| import sys |
| sys.path.insert(0, "Mask2Former") |
| import tempfile |
| from pathlib import Path |
| import numpy as np |
| import cv2 |
| import cog |
|
|
| |
| 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 |
|
|
| |
| 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 |
|
|
