Dataset Annotation

To recognize specific objects or improve recognition accuracy, users need to train their own models. Dataset collection and annotation are indispensable parts of this process.

1. Dataset Collection

Note: For recognition in specific scenarios, users can use a camera to record videos of objects from various angles, then extract frames from the video as the training set.

1.1. Extracting Images

A simple example: extract one frame every 15 frames from a video as the dataset.

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import cv2
import os
​
# Path to the input video file
video_file = '/home/pi/ultralytics/ultralytics/data/yahboom_data/orange_video/orange.mkv'
​
# Path to the output folder to save the frames
output_folder = '/home/pi/ultralytics/ultralytics/data/yahboom_data/orange_pic'
​
# Check if the output folder exists, if not, create it
if not os.path.exists(output_folder):
    os.makedirs(output_folder)
​
# Open the video file using OpenCV
cap = cv2.VideoCapture(video_file)
​
# Get the total number of frames in the video
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
​
# Get the frames per second (fps) of the video
fps = cap.get(cv2.CAP_PROP_FPS)
​
# Frame counter to keep track of the current frame number
frame_number = 0
​
# Define the interval for extracting frames (every 15th frame)
frame_interval = 15
​
# Initialize a new counter for saved frames (starting from 1)
saved_frame_number = 1
​
while True:
    ret, frame = cap.read()
    
    # If the frame is not successfully read, exit the loop
    if not ret:
        break
​
    # Save every 15th frame
    if frame_number % frame_interval == 0:
        # Format the frame number starting from 1 (e.g., 1.png, 2.png)
        image_name = f'{saved_frame_number}.png'
        image_path = os.path.join(output_folder, image_name)
​
        # Save the current frame as a PNG image
        cv2.imwrite(image_path, frame)
        print(f'Saving frame {frame_number}/{total_frames} as {image_name}')
​
        # Increment the saved frame counter
        saved_frame_number += 1
​
    # Increment the frame counter for the video
    frame_number += 1
​
# Release the video capture object
cap.release()
​
# Print message when processing is complete
print("Video processing complete! Every 15th frame saved as an image!")

1.2. Dataset Paths

Example video: /home/sunrise/ultralytics/ultralytics/data/yahboom_data/orange_video

Example images: /home/sunrise/ultralytics/ultralytics/data/yahboom_data/orange_pic

2. Using Label Studio

Label Studio is an open-source data annotation platform for data labeling and annotation task management, supporting various types of data input and annotation formats.

2.1. Installing Label Studio

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pip install label-studio -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com

2.2. Starting Label Studio

Enter in the terminal:

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label-studio start

2.2.3. Accessing Label Studio

Host Machine

Access method for RDK Pi Orin series boards. First-time access requires entering a username and password.

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http://localhost:8080/

Same Local Network

Devices on the same local network can access via the board's IP:8080.

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http://BoardIP:8080/ # For example: http://192.168.2.99:8080/

First Use

First-time users need to register and log in. Default factory settings (the account information is fictitious):

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Account: yahboom@163.com
Password: yahboom@163.com

After registration, the website will automatically log in:

3. Dataset Annotation

The demonstration dataset is on the board, so access Label Studio on the board.

3.1. Creating a Project

3.2. Project Name

The project name can be named freely according to your training set:

3.3. Importing Training Set

Manually import the pre-prepared dataset: Do not import too many images at once. It is recommended to select 50-100 images each time, then import multiple times.

3.4. Label Settings

We are demonstrating orange recognition, so select "Object Detection".

Delete the built-in labels:

Add new labels:

3.5. Dataset Annotation

Click to annotate all tasks:

Annotation steps: First click the "Orange" label, then use the mouse to draw a box around the corresponding orange in the image. After completing, click Submit to proceed to the next annotation.

If the boxed object is not a complete orange, you can click on the drawn box and readjust the position of each edge:

3.6. Exporting Dataset

Select YOLO format for export. The browser will automatically download a compressed package.

3.7. Dataset Directory Structure

The directory structure of the compressed package exported from Label Studio:

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.
├── images
├── labels
├── classes.txt
└── notes.json

We need to split the exported images and labels from Label Studio into train (training set) and val (validation set), with a ratio of approximately 8:2. The images and labels in both train and val must have corresponding names.

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.
├── classes.txt
├── notes.json
├── train
│   ├── images
│   └── labels
└── val
    ├── images
    └── labels

3.8. Dataset Configuration File

Contains the paths to the training and validation sets, as well as the number and names of classes.

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path: /home/sunrise/ultralytics/ultralytics/data/yahboom_data/orange_data
train: train/images
val: val/images
​
nc: 1
​
# Classes
names:
  0: Orange

References

https://github.com/HumanSignal/label-studio