python - 您好，有谁知道如何使用 LIME 突出显示图像上的预测区域并将其托管在 streamlit 上？

Question

import cv2                                                                                                                                                                
from PIL import Image, ImageOps                                                                                                                                           
import numpy as np                                                                                                                                                        
from tensorflow.keras.models import load_model                                                                                                                            
import streamlit as st                                                                                                                                                    
from lime import lime_image                                                                                                                                               
import matplotlib.pyplot as plt                                                                                                                                           
import streamlit.components.v1 as components                                                                                                                              
from tensorflow.keras.preprocessing import image as IM                                                                                                                    
from skimage.segmentation import mark_boundaries                                                                                                                          
                                                                                                                                                                          
model_path = "C:/Users/akhil/Desktop/MalariaDetectionWebapp/malaria_calssifier_model.h5"                                                                                  
model = load_model(model_path)                                                                                                                                            
                                                                                                                                                                          
                                                                                                                                                                          
def import_and_predict(image_data, model):                                                                                                                                
    size = (50, 50)                                                                                                                                                       
    image = ImageOps.fit(image_data, size, Image.ANTIALIAS)                                                                                                               
    image = np.asarray(image)                                                                                                                                             
    img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)                                                                                                                          
    img_resize = (                                                                                                                                                        
        cv2.resize(img, dsize=(50, 50), interpolation=cv2.INTER_CUBIC)                                                                                                    
    ) / 255.0                                                                                                                                                             
                                                                                                                                                                          
    img_reshape = img_resize[np.newaxis, ...]                                                                                                                             
                                                                                                                                                                          
    prediction = model.predict(img_reshape)                                                                                                                               
                                                                                                                                                                          
    return prediction  

def explain_prediction(image_data, model):                                                                                                                                
    image = IM.load_img(image_data, target_size=(50, 50))                                                                                                                 
    image = IM.img_to_array(image)                                                                                                                                        
    img = np.expand_dims(image, axis=0)                                                                                                                                   
                                                                                                                                                                          
    explainer = lime_image.LimeImageExplainer()                                                                                                                           
    explanation = explainer.explain_instance(                                                                                                                             
        img[0].astype("double"),                                                                                                                                          
        model.predict,                                                                                                                                                    
        top_labels=2,                                                                                                                                                     
        hide_color=0,                                                                                                                                                     
        num_samples=1000,                                                                                                                                                 
    )                                                                                                                                                                     
    temp_1, mask_1 = explanation.get_image_and_mask(                                                                                                                      
        explanation.top_labels[0], positive_only=True, num_features=5, hide_rest=True                                                                                     
    )                                                                                                                                                                     
    temp_2, mask_2 = explanation.get_image_and_mask(                                                                                                                      
        explanation.top_labels[0], positive_only=False, num_features=10, hide_rest=False                                                                                  
    )                                                                                                                                                                     
                                                                                                                                                                          
    return temp_1, mask_1, temp_2, mask_2                                                                                                                                 
                                                                                                                                                                          
                                                                                                                                                                          
st.write(                                                                                                                                                                 
    """                                                                                                                                                                   
    # Malaria Detection Webapp                                                                                                                                            
    """                                                                                                                                                                   
)                                                                                                                                                                         
                                                                                                                                                                          
st.write(                                                                                                                                                                 
    "This is a simple Malaria classification web app to predict Parasitisized or Unifected Malaria cell"                                                                  
)                                                                                                                                                                         
                                                                                                                                                                          
file = st.file_uploader("Please upload an image file", type=["jpg", "png"])                                                                                               
#                                                                                                                                                                         

if file is None:                                                                                                                                                          
    st.text("You haven't uploaded an image file")                                                                                                                         
else:                                                                                                                                                                     
    image = Image.open(file)                                                                                                                                              
    st.image(image, use_column_width=True)                                                                                                                                
    prediction = import_and_predict(image, model)                                                                                                                         
    # temp_1, mask_1, temp_1, mask_1 = explain_prediction(image, model)                                                                                                   
    # st.image(mark_boundaries(temp_1, mask_1))                                                                                                                           
    if np.argmax(prediction) == 0:                                                                                                                                        
        st.write("The Image is Infected !")                                                                                                                               
    else:                                                                                                                                                                 
        st.write("The Image is Not Infected !")                                                                                                                           


        

这是我的代码（粗略）。我无法创建假设如果我上传图像的部分，我会将该图像视为输出。

不完全像这张图片，但如果我上传一张合适类别的图片，预测应该是下面这张图片的输出。

请如果有人有任何线索，请告诉我！