Hello there! This is the fourth and the final part in our series on adapting image augmentation methods for object detection tasks. In the last three posts we have covered a variety of image augmentation techniques such as Flipping, rotation, shearing, scaling and translating. This part is about how to bring it all together and bake it into the input pipeline for your deep network. So, let's get started. This series has 4 parts. Part 1: Basic Design and Horizontal Flipping 2.
Part 2: Scaling and Translation 3. Part 3: Rotation and Shearing 4. Part 4: Baking augmentation into input pipelines. Everything from this article and the entire augmentation library can be found in the following Github Repo. Now, if you want to apply multiple transformations, you can probably do them by applying them Sequentially, one by one. For example, if I were to apply flipping, followed by scaling and rotating here is how I would accomplish it.
At this point, We will implement a function that solely combines multiple data augmentations. Let's write this function. The self. There is also another attribute, self. While a lot of architectures these days are fully convolutional, and hence are size invariant, we often end up choosing a constant input size for sake uniformity and to facilitate putting our images into batches which help in speed gains. Therefore, we'd like to have a image transform which resizes our images to a constant size as well as our bounding boxes.
We also want to maintain our aspect ratio. In the example above, the original image is of the size x When we have to resize a rectangular image to a square dimension keeping the aspect ratio constant, we resize the image isotropically keeping aspect ratio constant so that longer side is equal to the input dimension.
Now, that we have our augmentations done, and also a way to combine these augmentations, we can actually think about designing an input pipeline that serves us images and annotations from the COCO dataset, with augmentations applied on the fly. In deep networks, augmentation may be done using two ways.
Offline augmentation and online augmentation. This can help us multiply our train example by as many times as we want. Since, we have variety of augmentations, applying them stochastically can help us increase our training data many folds before we start to be repetitive. However, there is one drawback to this method that it isn't as suitable when the size of our data is too big. Consider a training dataset that occupies 50 GB of memory.
Augmenting it once alone would make the size go up to GB. In online augmentations, augmentations are applied just before the images are fed to the neural network. This has a couple of benifits over our previous approach. On a side note, from a computationally point of view, one might wonder whether the CPU or the GPU should be doing the online augmentations during the training loop.
Can anyone suggest an image labeling tool? I need a tool to label object s in image and use them as training data for object detection, any suggestions?
Any suggestions will be appreciated, thanks! Object Detection. Most recent answer. Abder-Rahman Ali. University of Stirling. Popular Answers 1.
Xiuliang Jin. Chinese Academy of Agricultural Sciences. You can use 'labelImg'. You can touch me if you have any questions.According to cocodataset.
It is one of the best image datasets available, so it is widely used in cutting edge image recognition artificial intelligence research. I highly recommend you read that page to understand how it works.
Even reading that page, it wasn't fully clear to me how to do it, so I've created some example code and JSON to help. I'm going to use the following two images for an example. The COCO dataset only contains 80 categories, and surprisingly "lamp" is not one of them. I'm going to create this COCO-like dataset with 4 categories: houseplant, book, bottle, and lamp. The first 3 are in COCO. The first step is to create masks for each item of interest in the scene.
That's 5 objects between the 2 images here.COCO Dataset Format - Complete Walkthrough
In the method I'm teaching here, it doesn't matter what color you use, as long as there is a distinct color for each object. You also don't need to be consistent with what colors you use, but you will need to make a note of what colors you used for each category. Important note 1: Even if there were 2 books in one scene, they would need different colors, because overlapping books would be impossible to distinguish by the code we are going to write.
Important note 2: Make sure each pixel is a solid color. Some image applications will perform smoothing around the edges, so you'll get blended colors and this method won't work.
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Note the rough edges below. Image 1 colors: 0,0 : houseplant; 0, 0, : book Image 2 colors:, 0 : bottle;0, : book;, 0 : lamp. This is the only difficult part, really. Convert the information to JSON. Here's a python function that will take in a mask Image object and return a dictionary of sub-masks, keyed by RGB color.
Note that it adds a padding pixel which we'll account for later. Here's a python function that will take a sub-mask, create polygons out of the shapes inside, and then return an annotation dictionary. This is where we remove the padding mentioned above. Home Blog Tutorials Courses. The annotations are stored using JSON. Object Detection I'm going to use the following two images for an example. Create sub-masks Here's a python function that will take in a mask Image object and return a dictionary of sub-masks, keyed by RGB color.
To be continuedYou can import upload these images into an existing PowerAI Vision data set, along with the COCO annotation file, to inter-operate with other collections of information and to ease your labeling effort. If you are using a. Only "object detection" annotations are supported. You can review the annotation format on the COCO data format page. When you import images with COCO annotations, PowerAI Vision only keeps the information it will use, as follows: PowerAI Vision extracts the information from the imagescategoriesand annotations lists and ignores everything else.
Unused annotations are not saved. For example, if there is annotation information for clockbut no image is tagged with a clock, then the clock object called category in COCO is not saved. Only the bounding box is used. Note: Images without tags are saved. The data set must exist before importing the COCO annotated data. Download the images that you want to import. If you downloaded train Therefore, you must make a new file that contains just the images you want to train.
Create a zip file that contains the annotations file and the images. There can be only one. If more that one. The images and the annotation file can reside in different directories. Import the zip file into an existing PowerAI Vision data set.
Note: COCO data sets are created for competition and are designed to be challenging to identify objects. Therefore, do not be surprised if the accuracy numbers achieved when training are relatively low, especially with the default iterations.Previouslywe have trained a mmdetection model with custom annotated dataset in Pascal VOC data format.
We will start with a brief introduction to the two annotation formats, followed with an introduction to the conversion script to convert VOC to COCO format, finally, we will validate the converted result by plotting the bounding boxes and class labels. As you can see the bounding box is defined by two points, the upper left and bottom right corners. The bounding box is express as the upper left starting coordinate and the box width and height, like "bbox" :[x,y,width,height].
Then you can run the voc2coco. The first argument is the image id, for our demo datasets, there are totally 18 images, so you can try setting it from 0 to How to train an object detection model with mmdetection - my previous post about creating custom Pascal VOC annotation files and train an object detection model with PyTorch mmdetection framework.
Get the source code for this post, check out my GitHub repo. Everything Blog posts Pages. Home About Me Blog Support. HTML html. Current rating: 4.Learn how to convert your dataset into one of the most popular annotated image formats used today. Or at least Jack or COCO was one of the first large scale datasets to annotate objects with more than just bounding boxes, and because of that it became a popular benchmark to use when testing out new detection models.
The format COCO uses to store annotations has since become a de facto standard, and if you can convert your dataset to its style, a whole world of state-of-the-art model implementations opens up. This is where pycococreator comes in. The shapes dataset has xpx jpeg images of random colored and sized circles, squares, and triangles on a random colored background. It also has binary mask annotations encoded in png of each of the shapes. This binary mask format is fairly easy to understand and create.
There are several variations of COCO, depending on if its being used for object instances, object keypoints, or image captions.
Luckily we have pycococreator to handle that part for us. Okay, with the first three done we can continue with images and annotations. All we have to do is loop through each image jpeg and its corresponding annotation pngs and let pycococreator generate the correctly formatted items. Lines 90 and 91 create our image entries, while lines take care of annotations. Single objects are encoded using a list of points along their contours, while crowds are encoded using column-major RLE Run Length Encoding.
RLE is a compression method that works by replaces repeating values by the number of times they repeat. Column-major just means that instead of reading a binary mask array left-to-right along rows, we read them up-to-down along columns.
The COCO Dataset
The tolerance option in pycococreatortools. The higher the number, the lower the quality of annotation, but it also means a lower file size. Using the example Jupyter Notebook in the pycococreator repoyou should see something like this:. You can find the full script used to convert the shapes dataset along with pycococreator itself on github. Take a look below for links to some of the amazing models using COCO.
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Does anyone have an experience with this?
I have images, and annotations, as well as ground truth masks. In order to convert a mask array of 0's and 1's into a polygon similar to the COCO-style dataset, use skimage. I built a very simple tool to create COCO-style datasets. I'm working on a python library which has many useful classes and functions for doing this. It's called Image Semantics. Let's assume that we want to create annotations and results files for an object detection task So, we are interested in just bounding boxes.
Here is a simple and light-weight example which shows how one can create annoatation and result files appropriately formatted for using COCO API metrics. Learn more.
Asked 2 years, 8 months ago. Active 11 months ago. Viewed 7k times. Ven Active Oldest Votes. Hans Krupakar Hans Krupakar 2, 2 2 gold badges 8 8 silver badges 16 16 bronze badges. I haven't looked into it myself but if they are just collections of polygons, extending to multiple polygons would be to add multiple polygon support for a single object cluster. Annotation file: ann. Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. Post as a guest Name.
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COCO and Pascal VOC data format for Object detection
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