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# Some basic setup:
# Setup detectron2 logger
import detectron2
from detectron2.utils.logger import setup_logger
setup_logger()


# import some common libraries

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# install dependencies:
!pip install pyyaml==5.1 pycocotools>=2.0.1
import torch, torchvision
print(torch.__version__, torch.cuda.is_available())
!gcc - version
# opencv is pre-installed on colab

# install detectron2: (colab has CUDA 10.1 + torch 1.6):
# See https://detectron2.readthedocs.io/tutorials/install.html for instructions
assert torch.__version__.startswith("1.6")

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#getting Image from COCO dataset
!wget http://images.cocodataset.org/val2017/000000439715.jpg -q -O input.jpg
im = cv2.imread("./input.jpg")
cv2_imshow(im)

#Creating a detectron2 config and a detectron2 `DefaultPredictor` to run inference on this image.
cfg = get_cfg()
# add project-specific config (e.g., TensorMask) here if you're not running a model in detectron2's core library
cfg.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"))
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5  # set threshold for this model