Merge pull request UppuluriKalyani#359 from mahipalimkar/Mahi

Added file for 3d pose estimation

Computer Vision/3d_pose_estimation/3d_pose_estimation.py

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+import cv2
+import mediapipe as mp
+import numpy as np
+import json
+import socket
+
+# Set up the UDP socket
+sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
+server_address = ('localhost', 12345)  # Port for Unity to listen to
+
+# Function to read and return 3D landmark positions
+def read_landmark_positions_3d(results):
+    if results.pose_world_landmarks is None:
+        return None
+    else:
+        # Extract 3D landmark positions
+        landmarks = [results.pose_world_landmarks.landmark[lm] for lm in mp.solutions.pose.PoseLandmark]
+        return np.array([(lm.x, lm.y, lm.z) for lm in landmarks])
+
+# Function to draw landmarks on the image
+def draw_landmarks_on_image(frame, results):
+    if results.pose_landmarks is not None:
+        mp_drawing = mp.solutions.drawing_utils
+        mp_pose = mp.solutions.pose
+        mp_drawing.draw_landmarks(
+            frame,
+            results.pose_landmarks,
+            mp_pose.POSE_CONNECTIONS,
+            mp_drawing.DrawingSpec(color=(245, 117, 66), thickness=2, circle_radius=2),
+            mp_drawing.DrawingSpec(color=(245, 66, 230), thickness=2, circle_radius=2),
+        )
+
+# Real-time 3D pose estimation function
+def real_time_pose_estimation():
+    # Initialize webcam or video
+    cap = cv2.VideoCapture(0)  # Use 0 for webcam
+
+    # Initialize Mediapipe Pose model
+    mp_pose = mp.solutions.pose
+    pose_detector = mp_pose.Pose(static_image_mode=False, model_complexity=2)
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        # Convert the frame to RGB (required by Mediapipe)
+        frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+
+        # Process the frame to obtain pose landmarks
+        results = pose_detector.process(frame_rgb)
+
+        # Draw landmarks on the frame (optional for visualization)
+        draw_landmarks_on_image(frame, results)
+
+        # Extract 3D landmarks
+        landmark_positions_3d = read_landmark_positions_3d(results)
+        if landmark_positions_3d is not None:
+            # Send landmark positions to Unity via UDP
+            data = json.dumps(landmark_positions_3d.tolist())  # Convert to JSON format
+            sock.sendto(data.encode('utf-8'), server_address)  # Send data to Unity
+            print(f'3D Landmarks: {landmark_positions_3d}')  # Optional: Print landmarks to console
+
+        # Display the frame with landmarks drawn
+        cv2.imshow('Real-Time 3D Pose Estimation', frame)
+
+        # Exit loop when 'q' key is pressed
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
+
+    cap.release()
+    cv2.destroyAllWindows()
+
+if __name__ == "__main__":
+    real_time_pose_estimation()

Computer Vision/3d_pose_estimation/readme.md

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+# 3D Pose Estimation
+
+This project implements real-time **3D human pose estimation** using **Mediapipe** and **OpenCV**. The goal is to estimate 3D coordinates of human body joints (keypoints) from a video or live webcam feed and send this data to external applications (e.g., Unity) via **UDP** for real-time animations or simulations.
+
+## Features
+- Real-time 3D pose estimation from live video or webcam input.
+- Visualizes the detected pose landmarks on the video feed.
+- Sends 3D pose data (in JSON format) over a network using a UDP socket for integration with other applications.
+- Flexible and easy to integrate with game engines, AR/VR applications, or robotics projects.
+
+## Requirements
+
+Make sure to have the following installed:
+- Python 3.7+
+- OpenCV
+- Mediapipe
+- Numpy
+- Socket Programming (standard Python library)
+- JSON (standard Python library)
+
+You can install the required packages using pip:
+
+```bash
+pip install opencv-python mediapipe numpy