I have a very basic question,So kindly bear with me
The task im trying to do is classify 12 labels,There are 12 folders which each which have about 300-400 images which i plan to feed to a network,I am Not exactly sure how do i go about reading these images in the 12 folders,i know i have to convert them into arrays,What i currently have in mind is ill create 12 assignment variables(one for each label) and read each image as an array,Does this make sense or is there a better way to do this?
Thanks in advance
Read all the images for each folder and label the class for each image (same label) , do the same process for each folder and add the images to global list. At the end you get big collection with each item having image data(array) and corresponding label , this way you get 3600 (12*300) items. You can use this for training. Sample item [image array,class label].
Related
I am working on a project where my task is to identify machine part by its part number written on label attached to it or engraved on its surface. One such example of label and engraved part is shown in below figures.
My task is to recognise 9 or 10 alphanumerical number (03C 997 032 D in 1st image and 357 955 531 in 2nd image). This seems to be easy task however I am facing problem in distinguishing between useful information in the image and rest of the part i.e. there are many other numbers and characters in both image and I want to focus on only mentioned numbers. I tried many things but no success as of now. Does anyone know the image pre processing methods or any ML/DL model which I should apply to get desired result?
Thanks in advance!
JD
You can use OCR to the get all characters from the image and then use regular expressions to extract the desired patterns.
You can use OCR method, like Tesseract.
Maybe, you want to clean the images before running the text-recognition system, by performing some filtering to remove noise / remove extra information, such as:
Convert to gray scale (colors are not relevant, aren't them?)
Crop to region of interest
Canny Filter
A good start can be one of this tutorial:
OpenCV OCR with Tesseract (Python API)
Recognizing text/number with OpenCV (C++ API)
I was given a task to process image files, and analyze data on them.
Imagine an exam paper with A, B, C, D answers to fill in (Picture1).
A vision sensor inspects this paper, and saves an image file of it on the computer. I would like to have this image file analyzed (check for the correct filled in circles) and create a document with the results.
With close to no programming skills, I am kind of clueless on how to even start this project. I basically need something to detect if the red circles are filled in or at least have some % of the area filled (Picture2), and the others in the row are not, and give scores accordingly.
I don't know that this could help you and I even do not know that is the right answer but I can not make comments yet.
So basically you should make some application where you can process every picture and check pixels in some area that covers your template with good answers. Then you store that there was true/false result in inspection and sum this up to store the score.
Maybe this will be helpful: Images and Pixels by Daniel Shiffman
But also I think that with no programming skills this could be very hard to accomplish your task.
I want to use NiftyNet to implement Deep Learning on medical image processing. However, there is one thing I haven't figured out regarding the data input: how does it join the multi-modality images? I saw the demo of BRATS2017, they seems to use 4 different modalities, and in the configuration file, they just included the directory of the images and they claim it will "concatenate" the images. But I want to know more, as those images are 3D, how are they concatenated? [slice1-30]:[slice1-30].. or [slice1, slice1, slice1 ...]:[slice2, slice2, slice2...]?
And can we control the data organization part? If so, which file should I modify?
Any suggestion would be greatly appreciated!
In this case, the 3D images are concatenated in an additional dimension. You control the order they're concatenated in by specifying the order of files to load in the *.ini files.
However, as long as you're consistent, it shouldn't matter what order the modalities go in.
The images are concatenated in the channel dimension. For 2D images, the dimensions are NSSC: batch size, 2 spatial dimensions, then channel. For 3D images, the dimensions are NSSSC: batch size, 3 spatial dimensions, then channel.
Apologies for tagging this just ImageJ - it's a problem regarding MicroManager, a microscopy plugin for it and I thought this would be best.
I'd recently taken images for an important experiment using MicroManager (a recent version, though I cannot recall the exact number). The IT services at my institution have recently been having some networking problems and my saved preferences for the software had been erased. I'd got half way through my experiment when I realised that I'd saved my images as separate image files (three greyscale TIFFs plus metadata text files) instead of OME-TIFF iamge stacks.
All of my ImageJ macros for image processing rely on having a multiple channel image stack, so this is a bit of a problem. Is there any easy way in MicroManager (or ImageJ) to bulk convert these single channel greyscale images into the OME-TIFF image stack after the images have already been taken?
Cheers.
You can start with a macro like this one:
// Convert your images to a stack
run("Images to Stack", "name=Stack title=[] use");
// The stack will default the images to time points. Convert to channels
run("Stack to Hyperstack...", "order=xyczt(default) channels=3 slices=1 frames=1 display=Color");
// Export as OME-TIFF
run("Bio-Formats Exporter");
This is designed to reconstruct one dataset at a time (open 3 images, run the macro and export the OME-TIFF).
If you don't want any dialogs to show you can pass an output directory to the Bio-Formats exporter:
run("Bio-Formats Exporter", "save=/path/to/image.ome.tif export compression=Uncompressed");
For the output file name you can get the original image name in the macro with getTitle()
There is also a template example on iterating over all the files in a directory, if you want to completely automate the macro. However this may take some tweaking since you want to operate on your images 3 at a time.
Hope that helps!
I'm planning to process quite a large number of images and would like to average every 5 consecutive images. My images are saved as .dm4 file format.
Essentially, I want to produce a single averaged image output for each 5 images that I can save. So for instance, if I had 400 images, I would like to get 80 averaged images that would represent the 400 images.
I'm aware that there's the Running Z Projector plugin but it does a running average and doesn't give me the reduced number of images I'm looking for. Is this something that has already been done before?
Thanks for the help!
It looks like the Image>Stacks>Tools>Grouped Z_Projector does exactly you want.
I found it by opening the command finder ('L') and filtering on "project".