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| 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.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.cache_data(show_spinner=False, 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.markdown( | |
| """ | |
| <style> | |
| .logo-img { | |
| margin-top: auto; | |
| margin-left: 30%; | |
| width: 50%; | |
| } | |
| .logo-img-2 { | |
| margin-top: auto; | |
| margin-left: 20%; | |
| width: 35%; | |
| } | |
| </style> | |
| """, | |
| 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""" | |
| <img class="logo-img" src="data:image/png;base64,{base64.b64encode(open(LOGO_IMAGE, "rb").read()).decode()}"> | |
| """, | |
| unsafe_allow_html=True | |
| ) | |
| with col2: | |
| # st.image('./aida_logo.png') | |
| st.markdown( | |
| f""" | |
| <img class="logo-img-2" src="data:image/png;base64,{base64.b64encode(open("aida_logo.png", "rb").read()).decode()}"> | |
| """, | |
| 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") |