

In [8]:

    
import json
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import cv2



In [9]:

    
jf = "m247514_Site3Annotation_MN_global.json"



In [10]:

    
with open("m247514_Site3Annotation_MN_global.json") as jd:
    d = json.load(jd)

Looking at the first set of points



In [11]:

    
al0 = d['area_lists'][0]



In [12]:

    
for i in range(len(al0['areas'])):
    x = al0['areas'][i]['global_path']



In [13]:

    
x = [i[0] for i in al0['areas'][0]['global_path']]
y = [-i[1] for i in al0['areas'][0]['global_path']]
z = [al0['areas'][0]['z'] for i in al0['areas'][0]['global_path']]



In [14]:

    
len(d['area_lists'])









    Out[14]:





1046



In [15]:

    
plt.plot(x, y, 'ro')
plt.plot(np.mean(x), np.mean(y), 'ro', color = 'blue')
plt.show()

Functions and grabbing all locations



In [16]:

    
def colMean(globalPath, z):
    x = np.mean([i[0] for i in globalPath])
    y = np.mean([i[1] for i in globalPath])
    z = np.mean(z)
    means = [x,y,z]
    return(means)

def colMedian(globalPath, z):
    x = np.median([i[0] for i in globalPath])
    y = np.median([i[1] for i in globalPath])
    z = np.median(z)
    medians = [x,y,z]
    return(medians)

def colm(globalPath, z):
    xmin = np.min([i[0] for i in globalPath])
    ymin = np.min([i[1] for i in globalPath])
    zmin = np.min(z)
    mins = [(xmin, ymin, zmin)]
    return(mins)

def colM(globalPath, z):
    xmax = np.max([i[0] for i in globalPath])
    ymax = np.max([i[1] for i in globalPath])
    zmax = np.max(z)
    maxs = [(xmax, ymax, zmax)]
    return(maxs)



In [17]:

    
am = []
arm = []
arM = []
ids = []
for ai in d['area_lists']:
    anno = { 'id': ai['id'], 'ggp': [], 'z': []}
    ids.append(ai['id'])
    for gp in ai['areas']:
        anno['ggp'].extend(gp['global_path'])
        anno['z'].append(gp['z'])
    am.append([colMean(anno['ggp'], anno['z']), anno['id']])
    arm.append(colm(anno['ggp'], anno['z']))
    arM.append(colM(anno['ggp'], anno['z']))



In [18]:

    
idsp1 = [i + 1 for i in ids]



In [19]:

    
anno_at = np.asarray(np.round([[(i[0][0]*3)/96, (i[0][1]*3)/96, i[0][2]] for i in am]))
anno_em = np.asarray(np.round([[i[0][0], i[0][1], i[0][2]] for i in am]))
anno_nm = np.asarray(([[i[0][0]/3, i[0][1]/3, i[0][2]] for i in am]))



In [20]:

    
tmp = anno_at
dfg = np.column_stack([tmp,ids])



In [39]:

    
#anno_at[0]
tmp  = np.delete(np.vstack(np.asarray(arm)), 6,0)

ranges =  {
    'xmin':(np.min(tmp[:,0])*3/96),
    'xmax':(np.max(tmp[:,0])*3/96),
    'ymin':(np.min(tmp[:,1])*3/96),
    'ymax':(np.max(tmp[:,1])*3/96),
    'zmin':(np.min(tmp[:,2])),
    'zmax':(np.max(tmp[:,2]))}
#ranges =  {
#    'xmin':(np.round(np.min(tmp[:,0]))),
#    'xmax':(np.round(np.max(tmp[:,0]))),
#    'ymin':(np.round(np.min(tmp[:,1]))),
#    'ymax':(np.round(np.max(tmp[:,1]))),
#    'zmin':np.round(np.min(tmp[:,2])),
#    'zmax':np.round(np.max(tmp[:,2]))}
ranges









    Out[39]:





{'xmax': 316.2684548908941,
 'xmin': 186.28543384089252,
 'ymax': 286.03090848932817,
 'ymin': 152.8437499965002,
 'zmax': 49.0,
 'zmin': 0.0}



In [ ]:

save locations to csv files



In [32]:

    
if False:
    #a = np.asarray(np.round(am))
    np.savetxt("rorb_locations_nm.csv", np.column_stack([anno_nm, ids]), delimiter=",", fmt = "%f")
    np.savetxt("rorb_locations_at.csv", np.column_stack([anno_at, ids]), delimiter=",", fmt = "%d")
    np.savetxt("rorb_locations_em.csv", np.column_stack([anno_em, ids]), delimiter=",", fmt = "%d")



In [35]:

    
if False:
    #a = np.asarray(np.round(am))
    np.savetxt("rorb_locations_nm_idp1.csv", np.column_stack([anno_nm, idsp1]), delimiter=",", fmt = "%f")
    np.savetxt("rorb_locations_at_idp1.csv", np.column_stack([anno_at, idsp1]), delimiter=",", fmt = "%d")
    np.savetxt("rorb_locations_em_idp1.csv", np.column_stack([anno_em, idsp1]), delimiter=",", fmt = "%d")



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