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test_SENSE.py
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test_SENSE.py
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import datasets
from config import cfg
import numpy as np
import torch
import datasets
from misc.utils import *
from model.VIC import Video_Individual_Counter
from tqdm import tqdm
import torch.nn.functional as F
from pathlib import Path
import argparse
import matplotlib.cm as cm
from train import compute_metrics_single_scene,compute_metrics_all_scenes
import os.path as osp
from model.MatchTool.compute_metric import associate_pred2gt_point_vis
parser = argparse.ArgumentParser(
description='VIC test and demo',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument(
'--DATASET', type=str, default='SENSE',
help='Directory where to write output frames (If None, no output)')
parser.add_argument(
'--output_dir', type=str, default='../dataset/demo_den_test',
help='Directory where to write output frames (If None, no output)')
parser.add_argument(
'--test_intervals', type=int, default=15,
help='Directory where to write output frames (If None, no output)')
parser.add_argument(
'--skip_flag', type=bool, default=False,
help='if you need to caculate the MIAE and MOAE, it should be False')
parser.add_argument(
'--SEED', type=int, default=3035,
help='Directory where to write output frames (If None, no output)')
parser.add_argument(
'--GPU_ID', type=str, default='2',
help='Directory where to write output frames (If None, no output)')
parser.add_argument(
'--model_path', type=str, default='./model/pretrained_models/SenseCrowd.pth',
help='pretrained weight path')
# parser.add_argument(
# '--model_path', type=str, default='./exp/SENSE/03-22_17-33_SENSE_VGG16_FPN_5e-05/ep_15_iter_115000_mae_2.211_mse_3.677_seq_MAE_6.439_WRAE_9.506_MIAE_1.447_MOAE_1.474.pth',
# help='pretrained weight path')
opt = parser.parse_args()
opt.output_dir = opt.output_dir+'_'+opt.DATASET
def test(cfg_data):
net = Video_Individual_Counter(cfg, cfg_data)
with open(osp.join(cfg_data.DATA_PATH, 'scene_label.txt'), 'r') as f:
lines = f.readlines()
scene_label = {}
for line in lines:
line = line.rstrip().split(' ')
scene_label.update({line[0]: [int(i) for i in line[1:]] })
test_loader, restore_transform = datasets.loading_testset(opt.DATASET, test_interval=opt.test_intervals,mode='test')
state_dict = torch.load(opt.model_path)
net.load_state_dict(state_dict, strict=True)
net.eval()
scenes_pred_dict = {'all':[], 'in':[], 'out':[], 'day':[],'night':[], 'scenic0':[], 'scenic1':[],'scenic2':[],
'scenic3':[],'scenic4':[],'scenic5':[], 'density0':[],'density1':[],'density2':[], 'density3':[],'density4':[] }
scenes_gt_dict = {'all':[], 'in':[], 'out':[], 'day':[],'night':[], 'scenic0':[], 'scenic1':[],'scenic2':[],
'scenic3':[],'scenic4':[],'scenic5':[], 'density0':[],'density1':[],'density2':[], 'density3':[],'density4':[] }
if opt.skip_flag:
intervals = 1
else:
intervals = opt.test_intervals
for scene_id, sub_valset in enumerate(test_loader, 0):
# if scene_id>2:
# break
gen_tqdm = tqdm(sub_valset)
video_time = len(sub_valset) + opt.test_intervals
print(video_time)
scene_name = ''
pred_dict = {'id': scene_id, 'time': video_time, 'first_frame': 0, 'inflow': [], 'outflow': []}
gt_dict = {'id': scene_id, 'time': video_time, 'first_frame': 0, 'inflow': [], 'outflow': []}
for vi, data in enumerate(gen_tqdm, 0):
img, target = data
# import pdb
# pdb.set_trace()
img, target = img[0], target[0]
scene_name = target[0]['scene_name']
img = torch.stack(img, 0)
with torch.no_grad():
b, c, h, w = img.shape
if h % 16 != 0:
pad_h = 16 - h % 16
else:
pad_h = 0
if w % 16 != 0:
pad_w = 16 - w % 16
else:
pad_w = 0
pad_dims = (0, pad_w, 0, pad_h)
img = F.pad(img, pad_dims, "constant")
if vi % opt.test_intervals == 0 or vi == len(sub_valset) - 1:
frame_signal = 'match'
else:
frame_signal = 'skip'
if frame_signal == 'match' or not opt.skip_flag:
pred_map, gt_den, matched_results = net.val_forward(img, target, frame_signal)
# save_inflow_outflow_density(img, matched_results['scores'], matched_results['pre_points'],
# matched_results['target'], matched_results['match_gt'],
# osp.join(opt.output_dir, scene_name), scene_name, vi, opt.test_intervals)
# -----------Counting performance------------------
gt_count, pred_cnt = gt_den[0].sum().item(), pred_map[0].sum().item()
# ============================================================
pred_cnt = pred_map[0].sum().item()
#===================================================================
if vi == 0:
pred_dict['first_frame'] = pred_map[0].sum().item()
gt_dict['first_frame'] = len(target[0]['person_id'])
pred_dict['inflow'].append(matched_results['pre_inflow'])
pred_dict['outflow'].append(matched_results['pre_outflow'])
gt_dict['inflow'].append(matched_results['gt_inflow'])
gt_dict['outflow'].append(matched_results['gt_outflow'])
if frame_signal == 'match':
pre_crowdflow_cnt, gt_crowdflow_cnt, _, _ = compute_metrics_single_scene(pred_dict, gt_dict, intervals)
print('den_gt: %.2f den_pre: %.2f gt_crowd_flow: %.2f, pre_crowd_flow: %.2f gt_inflow: %.2f pre_inflow:%.2f'
% (gt_count, pred_cnt, gt_crowdflow_cnt, pre_crowdflow_cnt, matched_results['gt_inflow'],
matched_results['pre_inflow']))
kpts0 = matched_results['pre_points'][0][:, 2:4].cpu().numpy()
kpts1 = matched_results['pre_points'][1][:, 2:4].cpu().numpy()
matches = matched_results['matches0'].cpu().numpy()
confidence = matched_results['matching_scores0'].cpu().numpy()
if kpts0.shape[0] > 0 and kpts1.shape[0] > 0:
save_visImg(kpts0, kpts1, matches, confidence, vi, img[0].clone(), img[1].clone(),
opt.test_intervals, osp.join(opt.output_dir,scene_name), None, None, scene_name, restore_transform)
save_inflow_outflow_density(img, matched_results['scores'], matched_results['pre_points'],
matched_results['target'], matched_results['match_gt'],
osp.join(opt.output_dir,scene_name), scene_name, vi, opt.test_intervals)
scenes_pred_dict['all'].append(pred_dict)
scenes_gt_dict['all'].append(gt_dict)
scene_l = scene_label[scene_name]
if scene_l[0] == 0: scenes_pred_dict['in'].append(pred_dict); scenes_gt_dict['in'].append(gt_dict)
if scene_l[0] == 1: scenes_pred_dict['out'].append(pred_dict); scenes_gt_dict['out'].append(gt_dict)
if scene_l[1] == 0: scenes_pred_dict['day'].append(pred_dict); scenes_gt_dict['day'].append(gt_dict)
if scene_l[1] == 1: scenes_pred_dict['night'].append(pred_dict); scenes_gt_dict['night'].append(gt_dict)
if scene_l[2] == 0: scenes_pred_dict['scenic0'].append(pred_dict); scenes_gt_dict['scenic0'].append(gt_dict)
if scene_l[2] == 1: scenes_pred_dict['scenic1'].append(pred_dict); scenes_gt_dict['scenic1'].append(gt_dict)
if scene_l[2] == 2: scenes_pred_dict['scenic2'].append(pred_dict); scenes_gt_dict['scenic2'].append(gt_dict)
if scene_l[2] == 3: scenes_pred_dict['scenic3'].append(pred_dict); scenes_gt_dict['scenic3'].append(gt_dict)
if scene_l[2] == 4: scenes_pred_dict['scenic4'].append(pred_dict); scenes_gt_dict['scenic4'].append(gt_dict)
if scene_l[2] == 5: scenes_pred_dict['scenic5'].append(pred_dict); scenes_gt_dict['scenic5'].append(gt_dict)
if scene_l[3] == 0: scenes_pred_dict['density0'].append(pred_dict); scenes_gt_dict['density0'].append(gt_dict)
if scene_l[3] == 1: scenes_pred_dict['density1'].append(pred_dict); scenes_gt_dict['density1'].append(gt_dict)
if scene_l[3] == 2: scenes_pred_dict['density2'].append(pred_dict); scenes_gt_dict['density2'].append(gt_dict)
if scene_l[3] == 3: scenes_pred_dict['density3'].append(pred_dict); scenes_gt_dict['density3'].append(gt_dict)
if scene_l[3] == 4: scenes_pred_dict['density4'].append(pred_dict); scenes_gt_dict['density4'].append(gt_dict)
for key in scenes_pred_dict.keys():
s_pred_dict = scenes_pred_dict[key]
s_gt_dict = scenes_gt_dict[key]
MAE, MSE, WRAE, MIAE, MOAE, cnt_result = compute_metrics_all_scenes(s_pred_dict, s_gt_dict, intervals)
if key == 'all':save_cnt_result = cnt_result
print('='*20, key, '='*20)
print('MAE: %.2f, MSE: %.2f WRAE: %.2f WIAE: %.2f WOAE: %.2f' % (MAE.data, MSE.data, WRAE.data, MIAE.data, MOAE.data))
print(save_cnt_result)
os.makedirs('./results',mode=0o777, exist_ok=True)
np.save(os.path.join('./results',str(opt.test_intervals)+'_SENSE_cnt.py'),save_cnt_result.numpy())
def save_visImg( kpts0, kpts1, matches, confidence, vi, last_frame, cur_frame, intervals,
save_path, id0=None, id1=None, scene_id='',restore_transform=None):
valid = matches > -1
mkpts0 = kpts0[valid].reshape(-1, 2)
mkpts1 = kpts1[matches[valid]].reshape(-1, 2)
color = cm.jet(confidence[valid])
text = [
'VIC',
'Keypoints: {}:{}'.format(len(kpts0), len(kpts1)),
'Matches: {}'.format(len(mkpts1))
]
small_text = [
'Match Threshold: {:.2f}'.format(0.1),
'Image Pair: {:06}:{:06}'.format(vi - intervals, vi)
]
out, out_by_point = make_matching_plot_fast(
last_frame, cur_frame, kpts0, kpts1, mkpts0, mkpts1, color, text,
path=None, show_keypoints=True, small_text=small_text, restore_transform=restore_transform,
id0=id0, id1=id1)
if save_path is not None:
# print('==> Will write outputs to {}'.format(save_path))
os.makedirs(save_path,mode =0o777, exist_ok=True)
stem = '{}_{}_{}_matches'.format(scene_id, vi, vi + intervals)
out_file = str(Path(save_path, stem + '.png'))
print('\nWriting image to {}'.format(out_file))
cv2.imwrite(out_file, out)
out_file = str(Path(save_path, stem + '_vis.png'))
cv2.imwrite(out_file, out_by_point)
def generate_cycle_mask(height, width, back_color, fore_color):
x, y = np.ogrid[-height:height + 1, -width:width + 1]
# ellipse mask
cir_idx = ((x) ** 2 / (height ** 2) + (y) ** 2 / (width ** 2) <= 1)
mask = np.zeros((2 * height + 1, 2 * width + 1, 3)).astype(np.uint8)
mask[cir_idx == 0] = back_color
mask[cir_idx == 1] = fore_color
return mask
def save_inflow_outflow_density(img, scores, pre_points, target, match_gt, save_path, scene_id, vi, intervals):
scores = scores.cpu().numpy()
_, __, img_h, img_w = img.size()
gt_inflow = np.zeros((img_h, img_w, 3)).astype(np.uint8)
gt_outflow = np.zeros((img_h, img_w, 3)).astype(np.uint8)
pre_inflow = np.zeros((img_h, img_w, 3)).astype(np.uint8)
pre_outflow = np.zeros((img_h, img_w, 3)).astype(np.uint8)
RoyalBlue1 = np.array([255, 118, 72]) # np.array([205,82,180])
red = [0, 0, 255]
green = [0, 255, 0]
blue = [255, 0, 0]
gt_inflow[:, :, 0:3] = RoyalBlue1
gt_outflow[:, :, 0:3] = RoyalBlue1
pre_inflow[:, :, 0:3] = RoyalBlue1
pre_outflow[:, :, 0:3] = RoyalBlue1
# matched_mask = np.zeros(scores.shape)
# matched_mask[match_gt['a2b'][:, 0], match_gt['a2b'][:, 1]] = 1
# matched_mask[match_gt['un_a'], -1] = 1
# matched_mask[-1, match_gt['un_b']] = 1
kernel = 8
wide = 2 * kernel + 1
pre_outflow_p = pre_points[0][scores[:-1, -1] > 0.4][:, 2:4]
tp_pred_index, fp_pred_index, tp_gt_index, fn_gt_index = associate_pred2gt_point_vis(pre_outflow_p, target[0], match_gt['un_a'].cpu().numpy())
# import pdb
# pdb.set_trace()
for row_id, pos in enumerate(pre_outflow_p, 0):
w, h = pos.cpu().numpy().astype(np.int64)
h_min, h_max = max(0, h - kernel), min(img_h, h + kernel + 1)
w_min, w_max = max(0, w - kernel), min(img_w, w + kernel + 1)
if row_id in tp_pred_index:
mask = generate_cycle_mask(kernel, kernel, RoyalBlue1, red)
if row_id not in tp_pred_index:
mask = generate_cycle_mask(kernel, kernel, RoyalBlue1, green)
pre_outflow[h_min:h_max, w_min:w_max] = mask[max(kernel - h, 0):wide - max(0, kernel + 1 + h - img_h),
max(kernel - w, 0):wide - max(0, kernel + 1 + w - img_w)]
for pos in (target[0]['points'][match_gt['un_a']][fn_gt_index]):
w, h = pos.cpu().numpy().astype(np.int64)
h_min, h_max = max(0, h - kernel), min(img_h, h + kernel + 1)
w_min, w_max = max(0, w - kernel), min(img_w, w + kernel + 1)
mask = generate_cycle_mask(kernel, kernel, RoyalBlue1, blue)
pre_outflow[h_min:h_max, w_min:w_max] = mask[max(kernel - h, 0):wide - max(0, kernel + 1 + h - img_h),
max(kernel - w, 0):wide - max(0, kernel + 1 + w - img_w)]
# ================================pre_inflow========================
pre_inflow_p = pre_points[1][scores[-1, :-1] > 0.4][:, 2:4]
tp_pred_index, fp_pred_index, tp_gt_index, fn_gt_index = associate_pred2gt_point_vis(pre_inflow_p, target[1], match_gt['un_b'].cpu().numpy())
for column_id, pos in enumerate(pre_inflow_p, 0):
w, h = pos.cpu().numpy().astype(np.int64)
h_min, h_max = max(0, h - kernel), min(img_h, h + kernel + 1)
w_min, w_max = max(0, w - kernel), min(img_w, w + kernel + 1)
if column_id in tp_pred_index:
mask = generate_cycle_mask(kernel, kernel, RoyalBlue1, red)
if column_id not in tp_pred_index:
mask = generate_cycle_mask(kernel, kernel, RoyalBlue1, green)
pre_inflow[h_min:h_max, w_min:w_max] = mask[max(kernel - h, 0):wide - max(0, kernel + 1 + h - img_h),
max(kernel - w, 0):wide - max(0, kernel + 1 + w - img_w)]
for pos in (target[1]['points'][match_gt['un_b']][fn_gt_index]):
w, h = pos.cpu().numpy().astype(np.int64)
h_min, h_max = max(0, h - kernel), min(img_h, h + kernel + 1)
w_min, w_max = max(0, w - kernel), min(img_w, w + kernel + 1)
mask = generate_cycle_mask(kernel, kernel, RoyalBlue1, blue)
pre_inflow[h_min:h_max, w_min:w_max] = mask[max(kernel - h, 0):wide - max(0, kernel + 1 + h - img_h),
max(kernel - w, 0):wide - max(0, kernel + 1 + w - img_w)]
# pred_inflow_map = cv2.applyColorMap((255 * pre_inflow / (pre_inflow.max() + 1e-10)).astype(np.uint8).squeeze(), cv2.COLORMAP_JET)
for row_id in match_gt['un_a'].cpu().numpy():
# import pdb
# pdb.set_trace()
w, h = target[0]['points'][row_id].cpu().numpy().astype(np.int64)
h_min, h_max = max(0, h - kernel), min(img_h, h + kernel + 1)
w_min, w_max = max(0, w - kernel), min(img_w, w + kernel + 1)
mask = generate_cycle_mask(kernel, kernel, RoyalBlue1, [0, 0, 255])
gt_outflow[h_min:h_max, w_min:w_max] = mask[max(kernel - h, 0):wide - max(0, kernel + 1 + h - img_h),
max(kernel - w, 0):wide - max(0, kernel + 1 + w - img_w)]
print(w, h)
# gt_outflow_map = cv2.applyColorMap((255 * gt_outflow / (gt_outflow.max() + 1e-10)).astype(np.uint8).squeeze(), cv2.COLORMAP_JET)
for column_id in match_gt['un_b'].cpu().numpy():
w, h = target[1]['points'][column_id].cpu().numpy().astype(np.int64)
h_min, h_max = max(0, h - kernel), min(img_h, h + kernel + 1)
w_min, w_max = max(0, w - kernel), min(img_w, w + kernel + 1)
mask = generate_cycle_mask(kernel, kernel, RoyalBlue1, [0, 0, 255])
gt_inflow[h_min:h_max, w_min:w_max] = mask[max(kernel - h, 0):wide - max(0, kernel + 1 + h - img_h),
max(kernel - w, 0):wide - max(0, kernel + 1 + w - img_w)]
# gt_inflow_map = cv2.applyColorMap((255 * gt_inflow / (gt_inflow.max() + 1e-10)).astype(np.uint8).squeeze(), cv2.COLORMAP_JET)
os.makedirs(save_path, mode=0o777, exist_ok=True)
stem = '{}_{}_{}_matches_outflow_pre_{}'.format(scene_id, vi, vi + intervals, np.round(scores[:-1, -1].sum(), 2))
out_file = str(Path(save_path, stem + '.png'))
print('\n Writing image to {}'.format(out_file))
cv2.imwrite(out_file, pre_outflow)
stem = '{}_{}_{}_matches_inflow_pre_{}'.format(scene_id, vi, vi + intervals, np.round(scores[-1, :-1].sum(), 2))
out_file = str(Path(save_path, stem + '.png'))
print('\n Writing image to {}'.format(out_file))
cv2.imwrite(out_file, pre_inflow)
stem = '{}_{}_{}_matches_outflow_gt_{}'.format(scene_id, vi, vi + intervals, match_gt['un_a'].size(0))
out_file = str(Path(save_path, stem + '.png'))
print('\n Writing image to {}'.format(out_file))
cv2.imwrite(out_file, gt_outflow)
stem = '{}_{}_{}_matches_inflow_gt_{}'.format(scene_id, vi, vi + intervals, match_gt['un_b'].size(0))
out_file = str(Path(save_path, stem + '.png'))
print('\n Writing image to {}'.format(out_file))
cv2.imwrite(out_file, gt_inflow)
# import pdb
# pdb.set_trace()
if __name__=='__main__':
import os
import numpy as np
import torch
from config import cfg
from importlib import import_module
# ------------prepare enviroment------------
seed = opt.SEED
if seed is not None:
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
os.environ["CUDA_VISIBLE_DEVICES"] = opt.GPU_ID
torch.backends.cudnn.benchmark = True
# ------------prepare data loader------------
data_mode = opt.DATASET
datasetting = import_module(f'datasets.setting.{data_mode}')
cfg_data = datasetting.cfg_data
# ------------Start Training------------
pwd = os.path.split(os.path.realpath(__file__))[0]
test(cfg_data)
# ==================== all ====================
# MAE: 12.29, MSE: 24.71 WRAE: 12.73 WIAE: 1.98 WOAE: 2.01
# ==================== in ====================
# MAE: 12.61, MSE: 23.44 WRAE: 16.94 WIAE: 1.98 WOAE: 1.94
# ==================== out ====================
# MAE: 12.19, MSE: 25.09 WRAE: 11.44 WIAE: 1.97 WOAE: 2.04
# ==================== day ====================
# MAE: 11.78, MSE: 22.94 WRAE: 12.50 WIAE: 2.05 WOAE: 2.02
# ==================== night ====================
# MAE: 14.06, MSE: 30.04 WRAE: 13.53 WIAE: 1.72 WOAE: 2.00
# ==================== scenic0 ====================
# MAE: 8.35, MSE: 17.04 WRAE: 9.61 WIAE: 1.55 WOAE: 1.80
# ==================== scenic1 ====================
# MAE: 11.15, MSE: 20.78 WRAE: 15.61 WIAE: 1.86 WOAE: 1.87
# ==================== scenic2 ====================
# MAE: 18.08, MSE: 29.65 WRAE: 17.41 WIAE: 2.29 WOAE: 2.13
# ==================== scenic3 ====================
# MAE: 33.56, MSE: 50.17 WRAE: 18.21 WIAE: 4.82 WOAE: 3.76
# ==================== scenic4 ====================
# MAE: 11.21, MSE: 21.40 WRAE: 13.06 WIAE: 1.97 WOAE: 1.69
# ==================== scenic5 ====================
# MAE: 29.91, MSE: 50.06 WRAE: 21.60 WIAE: 3.96 WOAE: 3.77
# ==================== density0 ====================
# MAE: 4.08, MSE: 5.75 WRAE: 12.58 WIAE: 1.08 WOAE: 1.21
# ==================== density1 ====================
# MAE: 7.99, MSE: 11.13 WRAE: 10.47 WIAE: 1.68 WOAE: 1.77
# ==================== density2 ====================
# MAE: 23.25, MSE: 32.89 WRAE: 14.51 WIAE: 3.14 WOAE: 3.07
# ==================== density3 ====================
# MAE: 50.03, MSE: 64.07 WRAE: 19.93 WIAE: 6.00 WOAE: 5.10
# ==================== density4 ====================
# MAE: 76.95, MSE: 83.93 WRAE: 24.46 WIAE: 6.32 WOAE: 5.99
# tensor([[ 89.4147, 93.0000],
# [148.2149, 259.0000],
# [ 29.7278, 37.0000],
# [ 71.6076, 87.0000],
# [ 38.6680, 43.0000],
# [ 61.2983, 65.0000],
# [ 70.3064, 77.0000],
# [154.6082, 227.0000],
# [ 48.5384, 50.0000],
# [ 40.7733, 44.0000],
# [172.4883, 284.0000],
# [ 65.9117, 77.0000],
# [164.5734, 232.0000],
# [ 11.7632, 12.0000],
# [ 41.9911, 39.0000],
# [ 73.3622, 81.0000],
# [ 15.7980, 22.0000],
# [ 58.1445, 76.0000],
# [ 78.0700, 81.0000],
# [ 96.6829, 100.0000],
# [123.0839, 127.0000],
# [ 41.0895, 40.0000],
# [ 46.0548, 43.0000],
# [ 50.4754, 50.0000],
# [ 54.0197, 53.0000],
# [ 23.2172, 22.0000],
# [ 75.5792, 69.0000],
# [ 84.4526, 89.0000],
# [121.0936, 115.0000],
# [243.7008, 306.0000],
# [ 47.3643, 45.0000],
# [ 43.6313, 62.0000],
# [ 77.0213, 71.0000],
# [ 62.3260, 71.0000],
# [ 55.6199, 54.0000],
# [ 58.6624, 55.0000],
# [ 37.3744, 35.0000],
# [ 27.4079, 30.0000],
# [ 38.2823, 41.0000],
# [ 83.2303, 78.0000],
# [ 26.2175, 31.0000],
# [147.9063, 164.0000],
# [116.8620, 160.0000],
# [ 29.7327, 35.0000],
# [ 27.4655, 26.0000],
# [ 75.3924, 86.0000],
# [157.0550, 180.0000],
# [111.5766, 191.0000],
# [163.5789, 171.0000],
# [ 80.2234, 75.0000],
# [ 88.8087, 103.0000],
# [ 35.8673, 34.0000],
# [107.7521, 104.0000],
# [ 77.2214, 97.0000],
# [ 73.8145, 70.0000],
# [ 55.1942, 61.0000],
# [221.1072, 237.0000],
# [ 52.5339, 60.0000],
# [ 64.5467, 71.0000],
# [ 68.5728, 71.0000],
# [ 53.1565, 47.0000],
# [ 94.2252, 100.0000],
# [ 57.4984, 49.0000],
# [ 37.9518, 29.0000],
# [ 40.8593, 33.0000],
# [ 35.7516, 35.0000],
# [ 80.8944, 98.0000],
# [111.9066, 135.0000],
# [ 30.5592, 30.0000],
# [ 33.7159, 37.0000],
# [ 99.1224, 101.0000],
# [136.8730, 232.0000],
# [ 71.0780, 69.0000],
# [ 21.6167, 28.0000],
# [ 46.0675, 45.0000],
# [ 22.1183, 37.0000],
# [ 52.8302, 54.0000],
# [ 19.6272, 23.0000],
# [ 35.3046, 34.0000],
# [ 39.5665, 57.0000],
# [ 28.2371, 16.0000],
# [ 75.8426, 77.0000],
# [ 26.7141, 29.0000],
# [114.5060, 134.0000],
# [ 72.5601, 88.0000],
# [ 68.5220, 67.0000],
# [112.4594, 130.0000],
# [ 84.0589, 62.0000],
# [227.6907, 329.0000],
# [ 33.1534, 35.0000],
# [ 48.8285, 47.0000],
# [ 58.0883, 52.0000],
# [ 57.2759, 52.0000],
# [ 45.3223, 46.0000],
# [ 68.1864, 64.0000],
# [120.8597, 169.0000],
# [ 59.3080, 64.0000],
# [191.3976, 250.0000],
# [175.0085, 233.0000],
# [ 10.6638, 11.0000],
# [ 80.5845, 97.0000],
# [ 81.8619, 96.0000],
# [ 35.5707, 31.0000],
# [232.4688, 316.0000],
# [ 70.8171, 81.0000],
# [ 39.7070, 41.0000],
# [ 55.2743, 57.0000],
# [ 87.0791, 108.0000],
# [177.9434, 190.0000],
# [ 91.6090, 83.0000],
# [ 51.4202, 48.0000],
# [ 73.2659, 74.0000],
# [ 47.3627, 70.0000],
# [144.2767, 175.0000],
# [ 54.6253, 63.0000],
# [ 98.3246, 97.0000],
# [ 56.5126, 61.0000],
# [ 38.2883, 23.0000],
# [ 70.7073, 69.0000],
# [ 38.0360, 22.0000],
# [ 53.3014, 57.0000],
# [ 65.6217, 69.0000],
# [ 45.7053, 53.0000],
# [ 49.8301, 44.0000],
# [ 23.4349, 22.0000],
# [ 75.9271, 76.0000],
# [ 76.7114, 76.0000],
# [ 24.8107, 26.0000],
# [105.8516, 116.0000],
# [ 72.5226, 73.0000],
# [ 68.9267, 68.0000],
# [ 66.7460, 152.0000],
# [ 48.6040, 56.0000],
# [ 54.7598, 62.0000],
# [ 65.3288, 75.0000],
# [ 44.4746, 50.0000],
# [158.6062, 230.0000],
# [ 67.7573, 73.0000],
# [ 49.4845, 37.0000],
# [ 87.4544, 71.0000],
# [ 86.9877, 86.0000],
# [ 85.2368, 80.0000],
# [ 77.9463, 75.0000],
# [ 58.9658, 77.0000],
# [ 51.8347, 53.0000],
# [123.8188, 142.0000],
# [112.5238, 164.0000],
# [ 69.3425, 74.0000],
# [ 62.7756, 67.0000],
# [110.3728, 108.0000],
# [ 50.3846, 58.0000],
# [164.9739, 202.0000],
# [ 26.0592, 23.0000],
# [ 98.3755, 97.0000],
# [ 64.1170, 62.0000],
# [ 23.0675, 24.0000],
# [ 48.8955, 59.0000],
# [ 53.0321, 52.0000],
# [ 45.3135, 43.0000],
# [ 95.5922, 98.0000],
# [ 46.4823, 43.0000],
# [ 56.9812, 84.0000],
# [172.6967, 179.0000],
# [ 80.6760, 73.0000],
# [ 25.7448, 25.0000],
# [ 16.1685, 26.0000],
# [ 62.0806, 55.0000],
# [ 93.0958, 102.0000],
# [ 37.5434, 51.0000],
# [ 20.9470, 21.0000],
# [ 42.8672, 57.0000],
# [ 37.6022, 22.0000],
# [ 26.5846, 26.0000],
# [ 34.8923, 37.0000],
# [ 42.0879, 46.0000],
# [ 41.3744, 45.0000],
# [116.4209, 131.0000],
# [143.4008, 142.0000],
# [ 28.7674, 29.0000],
# [ 71.7286, 66.0000],
# [ 42.6456, 69.0000],
# [ 47.6907, 44.0000],
# [ 26.2184, 22.0000],
# [ 20.1989, 23.0000],
# [ 36.0446, 36.0000],
# [ 58.5359, 58.0000],
# [ 17.0322, 19.0000],
# [ 55.8627, 75.0000],
# [126.8426, 124.0000],
# [159.0815, 184.0000],
# [ 37.2157, 38.0000],
# [ 76.2001, 92.0000],
# [106.7688, 105.0000],
# [ 63.7852, 64.0000],
# [247.1487, 312.0000],
# [ 31.1691, 32.0000],
# [ 68.5482, 67.0000],
# [ 84.2573, 76.0000],
# [ 64.7561, 67.0000],
# [110.7983, 120.0000],
# [ 51.1362, 50.0000],
# [ 14.2311, 19.0000],
# [ 89.2847, 87.0000],
# [122.7155, 142.0000],
# [ 53.4915, 54.0000],
# [ 80.5151, 89.0000],
# [ 16.3571, 18.0000],
# [ 44.3656, 37.0000],
# [118.4887, 146.0000],
# [ 30.9687, 30.0000],
# [ 99.9197, 108.0000],
# [ 83.3560, 93.0000],
# [ 40.8250, 50.0000],
# [ 49.4679, 41.0000],
# [264.1026, 390.0000],
# [ 14.9821, 13.0000],
# [ 86.6475, 91.0000],
# [123.1816, 122.0000],
# [ 32.7715, 36.0000],
# [ 26.4360, 26.0000],
# [ 49.7715, 52.0000],
# [ 20.3532, 18.0000],
# [ 20.4764, 18.0000],
# [ 35.3715, 33.0000],
# [ 75.1884, 77.0000],
# [119.4564, 129.0000],
# [ 47.2260, 46.0000],
# [ 58.8956, 67.0000],
# [ 58.2124, 69.0000],
# [ 26.4284, 27.0000],
# [133.6892, 137.0000],
# [ 40.5817, 42.0000],
# [ 32.0720, 35.0000],
# [ 90.3207, 98.0000],
# [ 68.1723, 77.0000],
# [ 57.4718, 68.0000],
# [ 21.4721, 25.0000],
# [129.9447, 255.0000],
# [137.8681, 112.0000],
# [ 60.6594, 61.0000],
# [ 26.3088, 43.0000],
# [109.1689, 110.0000],
# [ 77.0445, 84.0000],
# [ 48.9617, 50.0000],
# [ 84.1961, 81.0000],
# [ 96.4772, 87.0000],
# [ 56.4531, 61.0000],
# [ 38.2997, 45.0000],
# [ 64.8246, 63.0000],
# [ 50.9867, 54.0000],
# [ 26.2117, 25.0000],
# [ 48.4944, 49.0000],
# [ 45.4879, 79.0000],
# [ 36.5422, 51.0000]])