output_dir = './output/'
model_name = os.path.basename(model_path).split('.')[0]
file_results = os.path.join(output_dir,'results_' + model_name + '_.txt')
if not os.path.exists(output_dir):
os.mkdir(output_dir)
output_dir = os.path.join(output_dir, 'density_maps_' + model_name)
if not os.path.exists(output_dir):
os.mkdir(output_dir)
net = CrowdCounter()
trained_model = os.path.join(model_path)
network.load_net(trained_model, net)
net.cuda()
net.eval()
mae = 0.0
mse = 0.0
#load test data
data_loader = ImageDataLoader(data_path, gt_csv_path, shuffle=False, gt_downsample=True, pre_load=True)
#load test data gt
gt_files = os.listdir(gt_path)
gt_files.sort()
for i, blob in enumerate(data_loader):
im_data = blob['data']
gt_data = blob['gt_density']
density_map = net(im_data, gt_data)
density_map = density_map.data.cpu().numpy()
def open_model(model_path):
model = CrowdCounter()
network.load_net(model_path, model)
model.eval()
return model