#! /usr/bin/env python
# -*- coding: utf-8 -*-
import os
import cv2
import glob
import numpy as np
import scipy.io as sio
from show_fcnout import show_fcnout
def stitch(featfolder, savefolder, dirname):
root_folder = '/data/lc/data/AMIDA13/mitoses_ground_truth/'
show_fcnout(featfolder, dirname)
for i in range(len(dirname)):
filepath = root_folder + dirname[i] + '/*.csv'
img = glob.glob(filepath)
n = len(img)
for j in range(n):
imgname = img[j].split('/')[-1]
stitchPatch(root_folder, dirname[i], imgname, featfolder, savefolder)
stitchPatchImg(root_folder, dirname[i], imgname,savefolder)
def stitchPatch(root_folder, dir1, imgname, featfolder, savefolder):
# stitch the features of patches to feature of full image
name = os.path.join(dir1, imgname)
print 'name:%s\n' %(name)
Im = os.path.join(featfolder, name[0:-4])
I = [None]*16
for i in range(9):
dict1 = sio.loadmat(Im+'_0'+str(i+1)+'.mat')
I[i] = dict1['feat']
for i in range(9,16):
dict2 = sio.loadmat(Im+'_'+str(i+1)+'.mat')
I[i] = dict2['feat']
A = np.zeros((4*500,4*500))
for row in range(4):
for col in range(4):
A[row*500:(row+1)*500,col*500:(col+1)*500] = I[row*4+col]
sio.savemat(savefolder+name[0:-4], {'A':np.mat(A)})
def stitchPatchImg(root_folder, dir1, imgname, savefolder):
# stitch the visual feature map of patches to feature map of full image
name = os.path.join(dir1, imgname[0:-4])
imgfolder = '/data/lc/data/AMIDA13/SegMitos-out/AMIDA13/visualFCNTemp/'
Im = imgfolder + name
I = [None]*16
for i in range(9):
patch = cv2.imread(Im+'_0'+str(i+1)+'.bmp')
I[i] = patch
for i in range(9,16):
patch = cv2.imread(Im+'_'+str(i+1)+'.bmp')
I[i] = patch
A = np.zeros((4*500,4*500,3))
for row in range(4):
for col in range(4):
A[row*500:(row+1)*500,col*500:(col+1)*500] = I[row*4+col]
savefolder = savefolder[0:-9]+'fullImg/'
cv2.imwrite(savefolder+name+'.jpg', A)
print savefolder + name + '.jpg'
