Pros of openpose

• More comprehensive body keypoint detection, including face and hand keypoints
• Better performance and real-time capabilities on GPU
• Extensive documentation and community support

Cons of openpose

• Higher system requirements and more complex setup process
• Less flexibility in terms of customization and model architecture

Code Comparison

openpose:

// Configuring OpenPose
op::Wrapper opWrapper{op::ThreadManagerMode::Asynchronous};
opWrapper.configure(opWrapper);
opWrapper.start();

// Process and display results
opWrapper.emplaceAndPop(datumProcessed);

pose-tensorflow:

# Running inference
poseLifting = PoseLifting(model_file)
pose_2d = estimate_pose(image)
pose_3d = poseLifting.inference(pose_2d)

# Visualize results
visualize(image, pose_2d, pose_3d)

The openpose implementation is in C++ and focuses on real-time processing, while pose-tensorflow uses Python and provides a more straightforward API for 2D and 3D pose estimation. openpose offers more advanced features and better performance, but pose-tensorflow may be easier to integrate into existing Python projects and customize for specific needs.

Pros of Detectron2

• More comprehensive and actively maintained, with a wider range of object detection and segmentation models
• Built on PyTorch, offering better flexibility and ease of use for deep learning researchers
• Extensive documentation and community support

Cons of Detectron2

• Steeper learning curve due to its more complex architecture
• Potentially higher computational requirements for some models

Code Comparison

pose-tensorflow:

import tensorflow as tf
from pose_tensorflow.nnet import predict
config = tf.ConfigProto()
sess = tf.Session(config=config)
poseconfig = predict.load_config("path/to/config.yaml")

Detectron2:

from detectron2.config import get_cfg
from detectron2.engine import DefaultPredictor

cfg = get_cfg()
cfg.merge_from_file("path/to/config.yaml")
predictor = DefaultPredictor(cfg)

Key Differences

• pose-tensorflow focuses specifically on human pose estimation, while Detectron2 covers a broader range of computer vision tasks
• Detectron2 offers more pre-trained models and easier integration with other PyTorch-based projects
• pose-tensorflow may be simpler to use for those specifically interested in pose estimation and familiar with TensorFlow

Pros of tfjs-models

• Runs in the browser, enabling client-side pose estimation without server dependencies
• Supports a wider range of pre-trained models for various tasks beyond pose estimation
• Actively maintained by the TensorFlow team with frequent updates and improvements

Cons of tfjs-models

• May have slower performance compared to native TensorFlow implementations
• Limited to JavaScript environments, reducing flexibility for other programming languages
• Potentially larger model sizes due to browser compatibility requirements

Code Comparison

pose-tensorflow:

import tensorflow as tf
from pose_tensorflow import PoseEstimator

estimator = PoseEstimator()
poses = estimator.estimate(image)

tfjs-models:

import * as poseDetection from '@tensorflow-models/pose-detection';

const detector = await poseDetection.createDetector(poseDetection.SupportedModels.MoveNet);
const poses = await detector.estimatePoses(image);

Summary

While pose-tensorflow offers native TensorFlow performance and Python integration, tfjs-models provides browser-based functionality and a broader range of pre-trained models. The choice between them depends on specific project requirements, such as deployment environment and performance needs.

Pros of Mask_RCNN

• More comprehensive object detection and instance segmentation capabilities
• Better documentation and community support
• Includes pre-trained models on COCO dataset

Cons of Mask_RCNN

• Higher computational requirements
• More complex architecture, potentially harder to customize
• Less focused on human pose estimation specifically

Code Comparison

Mask_RCNN:

import mrcnn.model as modellib
from mrcnn import utils

class InferenceConfig(coco.CocoConfig):
    GPU_COUNT = 1
    IMAGES_PER_GPU = 1

model = modellib.MaskRCNN(mode="inference", config=InferenceConfig(), model_dir=MODEL_DIR)

pose-tensorflow:

from pose_tensorflow.nnet import predict
from pose_tensorflow.config import load_config

cfg = load_config("pose_cfg.yaml")
pose_estimator = predict.setup_pose_prediction(cfg)
pose_estimator.predict(image)

The code snippets show that Mask_RCNN has a more object-oriented approach, while pose-tensorflow uses a functional style. Mask_RCNN's setup appears more involved, reflecting its broader capabilities, while pose-tensorflow's code is more straightforward for pose estimation tasks.

Pros of human-pose-estimation.pytorch

• Implemented in PyTorch, which offers dynamic computational graphs and easier debugging
• More recent and actively maintained repository
• Includes pre-trained models and demo scripts for quick start

Cons of human-pose-estimation.pytorch

• Limited to 2D pose estimation, while pose-tensorflow supports both 2D and 3D
• Fewer customization options compared to pose-tensorflow's modular architecture
• Less extensive documentation and fewer examples than pose-tensorflow

Code Comparison

pose-tensorflow:

from config import load_config
from nnet import predict
from util import visualize

cfg = load_config("path/to/config.yaml")
pose_predictor = predict.setup_pose_prediction(cfg)
pose = pose_predictor.predict(image)
visualize.show_heatmaps(cfg, image, pose)

human-pose-estimation.pytorch:

from models.pose_resnet import get_pose_net
from utils.transforms import get_affine_transform

model = get_pose_net(cfg, is_train=False)
model.load_state_dict(torch.load(model_file))
trans = get_affine_transform(c, s, r, image_size)
input = cv2.warpAffine(image, trans, (int(image_size[0]), int(image_size[1])))
output = model(input)