def pre_MCNN(): MCNN_model_path = './saved_models/mcnn_shtechA_58.h5' #MCNN模型路径 net = CrowdCounter() trained_model = os.path.join(MCNN_model_path) network.load_net(trained_model, net) net.cuda() net.eval() return net
def evaluate_model(trained_model, data_loader): net = CrowdCounter() network.load_net(trained_model, net) net.cuda() net.eval() mae = 0.0 mse = 0.0 for blob in data_loader: im_data = blob['data'] gt_data = blob['gt_density'] density_map = net.forward(im_data, gt_data) density_map = density_map.data.cpu().numpy() gt_count = np.sum(gt_data) et_count = np.sum(density_map) mae += abs(gt_count - et_count) mse += ((gt_count - et_count) * (gt_count - et_count)) mae = mae / data_loader.get_num_samples() mse = np.sqrt(mse / data_loader.get_num_samples()) return mae, mse
model_path = './oilpalm_saved_models/mcnn_oilpalm_70.h5' 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 rmse = 0.0 mrmse = 0.0 # load test data data_loader = ImageDataLoader(data_path, gt_path, shuffle=False, gt_downsample=True, pre_load=True) for blob in data_loader: im_data = blob['data'] gt_data = blob['gt_density']
def testimage(modelname, camname): torch.backends.cudnn.enabled = True torch.backends.cudnn.benchmark = False vis = False save_output = False #test data and model file path if camname == 0: data_path = '../data/test/images/' else: data_path = '../data/test/images2/' if modelname == 'A': model_path = './final_models/cmtl_shtechA_204.h5' else: model_path = './final_models/cmtl_shtechB_768.h5' print("Model name:", modelname, " Camname: ", camname) gt_flag = False if gt_flag: gt_path = '../dataset/ShanghaiTech/part_A/test_data/ground_truth/' # ============================================================================= # for i in range(1, 4): # gt_name = os.path.join(gt_path,'img_' + format(i, '04') + '_ann.mat') # print(gt_name) # x = loadmat(gt_name) # print (len(x['annPoints'])) # # ============================================================================= 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) #load test data data_loader = ImageDataLoader(data_path, shuffle=False, gt_downsample=True, pre_load=True) 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 i = 1 #df = pd.read_csv("../etcount.csv") #df = df.set_index('IMG_NAME') #df['GROUND_TRUTH'] = 0.0 #df['MTL-v4-A10'] = 0.0 for blob in data_loader: if gt_flag: gt_name = os.path.join( gt_path, 'GT_' + format(blob['fname'].split('.')[0]) + '.mat') x = loadmat(gt_name) #gt_count = len(x['image_info'][0][0][0][0][0]) #df.at[blob['fname'].split('.')[0], 'GROUND_TRUTH'] = gt_count i += 1 im_data = blob['data'] density_map = net(im_data) density_map = density_map.data.cpu().numpy() x = len(density_map[0][0]) y = len(density_map[0][0][0]) half = (int)(x / 2) density_map1 = density_map[0][0][0:half][:] density_map2 = density_map[0][0][half:][:] print(x, y) et_c1 = np.sum(density_map1) et_c2 = np.sum(density_map2) side = 'none' if et_c1 > et_c2: side = 'right' else: side = 'left' print(et_c1, et_c2) et_count = np.sum(density_map) print(blob['fname'].split('.')[0], ' Model Estimated count : ', et_count) #df.at[blob['fname'].split('.')[0], 'MTL-v4-A'] = et_count if vis: utils.display_results(im_data, density_map) if save_output: utils.save_density_map( density_map, output_dir, 'output_' + blob['fname'].split('.')[0] + '.png') return (et_count, side) #df.to_csv('../etcount.csv') #testimage('A', 1)