Added crop by class about ISD data type

[HunJ93]

목적

• ELStateDetection 데이터 타입의 각 라벨 crop 기능 추가

내용

• class.yaml 파일에서 ELStateDetection class를 받아와 각 class 디렉토리를 만듭니다.
• 각 이미지의 json을 순회하며 label을 original 이미지에서 crop을 진행합니다.
• original 이미지에서 crop된 이미지는 각 라벨 class 디렉토리로 들어갑니다.
• 모든 작업이 끝나면 class 디렉토리가 비어져있으면 삭제합니다.

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기능 동작 검증

• 로컬 개발 환경에서 테스트 완료

---

각 class별 디렉토리

디렉토리 안 crop된 이미지

[gemini-code-assist]

Summary of Changes

Hello @HunJ93, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request implements a significant new utility within the application, enabling the automated extraction and categorization of image regions based on their annotations. The primary goal is to streamline the process of creating class-specific image datasets from existing labeled data, which is crucial for machine learning workflows. This feature is integrated into the GUI for ease of use and includes robust handling for various scenarios, such as managing output directories and supporting only rectangular annotations.

Highlights

• New Feature: Crop Labels by Class: I've introduced a new feature that allows users to crop labeled objects from images and organize them into class-specific directories. This is particularly designed for the ELStateDetection data type, enabling automated dataset preparation for specific object classes.
• UI Integration: A new 'Crop Classes' action has been added to the application's user interface, accessible via the 'Administrator' menu and the main toolbar. This provides a convenient way to trigger the new cropping functionality directly from the GUI.
• Dependency Update: The opencv-python library has been added as a new development dependency to support the image processing operations required for cropping.

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[Copilot]

Pull Request Overview

Adds a crop-by-class feature for ELStateDetection labels, organizing cropped images into class-specific folders.

• Introduces OpenCV for image processing
• Implements a new CLI tool to parse JSON annotations and crop images by class
• Integrates a "Crop Classes" action with a keyboard shortcut into the GUI

Reviewed Changes

Copilot reviewed 4 out of 4 changed files in this pull request and generated 3 comments.

File	Description
requirements-dev.txt	Added opencv-python for cropping functionality
labelme/config/default_config.yaml	Added crop_classes keyboard shortcut
labelme/cli/crop_label_class.py	New script to crop and organize images by class
labelme/app.py	Integrated crop action into the GUI

Comments suppressed due to low confidence (2)

labelme/cli/crop_label_class.py:20

• [nitpick] Consider renaming allow_class to allowed_classes to clearly indicate this holds multiple class names.

        self.allow_class = self.config['ELStateDetection']

labelme/cli/crop_label_class.py:50

• [nitpick] The method name _created_class_dir is past tense; rename it to _create_class_dirs to reflect its action of creating directories.

    def _created_class_dir(self, input_dir):

[gemini-code-assist]

Code Review

This pull request adds a new feature to crop labeled objects from images based on their class. The implementation is a good start, but I've identified several critical and high-severity issues related to security, error handling, and application stability. My review includes suggestions to use yaml.safe_load for security, improve error handling to prevent crashes, and replace sys.exit() with proper exception handling to ensure a better user experience in the GUI application. Addressing these points will make the new feature much more robust and secure.

[kjwoo31]

변경 내용 확인하였습니다! 고생하셨습니다

[RobotisDongun]

확인했습니다 고생하셨습니다