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fn.py
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fn.py
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#
# These two functions from here:
# https://github.com/MVIG-SJTU/AlphaPose/tree/pytorch
#
# Some proper changes has been done
import torch
import os
import cv2
import time
import matplotlib.pyplot as plt
from PIL import Image
import numpy as np
import math
import copy
def vis_frame_fast(frame, im_res, format='coco'):
'''
frame: frame image
im_res: im_res of predictions
format: coco or mpii
return rendered image
'''
if format == 'coco':
l_pair = [
(0, 1), (0, 2), (1, 3), (2, 4), # Head
(5, 6), (5, 7), (7, 9), (6, 8), (8, 10),
(17, 11), (17, 12), # Body
(11, 13), (12, 14), (13, 15), (14, 16)
]
p_color = [(0, 255, 255), (0, 191, 255),(0, 255, 102),(0, 77, 255), (0, 255, 0), #Nose, LEye, REye, LEar, REar
(77,255,255), (77, 255, 204), (77,204,255), (191, 255, 77), (77,191,255), (191, 255, 77), #LShoulder, RShoulder, LElbow, RElbow, LWrist, RWrist
(204,77,255), (77,255,204), (191,77,255), (77,255,191), (127,77,255), (77,255,127), (0, 255, 255)] #LHip, RHip, LKnee, Rknee, LAnkle, RAnkle, Neck
line_color = [(0, 215, 255), (0, 255, 204), (0, 134, 255), (0, 255, 50),
(77,255,222), (77,196,255), (77,135,255), (191,255,77), (77,255,77),
(77,222,255), (255,156,127),
(0,127,255), (255,127,77), (0,77,255), (255,77,36)]
elif format == 'mpii':
l_pair = [
(8, 9), (11, 12), (11, 10), (2, 1), (1, 0),
(13, 14), (14, 15), (3, 4), (4, 5),
(8, 7), (7, 6), (6, 2), (6, 3), (8, 12), (8, 13)
]
p_color = [PURPLE, BLUE, BLUE, RED, RED, BLUE, BLUE, RED, RED, PURPLE, PURPLE, PURPLE, RED, RED,BLUE,BLUE]
else:
NotImplementedError
img = frame
for human in im_res:
part_line = {}
pose = np.array(human['keypoints']).reshape(-1,3)
kp_preds = pose[:,:2]
kp_scores = np.expand_dims(pose[:,-1], 1)
aa = np.expand_dims((kp_preds[5,:]+kp_preds[6,:])/2, 0)
bb = np.expand_dims((kp_scores[5,:]+kp_scores[6,:])/2, 0)
kp_preds = np.concatenate((kp_preds, aa) )
kp_scores = np.concatenate((kp_scores, bb))
# Draw keypoints
for n in range(kp_scores.shape[0]):
if kp_scores[n] <= 0.05:
continue
cor_x, cor_y = int(kp_preds[n, 0]), int(kp_preds[n, 1])
part_line[n] = (cor_x, cor_y)
cv2.circle(img, (cor_x, cor_y), 4, p_color[n], -1)
# Draw limbs
for i, (start_p, end_p) in enumerate(l_pair):
if start_p in part_line and end_p in part_line:
start_xy = part_line[start_p]
end_xy = part_line[end_p]
cv2.line(img, start_xy, end_xy, line_color[i], 2*(int(kp_scores[start_p]) + int(kp_scores[end_p])) + 1)
return img
def vis_frame(frame, im_res, format='coco'):
'''
frame: frame image
im_res: im_res of predictions
format: coco or mpii
return rendered image
'''
if format == 'coco':
l_pair = [
(0, 1), (0, 2), (1, 3), (2, 4), # Head
(5, 6), (5, 7), (7, 9), (6, 8), (8, 10),
(17, 11), (17, 12), # Body
(11, 13), (12, 14), (13, 15), (14, 16)
]
p_color = [(0, 255, 255), (0, 191, 255),(0, 255, 102),(0, 77, 255), (0, 255, 0), #Nose, LEye, REye, LEar, REar
(77,255,255), (77, 255, 204), (77,204,255), (191, 255, 77), (77,191,255), (191, 255, 77), #LShoulder, RShoulder, LElbow, RElbow, LWrist, RWrist
(204,77,255), (77,255,204), (191,77,255), (77,255,191), (127,77,255), (77,255,127), (0, 255, 255)] #LHip, RHip, LKnee, Rknee, LAnkle, RAnkle, Neck
line_color = [(0, 215, 255), (0, 255, 204), (0, 134, 255), (0, 255, 50),
(77,255,222), (77,196,255), (77,135,255), (191,255,77), (77,255,77),
(77,222,255), (255,156,127),
(0,127,255), (255,127,77), (0,77,255), (255,77,36)]
elif format == 'mpii':
l_pair = [
(8, 9), (11, 12), (11, 10), (2, 1), (1, 0),
(13, 14), (14, 15), (3, 4), (4, 5),
(8, 7), (7, 6), (6, 2), (6, 3), (8, 12), (8, 13)
]
p_color = [PURPLE, BLUE, BLUE, RED, RED, BLUE, BLUE, RED, RED, PURPLE, PURPLE, PURPLE, RED, RED, BLUE, BLUE]
line_color = [PURPLE, BLUE, BLUE, RED, RED, BLUE, BLUE, RED, RED, PURPLE, PURPLE, RED, RED, BLUE, BLUE]
else:
raise NotImplementedError
img = frame
height,width = img.shape[:2]
img = cv2.resize(img,(int(width/2), int(height/2)))
for human in im_res:
part_line = {}
pose = np.array(human['keypoints']).reshape(-1,3)
kp_preds = pose[:,:2]
kp_scores = np.expand_dims(pose[:,-1], 1)
aa = np.expand_dims((kp_preds[5,:]+kp_preds[6,:])/2, 0)
bb = np.expand_dims((kp_scores[5,:]+kp_scores[6,:])/2, 0)
kp_preds = np.concatenate((kp_preds, aa) )
kp_scores = np.concatenate((kp_scores, bb))
# Draw keypoints
for n in range(kp_scores.shape[0]):
if kp_scores[n] <= 0.05:
continue
cor_x, cor_y = int(kp_preds[n, 0]), int(kp_preds[n, 1])
part_line[n] = (int(cor_x/2), int(cor_y/2))
bg = img.copy()
cv2.circle(bg, (int(cor_x/2), int(cor_y/2)), 2, p_color[n], -1)
# Now create a mask of logo and create its inverse mask also
transparency = max(0, min(1, kp_scores[n]))
img = cv2.addWeighted(bg, transparency, img, 1-transparency, 0)
# Draw limbs
for i, (start_p, end_p) in enumerate(l_pair):
if start_p in part_line and end_p in part_line:
start_xy = part_line[start_p]
end_xy = part_line[end_p]
bg = img.copy()
X = (start_xy[0], end_xy[0])
Y = (start_xy[1], end_xy[1])
mX = np.mean(X)
mY = np.mean(Y)
length = ((Y[0] - Y[1]) ** 2 + (X[0] - X[1]) ** 2) ** 0.5
angle = math.degrees(math.atan2(Y[0] - Y[1], X[0] - X[1]))
stickwidth = (kp_scores[start_p] + kp_scores[end_p]) + 1
polygon = cv2.ellipse2Poly((int(mX),int(mY)), (int(length/2), int(stickwidth)), int(angle), 0, 360, 1)
cv2.fillConvexPoly(bg, polygon, line_color[i])
#cv2.line(bg, start_xy, end_xy, line_color[i], (2 * (kp_scores[start_p] + kp_scores[end_p])) + 1)
transparency = max(0, min(1, 0.5*(kp_scores[start_p] + kp_scores[end_p])))
img = cv2.addWeighted(bg, transparency, img, 1-transparency, 0)
img = cv2.resize(img,(width,height),interpolation=cv2.INTER_CUBIC)
return img