for i, t in enumerate(set(y)):
idx = y == t
plt.scatter(x_embedded[idx, 0], x_embedded[idx, 1], label=t)
plt.title(title, fontsize=16)
plt.legend()
if save is not None:
plt.savefig(save)
with open(CONFIG_FILE, 'r') as stream:
config = yaml.load(stream)
nclasses = config['nclasses']
model = PCB(nclasses)
model.load_state_dict(torch.load(WEIGHT_FILE))
model = model.eval().cuda()
features = []
labels = []
fol_list = os.listdir(SAMPLE_FOLDER)
for folder in fol_list:
if folder == 'ALL':
continue
fol = os.path.join(SAMPLE_FOLDER, folder)
if not os.path.isdir(fol):
continue
print('Folder : ' + folder)
cls_name = folder
dataset_filename = []
for i in range(len(dataset_list)):
cam, label, filename = get_id(dataset_path[i])
dataset_cam.append(cam)
dataset_label.append(label)
dataset_filename.append(filename)
######################################################################
# Load Collected data Trained model
print('-------test-----------')
class_num = len(os.listdir(os.path.join(data_dir, 'train_all')))
model = PCB(class_num)
if 'st' in opt.which_epoch:
model = load_whole_network(model, name, opt.which_epoch + '_' + str(opt.net_loss_model))
else:
model = load_whole_network(model, name, opt.which_epoch)
model = model.eval()
if use_gpu:
model = model.cuda()
# Extract feature
dataset_feature = []
with torch.no_grad():
for i in range(len(dataset_list)):
dataset_feature.append(extract_feature(model, dataloaders[dataset_list[i]]))
result = {'gallery_f': dataset_feature[0].numpy(), 'gallery_label': dataset_label[0],
'gallery_name': dataset_filename[0], 'gallery_cam': dataset_cam[0],
'query_f': dataset_feature[1].numpy(), 'query_label': dataset_label[1], 'query_name': dataset_filename[1],
'query_cam': dataset_cam[1]}
scipy.io.savemat('pytorch_result.mat', result)
alarm_cooldown = 15 # second.
# Yolo model for object detection.
print('Load Detection Model.')
print('Use YOLO model for Detection.')
yolo = YOLO()
# Human feature extraction.
print('Load Feature Model.')
print('Use PCB model for features extractor.')
with open(CONFIG_FILE, 'r') as stream:
config = yaml.load(stream)
nclasses = config['nclasses']
encoder = PCB(nclasses)
encoder.load_state_dict(torch.load(WEIGHT_FILE))
encoder = encoder.eval().cuda()
print('Load Recognition Model.')
if args.SVM:
print('Use SVM for matching.')
recog = pickle.load(open(RECOG_MODEL, 'rb')) # SVM match.
_, _, cls_names = pickle.load(open(RECOG_SAMPLE, 'rb'))
else:
print('Use Cosine for matching.')
ft_set, lb_set, cls_names = pickle.load(open(RECOG_SAMPLE, 'rb'))
# ft_set, lb_set = reduceFeaturesSample(np.array(ft_set), lb_set, 20)
spl = args.resize.split('x')
w = int(spl[0])
h = int(spl[1])
