Arulkumar03/Fox_Sheep_Detector_Computer_Vision_model

1import cv22import numpy as np3import gradio as gr4from detectron2 import model_zoo5from detectron2.config import get_cfg6from detectron2.engine import DefaultPredictor7from detectron2.utils.visualizer import Visualizer8from detectron2.data import MetadataCatalog9 10def initialize_model():11    for d in ["train", "test"]:12        #DatasetCatalog.register("Animals_" + d, lambda d=d: get_wheat_dicts("Animal_Detection/" + d))13        MetadataCatalog.get("Animals_" + d).set(thing_classes=["fox","sheep"])14 15    wheat_metadata = MetadataCatalog.get("Animals_train")  16    cfg = get_cfg()17    cfg.MODEL.DEVICE = "cpu"18    cfg.DATALOADER.NUM_WORKERS = 019    cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-InstanceSegmentation/mask_rcnn_R_101_C4_3x.yaml")20    cfg.SOLVER.IMS_PER_BATCH = 221    cfg.SOLVER.BASE_LR = 0.0002522    cfg.SOLVER.STEPS = []23    cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 12824    cfg.MODEL.ROI_HEADS.NUM_CLASSES = 225    cfg.MODEL.WEIGHTS = "output/model_final.pth"26    cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.9527    predictor = DefaultPredictor(cfg)28    return predictor29 30def process_image(predictor, img):31    outputs = predictor(img)32    wheat_metadata = MetadataCatalog.get("Animals_train")33    v = Visualizer(img[:, :, ::-1],34                   metadata=wheat_metadata, 35                   scale=1.5, 36                   instance_mode="segmentation")37    out = v.draw_instance_predictions(outputs["instances"].to("cpu"))38    processed_img = cv2.cvtColor(out.get_image()[:, :, ::-1], cv2.COLOR_BGR2RGB)39    return processed_img40 41def main(img):42    predictor = initialize_model()43    processed_img = process_image(predictor, img)44    return processed_img45 46 47iface = gr.Interface(48    fn=main,49    inputs="image",50    outputs="image",51    title="Fox & Sheep Computer Vision detector",52    cache_examples=False,input_size=(8000, 8000), output_size=(8000, 8000)53)54iface.launch()