def make_error_image(truth, pred):
h, w = truth.shape
patch_img = 255 * np.ones((h, w, 3), dtype=np.uint8)
mask = truth - pred
patch_img = util_functions.add_mask(patch_img, truth, [0, 255, 0], 1)
patch_img = util_functions.add_mask(patch_img, mask, [0, 0, 255], 1)
patch_img = util_functions.add_mask(patch_img, mask, [255, 0, 0], -1)
return patch_img
def check_gt_plot2(rgb_file, city_name, city_id, tower_gt):
if city_name == 'USA_KS_Colwich_Maize':
city_name = 'Colwich_Maize'
if city_name == 'NZ_Palmerston North':
city_name = 'PalmerstonNorth'
try:
line_gt = misc_utils.load_file(
os.path.join(
r'/media/ei-edl01/data/uab_datasets/lines_v3/data/Original_Tiles',
'{}{}_GT.png'.format(city_name, city_id)))
line_gt = cv2.dilate(line_gt, np.ones((5, 5), np.uint8), iterations=3)
plt.figure(figsize=(8, 6))
rgb = misc_utils.load_file(rgb_file)
rgb = util_functions.add_mask(rgb, line_gt, [0, 255, 0], 1)
plt.imshow(rgb)
# visualize_with_connected_pairs(rgb, tower_gt, line_gt, style='r', add_fig=True)
center_points = np.array(tower_gt)
plt.scatter(center_points[:, 1],
center_points[:, 0],
c='g',
s=40,
marker='o',
alpha=1,
edgecolors='k')
plt.title('{} {}'.format(city_name, city_id))
plt.axis('off')
plt.tight_layout()
plt.show()
except:
pass
def check_gt_plot(city_name, city_id, tower_gt, line_gt):
if city_name == 'Colwich':
city_name = 'Colwich_Maize'
img_dir = r'/home/lab/Documents/bohao/data/transmission_line/raw'
rgb_file = glob(
os.path.join(img_dir, '*_{}_{}.tif'.format(city_name, city_id)))
line_gt = misc_utils.load_file(
os.path.join(
r'/media/ei-edl01/data/uab_datasets/lines/data/Original_Tiles',
'{}{}_GT.png'.format(city_name, city_id)))
line_gt = cv2.dilate(line_gt, np.ones((5, 5), np.uint8), iterations=10)
plt.figure(figsize=(8, 6))
assert len(rgb_file) == 1
rgb = misc_utils.load_file(rgb_file[0])
rgb = util_functions.add_mask(rgb, line_gt, [0, 255, 0], 1)
plt.imshow(rgb)
# visualize_with_connected_pairs(rgb, tower_gt, line_gt, style='r', add_fig=True)
center_points = np.array(tower_gt)
plt.scatter(center_points[:, 1],
center_points[:, 0],
c='g',
s=40,
marker='o',
alpha=1,
edgecolors='k')
plt.title('{} {}'.format(city_name, city_id))
plt.axis('off')
plt.tight_layout()
plt.show()
def visualize_results(img_dir,
city_id,
tile_id,
raw_rgb,
line_gt,
tower_pred,
tower_gt,
connected_pairs,
connected_towers,
unconnected_towers,
save_fig=False,
post_str='',
close_file=False):
plt.figure(figsize=(8.5, 8))
img_with_line = util_functions.add_mask(raw_rgb, line_gt, [0, 255, 0], 1)
visualize_with_connected_pairs(img_with_line,
tower_pred,
connected_pairs,
add_fig=True)
add_points(tower_gt, 'b', marker='s', size=80, alpha=1, edgecolor='k')
if connected_towers is not None and unconnected_towers is not None:
add_points([tower_pred[a] for a in connected_towers],
'r',
marker='o',
alpha=1,
edgecolor='k')
try:
add_points([tower_pred[a] for a in unconnected_towers],
'r',
marker='o',
alpha=1,
edgecolor='k')
except IndexError:
print('No more unconnected towers')
else:
add_points(tower_pred, 'r', marker='o', alpha=1, edgecolor='k')
plt.axis('off')
plt.tight_layout()
if save_fig:
if 'NZ' in post_str:
plt.title('{}_{}'.format(city_list2[city_id], tile_id))
plt.savefig(
os.path.join(
img_dir,
'{}_{}_post_result{}.png'.format(city_list2[city_id],
tile_id, post_str)))
else:
plt.title('{}_{}'.format(city_list[city_id], tile_id))
plt.savefig(
os.path.join(
img_dir,
'{}_{}_post_result{}.png'.format(city_list[city_id],
tile_id, post_str)))
if close_file:
plt.close()
else:
plt.show()
gt_list.append([y, x])
# get line confidences
pairs, dists, confs = get_edge_info(center_list, conf_img, radius=radius, width=width,
tile_min=(0, 0), tile_max=raw_rgb.shape)
# connect lines
connected_pairs = connect_lines(pairs, confs, th, cut_n=2)
connected_pairs, unconnected_pairs = prune_pairs(connected_pairs, center_list)
# get towers that are not connected
connected_towers = []
for p in connected_pairs:
connected_towers.append(p[0])
connected_towers.append(p[1])
connected_towers = list(set(connected_towers))
unconnected_towers = [a for a in range(len(center_list)) if a not in connected_towers]
# visualize results
plt.figure(figsize=(8.5, 8))
img_with_line = util_functions.add_mask(raw_rgb, line_gt, [0, 255, 0], 1)
visualize_with_connected_pairs(raw_rgb, center_list, connected_pairs, add_fig=True)
# visualize_with_connected_pairs(raw_rgb, center_list, unconnected_pairs, style='k', add_fig=True)
add_points(gt_list, 'b', marker='s', size=80, alpha=1, edgecolor='k')
add_points([center_list[a] for a in connected_towers], 'r', marker='o', alpha=1, edgecolor='k')
add_points([center_list[a] for a in unconnected_towers], 'yellow', marker='o', alpha=1, edgecolor='k')
plt.axis('off')
plt.tight_layout()
# plt.savefig(os.path.join(img_dir, '{}_{}_post_result.png'.format(city_list[city_id], tile_id)))
plt.show()
import os
import imageio
import numpy as np
from glob import glob
from util_functions import add_mask
import sis_utils
deeplab_pred_dir = r'/hdd/Results/gbdx_cmp/DeeplabV3_res101_inria_aug_grid_0_PS(321, 321)_BS5_EP100_LR1e-05_DS40_DR0.1_SFN32/sp/pred'
unet_pred_dir = r'/hdd/Results/gbdx_cmp/UnetCrop_inria_aug_grid_0_PS(572, 572)_BS5_EP100_LR0.0001_DS60_DR0.1_SFN32/sp/pred'
gt_dir = r'/media/ei-edl01/data/uab_datasets/sp/DATA_BUILDING_AND_PANEL'
tile_ids = [
os.path.basename(a).split('_')[0]
for a in glob(os.path.join(gt_dir, '*_GT.png'))
]
img_dir, task_dir = sis_utils.get_task_img_folder()
for img_cnt in range(len(tile_ids)):
img_name = '{}.png'.format(tile_ids[img_cnt])
rgb_name = '{}_RGB.jpg'.format(tile_ids[img_cnt])
unet_pred = imageio.imread(os.path.join(unet_pred_dir, img_name))
deeplab_pred = imageio.imread(os.path.join(deeplab_pred_dir, img_name))
ensemble_pred = unet_pred + deeplab_pred - unet_pred * deeplab_pred
sp_rgb = imageio.imread(os.path.join(gt_dir, rgb_name))
masked = add_mask(sp_rgb, ensemble_pred, [255, None, None], mask_1=1)
img_name = os.path.join(img_dir, 'ca_building', 'ensemble',
'{}_building_mask.png'.format(tile_ids[img_cnt]))
imageio.imsave(img_name, masked)
tile_ids = [
os.path.basename(a).split('_')[0]
for a in glob(os.path.join(pred_base_dir, 'deeplab', '*.png'))
]
for img_cnt in range(len(tile_ids)):
img_name = '{}_building_mask.png'.format(tile_ids[img_cnt])
gt_name = '{}_GT.png'.format(tile_ids[img_cnt])
unet_pred = imageio.imread(os.path.join(pred_base_dir, 'unet', img_name))
deeplab_pred = imageio.imread(
os.path.join(pred_base_dir, 'deeplab', img_name))
ensemble_pred = imageio.imread(
os.path.join(pred_base_dir, 'ensemble', img_name))
sp_gt = imageio.imread(os.path.join(gt_dir, gt_name))
masked_unet = add_mask(unet_pred, sp_gt, [0, 0, 255], mask_1=1)
masked_deeplab = add_mask(deeplab_pred, sp_gt, [0, 0, 255], mask_1=1)
masked_ensemble = add_mask(ensemble_pred, sp_gt, [0, 0, 255], mask_1=1)
'''plt.figure(figsize=(15, 8))
ax1 = plt.subplot2grid((2, 3), (0, 0), colspan=2, rowspan=2)
plt.imshow(masked_ensemble)
#plt.axis('off')
plt.title('ENSEMBLE')
ax2 = plt.subplot2grid((2, 3), (0, 2), sharex=ax1, sharey=ax1)
plt.imshow(masked_unet)
#plt.axis('off')
plt.title('UNET')
ax3 = plt.subplot2grid((2, 3), (1, 2), sharex=ax1, sharey=ax1)
plt.imshow(masked_deeplab)
# plt.axis('off')
plt.title('DEEPLAB')
import numpy as np
import matplotlib.pyplot as plt
import sis_utils
import util_functions
orig_dir = r'/media/ei-edl01/user/as667/BOHAO/gbdx_results'
rgb_orig_dir = r'/media/ei-edl01/user/as667'
task_id = 'Honolulu_chunks'
tile_id = '104001000832C600_0'
img_name = os.path.join(rgb_orig_dir, task_id, '{}.tif'.format(tile_id))
bgt_name = os.path.join(orig_dir, task_id, 'building_{}.tif'.format(tile_id))
sgt_name = os.path.join(orig_dir, task_id, 'sp_{}.tif'.format(tile_id))
img_dir, task_dir = sis_utils.get_task_img_folder()
img = imageio.imread(img_name)
bmask = np.copy(img)
smask = np.copy(img)
bgt = imageio.imread(bgt_name)
sgt = imageio.imread(sgt_name)
b_mask = util_functions.add_mask(bmask, bgt, [None, 255, None], mask_1=255)
s_mask = util_functions.add_mask(smask, sgt, [255, None, None], mask_1=255)
plt.figure(figsize=(12, 6))
ax1 = plt.subplot(121)
plt.imshow(b_mask)
plt.axis('off')
ax2 = plt.subplot(122, sharex=ax1, sharey=ax1)
plt.imshow(s_mask)
plt.axis('off')
plt.show()
for img_id in [1, 2, 3]:
img_name = '{}_0{}.tif'.format(city_name, img_id)
gt_file_name = '{}_0{}_roadCoord.csv'.format(city_name, img_id)
info_reader = read_polygon_info(os.path.join(img_dir, gt_file_name), city_name)
img = imageio.imread(os.path.join(img_dir, img_name))
gt_img = np.zeros(img.shape, np.uint8)
gt_img = add_polygons(gt_img, info_reader)
img = down_sample(img, 2)
gt_img = down_sample(gt_img, 2, dim=2)
new_img_name = '{}{}_RGB.tif'.format(city_name, img_id)
new_gt_name = '{}{}_GT.png'.format(city_name, img_id)
imageio.imsave(os.path.join(img_dir, new_img_name), img)
imageio.imsave(os.path.join(img_dir, new_gt_name), gt_img)
if check_result:
plt.figure(figsize=(10, 8))
# ax1 = plt.subplot(211)
img = util_functions.add_mask(img, gt_img, [255, 0, 0], mask_1=1)
plt.imshow(img)
plt.axis('off')
'''ax2 = plt.subplot(212, sharex=ax1, sharey=ax1)
plt.imshow(gt_img)
plt.axis('off')'''
plt.tight_layout()
plt.show()
pred_4 = evaluate_on_a_patch([chip_size, chip_size], X_batch_4)
# stitch
pred_stitch = np.zeros_like(pred)
pred_stitch[6:6 + 324, 6:6 + 324] = pred_1
pred_stitch[330:330 + 324, 6:6 + 324] = pred_2
pred_stitch[6:6 + 324, 330:330 + 324] = pred_3
pred_stitch[330:330 + 324, 330:330 + 324] = pred_4
# diff
pred_diff = pred - pred_stitch
gt_window = gt[92:chip_size_large - 92, 92:chip_size_large - 92]
gt_plot = tile[92:chip_size_large-92,92:chip_size_large-92,:] + img_mean
gt_cmp_large = 255 + np.zeros((gt_window.shape[0], gt_window.shape[1], 3), dtype=np.float32)
gt_cmp_small = 255 + np.zeros((gt_window.shape[0], gt_window.shape[1], 3), dtype=np.float32)
gt_plot = util_functions.add_mask(gt_plot, gt_window, [0, 0, 0], 255)
gt_cmp_large = util_functions.add_mask(gt_cmp_large, gt_window, [0, 0, 0], 255)
gt_cmp_large = util_functions.add_mask(gt_cmp_large, gt_window/255-pred, [255, 0, 0], 1)
gt_cmp_large = util_functions.add_mask(gt_cmp_large, gt_window/255-pred, [0, 255, 0], -1)
gt_cmp_small = util_functions.add_mask(gt_cmp_small, gt_window, [0, 0, 0], 255)
gt_cmp_small = util_functions.add_mask(gt_cmp_small, gt_window/255-pred_stitch, [255, 0, 0], 1)
gt_cmp_small = util_functions.add_mask(gt_cmp_small, gt_window/255-pred_stitch, [0, 255, 0], -1)
# compute iou
iou_pred = util_functions.iou_metric(gt_window[6:-6, 6:-6]/255, pred[6:-6, 6:-6]) * 100
iou_stitch = util_functions.iou_metric(gt_window[6:-6, 6:-6]/255, pred_stitch[6:-6, 6:-6]) * 100
if not (np.isnan(iou_pred) or np.isnan(iou_stitch)):
large_ious.append(iou_pred)
small_ious.append(iou_stitch)
# show
rgb_file = os.path.join(orig_dir, tile_id.replace('_', '-') + '_RGB.jpg')
adjust_file = os.path.join(adjust_save_dir, tile_id.replace('_', '-') + '_RGB.jpg')
p = imageio.imread(pred_file)
g = imageio.imread(gt_file)
rgb_orig = imageio.imread(rgb_file)
rgb_adju = imageio.imread(adjust_file)
_, cc_p = scipy.ndimage.label(p)
_, cc_g = scipy.ndimage.label(g)
pred_target_pixels[cnt_2, cnt_1] = np.sum(p)
gt_target_pixels[cnt_2, cnt_1] = np.sum(g)
pred_target_num[cnt_2, cnt_1] = cc_p
gt_target_num[cnt_2, cnt_1] = cc_g
masked_orig = add_mask(rgb_orig, g, [255, None, None], mask_1=1)
masked_adju = add_mask(rgb_adju, p, [255, None, None], mask_1=1)
plt.figure(figsize=(12, 6))
ax1 = plt.subplot(121)
plt.imshow(masked_orig)
plt.axis('off')
plt.title('CT')
ax2 = plt.subplot(122, sharex=ax1, sharey=ax1)
plt.imshow(masked_adju)
plt.axis('off')
plt.title('GBDX')
plt.suptitle('{}'.format(tile_id))
plt.tight_layout()
plt.show()
def get_error_mask_img(img, gt, mask):
img = add_mask(img, gt, [None, None, None], mask_1=255)
img = add_mask(img, mask, [255, None, None], mask_1=1)
img = add_mask(img, mask, [None, None, 255], mask_1=-1)
return img
