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# -*- coding: utf-8 -*- | |
""" | |
Created on Tue Dec 17 20:35:41 2023 | |
@author: luofeng | |
""" | |
from transformers import ViTImageProcessor, ViTForImageClassification | |
from PIL import Image | |
#import requests | |
#url = 'http://images.cocodataset.org/val2017/000000039769.jpg' | |
#image = Image.open(requests.get(url, stream=True).raw) | |
processor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224') | |
model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224') | |
#inputs = processor(images=image, return_tensors="pt") | |
#outputs = model(**inputs) | |
#logits = outputs.logits | |
# model predicts one of the 1000 ImageNet classes | |
#predicted_class_idx = logits.argmax(-1).item() | |
#print("Predicted class:", model.config.id2label[predicted_class_idx]) | |
def imageClassification(image_path): | |
image = Image.open(image_path) | |
inputs = processor(images=image, return_tensors="pt") | |
outputs = model(**inputs) | |
logits = outputs.logits | |
# model predicts one of the 1000 ImageNet classes | |
predicted_class_idx = logits.argmax(-1).item() | |
classification_result = model.config.id2label[predicted_class_idx] | |
print("Predicted class:", classification_result) | |
return classification_result | |