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')