I have images saved as png where the object I want to generate coco style segmentation annotations are present. take this image of a bulb as an example:bulb
Now let's say I want to generate segmentation annotations as in coco format from this image automatically by using a program, how can I do it ?
One thing we can do is write a program to store a fixed number of co-ordinates which are on the edges, and then use those co-ordinates in the segmentation field in the .json file, but that could create problems where the number or co-ordinates needed to accurately capture the boundary of an object would differ.
Any kind of help is greatly appreciated.
Related
I want to crop images which is already annotated for yolov5 format ".txt", but the coordinates will change on cropped so how can i update it and the image crop coordinate will also be one of the class in annotation .txt file. For example: there is two model i want to train first will just detect odometer and second will detect the digits in it so for second model first model will crop odometer image and send to second, therefore for training the second model i need this coordinates corrected. Because I already have full annotation ready and don't want to redo annotation on cropped images of like more than 2k images
As I have mentioned in the comments, I think I can write that as an answer as well.
You can crop your images from yolov5 with --save-crop flag of yolov5. And then for the annotations, you can actually give out full image size as the bounding box sizes. I suggest you check out this thread as well.
I have a large geotiff file of elevation data that I would like to use for object detection. I've labelled the objects (originally as a .shp) and converted the labels into a single geojson.
From reading object detection tutorials it seems that I need to split this large image into multiple images for training/testing. Is there a way to do this using the original labels, so that I don't need to re-label each smaller image?
If anyone has any useful tutorials/end to end examples of preparing satellite data for object detection that would also be really helpful.
The GeoJSON file you have should have the co-ordinates to get the bounding box for the named portion of the original image. (If you want to know how to do that, see here: https://gis.stackexchange.com/a/313023/120175). Once you have the bounding box, you can use any imaging library (Pillow or Pillow-SIMD) to get the sub-image that you have named (with the name in the same geojson object that contained the coordinates you took for getting bounding box). You can operate them while they're in memory or save them (they can be treated as independent images themselves) with these imaging library. These images can be used for training.
I am trying to make a yolo v4 tiny custom data set using google collab. I am using labelImg.py for image annotations which is shown in https://github.com/tzutalin/labelImg.
I have annotated one image as shown as below,
The .txt file with the annotated coordinates looks as following,
0 0.580859 0.502083 0.303906 0.404167
I only have one class which is calculator class. I want to use this one image to produce 4 more annotated images. I want to rotate the annotated image 45 degrees every time and create a new annotated image and a.txt coordinate file. I have seen something like this done in roboflow but I cant figure out how to do it manually with a python script. Is it possible to do it? If so how?
You can look into the repo and article below for python based data augmentation including rotation, shearing, resizing, translation, flipping etc.
https://github.com/Paperspace/DataAugmentationForObjectDetection
https://blog.paperspace.com/data-augmentation-for-bounding-boxes/
If you are using AlexeyAB's darknet repo for yolov4, then there are some augmentations you can use to increase training data size and variation.
https://github.com/AlexeyAB/darknet/wiki/CFG-Parameters-in-the-%5Bnet%5D-section
Look into Data augmentation section where you can use various defined augmentations for object detection by adding them to yolo cfg file.
I am trying to analyse an image and extract each number to then process using a CNN trained with MNIST. The images show garments with a grid-like pattern in each intersection of the grid there is a number (e.g. 0412). I want to analyse and detect which number it is to then store it's coordinates. Does anyone have any recommendations on how to preprocess the image given that it is quite noisy and with multiple numbers. I have tried using contours and it didn't work. I also put the image into binary and there are areas of the image which are unreadable. My initial idea was to isolate each number to then process.
Thanks in advance!
I'm trying to create curved text images with python as fake image to train my network.
sample desired image :
I tried using TextRecognitionDataGenerator but it's output wrap characters but I want characters to be on curve not distorted.
I saw also this example matplotlib but can't work on it. I can create with photoshop but because of large amount of images it is impossible.
regards