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This is a medium difficulty Coding & Algorithms question, commonly asked during Technical Screen rounds at Anthropic.

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This question is commonly asked for Software Engineer candidates at Anthropic during technical interviews.

Implement a Batch Image Processor | Anthropic Interview Question

Q: Implement a Batch Image Processor

This question evaluates a candidate's competency in image processing operations and parallel/concurrent programming, covering basic transformations (grayscale, scale, resize) and considerations for CPU-bound batch processing.

Implement a program that applies basic image transformations to a batch of images.

You are given a list of input image paths and, for each image, a sequence of operations to apply in order. Support the following operations:

grayscale : convert the image to grayscale.
scale(factor) : multiply both width and height by a positive floating-point scale factor.
resize(width, height) : resize the image to the exact target dimensions.

Part 1

Write a function that processes a small batch of images correctly and saves the transformed results.

Example function shape:

process_images(tasks: list[ImageTask], output_dir: str) -> list[str]

Where each ImageTask contains:

input_path : path to the source image
operations : ordered list of operations such as grayscale , scale(0.5) , or resize(200, 300)

The function should return the output paths in the same order as the input tasks.

Part 2

Now assume the images are very large and the batch size is also large. The interviewer asks you to improve performance on a multi-core machine.

Update your approach so that:

multiple images can be processed in parallel,
output ordering is preserved,
a failure in one task does not corrupt other results,
the design is appropriate for CPU-heavy image processing.

You may use a standard Python imaging library such as Pillow.

Implement a program that applies basic image transformations to a batch of images.

You are given a list of input image paths and, for each image, a sequence of operations to apply in order. Support the following operations:

grayscale : convert the image to grayscale.
scale(factor) : multiply both width and height by a positive floating-point scale factor.
resize(width, height) : resize the image to the exact target dimensions.

Part 1

Write a function that processes a small batch of images correctly and saves the transformed results.

Example function shape:

process_images(tasks: list[ImageTask], output_dir: str) -> list[str]

Where each ImageTask contains:

input_path : path to the source image
operations : ordered list of operations such as grayscale , scale(0.5) , or resize(200, 300)

The function should return the output paths in the same order as the input tasks.

Part 2

Now assume the images are very large and the batch size is also large. The interviewer asks you to improve performance on a multi-core machine.

Update your approach so that:

multiple images can be processed in parallel,
output ordering is preserved,
a failure in one task does not corrupt other results,
the design is appropriate for CPU-heavy image processing.

You may use a standard Python imaging library such as Pillow.

Implement a Batch Image Processor

Quick Overview

Implement a Batch Image Processor

Part 1

Part 2

Submit Your Answer to Earn 20XP

Implement a Batch Image Processor

Quick Overview

Implement a Batch Image Processor

Part 1

Part 2

Submit Your Answer to Earn 20XP