Spaces:
Sleeping
Sleeping
| import cv2 | |
| import torch | |
| from transformers import AutoImageProcessor, Swinv2ForImageClassification | |
| from lib.cam import ClassActivationMap | |
| class GlaucomaModel(object): | |
| def __init__(self, | |
| cls_model_path="pamixsun/swinv2_tiny_for_glaucoma_classification", | |
| device=torch.device('cpu')): | |
| # where to load the model, gpu or cpu ? | |
| self.device = device | |
| # classification model for nails disease | |
| self.cls_extractor = AutoImageProcessor.from_pretrained(cls_model_path) | |
| self.cls_model = Swinv2ForImageClassification.from_pretrained(cls_model_path).to(device).eval() | |
| # class activation map | |
| self.cam = ClassActivationMap(self.cls_model, self.cls_extractor) | |
| # classification id to label | |
| self.id2label = self.cls_model.config.id2label | |
| # number of classes for nails disease | |
| self.num_diseases = len(self.id2label) | |
| def glaucoma_pred(self, image): | |
| """ | |
| Args: | |
| image: image array in RGB order. | |
| """ | |
| inputs = self.cls_extractor(images=image.copy(), return_tensors="pt") | |
| with torch.no_grad(): | |
| inputs.to(self.device) | |
| outputs = self.cls_model(**inputs).logits | |
| disease_idx = outputs.cpu()[0, :].detach().numpy().argmax() | |
| return disease_idx | |
| def process(self, image): | |
| """ | |
| Args: | |
| image: image array in RGB order. | |
| """ | |
| image_shape = image.shape[:2] | |
| disease_idx = self.glaucoma_pred(image) | |
| cam = self.cam.get_cam(image, disease_idx) | |
| cam = cv2.resize(cam, image_shape[::-1]) | |
| return disease_idx, cam | |