Integration Guide

Django

Django's ImageField depends on Pillow for image validation. For more advanced tasks like generating thumbnails or watermarking on upload, use Django signals or a dedicated library like django-imagekit.

Django: Process Upload

# models.py from django.db import models class Photo(models.Model): image = models.ImageField(upload_to="photos/") # signals.py — auto-generate thumbnail on upload from django.db.models.signals import post_save from PIL import Image import os def create_thumbnail(sender, instance, **kwargs): if instance.image: with Image.open(instance.image.path) as img: img.thumbnail((300, 300), Image.Resampling.LANCZOS) thumb_path = instance.image.path.replace("/photos/", "/thumbs/") img.save(thumb_path) post_save.connect(create_thumbnail, sender=Photo)

FastAPI

FastAPI's file upload handling works seamlessly with Pillow via UploadFile → BytesIO → Image.open().

FastAPI Image Upload Handler

from fastapi import FastAPI, UploadFile, File from fastapi.responses import StreamingResponse from PIL import Image import io app = FastAPI() @app.post("/resize") async def resize_image(file: UploadFile = File(...)): contents = await file.read() img = Image.open(io.BytesIO(contents)) img.thumbnail((800, 800), Image.Resampling.LANCZOS) buf = io.BytesIO() img.save(buf, format="WEBP", quality=85) buf.seek(0) return StreamingResponse(buf, media_type="image/webp")

Flask

Flask Upload + Process

from flask import Flask, request, send_file from PIL import Image import io app = Flask(__name__) @app.post("/convert") def convert(): file = request.files["image"] img = Image.open(file.stream).convert("RGB") buf = io.BytesIO() img.save(buf, "JPEG", quality=90) buf.seek(0) return send_file(buf, mimetype="image/jpeg")

NumPy

Pillow and NumPy arrays can be converted freely. This allows you to apply arbitrary mathematical operations on pixel data and then render back to an image.

Pillow ↔ NumPy

import numpy as np from PIL import Image # PIL → NumPy array img = Image.open("photo.jpg") arr = np.array(img) print(arr.shape) # (height, width, 3) for RGB # Apply NumPy operations inv = 255 - arr # invert all values # NumPy array → PIL Image result = Image.fromarray(inv.astype(np.uint8)) result.save("inverted.jpg")

PyTorch / torchvision

PyTorch uses Pillow as its default image loader. torchvision.transforms accepts PIL Images natively. When you load a dataset with torchvision.datasets.ImageFolder, Pillow does the heavy lifting under the hood.

torchvision Pipeline

from torchvision import transforms from PIL import Image preprocess = transforms.Compose([ transforms.Resize(256), # Pillow resize transforms.CenterCrop(224), # Pillow crop transforms.ToTensor(), # PIL → torch.Tensor [0,1] transforms.Normalize( mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] ) ]) pil_img = Image.open("photo.jpg") tensor = preprocess(pil_img) print(tensor.shape) # torch.Size([3, 224, 224])

OpenCV

OpenCV and Pillow use different colour channel orders. OpenCV is BGR, Pillow is RGB. You must convert when passing data between libraries.

Pillow ↔ OpenCV

import cv2 import numpy as np from PIL import Image # Pillow → OpenCV (RGB → BGR) pil_img = Image.open("photo.jpg") cv_img = cv2.cvtColor(np.array(pil_img), cv2.COLOR_RGB2BGR) # OpenCV → Pillow (BGR → RGB) cv_img = cv2.imread("photo.jpg") pil_img = Image.fromarray(cv2.cvtColor(cv_img, cv2.COLOR_BGR2RGB))

Tkinter

Tkinter's native PhotoImage only supports GIF and PGM. Use ImageTk.PhotoImage from Pillow to display any image format in a Tkinter window.

Tkinter Display

import tkinter as tk from PIL import Image, ImageTk root = tk.Tk() img = Image.open("photo.jpg").resize((400, 300)) photo = ImageTk.PhotoImage(img) label = tk.Label(root, image=photo) label.pack() root.mainloop()

Qt (PyQt5 / PySide6)

Convert Pillow images to Qt's QImage via NumPy as an intermediate layer.

Pillow → QImage

from PyQt5.QtGui import QImage, QPixmap from PIL import Image import numpy as np img = Image.open("photo.jpg").convert("RGBA") data = np.array(img) h, w, ch = data.shape qimage = QImage(data.tobytes(), w, h, ch * w, QImage.Format_RGBA8888) pixmap = QPixmap.fromImage(qimage)