Is It Better To Adjust Color Before Or After Resizing
Resizing images is an integral part of the web, whether to brandish images on your website or app, store lower-resolution images, or generate a preparation set for neural networks.
Python offers a rich ready of options to perform some of the routine image resizing tasks. This commodity will walk you through those options and look at ImageKit - a cloud-based, ready-to-use solution that offers real-fourth dimension image manipulation.
We volition look at
- Resizing Images using Pillow
- Resizing Images using OpenCV
- Simplify all of information technology past using ImageKit, a complete image optimization product
Before we go started
Make sure you have a contempo version of Python installed on your system, preferably Python iii.6+, then spin upwards a virtual environment.
# create virtual env Python3 -m venv epitome-resize # activate source image-resize/bin/activate When nosotros get to ImageKit afterwards in this article, you will need to sign up for a costless business relationship on ImageKit's website. The costless plan has admission to all the features nosotros demand for image resizing and other transformations.
We will exist using an image by Asad from Pexels for all examples in this article. This image is too hosted on ImageKit.
https://ik.imagekit.io/ikmedia/python-resizing/sunset_SLoRHsWVo.jpg Resizing Images using Pillow (PIL)
Pillow is one of the about pop options for performing basic image manipulation tasks such as cropping, resizing, or calculation watermarks.
Install the latest version of Pillow with pip.
Python3 -one thousand pip install Pillow Pillow provides the resize() method, which takes a (width, height) tuple as an argument.
from PIL import Paradigm paradigm = Image.open('sunset.jpg') print(f"Original size : {epitome.size}") # 5464x3640 sunset_resized = epitome.resize((400, 400)) sunset_resized.save('sunset_400.jpeg') We read our image using the open() method from the Image module. And so we employ resize with a tuple, representing the width and height of the output image, respectively, as an argument containing the new size.
Here is how our resized epitome looks similar
Maintain Attribute Ratio while Resizing
The resize method is pretty handy and quick to piece of work with, simply it doesn't e'er requite the all-time results. Images resized using this method may be stretched or forced to fit in a box and therefore skewed.
ImageKit makes it ridiculously piece of cake to resize images while maintaining the aspect ratio. Run into how ImageKit works for resizing.
Although the thumbnail() method can accost the problem of skewing, it creates a thumbnail version of the paradigm whose size does not exceed the dimensions specified in the arguments.
prototype.thumbnail((400, 400))image.relieve("sunset-attribute.jpeg") The thumbnail method resizes the epitome in identify without returning an epitome object. Therefore, brand certain to save the file with a dissimilar proper noun.
Cropping an prototype with Pillow
The crop() method used to ingather an prototype accepts a 4-tuple of the ten and y coordinates of the summit-left and the bottom-right corner of the crop area.
So, if we want to crop an image from the (300,300) indicate to (700,900) betoken, the lawmaking would be
# Ingather the epitome box = (300, 300, 700, 900) cropped_image = image.crop(box) cropped_image.save('cropped-image.jpg') # 400x600 size of the paradigm print(cropped_image.size) 
In that location is an easier method of using ImageOps. The ImageOps.ingather() method accepts 2 arguments:
-
img- The paradigm to crop -
border- The width of the surface area to exist removed. The aforementioned width is removed from all four sides of the image.
from PIL import Epitome, ImageOps image = Prototype.open('sunset.jpg') cropped = ImageOps.crop(image, 600) cropped.save("crop-imageops-600.jpg") Here is our sample image cropped by 600px from all sides.
You should check out the pillow handbook for more complex image operations.
Bonus: Adding a watermark to an paradigm
To add watermarks, we need to use the ImageDraw and ImageFont methods. The ImageDraw module provides elementary-to-use APIs for working with second graphics, from creating polygons to writing text. ImageFont sets the font of the watermark text.
from PIL import Epitome, ImageDraw, ImageFont im = Paradigm.open up('sunset-aspect.jpg') width, height = im.size # 400x266 draw = ImageDraw.Draw(im) text = "Sunset" font = ImageFont.truetype('/usr/share/fonts/truetype/ubuntu/UbuntuMono-RI.ttf', 20) textwidth, textheight = depict.textsize(text, font) # calculate new x,y coordinates of the text ten = (width - textwidth)/2 y = (tiptop - textheight)/2 # depict watermark in the center draw.text((x, y), text, font=font) im.salve('pillow-watermark.jpg') Make sure to choose a font that is available on your system. You can set the position, fill, anchor, or language. You lot can also download a font of your option and provide the path to it.
Here is the sample output -
Resizing images using OpenCV
OpenCV is the de-facto library used to perform complex image processing tasks such equally face detection, pixel transformations, and 3D modeling. But, it can perform more generic tasks like epitome resizing besides.
First, let's install the latest opencv for Python using pip.
Python3 -g pip install opencv-Python Note that nosotros are using OpenCV 4.5 for this tutorial. cv2 is the name of the Python wrapper.
Resizing with OpenCV
Although OpenCV is a feasible choice for image resizing, it is best suited for heavy-duty tasks like object detection. Stick to Pillow for basic image manipulation or scroll below to meet how ImageKit does that.
Resizing images can be done by cv2.resize() method.
import cv2 img = cv2.imread('sunset.jpg') # Get original superlative and width print(f"Original Dimensions : {img.shape}") # resize image by specifying custom width and tiptop resized = cv2.resize(img, (2000, 1500)) print(f"Resized Dimensions : {resized.shape}") cv2.imwrite('resized_imaged.jpg', resized) Cropping an paradigm with OpenCV
OpenCV uses a NumPy assortment under the hood for representing images. We can leverage array slicing to excerpt the part of the pixels we need, i.e., crop the image.
Since this is a 2nd array, we need to specify the beginning and end coordinates, just similar we did while cropping images with Pillow. Though, the syntax here is slightly different.
If we want to crop an image starting at (0,0) indicate to (2732, 3640) betoken, then we can do this with OpenCV as shown below
# image[startY:endY, startX:endX] cropped_image = img[0:3640, 0:2732] cv2.imwrite('cropped-image-opencv.jpg', cropped_image) impress(f"Cropped Prototype Dimensions : {cropped_image.shape}") # 2732x3640 You can read more than almost paradigm processing tutorials from OpenCV.
Using ImageKit to resize and crop images
Both Pillow and OpenCV are good options to resize and crop images. Simply these solutions require all-encompassing setup and maintenance in line with the changing concern needs.
This might not be the all-time utilization of your or your team'due south time when tools similar ImageKit.io can do the same job for you with minimal endeavour.
ImageKit.io is a real-time image and video optimization, manipulation, and storage product that can assistance you lot do basic resizing and cropping on your images and more.
You have the flexibility to either upload your images to the integrated media library or attach your existing deject storage or servers to ImageKit and starting time delivering optimized files. Several image and video optimizations are done automatically, considering multiple factors, whereas resizing, cropping, and other transformations can exist washed by but modifying the URL in real-time.
Setup with ImageKit
In our example, nosotros have already uploaded the sample image to ImageKit.
https://ik.imagekit.io/ikmedia/python-resizing/sunset_SLoRHsWVo.jpg You should sign up for a gratuitous business relationship on ImageKit that comes with an aplenty amount of storage and commitment bandwidth. Y'all tin can download the original image from Pexels from here and upload it to your media library.
We volition be using ImageKit's Python SDK to create the resized and cropped epitome URLs. You can install this SDK using the following command -
Python3 -m pip install imagekitio Python-dotenv Basic resizing by changing height and width
Resizing images in ImageKit is simple. Merely add together the required dimension transformation to the URL, and you lot will get the resized image in real-time.
For example, to get the sample prototype with a width of 200px, the URL will be -
https://ik.imagekit.io/ikmedia/python-resizing/sunset_SLoRHsWVo.jpg?tr=west-200 While this is a simple string append operation, the Python SDK makes the lawmaking simpler and readable.
image_url = imagekit.url({ "path": "/python-resizing/sunset_SLoRHsWVo.jpg", "transformation_position": "query", "transformation": [ {"width": "200"} ] }) Similarly, if we want to get a 400 x 300px resized image from ImageKit, the URL will comprise meridian and width transformation parameters.
https://ik.imagekit.io/ikmedia/python-resizing/sunset_SLoRHsWVo.jpg?tr=w-400,h-300 The corresponding code with the Python SDK will be
image_url = imagekit.url({ "path": "/python-resizing/sunset_SLoRHsWVo.jpg", "transformation_position": "query", "transformation": [ {"width": "400", height : "300"} ] }) There are several other image transformation options with ImageKit. You can read about them from the production documentation or from the Python SDK documentation.
Cropping images with a specific attribute ratio
Some other transformation available in ImageKit is the aspect ratio transformation. You tin can utilize it with either the width or the superlative transformation to gear up the output paradigm's aspect ratio and transformations.
For example, to get an paradigm at 800px width with an aspect ratio 4:3, the URL will be
https://ik.imagekit.io/ikmedia/python-resizing/sunset_SLoRHsWVo.jpg?tr=w-800,ar-iv-3 Cropping options bachelor with ImageKit
In the above examples, if the output aspect ratio doesn't lucifer the input aspect ratio, the prototype gets cropped around the center of the original past default.
Yet, ImageKit gives the states multiple cropping options that can preclude cropping on the image and aid us get the required output. Let's await at some of the standard cropping options below.
i. No cropping - Forcing Requested Dimensions
Y'all can exercise this by adding the c-force transformation parameter to the URL. This volition force-fit the epitome to the size requested. Though, because this is a force fit, information technology may cause the image to get skewed.
https://ik.imagekit.io/ikmedia/python-resizing/sunset_SLoRHsWVo.jpg?tr=w-400,h-200,c-force 
In the Python SDK, this can be specified using the ingather transformation
image_url = imagekit.url({ "path": "/python-resizing/sunset_SLoRHsWVo.jpg", "transformation_position": "query", "transformation": [ { "peak": "200", "width": "400", "crop": "force", }, ] }) two. Fit within a container
The at_max ingather strategy fits the image inside a container with a divers height and width while preserving the aspect ratio. Either the pinnacle or width of the prototype may differ from the requested dimensions, and the resulting epitome will always be smaller than the dimensions specified.
https://ik.imagekit.io/ikmedia/python-resizing/sunset_SLoRHsWVo.jpg?tr=west-400,h-200,c-at_max 
iii. Fit outside a container
Opposite to the at_max crop, the at_least crop results in an paradigm that is at least the size that is specified using the elevation and width dimensions. Either the height or width of the paradigm may differ from the requested dimensions, and the resulting image will always be larger than the dimensions specified.
https://ik.imagekit.io/ikmedia/python-resizing/sunset_SLoRHsWVo.jpg?tr=west-400,h-200,c-at_least 
4. Pad an image
If you still want the resulting image to match the output dimension requested merely non get cropped, you can utilize the pad_resize ingather mode. Note that this is unlike from the ingather transformations used to a higher place and is specified using the crop_mode transformation. You can as well specify the padding colour that is added around the image using the groundwork transformation.
image_url = imagekit.url({ "path": "/python-resizing/sunset_SLoRHsWVo.jpg", "transformation_position": "query", "transformation": [ { "height": "200", "width": "400", "crop_mode": "pad_resize", "background" : "DADADA" }, ] }) The resulting URL is
https://ik.imagekit.io/ikmedia/python-resizing/sunset_SLoRHsWVo.jpg?tr=w-400,h-200,cm-pad_resize,bg-DADADA 
You can meet grayness-colored padding around the image, which perfectly fits the 400 10 200px output dimensions without cropping the prototype.
Bonus: Adding watermarks to an image using ImageKit
ImageKit offers more than than forty+ dissimilar real-time transformation parameters for images.
We can use them to add both epitome and text overlays to our images and control how and where they are displayed on our images. Yous can refer to the complete overlays documentation hither.
Let's look at an example to add together text on the image in the meridian-left corner of our base image. Here we specify the text, its font size, and font color in the URL itself.
image_url = imagekit.url({ "path": "/python-resizing/sunset_SLoRHsWVo.jpg", "transformation_position": "query", "transformation": [ { "height": "400", "overlay_text": "Sunset", "overlay_text_font_size": "30", "overlay_text_color": "FFFFFF" } ] }) The resulting image URL is
https://ik.imagekit.io/ikmedia/python-resizing/sunset_SLoRHsWVo.jpg?tr=h-400,ot-Sunset,ots-thirty,otc-FFFFFF 
Similarly, we can add together image watermarks or combine multiple watermarks in unlike positions and sizes on an image in real-time. You volition notice such examples in the documentation for overlays and chained transformations.
Conclusion
Here'south a quick summary of what we learned today. Nosotros looked at three different means of resizing images in Python.
- Pillow provides easy-to-setup and usable methods for performing basic prototype manipulation tasks. The Pillow Handbook contains examples of different operations you can perform on the image.
- OpenCV can too resize images using the cv2.resize() utility, but information technology is generally slower than other bachelor methods.
- ImageKit is a third-party deject-based prototype transformation service that you can utilize to dispense images at scale using URL-based transformation parameters.
Sign upwards at present for ImageKit's forever gratis plan and start resizing images in real-time with minimal try.
Source: https://imagekit.io/blog/image-resizing-in-python/
Posted by: sungthisione.blogspot.com
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