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| import torch | |
| from PIL import Image | |
| from transformers import BlipProcessor | |
| from caption_anything.utils.utils import load_image | |
| from .modeling_blip import BlipForConditionalGeneration | |
| import numpy as np | |
| from typing import Union | |
| from .base_captioner import BaseCaptioner | |
| import torchvision.transforms.functional as F | |
| class BLIPCaptioner(BaseCaptioner): | |
| def __init__(self, device, enable_filter=False): | |
| super().__init__(device, enable_filter) | |
| self.device = device | |
| self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32 | |
| self.processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large") | |
| self.model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large", | |
| torch_dtype=self.torch_dtype).to(self.device) | |
| def inference(self, image: Union[np.ndarray, Image.Image, str], filter=False, args={}): | |
| image = load_image(image, return_type="pil") | |
| inputs = self.processor(image, return_tensors="pt").to(self.device, self.torch_dtype) | |
| out = self.model.generate(**inputs, max_new_tokens=50) | |
| captions = self.processor.decode(out[0], skip_special_tokens=True).strip() | |
| result = {} | |
| if self.enable_filter and filter: | |
| clip_score = self.filter_caption(image, captions) | |
| result['clip_score'] = clip_score | |
| result.update({'caption':captions}) | |
| print(f"\nProcessed ImageCaptioning by BLIPCaptioner, Output Text: {captions}") | |
| return {'caption': captions} | |
| def inference_with_reduced_tokens(self, image: Union[np.ndarray, Image.Image, str], seg_mask, crop_mode="w_bg", | |
| filter=False, disable_regular_box=False): | |
| result = {} | |
| crop_save_path = self.generate_seg_cropped_image(image=image, seg_mask=seg_mask, crop_mode=crop_mode, | |
| disable_regular_box=disable_regular_box) | |
| image = load_image(image, return_type="pil") | |
| inputs = self.processor(image, return_tensors="pt") | |
| pixel_values = inputs.pixel_values.to(self.device, self.torch_dtype) | |
| _, _, H, W = pixel_values.shape | |
| seg_mask = Image.fromarray(seg_mask.astype(float)) | |
| seg_mask = seg_mask.resize((H, W)) | |
| seg_mask = F.pil_to_tensor(seg_mask) > 0.5 | |
| seg_mask = seg_mask.float() | |
| pixel_masks = seg_mask.unsqueeze(0).to(self.device) | |
| out = self.model.generate(pixel_values=pixel_values, pixel_masks=pixel_masks, max_new_tokens=50) | |
| captions = self.processor.decode(out[0], skip_special_tokens=True).strip() | |
| if self.enable_filter and filter: | |
| clip_score = self.filter_caption(image, captions) | |
| result['clip_score'] = clip_score | |
| result.update({'caption':captions, 'crop_save_path':crop_save_path}) | |
| print(f"\nProcessed ImageCaptioning by BLIPCaptioner, Output Text: {captions}") | |
| return result | |
| if __name__ == '__main__': | |
| model = BLIPCaptioner(device='cuda:0') | |
| # image_path = 'test_images/img2.jpg' | |
| image_path = 'image/SAM/img10.jpg' | |
| seg_mask = np.zeros((15, 15)) | |
| seg_mask[5:10, 5:10] = 1 | |
| seg_mask = 'test_images/img10.jpg.raw_mask.png' | |
| image_path = 'test_images/img2.jpg' | |
| seg_mask = 'test_images/img2.jpg.raw_mask.png' | |
| print(f'process image {image_path}') | |
| print(model.inference_with_reduced_tokens(image_path, seg_mask)) | |