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| import streamlit as st | |
| import tensorflow as tf | |
| import numpy as np | |
| from PIL import Image | |
| import cv2 | |
| labels = ["Column","Header","Table"] | |
| threshold = 0.75 | |
| model = tf.saved_model.load("best_saved_model") #Loading the saved model | |
| def process_img(img): | |
| img = img.resize((640,640)) # resize the image as reqired for the model input | |
| img = np.array(img) # Convert Pil Object into numpy array | |
| copy_img = img.copy() # Save a copy for backup | |
| img = img/255 # Normalizing the pixel value | |
| img = img.astype("float32") # Convert the format double to format float | |
| img = np.expand_dims(img,axis=0) # exapanding dimension to add batch | |
| return img,copy_img | |
| st.title("Table Extract") | |
| file_name = st.file_uploader("Upload a report image") | |
| if file_name is not None: | |
| col1, col2 = st.columns(2) | |
| image = Image.open(file_name) | |
| input_image, copy_img= process_img(image) | |
| col1.header("Input Image") | |
| col1.image(input_image, use_column_width=True) | |
| bbox,confidance,classes,nc = model(input_image) | |
| bbox,confidance , classes , nc = bbox[0].numpy(),confidance[0].numpy(),classes[0].numpy(),nc[0].numpy() | |
| st.subheader("Detected Result") | |
| table_count =0 | |
| header_count = 0 | |
| column_count = 0 | |
| for i in range(nc): | |
| if confidance[i] >= threshold: | |
| x1,y1,x2,y2 = bbox[i]*640 | |
| class_name = labels[int(classes[i])] | |
| st.text(class_name+" : "+str(int(confidance[i]*100))+"%") | |
| if class_name =="Header": | |
| header_count+=1 | |
| color = (0,0,255) #Blue color | |
| cv2.rectangle(copy_img, (int(x1), int(y1)), (int(x2), int(y2)),color, 2) | |
| if class_name =="Column": | |
| column_count+=1 | |
| color = (0,255,0) #Green color | |
| cv2.rectangle(copy_img, (int(x1), int(y1)), (int(x2), int(y2)),color, 2) | |
| if class_name =="Table": | |
| table_count+=1 | |
| color = (255,0,0) #Red color | |
| cv2.rectangle(copy_img, (int(x1), int(y1)), (int(x2), int(y2)),color, 2) | |
| st.text("No of Table Detected : "+str(table_count)) | |
| st.text("No of Header Detected : "+str(header_count)) | |
| st.text("No of Column Detected : "+str(column_count)) | |
| col2.header("Output Result") | |
| col2.image(copy_img, use_column_width=True) | |