Write Tests for an Image Pipeline
A new crop operation silently moves every annotation.
Read the full lesson and open its transcript below. A narrated video will be added after production.
What you will learn
A new crop operation silently moves every annotation.
A practical question
A new crop function moves every scratch annotation ten pixels, yet the processed photographs still look normal. A small test with known coordinates could catch the problem before any inspection decision is made.
The useful idea
Create a tiny image with a simple box whose expected crop and resize coordinates you can calculate by hand. Test the output dimensions, allowed pixel range, and transformed box against those independent expected numbers. For a class mask, check that the labels remain valid and line up with the visible object. Don't calculate the expected values with the same code being tested.
Work through the example
Add a known boundary case: a box partly outside the crop, or a bright pixel at the image edge. Real photos are useful for visual review, but tiny synthetic examples make exact behavior easier to verify. If a future edit changes the result, the test tells you where to investigate. Start with a tiny synthetic image whose expected pixel values and coordinates can be worked out by hand. Apply one crop or resize and verify both the image and its annotation. Add a case at the edge, an empty mask, and a non-square input. A test that merely repeats the production function may reproduce its mistake. Keep expected answers independent and check the whole pipeline for broken assumptions when operations are combined. Use this case to test your understanding: Transform a small synthetic image whose expected box coordinates are easy to calculate. Here is the limitation to watch for: Tests should detect meaningful mistakes, not merely repeat the same implementation logic. This supports a careful conclusion: Pipeline tests can check shape, value ranges, label alignment, and known examples at each step.
Limits and next step
Save the test image and its expected output as part of the pipeline. Next, we'll bring all these steps together around one final inspection question.
Worked example
Transform a small synthetic image whose expected box coordinates are easy to calculate.
Quick check: test your understanding
Try it yourself: Consider this case: Transform a small synthetic image whose expected box coordinates are easy to calculate. Explain how you would check the result. In your answer, address this limitation: Tests should detect meaningful mistakes, not merely repeat the same implementation logic.
Check the reasoning
Pipeline tests can check shape, value ranges, label alignment, and known examples at each step. Tests should detect meaningful mistakes, not merely repeat the same implementation logic.
What is the main idea behind write tests for an image pipeline?
Pipeline tests can check shape, value ranges, label alignment, and known examples at each step.
How is it used in the example?
Transform a small synthetic image whose expected box coordinates are easy to calculate.
What limitation should be checked?
Tests should detect meaningful mistakes, not merely repeat the same implementation logic.
Further reading
Digital Image Processing, 4th edition — Rafael C. Gonzalez and Richard E. Woods.
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Accessible referenceLesson 76 transcript301 words · Narration ready for the upcoming videoOpen transcript
A new crop function moves every scratch annotation ten pixels, yet the processed photographs still look normal. A small test with known coordinates could catch the problem before any inspection decision is made.
Create a tiny image with a simple box whose expected crop and resize coordinates you can calculate by hand. Test the output dimensions, allowed pixel range, and transformed box against those independent expected numbers. For a class mask, check that the labels remain valid and line up with the visible object. Don't calculate the expected values with the same code being tested.
Add a known boundary case: a box partly outside the crop, or a bright pixel at the image edge. Real photos are useful for visual review, but tiny synthetic examples make exact behavior easier to verify. If a future edit changes the result, the test tells you where to investigate. Start with a tiny synthetic image whose expected pixel values and coordinates can be worked out by hand. Apply one crop or resize and verify both the image and its annotation. Add a case at the edge, an empty mask, and a non-square input. A test that merely repeats the production function may reproduce its mistake. Keep expected answers independent and check the whole pipeline for broken assumptions when operations are combined. Use this case to test your understanding: Transform a small synthetic image whose expected box coordinates are easy to calculate. Here is the limitation to watch for: Tests should detect meaningful mistakes, not merely repeat the same implementation logic. This supports a careful conclusion: Pipeline tests can check shape, value ranges, label alignment, and known examples at each step.
Save the test image and its expected output as part of the pipeline. Next, we'll bring all these steps together around one final inspection question.