The mse function takes three arguments: imageA and imageB, which are the two images we are going to compare, and then the title of our figure. We then compute the MSE and SSIM between the two images on Lines 21 and 22. Lines 25-39 handle some simple matplotlib plotting. Some parts of the image can be excluded from the comparison. Can be used for automation QA tests. GitHub - romankh3/image-comparison: Published on Maven Central Java Library that compares 2 images with the same sizes and shows the differences visually by drawing rectangles. Some parts of the image can be excluded from the comparison.

Example Usage • Option Summary

Once you have your totals, compare it to the totals for a second image. You might find that each image has a fairly unique footprint, enough to identify matches. How about using Edge Detection. (source: wikimedia.org) With two similar pictures edge detection should provide you with a usable and fairly reliable unique footprint. “An Industry-acclaimed duplicate photo remover for Windows” Available for: Windows 10, 8.1. Product Version: 1.0.0.0. Best Feature: Supports external devices (e.g. Pen drives) If you want to get a duplicate-free photo library in no time, then Quick Photo Finder is among the best duplicate photo finders and cleaner software that you.

Compare 2 Photo Libraries In Windows 10

JuxtaposeJS Embed. Juxtapose helps storytellers compare two pieces of similar media, including photos, and GIFs. It’s ideal for highlighting then/now stories that explain slow changes over time (growth of a city skyline, regrowth of a forest, etc.) or before/after stories that show the impact of single dramatic events (natural disasters.

Use the compare program to mathematically and visually annotate the difference between an image and its reconstruction. See Command Line Processing for advice on how to structure your compare command or see below for example usages of the command.

Example Usage

We list a few examples of the compare command here to illustrate its usefulness and ease of use. To get started, lets compare an image to one thats been sharpened:

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The red areas of the difference image emphasizes (highlight) pixels that are affected by the image sharpening, whereas white de-emphasizes (lowlight) pixels that are untouched by the sharpening process.

In addition to the visual interpretation of the difference in an image and its reconstruction, we report a mathematical measure of the difference:

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Or, if you just want the red channel distortion, use this command:

Or, if you just want the overall image distortion, use this command:

If the reconstructed image is a subimage of the image, the compare program returns the best match offset. In addition, it returns a similarity image such that an exact match location is completely white and if none of the pixels match, black, otherwise some gray level in-between:

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You can find additional examples of using compare in Examples of ImageMagick Usage.

Two images are considered similar if their difference according to the specified metric and fuzz value is 0, with the exception of the normalized cross correlation metric (NCC), where two images are considered similar when their normalized cross correlation is 1. The default metric is NCC.

The compare program returns 2 on error, 0 if the images are similar, or a value between 0 and 1 if they are not similar.

Option Summary

The compare command recognizes these options. Click on an option to get more details about how that option works.

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Option	Description
-alpha	on, activate, off, deactivate, set, opaque, copy',transparent, extract, background, or shape the alpha channel
-authenticate `value`	decrypt image with this password
-background `color`	background color
-brightness-contrast `geometry`	improve brightness / contrast of the image
-channel `type`	apply option to select image channels
-colorspace `type`	set image colorspace
-compose `operator`	set image composite operator
-crop `geometry`	crop the image
-decipher `filename`	convert cipher pixels to plain
-debug `events`	display copious debugging information
-define `format:option`	define one or more image format options
-density `geometry`	horizontal and vertical density of the image
-depth `value`	image depth
-dissimilarity-threshold `value`	maximum distortion for (sub)image match (default 0.2)
-distort `type coefficients`	distort image
-encipher `filename`	convert plain pixels to cipher pixels
-extract `geometry`	extract area from image
-fuzz `distance`	colors within this distance are considered equal
-gravity `type`	horizontal and vertical text placement
-help	print program options
-highlight-color `color`	emphasize pixel differences with this color
-identify	identify the format and characteristics of the image
-interlace `type`	type of image interlacing scheme
-level `value`	adjust the level of image contrast
-limit `type value`	pixel cache resource limit
-log `format`	format of debugging information
-lowlight-color `color`	de-emphasize pixel differences with this color
-metric `type`	measure differences between images with this metric
-negate	replace each pixel with its complementary color
-profile `filename`	add, delete, or apply an image profile
-quality `value`	JPEG/MIFF/PNG compression level
-quantize `colorspace`	reduce image colors in this colorspace
-quiet	suppress all warning messages
-read-mask `filename`	associate a read mask with the image
-regard-warnings	pay attention to warning messages.
-repage `geometry`	size and location of an image canvas
-resize `geometry`	resize the image
-respect-parentheses	settings remain in effect until parenthesis boundary.
-rotate `degrees`	apply Paeth rotation to the image
-sampling-factor `geometry`	horizontal and vertical sampling factor
-seed `value`	seed a new sequence of pseudo-random numbers
-separate	separate an image channel into a grayscale image
-set `attribute value`	set an image attribute
-sigmoidal-contrast `geometry`	increase the contrast without saturating highlights or shadows
-similarity-threshold `value`	minimum distortion for (sub)image match (default 0.0)
-size `geometry`	width and height of image
-subimage-search	search for subimage
-synchronize	synchronize image to storage device
-taint	mark the image as m