app.py

# Preparing Dataset for Pose Estimation
import mediapipe as mp
import cv2
import time
import numpy as np
import pandas as pd
import os
mpPose = mp.solutions.pose
pose = mpPose.Pose()
mpDraw = mp.solutions.drawing_utils # For drawing keypoints
points = mpPose.PoseLandmark # Landmarks
path = "DATASET/TRAIN/plank" # enter dataset path
data = []
for p in points:
        x = str(p)[13:]
        data.append(x + "_x")
        data.append(x + "_y")
        data.append(x + "_z")
        data.append(x + "_vis")
data = pd.DataFrame(columns = data) # Empty dataset

count = 0

for img in os.listdir(path):

        temp = []

        img = cv2.imread(path + "/" + img)

        imageWidth, imageHeight = img.shape[:2]

        imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

        blackie = np.zeros(img.shape) # Blank image

        results = pose.process(imgRGB)

        if results.pose_landmarks:

                # mpDraw.draw_landmarks(img, results.pose_landmarks, mpPose.POSE_CONNECTIONS) #draw landmarks on image

                mpDraw.draw_landmarks(blackie, results.pose_landmarks, mpPose.POSE_CONNECTIONS) # draw landmarks on blackie

                landmarks = results.pose_landmarks.landmark

                for i,j in zip(points,landmarks):

                        temp = temp + [j.x, j.y, j.z, j.visibility]

                data.loc[count] = temp

                count +=1

        cv2.imshow("Image", img)

        cv2.imshow("blackie",blackie)

        cv2.waitKey(100)

data.to_csv("dataset3.csv") # save the data as a csv file


# Creating the Pose Estimation model

from sklearn.svm import SVC
data = pd.read_csv("dataset3.csv")
X,Y = data.iloc[:,:132],data['target']
model = SVC(kernel = 'poly')
model.fit(X,Y)
mpPose = mp.solutions.pose
pose = mpPose.Pose()
mpDraw = mp.solutions.drawing_utils
path = "enter image path"
img = cv2.imread(path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
results = pose.process(imgRGB)
if results.pose_landmarks:
        landmarks = results.pose_landmarks.landmark
        for j in landmarks:
                temp = temp + [j.x, j.y, j.z, j.visibility]
        y = model.predict([temp])
        if y == 0:
            asan = "plank"
        else:
            asan = "goddess"
        print(asan)
        cv2.putText(img, asan, (50,50), cv2.FONT_HERSHEY_SIMPLEX,1,(255,255,0),3)
        cv2.imshow("image",img)