Get dimensions of objects within an image - image

i'm planning to build a web app where following feature is used:
Imagine uploading an image,
and the dimensions within the image need to be retrieved
e.g. I would like to know the height and width of the input field.
given the fact I can provide the base image sizes and aspect ratio's.
How would one go about getting the marked dimensions out of the given image?
is there an open API that could do this?
Is this even a possiblity?

Related

Liquid Pixels check source image mime type

unfortunately, I cannot tag this post with the correct "technology" because it does not exist and i dont have 1500 reputation to create it.
We are using a cloud service called "Liquid Pixels" to render some stuff on our images.
Lets say we have an image chain that is currently rendering a ribbon on the given JPEG image. This chain is working fine.
Then I adapted the chain to work with animated gif images, therefore I changed the sink format to gif (sink=format[gif]). That was working fine as well.
Now I want to combine the two cases in one chain, because the only difference is the sink command. The plan is to check the MIME type of the source image and then either render a gif or a jpg image.
I rendered the image as xml to view the metadata map.
I thought i can do it like this.
source=url[https://something.com/1x1_sample.gif],name[testImage]
sink=format[gif],if[('testImage.format' eq 'GIF')]
sink=format[jpg],if[('testImage.format' ne 'GIF’)]
But for some reason I cannot access the format attribute. I am used to grab some parameters like “testImage.width” or “testImage.height”, but for some reason i cannot access the format=“GIF” property. I guess that has happens because the width and height are on a different hierarchy level in the metadata map.
I hope you guys can help me.
The image does not actually have a "format" during the render. Only a file has a format. During processing the image is simply on memory as either raster or vector data; it is only when you sink that it becomes a file in whatever format. Also, LiquiFire OS uses the image data to determine the original format when acquiring an image from a source, never the image name itself.
If you need operations in your LiquiFire Image Chain to react to the source image URL, you can test the last part of the image name by applying a regular expression to see if it is either .GIF or .gif. An example of how that can be done:
set=imageURL[https://your.server.com/sample.gif]
source=url[global.imageURL],name[testImage]
regexcase=name[isGif],key[global.imageURL],cases[\.gif$|\.GIF$|\.\w+$],values[yes|yes|no]
sink=format[gif],if[('global.isGif' eq 'yes')]
sink=format[jpg],if[('global.isGif' eq 'no’)]

In FineUploader Plugin scale , how can i define the height and width not the full width (max width option)

how can I set height and width in scaling and can I depend on the image generated (quality and professional scale generation).
how can I set height and width in scaling
You can't. Specify a maxSize for each scaling.sizes entry and Fine Uploader will proportionally scale the image.
can I depend on the image generated (quality
Quality will be limited if you rely on the browser only. There is an entire section in the documentation that explains how you can generate higher-quality resizes by integrating a third-party resize library. I also discuss why you may or may not want to do this. From the documentation:
Fine Uploader's internal image resize code delegates to the drawImage
method on the browser's native CanvasRenderingContext2D object. This
object is used to manipulate a element, which represents a
submitted image File or Blob. Most browsers use linear interpolation
when resizing images. This can lead to extreme aliasing and moire
patterns which is a deal breaker for anyone resizing images for
art/photo galleries, albums, etc. These kinds of artifacts are
impossible to remove after the fact.
If speed is most important, and precise scaled image generation is not
paramount, you should continue to use Fine Uploader's internal scaling
implementation. However, if you want to generate higher quality scaled
images for upload, you should instead use a third-party library to
resize submitted image files, such as pica or limby-resize. As of
version 5.10 of Fine Uploader, it is extremely easy to integrate such
a plug-in into this library. In fact, Fine Uploader will continue to
properly orient the submitted image file and then pass a properly
sized to the image scaling library of your choice to receive
the resized image file, along with the original full-sized image file
drawn onto a for reference. The only caveat is that, due to
issues with scaling larger images in iOS, you may need to continue to
use Fine Uploader's internal scaling algorithm for that particular OS,
as other third-party scaling libraries most likely do not contain
logic to handle this complex case. Luckily, that is easy to account
for as well.
If you'd like to, for example, use pica to generate higher-quality
scaled images, simply pull pica into your project, and contribute a
scaling.customResizer function, like so:
scaling: {
customResizer: !qq.ios() && function(resizeInfo) {
return new Promise(function(resolve, reject) {
pica.resizeCanvas(resizeInfo.sourceCanvas, resizeInfo.targetCanvas, {}, resolve)
})
},
...
}

Drupal resize image on the fly

Is it possible to resize images on the fly and cache the result with Drupal?
I have some big images (e.g. 2000x2000px) and I want to display a preview of the e.g. 100x100px.
I know there is a theme_image_style function. But it seams to only create the <img> with the right size and not effectively resize the image.
I look at modules/images/image.admin.inc and they used the function [image_style_create_derivative][2].
Yes, you should use Drupal's Image styles (Configuration -> Media -> Image styles). There you should create your style.
Then, on front-end, when ever you want to display image with that style (in that resolution) you can use image_style_url() function:
https://api.drupal.org/api/drupal/modules!image!image.module/function/image_style_url/7
It accepts 2 parameters - one is image style machine name and other is image URI, which you can get if you print out all image field properties.
You can also select image styles from back-end interface...i.e. when creating a view for some image you can select to be displayed in specific image style.
In both cases those image styles are generated the first time image is used.
In response to your comment on MilanG's answer, using image_style_url() is the best option on the backend. There is also
https://www.drupal.org/project/resp_img
which may be something worth looking into. From a UX perspective, you don't want to force the user to load a 2000x2000 px image every time they load the page. Regardless of the outputted size, the image is still going to render as a 2000x2000 px image with a large size. image_style_url() or using image styles in the GUI create a new file that will load much quicker and is the preferred method.

How to make curve and rounded image in Blackberry 10 native?

I am trying to curve and round the image but I am not able to do it perfectly. I have tried to create an .amd file and set it as the background but this is not working perfectly. Is there any other way through which I can make the image round as well as curved on a Blackberry - 10.?
I am getting an image as a response from the server like below:
I want something like the following images.Images are not static they are dynamic it's comes from web service.
I have checked the links from the BlackBerry forums also but did not get a proper solution. If anyone knows then please let me know.
To put rounded corners on an image I would use the the Nine Slice feature described in the API. Using a drawing program crreate a small square example of the frame. Using the nine slice system to scale it to the size of your image and lay it over your image.
The same procedure will work for cicularly vignetting images. Depending on howmany sizes you want you may have to draw them on the fly or have several sizes and scale to other sizes.

Image similarity detection

I've been playing around writing a scraper that scrapes Deviantart.com. It saves a copy of new images locally, and also creates a record in a Postgresql DB for the image. My problem: as new images come in, how do I know if this new image corresponds to an image I've seen before? Dupes are fairly rare on DA, but at the same time, this is an interesting problem in a more general sense.
Thoughts on ways to proceed?
Right now the Postgresql DB is populated as I scrape images, and which has a table which looks like:
CREATE TABLE Image
(
id SERIAL PRIMARY KEY NOT NULL,
url varchar(5000) UNIQUE NOT NULL,
dateadded timestamp without time zone default (now() at time zone 'utc'),
width int,
height int
);
Where url is the link to the image as I scraped it from DA (ex: http://th05.deviantart.net/fs70/PRE/f/2014/222/2/3/sketch_dump_56_by_lilaira-d7uj8pe.png), dateadded is the datetime the scraper found the image, and width & height are the image dimensions.
I currently don't store the image itself in the database, but I do keep a local mirror -- I take the url for the image and wget -r -nc the file. So for a url: http://th05.deviantart.net/fs70/PRE/f/2014/222/2/3/sketch_dump_56_by_lilaira-d7uj8pe.png I keep a local copy at <somedir>/th05.deviantart.net/fs70/PRE/f/2014/222/2/3/sketch_dump_56_by_lilaira-d7uj8pe.png
Now, image recognition in the general case is quite hard. I want to be able to handle things like slight resizes, which I could account for by normalizing all images kept to a specific resolution, and normalize the query image to that same resolution at query time. I want to be able to handle things like change of format (PNG vs JPG vs etc) which I could do by reading an image file into a normalized format (ex: uncompressed RGB values for each pixel, though ideally some "slack" would be tolerated here).
Nice to haves (would be willing to give up for simplification/better accuracy):
I'd like to be able to handle cropping an image (ex: I've previously seen imageA, and somebody takes imageA and crops it and uploads it as imageB I'd like to notice that as a duplicate).
I'd like to be able to handle watermarking an image with a logo
I'd like to be able to handle cropping in a case where the new image to classify is a subimage of a previously seen image (ie - I have imageA stored, somebody takes imageA and crops it, I'd like to be able to map that cropped image to imageA)
Constraints/extra info:
I'm not at all interested in finding images that are different yet similar (ex: two distinct photos of the same Red Bus should be reported as two distinct images)
while I'm not entirely opposed to using metadata (ex: artist, image category, etc), I'd like to keep this as constrained to just the image data (EXIF data, resolution, RBG colour values) as possible.
an image that is sized down and appears in a new larger image I wish to consider as different. Ex: I have imageA, I resize it to 50x50, and that 50x50 grid appears in a new image, I would not consider the new image "the same" as imageA (though I suppose by the criteria outlined previously I would consider imageA a duplicate of the new image)
It would be nice but not required if one could detect "minor" revisions in the image (ex: a blanket change to the the gamma value in an image, etc)
Thoughts? Suggestions?
For my use case I'm far more concerned about false positives than false negatives, and as such a "fuzzy match" approach should err on the side of caution.
In case it matters I'm writing all of this in Python, though TBH I'm happy to use an alternate tech if it solves my problem elegantly/efficiently.
I would grab a small subimage somewhere not near the edges, and cross correlate this within the vicinity of its source location in your database images. You can resample it prior to cross correlation to account for small resizes, and you can choose the size of the vicinity that you match against to account for asymmetrical crops of a certain percentage.
To avoid percect fits on featureless regions (e.g. the sky) you could use local image variation as a selection criterion for the subimage location.
This would still be quite slow, so it will be necessary to use a global image metric to first select candidate duplicates from the database (e.g. the color histograms mentioned by danf).

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