Chain custom Amazon SageMaker Ground Truth jobs for image processing

Figure 2: A closeup of a single bin, which reveals 2 adjoining bins.

Amazon SageMaker Ground Truth supports various types of labeling jobs, including numerous image-based labeling workflows like image-level labels, bounding box-specific labels, or pixel-level labeling. For circumstances not covered by these standard approaches, Ground Truth also supports custom image-based labeling, which permits you to develop a labeling workflow with a completely distinct UI and associated processing. Beyond that, you can chain various Ground Truth labeling jobs together so that the output of one job acts as the input to another job, to permit much more versatility in a labeling workflow by breaking the job into numerous phases.
In this post, we demonstrate how to chain two customized Ground Truth tasks together to carry out advanced image manipulations, consisting of separating parts of images, and de-skewing images that were photographed from an angle. Additionally, we show numerous techniques for enhancing source images, which are valuable for situations where you have a limited number of source images.
Extracting regions of an image
Expect were charged with creating an artificial intelligence (ML) model that processes a picture of a shelving system and figures out whether any of the bins because shelving unit require restocking. Due to the size of the storeroom, a single electronic camera is used to record pictures of several shelving units, each from a different angle The following image is an example of such a shelving system.

Figure 1: A shelving unit with numerous bins full, photographed from an angle.

In this example, weve separated a rectangle-shaped area that bounds a provided bin, but due to the fact that the image was drawn from an angle, portions of the bins on the left and right are also partially consisted of. An image like this performs improperly when utilized for training or for inference since a rectangular area consists of information from other bins.
To fix this, we can pick a non-rectangular section of the initial image and warp it to develop a new image. The following image demonstrates the results of a warp change used to the original image.

For training or reasoning, we require images of specific bins, instead of the overall shelving unit. The design were establishing takes a picture of a single bin, and return a classification of Empty or Full. This classification feeds into an automated restocking system, enabling us to maintain stock levels at the bin level without the problem of someone physically inspecting the levels.
Sadly, since the shelf images are taken at an angle, each bin is skewed and has a different size and shape. Due to the fact that any bin images extracted from the primary image are rectangular, the extracted images consist of unwanted material, as revealed in the following picture of 2 adjacent bins

Figure 3: Original shelving unit with just the bins isolated, and the image distorted to make it orthogonal

Figure 6: The custom-made Ground Truth UI for the first labeling task.

For custom-made Ground Truth user interfaces, a set of custom-made tags is offered, referred to as Crowd tags. These tags consist of bounding boxes, lines, points, and other interface components that you can utilize to develop a labeling UI. In this case, we use the crowd-polygon tag, which is displayed as a yellow polygon.
After the labeler draws a polygon with four corners on the UI for all source images, they leave the UI by picking Done. At this point, the post-UI Lambda function is run and each de-skewed image is conserved to Amazon S3. When the function is total, control is passed to the next chained Ground Truth task.
Typically, chained Ground Truth tasks recycle an output manifest file as the input manifest apply for the next (chained) labeling task. In this case, we developed a brand-new image, so we customize the pre-UI Lambda function so it passes in the proper (de-skewed) file name, instead of the initial, manipulated image file name.
The second task in the chain uses the bounding box-based labeling performance that is integrated in to Ground Truth. The bounding boxes do not cover the whole contents of each bin, but they do cover the openings of the bins. This provides enough information to create a design to find whether a bin is complete or empty.

Customized Ground Truth tasks supply a great deal of flexibility, and utilizing them with images allows sophisticated functionality like cropping and de-skewing images, as well as carrying out custom image augmentation. The provided Crowd HTML tags support numerous various labeling approaches like polygons, lines, text boxes, modal notifies, key point positioning, and others. Combined with the power of post-ui and pre-ui Lambda functions, a customized Ground Truth task permits you to construct complicated labeling tasks to support a wide array of use cases, and integrating various customized tasks by chaining them together offers a lot more alternatives.
You can use the GitHub repo related to this post as a beginning point for your own chained image labeling jobs. You can also extend the code to support additional image augmentation approaches (like cropping or turning the source images), or customize it to fit your particular use case.
To read more about chained Ground Truth jobs, see Chaining Labeling Jobs.
For more info about the Crowd tags you can utilize in the Ground Truth UI, see Crowd HTML Elements Reference.

Compare the following two bin images: the image on the left is drawn out from the initial image, and the image on the right is the very same bin, drawn out from the de-skewed image.

This warping achieves two tasks. First, weve chosen simply the shelving system, cropping out the neighboring walls, floor, and any other irrelevant locations near the edges of the racks. Second, the warping of the image leads to each bin being more rectangular than the original version.
This deformed image doesnt have any brand-new material– its simply a distortion of the original image. But by performing this warping, each bin can be selected utilizing a rectangular bounding box, which provides required consistency, no matter what position a bin is in. Compare the following 2 bin images: the image left wing is drawn out from the initial image, and the image on the right is the very same bin, drawn out from the de-skewed image.

Figure 7: De-skewed image with bounding boxes from the 2nd chained Ground Truth labeling job.

Figure 5: Architecture diagram revealing 2 chained Ground Truth tasks, each with a Pre- and Post- UI Lambda function.

The very first Ground Truth job obtains images from Amazon S3 and shows them one at a time, waiting for the user to specify the four corners of the shelving unit within the image, using a custom-made UI. Typically, image augmentation is performed by taking a source image and producing numerous versions of it, altering aspects like brightness and contrast, coloring, and even cropping or turning images. With this technique, a single source image of a shelving system with 24 bins produces 14 versions for each bin image, for a total of 336 images that can be used for training a model. Customized Ground Truth tasks offer an excellent deal of versatility, and using them with images enables advanced functionality like cropping and de-skewing images, as well as performing customized image augmentation.

Images that require to be labeled are kept in Amazon Simple Storage Solution (Amazon S3). The first Ground Truth task retrieves images from Amazon S3 and shows them one at a time, waiting for the user to specify the 4 corners of the shelving unit within the image, using a custom-made UI. When that step is complete, the post-UI Lambda function utilizes the corner collaborates to warp or de-skew each image, which is then saved to the exact same S3 pail that the initial image lives in. Keep in mind that its not needed to do this throughout inference– for a circumstance where the electronic camera remains in a repaired area, you can save those corner collaborates for later usage during inference.
After the very first Ground Truth job has actually de-skewed the source image, the second task utilizes basic bounding boxes to identify each bin within the de-skewed image. The post-UI Lambda function then draws out the private bin images, augments them with rotations, turning, and color and brightness alterations, and writes the resulting information to Amazon S3, where it can be utilized for model training or other functions.
You can discover example code and deployment guidelines in the GitHub repo.
Custom user interface.
From a labelers viewpoint, after they visit and select a task, they utilize the customized UI to pick the four corners of a bin.

At this point, the post-UI Lambda function runs and crops out each bin image, makes variations of it for image augmentation functions, and saves the variations into a folder structure in Amazon S3 based on classification. Each subfolder consists of images of bins that are full or either empty, ideal for usage in design training.
Image augmentation.
Image augmentation is a strategy sometimes used in image-based ML work. Its particularly helpful when the variety of source images is low, or restricted in the variety of variants. Usually, image enhancement is performed by taking a source image and developing multiple variations of it, changing elements like brightness and contrast, coloring, and even cropping or turning images. These variations assist the resulting design be more robust and capable of dealing with images that are different to the original training images.
In this example, we utilize image enhancement approaches in the post-UI Lambda function of the 2nd Ground Truth job. The labeler has specified the bounding boxes for each bin image in the Ground Truth UI, and that data is used to draw out parts of the general image. Those extracted portions are of the specific bins, and these smaller images are utilized as input into our image augmentation procedure.
In our case, we develop 14 variants of each bin image, with variations of brightness, sharpness, and contrast, as well horizontal flipping integrated with these variations. With this technique, a single source picture of a shelving system with 24 bins creates 14 variations for each bin image, for an overall of 336 images that can be used for training a design. The following reveals an original bin image (upper left) and each of its versions.

The bottom opening of the bin was initially at an angle, and now its horizontal. In general, weve lowered the quantity of the bin shown, and increased the proportion of the contents of the bin within the image. This enhances our ML training process, due to the fact that each bin image has less superfluous content.
Ground Truth jobs.
Each customized Ground Truth labeling job is defined with a web-based interface and 2 associated AWS Lambda functions (to find out more, see Processing with AWS Lambda). One function runs prior to each image shown by the UI, and the other follow the user completes the labeling task for all the images. Ground Truth uses a number of pre-made user interfaces (like bounding box-based selection), but you can likewise produce your own customized UI if needed, as we provide for this example.
When Ground Truth tasks are chained together, the output of one task is used as the input of another task. For this task, we utilize 2 chained jobs to process our images, as shown in the following diagram.

Figure 4: A single bin from the original image (left) compared with the bin from the distorted image (right).

About the Author.
Greg Sommerville is a Senior Prototyping Architect on the AWS Envision Engineering Americas Prototyping group, where he helps AWS consumers execute innovative solutions to difficult problems with machine knowing, IoT and serverless innovations. He lives in Ann Arbor, Michigan and enjoys practicing yoga, accommodating his pets, and playing poker.

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