import torch import numpy as np import cv2 from huggingface_hub import hf_hub_download REPO_ID = "idml/Detectron2-FasterRCNN_InsectDetect" FILENAME = "model.pth" FILENAME_CONFIG = "config.yml" # Ensure you have the model file import cv2 from detectron2.config import get_cfg from detectron2.engine import DefaultPredictor from detectron2.data import MetadataCatalog from detectron2.utils.visualizer import Visualizer, ColorMode import matplotlib.pyplot as plt viz_classes = {'thing_classes': ['Acrididae', 'Agapeta', 'Agapeta hamana', 'Animalia', 'Anisopodidae', 'Aphididae', 'Apidae', 'Arachnida', 'Araneae', 'Arctiidae', 'Auchenorrhyncha indet.', 'Baetidae', 'Cabera', 'Caenidae', 'Carabidae', 'Cecidomyiidae', 'Ceratopogonidae', 'Cercopidae', 'Chironomidae', 'Chrysomelidae', 'Chrysopidae', 'Chrysoteuchia culmella', 'Cicadellidae', 'Coccinellidae', 'Coleophoridae', 'Coleoptera', 'Collembola', 'Corixidae', 'Crambidae', 'Culicidae', 'Curculionidae', 'Dermaptera', 'Diptera', 'Eilema', 'Empididae', 'Ephemeroptera', 'Erebidae', 'Fanniidae', 'Formicidae', 'Gastropoda', 'Gelechiidae', 'Geometridae', 'Hemiptera', 'Hydroptilidae', 'Hymenoptera', 'Ichneumonidae', 'Idaea', 'Insecta', 'Lepidoptera', 'Leptoceridae', 'Limoniidae', 'Lomaspilis marginata', 'Miridae', 'Mycetophilidae', 'Nepticulidae', 'Neuroptera', 'Noctuidae', 'Notodontidae', 'Object', 'Opiliones', 'Orthoptera', 'Panorpa germanica', 'Panorpa vulgaris', 'Parasitica indet.', 'Plutellidae', 'Psocodea', 'Psychodidae', 'Pterophoridae', 'Pyralidae', 'Pyrausta', 'Sepsidae', 'Spilosoma', 'Staphylinidae', 'Stratiomyidae', 'Syrphidae', 'Tettigoniidae', 'Tipulidae', 'Tomoceridae', 'Tortricidae', 'Trichoptera', 'Triodia sylvina', 'Yponomeuta', 'Yponomeutidae']} def detectron_process_image(image): cfg = get_cfg() cfg.merge_from_file(hf_hub_download(repo_id=REPO_ID, filename=FILENAME_CONFIG)) cfg.MODEL.WEIGHTS = hf_hub_download(repo_id=REPO_ID, filename=FILENAME) cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.2 cfg.MODEL.DEVICE='cpu' predictor = DefaultPredictor(cfg) numpy_image = np.array(image) im = cv2.cvtColor(numpy_image, cv2.COLOR_RGB2BGR) v = Visualizer(im[:, :, ::-1], viz_classes, scale=0.5) outputs = predictor(im) out = v.draw_instance_predictions(outputs["instances"].to("cpu")) results = out.get_image()[:, :, ::-1] rgb_image = cv2.cvtColor(results, cv2.COLOR_BGR2RGB) return rgb_image