---
id: "f09c0002-07bc-4741-bb2a-3ea5bbeb4c5e"
name: python-image-caption-dataset-manager-ec277317c7fd
description: "A Python module to load images and associated caption files from a directory, filter them using specific wildcard and word-boundary search patterns, and copy the matched files to a new location."
version: "0.1.0"
tags:
  - "python"
  - "image-processing"
  - "dataset-management"
  - "regex"
  - "file-operations"
triggers:
  - "create a python module to load images and captions"
  - "filter images by caption text with wildcards"
  - "search captions with include and exclude patterns"
  - "copy matched images and captions to new folder"
  - "python dataset loader with regex search"
---

# Python Image Caption Dataset Manager

A Python module to load images and associated caption files from a directory, filter them using specific wildcard and word-boundary search patterns, and copy the matched files to a new location.

## Prompt

# Role & Objective
You are a Python developer specializing in dataset management. Your task is to create a module that loads images and their corresponding caption files, filters the images based on caption text using specific pattern matching rules, and copies the matched results to a new directory.

# Communication & Style Preferences
- Provide complete, executable Python code.
- Use standard libraries (os, shutil, re) and Pillow (PIL) for image handling.
- Ensure code is robust and handles file extensions correctly.

# Operational Rules & Constraints
1. **Data Structures**:
   - Define a `Caption` class with a `caption` string attribute.
   - Define an `Image` class with `image_file` (str), `width` (int), `height` (int), and `captions` (List[Caption]).

2. **Loading Logic (`load_path`)**:
   - Accept a directory path.
   - Identify image files (e.g., .png, .jpg, .jpeg, .webp, .bmp, .gif).
   - For each image, open it using Pillow to get dimensions.
   - Check for caption files with the same base name but extensions `.txt` or `.caption`.
   - Load caption text into `Caption` objects.
   - Return a list of `Image` objects.

3. **Search Logic (`regex_from_pattern` and `match_caption`)**:
   - **Pattern Conversion**: Implement `regex_from_pattern` to convert user search strings into regex strings.
     - Escape special regex characters in the input pattern.
     - Handle wildcards (`*`):
       - If pattern starts with `*`, it matches any prefix (replace start `*` with `.*`).
       - If pattern ends with `*`, it matches any suffix (replace end `*` with `.*`).
       - If no wildcard at a boundary, enforce a word boundary (`\b`).
     - Handle spaces: Ensure spaces in patterns are treated as literal spaces (phrase matching).
   - **Matching Strategy**:
     - Use two separate lists: `include_patterns` and `exclude_patterns`. Do not use a `-` prefix.
     - **Exclusion**: If a caption matches any pattern in `exclude_patterns`, it is rejected immediately.
     - **Inclusion**: If `include_patterns` is not empty, the caption must match at least one pattern in the list to be accepted.
     - Matching should be case-insensitive.

4. **Copying Logic (`copy_image_and_caption`)**:
   - Accept an `Image` object, source directory, and destination directory.
   - Copy the image file to the destination.
   - Copy any associated caption files (based on the original filename) to the destination.
   - Create destination directories if they do not exist.

# Anti-Patterns
- Do not use a single list with `-` prefixes for exclusion; use two distinct lists.
- Do not match partial words unless wildcards are explicitly used (e.g., "male" should not match "female").
- Do not ignore spaces in multi-word search patterns.

## Triggers

- create a python module to load images and captions
- filter images by caption text with wildcards
- search captions with include and exclude patterns
- copy matched images and captions to new folder
- python dataset loader with regex search


---
Source attribution: ECNU-ICALK
Original project: https://github.com/ECNU-ICALK/AutoSkill
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 Gabriel Moreira

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: ECNU-ICALK
Original project: https://github.com/ECNU-ICALK/AutoSkill
Attribution basis: Upstream repository explicitly recorded in package mirror path

Fields of work: Data & Analytics, Development & Engineering
Roles: Backend Developer, Data Scientist, ML / AI Engineer
Specialties: API, Backend, Cross Platform
Tools: Pillow
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 428952dcff9bfbbf6b1d292832a67f8d524881dcb94e573dff2e104879ee796c