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import cv2
from time import time
import pickle as pk
import mediapipe as mp
import pandas as pd
import pyttsx4
import multiprocessing as mtp
from recommendations import check_pose_angle
from landmarks import extract_landmarks
from calc_angles import rangles
def init_cam():
cam = cv2.VideoCapture(0)
cam.set(cv2.CAP_PROP_AUTOFOCUS, 0)
cam.set(cv2.CAP_PROP_FOCUS, 360)
cam.set(cv2.CAP_PROP_BRIGHTNESS, 130)
cam.set(cv2.CAP_PROP_SHARPNESS, 125)
cam.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)
cam.set(cv2.CAP_PROP_FRAME_HEIGHT, 720)
return cam
def get_pose_name(index):
names = {
0: "Adho Mukha Svanasana",
1: "Phalakasana",
2: "Utkata Konasana",
3: "Vrikshasana",
}
return str(names[index])
def init_dicts():
landmarks_points = {
"nose": 0,
"left_shoulder": 11, "right_shoulder": 12,
"left_elbow": 13, "right_elbow": 14,
"left_wrist": 15, "right_wrist": 16,
"left_hip": 23, "right_hip": 24,
"left_knee": 25, "right_knee": 26,
"left_ankle": 27, "right_ankle": 28,
"left_heel": 29, "right_heel": 30,
"left_foot_index": 31, "right_foot_index": 32,
}
landmarks_points_array = {
"left_shoulder": [], "right_shoulder": [],
"left_elbow": [], "right_elbow": [],
"left_wrist": [], "right_wrist": [],
"left_hip": [], "right_hip": [],
"left_knee": [], "right_knee": [],
"left_ankle": [], "right_ankle": [],
"left_heel": [], "right_heel": [],
"left_foot_index": [], "right_foot_index": [],
}
col_names = []
for i in range(len(landmarks_points.keys())):
name = list(landmarks_points.keys())[i]
col_names.append(name + "_x")
col_names.append(name + "_y")
col_names.append(name + "_z")
col_names.append(name + "_v")
cols = col_names.copy()
return cols, landmarks_points_array
engine = pyttsx4.init()
def tts(tts_q):
while True:
objects = tts_q.get()
if objects is None:
break
message = objects[0]
engine.say(message)
engine.runAndWait()
tts_q.task_done()
def cv2_put_text(image, message):
cv2.putText(
image,
message,
(50, 50),
cv2.FONT_HERSHEY_SIMPLEX,
2,
(255, 0, 0),
5,
cv2.LINE_AA
)
def destory(cam, tts_proc, tts_q):
cv2.destroyAllWindows()
cam.release()
tts_q.put(None)
tts_q.close()
tts_q.join_thread()
tts_proc.join()
if __name__ == "__main__":
cam = init_cam()
model = pk.load(open("./models/4_poses.model", "rb"))
cols, landmarks_points_array = init_dicts()
angles_df = pd.read_csv("./csv_files/4_angles_poses_angles.csv")
mp_drawing = mp.solutions.drawing_utils
mp_pose = mp.solutions.pose
tts_q = mtp.JoinableQueue()
tts_proc = mtp.Process(target=tts, args=(tts_q, ))
tts_proc.start()
tts_last_exec = time() + 5
while True:
result, image = cam.read()
flipped = cv2.flip(image, 1)
resized_image = cv2.resize(
flipped,
(640, 360),
interpolation=cv2.INTER_AREA
)
key = cv2.waitKey(1)
if key == ord("q"):
destory(cam, tts_proc, tts_q)
break
if result:
err, df, landmarks = extract_landmarks(
resized_image,
mp_pose,
cols
)
if err == False:
prediction = model.predict(df)
probabilities = model.predict_proba(df)
mp_drawing.draw_landmarks(
flipped,
landmarks,
mp_pose.POSE_CONNECTIONS
)
if probabilities[0, prediction[0]] > 0.85:
cv2_put_text(
flipped,
get_pose_name(prediction[0])
)
angles = rangles(df, landmarks_points_array)
suggestions = check_pose_angle(
prediction[0], angles, angles_df)
if time() > tts_last_exec:
tts_q.put([
suggestions[0]
])
tts_last_exec = time() + 5
else:
cv2_put_text(
flipped,
"No Pose Detected"
)
cv2.imshow("Frame", flipped)
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