# scale = 0.8,
# instance_mode = ColorMode.IMAGE_BW)
# v = v.draw_instance_predictions(outputs['instances'].to('cpu'))
# cv2.imshow('image', v.get_image()[:, :, ::-1])
# cv2.waitKey(0)
if args.mode == 'infer':
val_dicts = get_val_dicts()
for d in random.sample(val_dicts, 10):
im = cv2.imread(d['file_name'])
outputs = predictor(im)
v = Visualizer(im[:, :, ::-1],
metadata=openimages_val_metadata,
scale=0.8,
instance_mode=ColorMode.IMAGE_BW)
v = v.draw_instance_predictions(outputs['instances'].to('cpu'))
cv2.imshow('image', v.get_image()[:, :, ::-1])
cv2.waitKey(0)
if args.mode == 'evaluate':
print(cfg.DATASETS.TEST[0])
trainer = DefaultTrainer(cfg)
model = trainer.build_model(cfg)
DetectionCheckpointer(model).load(args.model_pth)
evaluator = COCOEvaluator(cfg.DATASETS.TEST[0],
cfg,
False,
output_dir="./output/")
val_loader = build_detection_test_loader(cfg, "openimages_val")
inference_on_dataset(model, val_loader, evaluator)
v = Visualizer(im[:, :, ::-1],
MetadataCatalog.get(cfg.DATASETS.TRAIN[0]),
scale=0.8) #为什么这里是cfg.DATASETS.TRAIN[0]而不是TEST呢？
out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
# cv2_imshow(out.get_image()[:, :, ::-1])
out.save("./model_output.jpg")
i += 1
# #from detectron2.utils.visualizer import ColorMode
# dataset_dicts = load_coco_json("/home/xuyifei/anaconda3/envs/detectron2/lib/python3.7/site-packages/detectron2/datasets/coco/annotations/instances_val.json", "/home/xuyifei/anaconda3/envs/detectron2/lib/python3.7/site-packages/detectron2/datasets/coco/val")
# for d in random.sample(dataset_dicts, 3):
# im = cv2.imread(d["file_name"])
# outputs = predictor(im)
# v = Visualizer(im[:, :, ::-1],
# metadata=coco_train_metadata,
# scale=0.8,
# instance_mode=ColorMode.IMAGE_BW # remove the colors of unsegmented pixels
# )
# out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
# cv2_imshow(out.get_image()[:, :, ::-1])
print(os.getcwd())
from detectron2.evaluation import COCOEvaluator, inference_on_dataset
from detectron2.data import build_detection_test_loader
evaluator = COCOEvaluator("coco_val_2", cfg, True, output_dir="./output")
val_loader = build_detection_test_loader(cfg, "coco_val_2")
print(inference_on_dataset(trainer.model, val_loader, evaluator))
# another equivalent way is to use trainer.test
def build_test_loader(cls, cfg, dataset_name):
return build_detection_test_loader(cfg,
dataset_name,
mapper=DatasetMapper(cfg, False))
def build_test_loader(cls, cfg, dataset_name):
"""
Returns:
iterable
"""
return build_detection_test_loader(cfg, dataset_name)