Slice any image into precise rectangular tiles. Perfect for UI spritesheets, game asset grids, photomosaic planning, map tiles, or training datasets.
Image slicing into uniform grids is a fundamental operation in digital content creation. From CSS sprites that accelerate web performance, to game texture atlases, to machine learning datasets (medical imaging patches, satellite tiles), the ability to split an image accurately and losslessly is critical. Our tool uses native canvas rendering to extract each tile with subpixel precision and deterministic coordinates, preserving original quality.
⚙️ Algorithm core: Given source width (W) and height (H), rows = R, cols = C → each tile dimensions:
tileWidth = floor(W / C) , tileHeight = floor(H / R). Remainder pixels (if any) are assigned to the last column/row to avoid distortion. Edge tiles keep exact boundaries – no stretching, no interpolation artifacts.
Break down character spritesheets, environment tilesets, or UI elements into individual assets. Use the ZIP output to directly import into Unity, Godot, or RPG Maker.
Prepare training data for convolutional neural networks: slice histology slides, aerial images, or artworks into manageable patches. Each tile becomes a labeled sample.
Deconstruct a master image into a grid, then rearrange or replace tiles to build stunning mosaic art. Plan each tile's content.
Split large format designs for multi-page printing (posters, wall murals). Perfect for exact tile dimensions before sending to wide-format printers.
Split Image & Generate Tiles if you want to manually trigger, but auto‑refresh is enabled.
Download All Tiles (ZIP) to get a structured archive: each tile named tile_row{row}_col{col}.png.
When an image width is not perfectly divisible by columns, the splitter distributes remainder to rightmost tiles. For pixel‑perfect applications (like sprite alignment), this approach maintains integer tile sizes. Advanced users can also compute tile indices manually; this tool automates the process, giving educators a live demonstration of grid geometry. The division strategy follows "floor division + remainder distribution", widely adopted in graphics libraries (Pillow, OpenCV).
For images larger than 4000×4000 pixels, limit rows/cols to ≤10 to avoid browser memory pressure. Use PNG for transparency; JPEG works but tiles will be lossy. To split extremely large images (e.g., 10000×10000), consider pre‑scaling or using a desktop tool. Our algorithm maintains the original color profile (sRGB) and never downsamples—each tile retains source quality. The auto‑update feature uses a 300ms debounce to prevent excessive recalculations while typing.
drawImage with integer source coordinates, producing pixel-accurate crops. The same methodology is used in major image processing libraries. Additionally, the tool provides visual verification: every tile is shown before download.
When the user uploads an image, we create an offscreen HTMLImageElement and draw it onto a temporary canvas to get exact pixel dimensions. For each cell (i, j), the source rectangle is: sx = j * tileWidth, sy = i * tileHeight, sw = tileWidth + (j == cols-1 ? remainderW : 0) similarly for height. This guarantees full coverage without gaps. Each tile is rendered to an individual canvas and exported as a PNG blob. The ZIP archive is built with JSZip, respecting original filenames with row/col indices. The tool has been validated by graphic designers and game artists for accurate edge handling: no color bleeding, no antialiasing artifacts at cut borders.