All the generated images by Roboflow are not being included in the dataset export (Yolov5).
Only 797 images of the 6376 generated trainings set images are included in the zip...
Is this a bug, or a setting that needs to be adapted somewhere?
Related
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.
So, I have 20 positive samples and 500 negative samples. I created the .vec file using createsample utility.Now, when i try to train the classifier using the traincascade.exe utility, I run into the following error:
I have looked into many solutions given to people who have faced similar issues, but none of them worked.
Things I tried: 1. Increasing the negative sample size 2. Checking the path of the negative(or background images) stored in the Negative.txt file 3. Varying different parameters.
Here is some information regarding the path: My working directory has the following files: 1. Traincascade.exe 2. Positive image folder 3. NegativeImageFolder 4. vec file 5. Negative.txt (file that has path to images in the negative image folder)
My Negative.txt file has the absolute file path for the images in the negative image folder. I also tried changing the file path to the following format:
NegativeImageFolder\Image1.pgm
but didn't work! I tried both front and backslash too!
I have run out of ways to change the file path or make any modification to make this work!
First of all: is NumStages 1 and maxDepth 1 intentional?
Looking at Opencv's source code (cascadeclassifier.cpp, imagestorage.cpp), the error is thrown when in function
bool CvCascadeClassifier::updateTrainingSet( double& acceptanceRatio)
a number, negCount=500, of negative samples cannot be filled.
Before, everything was ok with positive samples (and the line about pos count that was printed on the screen is a proof of this).
Digging deep into source code negCount cannot be filled when imgReader.getNeg( img ) returns false, this means it cannot provide any image, which in turn happens when the list of source negatives is empty.
So you have to concentrate all your efforts in the direction of providing the algorithm with the correct list of negative images.
There are two ways to solve this: make sure that Negative.txt is read and all paths are regular and that every image in the list can be read regularly.
Is the file name “Negative.txt” or “Negatives.txt”?
Anyway with so few positive and negative samples you won’t train anything functioning, it is only useful to make you understand how the process of training works.
Well I was able to resolve the issue and run the train the classifier successfully. However, I am not 100% sure as to how the change I made helped.
This is what I did:
I was generating the Negative.txt file using Excel. I would enter the file path of one image and increment the image filename (since my images were name image1, image2, image3...). So the format as mentioned earlier would be :
C:\OpenCV-3.0.0\opencv\build\x64\vc12\bin\Negative\Image1.pgm
And finally save the file as a Unicode txt document. However, saving it as a unicode txt document gave me the error stated in the question. I saved it as a Text (tab delimited) file and it worked.
I run caffe using an image_data_layer and don't want to create an LMDB or LevelDB for the data, But The compute_image_mean tool only works with LMDB/LevelDB databases.
Is there a simple solution for creating a mean file from a list of files (the same format that image_data_layer is using)?
You may notice that recent models (e.g., googlenet) do not use a mean file the same size as the input image, but rather a 3-vector representing a mean value per image channel. These values are quite "immune" to the specific dataset used (as long as it is large enough and contains "natural images").
So, as long as you are working with natural images you may use the same values as e.g., GoogLenet is using: B=104, G=117, R=123.
The simplest solution is to create a LMDB or LevelDB database of the image set.
The complicated solution is to write a tool similar to compute_image_mean, which takes image inputs and do the transformations and find the mean!
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 know the first step is to create two file lists with the corresponding labels, one for the training and one for the test set. Suppose the former is called train.txt and the latter val.txt. The paths in these file lists should be relative. The labels should start at 0 and look similar to this:
relative/path/img1.jpg 0
relative/path/img2.jpg 0
relative/path/img3.jpg 1
relative/path/img4.jpg 1
relative/path/img5.jpg 2
For each of these two sets, we will create a separate LevelDB. Is this formatted as a text file? I thought I would create a directory with several subdirectories for each of my classes. Do I manually have to create a text file?
Please see this tutorial on how to use convert_imageset to build levelDb or lmdb datasets for caffe's training.
As you can see from these instruction it does not matter how you arrange the image files on your disk (same folder/different folders...) as long as you have the correct paths in your 'train.txt'/'val.txt' files relative to '/path/to/jpegs/' argument. But if you want to use convert_imageset tool, you'll have to create a text file listing all the images you want to use.