Spaces:
Runtime error
Runtime error
File size: 4,191 Bytes
4ba516b cb0074d 4ba516b cb0074d 4ba516b cb0074d e2dff2b cb0074d e2dff2b cb0074d e2dff2b cb0074d e2dff2b cb0074d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 |
import cv2
from time import time
import pickle as pk
import mediapipe as mp
import pandas as pd
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
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 destroy(cam):
cv2.destroyAllWindows()
cam.release()
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_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"):
destroy(cam)
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:
# Display suggestions on screen
cv2_put_text(
flipped,
suggestions[0]
)
tts_last_exec = time() + 5
else:
cv2_put_text(
flipped,
"No Pose Detected"
)
cv2.imshow("Frame", flipped)
|