV2-' + args.model_name + '-TestMode(' + str(epoch_num) + 'epoch).npy'
np.save(score_save_name, score)
# # Step 5: Get ROC Curves and TPR@FPR Table
# In[38]:
score_save_path = './IJBC/result'
files = glob.glob(score_save_path + '/MS1MV2*.npy')
methods = []
scores = []
for file in files:
methods.append(Path(file).stem)
scores.append(np.load(file))
methods = np.array(methods)
scores = dict(zip(methods,scores))
colours = dict(zip(methods, sample_colours_from_colourmap(methods.shape[0], 'Set2')))
#x_labels = [1/(10**x) for x in np.linspace(6, 0, 6)]
x_labels = [10**-6, 10**-5, 10**-4,10**-3, 10**-2, 10**-1]
tpr_fpr_table = PrettyTable(['Methods'] + map(str, x_labels))
# fig = plt.figure()
for method in methods:
fpr, tpr, _ = roc_curve(label, scores[method])
roc_auc = auc(fpr, tpr)
fpr = np.flipud(fpr)
tpr = np.flipud(tpr) # select largest tpr at same fpr
# plt.plot(fpr, tpr, color=colours[method], lw=1, label=('[%s (AUC = %0.4f %%)]' % (method.split('-')[-1], roc_auc*100)))
tpr_fpr_row = []
tpr_fpr_row.append(method)
for fpr_iter in np.arange(len(x_labels)):
_, min_index = min(list(zip(abs(fpr-x_labels[fpr_iter]), range(len(fpr)))))
tpr_fpr_row.append('%.4f' % tpr[min_index])
tpr_fpr_table.add_row(tpr_fpr_row)
#plt.xlim([10**-6, 0.1])
#plt.ylim([0.3, 1.0])
#plt.grid(linestyle='--', linewidth=1)
#plt.xticks(x_labels)
#plt.yticks(np.linspace(0.3, 1.0, 8, endpoint=True))
#plt.xscale('log')
#plt.xlabel('False Positive Rate')
#plt.ylabel('True Positive Rate')
#plt.title('ROC on IJB-C')
#plt.legend(loc="lower right")
#plt.show()
#fig.savefig('IJB-B.pdf')
# In[39]:
print(tpr_fpr_table)
