def process(self, inputs, outputs):
import sys
sys.path.insert(
0, '/home/sk49/new_workspace/trj/cityscapesScripts-master')
from cityscapesscripts.helpers.labels import trainId2label
print(outputs[0])
for input, output in zip(inputs, outputs):
file_name = input["file_name"]
basename = os.path.splitext(os.path.basename(file_name))[0]
pred_filename = os.path.join(self._temp_dir,
basename + "_pred.png")
pred_filename1 = os.path.join(
'/home/sk49/new_workspace/trj/detectron2-master/projects/PointRend/output',
(basename + "_pred.png"))
output = output["sem_seg"].argmax(dim=0).to(
self._cpu_device).numpy()
pred = 255 * np.ones(output.shape, dtype=np.uint8)
for train_id, label in trainId2label.items():
if label.ignoreInEval:
continue
pred[output == train_id] = label.id
Image.fromarray(pred).save(pred_filename)
Image.fromarray(pred).save(pred_filename1)
def process(self, inputs, outputs):
from cityscapesscripts.helpers.labels import trainId2label
for input, output in zip(inputs, outputs):
file_name = input["file_name"]
basename = os.path.splitext(os.path.basename(file_name))[0]
pred_filename = os.path.join(self._temp_dir, basename + "_pred.png")
output = output["sem_seg"].argmax(dim=0).to(self._cpu_device).numpy()
pred = 255 * np.ones(output.shape, dtype=np.uint8)
for train_id, label in trainId2label.items():
if label.ignoreInEval:
continue
pred[output == train_id] = label.id
Image.fromarray(pred).save(pred_filename)