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Add v1 app
Browse files- Examples/meme_1.jpg +0 -0
- Examples/meme_2.jpg +0 -0
- Examples/meme_3.jpg +0 -0
- aida_logo.png +0 -0
- app_inpaint.py +204 -0
- inpaint_me_logo.png +0 -0
Examples/meme_1.jpg
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Examples/meme_2.jpg
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Examples/meme_3.jpg
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aida_logo.png
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app_inpaint.py
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1 |
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import streamlit as st
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import spacy
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from streamlit_echarts import st_echarts
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from annotated_text import annotated_text
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import time
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import easyocr
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import math
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from pathlib import Path
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from PIL import Image, ImageDraw
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import PIL
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import io
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import os
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import random
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import matplotlib.pyplot as plt
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import cv2
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# from google.colab.patches import cv2_imshow
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import numpy as np
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from tqdm.auto import tqdm
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import shutil
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import base64
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import logging
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logging.basicConfig(format='%(asctime)s - %(message)s',
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datefmt='%Y-%m-%d %H:%M:%S',
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level=logging.INFO,
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)
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@st.cache(show_spinner=False, allow_output_mutation=True, suppress_st_warning=True)
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def load_models():
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#specify shortform of language you want to extract,
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# I am using Spanish(es) and English(en) here by list of language ids
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reader = easyocr.Reader(['en'],)
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return reader
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reader = load_models()
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def midpoint(x1, y1, x2, y2):
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x_mid = int((x1 + x2)/2)
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y_mid = int((y1 + y2)/2)
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return (x_mid, y_mid)
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def inpaint_text(img, text_coordinates):
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# read image
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# img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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# generate (word, box) tuples
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mask = np.zeros(img.shape[:2], dtype="uint8")
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for box in text_coordinates:
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x0, y0 = box[0]
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x1, y1 = box[1]
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x2, y2 = box[2]
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x3, y3 = box[3]
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x_mid0, y_mid0 = midpoint(x1, y1, x2, y2)
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x_mid1, y_mi1 = midpoint(x0, y0, x3, y3)
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thickness = int(math.sqrt( (x2 - x1)**2 + (y2 - y1)**2 ))
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cv2.line(mask, (x_mid0, y_mid0), (x_mid1, y_mi1), 255,
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thickness)
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img = cv2.inpaint(img, mask, 7, cv2.INPAINT_NS)
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return(img)
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def file_selector(folder_path='.'):
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filenames = os.listdir(folder_path)
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selected_filename = st.selectbox('Select a file', filenames)
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return os.path.join(folder_path, selected_filename)
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st.set_page_config(
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page_title="Inpaint Me",
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page_icon=":art:",
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layout="wide",
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initial_sidebar_state="expanded",
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menu_items={
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'Get Help': 'https://www.extremelycoolapp.com/help',
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'Report a bug': "https://www.extremelycoolapp.com/bug",
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'About': "# This is a header. This is an *extremely* cool app!"
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}
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)
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st.markdown(
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"""
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<style>
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.logo-img {
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margin-top: auto;
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margin-left: 30%;
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width: 50%;
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}
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.logo-img-2 {
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margin-top: auto;
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margin-left: 20%;
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width: 35%;
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}
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</style>
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""",
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unsafe_allow_html=True
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)
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LOGO_IMAGE = "inpaint_me_logo.png"
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col1, col2= st.columns([2, 2])
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with col1:
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# st.image('./aida_logo.png')
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st.markdown(
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f"""
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<img class="logo-img" src="data:image/png;base64,{base64.b64encode(open(LOGO_IMAGE, "rb").read()).decode()}">
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""",
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unsafe_allow_html=True
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)
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with col2:
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# st.image('./aida_logo.png')
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st.markdown(
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f"""
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<img class="logo-img-2" src="data:image/png;base64,{base64.b64encode(open("aida_logo.png", "rb").read()).decode()}">
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""",
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unsafe_allow_html=True
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)
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st.header("")
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with st.expander("Project Description", expanded=False):
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st.write("""
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Developed in Applied Intelligence and Data Analysis ([AI+DA](http://aida.etsisi.upm.es/)) group at Polytech University of Madrid (UPM).
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To rule out the possibility of text misleading image Deep Learning models (e.g., CNNs) it is useful to remove text from images. Hence,
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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.
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""")
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filename_example = None
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if st.checkbox('Select a example'):
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folder_path = './Examples/'
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# if st.checkbox('Change directory'):
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# folder_path = st.text_input('Enter folder path', '.')
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filename_example = file_selector(folder_path=folder_path)
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st.write('You selected `%s`' % filename_example)
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uploaded_file = st.file_uploader(label="Upload image",
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type="jpg",
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accept_multiple_files=False,
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key=None,
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help=None,
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on_change=None,
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args=None,
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kwargs=None,
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)
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col1, col2, col3 = st.columns([2, 0.5, 2])
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if filename_example:
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with col1:
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st.header("Original")
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img = Image.open( filename_example )
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st.image(img, caption=None, width=None, use_column_width=None, clamp=False, channels="RGB", output_format="auto")
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with col3:
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st.header("Inpainted")
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with st.spinner('Wait for it...'):
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img_array = np.array(Image.open( filename_example ))
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# detect text
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bounds = reader.readtext(img_array, detail=1) #detail=1 # [(coordinates, detected text, confidence threshold)]
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text_coordinates = [ bound[0] for bound in bounds]
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# inpaint text coordinates
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inpaint_image = inpaint_text(img_array, text_coordinates)
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st.image(inpaint_image, caption=None, width=None, use_column_width=None, clamp=False, channels="RGB", output_format="auto")
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if uploaded_file:
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with col1:
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st.header("Original")
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st.image(uploaded_file, caption=None, width=None, use_column_width=None, clamp=False, channels="RGB", output_format="auto")
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with col3:
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st.header("Inpainted")
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with st.spinner('Wait for it...'):
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# Transform loaded file to bytes
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bytes_data = uploaded_file.getvalue()
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# bytes to numpy array
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img_array = np.array(Image.open(io.BytesIO(bytes_data)))
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# detect text
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bounds = reader.readtext(img_array, detail=1) #detail=1 # [(coordinates, detected text, confidence threshold)]
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text_coordinates = [ bound[0] for bound in bounds]
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# inpaint text coordinates
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inpaint_image = inpaint_text(img_array, text_coordinates)
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st.image(inpaint_image, caption=None, width=None, use_column_width=None, clamp=False, channels="RGB", output_format="auto")
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inpaint_me_logo.png
ADDED
![]() |