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In [34]:

    
import matplotlib.pyplot as plt
import pylab
import numpy as np
from skimage.transform import rotate
from scipy.spatial.distance import pdist
from scipy.spatial.distance import squareform
import os
import pickle
import shutil

#Rotation: RS140_x5_0000_gabor-blueNisslWide-segm-blueNisslRegular_spMaxDirAngle.npys
#RAW:   "/oasis/projects/nsf/csd181/yuncong/DavidData2014tif/RS140/x5/0000/RS140_x5_0000.tif"
#PATH: " /oasis/projects/nsf/csd181/yuncong/DavidData2014results/RS140/0000/"
#Example: "/oasis/projects/nsf/csd181/yuncong/DavidData2014v4/RS141/x5/0001/pipelineResults/RS141_x5_0001_segm-blueNissl_segmentation.npy"



In [35]:

    
IMG_PATH    = "/oasis/projects/nsf/csd181/yuncong/DavidData2014tif/RS140/x5/0000/RS140_x5_0000.tif"
DATA_PATH = "/oasis/projects/nsf/csd181/yuncong/DavidData2014results/RS140/0000/"
PROC_PATH = "RS140/x5/0000"
CODE_NAME = "blueNisslRegular"

FNAMES = PROC_PATH.replace("/", "_")
FPATH = DATA_PATH



In [106]:

    
# Load segments data
SEGMENTS  = FPATH + FNAMES + "_segm-" + CODE_NAME + "_segmentation.npy" 
ROTATIONS = FPATH + FNAMES + "_gabor-blueNisslWide-segm-" + CODE_NAME + "_spMaxDirAngle.npy" 
NEIBRS         = FPATH + FNAMES + "_segm-" + CODE_NAME + "_neighbors.npy"
TEXT_DIST   = FPATH + FNAMES + "_gabor-blueNisslWide-segm-" + CODE_NAME + "-vq-blueNissl_texHistPairwiseDist.npy" 
TEXT_MAP   = FPATH + FNAMES + "_gabor-blueNisslWide-vq-blueNissl_texMap.npy"
PROPS          = FPATH + FNAMES + "_segm-" + CODE_NAME + "_spProps.npy"
data = np.load(SEGMENTS)
rotData = np.load(ROTATIONS)
nbrs = np.load(NEIBRS)
texton_dist = np.load(TEXT_DIST)
texton_map = np.load(TEXT_MAP)
sp_props = np.load(PROPS)



In [107]:

    
print texton_map.min(), texton_map.max()



In [108]:

    
# Input image
img = plt.imread(IMG_PATH)
plt.imshow(img)









    Out[108]:





<matplotlib.image.AxesImage at 0x54fd310>



In [109]:

    
try:
    os.stat("images")
except:
    os.mkdir("images")



In [110]:

    
center_dists = pdist(sp_props[:, :2])
center_dist_matrix = squareform(center_dists)
k = 8
k_neighbors = np.argsort(center_dist_matrix, axis=1)[:, 1:k+1]



In [176]:

    
def getXYwithNbrs(v, trsh):
    minY, minX, maxY, maxX = sp_props[v, 4:]
    
    for o in k_neighbors[v]:
        if texton_dist[v, o] > trsh:
            return True, 0, 0, 0, 0
        
        minoY, minoX, maxoY, maxoX = sp_props[o, 4:]
        
        minX = min(minX, minoX)
        maxX = max(maxX, maxoX)
        minY = min(minY, minoY)
        maxY = max(maxY, maxoY)
        
    return False, minX, maxX, minY, maxY



In [190]:

    
lenX, lenY = len(img), len(img[0])
mapped = {}
N = data.max() + 1
T = 0.07

for v in range(0, N):
    if v == -1: 
        continue
    
    # Mark all values equal to v
    b, minX, maxX, minY, maxY = getXYwithNbrs(v, T)
    if b:
        continue
    
    # Crop patch
    degree = np.degrees(rotData[v])
    H = maxX - minX
    W = maxY - minY
    buffX = H / 2 #np.min((np.max((20, minX - 1)), lenX - maxX - 1))
    buffY = W / 2 #np.min((np.max((20, minY - 1)), lenY - maxY - 1))
    patch = img[minY - buffY: maxY + buffY, minX - buffX : maxX + buffX]
    #patch2 = img[minX : maxX, minY : maxY]
    #imshow(patch2)
    #break

    if patch.shape[0] == 0 or patch.shape[1] == 0:
        continue
    patch = rotate(patch, degree, True)
    #imshow(patch)
    #break
    
    nCY, nCX, _ = patch.shape
    patch = patch[(nCY - W) / 2 : (nCY + W) / 2, (nCX - H) / 2 : (nCX + H) / 2]
    nCY, nCX, _ = patch.shape
    
    radius = int(np.sqrt((nCX / 2) ** 2 + (nCY / 2) ** 2) / 2)
    pointX = int(maxX - H / 2);
    pointY = int(maxY - W / 2);
    
    textons = [0] * 8
    '''for r in range(0, radius + 1):
        for x in range(0, radius + 1):
            for y in range(0, radius + 1):
                if x ** 2 + y ** 2 == r ** 2:
                    rx = minX + x
                    ry = minY + y
                    val = texton_map[ry, rx]
                    if val >= 0:
                        textons[val] += 1'''

    for x in range(int(minX), int(maxX)):
        for y in range(int(minY), int(maxY)):
            val = texton_map[y, x]
            if val >= 0:
                textons[val] += 1
    
    suffixName = str(v) + "_" + str(pointX) + "_" + str(pointY) + "_" + str(radius)
    imgName = "WebStem/photos/" + suffixName
    mapped[v] = suffixName  + ".png"
    plt.imsave(imgName + ".png", patch)
    
    plt.hist(range(8), bins=8, weights=textons, normed = True, label=None)
    plt.axes().get_xaxis().set_visible(False)
    plt.axes().get_yaxis().set_visible(False)
    plt.savefig(imgName + '_hist.png')
    plt.cla()

Create Cover



In [191]:

    
# save the map in case of a crash
#file = open("images/v2name.pck", "w")
#pickle.dump(mapped, file)
#file.close()
print "done"









    



done



In [192]:

    
# load the map in case of a crash
#file  = open ("images/v2name.pck")
#mapped = pickle.load(file)
#file.close();
print "done"









    



done



In [192]:



In [193]:

    
maxKey = max(mapped.keys())
potential = mapped.copy()
index = 1
T = 0.1
S = []
nS = set()

while potential:
    folderName = "WebStem/photos/group_" + str(index) + "/"
    shutil.rmtree(folderName, True)

    candidate = potential.popitem()
    nS.add(candidate)
    
    iter = potential.iteritems()
    remove = set()
    
    
    while True:
        try:
            next = iter.next()
        except:
            break
        succ = True
        v1 = next[0]
        for k in nS:
            v2 = k[0]
          #  print v1, v2
            if texton_dist[v1, v2] > T:
                succ = False
                break
        
        if succ:
            remove.add(next[0])
            nS.add(next)
            
    for i in remove:
        del potential[i]
        
    if (len(nS) >= 8):
        os.mkdir(folderName)
        #os.mkdir(folderName + "images")
        for n in nS:
            print n[1], folderName + n[1]
            os.symlink(n[1], folderName + n[1])
        index = index + 1
        S.append(nS)
    nS = set()
    
for s in S:
    print s









    



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set([(1522, '1522_888_1415_52.png'), (133, '133_407_361_52.png'), (115, '115_1036_314_52.png'), (1501, '1501_4538_1369_51.png'), (105, '105_313_313_52.png'), (3285, '3285_2041_2330_52.png'), (2012, '2012_4105_1609_50.png'), (1479, '1479_2472_1368_50.png'), (3339, '3339_1894_2431_55.png'), (170, '170_938_409_51.png'), (1241, '1241_2426_1270_52.png'), (2325, '2325_3961_1752_51.png'), (731, '731_2666_984_51.png'), (3274, '3274_1945_2330_51.png'), (48, '48_502_207_56.png'), (2873, '2873_3192_2041_51.png'), (88, '88_1035_264_53.png'), (634, '634_936_936_51.png'), (694, '694_255_984_54.png'), (912, '912_2665_1081_51.png'), (720, '720_1033_984_50.png'), (91, '91_458_259_53.png'), (1176, '1176_1321_1226_51.png'), (2951, '2951_3192_2090_51.png'), (785, '785_309_1033_53.png'), (113, '113_938_313_51.png'), (1251, '1251_2473_1272_51.png')])
set([(2440, '2440_4922_1805_53.png'), (2342, '2342_4970_1755_51.png'), (2581, '2581_986_1897_52.png'), (2389, '2389_4682_1802_51.png'), (2439, '2439_4872_1802_51.png'), (2343, '2343_5019_1754_53.png'), (2290, '2290_841_1754_51.png'), (2458, '2458_1035_1849_52.png'), (1683, '1683_2569_1465_50.png'), (2340, '2340_4872_1753_51.png')])



In [ ]:

    
print "Done"



In [ ]: