def get_tower_truth_pred(dirs, city_id, tile_id):
gt_list = []
csv_file_name = os.path.join(
dirs['raw'], 'USA_{}_{}.csv'.format(city_list[city_id], tile_id))
for label, bbox in read_polygon_csv_data(csv_file_name):
y, x = get_center_point(*bbox)
gt_list.append([y, x])
return gt_list
def plot_across_model(link_r=20,
model_names=('faster_rcnn', 'faster_rcnn_res101',
'faster_rcnn_res50')):
plt.figure(figsize=(10, 8))
city_list = ['AZ_Tucson', 'KS_Colwich_Maize', 'NC_Clyde', 'NC_Wilmington']
for city_id in range(4):
plt.subplot(221 + city_id)
for model_name in model_names:
pred_list_all = []
gt_list_all = []
cf_list_all = []
for tile_id in [1, 2, 3]:
# load data
pred_file_name = os.path.join(
task_dir, model_name,
'USA_{}_{}.txt'.format(city_list[city_id], tile_id))
preds = ersa_utils.load_file(pred_file_name)
csv_file_name = os.path.join(
raw_dir, 'USA_{}_{}.csv'.format(city_list[city_id],
tile_id))
pred_list = []
gt_list = []
cf_list = []
center_list, conf_list, _ = local_maxima_suppression(preds)
for center, conf in zip(center_list, conf_list):
pred_list.append(center.tolist())
cf_list.append(conf)
for label, bbox in read_polygon_csv_data(csv_file_name):
y, x = get_center_point(*bbox)
gt_list.append([y, x])
pred_list_all.extend(pred_list)
gt_list_all.extend(gt_list)
cf_list_all.extend(cf_list)
f1, y_true, y_score = radius_scoring(pred_list_all, gt_list_all,
cf_list_all, link_r)
ap = average_precision_score(y_true, y_score)
precision, recall, _ = precision_recall_curve(y_true, y_score)
plt.step(recall[1:],
precision[1:],
alpha=1,
where='post',
label='{}, AP={:.2f}'.format(model_name, ap))
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.ylim([0.0, 1.05])
plt.xlim([0.0, 1.0])
plt.title('{} Performance Comparison'.format(city_list[city_id]))
plt.legend(loc='lower left')
plt.tight_layout()
plt.savefig(os.path.join(img_dir, 'cmp_tile_pr.png'))
plt.show()
def compare_towers(task_dir, raw_dir):
city_list = ['AZ_Tucson', 'KS_Colwich_Maize', 'NC_Clyde', 'NC_Wilmington']
model_names = ['faster_rcnn_res101', 'faster_rcnn_res50', 'faster_rcnn']
for model_cnt, model_name in enumerate(model_names):
print(model_name)
tp_all, p_d_all, r_d_all = 0, 0, 0
for city_id in range(4):
pred_list_all = []
gt_list_all = []
for tile_id in [1, 2, 3]:
# load data
pred_file_name = os.path.join(task_dir, model_name+'_all',
'USA_{}_{}.txt'.format(city_list[city_id], tile_id))
preds = ersa_utils.load_file(pred_file_name)
csv_file_name = os.path.join(raw_dir, 'USA_{}_{}.csv'.format(city_list[city_id], tile_id))
pred_list = []
gt_list = []
center_list, conf_list, _ = local_maxima_suppression(preds)
for center, conf in zip(center_list, conf_list):
pred_list.append(center.tolist())
for label, bbox in read_polygon_csv_data(csv_file_name):
y, x = get_center_point(*bbox)
gt_list.append([y, x])
pred_list_all.extend(pred_list)
gt_list_all.extend(gt_list)
f1, tp, p_d, r_d = radius_scoring_f1(pred_list_all, gt_list_all)
tp_all += tp
p_d_all += p_d
r_d_all += r_d
print('{}: f1={:.2f}'.format(city_list[city_id], f1))
p = tp_all / p_d_all
r = tp_all / r_d_all
f1 = 2 * p * r / (p + r)
print('overall f1={:.2f}'.format(f1))
def plot_across_model_v2(link_r=20,
model_names=('faster_rcnn_v2',
'faster_rcnn_res101_v2',
'faster_rcnn_res50_v2')):
from glob import glob
from natsort import natsorted
plt.figure(figsize=(14, 8))
model_name_dict = {
'faster_rcnn_v2': 'Inception',
'faster_rcnn_res101_v2': 'ResNet101',
'faster_rcnn_res50_v2': 'ResNet50'
}
city_list = ['Tucson', 'Colwich', 'NZ']
# cmap = ersa_utils.get_default_colors()
cmap = plt.get_cmap('tab20')
width = 0.1
model_n_tp = np.zeros((3, len(model_names)))
model_n_recall = np.zeros((3, len(model_names)))
model_n_precision = np.zeros((3, len(model_names)))
for city_id in range(3):
plt.subplot(221 + city_id)
for model_cnt, model_name in enumerate(model_names):
tp_all = 0
n_recall_all = 0
n_precision_all = 0
pred_files_1 = natsorted(
glob(os.path.join(task_dir, model_name, '*.txt')))
csv_files_temp = natsorted(glob(os.path.join(raw_dir, '*.csv')))
csv_files = []
for f in csv_files_temp:
if 'Tucson' in f or 'Colwich' in f or 'NZ' in f:
if '_1_' in f or '_2_' in f or '_3_' in f or '_1.' in f or '_2.' in f or '_3.' in f:
if 'NZ' not in f:
csv_files.append(f)
else:
if 'resize' in f:
csv_files.append(f)
for pred_file_1, csv_file_name in zip(pred_files_1, csv_files):
if city_list[city_id] not in pred_file_1:
continue
local_tile_id = int(''.join(
[a for a in os.path.basename(pred_file_1) if a.isdigit()]))
if 'Tucson' in pred_file_1:
local_city_id = 0
pred_file_2 = os.path.join(
task_dir, 'post_{}_{}_{}_pred_v2.npy'.format(
model_name[:-3], local_city_id, local_tile_id))
conn_file = os.path.join(
task_dir, 'post_{}_{}_{}_conn_v2.npy'.format(
model_name[:-3], local_city_id, local_tile_id))
elif 'Colwich' in pred_file_1:
local_city_id = 1
pred_file_2 = os.path.join(
task_dir, 'post_{}_{}_{}_pred_v2.npy'.format(
model_name[:-3], local_city_id, local_tile_id))
conn_file = os.path.join(
task_dir, 'post_{}_{}_{}_conn_v2.npy'.format(
model_name[:-3], local_city_id, local_tile_id))
else:
if 'Dunedin' in pred_file_1:
local_city_id = 0
pred_file_2 = os.path.join(
task_dir, 'post_{}_NZ_{}_{}_pred_v2.npy'.format(
model_name[:-3], local_city_id, local_tile_id))
conn_file = os.path.join(
task_dir, 'post_{}_NZ_{}_{}_conn_v2.npy'.format(
model_name[:-3], local_city_id, local_tile_id))
elif 'Gisborne' in pred_file_1:
local_city_id = 1
pred_file_2 = os.path.join(
task_dir, 'post_{}_NZ_{}_{}_pred_v2.npy'.format(
model_name[:-3], local_city_id, local_tile_id))
conn_file = os.path.join(
task_dir, 'post_{}_NZ_{}_{}_conn_v2.npy'.format(
model_name[:-3], local_city_id, local_tile_id))
elif 'Palmerston North' in pred_file_1:
local_city_id = 2
pred_file_2 = os.path.join(
task_dir, 'post_{}_NZ_{}_{}_pred_v2.npy'.format(
model_name[:-3], local_city_id, local_tile_id))
conn_file = os.path.join(
task_dir, 'post_{}_NZ_{}_{}_conn_v2.npy'.format(
model_name[:-3], local_city_id, local_tile_id))
elif 'Rotorua' in pred_file_1:
local_city_id = 3
pred_file_2 = os.path.join(
task_dir, 'post_{}_NZ_{}_{}_pred_v2.npy'.format(
model_name[:-3], local_city_id, local_tile_id))
conn_file = os.path.join(
task_dir, 'post_{}_NZ_{}_{}_conn_v2.npy'.format(
model_name[:-3], local_city_id, local_tile_id))
elif 'Tauranga' in pred_file_1:
local_city_id = 4
pred_file_2 = os.path.join(
task_dir, 'post_{}_NZ_{}_{}_pred_v2.npy'.format(
model_name[:-3], local_city_id, local_tile_id))
conn_file = os.path.join(
task_dir, 'post_{}_NZ_{}_{}_conn_v2.npy'.format(
model_name[:-3], local_city_id, local_tile_id))
preds = ersa_utils.load_file(pred_file_1)
pred_list = ersa_utils.load_file(pred_file_2)
cp_list = ersa_utils.load_file(conn_file)
tower_gt = []
for label, bbox in read_polygon_csv_data(csv_file_name):
y, x = get_center_point(*bbox)
tower_gt.append([y, x])
connected_pairs = read_lines_truth(csv_file_name, tower_gt)
pred_list_orig = []
center_list, conf_list, _ = local_maxima_suppression(preds)
for center, conf in zip(center_list, conf_list):
if conf > 0.8:
pred_list_orig.append(center.tolist())
if len(pred_list) == 0:
pred_list = np.array([
(0, 0),
])
link_list = link_pred_gt(pred_list, tower_gt, link_r)
tp, n_recall, n_precision = grid_score(tower_gt, pred_list,
connected_pairs,
cp_list, link_list)
tp_all += tp
n_recall_all += n_recall
n_precision_all += n_precision
model_n_tp[0, model_cnt] += tp
model_n_recall[0, model_cnt] += n_recall
model_n_precision[0, model_cnt] += n_precision
recall = tp_all / n_recall_all
precision = tp_all / n_precision_all
f1 = 2 * (precision * recall) / ((precision + recall) + 1e-6)
X = np.arange(2)
plt.bar(X + width * (3 * model_cnt), [precision, recall],
width=width,
color=cmap(2 * model_cnt),
label='{} f1={:.2f}'.format(model_name_dict[model_name],
f1),
edgecolor='k')
offset_x = 0.04
offset_y = 0.01
plt.text(width * (3 * model_cnt) - offset_x,
precision + offset_y,
'{:.2f}'.format(precision),
fontsize=8)
plt.text(1 + width * (3 * model_cnt) - offset_x,
recall + offset_y,
'{:.2f}'.format(recall),
fontsize=8)
#plt.xlabel('Recall')
plt.xticks(X + width * 3.5, ['Precision', 'Recall'])
plt.ylabel('Score')
plt.ylim([0.0, 1.2])
plt.legend()
plt.title(city_list[city_id])
plt.tight_layout()
plt.savefig(os.path.join(img_dir, 'fvis_performance_comparison.png'))
plt.show()
recall_overall = model_n_tp / model_n_recall
precision_overall = model_n_tp / model_n_precision
f1_overall = 2 * (recall_overall * precision_overall) / (recall_overall +
precision_overall)
print(f1_overall)
def plot_across_model_grid_with_graph_all(link_r=20,
model_names=('faster_rcnn',
'faster_rcnn_res101',
'faster_rcnn_res50')):
plt.figure(figsize=(14, 8))
model_name_dict = {
'faster_rcnn': 'Inception',
'faster_rcnn_res101': 'ResNet101',
'faster_rcnn_res50': 'ResNet50'
}
city_list = ['AZ_Tucson', 'KS_Colwich_Maize', 'NC_Clyde', 'NC_Wilmington']
# cmap = ersa_utils.get_default_colors()
cmap = plt.get_cmap('tab20')
width = 0.1
model_n_tp = np.zeros((3, len(model_names)))
model_n_recall = np.zeros((3, len(model_names)))
model_n_precision = np.zeros((3, len(model_names)))
for city_id in range(4):
plt.subplot(221 + city_id)
for model_cnt, model_name in enumerate(model_names):
tp_all, tp_all_graph, tp_all_graph_combine = 0, 0, 0
n_recall_all, n_recall_all_graph, n_recall_all_graph_combine = 0, 0, 0
n_precision_all, n_precision_all_graph, n_precision_all_graph_combine = 0, 0, 0
for tile_id in [1, 2, 3]:
# load data
pred_file_name = os.path.join(
task_dir, model_name + '_all',
'USA_{}_{}.txt'.format(city_list[city_id], tile_id))
preds = ersa_utils.load_file(pred_file_name)
pred_file_name = os.path.join(
task_dir, 'overall_post_{}_{}_{}_pred2.npy'.format(
model_name, city_id, tile_id))
pred_list = ersa_utils.load_file(pred_file_name)
pred_file_name = os.path.join(
task_dir, 'overall_post_{}_{}_{}_conn2.npy'.format(
model_name, city_id, tile_id))
cp_list = ersa_utils.load_file(pred_file_name)
csv_file_name = os.path.join(
raw_dir, 'USA_{}_{}.csv'.format(city_list[city_id],
tile_id))
#cp_file_name = os.path.join(task_dir, '{}_{}_cp.npy'.format(city_list[city_id], city_id))
#connected_pairs = ersa_utils.load_file(cp_file_name)
cp_file_name_graph = os.path.join(
task_dir, '{}_graph_rnn_{}_{}_orig_all.npy'.format(
model_name, city_id, tile_id))
cp_graph = ersa_utils.load_file(cp_file_name_graph)
cp_file_name_graph = os.path.join(
task_dir, '{}_graph_rnn_{}_{}_all.npy'.format(
model_name, city_id, tile_id))
cp_graph_combine = ersa_utils.load_file(cp_file_name_graph)
tower_gt = []
for label, bbox in read_polygon_csv_data(csv_file_name):
y, x = get_center_point(*bbox)
tower_gt.append([y, x])
line_file = os.path.join(
raw_dir, 'USA_{}_{}.csv'.format(city_list[city_id],
tile_id))
connected_pairs = read_lines_truth(line_file, tower_gt)
pred_list_orig = []
center_list, conf_list, _ = local_maxima_suppression(preds)
for center, conf in zip(center_list, conf_list):
if conf > 0.5:
pred_list_orig.append(center.tolist())
if len(pred_list) == 0:
pred_list = np.array([
(0, 0),
])
link_list = link_pred_gt(pred_list, tower_gt, link_r)
tp, n_recall, n_precision = grid_score(tower_gt, pred_list,
connected_pairs,
cp_list, link_list)
tp_graph_combine, n_recall_graph_combine, n_precision_graph_combine = \
grid_score(tower_gt, pred_list, connected_pairs, cp_graph_combine, link_list)
link_list = link_pred_gt(center_list, tower_gt, link_r)
tp_graph, n_recall_graph, n_precision_graph = grid_score(
tower_gt, pred_list, connected_pairs, cp_graph, link_list)
tp_all += tp
n_recall_all += n_recall
n_precision_all += n_precision
tp_all_graph += tp_graph
n_recall_all_graph += n_recall_graph
n_precision_all_graph += n_precision_graph
tp_all_graph_combine += tp_graph_combine
n_recall_all_graph_combine += n_recall_graph_combine
n_precision_all_graph_combine += n_precision_graph_combine
model_n_tp[0, model_cnt] += tp
model_n_tp[1, model_cnt] += tp_graph
model_n_tp[2, model_cnt] += tp_graph_combine
model_n_recall[0, model_cnt] += n_recall
model_n_recall[1, model_cnt] += n_recall_graph
model_n_recall[2, model_cnt] += n_recall_graph_combine
model_n_precision[0, model_cnt] += n_precision
model_n_precision[1, model_cnt] += n_precision_graph
model_n_precision[2, model_cnt] += n_precision_graph_combine
recall = tp_all / n_recall_all
precision = tp_all / n_precision_all
f1 = 2 * (precision * recall) / (precision + recall)
recall_graph = tp_all_graph / n_recall_all_graph
precision_graph = tp_all_graph / n_precision_all_graph
f1_graph = 2 * (precision_graph *
recall_graph) / (precision_graph + recall_graph)
recall_graph_combine = tp_all_graph_combine / n_recall_all_graph_combine
precision_graph_combine = tp_all_graph_combine / n_precision_all_graph_combine
f1_graph_combine = 2 * (
precision_graph_combine * recall_graph_combine) / (
precision_graph_combine + recall_graph_combine)
X = np.arange(2)
plt.bar(X + width * (3 * model_cnt), [precision, recall],
width=width,
color=cmap(2 * model_cnt),
label='{} f1={:.2f}'.format(model_name_dict[model_name],
f1),
edgecolor='k')
plt.bar(X + width * (3 * model_cnt + 1),
[precision_graph, recall_graph],
width=width,
color=cmap(2 * model_cnt + 1),
label='GRN f1={:.2f}'.format(f1_graph),
edgecolor='k')
plt.bar(X + width * (3 * model_cnt + 2),
[precision_graph_combine, recall_graph_combine],
width=width,
color=cmap(2 * model_cnt + 1),
label='Combine f1={:.2f}'.format(f1_graph_combine),
edgecolor='k',
hatch='//')
offset_x = 0.04
offset_y = 0.01
plt.text(width * (3 * model_cnt) - offset_x,
precision + offset_y,
'{:.2f}'.format(precision),
fontsize=8)
plt.text(1 + width * (3 * model_cnt) - offset_x,
recall + offset_y,
'{:.2f}'.format(recall),
fontsize=8)
plt.text(width * (3 * model_cnt + 1) - offset_x,
precision_graph + offset_y,
'{:.2f}'.format(precision_graph),
fontsize=8)
plt.text(1 + width * (3 * model_cnt + 1) - offset_x,
recall_graph + offset_y,
'{:.2f}'.format(recall_graph),
fontsize=8)
plt.text(width * (3 * model_cnt + 2) - offset_x,
precision_graph_combine + offset_y,
'{:.2f}'.format(precision_graph_combine),
fontsize=8)
plt.text(1 + width * (3 * model_cnt + 2) - offset_x,
recall_graph_combine + offset_y,
'{:.2f}'.format(recall_graph_combine),
fontsize=8)
#plt.xlabel('Recall')
plt.xticks(X + width * 4, ['Precision', 'Recall'])
plt.ylabel('Score')
plt.ylim([0.0, 1.2])
#plt.xlim([0.0, 1.0])
plt.title('{} Performance Comparison'.format(
city_list[city_id].split('_')[1]))
handles, labels = plt.gca().get_legend_handles_labels()
order = [0, 3, 6, 1, 4, 7, 2, 5, 8]
plt.legend([handles[idx] for idx in order],
[labels[idx] for idx in order],
loc='upper left',
ncol=3,
fontsize=9)
plt.tight_layout()
# plt.savefig(os.path.join(img_dir, 'tile_performance_graph_rnn_all.png'))
plt.show()
recall_overall = model_n_tp / model_n_recall
precision_overall = model_n_tp / model_n_precision
f1_overall = 2 * (recall_overall * precision_overall) / (recall_overall +
precision_overall)
print(f1_overall)
def plot_across_model_grid(link_r=20,
model_names=('faster_rcnn', 'faster_rcnn_res101',
'faster_rcnn_res50')):
plt.figure(figsize=(10, 8))
model_name_dict = {
'faster_rcnn': 'Inception',
'faster_rcnn_res101': 'ResNet101',
'faster_rcnn_res50': 'ResNet50'
}
city_list = ['AZ_Tucson', 'KS_Colwich_Maize', 'NC_Clyde', 'NC_Wilmington']
cmap = ersa_utils.get_default_colors()
width = 0.15
for city_id in range(4):
plt.subplot(221 + city_id)
for model_cnt, model_name in enumerate(model_names):
tp_all = 0
n_recall_all = 0
n_precision_all = 0
for tile_id in [1, 2, 3]:
# load data
pred_file_name = os.path.join(
task_dir, model_name,
'USA_{}_{}.txt'.format(city_list[city_id], tile_id))
preds = ersa_utils.load_file(pred_file_name)
pred_file_name = os.path.join(
task_dir,
'post_{}_{}_{}_pred2.npy'.format(model_name, city_id,
tile_id))
pred_list = ersa_utils.load_file(pred_file_name)
pred_file_name = os.path.join(
task_dir,
'post_{}_{}_{}_conf2.npy'.format(model_name, city_id,
tile_id))
cf_list = ersa_utils.load_file(pred_file_name)
pred_file_name = os.path.join(
task_dir,
'post_{}_{}_{}_conn2.npy'.format(model_name, city_id,
tile_id))
cp_list = ersa_utils.load_file(pred_file_name)
csv_file_name = os.path.join(
raw_dir, 'USA_{}_{}.csv'.format(city_list[city_id],
tile_id))
cp_file_name = os.path.join(
task_dir, '{}_{}_cp.npy'.format(city_list[city_id],
city_id))
connected_pairs = ersa_utils.load_file(cp_file_name)
tower_gt = []
for label, bbox in read_polygon_csv_data(csv_file_name):
y, x = get_center_point(*bbox)
tower_gt.append([y, x])
pred_list_orig = []
center_list, conf_list, _ = local_maxima_suppression(preds)
for center, conf in zip(center_list, conf_list):
if conf > 0.5:
pred_list_orig.append(center.tolist())
link_list = link_pred_gt(pred_list, tower_gt, link_r)
tp, n_recall, n_precision = grid_score(tower_gt, pred_list,
connected_pairs,
cp_list, link_list)
tp_all += tp
n_recall_all += n_recall
n_precision_all += n_precision
recall = tp_all / n_recall_all
precision = tp_all / n_precision_all
X = np.arange(2)
plt.bar(X + width * model_cnt, [precision, recall],
width=width,
color=cmap[model_cnt],
label=model_name_dict[model_name])
plt.text(width * model_cnt - 0.05, precision,
'{:.2f}'.format(precision))
plt.text(1 + width * model_cnt - 0.05, recall,
'{:.2f}'.format(recall))
#plt.xlabel('Recall')
plt.xticks(X + width, ['Precision', 'Recall'])
plt.ylabel('Score')
plt.ylim([0.0, 1])
#plt.xlim([0.0, 1.0])
plt.title('{} Performance Comparison'.format(city_list[city_id]))
plt.legend(loc='upper left', ncol=3, fontsize=9)
plt.tight_layout()
# plt.savefig(os.path.join(img_dir, 'tile_performance_graph.png'))
plt.show()
# load data
pred_file_name = os.path.join(task_dir, 'USA_{}_{}.txt'.format(city_list[city_id], tile_id))
preds = ersa_utils.load_file(pred_file_name)
raw_rgb = ersa_utils.load_file(os.path.join(raw_dir, 'USA_{}_{}.tif'.format(city_list[city_id], tile_id)))
csv_file_name = os.path.join(raw_dir, 'USA_{}_{}.csv'.format(city_list[city_id], tile_id))
pred_list = []
gt_list = []
cf_list = []
center_list, conf_list, _ = local_maxima_suppression(preds)
for center, conf in zip(center_list, conf_list):
pred_list.append(center.tolist())
cf_list.append(conf)
for label, bbox in read_polygon_csv_data(csv_file_name):
y, x = get_center_point(*bbox)
gt_list.append([y, x])
pred_list_all.extend(pred_list)
gt_list_all.extend(gt_list)
dist.extend(get_linked_dist(pred_list_all, gt_list_all, link_r))
plt.figure(figsize=(8, 4))
plt.hist(dist, bins=200)
dist_range = np.arange(0, 70, 5)
plt.xticks(dist_range, dist_range*0.15)
plt.xlabel('Dist(m)')
plt.ylabel('Cnt')
plt.title('Histogram of Linked Towers\' Distance')
plt.tight_layout()
def ablation_line(model_name, link_r=20):
city_list = ['AZ_Tucson', 'KS_Colwich_Maize', 'NC_Clyde', 'NC_Wilmington']
model_n_tp = 0
model_n_recall = 0
model_n_precision = 0
for city_id in range(4):
tp_all, n_precision_all, n_recall_all = 0, 0, 0
for tile_id in [1, 2, 3]:
# load data
pred_file_name = os.path.join(
task_dir, model_name,
'USA_{}_{}.txt'.format(city_list[city_id], tile_id))
preds = ersa_utils.load_file(pred_file_name)
csv_file_name = os.path.join(
raw_dir, 'USA_{}_{}.csv'.format(city_list[city_id], tile_id))
cp_file_name_graph = os.path.join(
task_dir,
'{}_graph_rnn_normal_{}_{}.npy'.format(model_name, city_id,
tile_id))
cp_graph = ersa_utils.load_file(cp_file_name_graph)
tower_gt = []
for label, bbox in read_polygon_csv_data(csv_file_name):
y, x = get_center_point(*bbox)
tower_gt.append([y, x])
line_file = os.path.join(
raw_dir, 'USA_{}_{}.csv'.format(city_list[city_id], tile_id))
connected_pairs = read_lines_truth(line_file, tower_gt)
pred_list_orig = []
center_list, conf_list, _ = local_maxima_suppression(preds)
for center, conf in zip(center_list, conf_list):
if conf > 0.5:
pred_list_orig.append(center.tolist())
if len(center_list) != 0:
link_list = link_pred_gt(center_list, tower_gt, link_r)
tp, n_recall, n_precision = grid_score(tower_gt, center_list,
connected_pairs,
cp_graph, link_list)
else:
tp = 0
n_recall = 0
n_precision = 0
tp_all += tp
n_recall_all += n_recall
n_precision_all += n_precision
model_n_tp += tp
model_n_recall += n_recall
model_n_precision += n_precision
p = tp_all / n_precision_all
r = tp_all / n_recall_all
f1 = 2 * (p * r) / (p + r)
print('{}: f1={:.2f}'.format(city_list[city_id], f1))
recall_overall = model_n_tp / model_n_recall
precision_overall = model_n_tp / model_n_precision
f1_overall = 2 * (recall_overall * precision_overall) / (recall_overall +
precision_overall)
print('Overall: f1={:.2f}'.format(f1_overall))
def plot_across_model_grid_with_graph(link_r=20,
model_names=('faster_rcnn',
'faster_rcnn_res101',
'faster_rcnn_res50')):
plt.figure(figsize=(14, 8))
model_name_dict = {
'faster_rcnn': 'Inception',
'faster_rcnn_res101': 'ResNet101',
'faster_rcnn_res50': 'ResNet50'
}
city_list = ['AZ_Tucson', 'KS_Colwich_Maize', 'NC_Clyde', 'NC_Wilmington']
# cmap = ersa_utils.get_default_colors()
cmap = plt.get_cmap('tab20')
width = 0.1
model_n_tp = np.zeros((3, len(model_names)))
model_n_recall = np.zeros((3, len(model_names)))
model_n_precision = np.zeros((3, len(model_names)))
for city_id in range(4):
plt.subplot(221 + city_id)
for model_cnt, model_name in enumerate(model_names):
tp_all, tp_all_graph, tp_all_graph_combine = 0, 0, 0
n_recall_all, n_recall_all_graph, n_recall_all_graph_combine = 0, 0, 0
n_precision_all, n_precision_all_graph, n_precision_all_graph_combine = 0, 0, 0
for tile_id in [1, 2, 3]:
# load data
pred_file_name = os.path.join(
task_dir, model_name,
'USA_{}_{}.txt'.format(city_list[city_id], tile_id))
preds = ersa_utils.load_file(pred_file_name)
pred_file_name = os.path.join(
task_dir, 'w1_post_{}_{}_{}_pred2.npy'.format(
model_name, city_id, tile_id))
pred_list = ersa_utils.load_file(pred_file_name)
pred_file_name = os.path.join(
task_dir, 'w1_post_{}_{}_{}_conn2.npy'.format(
model_name, city_id, tile_id))
cp_list = ersa_utils.load_file(pred_file_name)
csv_file_name = os.path.join(
raw_dir, 'USA_{}_{}.csv'.format(city_list[city_id],
tile_id))
#cp_file_name = os.path.join(task_dir, '{}_{}_cp.npy'.format(city_list[city_id], city_id))
#connected_pairs = ersa_utils.load_file(cp_file_name)
cp_file_name_graph = os.path.join(
task_dir, '{}_graph_rnn_{}_{}_orig.npy'.format(
model_name, city_id, tile_id))
cp_graph = ersa_utils.load_file(cp_file_name_graph)
cp_file_name_graph = os.path.join(
task_dir,
'{}_graph_rnn_{}_{}.npy'.format(model_name, city_id,
tile_id))
cp_graph_combine = ersa_utils.load_file(cp_file_name_graph)
tower_gt = []
for label, bbox in read_polygon_csv_data(csv_file_name):
y, x = get_center_point(*bbox)
tower_gt.append([y, x])
line_file = os.path.join(
raw_dir, 'USA_{}_{}.csv'.format(city_list[city_id],
tile_id))
connected_pairs = read_lines_truth(line_file, tower_gt)
pred_list_orig = []
center_list, conf_list, _ = local_maxima_suppression(preds)
for center, conf in zip(center_list, conf_list):
if conf > 0.5:
pred_list_orig.append(center.tolist())
link_list = link_pred_gt(pred_list, tower_gt, link_r)
tp, n_recall, n_precision = grid_score(tower_gt, pred_list,
connected_pairs,
cp_list, link_list)
tp_graph_combine, n_recall_graph_combine, n_precision_graph_combine = \
grid_score(tower_gt, pred_list, connected_pairs, cp_graph_combine, link_list)
link_list = link_pred_gt(center_list, tower_gt, link_r)
tp_graph, n_recall_graph, n_precision_graph = grid_score(
tower_gt, pred_list, connected_pairs, cp_graph, link_list)
tp_all += tp
n_recall_all += n_recall
n_precision_all += n_precision
tp_all_graph += tp_graph
n_recall_all_graph += n_recall_graph
n_precision_all_graph += n_precision_graph
tp_all_graph_combine += tp_graph_combine
n_recall_all_graph_combine += n_recall_graph_combine
n_precision_all_graph_combine += n_precision_graph_combine
model_n_tp[0, model_cnt] += tp
model_n_tp[1, model_cnt] += tp_graph
model_n_tp[2, model_cnt] += tp_graph_combine
model_n_recall[0, model_cnt] += n_recall
model_n_recall[1, model_cnt] += n_recall_graph
model_n_recall[2, model_cnt] += n_recall_graph_combine
model_n_precision[0, model_cnt] += n_precision
model_n_precision[1, model_cnt] += n_precision_graph
model_n_precision[2, model_cnt] += n_precision_graph_combine
recall = tp_all / n_recall_all
precision = tp_all / n_precision_all
f1 = 2 * (precision * recall) / (precision + recall)
recall_graph = tp_all_graph / n_recall_all_graph
precision_graph = tp_all_graph / n_precision_all_graph
f1_graph = 2 * (precision_graph *
recall_graph) / (precision_graph + recall_graph)
recall_graph_combine = tp_all_graph_combine / n_recall_all_graph_combine
precision_graph_combine = tp_all_graph_combine / n_precision_all_graph_combine
f1_graph_combine = 2 * (
precision_graph_combine * recall_graph_combine) / (
precision_graph_combine + recall_graph_combine)
recall_overall = model_n_tp / model_n_recall
precision_overall = model_n_tp / model_n_precision
f1_overall = 2 * (recall_overall * precision_overall) / (recall_overall +
precision_overall)
print(f1_overall)