Pose-Estimation

3D pose estimation is a process of predicting the transformation of an object from a user-defined reference pose, given an image or a 3D scan.

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Clone this repository in your local environment by running the code on your bash.

• git clone https://github.com/YOUR-USERNAME/Pose-Estimation.git

Now, install the required packages:

• pip install opencv-python

OpenCV is a library of programming functions mainly aimed at real-time computer vision. Originally developed by Intel, it was later supported by Willow Garage then Itseez. The library is cross-platform and free for use under the open-source Apache 2 License.

Usage:

• To access your webcam through opencv

import cv2

cap = cv2.VideoCapture(0)

while True:
    success, vid = cap.read()
    cv2.imshow("Video", vid)
    cv2.waitKey(1)

• To show fps count on the screen

import time
import cv2 as cv

pTime = 0
cTime = time.time()
fps = 1 / (cTime - pTime)
pTime = cTime
cv.putText(flipped, f'FPS:{int(fps)}', (10, 70),
               cv.FONT_HERSHEY_PLAIN, 3, (0, 255, 0), 2)

• pip install mediapipe

MediaPipe Pose is a ML solution for high-fidelity body pose tracking, inferring 3D landmarks and background segmentation mask on the whole body from RGB video frames utilizing our BlazePose research that also powers the ML Kit Pose Detection API.