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| import streamlit as st | |
| # from img_classification import teachable_machine_classification | |
| from PIL import Image, ImageOps | |
| import streamlit_authenticator as stauth | |
| import yaml | |
| from yaml.loader import SafeLoader | |
| import os.path as osp | |
| import glob | |
| # import cv2 | |
| import numpy as np | |
| from transformers import BlipProcessor, BlipForConditionalGeneration | |
| from PIL import Image | |
| import requests | |
| processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base") | |
| model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base") | |
| # authentification | |
| with open('./config.yaml') as file: | |
| config = yaml.load(file, Loader=SafeLoader) | |
| authenticator = stauth.Authenticate( | |
| config['credentials'], | |
| config['cookie']['name'], | |
| config['cookie']['key'], | |
| config['cookie']['expiry_days'], | |
| config['preauthorized'] | |
| ) | |
| name, authentication_status, username = authenticator.login('Login', 'main') | |
| if authentication_status: | |
| authenticator.logout('Logout', 'main') | |
| page = st.sidebar.selectbox("探索或预测", ("image_caption", | |
| "image_to_text" | |
| )) | |
| if page == "image_caption": | |
| st.title("Image caption") | |
| st.write("Model[link](https://huggingface.co/Salesforce/blip-image-captioning-base)") | |
| uploaded_file = st.file_uploader("Select..", type=["jpg","png","jpeg"]) | |
| if uploaded_file is not None: | |
| raw_image = Image.open(uploaded_file).convert('RGB') | |
| st.image(raw_image, caption='image', use_column_width=True) | |
| st.write("") | |
| # unconditional image captioning | |
| inputs = processor(raw_image, return_tensors="pt") | |
| out = model.generate(**inputs) | |
| st.text(processor.decode(out[0], skip_special_tokens=True)) | |
| # st.text(generated_text) | |
| urll = st.text_input("image url", value="") | |
| if st.button("send"): | |
| raw_image = Image.open(requests.get(urll, stream=True).raw).convert('RGB') | |
| inputs = processor(raw_image, return_tensors="pt") | |
| out = model.generate(**inputs) | |
| st.text(processor.decode(out[0], skip_special_tokens=True)) | |
| elif page == "image_to_text": | |
| pass | |
| # page = st.sidebar.selectbox("探索或预测", ("将图像放大为高清", | |
| # "肺炎x_ray图像分类", | |
| # "生成动漫人脸图像" | |
| # )) | |
| # if page == "肺炎x_ray图像分类": | |
| # st.title("使用谷歌的可教机器进行图像分类") | |
| # st.write("Google Teachable machine"" [link](https://teachablemachine.withgoogle.com/train/image)") | |
| # st.header("肺炎x_ray") | |
| # st.text("上传肺x_ray图片") | |
| # uploaded_file = st.file_uploader("选择..", type=["jpg","png","jpeg"]) | |
| # if uploaded_file is not None: | |
| # image = Image.open(uploaded_file).convert('RGB') | |
| # st.image(image, caption='上传了图片。', use_column_width=True) | |
| # st.write("") | |
| # st.write("分类...") | |
| # label = teachable_machine_classification(image, 'pneumonia__x_ray_image_classify_normal_vs_penumonia.h5') | |
| # if label == 0: | |
| # st.write("正常") | |
| # else: | |
| # st.write("肺炎") | |
| # st.text("类:正常,肺炎") | |
| # # 0 normal | |
| # # 1 pneumonia | |
| # elif page =="将图像放大为高清": | |
| # st.title("使用 ESGAN 放大图像") | |
| # st.write("ESGAN 安装"" [link](https://github.com/xinntao/ESRGAN)") | |
| # st.write("ESGAN 模型下载"" [link](https://drive.google.com/drive/u/0/folders/17VYV_SoZZesU6mbxz2dMAIccSSlqLecY)") | |
| # st.header("将图像放大为高清") | |
| # st.text("上传图片") | |
| # model_path = './RRDB_ESRGAN_x4.pth' # models/RRDB_ESRGAN_x4.pth OR models/RRDB_PSNR_x4.pth | |
| # # device = torch.device('cuda') # if you want to run on CPU, change 'cuda' -> cpu | |
| # device = torch.device('cpu') | |
| # # test_img_folder = 'LR/*' | |
| # uploaded_file = st.file_uploader("选择..", type=["jpg","png","jpeg"]) | |
| # if uploaded_file is not None: | |
| # img = Image.open(uploaded_file).convert('RGB') | |
| # st.image(img, caption='上传了图片。', use_column_width=True) | |
| # st.write("") | |
| # st.write("") | |
| # st.write("放大图像，大约等待时间：1 分钟,请稍候...") | |
| # rrdb_esrgan_model = arch.RRDBNet(3, 3, 64, 23, gc=32) | |
| # rrdb_esrgan_model.load_state_dict(torch.load(model_path), strict=True) | |
| # rrdb_esrgan_model.eval() | |
| # rrdb_esrgan_model = rrdb_esrgan_model.to(device) | |
| # idx = 0 | |
| # # img = np.array(img.getdata()).reshape(img.size[0], img.size[1], 3) * 1.0 / 255 | |
| # # uploaded_file = st.file_uploader("Upload Image") | |
| # # image = Image.open(uploaded_file) | |
| # # st.image(image, caption='Input', use_column_width=True) | |
| # img = np.array(img)* 1.0 / 255 | |
| # # cv2.imwrite('out.jpg', cv2.cvtColor(img_array, cv2.COLOR_RGB2BGR)) | |
| # img = torch.from_numpy(np.transpose(img[:, :, [2, 1, 0]], (2, 0, 1))).float() | |
| # img_LR = img.unsqueeze(0) | |
| # img_LR = img_LR.to(device) | |
| # with torch.no_grad(): | |
| # output = rrdb_esrgan_model(img_LR).data.squeeze().float().cpu().clamp_(0, 1).numpy() | |
| # output = np.transpose(output[[2, 1, 0], :, :], (1, 2, 0)) | |
| # output = torch.tensor((output * 255.0).round()) | |
| # fig1 = plt.figure(figsize=(14,8)) | |
| # fig1.suptitle("Upscaled image") | |
| # plt.imshow(np.transpose(vutils.make_grid(output, padding=2, normalize=True), (0,1, 2))) | |
| # st.pyplot(fig1) | |
| # elif page =="生成动漫人脸图像": | |
| # # Number of GPUs available. Use 0 for CPU mode. | |
| # ngpu = 1 | |
| # # device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| # device = torch.device("cpu") | |
| # # anime_face_gan_gen_model = AnimeFaceGenerator(ngpu).to(device) | |
| # anime_face_gan_gen_model = torch.load("./anime_face_gan_generator64_64.pt",map_location=torch.device('cpu') ) | |
| # pp1=st.slider("p1",-5.01,5.00) | |
| # pp2=st.slider("p2",-5.01,5.00) | |
| # pp3=st.slider("p3",-5.01,5.00) | |
| # pp4=st.slider("p4",-5.01,5.00) | |
| # pp5=st.slider("p5",-5.01,5.00) | |
| # pp6=st.slider("p6",-5.01,5.00) | |
| # pp7=st.slider("p7",-5.01,5.00) | |
| # pp8=st.slider("p8",-5.01,5.00) | |
| # anime_face_gan_gen_model.eval() | |
| # bla = [pp1,pp2,pp3,pp4,pp5,pp6,pp7,pp8] | |
| # randomlist = [] | |
| # for i in range(0,92): | |
| # n = random.random() | |
| # randomlist.append(n) | |
| # res = bla + randomlist | |
| # # print(res) | |
| # fixed_noise = torch.tensor(res).reshape(1,100,1,1) | |
| # # fixed_noise = torch.randn(1, nz, 1, 1, device=device) | |
| # print(fixed_noise) | |
| # fake = anime_face_gan_gen_model(fixed_noise) | |
| # fig1 = plt.figure(figsize=(14,8)) | |
| # fig1.suptitle("随机生成的动漫脸") | |
| # plt.imshow(np.transpose(vutils.make_grid(fake, padding=2, normalize=True), (1, 2, 0))) | |
| # st.pyplot(fig1) | |
| elif authentication_status == False: | |
| st.error("用户名/密码不正确") | |
| elif authentication_status == None: | |
| st.warning('请输入您的用户名和密码') | |