Could you tell me annotation tool for pose estimation?

4月 1, 2023

Yes, there are several annotation tools available for pose estimation tasks. Pose estimation involves detecting the position and orientation of human body parts in an image or video. Here are some popular annotation tools for pose estimation:

1. VGG Image Annotator (VIA) – Developed by the Visual Geometry Group at the University of Oxford, this open-source tool is designed for image, text, and audio annotations. It supports keypoints and skeletons for pose estimation tasks. You can find more information about VIA here: http://www.robots.ox.ac.uk/~vgg/software/via/
2. RectLabel – This annotation tool is specifically designed for Mac users. It supports bounding box, polygon, and keypoints annotations for various computer vision tasks, including pose estimation. More information can be found at https://rectlabel.com/
3. OpenPose – Developed by Carnegie Mellon University, OpenPose is an open-source real-time system for detecting body, face, and hand keypoints. Although not a dedicated annotation tool, OpenPose can be useful for generating pose estimation annotations automatically. Learn more about OpenPose here: https://github.com/CMU-Perceptual-Computing-Lab/openpose
4. CVAT (Computer Vision Annotation Tool) – Developed by Intel, CVAT is a popular open-source tool for annotating images and videos. It supports various annotation types, including keypoints, which can be used for pose estimation. Find more about CVAT here: https://github.com/openvinotoolkit/cvat
5. labelbox – Labelbox is a widely-used, web-based platform for annotating images and videos. It supports keypoints and bounding box annotations, making it suitable for pose estimation tasks. Labelbox offers both free and paid plans. You can learn more about Labelbox here: https://www.labelbox.com/

These are just a few examples; there are many other tools available depending on your specific needs and requirements. When selecting a tool, consider factors like ease of use, collaboration features, and the level of support and documentation available.

Yes, there are other tools available for annotating human pose skeletons. Here are a few more options:

1. Annotorious: An open-source image annotation library that supports keypoint annotations. Annotorious is lightweight and can be easily integrated into your web application. More information can be found at https://annotorious.github.io/.
2. Supervisely: Supervisely is a web-based platform for data annotation, particularly for computer vision tasks. It supports various annotation types, including keypoints and skeletons, making it suitable for pose estimation. Supervisely offers both free and paid plans. Learn more at https://supervise.ly/.
3. Labelbox Keypoints: While I mentioned Labelbox previously, it’s worth noting that their Keypoints feature specifically supports pose estimation tasks with skeleton annotations. Find more information on Labelbox Keypoints here: https://labelbox.com/docs/guides/keypoints
4. DeepLabCut: DeepLabCut is an open-source toolbox for markerless pose estimation using deep learning. Although it is primarily designed for animal pose estimation, it can also be used for human pose estimation tasks. DeepLabCut includes annotation tools for labeling keypoints in images and videos. More information can be found at https://github.com/DeepLabCut/DeepLabCut.
5. PoseAnnotationTool: This is an open-source tool specifically designed for annotating human body keypoints for pose estimation tasks. It is built with Python and uses OpenCV for image processing. The tool can be found at https://github.com/ArashJavan/pose_annotation_tool.

These are additional examples of tools available for annotating human pose skeletons. As mentioned previously, when selecting a tool, consider factors like ease of use, collaboration features, and the level of support and documentation available.

Yes, there are standalone annotation tools for pose estimation that you can use without relying on web services. Here are a couple of options:

1. PoseAnnotationTool: As mentioned in a previous response, this open-source tool is specifically designed for annotating human body keypoints for pose estimation tasks. It is built with Python and uses OpenCV for image processing. You can run it on your local machine without needing an internet connection. More information and the source code can be found at https://github.com/ArashJavan/pose_annotation_tool.
2. OpenCV Annotate: You can create a custom annotation tool using the OpenCV library in Python. OpenCV is a powerful library for computer vision tasks, and it can be used to build a standalone tool for annotating keypoints in images. A tutorial on creating a custom keypoint annotator with OpenCV can be found here: https://learnopencv.com/human-pose-estimation-using-keypoint-annotations/

While these tools are standalone and can be used offline, keep in mind that they might require some setup and configuration on your local machine. Additionally, you might need to have some programming knowledge to modify the tools to fit your specific requirements.

Certainly! I can provide you with a basic implementation of an annotation tool for pose estimation using Python and OpenCV. You can modify and expand this code to fit your specific needs.

import cv2
import json
import numpy as np

# List of keypoints you want to annotate
keypoints = ['nose', 'left_eye', 'right_eye', 'left_ear', 'right_ear',
             'left_shoulder', 'right_shoulder', 'left_elbow', 'right_elbow',
             'left_wrist', 'right_wrist', 'left_hip', 'right_hip',
             'left_knee', 'right_knee', 'left_ankle', 'right_ankle']

# Colors for each keypoint
colors = [tuple(np.random.randint(0, 255, 3).tolist()) for _ in range(len(keypoints))]

def click_event(event, x, y, flags, param):
    if event == cv2.EVENT_LBUTTONDOWN:
        selected_keypoint = param["selected_keypoint"]
        img = param["img"].copy()

        if 0 <= selected_keypoint < len(keypoints):
            # Save the clicked point in the annotations
            param["annotations"][keypoints[selected_keypoint]] = (x, y)

            # Draw the keypoint
            cv2.circle(img, (x, y), 5, colors[selected_keypoint], -1)
            cv2.imshow('image', img)

def main():
    # Read your image
    image_path = "your_image_path_here.jpg"
    img = cv2.imread(image_path)

    # Initialize the annotations dictionary
    annotations = {keypoint: None for keypoint in keypoints}

    # Set up OpenCV window and mouse callback
    cv2.namedWindow('image')
    cv2.setMouseCallback('image', click_event, {"img": img, "selected_keypoint": 0, "annotations": annotations})

    while True:
        # Display instructions
        print(f"Annotate {keypoints[0]}")
        print("Press 'n' for next keypoint, 'p' for previous keypoint, or 'q' to quit and save annotations.")

        # Show the image
        cv2.imshow('image', img)

        # Wait for key press
        key = cv2.waitKey(0) & 0xFF

        if key == ord('n'):
            # Move to the next keypoint
            if 0 <= selected_keypoint < len(keypoints) - 1:
                selected_keypoint += 1
        elif key == ord('p'):
            # Move to the previous keypoint
            if 0 < selected_keypoint:
                selected_keypoint -= 1
        elif key == ord('q'):
            # Quit and save annotations
            break

    cv2.destroyAllWindows()

    # Save the annotations to a JSON file
    with open('annotations.json', 'w') as outfile:
        json.dump(annotations, outfile)

    print("Annotations saved to 'annotations.json'")

if __name__ == '__main__':
    main()

Make sure to replace “your_image_path_here.jpg" with the path to your image file. This script allows you to annotate keypoints on an image by clicking on the corresponding locations. Press 'n’ to move to the next keypoint, 'p’ to move to the previous keypoint, and 'q’ to quit and save the annotations.

To use this code, you’ll need Python and OpenCV installed on your machine. You can install OpenCV using the following command:

pip install opencv-python-headless

This basic implementation can be further customized and improved to handle multiple images, visualize skeletons, and improve the user interface.

Here’s an extended version of the previous code that includes support for multiple images and visualizes the skeleton as you annotate keypoints:

import cv2
import json
import numpy as np
import os

# List of keypoints you want to annotate
keypoints = ['nose', 'left_eye', 'right_eye', 'left_ear', 'right_ear',
             'left_shoulder', 'right_shoulder', 'left_elbow', 'right_elbow',
             'left_wrist', 'right_wrist', 'left_hip', 'right_hip',
             'left_knee', 'right_knee', 'left_ankle', 'right_ankle']

# Colors for each keypoint
colors = [tuple(np.random.randint(0, 255, 3).tolist()) for _ in range(len(keypoints))]

# Skeleton structure
skeleton = [
    ('nose', 'left_eye'), ('nose', 'right_eye'),
    ('left_eye', 'left_ear'), ('right_eye', 'right_ear'),
    ('left_shoulder', 'right_shoulder'),
    ('left_shoulder', 'left_elbow'), ('right_shoulder', 'right_elbow'),
    ('left_elbow', 'left_wrist'), ('right_elbow', 'right_wrist'),
    ('left_hip', 'right_hip'),
    ('left_hip', 'left_knee'), ('right_hip', 'right_knee'),
    ('left_knee', 'left_ankle'), ('right_knee', 'right_ankle')
]

def click_event(event, x, y, flags, param):
    if event == cv2.EVENT_LBUTTONDOWN:
        selected_keypoint = param["selected_keypoint"]
        img = param["img"].copy()

        if 0 <= selected_keypoint < len(keypoints):
            # Save the clicked point in the annotations
            param["annotations"][keypoints[selected_keypoint]] = (x, y)

            # Draw the keypoint
            cv2.circle(img, (x, y), 5, colors[selected_keypoint], -1)

            # Draw the skeleton
            for start, end in skeleton:
                if start in param["annotations"] and end in param["annotations"]:
                    start_point = param["annotations"][start]
                    end_point = param["annotations"][end]
                    cv2.line(img, start_point, end_point, (0, 255, 0), 2)

            cv2.imshow('image', img)

def main():
    # Read your images
    image_folder = "your_image_folder_path_here"
    image_files = [f for f in os.listdir(image_folder) if f.lower().endswith(('.png', '.jpg', '.jpeg'))]

    for image_file in image_files:
        image_path = os.path.join(image_folder, image_file)
        img = cv2.imread(image_path)

        # Initialize the annotations dictionary
        annotations = {keypoint: None for keypoint in keypoints}

        # Set up OpenCV window and mouse callback
        cv2.namedWindow('image')
        cv2.setMouseCallback('image', click_event, {"img": img, "selected_keypoint": 0, "annotations": annotations})

        while True:
            # Display instructions
            print(f"Annotating {image_file}: {keypoints[0]}")
            print("Press 'n' for next keypoint, 'p' for previous keypoint, 's' to skip this image, or 'q' to quit and save annotations.")

            # Show the image
            cv2.imshow('image', img)