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Smooth Pose Estimation Data
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import pandas as pd | |
import numpy as np | |
import cv2, os | |
import scipy | |
from scipy import signal | |
import csv | |
circle_color, line_color = (255,255,0), (0,0,255) | |
window_length, polyorder = 13, 2 | |
sd = "workout" | |
input_source = "/home/stephen/Desktop/" + sd + '.MP4' | |
# Get pose data - data is generated by OpenPose | |
df = pd.read_csv('/home/stephen/Desktop/' +sd+ '.csv') | |
cap = cv2.VideoCapture(input_source) | |
hw = 720 | |
out = cv2.VideoWriter('/home/stephen/Desktop/smooth_pose.avi', | |
cv2.VideoWriter_fourcc('M','J','P','G'), 30, (hw,hw)) | |
# There are 15 points in the skeleton | |
pairs = [[0,1], # head | |
[1,2],[1,5], # sholders | |
[2,3],[3,4],[5,6],[6,7], # arms | |
[1,14],[14,11],[14,8], # hips | |
[8,9],[9,10],[11,12],[12,13]] # legs | |
# Smooth it out | |
for i in range(30): df[str(i)] = signal.savgol_filter(df[str(i)], window_length, polyorder) | |
frame_number = 0 | |
while True: | |
print(frame_number) | |
ret, img = cap.read() | |
if not ret: break | |
#img = np.zeros_like(img) | |
values = np.array(df.values[frame_number], int) | |
points, lateral_offset = [], 18 | |
points = list(zip(values[:15]+lateral_offset, values[15:])) | |
cc = 0 | |
for point in points: | |
cc += 90 | |
xy = tuple(np.array([point[0], point[1]], int)) | |
cv2.circle(img, xy, 5, (cc,cc,cc), 5) | |
# Draw Skeleton | |
for pair in pairs: | |
partA = pair[0] | |
partB = pair[1] | |
cv2.line(img, points[partA], points[partB], line_color, 3, lineType=cv2.LINE_AA) | |
cv2.imshow('Output-Skeleton', img) | |
k = cv2.waitKey(100) | |
if k == 27: break | |
out.write(img) | |
frame_number+=1 | |
cv2.destroyAllWindows() |
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