May 08, 2025●41 reads●No License

Image Splitter: Divide an Image into Grid-based Pieces

m
@manisht0914

🧠 Image Splitter Using OpenCV

📂 Ready Tensor Labs – Assignment 2 Solution

Welcome to Ready Tensor’s image processing mini-project! In this guide, we demonstrate how to split an image into smaller tiles using Python and OpenCV.

📌 Objective

The goal is to split a given image into smaller segments based on a specified number of rows and columns and save each segment as an individual image. This is commonly used in:

Image annotation
Machine learning datasets
Puzzle/game creation
UI design slices

🛠️ Prerequisites

Install the required Python libraries using pip:

pip install numpy opencv-python

Step-by-Step Breakdown

Import Libraries and Read the Image
Using cv2 to read the image and os to handle file system operations.

import numpy as np
import cv2
import os

image_path = "assignment-2.jpg"  
image = cv2.imread(image_path)

if image is None:
    print("Error: Could not load image. Check the file path.")
    exit()

Define Number of Rows and Columns

num_rows = 6
num_cols = 3

Calculating Sub-Image Dimensions

image_height, image_width, _ = image.shape
sub_image_height = image_height // num_rows
sub_image_width = image_width // num_cols

Creating Output Directory

output_dir = "output_bars"
if not os.path.exists(output_dir):
    os.makedirs(output_dir)

Loop Through and Save Cropped Pieces

for row in range(num_rows):
    for col in range(num_cols):
        start_x = col * sub_image_width
        start_y = row * sub_image_height
        end_x = (col + 1) * sub_image_width
        end_y = (row + 1) * sub_image_height

        sub_image = image[start_y:end_y, start_x:end_x]
        output_path = os.path.join(output_dir, f"piece_{row}_{col}.jpg")
        cv2.imwrite(output_path, sub_image)
        print(f"Saved: {output_path}")

print("Image successfully split into pieces!")

Output Format

output_bars/
├── piece_0_0.jpg
├── piece_0_1.jpg
├── piece_0_2.jpg
├── ...
└── piece_5_2.jpg

Tips:

For better image quality, save images as .png instead of .jpg.
Add support for images that don't divide evenly by resizing or padding.
This can be extended to batch-process multiple images.
Build a GUI using Tkinter or deploy it as a Flask web app.