def get_iou(data_list, class_num, save_path=None):
from multiprocessing import Pool
from utils.metric import ConfusionMatrix
ConfM = ConfusionMatrix(class_num)
f = ConfM.generateM
pool = Pool()
m_list = pool.map(f, data_list)
pool.close()
pool.join()
for m in m_list:
ConfM.addM(m)
aveJ, j_list, M = ConfM.jaccard()
classes = np.array(('background', # always index 0
'aeroplane', 'bicycle', 'bird', 'boat',
'bottle', 'bus', 'car', 'cat', 'chair',
'cow', 'diningtable', 'dog', 'horse',
'motorbike', 'person', 'pottedplant',
'sheep', 'sofa', 'train', 'tvmonitor'))
for i, iou in enumerate(j_list):
print('class {:2d} {:12} IU {:.2f}'.format(i, classes[i], j_list[i]))
print('meanIOU: ' + str(aveJ) + '\n')
if save_path:
with open(save_path, 'w') as f:
for i, iou in enumerate(j_list):
f.write('class {:2d} {:12} IU {:.2f}'.format(i, classes[i], j_list[i]) + '\n')
f.write('meanIOU: ' + str(aveJ) + '\n')
def get_iou(data_list, class_num, save_path=None):
from multiprocessing import Pool
from utils.metric import ConfusionMatrix
ConfM = ConfusionMatrix(class_num)
f = ConfM.generateM
pool = Pool()
m_list = pool.map(f, data_list)
pool.close()
pool.join()
for m in m_list:
ConfM.addM(m)
aveJ, j_list, M = ConfM.jaccard()
classes = np.array((
'background', # always index 0
'REA',
'SRF',
'PED'))
for i, iou in enumerate(j_list):
print('class {:2d} {:12} IU {:.2f}'.format(i, classes[i], j_list[i]))
print('meanIOU: ' + str(aveJ) + '\n')
if save_path:
with open(save_path, 'w') as f:
for i, iou in enumerate(j_list):
f.write('class {:2d} {:12} IU {:.2f}'.format(
i, classes[i], j_list[i]) + '\n')
f.write('meanIOU: ' + str(aveJ) + '\n')
def get_iou(data_list, class_num, dataset, save_path=None):
from multiprocessing import Pool
from utils.metric import ConfusionMatrix
ConfM = ConfusionMatrix(class_num)
f = ConfM.generateM
pool = Pool()
m_list = pool.map(f, data_list)
pool.close()
pool.join()
for m in m_list:
ConfM.addM(m)
aveJ, j_list, M = ConfM.jaccard()
classes = np.array(("road", "sidewalk", "building", "wall", "fence",
"pole", "traffic_light", "traffic_sign", "vegetation",
"terrain", "sky", "person", "rider", "car", "truck",
"bus", "train", "motorcycle", "bicycle"))
for i, iou in enumerate(j_list):
print('class {:2d} {:12} IU {:.2f}'.format(i, classes[i],
100 * j_list[i]))
print('meanIOU: ' + str(aveJ) + '\n')
if save_path:
with open(save_path, 'w') as f:
for i, iou in enumerate(j_list):
f.write('class {:2d} {:12} IU {:.2f}'.format(
i, classes[i], 100 * j_list[i]) + '\n')
f.write('meanIOU: ' + str(aveJ) + '\n')
return aveJ
def get_iou(data_list, class_num, save_path=None):
from multiprocessing import Pool
from utils.metric import ConfusionMatrix
ConfM = ConfusionMatrix(class_num)
f = ConfM.generateM
pool = Pool()
m_list = pool.map(f, data_list)
pool.close()
pool.join()
for m in m_list:
ConfM.addM(m)
aveJ, j_list, M = ConfM.jaccard()
classes = np.array(('background', # always index 0
'aeroplane', 'bicycle', 'bird', 'boat',
'bottle', 'bus', 'car', 'cat', 'chair',
'cow', 'diningtable', 'dog', 'horse',
'motorbike', 'person', 'pottedplant',
'sheep', 'sofa', 'train', 'tvmonitor'))
for i, iou in enumerate(j_list):
print('class {:2d} {:12} IU {:.2f}'.format(i, classes[i], j_list[i]))
print('meanIOU: ' + str(aveJ) + '\n')
if save_path:
with open(save_path, 'w') as f:
for i, iou in enumerate(j_list):
f.write('class {:2d} {:12} IU {:.2f}'.format(i, classes[i], j_list[i]) + '\n')
f.write('meanIOU: ' + str(aveJ) + '\n')
def get_iou(data_list, class_num, dataset, save_path=None):
from multiprocessing import Pool
from utils.metric import ConfusionMatrix
ConfM = ConfusionMatrix(class_num)
f = ConfM.generateM
pool = Pool()
m_list = pool.map(f, data_list)
pool.close()
pool.join()
for m in m_list:
ConfM.addM(m)
aveJ, j_list, M = ConfM.jaccard()
if dataset == 'pascal_voc':
classes = np.array((
'background', # always index 0
'aeroplane',
'bicycle',
'bird',
'boat',
'bottle',
'bus',
'car',
'cat',
'chair',
'cow',
'diningtable',
'dog',
'horse',
'motorbike',
'person',
'pottedplant',
'sheep',
'sofa',
'train',
'tvmonitor'))
elif dataset == 'cityscapes':
classes = np.array(
("road", "sidewalk", "building", "wall", "fence", "pole",
"traffic_light", "traffic_sign", "vegetation", "terrain", "sky",
"person", "rider", "car", "truck", "bus", "train", "motorcycle",
"bicycle"))
for i, iou in enumerate(j_list):
print('class {:2d} {:12} IU {:.2f}'.format(i, classes[i], j_list[i]))
print('meanIOU: ' + str(aveJ) + '\n')
if save_path:
with open(save_path, 'w') as f:
for i, iou in enumerate(j_list):
f.write('class {:2d} {:12} IU {:.2f}'.format(
i, classes[i], j_list[i]) + '\n')
f.write('meanIOU: ' + str(aveJ) + '\n')
return aveJ
def get_iou(data_list, class_num, save_path=None):
ConfM = ConfusionMatrix(class_num)
f = ConfM.generateM
pool = Pool()
m_list = pool.map(f, data_list)
pool.close()
pool.join()
for m in m_list:
ConfM.addM(m)
aveJ, j_list, M = ConfM.jaccard()
#print('meanIOU: ' + str(aveJ) + '\n')
if save_path:
with open(save_path, 'w') as f:
f.write('meanIOU: ' + str(aveJ) + '\n')
f.write(str(j_list)+'\n')
f.write(str(M)+'\n')
return aveJ
args = parse_args()
m_list = []
data_list = []
test_ids = [i.strip() for i in open(args.test_ids) if not i.strip() == '']
for index, img_id in enumerate(test_ids):
if index % 100 == 0:
print('%d processd'%(index))
pred_img_path = os.path.join(args.pred_dir, img_id+'.png')
gt_img_path = os.path.join(args.gt_dir, img_id+'.png')
pred = cv2.imread(pred_img_path, cv2.IMREAD_GRAYSCALE)
gt = cv2.imread(gt_img_path, cv2.IMREAD_GRAYSCALE)
# show_all(gt, pred)
data_list.append([gt.flatten(), pred.flatten()])
ConfM = ConfusionMatrix(args.class_num)
f = ConfM.generateM
pool = Pool()
m_list = pool.map(f, data_list)
pool.close()
pool.join()
for m in m_list:
ConfM.addM(m)
aveJ, j_list, M = ConfM.jaccard()
with open(args.save_path, 'w') as f:
f.write('meanIOU: ' + str(aveJ) + '\n')
f.write(str(j_list)+'\n')
f.write(str(M)+'\n')
def get_iou(args, data_list, class_num, save_path=None):
from multiprocessing import Pool
from utils.metric import ConfusionMatrix
ConfM = ConfusionMatrix(class_num)
f = ConfM.generateM
pool = Pool()
m_list = pool.map(f, data_list)
pool.close()
pool.join()
for m in m_list:
ConfM.addM(m)
aveJ, j_list, M = ConfM.jaccard()
if args.dataset == 'pascal_voc':
classes = np.array((
'background', # always index 0
'aeroplane',
'bicycle',
'bird',
'boat',
'bottle',
'bus',
'car',
'cat',
'chair',
'cow',
'diningtable',
'dog',
'horse',
'motorbike',
'person',
'pottedplant',
'sheep',
'sofa',
'train',
'tvmonitor'))
elif args.dataset == 'pascal_context':
classes = np.array(
('background', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle',
'bus', 'car', 'cat', 'chair', 'cow', 'table', 'dog', 'horse',
'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train',
'tvmonitor', 'bag', 'bed', 'bench', 'book', 'building', 'cabinet',
'ceiling', 'cloth', 'computer', 'cup', 'door', 'fence', 'floor',
'flower', 'food', 'grass', 'ground', 'keyboard', 'light',
'mountain', 'mouse', 'curtain', 'platform', 'sign', 'plate',
'road', 'rock', 'shelves', 'sidewalk', 'sky', 'snow',
'bedclothes', 'track', 'tree', 'truck', 'wall', 'water', 'window',
'wood'))
elif args.dataset == 'cityscapes':
classes = np.array(
("road", "sidewalk", "building", "wall", "fence", "pole",
"traffic_light", "traffic_sign", "vegetation", "terrain", "sky",
"person", "rider", "car", "truck", "bus", "train", "motorcycle",
"bicycle"))
for i, iou in enumerate(j_list):
if j_list[i] > 0:
print('class {:2d} {:12} IU {:.2f}'.format(i, classes[i],
j_list[i]))
print('meanIOU: ' + str(aveJ) + '\n')
if save_path:
with open(save_path, 'w') as f:
for i, iou in enumerate(j_list):
f.write('class {:2d} {:12} IU {:.2f}'.format(
i, classes[i], j_list[i]) + '\n')
f.write('meanIOU: ' + str(aveJ) + '\n')
