def _trigger(self):
global args
self.val_ds = get_data('val', args.base_dir, args.meta_dir, 1)
self.val_ds.reset_state()
self.stat = MIoUStatistics(self.nb_class)
def mypredictor(input_img):
# input image: 1*H*W*3
# output : H*W*C
output = self.pred(input_img)
return output[0][0]
for image, label in tqdm(self.val_ds.get_data()):
label = np.squeeze(label)
image = np.squeeze(image)
prediction = predict_scaler(image,
mypredictor,
scales=[0.5, 0.75, 1, 1.25, 1.5],
classes=CLASS_NUM,
tile_size=CROP_SIZE,
is_densecrf=False)
prediction = np.argmax(prediction, axis=2)
self.stat.feed(prediction, label)
self.trainer.monitors.put_scalar("mIoU", self.stat.mIoU)
self.trainer.monitors.put_scalar("mean_accuracy",
self.stat.mean_accuracy)
self.trainer.monitors.put_scalar("accuracy", self.stat.accuracy)
def proceed_validation(args, is_save=True, is_densecrf=False):
import cv2
#name = "ningbo_val"
name = "val"
ds = dataset.PSSD(args.base_dir, args.meta_dir, name)
ds = BatchData(ds, 1)
pred_config = PredictConfig(model=Model(),
session_init=get_model_loader(args.load),
input_names=['image'],
output_names=['prob'])
predictor = OfflinePredictor(pred_config)
from tensorpack.utils.fs import mkdir_p
result_dir = "result/pssd_apr26"
#result_dir = "ningbo_validation"
mkdir_p(result_dir)
i = 1
stat = MIoUStatistics(CLASS_NUM)
logger.info("start validation....")
for image, label in tqdm(ds.get_data()):
label = np.squeeze(label)
image = np.squeeze(image)
def mypredictor(input_img):
#input image: 1*H*W*3
#output : H*W*C
output = predictor(input_img)
return output[0][0]
prediction = predict_scaler(image,
mypredictor,
scales=[0.5, 0.75, 1, 1.25, 1.5],
classes=CLASS_NUM,
tile_size=CROP_SIZE,
is_densecrf=is_densecrf)
prediction = np.argmax(prediction, axis=2)
stat.feed(prediction, label)
if is_save:
cv2.imwrite(
os.path.join(result_dir, "{}.png".format(i)),
np.concatenate((image, visualize_label(label),
visualize_label(prediction)),
axis=1))
#imwrite_grid(image,label,prediction, border=512, prefix_dir=result_dir, imageId = i)
i += 1
logger.info("mIoU: {}".format(stat.mIoU))
logger.info("mean_accuracy: {}".format(stat.mean_accuracy))
logger.info("accuracy: {}".format(stat.accuracy))
def proceed_test_dir(args):
import cv2
ll = os.listdir(args.test_dir)
pred_config = PredictConfig(model=Model(),
session_init=get_model_loader(args.load),
input_names=['image'],
output_names=['prob'])
predictor = OfflinePredictor(pred_config)
from tensorpack.utils.fs import mkdir_p
result_dir = "test-from-dir"
visual_dir = os.path.join(result_dir, "visualization")
final_dir = os.path.join(result_dir, "final")
import shutil
shutil.rmtree(result_dir, ignore_errors=True)
mkdir_p(result_dir)
mkdir_p(visual_dir)
mkdir_p(final_dir)
logger.info("start validation....")
def mypredictor(input_img):
# input image: 1*H*W*3
# output : H*W*C
output = predictor(input_img[np.newaxis, :, :, :])
return output[0][0]
for i in tqdm(range(len(ll))):
filename = ll[i]
image = cv2.imread(os.path.join(args.test_dir, filename))
prediction = predict_scaler(image,
mypredictor,
scales=[0.5, 0.75, 1, 1.25, 1.5],
classes=CLASS_NUM,
tile_size=CROP_SIZE,
is_densecrf=False)
prediction = np.argmax(prediction, axis=2)
cv2.imwrite(os.path.join(final_dir, "{}".format(filename)), prediction)
cv2.imwrite(
os.path.join(visual_dir, "{}".format(filename)),
np.concatenate((image, visualize_label(prediction)), axis=1))
def proceed_test(args, is_densecrf=False):
import cv2
ds = dataset.Aerial(args.base_dir, args.meta_dir, "test")
imglist = ds.imglist
ds = BatchData(ds, 1)
pred_config = PredictConfig(model=Model(),
session_init=get_model_loader(args.load),
input_names=['image'],
output_names=['prob'])
predictor = OfflinePredictor(pred_config)
from tensorpack.utils.fs import mkdir_p
result_dir = "test-{}".format(os.path.basename(__file__).rstrip(".py"))
import shutil
shutil.rmtree(result_dir, ignore_errors=True)
mkdir_p(result_dir)
mkdir_p(os.path.join(result_dir, "compressed"))
import subprocess
logger.info("start validation....")
_itr = ds.get_data()
for i in tqdm(range(len(imglist))):
image = next(_itr)
name = os.path.basename(imglist[i]).rstrip(".tif")
image = np.squeeze(image)
prediction = predict_scaler(image,
predictor,
scales=[0.9, 1, 1.1],
classes=CLASS_NUM,
tile_size=CROP_SIZE,
is_densecrf=is_densecrf)
prediction = np.argmax(prediction, axis=2)
prediction = prediction * 255 # to 0-255
file_path = os.path.join(result_dir, "{}.tif".format(name))
compressed_file_path = os.path.join(result_dir, "compressed",
"{}.tif".format(name))
cv2.imwrite(file_path, prediction)
command = "gdal_translate --config GDAL_PAM_ENABLED NO -co COMPRESS=CCITTFAX4 -co NBITS=1 " + file_path + " " + compressed_file_path
print command
subprocess.call(command, shell=True)
