Delete glaucoma.py

glaucoma.py

@@ -1,92 +0,0 @@
-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
-