Mask2Former / predict.py
Ahsen Khaliq
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import sys
sys.path.insert(0, "Mask2Former")
import tempfile
from pathlib import Path
import numpy as np
import cv2
import cog
# import some common detectron2 utilities
from detectron2.config import CfgNode as CN
from detectron2.engine import DefaultPredictor
from detectron2.config import get_cfg
from detectron2.utils.visualizer import Visualizer, ColorMode
from detectron2.data import MetadataCatalog
from detectron2.projects.deeplab import add_deeplab_config
# import Mask2Former project
from mask2former import add_maskformer2_config
class Predictor(cog.Predictor):
def setup(self):
cfg = get_cfg()
add_deeplab_config(cfg)
add_maskformer2_config(cfg)
cfg.merge_from_file("Mask2Former/configs/coco/panoptic-segmentation/swin/maskformer2_swin_large_IN21k_384_bs16_100ep.yaml")
cfg.MODEL.WEIGHTS = 'model_final_f07440.pkl'
cfg.MODEL.MASK_FORMER.TEST.SEMANTIC_ON = True
cfg.MODEL.MASK_FORMER.TEST.INSTANCE_ON = True
cfg.MODEL.MASK_FORMER.TEST.PANOPTIC_ON = True
self.predictor = DefaultPredictor(cfg)
self.coco_metadata = MetadataCatalog.get("coco_2017_val_panoptic")
@cog.input(
"image",
type=Path,
help="Input image for segmentation. Output will be the concatenation of Panoptic segmentation (top), "
"instance segmentation (middle), and semantic segmentation (bottom).",
)
def predict(self, image):
im = cv2.imread(str(image))
outputs = self.predictor(im)
v = Visualizer(im[:, :, ::-1], self.coco_metadata, scale=1.2, instance_mode=ColorMode.IMAGE_BW)
panoptic_result = v.draw_panoptic_seg(outputs["panoptic_seg"][0].to("cpu"),
outputs["panoptic_seg"][1]).get_image()
v = Visualizer(im[:, :, ::-1], self.coco_metadata, scale=1.2, instance_mode=ColorMode.IMAGE_BW)
instance_result = v.draw_instance_predictions(outputs["instances"].to("cpu")).get_image()
v = Visualizer(im[:, :, ::-1], self.coco_metadata, scale=1.2, instance_mode=ColorMode.IMAGE_BW)
semantic_result = v.draw_sem_seg(outputs["sem_seg"].argmax(0).to("cpu")).get_image()
result = np.concatenate((panoptic_result, instance_result, semantic_result), axis=0)[:, :, ::-1]
out_path = Path(tempfile.mkdtemp()) / "out.png"
cv2.imwrite(str(out_path), result)
return out_path