yolo-training-tools

1.Image-extractor-drag.py

A simple GUI tool for extracting frames from video files using OpenCV and Tkinter.

Features

• Drag and drop video files or folders for easy selection.
• Supports custom sample rates for frame extraction (frames per second).
• Option to rotate frames (90° clockwise or counterclockwise).
• Adjustable JPEG compression for output frames.
• Batch processing for multiple videos in a folder.

Requirements

• Python 3.6+
• Libraries: tkinter, cv2 (OpenCV), tkinterdnd2

Installation

1. Install required libraries:

   pip install opencv-python tkinterdnd2

2. Place the script in your desired directory.

Usage

1. Run the script:

   python video_frame_extractor.py

2. Use the GUI to:
   • Select a Video File: Click the "Select Video File" button or drag a video file to the designated area.
   • Select a Folder for Batch Processing: Click "Select Folder for Batch Processing" or drag a folder to the designated area.
   • Set Parameters:
     • Enter the desired sample rate (frames per second).
     • Choose a rotation option (No Rotation, Rotate Left 90°, or Rotate Right 90°).
     • Adjust the compression level using the slider.
   • Click Process Video to process a single file or Batch Process Folder to process all videos in the selected folder.

Output

• Extracted frames are saved in an output folder within the selected folder (for batch processing) or in a new folder named <video_name>_frames next to the video file.

Notes

• Supported video formats include .mp4, .avi, .mkv, .mov, .flv, .wmv, .mpeg, .mpg, and .3gp.
• Compression level ranges from 0 (lowest quality) to 100 (highest quality). The default value is 90.
• The default directory for file dialogs can be changed by modifying the default_dir variable in the script.

2. dataset-split-unsplit.py

This tool provides an easy-to-use graphical interface to manage annotated datasets. It includes two main functionalities:

1. Split Dataset: Splits a dataset into training, validation, and test sets.
2. Merge Dataset: Merges training, validation, and test datasets into a single dataset.

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Features

Split Dataset

• Splits a dataset into train, valid, and test subsets based on user-defined ratios.
• Handles missing labels:
  • If an image exists without a corresponding label file, it is still included (assumes no objects are annotated in the image).
  • If a label file exists without a corresponding image, the label file is removed, and a notification is printed in the console.
• Organizes the output into a structured format:

  ├── train
  │   ├── images
  │   └── labels
  ├── valid
  │   ├── images
  │   └── labels
  └── test
      ├── images
      └── labels
  

Merge Dataset

• Merges separate train, valid, and test datasets into a unified dataset containing images and labels.
• Names the merged dataset folder as merged_<date> (e.g., merged_2024-11-19).

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Requirements

• Python 3.6+
• Libraries:
  • tkinter
  • os
  • shutil
  • random
  • datetime

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Installation

1. Install Python 3.6+ if not already installed.
2. Save the script as dataset_tool.py.

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Usage

Running the Tool

Run the script:

python dataset_tool.py

Split Dataset

1. Select Source Folder:
   • Choose a folder containing two subfolders: images and labels.
2. Select Output Folder:
   • Choose where the split dataset will be saved.
3. Set Ratios:
   • Define the train and valid ratios (e.g., 0.7 and 0.2). The test ratio is calculated automatically.
4. Click Split Dataset.

Output Structure:

• The tool creates three folders: train, valid, and test.
• Each folder contains images and labels.

Handling Missing Data:

• Images without labels are included.
• Labels without corresponding images are removed, and their filenames are logged in the console.

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Merge Dataset

1. Select train, valid, and test Folders:
   • Choose the folders containing the datasets to merge.
2. Select Root Directory:
   • Choose a root directory to save the merged dataset.
3. The merged dataset is saved in a folder named merged_<date> inside the selected root directory.

Output Structure:

merged_<date>
├── images
└── labels

Notes

• The tool assumes .jpg images for label matching. Modify the script if other formats (e.g., .png) are used.
• The labels should be .txt files with the same names as their corresponding images.