def returnNum(category, index):
index = index - 1
if category == 'restaurant':
blob = data_loader_res.blob_list[index]
elif category == 'outdoor':
blob = data_loader_out.blob_list[index]
elif category == 'classroom':
blob = data_loader_class.blob_list[index]
im_data = blob['data']
density_map = net(im_data)
density_map = density_map.data.cpu().numpy()
et_count = sum(density_map) # preedicted_count
if save_output:
output_dir = output_dir_base + category + '/'
utils.save_density_map(
density_map, output_dir,
category + '_' + blob['fname'].split('.')[0] + '.png')
return et_count
for blob in data_loader:
im_data = blob['data']
gt_data = blob['gt_density']
# print('gt_data: ',gt_data)
density_map = net(im_data, gt_data)
density_map = density_map.data.cpu().numpy()
# print('density_map: ',density_map)
gt_count = np.sum(gt_data)
# print('gt_count: ',gt_count)
et_count = np.sum(density_map)
# print('et_count: ',et_count)
mae += abs(gt_count - et_count)
rmse += ((gt_count - et_count) * (gt_count - et_count))
if vis:
utils.display_results(im_data, gt_data, density_map)
if save_output:
utils.save_density_map(
density_map, output_dir,
'output_' + blob['fname'].split('.')[0] + '.png')
mae = mae / data_loader.get_num_samples()
rmse = np.sqrt(rmse / data_loader.get_num_samples())
print('rmse: ', rmse)
mrmse = np.sqrt(rmse / data_loader.get_num_samples()).mean()
print('\nMAE: %0.3f, RMSE: %0.3f' % (mae, rmse))
print('\nmRMSE:%0.3f' % (mrmse))
f = open(file_results, 'w')
f.write('MAE: %0.3f, MSE: %0.3f' % (mae, rmse))
f.close()
model_path = './saved_models/mcnn_shtechA_1998.h5'
output_dir = './output/'
net = CrowdCounter()
"""Load trained model"""
trained_model = os.path.join(model_path)
network.load_net(trained_model, net)
net.cuda()
net.eval()
gt_data = None
"""Load image from data_path"""
im_data = read_image(data_path)
"""Calculate density map"""
density_map = net(im_data, gt_data)
"""convert to numpy array"""
density_map = density_map.data.cpu().numpy()
"""estimation count from density map"""
et_count = np.sum(density_map)
"""Save result: density map; stack input image and density map"""
utils.save_results(im_data, int(et_count), density_map, output_dir)
utils.save_density_map(density_map, output_dir)
import matplotlib.pyplot as plt
img = cv2.imread('./output/results.png')
plt.imshow(img, cmap='gray')
plt.show()
print et_count
# gt_path = './data/original/shanghaitech/part_B_final/test_data/ground_truth_csv/'
model_path = './final_models/mcnn_shtechA_660.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()
net.training = False
im_data = cv2.imread(
'/home/haoxue/Pictures/Screenshot from 2018-11-05 15-26-25.png', 0)
im_data = im_data.reshape((1, 1, im_data.shape[0], im_data.shape[1]))
density_map = net(im_data)
density_map = density_map.data.cpu().numpy()
et_count = np.sum(density_map)
if vis:
utils.display_density(im_data, density_map)
if save_output:
utils.save_density_map(density_map, output_dir, 'output_' + '.png')
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)
