openpose

Pros of Detectron2

• More comprehensive object detection and segmentation capabilities
• Modular architecture allowing easier customization and extension
• Better performance on large-scale datasets and complex scenes

Cons of Detectron2

• Steeper learning curve due to more complex architecture
• Higher computational requirements for training and inference
• Less specialized for human pose estimation compared to OpenPose

Code Comparison

OpenPose example:

from openpose import pyopenpose as op
params = dict()
opWrapper = op.WrapperPython()
opWrapper.configure(params)
opWrapper.start()

Detectron2 example:

from detectron2.config import get_cfg
from detectron2.engine import DefaultPredictor
cfg = get_cfg()
cfg.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"))
predictor = DefaultPredictor(cfg)

Both libraries offer powerful computer vision capabilities, but Detectron2 provides a more versatile toolkit for various object detection and segmentation tasks, while OpenPose specializes in human pose estimation with potentially easier setup for that specific use case.

Pros of tfjs-models

• Browser-based: Runs directly in web browsers without additional dependencies
• Diverse models: Offers a variety of pre-trained models beyond pose estimation
• JavaScript ecosystem: Integrates easily with web applications and frameworks

Cons of tfjs-models

• Performance: Generally slower than native implementations like OpenPose
• Limited customization: Less flexibility for advanced users to modify core algorithms
• Dependency on TensorFlow.js: Requires loading additional libraries

Code Comparison

OpenPose (C++):

auto datum = op::Datum();
opWrapper.emplaceAndPop(op::VectorDatum{datum});
cv::imshow("OpenPose", datum.cvOutputData);

tfjs-models (JavaScript):

const net = await posenet.load();
const pose = await net.estimateSinglePose(imageElement);
drawKeypoints(pose.keypoints, 0.6, ctx);

Both repositories provide pose estimation capabilities, but OpenPose offers a more comprehensive and performant solution for desktop and server environments, while tfjs-models excels in web-based applications with its easy integration and diverse model offerings.

Pros of YOLOv5

• Faster inference speed and real-time object detection capabilities
• More versatile, supporting a wider range of object detection tasks
• Easier to deploy and integrate into various applications

Cons of YOLOv5

• Less specialized for human pose estimation compared to OpenPose
• May require more data and fine-tuning for specific pose-related tasks
• Potentially lower accuracy for complex human pose scenarios

Code Comparison

OpenPose example:

import cv2
import pyopenpose as op

params = dict()
params["model_folder"] = "../models/"
opWrapper = op.WrapperPython()
opWrapper.configure(params)
opWrapper.start()

datum = op.Datum()
imageToProcess = cv2.imread("image.jpg")
datum.cvInputData = imageToProcess
opWrapper.emplaceAndPop(op.VectorDatum([datum]))

YOLOv5 example:

import torch

model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
img = 'https://ultralytics.com/images/zidane.jpg'
results = model(img)
results.print()
results.save()

Pros of Mask_RCNN

• Provides instance segmentation in addition to object detection
• Supports a wider range of object classes and datasets
• More flexible architecture for various computer vision tasks

Cons of Mask_RCNN

• Generally slower inference time compared to OpenPose
• Requires more computational resources for training and inference
• Less specialized for human pose estimation tasks

Code Comparison

Mask_RCNN example:

import mrcnn.model as modellib

model = modellib.MaskRCNN(mode="inference", config=config, model_dir=MODEL_DIR)
results = model.detect([image], verbose=1)

OpenPose example:

from openpose import pyopenpose as op

opWrapper = op.WrapperPython()
opWrapper.configure(params)
opWrapper.start()
datum = op.Datum()
opWrapper.emplaceAndPop(op.VectorDatum([datum]))

Both repositories offer powerful computer vision capabilities, but they focus on different aspects. Mask_RCNN is more versatile for general object detection and instance segmentation, while OpenPose specializes in human pose estimation. The choice between them depends on the specific requirements of your project and the available computational resources.

Pros of Detectron

• Broader scope: Supports multiple computer vision tasks beyond pose estimation
• More flexible architecture: Modular design allows easier customization
• Better documentation and community support

Cons of Detectron

• Higher computational requirements
• Steeper learning curve for beginners
• Less specialized for real-time pose estimation

Code Comparison

OpenPose example:

#include <openpose/pose/poseExtractor.hpp>

auto poseExtractor = op::PoseExtractorCaffe::getInstance(poseModel, netInputSize, outputSize, keypointScaleMode, num_gpu_start);
poseExtractor->forwardPass(netInputArray, imageSize, scaleInputToNetInputs);

Detectron example:

from detectron2 import model_zoo
from detectron2.engine import DefaultPredictor

cfg = model_zoo.get_config_file("COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml")
predictor = DefaultPredictor(cfg)
outputs = predictor(image)

Both repositories offer powerful tools for computer vision tasks, with OpenPose specializing in real-time pose estimation and Detectron providing a more versatile framework for various detection and segmentation tasks. The choice between them depends on the specific requirements of your project and your familiarity with the respective ecosystems.

Pros of darknet

• Supports a wider range of object detection models (YOLO, Tiny-YOLO, etc.)
• Generally faster inference times for object detection tasks
• More flexible for various computer vision tasks beyond pose estimation

Cons of darknet

• Less specialized for human pose estimation compared to openpose
• May require more setup and configuration for specific use cases
• Documentation can be less comprehensive for certain features

Code Comparison

openpose:

auto opWrapper = op::Wrapper{op::ThreadManagerMode::Asynchronous};
opWrapper.configure(wrapperStructPose);
opWrapper.start();

// Process and display image
auto datumProcessed = opWrapper.emplaceAndPop(imageToProcess);
if (datumProcessed != nullptr)
    cv::imshow("OpenPose", datumProcessed->at(0)->cvOutputData);

darknet:

network *net = load_network("cfg/yolov3.cfg", "yolov3.weights", 0);
image im = load_image("data/dog.jpg", 0, 0, net->w, net->h);
float *X = im.data;
network_predict(net, X);
int nboxes = 0;
detection *dets = get_network_boxes(net, im.w, im.h, 0.5, 0.5, 0, 1, &nboxes);

Both repositories offer powerful computer vision capabilities, with openpose specializing in human pose estimation and darknet providing a broader range of object detection models. The choice between them depends on the specific requirements of your project.