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| from PIL import Image | |
| import requests | |
| from transformers import Blip2Processor, Blip2ForConditionalGeneration, BlipProcessor, BlipForConditionalGeneration | |
| import torch | |
| from utils.util import resize_long_edge | |
| class ImageCaptioning: | |
| def __init__(self, device): | |
| self.device = device | |
| self.processor, self.model = self.initialize_model() | |
| def initialize_model(self): | |
| if self.device == 'cpu': | |
| self.data_type = torch.float32 | |
| else: | |
| self.data_type = torch.float16 | |
| # uncomment for load stronger captioner | |
| # processor = Blip2Processor.from_pretrained("pretrained_models/blip2-opt-2.7b") | |
| # model = Blip2ForConditionalGeneration.from_pretrained( | |
| # "pretrained_models/blip2-opt-2.7b", torch_dtype=self.data_type | |
| # ) | |
| processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base") | |
| model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base") | |
| model.to(self.device) | |
| return processor, model | |
| def image_caption(self, image_src): | |
| image = Image.open(image_src) | |
| image = resize_long_edge(image) | |
| inputs = self.processor(images=image, return_tensors="pt").to(self.device, self.data_type) | |
| generated_ids = self.model.generate(**inputs) | |
| generated_text = self.processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip() | |
| print('\033[1;35m' + '*' * 100 + '\033[0m') | |
| print('\nStep1, BLIP2 caption:') | |
| print(generated_text) | |
| print('\033[1;35m' + '*' * 100 + '\033[0m') | |
| return generated_text | |
| def image_caption_debug(self, image_src): | |
| return "A dish with salmon, broccoli, and something yellow." |