I am new to Deep Learning. I have this question: I am trying to train a network with this data. Everything is in one folder and labels are in a different mat file.
I understand that I can read the data with scipy.io. But how can I get train X in one folder? If I use the built in flow_from_directory it shows no images, because every class should have it's own folder.
How can I create X with only one folder? Now it shows Found 0 images belonging to 0 classes
There is just a folder with images. All images are in 1 folder. I mean there is no classes folder. With flow_from_directory you should have something like cars/mercedes, cars/bmw, cars/audi, but my data doesn't have subfolders.
So my question is there any other way to create X data?
Set classes to None and put all images into one subfolder of your image folder.
For example:
flow_from_directory(directory = "/path/to/your/images/", class_mode="None", …)
put your images into /path/to/your/images/data
The link you posted also shows a download link to "A devkit, including class labels for training images and bounding boxes for all images".
You'll find the information there that you need in order to transform your data set into the desired folder structure required for flow_from_directory().
From the README.md
-cars_meta.mat:
Contains a cell array of class names, one for each class.
-cars_train_annos.mat:
Contains the variable 'annotations', which is a struct array of length
num_images and where each element has the fields:
bbox_x1: Min x-value of the bounding box, in pixels
bbox_x2: Max x-value of the bounding box, in pixels
bbox_y1: Min y-value of the bounding box, in pixels
bbox_y2: Max y-value of the bounding box, in pixels
class: Integral id of the class the image belongs to.
fname: Filename of the image within the folder of images.
Related
I am trying to work on a computer vision model but the dataset is completely new to me. It is as shown :
Dataset folders image
I have to extract each image in every folder and combine them into one train and one test folder. This is to get the filepaths of each image which I will use to decode jpeg. Can someone help me with the same ? Basically I need the filepaths of each image in one list. But the image names are also duplicate in each folder.
I am working on a multiclass image classification problem and there are 12 folders inside my training folder and I am trying to load all the images from these 12 folders along with their label in the numpy arrays both X and Y.
This is the code that I am using. I am able to get the images as an array, however I want to know how to get the corresponding labels as well. I'd appreciate the help.
This is the code that I am using.
I'm trying to tile a folder of 3 band images into 256 x 256 pixel squares, (keeping the original CRS), feed this into a Unet model and then re-organize my tiled data back into one complete image to view the results of my model.
I've used this answer but the last row of tiles are composed of the remaining pixels (edge cases).
Is there a way of either allowing for padding to be added or an automatically determined overlap to prevent this and ensure all tiles are 256x256?
makeLastPartFull = True is supposed to do this with cv2, so I am looking for something similar with GDAL?
I'm using Pycharm version 2021.1.
I have a Nifti object generated from a directory of dicom files.
It seems that the Nifti should know how many frames it holds, but all I can find in the header info is the shape. The problem is, the shape is at times (num_images, x, y) and at times (x, y, num_images).
The only nibabel functions I found relevant where from the Ecat library. I am not familiar with ecat format, but I want my method to work for any nii file. I am working with the nibabel library.
Is there a way to retrieve the number of images in a Nifti file?
I'm guessing you're looking at fMRI, DTI or ASL data.
Say your 4D nii stack is called 'data.nii.'
Just go into that directory and do:
mri = nib.load('data.nii')
mri.shape
The fourth element you see will be the number of volumes. You can access it thusly: mri.shape[3] if you need it for some kind of purpose in your programs.
This works consistently for me. If your data are "stacked" in an inconsistent orientation, you are going to have to get fancy.
You could include checks based off of the dimensionality of your images. For example if you know that your images are 128x128x128, then you can go ahead and get whichever element of mri.shape isn't 128, but this approach is suboptimal for a few reasons.
I want to compare an image of a front of a car with a database of images with other cars and tell me that most likely match of it.
Program firstly shall find key points and descriptors of the database of the images using SURF and try to compare them with Flann matcher.
But I simply can't understand how it should work. I'll have matches of the various pairs of images, but how can I compare the images and show relative output?