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I want to extend my jenkins image to have docker installed so it can build a Dockerfile out of a project.I found a nice install script for docker but only for Ubuntu 64bit. What I need to know is if the parent images of my jenkins image base on Ubuntu 64bit so I can use this install script without any problems.

I don't think there is currently any docker command that allows you to do that. It seems that the only way is to launch the container and run those commands: determine OS distribution of a docker image

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I wasn't sure what element I should use or if I should be using get, but it fails. When I look at the source code for the web page, the logo is hidden within the javascript (nodeJS, vueJS,and expressJS application) and I noticed the javascript seems to add a sequence of numbers and letters to the image when I go to the image page even though the image name in the assets folder does not have it on there. My-Logo.d63b7f9.png.

What would be the most efficient way to check if a local docker image is outdated compared to the remote image? All solutions I've found so far are bash scripts or external services that push on an update. I'd like to find a solution that is as native to docker as possible, and would like to refrain from pushing a notification from somewhere else (to alert the server of an updated image).

Docker Image Update Notifier is a CLI application written in Go and delivered as a single executable (and a Docker image) to receive notifications when a Docker image is updated on a Docker registry.

I have a bunch of image files with the extension ".png", but I'm fairly sure some of them are actually JPEGs that were renamed to PNGs. As far as I know, simply renaming the file doesn't actually change the formatting, compression, etc., so how do I check which are formatted accordingly to their extensions?

I have to write a test case in Python to check whether a jpg image is in color or grayscale. Can anyone please let me know if there is any way to do it with out installing extra libraries like OpenCV?

For faster processing, it is better to avoid loops on every pixel, using ImageChops, (but also to be sure that the image is truly grayscale, we need to compare colors on every pixel and cannot just use the sum):

A performance-enhance for fast results: since many images have black or white border, you'd expect faster termination by sampling a few random i,j-points from im and test them? Or use modulo arithmetic to traverse the image rows. First we sample(-without-replacement) say 100 random i,j-points; in the unlikely event that isn't conclusive, then we scan it linearly.

(Also my original remark stands, first you check the JPEG header, offset 6: number of components (1 = grayscale, 3 = RGB). If it's 1=grayscale, you know the answer already without needing to inspect individual pixels.)

Both of these approaches got me only like 53% accuracy in my dataset.I had to relax the condition for checking pixels in different channels and create a ratio to classify it as grey or color. With this approach, I was able to get 87.3% accuracy on my dataset.

In case of a grayscale image, all channels in a certain pixel are equal (if you only have one channel, then you don't have a problem).So basically, you can list all the pixels with their three channel values to check if each pixel has all three channels equal.

If len(color_count) exceeds 256 (default max value), this function returns None, meaning you had more than 256 color options in your pixel list, hence it is a colored image (grayscale can only have 256 colors, (0,0,0) to (255,255,255)).

Old question but I needed a different solution.Sometimes 3 channel images (eg RGB) might be almost grayscale without every pixel being identical in 3 channels. This checks every pixel but you can also subsample the image if needed. I used slope here but you can use checks on most of these parmaters from the regression. Linear regressions are usually very fast due to internal matrix multiply solution.

Repair a Windows image using DISM. You can repair offline Windows image in a WIM or VHD file, or an online Windows image. An online Windows image will also attempt to repair itself if it becomes unserviceable. The repair source for this operation is the same source that is used for Features on Demand and is determined by Group Policy settings. For more information, see Configure a Windows Repair Source. When you use the DISM tool to repair an online or offline image, you can use the /Source argument with the /RestoreHealth argument to specify additional repair source locations to use to search for the required files.

When you use the /CheckHealth sfc argument, the DISM tool will report whether the image is healthy, repairable, or non-repairable. If the image is non-repairable, you should discard the image and start again. If the image is repairable, you can use the /RestoreHealth argument to repair the image.

If you do not specify a /Source for the repair files, the default location for Features on Demand is used. For more information, see Configure a Windows Repair Source. If you specify more than one /Source, the files are copied from the first location where they are found and the rest of the locations are ignored. You can use /LimitAccess to prevent the DISM tool from using Windows Update as a repair source or as a backup repair source for online images.

In some cases, an image can be corrupted while modifying it with DISM. Use /Cleanup-MountPoints to repair it. This command will not unmount images that are already mounted, nor will it delete images that can be recovered using the /Remount-Image command.

A verifyImages rule can contain a list of attestors or authorities used to check the attached image signature. The type of attestor supported will vary based on the tool used to sign the image. For example, Sigstore Cosign supports public keys, certificates, and keyless attestors.

A verifyImages rule can contain a list of attestations i.e., signed metadata, to checked for the image. The nested attestations.attestors are used to verify the signature of the attestation. Any JSON data in an attestation can be verified using a set of attestations.conditions.

The rule mutates matching images to add the image digest, when mutateDigest is set to true (which is the default), if the digest is not already specified. Using an image digest has the benefit of making image references immutable and prevents spoofing attacks. Using a digest helps ensure that the version of the deployed image does not change and, for example, is the same version that was scanned and verified by a vulnerability scanning and detection tool.

The imageVerify rule first executes as part of the mutation webhook as the applying policy may insert the image digest. The imageVerify rules execute after other mutation rules are applied but before the validation webhook is invoked. This order allows other policy rules to first mutate the image reference if necessary, for example, to replace the registry address, before the image signature is verified.

The attestors declaration specifies one or more ways of checking image signatures or attestations. The attestors.count specifies the required count of attestors in the entries list that must be verified. By default, and when not specified, all attestors are verified.

Image verification requires multiple network calls and can be time consuming. Kyverno has a TTL based cache for image verification which caches successful outcomes of image verification. When cache is enabled, an image once verified by a policy will be considerd to be verified until TTL duration expires or there is a change in policy.

imageVerifyCacheEnabled: Enable a TTL cache for verified images. Default is true.imageVerifyCacheMaxSize: Maximum number of keys that can be stored in the TTL cache. Keys are a combination of policy elements along with the image reference. Default is 1000. 0 sets the value to default.imageVerifyCacheTTLDuration: Maximum TTL value for a cache expressed as duration. Default is 60m. 0 sets the value to default.

The cache is enabled by default and significantly helps with execution time of verify image policiess by making not accessing remote repository on every verification attempt. It should be noted that any change to the image/signature in the remote repository will not be reflected till the cache entry expires.

Hi-resolution images are at least 300 DPI. This resolution makes for good print quality and is important if you want to represent your brand or other important printed materials with clean and sharp images.

images can be expanded to be larger in size, the important thing to note is that the more you increase the size of your image, the less DPI the image contains. If you have a low-resolution image, you must reduce the size of your image so that the DPI can increase. (more info on doing this below)

Before you reuse content, make sure that its license is legitimate and check the exact terms of reuse. For example, the license might require that you give credit to the image creator when you use the image.

To find license details: Select an image, then below the image, select License details. We recommend that you always review the licensing requirements of any images you want to use.

The SHA256SUMS file contains checksums for all the available images (you can check this by opening the file) where a checksum exists - development and beta versions sometimes do not generate new checksums for each release.

Depending on your platform, you may or may not need to download the public key used to authenticate the checksum file (Ubuntu and most variants come with the relevant keys pre-installed). The easiest way to find out if you need the key is to run the authentication command:

If you get no results (or any result other than that shown above) then the ISO file does not match the checksum. This could be because the ISO has been altered, or it downloaded incorrectly - either way you should download a fresh ISO from a known good source. 006ab0faaa