Add v1 app

app_inpaint.py

@@ -0,0 +1,204 @@
+import streamlit as st
+import spacy
+from streamlit_echarts import st_echarts
+from annotated_text import annotated_text
+import time
+import easyocr
+import math
+from pathlib import Path
+from PIL import Image, ImageDraw
+import PIL
+import io
+import os
+import random
+import matplotlib.pyplot as plt
+import cv2
+# from google.colab.patches import cv2_imshow
+import numpy as np
+from tqdm.auto import tqdm
+import shutil
+import base64
+import logging
+logging.basicConfig(format='%(asctime)s - %(message)s',
+                    datefmt='%Y-%m-%d %H:%M:%S',
+                    level=logging.INFO,
+                    )
+
+
+@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)
+
+
+
+
+
+
+
+
+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(
+    """
+    <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.
+     """)          
+
+        
+        
+filename_example = None        
+if st.checkbox('Select a example'):
+    folder_path = './Examples/'
+    # if st.checkbox('Change directory'):
+    #     folder_path = st.text_input('Enter folder path', '.')
+    filename_example = file_selector(folder_path=folder_path)
+    st.write('You selected `%s`' % filename_example)        
+        
+uploaded_file = st.file_uploader(label="Upload image", 
+                 type="jpg", 
+                 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 filename_example:
+    with col1:
+        st.header("Original")
+        img = Image.open( filename_example )
+        st.image(img, caption=None, width=None, use_column_width=None, clamp=False, channels="RGB", output_format="auto")
+
+    with col3:
+        st.header("Inpainted")
+        with st.spinner('Wait for it...'):
+            img_array =  np.array(Image.open( filename_example ))
+            # 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.header("Original")
+            st.image(uploaded_file, caption=None, width=None, use_column_width=None, clamp=False, channels="RGB", output_format="auto")
+
+        with col3:
+            st.header("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")