import streamlit as st import time import easyocr import math from pathlib import Path from PIL import Image, ImageDraw import PIL import io import os import cv2 import numpy as np import shutil import base64 import logging @st.cache(show_spinner=False, allow_output_mutation=True, suppress_st_warning=True) def load_models(): #specify shortform of language you want to extract, # I am using Spanish(es) and English(en) here by list of language ids reader = easyocr.Reader(['en'],) return reader reader = load_models() def midpoint(x1, y1, x2, y2): x_mid = int((x1 + x2)/2) y_mid = int((y1 + y2)/2) return (x_mid, y_mid) def inpaint_text(img, text_coordinates): # read image # img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # generate (word, box) tuples mask = np.zeros(img.shape[:2], dtype="uint8") for box in text_coordinates: x0, y0 = box[0] x1, y1 = box[1] x2, y2 = box[2] x3, y3 = box[3] x_mid0, y_mid0 = midpoint(x1, y1, x2, y2) x_mid1, y_mi1 = midpoint(x0, y0, x3, y3) thickness = int(math.sqrt( (x2 - x1)**2 + (y2 - y1)**2 )) cv2.line(mask, (x_mid0, y_mid0), (x_mid1, y_mi1), 255, thickness) img = cv2.inpaint(img, mask, 7, cv2.INPAINT_NS) return(img) def file_selector(folder_path='.'): filenames = os.listdir(folder_path) selected_filename = st.selectbox('Select a file', filenames) return os.path.join(folder_path, selected_filename), selected_filename st.set_page_config( page_title="Inpaint Me", page_icon=":art:", layout="wide", initial_sidebar_state="expanded", menu_items={ 'Get Help': 'https://www.extremelycoolapp.com/help', 'Report a bug': "https://www.extremelycoolapp.com/bug", 'About': "# This is a header. This is an *extremely* cool app!" } ) st.markdown( """ """, unsafe_allow_html=True ) LOGO_IMAGE = "inpaint_me_logo.png" col1, col2= st.columns([2, 2]) with col1: # st.image('./aida_logo.png') st.markdown( f""" """, unsafe_allow_html=True ) with col2: # st.image('./aida_logo.png') st.markdown( f""" """, unsafe_allow_html=True ) st.header("") with st.expander("Project Description", expanded=False): st.write(""" Developed in Applied Intelligence and Data Analysis ([AI+DA](http://aida.etsisi.upm.es/)) group at Polytech University of Madrid (UPM). To rule out the possibility of text misleading image Deep Learning models (e.g., CNNs) it is useful to remove text from images. Hence, this tool uses [EasyOCR](https://github.com/JaidedAI/EasyOCR) and [OpenCV](https://pypi.org/project/opencv-python/) for detecting texts and inpainting them. Currently, only `JPG` files are supported. This tools has been tested on memes, feel free to try some examples or upload your own images. """) file_example_path = None if st.checkbox('Select a example'): folder_path = './Examples/' # if st.checkbox('Change directory'): # folder_path = st.text_input('Enter folder path', '.') file_example_path, example_file_name = file_selector(folder_path=folder_path) st.write('You selected `%s`' % file_example_path) uploaded_file = st.file_uploader(label="Upload image", type=["jpg", "jpeg"], accept_multiple_files=False, key=None, help=None, on_change=None, args=None, kwargs=None, ) col1, col2, col3 = st.columns([2, 0.5, 2]) if file_example_path and not uploaded_file: with col1: st.subheader("Original") # st.write(f"./Examples_inpainted/{example_file_name.strip(".jpg")}_inpainted.jpeg") img = Image.open( file_example_path ) st.image(img, caption=None, width=None, use_column_width=None, clamp=False, channels="RGB", output_format="auto") with col3: st.subheader("Inpainted") with st.spinner('Wait for it...'): time.sleep(1) example_file_name = example_file_name.strip(".jpg") inpaint_image = f"./Examples_inpainted/{example_file_name}_inpaint.jpeg" # img_array = np.array(Image.open( file_example_path )) # # detect text # bounds = reader.readtext(img_array, detail=1) #detail=1 # [(coordinates, detected text, confidence threshold)] # text_coordinates = [ bound[0] for bound in bounds] # # inpaint text coordinates # inpaint_image = inpaint_text(img_array, text_coordinates) st.image(inpaint_image, caption=None, width=None, use_column_width=None, clamp=False, channels="RGB", output_format="auto") if uploaded_file: with col1: st.subheader("Original") st.image(uploaded_file, caption=None, width=None, use_column_width=None, clamp=False, channels="RGB", output_format="auto") with col3: st.subheader("Inpainted") with st.spinner('Wait for it...'): # Transform loaded file to bytes bytes_data = uploaded_file.getvalue() # bytes to numpy array img_array = np.array(Image.open(io.BytesIO(bytes_data))) # detect text bounds = reader.readtext(img_array, detail=1) #detail=1 # [(coordinates, detected text, confidence threshold)] text_coordinates = [ bound[0] for bound in bounds] # inpaint text coordinates inpaint_image = inpaint_text(img_array, text_coordinates) st.image(inpaint_image, caption=None, width=None, use_column_width=None, clamp=False, channels="RGB", output_format="auto")