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app.py
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app.py
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import streamlit as st
import numpy as np
import math,pickle
from PIL import Image
import cv2
import mediapipe as mp
import time
mp_drawing = mp.solutions.drawing_utils
mp_pose = mp.solutions.pose
#hide the name:
# hide_name="""
# <style>
# #MainMenu {visibility:hidden;}
# #footer {visibilty:hidden;}
# </style>
# """
# st.markdown(hide_name,unsafe_allow_html=True)
#Import model
load_model=pickle.load(open('YogaModel.pkl','rb'))
def getAngle(a, b, c):
ang = math.degrees(math.atan2(c[1]-b[1], c[0]-b[0]) - math.atan2(a[1]-b[1], a[0]-b[0]))
return round(ang + 360 if ang < 0 else ang)
def feature_list(poseLandmarks,posename):
return [getAngle(poseLandmarks[16],poseLandmarks[14],poseLandmarks[12]),
getAngle(poseLandmarks[14],poseLandmarks[12],poseLandmarks[24]),
getAngle(poseLandmarks[13],poseLandmarks[11],poseLandmarks[23]),
getAngle(poseLandmarks[15],poseLandmarks[13],poseLandmarks[11]),
getAngle(poseLandmarks[12],poseLandmarks[24],poseLandmarks[26]),
getAngle(poseLandmarks[11],poseLandmarks[23],poseLandmarks[25]),
getAngle(poseLandmarks[24],poseLandmarks[26],poseLandmarks[28]),
getAngle(poseLandmarks[23],poseLandmarks[25],poseLandmarks[27]),
getAngle(poseLandmarks[26],poseLandmarks[28],poseLandmarks[32]),
getAngle(poseLandmarks[25],poseLandmarks[27],poseLandmarks[31]),
getAngle(poseLandmarks[0],poseLandmarks[12],poseLandmarks[11]),
getAngle(poseLandmarks[0],poseLandmarks[11],poseLandmarks[12]),
posename]
# set the layout width wide
st.set_page_config(layout="wide")
#sidebar
st.sidebar.title('YogaGuru')
app_mode=st.sidebar.selectbox('Select The Pose',['Tree','Mountain','Warrior2'])
# some funny views
# st.balloons()
# st.snow()
# st.success("yessss")
if app_mode=='Tree':
col1, col2 = st.columns([2,3])
with col1:
with st.container():
st.write("Tree Pose")
image = Image.open('tree.jpg')
st.image(image, caption='Tree Pose')
with col2:
st.write("Webcam Live Feed")
# run = st.checkbox('Start Video')
button=st.empty()
start=button.button('Start')
if start:
stop=button.button('Stop')
visible_message = st.empty()
FRAME_WINDOW = st.image([])
accuracytxtbox = st.empty()
cap = cv2.VideoCapture(0)
with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:
while cap.isOpened():
ret, frame = cap.read()
h,w,c=frame.shape
# Recolor image to RGB
image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
image.flags.writeable = False
# Make detection
results = pose.process(image)
# Recolor back to BGR
image.flags.writeable = True
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
# Render detections
mp_drawing.draw_landmarks(image, 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)
)
FRAME_WINDOW.image(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
poseLandmarks=[]
if results.pose_landmarks:
for lm in results.pose_landmarks.landmark:
poseLandmarks.append((int(lm.x*w),int(lm.y*h)))
if len(poseLandmarks)==0:
visible_message.text("Body Not Visible")
accuracytxtbox.text('')
continue
else:
visible_message.text("")
d=feature_list(poseLandmarks,1)
# print(load_model.predict(np.array(d).reshape(1, -1)))
# accuracytxtbox.text(f"Accuracy : {np.array_str(load_model.predict(np.array(d).reshape(1, -1))[0])}")
rt_accuracy=int(round(load_model.predict(np.array(d).reshape(1, -1))[0],0))
if rt_accuracy<75:
accuracytxtbox.text(f"Accuracy : Not so Good {rt_accuracy}")
elif rt_accuracy>75 and rt_accuracy<85:
accuracytxtbox.text(f"Accuracy : Good {rt_accuracy}")
elif rt_accuracy>85 and rt_accuracy<95:
accuracytxtbox.text(f"Accuracy : Very Good {rt_accuracy}")
elif rt_accuracy>95 and rt_accuracy<100:
accuracytxtbox.text(f"Accuracy : Near to perfection {rt_accuracy}")
elif rt_accuracy>=100:
accuracytxtbox.text(f"Accuracy : You reached your goal perfection 100")
if stop:
cap.release()
cv2.destroyAllWindows()
else:
st.write('Your Camera is Not Detected !')
elif app_mode=='Mountain':
col1, col2 = st.columns([2,3])
with col1:
with st.container():
st.write("Mountain Pose")
image = Image.open('mountain.jpg')
st.image(image, caption='Mountain Pose')
with col2:
st.write("Webcam Live Feed")
# run = st.checkbox('Start Video')
button=st.empty()
start=button.button('Start')
if start:
stop=button.button('Stop')
visible_message = st.empty()
FRAME_WINDOW = st.image([])
accuracytxtbox = st.empty()
cap = cv2.VideoCapture(0)
with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:
while cap.isOpened():
ret, frame = cap.read()
h,w,c=frame.shape
# Recolor image to RGB
image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
image.flags.writeable = False
# Make detection
results = pose.process(image)
# Recolor back to BGR
image.flags.writeable = True
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
# Render detections
mp_drawing.draw_landmarks(image, 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)
)
FRAME_WINDOW.image(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
poseLandmarks=[]
if results.pose_landmarks:
for lm in results.pose_landmarks.landmark:
poseLandmarks.append((int(lm.x*w),int(lm.y*h)))
if len(poseLandmarks)==0:
visible_message.text("Body Not Visible")
accuracytxtbox.text('')
continue
else:
visible_message.text("")
d=feature_list(poseLandmarks,2)
# print(load_model.predict(np.array(d).reshape(1, -1)))
# accuracytxtbox.text(f"Accuracy : {np.array_str(load_model.predict(np.array(d).reshape(1, -1))[0])}")
rt_accuracy=int(round(load_model.predict(np.array(d).reshape(1, -1))[0],0))
if rt_accuracy<75:
accuracytxtbox.text(f"Accuracy : Not so Good {rt_accuracy}")
elif rt_accuracy>75 and rt_accuracy<85:
accuracytxtbox.text(f"Accuracy : Good {rt_accuracy}")
elif rt_accuracy>85 and rt_accuracy<95:
accuracytxtbox.text(f"Accuracy : Very Good {rt_accuracy}")
elif rt_accuracy>95 and rt_accuracy<100:
accuracytxtbox.text(f"Accuracy : Near to perfection {rt_accuracy}")
elif rt_accuracy>=100:
accuracytxtbox.text(f"Accuracy : You reached your goal perfection 100")
if stop:
cap.release()
cv2.destroyAllWindows()
else:
st.write('Your Camera is Not Detected !')
elif app_mode=='Warrior2':
col1, col2 = st.columns([2,3])
with col1:
with st.container():
st.write("Warrior2 Pose")
image = Image.open('warrior2.jpg')
st.image(image, caption='Warrior2 Pose')
with col2:
st.write("Webcam Live Feed")
# run = st.checkbox('Start Video')
button=st.empty()
start=button.button('Start')
if start:
stop=button.button('Stop')
visible_message = st.empty()
FRAME_WINDOW = st.image([])
accuracytxtbox = st.empty()
cap = cv2.VideoCapture(0)
with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:
while cap.isOpened():
ret, frame = cap.read()
h,w,c=frame.shape
# Recolor image to RGB
image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
image.flags.writeable = False
# Make detection
results = pose.process(image)
# Recolor back to BGR
image.flags.writeable = True
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
# Render detections
mp_drawing.draw_landmarks(image, 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)
)
FRAME_WINDOW.image(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
poseLandmarks=[]
if results.pose_landmarks:
for lm in results.pose_landmarks.landmark:
poseLandmarks.append((int(lm.x*w),int(lm.y*h)))
if len(poseLandmarks)==0:
visible_message.text("Body Not Visible")
accuracytxtbox.text('')
continue
else:
visible_message.text("")
d=feature_list(poseLandmarks,3)
# print(load_model.predict(np.array(d).reshape(1, -1)))
# accuracytxtbox.text(f"Accuracy : {np.array_str(load_model.predict(np.array(d).reshape(1, -1))[0])}")
rt_accuracy=int(round(load_model.predict(np.array(d).reshape(1, -1))[0],0))
if rt_accuracy<75:
accuracytxtbox.text(f"Accuracy : Not so Good {rt_accuracy}")
elif rt_accuracy>75 and rt_accuracy<85:
accuracytxtbox.text(f"Accuracy : Good {rt_accuracy}")
elif rt_accuracy>85 and rt_accuracy<95:
accuracytxtbox.text(f"Accuracy : Very Good {rt_accuracy}")
elif rt_accuracy>95 and rt_accuracy<100:
accuracytxtbox.text(f"Accuracy : Near to perfection {rt_accuracy}")
elif rt_accuracy>=100:
accuracytxtbox.text(f"Accuracy : You reached your goal perfection 100")
if stop:
cap.release()
cv2.destroyAllWindows()
else:
st.write('Your Camera is Not Detected !')