juxtapose 0.0.35

🍿 Intro

Juxtapose is a 2D multi person pose detection, tracking, and estimation inference toolbox for sports + kinematics analysis. Visit Docs.

See how we integrated juxtapose into this app: Juxt Space

🍄 Overview

Code mostly adopted from four repos -> ultralytics, mmdeploy, mmdetection, mmpose.

Supported Detectors: rtmdet-s, rtmdet-m, rtmdet-l, groundingdino, yolov8
Supported Pose Estimators: rtmpose-s, rtmpose-m, rtmpose-l
Supported Trackers: bytetrack, botsort Supported Point Trackers: Tapnet

🥒 Updates

• 2024/05/16 Remove ultralytics dependency, port yolov8 to run in ONNX directly to improve speed.
• 2024/04/27 Added FastAPI to EXE example with ONNX GPU Runtime in examples/fastapi-pyinstaller.
• 2024/01/11 Added Nextra docs + deployed to Vercel at sdk.juxt.space.
• 2024/01/07 Reduce dependencies by removing MMCV, MMDet, MMPose SDK, run fully on ONNX.
• 2023/11/01 Added juxtapose to PYPI repository so that we can install it using pip install juxtapose.
• 2023/08/25 Added custom region of interests (ROI) drawing tools that enables multi ROIs filtering while performing pose estimation/tracking. See usage below.
• 2023/08/15 Added GroundingDino & YOLOv8 object detector.
• 2023/08/09 Added keypoints streaming to csv file using csv module.
• 2023/07/31 Added ByteTrack and BotSORT. Completed engineering effort for top down inferences in any sources. See supported sources below.
• 2023/06/15 Converted RTMDET (s/m/l) and RTMPOSE (s/m/l) to ONNX using MMDeploy.

👉 Getting Started

Install Using PIP

pip install juxtapose

Note: If you faced any issues, kindly review this github issue

🧀 Local Development

git clone https://github.com/ziqinyeow/juxtapose
pip install .

🤩 Feel The Magic

🌄 Basic Usage

from juxtapose import RTM

# Init a rtm model (including rtmdet, rtmpose, tracker)
model = RTM(
    det="rtmdet-m", # see type hinting
    pose="rtmpose-m", # see type hinting
    tracker="bytetrack", # see type hinting
    device="cpu",  # see type hinting
)

# Inference with directory (all the images and videos in the dir will get inference sequentially)
model("data")

# Inference with image
model("data/football.jpeg", verbose=False) # verbose -> disable terminal printing

# Inference with video
model("data/bike.mp4")

# Inference with the YouTube Source
model("https://www.youtube.com/watch?v=1vYvTbDJuFs&ab_channel=PeterGrant", save=True)

🎨 Select Region of Interests (ROIs)

It will first prompt the user to draw the ROIs, press r to remove the existing ROI drawn. After drawing, press SPACE or ENTER or q to accept the ROI drawn. The model will filter out the bounding boxes based on the ROIs.

😁 Note: Press SPACE again to redraw the bounding boxes. See custom implementation with cv2 here.

from juxtapose import RTM

model = RTM(det="groundingdino", pose="rtmpose-l", tracker="none")
model("data/bike.mp4", roi="rect") # rectangle roi

# 1. Draw ROI first
# 2. Press r or R to reset ROI
# 3. Press SPACE or Enter or q or Q to continue with the ROI

🚴‍♂️ Accessing result for each frame: More Flexibility

# Adding custom plot
import cv2
from juxtapose import RTM, Annotator

model = RTM()
annotator = Annotator(thickness=3, font_color=(128, 128, 128)) # see rtm.utils.plotting

# set show to true -> cv2.imshow the frame (you can use cv2 to plot anything in the frame)
# set plot to false -> if you want to ignore default plot -> see rtm.rtm (line `if plot:`)
for result in model("data/bike.mp4", show=True, plot=False, stream=True):
    # do what ever you want with the data
    im, bboxes, kpts = result.im, result.bboxes, result.kpts

    # e.g custom plot anything using cv2 API
    cv2.putText(
        im, "custom text", (100, 100), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (128, 128, 128)
    )

    # use the annotator class -> see rtm.utils.plotting
    annotator.draw_bboxes(
        im, bboxes, labels=[f"children_{i}" for i in range(len(bboxes))]
    )
    annotator.draw_kpts(im, kpts, thickness=4)
    annotator.draw_skeletons(im, kpts)

⚽️ Custom Forward Pass: Full Flexibility

# Custom model forward pass
import cv2
import torch
from juxtapose import RTMDet, RTMPose, Annotator

frame = cv2.imread("data/football.jpeg")
device = "cuda" if torch.cuda.is_available() else "cpu"

# s, m, l
rtmdet = RTMDet("l", device=device)
rtmpose = RTMPose("l", device=device)
annotator = Annotator()


bboxes, scores, labels = rtmdet(frame)  # [[x1, y1, x2, y2], ...], [], []
kpts = rtmpose(frame, bboxes=bboxes)  # shape: (number of human, 17, 2)

annotator.draw_bboxes(frame, bboxes, labels=[f"person_{i}" for i in range(len(bboxes))])
annotator.draw_kpts(frame, kpts, thickness=4)
annotator.draw_skeletons(frame, kpts)

cv2.imshow("frame", frame)
cv2.waitKey(0)
cv2.destroyAllWindows()

Supported Sources

Adopted from ultralytics repository -> see https://docs.ultralytics.com/modes/predict/