def write_features(raw_dir, city_list, city_id, tile_id, model):
raw_rgb = misc_utils.load_file(
os.path.join(raw_dir, 'USA_{}_{}.tif'.format(city_list[city_id],
tile_id)))
tile_dim = raw_rgb.shape
# get tower truth
gt_list = []
csv_file_name = os.path.join(
raw_dir, '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])
# get features
feature = []
for start, stop, online_points in read_line_csv_data(
csv_file_name, gt_list):
centers = add_point_if_not_nearby(start, stop,
[gt_list[a] for a in online_points])
for c in centers:
c = [int(a) for a in c]
region = [
c[0] - patch_size[0] // 2, c[1] - patch_size[1] // 2,
*patch_size
]
region = get_region_bounds(region, tile_dim)
img_patch = raw_rgb[region[0]:region[0] + region[2],
region[1]:region[1] + region[3], :]
feature.append(model.get_feature(img_patch))
return np.array(feature)
for city_id in range(4):
dist = []
for tile_id in range(3, tile_range[city_id]):
csv_file_name = os.path.join(
raw_dir, 'USA_{}_{}.csv'.format(city_list[city_id], tile_id))
raw_rgb = ersa_utils.load_file(
os.path.join(
raw_dir, 'USA_{}_{}.tif'.format(city_list[city_id],
tile_id)))
# get tower truth
gt_list = []
csv_file_name = os.path.join(
raw_dir, '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])
# visualize results
for start, stop, online_points in read_line_csv_data(
csv_file_name, gt_list):
centers = add_point_if_not_nearby(
start, stop, [gt_list[a] for a in online_points])
for i in range(len(centers) - 1):
dist.append(
np.linalg.norm(
np.array(centers[i]) - np.array(centers[i + 1])))
plt.subplot(2, 2, city_id + 1)
plt.hist(dist, bins=100)
plt.xlabel('Distance')