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| import cv2 | |
| import torch | |
| import numpy as np | |
| from torch import nn | |
| from transformers import AutoImageProcessor, Swinv2ForImageClassification, SegformerForSemanticSegmentation | |
| from cam import ClassActivationMap | |
| from utils import add_mask, simple_vcdr | |
| class GlaucomaModel(object): | |
| def __init__(self, | |
| cls_model_path="pamixsun/swinv2_tiny_for_glaucoma_classification", | |
| seg_model_path='pamixsun/segformer_for_optic_disc_cup_segmentation', | |
| device=torch.device('cpu')): | |
| # where to load the model, gpu or cpu ? | |
| self.device = device | |
| # classification model for glaucoma | |
| self.cls_extractor = AutoImageProcessor.from_pretrained(cls_model_path) | |
| self.cls_model = Swinv2ForImageClassification.from_pretrained(cls_model_path).to(device).eval() | |
| # segmentation model for optic disc and cup | |
| self.seg_extractor = AutoImageProcessor.from_pretrained(seg_model_path) | |
| self.seg_model = SegformerForSemanticSegmentation.from_pretrained(seg_model_path).to(device).eval() | |
| # class activation map | |
| self.cam = ClassActivationMap(self.cls_model, self.cls_extractor) | |
| # classification id to label | |
| self.cls_id2label = self.cls_model.config.id2label | |
| # segmentation id to label | |
| self.seg_id2label = self.seg_model.config.id2label | |
| # number of classes for classification | |
| self.num_diseases = len(self.cls_id2label) | |
| # number of classes for segmentation | |
| self.seg_classes = len(self.seg_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 optic_disc_cup_pred(self, image): | |
| """ | |
| Args: | |
| image: image array in RGB order. | |
| """ | |
| inputs = self.seg_extractor(images=image.copy(), return_tensors="pt") | |
| with torch.no_grad(): | |
| inputs.to(self.device) | |
| outputs = self.seg_model(**inputs) | |
| logits = outputs.logits.cpu() | |
| upsampled_logits = nn.functional.interpolate( | |
| logits, | |
| size=image.shape[:2], | |
| mode="bilinear", | |
| align_corners=False, | |
| ) | |
| pred_disc_cup = upsampled_logits.argmax(dim=1)[0] | |
| return pred_disc_cup.numpy().astype(np.uint8) | |
| 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]) | |
| disc_cup = self.optic_disc_cup_pred(image) | |
| try: | |
| vcdr = simple_vcdr(disc_cup) | |
| except: | |
| vcdr = np.nan | |
| _, disc_cup_image = add_mask(image, disc_cup, [1, 2], [[0, 255, 0], [255, 0, 0]], 0.2) | |
| return disease_idx, disc_cup_image, cam, vcdr | |