Share this:

Building Image Size Reducer Tool In Python

Image Size Reducer is much needed tool these days because most site wants us to upload various documents in the form of image but with the size limit. Modern days camera gives us image with size in megabytes but the server or site we need to upload the site has size limitation. There are sites like https://www.reduceimages.com to reduce size online but many have limitations like some have usage limits per day, some have too many ads and some have unfriendly usages. With the mindset to create own image size reducer which could have the features as I want, I am creating one using OpenCV and Streamlit. The version of streamlit is 1.12.0 on this blog. Below is the demo of an working app we are going to build.

Image Size Reducer

Project Structure

Since the project is currently in the beginning, its not a bad idea to follow the following project structure.

alt

Config File

Config file will be used to prepare the configuration of our app. In the future, we might want to change this because changing a config from web app is even easier for an admin. For now, config file will look like below:

{
    "version": "0.0.1",
    "logging": {
        "level": "DEBUG",
        "console_log": true
    },
    "allowed_modes":{
        "Image Size Reducer":{
            "extensions":"png,jpeg,jpg"
        }
    },
    "execution_mode":
    {
        "mode":"dev"
    }

}

In above config file, we have specified version, logging and allowed_modes. We have currently set up Image Size Reducer as an allowed mode but could add more later.

variables.py File

This file is used to create global variables by reading config file above so that we dont have to worry about global variables later. Here, we will simply read the file and assign values into variables.

"""Module to define global variables.
"""
import json
import inspect
import os

class Variables:
    """A class to read config file and hold variables.
    """

    # Reading paths
    curr_dir = os.path.dirname(os.path.abspath(
        inspect.getfile(inspect.currentframe())))
    root_dir = os.path.dirname(curr_dir)
    conf_dir = os.path.join(root_dir, "config")

    # Reading Config file path
    config_file_path = os.path.join(conf_dir, "config.json")
    path = (os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

    # Reading and loading config file
    with open(config_file_path, "r") as file:
        config = json.load(file)

    # logging variables
    logging = config["logging"]
    level = logging["level"]
    console_log = logging["console_log"]
    version = config["version"]

    # allowed modes
    allowed_modes_dict = config["allowed_modes"]
    allowed_modes = list(allowed_modes_dict.keys())

    def __getitem__(self, key):
        """Get the value from key."""
        return self.config[key]

try:
    var = Variables()
except Exception as err:
    raise err

Application File: app.py

This file will hold all the codes that will handle the UI and flow of the image reducing. Lets first import necessary modules.

  • Import streamlit, opencv, numpy, os, var, Image from PIL to read byte image data.
  • Set streamlit app's layout wide.
  • Prepare sidebar from where we will select modes.
  • Prepare first mode, Image Size Reducer.
  • If selected, show its selected.
  • Select an image with predefined extensions and set it in uploaded file.
  • If its not null, then read it and show the array image.
  • If no mode is selected, show it too.
import streamlit as st
import cv2
import numpy as np
import os
from utils.variables import var
from PIL import Image
st.set_page_config(layout="wide")

sidebar = st.sidebar
sidebar.markdown("## Modes ")

size_reducer = sidebar.checkbox("Image Size Reducer")

if size_reducer:
    st.markdown("## Selected Size Reducer")
    exts = var.allowed_modes_dict["Image Size Reducer"]["extensions"].split(",")
    uploaded_file = st.file_uploader(f"Select file: {exts}", type=exts)
    if uploaded_file is not None:
        img = Image.open(uploaded_file)
        img = np.array(img)
        st.image(img, use_column_width=True)

else:
    st.markdown("## No Mode Selected!!")

Developing Image Size Reducer

Store Temp Image and Remove Old Image

Lets use opencv to reduce image size first. Lets import time as well because we want to save images with the timestamp in file name.

if size_reducer:
    st.markdown("## Selected Size Reducer")
    exts = var.allowed_modes_dict["Image Size Reducer"]["extensions"].split(",")
    uploaded_file = st.file_uploader(f"Select file: {exts}", type=exts)
    if uploaded_file is not None:
        fname = f"data/{int(time.time())}."+uploaded_file.name.split(".")[-1]

        img = Image.open(uploaded_file).convert("RGB")        
        with open(fname, "wb") as f:
            f.write(uploaded_file.getbuffer())
            for f in os.listdir("data"):
                ts = float(f.split(".")[0])
                try:
                    if time.time()-ts > 120:
                        os.remove("data/"+f)
                except:
                    pass

In above code,

  • We prepared a file name to store it in our data folder.
  • We read the image and converted it into RGB because by default, we will have alpha in Image read by PIL. And we are only working with colorspace and image size so lets ignore it.
  • Next, create a image file using the byte data in uploaded_file by writing in a file pointer created with wb in fname.
  • Also, the files will get created everytime widget's states are updated so there will be a lot of file withing a minute inside our data folder, we need to remove them if its been more than 3 minutes since it has been created. We use the timestamp that we attached in the filename to find the minutes since its created.

Widgets for New Height/Width

Now that we have stored the file as it was uploaded, its time for us to show its default size, shape and image if needed. Also show the new height and width that we want to have.

        img = np.asarray(img)
        H,W,_=img.shape        
        show_image = st.checkbox("Show Image")
        if show_image:
            st.image(img, use_column_width=True)

        st.markdown(f"""Original Dimension of the image is: {H,W}. \\
                        Original Size of the image is: {os.path.getsize(fname)/1024}kbs \\
                        Please Select H and W.""")

        cols = st.columns(2)
        h = cols[0].number_input("Height", min_value=1, value=int(H))
        w = cols[1].number_input("Width",min_value=1, value=int(W))

In above code,

  • We showed the dimension of image, size of image in KBs.
  • Created two widgets for Height and Width. Height of Image is related to Y-axis and Width is related to X-axis. And the H,W,_ = img.shape gives the number of rows, cols.

Resize Image and Save it!

Lets use the values from the widgets above to resize image size and save it.

        if st.button("Reduce size!!"):
            nimg = cv2.resize(img, (int(w), int(H)), interpolation=cv2.INTER_AREA)
            nfname = fname.replace("data/", "data/temp_")

            nimg = cv2.cvtColor(nimg, cv2.COLOR_RGB2BGR)

            if show_image:
                st.image(img, use_column_width=True)

            cv2.imwrite(nfname, nimg)
            st.markdown(f"New file size: {os.path.getsize(nfname)/1024} kbs")

Download Resized Image

Lets download the resized image.

            with open(nfname, "rb") as fp:
                dbtn = st.download_button(label="Download image file.", data=fp,
                            file_name=nfname.split("/")[-1], mime="image/png")
                if dbtn:
                    st.markdown("Downloaded!!!!")

In above code, we tried to open recently saved resized image in byte format and then put it in download_button's data. Once its downloaded, Downloaded!!!! is shown.

Full Code

app.py

import streamlit as st
import cv2
import numpy as np
import os
from utils.variables import var
from PIL import Image
import time

st.set_page_config(layout="wide")

sidebar = st.sidebar
sidebar.markdown("## Modes ")

size_reducer = sidebar.checkbox("Image Size Reducer")

if size_reducer:
    st.markdown("## Selected Size Reducer")
    exts = var.allowed_modes_dict["Image Size Reducer"]["extensions"].split(",")
    uploaded_file = st.file_uploader(f"Select file: {exts}", type=exts)
    if uploaded_file is not None:
        fname = f"data/{int(time.time())}."+uploaded_file.name.split(".")[-1]

        img = Image.open(uploaded_file).convert("RGB")        
        with open(fname, "wb") as f:
            f.write(uploaded_file.getbuffer())
            for f in os.listdir("data"):
                ts = float(f.split(".")[0])
                try:
                    if time.time()-ts > 120:
                        os.remove("data/"+f)
                except:
                    pass

        img = np.asarray(img)
        H,W,_=img.shape        
        show_image = st.checkbox("Show Image")
        if show_image:
            st.image(img, use_column_width=True)

        st.markdown(f"""Original Dimension of the image is: {H,W}. \\
                        Original Size of the image is: {os.path.getsize(fname)/1024}kbs \\
                        Please Select H and W.""")

        cols = st.columns(2)
        h = cols[0].number_input("Height", min_value=1, value=int(H))
        w = cols[1].number_input("Width",min_value=1, value=int(W))

        if st.button("Reduce size!!"):
            nimg = cv2.resize(img, (int(w), int(H)), interpolation=cv2.INTER_AREA)
            nfname = fname.replace("data/", "data/temp_")

            nimg = cv2.cvtColor(nimg, cv2.COLOR_RGB2BGR)

            if show_image:
                st.image(img, use_column_width=True)

            cv2.imwrite(nfname, nimg)
            st.markdown(f"New file size: {os.path.getsize(nfname)/1024} kbs")

            with open(nfname, "rb") as fp:
                dbtn = st.download_button(label="Download image file.", data=fp,
                            file_name=nfname.split("/")[-1], mime="image/png")
                if dbtn:
                    st.markdown("Downloaded!!!!")
else:
    st.markdown("## No Mode Selected!!")

Please follow this link for the full codes.

This is all for now in this blog and there are a lot to come in this blog soon. I will add different features like color changing, convolving, edge detecting and many cool Image Processing algorithms. Stay Tuned!!

Leave a Reply

Share this:

Subscribe to our Newsletter

Hello surfer, thank you for being here. We are as excited as you are to share what we know about data. Please subscribe to our newsletter for weekly data blogs and many more. If you’ve already done it, please close this popup.



No, thank you. I do not want.
100% secure.
Scroll to Top