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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
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