Because of increasing usage of smartphones and computers, forward head posture is severe in modern young people. But actually by correcting their posture, it can be easily cured. And if you know your current posture, it is naturally followed by correcting posture. So we designed this program to visualize subject's posture by using OpenPose. OpenPose works best at Nvidia GPU environment.
1. Download OpenPose
Download OpenPose ZIP file below.(openpose-master.zip)
# Image Capture from Webcam
cap = cv2.VideoCapture(1)
while True :
# Read Images
ret, image = cap.read()
imageCopy = image
# Resize for Performance
imageCopy = cv2.resize(imageCopy, (1200,680))
image = cv2.resize(image,(300,170))
# Get Results from Model
imageHeight, imageWidth, _ = image.shape
inpBlob = cv2.dnn.blobFromImage(image, 1.0 / 255, (imageWidth, imageHeight), (0, 0, 0), swapRB=False, crop=False)
net.setInput(inpBlob)
output = net.forward()
H = output.shape[2]
W = output.shape[3]
print("이미지 ID : ", len(output[0]), ", H : ", output.shape[2], ", W : ",output.shape[3])
points = []
# Get Coordinates of the Results
for i in range(0,25):
probMap = output[0, i, :, :]
minVal, prob, minLoc, point = cv2.minMaxLoc(probMap)
x = (imageWidth * point[0]) / W
y = (imageHeight * point[1]) / H
if prob > 0.1 :
points.append((int(x), int(y)))
else :
points.append(None)
if i == 1 :
neckx = int(x)
necky = int(y)
if i == 8 :
waistx = int(x)
waisty = int(y)
if i == 17 :
headx = int(x)
heady = int(y)
if i == 18 and points[17] is None :
headx = int(x)
heady = int(y)
# Calculate the Degree(abs for every direction)
deg = abs(abs(math.atan((necky-waisty)/((neckx-waistx)+1.e-09))) - abs(math.atan((heady-necky)/((headx-neckx)+1.e-09))))*180/np.pi
# Draw Gauges and Text for Visualizing
imageCopy = cv2.rectangle(imageCopy, (0, 0), (350, 50), (255, 255, 255), -1)
text = str('lean back %s [deg]' % (int(deg)))
if deg <= 30 :
ratio = deg/30
bias = 1
elif deg > 30 :
ratio = 1
bias = 0
cv2.putText(imageCopy, text, (20, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, int(255*bias), int(255*ratio)), 2)
cv2.rectangle(imageCopy, (1100, 10), (1180, 670), (255-ratio*255, 255-ratio*255, 225 + 30*ratio), 1)
cv2.rectangle(imageCopy, (1100, int(670-ratio*660)), (1180, 670), (255-ratio*255, 255-ratio*255, 225 + 30*ratio), -1)
# Get Coordinates for Each Pose Pair
for pair in POSE_PAIRS:
partA = pair[0]
partA = BODY_PARTS[partA]
partB = pair[1]
partB = BODY_PARTS[partB]
# Draw Lines for Each Pairs
if points[partA] and points[partB]:
x1 = points[partA][0] * 4
y1 = points[partA][1] * 4
x2 = points[partB][0] * 4
y2 = points[partB][1] * 4
cv2.line(image, points[partA], points[partB], (0, int(255*bias), int(255*ratio)), 2)
cv2.line(imageCopy, (x1, y1), (x2, y2), (0, int(255*bias), int(255*ratio)), 2)
# Show Results
cv2.imshow("result",imageCopy)
if cv2.waitKey(1) == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
Result
(a) Trutle Neck
(b) Straight Neck
(c) Near Turtle
(d) Lean Back
Discussion
OpenPose is very heavy program, so it works very slow. Due to the low fps, output shows slightly obsolete results. And OpenPose requires the position of waist, the camera must be set in exact height and distant. The distance is about 1.5m and the height is about 1m, which will lead to inconvenience for use. We need to improve these problems.
Appendix
코드
import cv2
import math
from matplotlib.pyplot import hsv
import numpy as np
BODY_PARTS = { "Neck": 1, "Waist": 8, "Left Ear": 17, "Right Ear": 18, "Background": 25 }
POSE_PAIRS = [["Left Ear", "Neck"], ["Right Ear", "Neck"], ["Neck", "Waist"]]
protoFile = "pose_deploy.prototxt"
weightsFile = "pose_iter_584000.caffemodel"
net = cv2.dnn.readNetFromCaffe(protoFile, weightsFile)
cap = cv2.VideoCapture(1)
while True :
ret, image = cap.read()
imageCopy = image
imageCopy = cv2.resize(imageCopy, (1200,680))
image = cv2.resize(image,(300,170))
imageHeight, imageWidth, _ = image.shape
inpBlob = cv2.dnn.blobFromImage(image, 1.0 / 255, (imageWidth, imageHeight), (0, 0, 0), swapRB=False, crop=False)
net.setInput(inpBlob)
output = net.forward()
H = output.shape[2]
W = output.shape[3]
print("이미지 ID : ", len(output[0]), ", H : ", output.shape[2], ", W : ",output.shape[3])
points = []
for i in range(0,25):
probMap = output[0, i, :, :]
minVal, prob, minLoc, point = cv2.minMaxLoc(probMap)
x = (imageWidth * point[0]) / W
y = (imageHeight * point[1]) / H
if prob > 0.1 :
points.append((int(x), int(y)))
else :
points.append(None)
if i == 1 :
neckx = int(x)
necky = int(y)
if i == 8 :
waistx = int(x)
waisty = int(y)
if i == 17 :
headx = int(x)
heady = int(y)
if i == 18 and points[17] is None :
headx = int(x)
heady = int(y)
deg = abs(abs(math.atan((necky-waisty)/((neckx-waistx)+1.e-09))) - abs(math.atan((heady-necky)/((headx-neckx)+1.e-09))))*180/np.pi
imageCopy = cv2.rectangle(imageCopy, (0, 0), (350, 50), (255, 255, 255), -1)
text = str('lean back %s [deg]' % (int(deg)))
if deg <= 30 :
ratio = deg/30
bias = 1
elif deg > 30 :
ratio = 1
bias = 0
cv2.putText(imageCopy, text, (20, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, int(255*bias), int(255*ratio)), 2)
cv2.rectangle(imageCopy, (1100, 10), (1180, 670), (255-ratio*255, 255-ratio*255, 225 + 30*ratio), 1)
cv2.rectangle(imageCopy, (1100, int(670-ratio*660)), (1180, 670), (255-ratio*255, 255-ratio*255, 225 + 30*ratio), -1)
for pair in POSE_PAIRS:
partA = pair[0]
partA = BODY_PARTS[partA]
partB = pair[1]
partB = BODY_PARTS[partB]
if points[partA] and points[partB]:
x1 = points[partA][0] * 4
y1 = points[partA][1] * 4
x2 = points[partB][0] * 4
y2 = points[partB][1] * 4
cv2.line(image, points[partA], points[partB], (0, int(255*bias), int(255*ratio)), 2)
cv2.line(imageCopy, (x1, y1), (x2, y2), (0, int(255*bias), int(255*ratio)), 2)
cv2.imshow("result",imageCopy)
if cv2.waitKey(1) == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
