-
Notifications
You must be signed in to change notification settings - Fork 0
/
predict_fpn.py
62 lines (53 loc) · 2.21 KB
/
predict_fpn.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
import detectron2
from detectron2.utils.logger import setup_logger
setup_logger()
# import some common libraries
import numpy as np
import cv2
import random
import datetime
import time
import os
from detectron2 import model_zoo
from detectron2.engine import DefaultPredictor
from detectron2.config import get_cfg
from detectron2.utils.visualizer import Visualizer
from detectron2.data import MetadataCatalog
from detectron2.data.datasets import register_coco_instances
from detectron2.data import DatasetCatalog, MetadataCatalog
from detectron2.engine import DefaultTrainer
from detectron2.config import get_cfg
from detectron2.utils.visualizer import ColorMode
from detectron2.evaluation import COCOEvaluator, inference_on_dataset
from detectron2.data import build_detection_test_loader
# prediction and evaluation function
def Predict():
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
# register test dataset
register_coco_instances("custom", {}, "datasets/testdata/midv500_coco.json", "datasets/testdata/")
custom_metadata = MetadataCatalog.get("custom")
dataset_dicts = DatasetCatalog.get("custom")
# set cfg
cfg = get_cfg()
cfg.merge_from_file("configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml")
cfg.DATASETS.TEST = ("custom", )
cfg.MODEL.WEIGHTS = os.path.join(cfg.OUTPUT_DIR, "model_final.pth")
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5
cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = (512)
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 1
predictor = DefaultPredictor(cfg)
# save prediction image results
cnt=0
for d in dataset_dicts:
img = cv2.imread(d["file_name"])
outputs = predictor(img)
v = Visualizer(img[:, :, ::-1], metadata=custom_metadata, scale=1, instance_mode=ColorMode.IMAGE_BW)
v = v.draw_instance_predictions(outputs["instances"].to("cpu"))
cv2.imwrite('D:/eagletmp/detectron2-maskrcnn/outputimg/'+str(cnt)+'.png',v.get_image()[:, :, ::-1])
cnt+=1
# model evaulation
evaluator = COCOEvaluator("custom", cfg, False, output_dir="./output/")
val_loader = build_detection_test_loader(cfg, "custom")
print(inference_on_dataset(predictor.model, val_loader, evaluator))
if __name__ == "__main__":
Predict()