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hws=3 # half window size
batch_size=5 # batch size
import numpy as np
import cv2
from skimage.feature import local_binary_pattern
#from algae_core import cmap
import pickle
import os, fnmatch
#lbp
radius = 3
cmax=640
rmax=480
clmax=2
def get_image_pair(file_pair):
""" """
oim=cv2.imread(file_pair['raw'])#BGR
im=cv2.resize(oim, (cmax,rmax),interpolation = cv2.INTER_CUBIC)
ocl=cv2.imread(file_pair['label'],0)
cl=cv2.resize(ocl, (cmax,rmax),interpolation = cv2.INTER_NEAREST )
#print cl.shape
if cl is None:# not found
cl=np.zeros( (im.shape[0],im.shape[1]),dtype=np.uint8 )
print "raw: %s, label: %s"%(file_pair['raw'], file_pair['label'])
im_grey=cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
lbp = local_binary_pattern( im_grey, 8 * radius, radius, 'uniform')
#cv2.imshow("lbp", lbp)
return cl,im,lbp
def get_xy(train_pairs):
xrmax= ( (rmax-2*hws)*(cmax-2*hws)*len(train_pairs) )
xcmax= 3+(2*hws)**2
x=np.zeros((xrmax,xcmax), dtype=np.uint8)
yrmax= ( (rmax-2*hws)*(cmax-2*hws)*len(train_pairs) )
y=np.zeros((yrmax), dtype=np.uint8)
k=0
for count,i in enumerate(train_pairs):
#print "------- processed %d from %d"%(count,len(train_pairs))
cl,im,lbp=get_image_pair(i)
#show_overlay(cl,im)
if im.shape[0]!=rmax or im.shape[1]!=cmax or lbp.shape[0]!=rmax or lbp.shape[1]!=cmax:
print "Error: image pair has missmatch size."
for r in xrange(hws,lbp.shape[0]-hws):
for c in xrange(hws,lbp.shape[1]-hws):
xrow=np.concatenate([im[r,c],
lbp[r-hws:r+hws,c-hws:c+hws].reshape(1,-1)[0],])
x[k,:]=xrow
y[k]=cl[r,c]
k+=1
return x,y
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#%reload_ext autoreload
#%autoreload 2
from algae_app.models import *
from django.db.models import Q, Sum, Count
import pandas as pd
from django.utils import timezone
from django.core.files import File
import cv2
import uuid
import numpy as np
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pwd
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import os, fnmatch
#dataset_path="C:\\Users\\Wasit\\Google Drive\\Projects\\2014_algae\\dataset_2016_sept_labelled\\dataset"
dataset_path="/root/dataset"
os.listdir(dataset_path)
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raw_files=fnmatch.filter(os.listdir(dataset_path), '*.jpg')
raw_list=[i[:-4] for i in raw_files]
label_files=fnmatch.filter(os.listdir(dataset_path), '*.png')
label_list={i[:-4]:None for i in label_files}
train_pairs=[]
for count,i in enumerate(raw_list):
if i in label_list:
train_pairs.append({
'raw': os.path.join(dataset_path,i)+'.jpg',
'label': os.path.join(dataset_path,i)+'.png',
'recall': os.path.join(dataset_path,i+"_recall")+'.jpg',
})
cmap=np.array([
( 0 , 255, 255, ),
( 14 , 127, 255, ),
( 44 , 160, 44 , ),
( 40 , 39 , 214, ),
( 0 , 0 , 255, ),
( 0 , 255, 0 , ),
( 194, 119, 227, ),
( 255, 0 , 0 , ),
( 34 , 189, 188, ),
( 207, 190, 23 , ),])
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with open('forest.pic','rb') as f:
forest = pickle.load(f)
for i in train_pairs[:10]:
raw=RawImage()
fname=i['raw']
print fname
raw.img.save(fname, File(open(fname, 'rb')))
label=LabelImage(raw=raw)
fname=i['label']
label.img.save(fname, File(open(fname, 'rb')))
x,y=get_xy( [i] )
oim=cv2.imread(i['raw'])#BGR
im=cv2.resize(oim, (cmax,rmax),interpolation = cv2.INTER_CUBIC)
cl_p=forest.predict( x )
cl_p.resize((rmax-2*hws,cmax-2*hws))
#cl_p=cv2.imread(i['label'],cv2.CV_LOAD_IMAGE_GRAYSCALE)
ol=im.copy()
algae_pixel=0
for r in xrange(rmax-2*hws):
for c in xrange(cmax-2*hws):
predicting_class=cl_p[r,c]
if predicting_class>0:
ol[r+hws,c+hws] = cmap[ predicting_class-1 ]
algae_pixel+=1
ol_rgb = cv2.cvtColor(ol, cv2.COLOR_BGR2RGB)
temp_fname=str(uuid.uuid4())+'.jpg'
cv2.imwrite(temp_fname, ol)
recall=RecallImage(raw=raw,algae_pixel=algae_pixel)
fname=i['recall']
recall.img.save(fname, File(open(temp_fname, 'rb')))
os.remove(temp_fname)
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recall.img.name
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recall.img.path
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oim.shape
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im.shape
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cl_p.shape
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%matplotlib inline
import numpy as np
from matplotlib import pyplot as plt
cmap=np.array([
( 0 , 255, 255, ),
( 14 , 127, 255, ),
( 44 , 160, 44 , ),
( 40 , 39 , 214, ),
( 0 , 0 , 255, ),
( 0 , 255, 0 , ),
( 194, 119, 227, ),
( 255, 0 , 0 , ),
( 34 , 189, 188, ),
( 207, 190, 23 , ),])
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#dummy labelling for debug only
hws=3
cmax=640
rmax=480
oim=cv2.imread(i['raw'])#BGR
im=cv2.resize(oim, (cmax,rmax),interpolation = cv2.INTER_CUBIC)
#cl_p=forest.predict( x )
#cl_p.resize((rmax-2*hws,cmax-2*hws))
cl_p=cv2.imread(i['label'],cv2.CV_LOAD_IMAGE_GRAYSCALE)
cl_p=cv2.resize(cl_p, (cmax,rmax),interpolation = cv2.INTER_NEAREST)
ol=im.copy()
for r in xrange(rmax-2*hws):
for c in xrange(cmax-2*hws):
predicting_class=cl_p[r,c]
if predicting_class>0:
ol[r+hws,c+hws] = cmap[ predicting_class-1 ]
ol_rgb = cv2.cvtColor(ol, cv2.COLOR_BGR2RGB)
temp_fname=str(uuid.uuid4())+'.jpg'
cv2.imwrite(temp_fname, ol_rgb)
os.remove(temp_fname)
plt.imshow(ol_rgb)
plt.show()
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x=uuid.uuid4()
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