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detection_test.py
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detection_test.py
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# coding: utf-8
# In[1]:
import numpy as np
import cv2
import os
from os import listdir
from os.path import basename,isfile,join
import cPickle as pickle
#from matplotlib import pyplot as plt
from scipy import ndimage
from sklearn import neighbors
from sklearn import svm
from skimage.feature import hog
from skimage import feature
import random
import time
# In[2]:
def creat_posfile_group(pos_files_group):
i = 0
pos_files = []
group = []
for pfs in pos_files_group:
group_idx = []
for j in range(len(pfs)):
group_idx.append(i)
i = i+1
pos_files.append(pfs[j])
group.append(group_idx)
return pos_files,group
def get_pos(fi):
poses = []
with open(fi) as f:
lines = f.readlines()
for line in lines:
content = line.split()
file_dir = content[0]
tag = os.path.splitext(basename(file_dir))[0]
num_object = content[1]
for i in range(int(num_object)):
x = int(content[2+4*i])
y = int(content[2+4*i + 1])
w = int(content[2+4*i + 2])
h = int(content[2+4*i + 3])
poses.append([file_dir,x,y,w,h, tag +'_'+ str(i+1)])
return poses
def get_neg_images(fi):
neg_images = []
with open(fi) as f:
lines = f.readlines()
for line in lines:
content = line.split()
file_dir = content[0]
image = cv2.imread(file_dir.replace("\\", "/"))
num_object = content[1]
for i in range(int(num_object)):
x = int(content[2+4*i])
y = int(content[2+4*i + 1])
w = int(content[2+4*i + 2])
h = int(content[2+4*i + 3])
image[y:y+h, x:x+w] = 0
neg_images.append(image)
return neg_images
def unify_label(labels, group):
res = []
for a in labels:
for i in range(len(group)):
if a in group[i]:
res.append(i)
return np.array(res)
def highpass_and_imgback(pos,xsmall, xlarge):
cur_h, cur_w = pos.shape
if cur_h < 2*xlarge or cur_w < 2*xlarge:
#print cur_h, cur_w
resize_scale = int(max(2 * xlarge / (1.0*cur_w), 2 * xlarge / (cur_h*1.0))) + 1
#print resize_scale
pos = cv2.resize(pos,(0,0),fx = resize_scale,fy=resize_scale)
cur_h, cur_w = pos.shape
#print cur_h, cur_w
f = np.fft.fft2(pos)
fshift = np.fft.fftshift(f)
mask = np.zeros(pos.shape)
#mask = np.ones(pos.shape)
height = pos.shape[0]
width = pos.shape[1]
centerx = int(width/2)
centery = int(height/2)
mask[centery-xlarge:centery+xlarge,centerx-xlarge:centerx+xlarge] = 1
mask[centery-xsmall:centery+xsmall,centerx-xsmall:centerx+xsmall] = 0
fshift_filtered = fshift.copy() * mask
f_ishift_filtered = np.fft.ifftshift(fshift_filtered)
img_back = np.fft.ifft2(f_ishift_filtered)
mag_fshift = fshift_filtered[centery-xlarge:centery+xlarge,centerx-xlarge:centerx+xlarge]
epsilon = 10**-8
magnitude_spectrum = 20*np.log(np.abs(mag_fshift) + epsilon)
img_back = np.abs(img_back)
min_back = np.min(img_back)
max_back = np.max(img_back)
img_back = np.array((img_back-min_back)/(max_back-min_back)*255,dtype = np.uint8)
img_back = np.array(img_back, dtype = np.uint8)
img_back = np.clip(img_back, 0, 255)
return img_back, magnitude_spectrum
def get_sample(poses,xsmall, xlarge):
X = []
for p, l in poses:
x = p[1]
y = p[2]
w = p[3]
h = p[4]
image = cv2.imread(p[0].replace("\\", "/"))
image_preprocess = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
#image_preprocess = highpass_and_imgback(image_preprocess,xsmall, xlarge)
pos = image_preprocess[y:y + h, x:x + w]
"""
if (w < 2 * xlarge + 1) or (h < 2 * xlarge + 1):
scale = int(max(2 * xlarge / w, 2 * xlarge / h)) + 1
pos = cv2.resize(pos, (scale * pos.shape[0], scale * pos.shape[1]))
#print pos.shape
#h, w = pos.shape
#pos_back, magnitude = highpass_and_imgback(pos,xsmall, xlarge)
#pos_back = cv2.resize(pos_back, (h,w))
deg_step = 10
for deg in range(0,360, deg_step):
cur_pos = ndimage.interpolation.rotate(pos,deg, reshape=True)
cur_pos_back, magnitude = highpass_and_imgback(cur_pos,xsmall, xlarge)
X.append((magnitude,l,cur_pos))
"""
deg_step = 10
for deg in range(0,360, deg_step):
cur_pos = ndimage.interpolation.rotate(pos,deg, reshape=True)
cur_h, cur_w = cur_pos.shape
new_pos = cur_pos
new_pos = np.array(new_pos, np.uint8)
if (cur_w < 2 * xlarge + 1) or (cur_h < 2 * xlarge + 1):
scale = int(max(2 * xlarge / cur_w, 2 * xlarge / cur_h)) + 1
new_pos = cv2.resize(cur_pos, (scale * cur_pos.shape[1], scale * cur_pos.shape[0]))
new_pos = np.array(new_pos, np.uint8)
pos_back, magnitude = highpass_and_imgback(new_pos,xsmall, xlarge)
X.append((magnitude,l,new_pos))
#X.append((pos_back,l,pos))
return X
def PCA(data):
datamean = np.mean(data,0)
a = data - datamean
datamax_after_center = np.std(a,0) + 10e-8
b = a / datamax_after_center
x = b.transpose()
xcov = np.dot(x,x.transpose())/x.shape[1]
ux,sxx,vx = np.linalg.svd(xcov)
sx = sxx
return ux,sx,vx, datamean, datamax_after_center
def customPCA(data, ratio):
ux,sx,vx, datamean, datamax = PCA(data)
N = ux.shape[1]
sum_sx = np.sum(sx)
ac_sum = 0
k = 0
for _i in range(N):
if sx[_i] == 0:
break
k = _i
ac_sum += sx[_i]
if ac_sum/sum_sx >= ratio:
break
k += 1
return ux,sx,vx,datamean,datamax,k
def mydistance(a, b):
#return np.dot(a-b, (a-b).transpose() )
#return 1 - np.dot(a, b)
return 1 - np.dot(a, b)/(np.sqrt(np.dot(a,a)*np.dot(b,b)))
#return (1 - np.dot(a, b)/(np.sqrt(np.dot(a,a)*np.dot(b,b))))**2 + (1 - (np.sqrt(np.dot(a,a)/np.dot(b,b))))**2
# In[3]:
def check_foreground_hog(hypothesis, clf, _sz):
cur_pos = hypothesis
cur_h, cur_w = cur_pos.shape
if (cur_w < int(_sz[1])) or (cur_h < int(_sz[0])):
cur_pos = cv2.resize(cur_pos,(int(_sz[1]),int(_sz[0])))
hogFeature = feature.hog(cur_pos,
orientations=9,
pixels_per_cell=(8, 8),
cells_per_block=(2, 2),
transform_sqrt=True,
visualise=True,
feature_vector=False)[0]
predict = clf.predict([hogFeature.reshape(-1)])[0]
if predict == 1:
return True
else:
return False
# In[ ]:
def reconstruct_from_pca(x,ux,sx,vx,datamean, datamax,k):
N = ux.shape[1]
padded = np.zeros((N))
padded[:k] = x
reconstructed = np.dot(ux,padded)
return reconstructed
def check_foreground(hypothesis, max_error,min_norm, xsmall, xlarge,ux,sx,vx,datamean, datamax,k):
uxx = ux[:,:k]
cur_pos = hypothesis
cur_h, cur_w = cur_pos.shape
if (cur_w < 2 * xlarge + 1) or (cur_h < 2 * xlarge + 1):
scale = int(max(2 * xlarge / cur_w, 2 * xlarge / cur_h)) + 1
cur_pos = cv2.resize(cur_pos, (scale * cur_pos.shape[1], scale * cur_pos.shape[0]))
_, mag = highpass_and_imgback(cur_pos,xsmall, xlarge)
mag = mag.reshape(-1)
mag_norm = (mag-datamean)/datamax
mag_projected = np.dot(mag_norm, uxx)
mag_reconstructed = reconstruct_from_pca(mag_projected,ux,sx,vx,datamean, datamax,k)
reconstructed_error = np.sum((mag_reconstructed-mag_norm)**2)
if reconstructed_error > max_error or np.sqrt(np.sum(mag_projected[:k/2]**2)) < min_norm:
return False
else:
#print '%f vs %f, %f vs %f'%(reconstructed_error,max_error, np.sqrt(np.sum(mag_projected[:k/2]**2)), min_norm)
return True
pos_main_class = ['./annotation/Acanthamoeba_trophozoite_cyst.txt',
'./annotation/Balantidium_cyst_trophozoite.txt',
'./annotation/Cyclospora_oocyst_normal.txt',
'./annotation/Cystoisospora_oocyst_single.txt',
'./annotation/Giardia_trophozoite.txt',
'./annotation/Iodamoeba_cyst.txt',
'./annotation/Sarcocystis_oocyst_single.txt',
'./annotation/Toxoplasma_cyst_single.txt'
]
pos_files_group = [
[#'./annotation_subclass/Sarcocystis_oocyst_single_stage2.txt',
#'./annotation_subclass/Sarcocystis_oocyst_single_stage3.txt',
'./annotation_subclass/Sarcocystis_oocyst_stage2.txt',
'./annotation_subclass/Sarcocystis_oocyst_stage3.txt'],
[#'./annotation_subclass/Cystoisospora_oocyst_single_stage1.txt',
#'./annotation_subclass/Cystoisospora_oocyst_single_stage2.txt',
#'./annotation_subclass/Cystoisospora_oocyst_single_stage3.txt',
#'./annotation_subclass/Cystoisospora_oocyst_single_stage4.txt',
'./annotation_subclass/Cystoisospora_oocyst_stage1.txt',
'./annotation_subclass/Cystoisospora_oocyst_stage2.txt',
'./annotation_subclass/Cystoisospora_oocyst_stage3.txt',
'./annotation_subclass/Cystoisospora_oocyst_stage4.txt'],
['./annotation/Iodamoeba_cyst.txt'],
['./annotation_subclass/Cyclospora_oocyst_stage1.txt',
'./annotation_subclass/Cyclospora_oocyst_stage2.txt',
'./annotation_subclass/Cyclospora_oocyst_stage3.txt'],
['./annotation/Toxoplasma_cyst_single.txt'],
]
names = ['Sarcocystis','Cystoisospora','Iodamoeba','Cyclospora','Toxoplasma']
pos_files, group = creat_posfile_group(pos_files_group)
numclass = len(group)
num_label = len(pos_files)
numlabel = len(pos_files)
with open("myModel.pickle",'rb') as f:
classifier,names,sz,ux,sx,vx,datamean, datamax, k,comp_parameters,root_filters,max_errors,min_norms,xlarge,xsmall = pickle.load(f)
uxx = ux[:,:k]
detection_dirs = ['./data/Sarcocystis_oocyst/',
'./data/Iodamoeba_cyst/',
'./data/Toxoplasma_cyst/',
'./data/Cyclospora_oocyst/',
'./data/Cystoisospora_oocyst/']
list_images = []
for detection_dir in detection_dirs:
only_files = [detection_dir + f for f in listdir(detection_dir) if isfile(join(detection_dir,f))]
list_images.extend(only_files)
colors = [(0,0,255),(0,255,0),(255,0,0),(255,0,255),(0,255,255),(255,255,0)]
scales = [1,2,3,4,5,6]
save_dir = './result/'
for file_dir_idx in range(len(list_images)):
print '=========== %d / %d =============='%(file_dir_idx+1,len(list_images))
file_dir = list_images[file_dir_idx]
print file_dir
tag = os.path.splitext(basename(file_dir))[0]
image = cv2.imread(file_dir)
hypotheses = []
img = image.copy()
for scale in scales:
resized = cv2.resize(image,(0,0), fx=1./scale, fy=1./scale)
img_gray = cv2.cvtColor(resized, cv2.COLOR_BGR2GRAY)
for filter_sz_idx in range(len(sz)):
start_time = time.time()
img = resized.copy()
filter_sz = sz[filter_sz_idx]
comp_ux,comp_sx,comp_vx,comp_datamean, comp_datamax, comp_k = comp_parameters[filter_sz_idx]
comp_uxx = comp_ux[:,:comp_k]
#print comp_k
#for _i in range(0,img_gray.shape[0],int(filter_sz[0])/2 ):
# for _j in range(0,img_gray.shape[1],int(filter_sz[1])/2 ):
for _i in range(0,img_gray.shape[0],int(filter_sz[0])/8):
for _j in range(0,img_gray.shape[1], int(filter_sz[1])/8):
hypothesis = img_gray[_i:np.minimum(_i+int(filter_sz[0]), img_gray.shape[0]),
_j:np.minimum(_j+int(filter_sz[1]), img_gray.shape[1])]
#if check_foreground(hypothesis, max_errors[filter_sz_idx],min_norms[filter_sz_idx],
# xsmall, xlarge,ux,sx,vx,datamean, datamax,k):
if check_foreground_hog(hypothesis, root_filters[filter_sz_idx],filter_sz) and check_foreground(hypothesis, max_errors[filter_sz_idx],min_norms[filter_sz_idx],
xsmall, xlarge,ux,sx,vx,datamean, datamax,k):
#if check_foreground_hog(hypothesis, root_filters[filter_sz_idx],filter_sz):
#print hypothesis.shape
_, featureMagnitude = highpass_and_imgback(hypothesis,xsmall,xlarge)
featureMagnitude_norm = (featureMagnitude.reshape(-1)-datamean)/datamax
featureMagnitude_projected = np.dot(featureMagnitude_norm, uxx)
category = classifier.predict([featureMagnitude_projected])[0]
#==========================================================================
#featureMagnitude_norm = (featureMagnitude.reshape(-1)-comp_datamean)/comp_datamax
#featureMagnitude_projected = np.dot(featureMagnitude_norm, comp_uxx)
#category = comp_classifiers[filter_sz_idx].predict([featureMagnitude_projected])[0]
#==========================================================================
group_category = unify_label([category],group)[0]
#cv2.rectangle(img,(_j,_i),(_j+hypothesis.shape[1],_i+hypothesis.shape[0]),(0,255,0),3)
cv2.rectangle(img,(_j,_i),(_j+hypothesis.shape[1],_i+hypothesis.shape[0]),colors[group_category],3)
cv2.imwrite(save_dir + tag + '_hog_duplicate_hardNegative_res_detected_scale_%d_filter_%d.png'%(scale,filter_sz_idx),img )
print tag + '_hog_duplicate_res_detected_scale_%d_filter_%d.png'%(scale,filter_sz_idx)
print 'Detection %s seconds'%(time.time()-start_time)