Spaces:
Build error
Build error
| from PIL import Image | |
| import requests | |
| import torch | |
| from torchvision import transforms | |
| import os | |
| from torchvision.transforms.functional import InterpolationMode | |
| import matplotlib.pyplot as plt | |
| import matplotlib.image as mpimg | |
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| from models.blip import blip_decoder | |
| image_size = 384 | |
| transform = transforms.Compose([ | |
| transforms.Resize((image_size,image_size),interpolation=InterpolationMode.BICUBIC), | |
| transforms.ToTensor(), | |
| transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)) | |
| ]) | |
| model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_large_caption.pth' | |
| model = blip_decoder(pretrained=model_url, image_size=384, vit='large') | |
| model.eval() | |
| model = model.to(device) | |
| from models.blip_vqa import blip_vqa | |
| image_size_vq = 480 | |
| transform_vq = transforms.Compose([ | |
| transforms.Resize((image_size_vq,image_size_vq),interpolation=InterpolationMode.BICUBIC), | |
| transforms.ToTensor(), | |
| transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)) | |
| ]) | |
| model_url_vq = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model*_vqa.pth' | |
| model_vq = blip_vqa(pretrained=model_url_vq, image_size=480, vit='base') | |
| model_vq.eval() | |
| model_vq = model_vq.to(device) | |
| def inference(raw_image, model_n, question="", strategy=""): | |
| if model_n == 'Image Captioning': | |
| image = transform(raw_image).unsqueeze(0).to(device) | |
| with torch.no_grad(): | |
| if strategy == "Beam search": | |
| caption = model.generate(image, sample=False, num_beams=3, max_length=20, min_length=5) | |
| else: | |
| caption = model.generate(image, sample=True, top_p=0.9, max_length=20, min_length=5) | |
| return 'caption: '+caption[0] | |
| else: | |
| image_vq = transform_vq(raw_image).unsqueeze(0).to(device) | |
| with torch.no_grad(): | |
| answer = model_vq(image_vq, question, train=False, inference='generate') | |
| return 'answer: '+answer[0] | |
| #get caption for a single iamge | |
| def get_caption(image_path): | |
| img = Image.open(image_path) | |
| return inference(img, "Image Captioning")[9:] | |
| def display(image_path): | |
| img = mpimg.imread(image_path) | |
| img = Image.open(image_path) | |
| plt.imshow(img) | |
| print("Caption: " + get_caption(image_path)) | |
| #returns a dictionary with key -> img_path and value -> caption | |
| def get_captions(img_directory, print_status=True): | |
| #key is img path, value is the caption | |
| captions = {} | |
| length = 0 | |
| for file in os.listdir(img_directory): | |
| length+=1 | |
| count = 0 | |
| for file in os.listdir(img_directory): | |
| f = os.path.join(img_directory, file) | |
| captions[f] = inference(Image.open(f), "Image Captioning") | |
| if print_status: | |
| print("Images complete:", str(count) + "/" + str(length)) | |
| print("Caption:", captions[f]) | |
| return captions | |
| #writes dictionary to file, key and value seperated by ':' | |
| def write_to_file(filename, caption_dict): | |
| with open(filename, "w") as file: | |
| for i in caption_dict: | |
| file.write(i + ":" + caption_dict[i]) | |
| file.close() | |