#coding: utf-8
"""
PCKh Validation
For Single Person Pose Estimation
Human Pose Estimation Project in Lab of IP
Author: Liu Fangrui aka mpsk
Beijing University of Technology
College of Computer Science & Technology
Experimental Code
!!DO NOT USE IT AS DEPLOYMENT!!
ATTENTION:
A pre-gen Validation set is needed to do validation seperately
"""
import cv2
import time
import numpy as np
import matplotlib.pyplot as plt
import sys
sys.path.append("..")
import net.CPM as CPM
import net.MobileCPM as MobileCPM
import util.predict as predict
import util.Global as Global
import dataset.datagen_mpii as datagen
def visualize_accuracy(a, start=0.01, end=0.5, showlist=None, resolution=100):
""" Compute the Accuracy in rate
Args:
a: distance matrix like err in compute_distance
showlist: list of joints to be shown
resolutional: determine the
"""
if showlist is None:
showlist = range(0, len(Global.joint_list))
else:
show_list = showlist
plt.title("CPM PCKh benchmark on MPII")
plt.xlabel("Normalized distance")
plt.ylabel("Accuracy")
dists = np.linspace(start, end, resolution)
re = np.zeros((dists.shape[0], a.shape[1]), np.float)
av = np.zeros((dists.shape[0]), np.float)
for ridx in range(dists.shape[0]):
print "[*]\tProcessing Result in normalized distance of", dists[ridx]
for j in range(a.shape[1]):
condition = a[:,j] <= dists[ridx]
re[ridx, j] = len(np.extract(condition, a[:,j]))/float(a.shape[0])
print "[*]\t", Global.joint_list[j], " Accuracy :\t\t", re[ridx, j]
av[ridx] = np.average(re[ridx])
print "[*]\t\tTOTAL Accuracy :\t\t", av[ridx]
for j in show_list:
plt.plot(dists, re[:, j], label=Global.joint_list[j], linewidth = 2.0)
plt.plot(dists, av, label="Average",linewidth = 3.0)
plt.legend(loc='upper left')
plt.grid()
plt.show()
def compute_distance(model, dataset, metric='PCKh', debug=False):
"""
Args:
model: model to load
dataset: dataset to generate image & ground truth
Return:
An normalized distance matrix with shape of num_of_img x joint_num
"""
if metric == 'PCKh':
normJ_a = 9
normJ_b = 8
elif metric == 'PCK':
normJ_a = 12
normJ_b = 3
else:
raise ValueError
err = np.zeros((len(dataset.valid_set), len(Global.joint_list)), np.float)
print "[*]\tCreated error matrix with shape of ", err.shape
paral = 16
if debug:
paral = 2
for _iter in range(len(dataset.valid_set)/paral):
im_list = []
j_list = []
w_list = []
for n in range(paral):
img, new_j, w, _, _ = dataset.getSample(sample=dataset.valid_set[_iter*paral+n])
im_list.append(img)
j_list.append(new_j)
w_list.append(w)
w = np.array(w_list)
j_gt = np.array(j_list)
print "GT weight has shape of ", w.shape
# estimate by networkn
j_dt, w_dt = predict.predict(im_list, model=model, thresh=0.2, debug=False, is_name=False)
print "Predict joint has shape of ", j_dt.shape
w = np.transpose(np.hstack((np.expand_dims(w,1), np.expand_dims(w,1))), axes=[0,2,1])
w_dt = np.transpose(np.hstack((np.expand_dims(w_dt,1), np.expand_dims(w_dt,1))), axes=[0,2,1])
for n in range(j_dt.shape[0]):
temp_err = np.linalg.norm(w[n]*w_dt[n]*(j_gt[n,:,:]-j_dt[n,:,-1::-1]),axis=1) / np.linalg.norm(j_gt[n,normJ_a,:]-j_gt[n,normJ_b,:], axis=0)
print temp_err
err[_iter*paral+n] = temp_err
print "[*]\tTemp Error is ", np.average(err[_iter*paral:_iter*paral+paral], axis=0)
if debug:
return err
aver_err = np.average(err)
print "[*]\tAverage PCKh Normalised distance is ", aver_err
return err
def get_err(debug=False):
print('--Creating Dataset')
''' >>>>>>> Mobile CPM
in_size = 224
model = MobileCPM.MobileCPM(pretrained_model='model/mobile_model.npy', load_pretrained=True, training=False, cpu_only=False,
stage=6, in_size=in_size)
#<<<<<<<<<<<<'''
# ''' >>>>>>> original CPM
in_size = 368
model = CPM.CPM(pretrained_model='model/CPM_model_368_stage_6/model.npy', load_pretrained=True, training=False, cpu_only=False,
stage=6, in_size=in_size)
#<<<<<<<<<<<<'''
dataset = datagen.DataGenerator(Global.joint_list, Global.IMG_ROOT, Global.training_txt_file, remove_joints=None, in_size=in_size)
dataset._create_train_table()
dataset.valid_set = np.load('model/CPM_model_368_stage_6/Dataset-Validation-Set.npy')
model.BuildModel()
dist = compute_distance(model, dataset, metric='PCKh', debug=debug)
return dist
if __name__ == '__main__':
np.save('val_res.npy', get_err(debug=False))
visualize_accuracy(np.load('val_res.npy'), start=0.01, end=0.5, showlist=[0,1,4,5,10,11,14,15])
