app.py

import streamlit as st

# Install mediapipe
#!pip install mediapipe

# *******Import necessary libraries***************
import math
import cv2
import numpy as np
from time import time
import mediapipe as mp
import matplotlib.pyplot as plt
from PIL import Image
from transformers import pipeline
#*******************Initialize the Pose Detection Model*****************

# Initializing mediapipe pose class.
mp_pose = mp.solutions.pose

# Setting up the Pose function.
pose = mp_pose.Pose(static_image_mode=True, min_detection_confidence=0.3, model_complexity=2)

# Initializing mediapipe drawing class, useful for annotation.
mp_drawing = mp.solutions.drawing_utils

#*********Read an Image************************

# !pip install requests
# import requests
# # Function to read an image from a URL
#def read_image_from_url(url1):
#   response = requests.get(url1)
#   image_array = np.asarray(bytearray(response.content), dtype=np.uint8)
#   image = cv2.imdecode(image_array, cv2.IMREAD_COLOR)
#   return image

#   # GitHub URL of the image
# url1 = 'https://github.com/toanmolsharma/newprojecty/raw/main/media/sample.jpg'

# # Read the image from the URL
# sample_img = read_image_from_url(url1)

# # Read an image from the specified path.
# #sample_img = cv2.imread('media/sample.jpg')

# # Specify a size of the figure.
# plt.figure(figsize = [10, 10])

# # Display the sample image, also convert BGR to RGB for display.
#plt.title("Sample Image");plt.axis('off');plt.imshow(sample_img[:,:,::-1]);plt.show()



#*********************Pose Detection On Real-Time Webcam Feed/Video******

## Setup Pose function for video.
#pose_video = mp_pose.Pose(static_image_mode=False, min_detection_confidence=0.5, model_complexity=1)

## Initialize the VideoCapture object to read from the webcam.
#video = cv2.VideoCapture(1)

## Create named window for resizing purposes
#cv2.namedWindow('Pose Detection', cv2.WINDOW_NORMAL)


## Initialize the VideoCapture object to read from a video stored in the disk.
##video = cv2.VideoCapture('media/running.mp4')

## Set video camera size
#video.set(3,1280)
#video.set(4,960)

## Initialize a variable to store the time of the previous frame.
#time1 = 0

## Iterate until the video is accessed successfully.
#while video.isOpened():

  #  # Read a frame.
 #   ok, frame = video.read()

  #  # Check if frame is not read properly.
 #   if not ok:

  #      # Break the loop.
  #      break

  #  # Flip the frame horizontally for natural (selfie-view) visualization.
  #  frame = cv2.flip(frame, 1)

   # # Get the width and height of the frame
   # frame_height, frame_width, _ =  frame.shape

   # # Resize the frame while keeping the aspect ratio.
   # frame = cv2.resize(frame, (int(frame_width * (640 / frame_height)), 640))

   # # Perform Pose landmark detection.
    #frame, _ = detectPose(frame, pose_video, display=False)

    ## Set the time for this frame to the current time.
    ##time2 = time()

    # #Check if the difference between the previous and this frame time > 0 to avoid division by zero.
    #if (time2 - time1) > 0:

     #   # Calculate the number of frames per second.
      #  frames_per_second = 1.0 / (time2 - time1)

       # # Write the calculated number of frames per second on the frame.
        #cv2.putText(frame, 'FPS: {}'.format(int(frames_per_second)), (10, 30),cv2.FONT_HERSHEY_PLAIN, 2, (0, 255, 0), 3)

    ## Update the previous frame time to this frame time.
    ## As this frame will become previous frame in next iteration.
    #time1 = time2

    ## Display the frame.
    #cv2.imshow('Pose Detection', frame)

   # # Wait until a key is pressed.
   # # Retreive the ASCII code of the key pressed
   # k = cv2.waitKey(1) & 0xFF

   # # Check if 'ESC' is pressed.
    #if(k == 27):

    #    # Break the loop.
    #    break

## Release the VideoCapture object.
#video.release()

## Close the windows.
#cv2.destroyAllWindows()


#************************Create a Pose Detection Function*******************
def detectPose(image, pose, display=True):
    '''
    This function performs pose detection on an image.
    Args:
        image: The input image with a prominent person whose pose landmarks needs to be detected.
        pose: The pose setup function required to perform the pose detection.
        display: A boolean value that is if set to true the function displays the original input image, the resultant image,
                 and the pose landmarks in 3D plot and returns nothing.
    Returns:
        output_image: The input image with the detected pose landmarks drawn.
        landmarks: A list of detected landmarks converted into their original scale.
    '''

    # Create a copy of the input image.
    output_image = image.copy()

    # Convert the image from BGR into RGB format.
    imageRGB = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

    # Perform the Pose Detection.
    results = pose.process(imageRGB)

    # Retrieve the height and width of the input image.
    height, width, _ = image.shape

    # Initialize a list to store the detected landmarks.
    landmarks = []

    # Check if any landmarks are detected.
    if results.pose_landmarks:

        # Draw Pose landmarks on the output image.
        mp_drawing.draw_landmarks(image=output_image, landmark_list=results.pose_landmarks,
                                  connections=mp_pose.POSE_CONNECTIONS)

        # Iterate over the detected landmarks.
        for landmark in results.pose_landmarks.landmark:

            # Append the landmark into the list.
            landmarks.append((int(landmark.x * width), int(landmark.y * height),
                                  (landmark.z * width)))

    # Check if the original input image and the resultant image are specified to be displayed.
    if display:

        # Display the original input image and the resultant image.
        plt.figure(figsize=[22,22])
        plt.subplot(121);plt.imshow(image[:,:,::-1]);plt.title("Original Image");plt.axis('off');
        plt.subplot(122);plt.imshow(output_image[:,:,::-1]);plt.title("Output Image");plt.axis('off');

        # Also Plot the Pose landmarks in 3D.
        mp_drawing.plot_landmarks(results.pose_world_landmarks, mp_pose.POSE_CONNECTIONS)

    # Otherwise
    else:

        # Return the output image and the found landmarks.
        return output_image, landmarks



# ********************Pose Classification with Angle Heuristics*****************

def calculateAngle(landmark1, landmark2, landmark3):
    '''
    This function calculates angle between three different landmarks.
    Args:
        landmark1: The first landmark containing the x,y and z coordinates.
        landmark2: The second landmark containing the x,y and z coordinates.
        landmark3: The third landmark containing the x,y and z coordinates.
    Returns:
        angle: The calculated angle between the three landmarks.
    '''

    # Get the required landmarks coordinates.
    x1, y1, _ = landmark1
    x2, y2, _ = landmark2
    x3, y3, _ = landmark3

    # Calculate the angle between the three points
    angle = math.degrees(math.atan2(y3 - y2, x3 - x2) - math.atan2(y1 - y2, x1 - x2))

    # Check if the angle is less than zero.
    if angle < 0:

        # Add 360 to the found angle.
        angle += 360

    # Return the calculated angle.
    return angle


    
#***************************Create a Function to Perform Pose Classification***************

def classifyPose(landmarks, output_image, display=False):
    
    # Initialize the label of the pose. It is not known at this stage.
    label = "Unknown Pose"

    # Specify the color (Red) with which the label will be written on the image.
    color = (0, 0, 255)

    # Calculate the required angles.
    #----------------------------------------------------------------------------------------------------------------

    # Get the angle between the left shoulder, elbow and wrist points.
    left_elbow_angle = calculateAngle(landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value],
                                      landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value],
                                      landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value])

    # Get the angle between the right shoulder, elbow and wrist points.
    right_elbow_angle = calculateAngle(landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value],
                                       landmarks[mp_pose.PoseLandmark.RIGHT_ELBOW.value],
                                       landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value])

    # Get the angle between the left elbow, shoulder and hip points.
    left_shoulder_angle = calculateAngle(landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value],
                                         landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value],
                                         landmarks[mp_pose.PoseLandmark.LEFT_HIP.value])

    # Get the angle between the right hip, shoulder and elbow points.
    right_shoulder_angle = calculateAngle(landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value],
                                          landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value],
                                          landmarks[mp_pose.PoseLandmark.RIGHT_ELBOW.value])

    # Get the angle between the left hip, knee and ankle points.
    left_knee_angle = calculateAngle(landmarks[mp_pose.PoseLandmark.LEFT_HIP.value],
                                     landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value],
                                     landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value])

    # Get the angle between the right hip, knee and ankle points
    right_knee_angle = calculateAngle(landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value],
                                      landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value],
                                      landmarks[mp_pose.PoseLandmark.RIGHT_ANKLE.value])

    #----------------------------------------------------------------------------------------------------------------
    # Check for Five-Pointed Star Pose
    if abs(landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value][1] - landmarks[mp_pose.PoseLandmark.LEFT_HIP.value][1]) < 100 and \
       abs(landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value][1] - landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value][1]) < 100 and \
       abs(landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value][0] - landmarks[mp_pose.PoseLandmark.RIGHT_ANKLE.value][0]) > 200 and \
       abs(landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value][0] - landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value][0]) > 200:
        label = "Five-Pointed Star Pose"  
        
    # Check if it is the warrior II pose or the T pose.
    # As for both of them, both arms should be straight and shoulders should be at the specific angle.
    #----------------------------------------------------------------------------------------------------------------

    # Check if the both arms are straight.
    if left_elbow_angle > 165 and left_elbow_angle < 195 and right_elbow_angle > 165 and right_elbow_angle < 195:

        # Check if shoulders are at the required angle.
        if left_shoulder_angle > 80 and left_shoulder_angle < 110 and right_shoulder_angle > 80 and right_shoulder_angle < 110:

    # Check if it is the warrior II pose.
    #----------------------------------------------------------------------------------------------------------------

            # Check if one leg is straight.
            if left_knee_angle > 165 and left_knee_angle < 195 or right_knee_angle > 165 and right_knee_angle < 195:

                # Check if the other leg is bended at the required angle.
                if left_knee_angle > 90 and left_knee_angle < 120 or right_knee_angle > 90 and right_knee_angle < 120:

                    # Specify the label of the pose that is Warrior II pose.
                    label = 'Warrior II Pose'

    #----------------------------------------------------------------------------------------------------------------

    # Check if it is the T pose.
    #----------------------------------------------------------------------------------------------------------------

            # Check if both legs are straight
            if left_knee_angle > 160 and left_knee_angle < 195 and right_knee_angle > 160 and right_knee_angle < 195:

                # Specify the label of the pose that is tree pose.
                label = 'T Pose'
    
    #----------------------------------------------------------------------------------------------------------------

    # Check if it is the tree pose.
    #----------------------------------------------------------------------------------------------------------------

    # Check if one leg is straight
    if left_knee_angle > 165 and left_knee_angle < 195 or right_knee_angle > 165 and right_knee_angle < 195:

        # Check if the other leg is bended at the required angle.
        if left_knee_angle > 315 and left_knee_angle < 335 or right_knee_angle > 25 and right_knee_angle < 45:

            # Specify the label of the pose that is tree pose.
            label = 'Tree Pose'
    
    # Check for Upward Salute Pose
    if abs(landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value][0] - landmarks[mp_pose.PoseLandmark.LEFT_HIP.value][0]) < 100 and \
       abs(landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value][0] - landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value][0]) < 100 and \
       landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value][1] < landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value][1] and \
       landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value][1] < landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value][1] and \
       abs(landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value][1] - landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value][1]) < 50:
        label = "Upward Salute Pose"

   # Check for Hands Under Feet Pose
    if landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value][1] > landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value][1] and \
       landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value][1] > landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value][1] and \
       abs(landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value][0] - landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value][0]) < 50 and \
       abs(landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value][0] - landmarks[mp_pose.PoseLandmark.RIGHT_ANKLE.value][0]) < 50:
        label = "Hands Under Feet Pose"      
        
       
    #----------------------------------------------------------------------------------------------------------------

    # Check if the pose is classified successfully
    if label != 'Unknown Pose':

        # Update the color (to green) with which the label will be written on the image.
        color = (0, 255, 0)

    # Write the label on the output image.
    cv2.putText(output_image, label, (10, 30),cv2.FONT_HERSHEY_PLAIN, 2, color, 2)

    # Check if the resultant image is specified to be displayed.
    if display:

        # Display the resultant image.
        plt.figure(figsize=[10,10])
        plt.imshow(output_image[:,:,::-1]);plt.title("Output Image");plt.axis('off');

    else:

        # Return the output image and the classified label.
        return output_image, label

#******************************Pose Classification On Real-Time Webcam Feed*****************
'''
# Setup Pose function for video.
pose_video = mp_pose.Pose(static_image_mode=False, min_detection_confidence=0.5, model_complexity=1)

# Initialize the VideoCapture object to read from the webcam.
camera_video = cv2.VideoCapture(0)
camera_video.set(3,1280)
camera_video.set(4,960)

# Initialize a resizable window.
cv2.namedWindow('Pose Classification', cv2.WINDOW_NORMAL)
 
# Iterate until the webcam is accessed successfully.
while camera_video.isOpened():

    # Read a frame.
    ok, frame = camera_video.read()

    # Check if frame is not read properly.
    if not ok:

        # Continue to the next iteration to read the next frame and ignore the empty camera frame.
        continue

    # Flip the frame horizontally for natural (selfie-view) visualization.
    frame = cv2.flip(frame, 1)

    # Get the width and height of the frame
    frame_height, frame_width, _ =  frame.shape

    # Resize the frame while keeping the aspect ratio.
    frame = cv2.resize(frame, (int(frame_width * (640 / frame_height)), 640))

    # Perform Pose landmark detection.
    frame, landmarks = detectPose(frame, pose_video, display=False)

    # Check if the landmarks are detected.
    if landmarks:

        # Perform the Pose Classification.
        frame, _ = classifyPose(landmarks, frame, display=False)

    # Display the frame.
    cv2.imshow('Pose Classification', frame)

    # Wait until a key is pressed.
    # Retreive the ASCII code of the key pressed
    k = cv2.waitKey(1) & 0xFF

    # Check if 'ESC' is pressed.
    if(k == 27):

        # Break the loop.
        break

# Release the VideoCapture object and close the windows.
camera_video.release()
cv2.destroyAllWindows()


# Create a Gradio interface
iface = gr.Interface(
    fn=detect_yoga_poses,
    inputs=None,
    outputs=None,
    title="Live Yoga Pose Detection",
    description="This app detects yoga poses from the live camera feed using MediaPipe.",
)
'''

#import streamlit as st
#import cv2
#import numpy as np
#from PIL import Image
#from transformers import pipeline

# Function to load model from Hugging Face
@st.cache(allow_output_mutation=True)
def load_model():
    return pipeline("pose-detection", device=0)  # Adjust device as per your requirement

# Function to detect yoga pose from image
def detect_yoga_pose(image):
    # Convert PIL image to OpenCV format
    cv_image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
    # Your pose detection logic here
    # Replace the following line with your actual pose detection code
    return "Detected yoga pose: Warrior II"

def main():
    st.title("Yoga Pose Detection from Live Camera Feed")

    # Load the model
    model = load_model()

   
    

# Setup Pose function for video.
pose_video = mp_pose.Pose(static_image_mode=False, min_detection_confidence=0.5, model_complexity=1)

# Accessing web cam : Initialize the VideoCapture object to read from the webcam.
camera_video = cv2.VideoCapture(0)
camera_video.set(3,1280)
camera_video.set(4,960)

# Initialize a resizable window.
cv2.namedWindow('Pose Classification', cv2.WINDOW_NORMAL)

# Iterate until the webcam is accessed successfully.
while camera_video.isOpened():

    # Read a frame.
    ok, frame = camera_video.read()

    # Check if frame is not read properly.
    if not ok:

        # Continue to the next iteration to read the next frame and ignore the empty camera frame.
        continue

    # Flip the frame horizontally for natural (selfie-view) visualization.
    frame = cv2.flip(frame, 1)

    # Get the width and height of the frame
    frame_height, frame_width, _ =  frame.shape

    # Resize the frame while keeping the aspect ratio.
    frame = cv2.resize(frame, (int(frame_width * (640 / frame_height)), 640))

    # Perform Pose landmark detection.
    frame, landmarks = detectPose(frame, pose_video, display=False)

    # Check if the landmarks are detected.
    if landmarks:

        # Perform the Pose Classification.
        frame, _ = classifyPose(landmarks, frame, display=False)

    # Display the frame.
    cv2.imshow('Pose Classification', frame)

    # Wait until a key is pressed.
    # Retreive the ASCII code of the key pressed
    k = cv2.waitKey(1) & 0xFF

    # Check if 'ESC' is pressed.
    if(k == 27):

        # Break the loop.
        break

# Release the VideoCapture object, close Streamlit app and close the windows.
camera_video.release()
st.stop()
cv2.destroyAllWindows()

if __name__ == "__main__":
    main()