def list_views(self):
if self.active_connection.jenkins_views:
display_results(self.active_connection.view_names)
else:
print('No Jenkins views to display.')
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()
def list_connections(self):
if self.jenkins_connections:
display_results(self.connection_names)
else:
print('No Jenkins connections to display.')
def list_custom_reports(self):
if self.jenkins_reports:
display_results(self.report_names)
else:
print('No Jenkins reports to display.')
def single_img_estimate(input_img):
self_data_path = input_img
# print('get data path!')
gt_path = './data/mall_demo/rgb_den/' + self_data_path.split(
'/')[-1].replace('.jpg', '.csv')
# print('get gt path!')
# branch pre-train
model_path = './final_models/ms_pool_deconv_skip_mall_60_ms.h5'
self_output_dir = './data/mall_demo/demo_output/'
self_gt_dir = './data/mall_demo/demo_gt/'
model_name = os.path.basename(model_path).split('.')[0]
file_results = os.path.join(self_output_dir,
'results_' + model_name + '.txt')
if not os.path.exists(self_output_dir):
os.mkdir(self_output_dir)
self_output_dir = os.path.join(self_output_dir, 'den_' + model_name)
if not os.path.exists(self_output_dir):
os.mkdir(self_output_dir)
if not os.path.exists(self_gt_dir):
os.mkdir(self_gt_dir)
gt_dir = os.path.join(self_gt_dir, 'gt_' + model_name)
if not os.path.exists(self_gt_dir):
os.mkdir(self_gt_dir)
# print('mkdir done!')
net = CrowdCounter_cnterr_l1_out()
trained_model = os.path.join(model_path)
network.load_net(trained_model, net)
net.cuda(3)
net.eval()
mae = 0.0
mse = 0.0
# load test data
# downsample = True
# data_loader = ImageDataLoader(data_path, gt_path, shuffle=False, gt_downsample=True, pre_load=False)
# downsample = False
data_loader = SingleImageDataLoader(self_data_path,
gt_path,
shuffle=False,
gt_downsample=False,
pre_load=False)
for blob in data_loader:
im_data = blob['data']
gt_data = blob['gt_density']
t.tic()
density_map = net(im_data, gt_data, is_training=False)
density_map = density_map.data.cpu().numpy()
duration = t.toc()
print("time duration:" + str(duration))
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))
if vis:
utils.display_results(im_data, gt_data, density_map)
if save_output:
utils.save_demo_density_map(
density_map, self_output_dir,
'output_' + blob['fname'].split('.')[0] + '.png')
gt_data = 255 * gt_data / np.max(gt_data)
gt_data = gt_data.astype(np.uint8)
gt_data = cv2.applyColorMap(gt_data, cv2.COLORMAP_JET)
cv2.imwrite(
os.path.join(self_gt_dir,
'gt_' + blob['fname'].split('.')[0] + '.png'),
gt_data)
print('\nMAE: %0.2f, MSE: %0.2f' % (mae, mse))
# f = open(file_results, 'w')
# f.write('MAE: %0.2f, MSE: %0.2f' % (mae, mse))
# f.close()
# et_path = self.output_dir + 'output_' + blob['fname'].split('.')[0] + '.png'
# gt_path = self.gt_dir+'gt_'+blob['fname'].split('.')[0]+'.png'
return (self_output_dir + '/output_' + blob['fname'].split('.')[0] +
'.png',
self_gt_dir + '/gt_' + blob['fname'].split('.')[0] + '.png',
mae, mse, gt_count, et_count)
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)
def single_img_estimate(input_path):
data_path = input_path
gt_path = './data/formatted_trainval/mall_dataset/rgb_val_den/' + input_path.split(
'/')[-1].replace('.jpg', '.csv')
# branch pre-train
model_path = './final_models/mcnn_mall_perspective_28_ms.h5'
output_dir = './demo_output/'
gt_dir = './demo_gt/'
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)
if not os.path.exists(gt_dir):
os.mkdir(gt_dir)
gt_dir = os.path.join(gt_dir, 'density_maps_' + model_name)
if not os.path.exists(gt_dir):
os.mkdir(gt_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
# downsample = True
data_loader = SingleImageDataLoader(data_path,
gt_path,
shuffle=False,
gt_downsample=True,
pre_load=False)
# downsample = False
# data_loader = ImageDataLoader(data_path, gt_path, shuffle=False, gt_downsample=True, pre_load=False)
for blob in data_loader:
im_data = blob['data']
gt_data = blob['gt_density']
t.tic()
density_map = net(im_data, gt_data, False)
density_map = density_map.data.cpu().numpy()
duration = t.toc()
print("time duration:" + str(duration))
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))
if vis:
utils.display_results(im_data, gt_data, density_map)
if save_output:
utils.save_demo_density_map(
density_map, output_dir,
'output_' + blob['fname'].split('.')[0] + '.png')
gt_data = 255 * gt_data / np.max(gt_data)
gt_data = gt_data.astype(np.uint8)
gt_data = cv2.applyColorMap(gt_data, cv2.COLORMAP_JET)
cv2.imwrite(
os.path.join(gt_dir,
'gt_' + blob['fname'].split('.')[0] + '.png'),
gt_data)
print('\nMAE: %0.2f, MSE: %0.2f' % (mae, mse))
f = open(file_results, 'w')
f.write('MAE: %0.2f, MSE: %0.2f' % (mae, mse))
f.close()
return (output_dir + '/output_' + blob['fname'].split('.')[0] + '.png',
gt_dir + '/gt_' + blob['fname'].split('.')[0] + '.png', mae,
mse, gt_count, et_count)
