/
check_installation.py
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/
check_installation.py
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import detectron2
from detectron2.utils.logger import setup_logger
setup_logger()
# import some common libraries
import numpy as np
import cv2
import random
# import some common detectron2 utilities
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
import matplotlib.pyplot as plt
im = cv2.imread("./input.jpg")
plt.imshow(im)
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(
"./detectron2/detectron2/model_zoo/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"
)
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5
cfg.MODEL.WEIGHTS = "detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl"
predictor = DefaultPredictor(cfg)
outputs = predictor(im)
print(outputs["instances"].pred_classes)
print(outputs["instances"].pred_boxes)
v = Visualizer(im[:, :, ::-1], MetadataCatalog.get(cfg.DATASETS.TRAIN[0]), scale=1.2)
v = v.draw_instance_predictions(outputs["instances"].to("cpu"))
plt.imshow(v.get_image()[:, :, ::-1])
# save image
plt.savefig("output.jpg")
# DATA SHOULD BE SUPER EASY TO ADD
#from detectron2.data.datasets import register_coco_instances
#register_coco_instances("my_dataset", {}, "json_annotation.json", "path/to/image/dir")
#https://detectron2.readthedocs.io/tutorials/datasets.html#register-a-dataset
# test in CMD
# cd detectron2
# python demo/demo.py --config-file configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml --webcam --opts MODEL.WEIGHTS detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl