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)