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sample_feature_extractor.py
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import json
import numpy as np
import csv
with open('data_set/data32.txt') as data_file:
f = json.load(data_file)
def averageArea():
for item in f:
t_s=0
for k in range(len(item['data'])-1):
s=0
for i in range(1,5):
s =(np.array(item['data'][k][str(i)]['mcpPosition']) + np.array(
item['data'][k][str(i+1)]['mcpPosition']))/2.0
area=0.5*np.linalg.norm(np.cross(np.array(item['data'][k][str(i+1)]['tipPosition'])-np.array(item['data'][k][str(i)]['tipPosition']),s-np.array(item['data'][k][str(i)]['tipPosition'])))
t_s+=area
return t_s/(len(item['data']))
def averageSpread():
for item in f:
t_s=0
for val in item['data']:
s=0
for i in range(1,5):
s = np.sqrt(sum((np.array(val[str(i+1)]['tipPosition']) - np.array(val[str(i)]['tipPosition'])) ** 2))
t_s+=s
return t_s/len(item['data'])
def averageDistance():
for item in f:
t_s=0
for k in range(len(item['data'])-1):
s=0
for i in range(1,6):
s = np.sqrt(sum((np.array(item['data'][k+1][str(i)]['tipPosition']) - np.array(item['data'][k][str(i)]['tipPosition'])) ** 2))
t_s+=s
return t_s/(len(item['data'])-1)
def extended_distance(item,val,i):
s=0
if i == 1:
tip_s = np.sqrt(sum((np.array(val[str(6)]['position']) - np.array(val[str(i)]['tipPosition'])) ** 2))
dip_s = np.sqrt(sum((np.array(val[str(6)]['position']) - np.array(val[str(i)]['dipPosition'])) ** 2))
pip_s = np.sqrt(sum((np.array(val[str(6)]['position']) - np.array(val[str(i)]['pipPosition'])) ** 2))
s = max(np.linalg.norm(tip_s), np.linalg.norm(dip_s), np.linalg.norm(pip_s))
else:
tip_s = np.sqrt(
sum((np.array(val[str(6)]['position']) - np.array(val[str(i)]['tipPosition'])) ** 2))
dip_s = np.sqrt(
sum((np.array(val[str(6)]['position']) - np.array(val[str(i)]['dipPosition'])) ** 2))
pip_s = np.sqrt(
sum((np.array(val[str(6)]['position']) - np.array(val[str(i)]['pipPosition'])) ** 2))
mcp_s = np.sqrt(sum((np.array(val[str(6)]['position']) - np.array(val[str(i)]['mcpPosition'])) ** 2))
s=max(np.linalg.norm(tip_s),np.linalg.norm(dip_s),np.linalg.norm(pip_s),np.linalg.norm(mcp_s))
return s
def dip_tip_projection(item,val,i):
vector_s = np.array(val[str(i)]['dipPosition']) - np.array(val[str(i)]['tipPosition'])
scalar_vector= np.array(val[str(6)]['normal'])/np.linalg.norm(np.array(val[str(6)]['normal']))
dot_vector= np.dot(vector_s,np.array(val[str(6)]['normal']))
s = dot_vector*scalar_vector
return s
def unit_vector(vector):
return vector / np.linalg.norm(vector)
def angle_between(v1, v2):
v1_u = unit_vector(v1)
v2_u = unit_vector(v2)
return np.arccos(np.clip(np.dot(v1_u, v2_u), -1.0, 1.0))
def angle(item,val,i):
a=angle_between(np.array(val[str(i)]['direction']),np.array([1,0,1]))
return a
def writecsv_cluster():
with open('output_v.csv', 'wb') as o:
w = csv.DictWriter(o, ['pinch_strength', 'grab_strength','average_distance','average_spread','average_trispread',
'f1_extended_distance','f1_diptip_projection','f1_angle','f2_extended_distance', 'f2_diptip_projection', 'f2_angle',
'f3_extended_distance', 'f3_diptip_projection', 'f3_angle','f4_extended_distance', 'f4_diptip_projection', 'f4_angle',
'f5_extended_distance', 'f5_diptip_projection', 'f5_angle','label'])
w.writeheader()
for item in f:
for val in item['data']:
elem={}
for i in range(1, 6):
elem['pinch_strength']=val[str(6)]['pinch_strength']
elem['grab_strength'] = val[str(6)]['grab_strength']
elem['average_distance']=averageDistance()
elem['average_spread']=averageSpread()
elem['average_trispread']=averageArea()
elem['f'+str(i)+'_extended_distance']= extended_distance(item,val,i)
elem['f' + str(i) + '_diptip_projection'] = list(dip_tip_projection(item, val, i))
elem['f'+str(i)+'_angle']=angle(item, val, i)
elem['label']=item['label']
w.writerow(elem)
writecsv_cluster()