def correlate_image(self, image_one, image_two):
try:
max_range = c2d(image_one, image_one, mode='same')
img_result = c2d(image_one, image_two, mode='same')
result_final = ((img_result.max() / max_range.max()) * 100)
return result_final
except Exception, e:
flash("failed:" + str(e))
return 105
def correlate_image(self, image_one, image_two):
try:
max_range=c2d(image_one, image_one, mode='same')
img_result=c2d(image_one, image_two, mode='same')
result_final=((img_result.max()/max_range.max())*100)
return result_final
except Exception, e:
flash("failed:"+str(e))
return 105
def correlate_image(self, image_one, image_two):
try:
max_range=c2d(image_one, image_one, mode='same')
img_result=c2d(image_one, image_two, mode='same')
result_final=((img_result.max()/max_range.max())*100)
print "Images are", result_final,"percent similar\n"
if result_final==100.0:
print"Images are exactly same\n"
elif result_final==0.0:
print "Images are very different\n"
except:
print "\nAwwh, somethings not right! Try again\n"
exit()
def correlate_image(self, image_one, image_two):
try:
max_range = c2d(image_one, image_one, mode='same')
img_result = c2d(image_one, image_two, mode='same')
result_final = ((img_result.max() / max_range.max()) * 100)
print "Images are", result_final, "percent similar\n"
if result_final == 100.0:
print "Images are exactly same\n"
elif result_final == 0.0:
print "Images are very different\n"
except:
print "\nAwwh, somethings not right! Try again\n"
exit()
def correlate_image(self, image_one, image_two, filename):
try:
max_range=c2d(image_one, image_one, mode='same')
img_result=c2d(image_one, image_two, mode='same')
result_final=((img_result.max()/max_range.max())*100)
my_array.append({'name': filename, 'number': result_final})
print "with: " + filename + " | Images are", result_final,"percent similar\n"
if result_final==100.0:
print "with: " + filename + " | Images are exactly same\n"
elif result_final==0.0:
print "with: " + filename + " | Images are very different\n"
except:
print "\nAwwh, somethings not right! Try again\n"
exit()
def sim(img1, img2, scale_percent=15, multichannel=True):
'''sim: img1과 비교해서 img2와 얼마나 차이가 나는가'''
img1 = img_thresh(img1)
img2 = img_thresh(img2)
re_im1 = img_resize(scale_percent, img1)
re_im2 = img_resize(scale_percent, img2)
c11 = c2d(re_im1, re_im1, mode='same').ravel().tolist()
c12 = c2d(re_im1, re_im2, mode='same').ravel().tolist()
print(np.nanmax(c12), np.nanmax(c11))
if np.nanmax(c12) == np.nan:
print(c11)
print(c12)
return np.nanmax(c12) / np.nanmax(c11)
def cross_corr(project):
f = open('/home/cuda/Stefanie/HOST01to07/out' + project + '.txt', 'w')
for project in project_lst:
for shot in shots:
for slide in slides:
index1 = 0
pict_lst = os.listdir("/home/cuda/Stefanie/images75/" +
project + "/" + shot + "/" + slide + "/")
for x in range(len(pict_lst)):
im = "/home/cuda/Stefanie/images75/" + project + "/" + shot + "/" + slide + "/" + pict_lst[
index1]
for pic in pict_lst:
im2 = "/home/cuda/Stefanie/images75/" + project + "/" + shot + "/" + slide + "/" + pict_lst[
index2]
if (pict_lst[index2],
pict_lst[index1]) not in ready_lst:
data1 = get(im)
data2 = get(im2)
cc = c2d(data1, data2, mode='same')
f.write(project + '\t' + pict_lst[index1] + '\t' +
pict_lst[index2] + '\t' + str(cc.max()) +
'\n')
ready_lst.add((pict_lst[index1], pict_lst[index2]))
#print project, shot, slide, pict_lst[index1], pict_lst[index2], cc.max()
index2 = index2 + 1
index1 = index1 + 1
index2 = 0
ready_lst.clear()
def compare_images(imgfile1, imgfile2): '''Compare the images as per https://stackoverflow.com/questions/1819124/image-comparison-algorithm This is really slow, so we start with a tiny size and gradually go larger if we find them to look alike.''' size = 5 origimage1, origimage2 = read_image(imgfile1), read_image(imgfile2) if origimage1 == None or origimage2 == None: # bad image return False, 0, size while True: data1, data2 = get_resized_data(origimage1, size), get_resized_data(origimage2, size) correlation = c2d(data1, data2, mode="same").max() if correlation > size**2 * 0.75: if size >= 80: # seems like a good match # print "\tMATCH", size, imgfile1, imgfile2 return True, correlation, size else: # refine # print "\tPossible correlation!", size, imgfile1, imgfile2 size *= 2 else: # not a match return False, correlation, size
def compare_images(orig_url, file_name_1, file_name_2): file_path_1 = global_vars.img_filepath + file_name_1 + '-clipped' file_path_2 = global_vars.img_filepath + file_name_2 + '-clipped' hash_of_url = hashlib.md5(orig_url) hash_link = global_vars.cache_url + hash_of_url.hexdigest() + '/' f1_open = False if os.path.isfile(file_path_1 + '.png'): img1 = get(file_path_1) f1_open = True else: f = open(global_vars.failed_pages, 'a') f.write(file_name_1 + "\n") f.close() if os.path.isfile(file_path_2 + '.png'): if f1_open == True: img2 = get(file_path_2) #c11 = c2d(img1, img1, mode='same') c12 = c2d(img1, img2, mode='same') diff = c12.max() skew = c12.max()/30000. if skew < global_vars.quality_threshold: # write results to results file f = open(global_vars.comparison_results, 'a') f.write(file_name_1 + ',' + file_name_2 + ',' + orig_url + ',' + hash_link + ',' + str(skew) + "\n") f.close() else: f = open(global_vars.failed_pages, 'a') f.write(file_name_2 + "\n") f.close()
def compare_meanshift(dir_mainfolder):
##Compare_ls: Store score of comparation between images
compare_ls = []
##Base_img: Image draw from user
print "get base"
base_img = cv2.imread(dir_mainfolder + '/img_result/0.jpg')
img0 = cv2.cvtColor(base_img,cv2.COLOR_BGR2RGB)
hist_base = cv2.calcHist([img0], [0, 1, 2], None, [8, 8, 8],
[0, 256, 0, 256, 0, 256])
hist_base = cv2.normalize(hist_base).flatten()
base_img = cv2.resize(base_img, (100, 100))
##Get inner product user draw image
base_img = sp.inner(base_img, [299, 587, 114]) / 1000.0
base_img = (base_img - base_img.mean()) / base_img.std()
base_img.shape
num = 1
##Compare the user draw image to all images
list_img =[]
for img2 in glob.glob(dir_mainfolder + "/*.jpg"):
n= cv2.imread(img2)
list_img.append(n)
for img2 in glob.glob(dir_mainfolder + "/*.png"):
n= cv2.imread(img2)
list_img.append(n)
print "jump for"
for img in list_img:
img = get(num,dir_mainfolder,0)
img.shape
print "compare: " + num
##Use correlation 2d to compare between image
compare_img = c2d(base_img, img, mode='same')
value_c2d_pixel = compare_img.max()*0.4/1000
##Read image and calculated image histogram
data = imread(dir_mainfolder+'/img_meanshift/%s.jpg' % num)
img_1= cv2.cvtColor(data,cv2.COLOR_BGR2RGB)
hist_img = cv2.calcHist([img_1], [0, 1, 2], None, [8, 8, 8],
[0, 256, 0, 256, 0, 256])
hist_img = cv2.normalize(hist_img).flatten()
##Comare histograms
compare = cv2.compareHist(hist_base,hist_img,cv2.cv.CV_COMP_CORREL)
compare = compare*0.6
avg_value = (compare + value_c2d_pixel)/2
compare_ls.append((num,avg_value))
print "compare done"
num = num + 1
##Sorting the score of comparation DESC
compare_ls = sorted(compare_ls,key = itemgetter(1), reverse = True)
i = 1
##Write ranking image to folder
for ls in compare_ls:
cv2.imwrite(dir_mainfolder+"/img_rank/"+str(i)+".jpg",list_img[ls[0] - 1])
i = i+1
def main():
checker = True
while checker == True:
testfile = input('Enter the file path to JPG: ')
if os.path.exists(testfile):
checker = False
else:
print("Not valid file path.")
img_test = Image.open(testfile)
img_test.thumbnail([50,50], Image.ANTIALIAS)
img_test.save(testfile, "JPEG")
testValue = valueIt(testfile)
count = [0,0,0,0,0]
for eachkind in range(1, 6):
for eachfile in listdir("Data/0"+str(eachkind)):
img = Image.open("Data/0"+str(eachkind)+"/"+ eachfile)
img.thumbnail([50,50], Image.ANTIALIAS)
img.save("Data/0"+str(eachkind)+"/"+ eachfile, "JPEG")
compareValue = valueIt("Data/0"+str(eachkind)+"/"+ eachfile)
CompareCode = c2d(testValue, compareValue, mode='same')
count[eachkind-1] += CompareCode.max()
numofFile = []
for eachkind in range(1, 6):
numofFile.append(len(listdir("Data/0"+str(eachkind))))
fianlpercentage = []
fianlpercentage.append(count[0]/numofFile[0])
fianlpercentage.append(count[1]/numofFile[1])
fianlpercentage.append(count[2]/numofFile[2])
fianlpercentage.append(count[3]/numofFile[3])
fianlpercentage.append(count[4]/numofFile[4])
thehighest = 0
theindex = 0
for eachindex in range(0,5):
if fianlpercentage[eachindex] > thehighest:
thehighest = fianlpercentage[eachindex]
theindex = eachindex
if theindex == 0:
print("It is Smile")
elif theindex == 1:
print("It is Hat")
elif theindex == 2:
print("It is Hash")
elif theindex == 3:
print("It is Heart")
elif theindex == 4:
print("It is Dollar")
def corelationImage1Image2(imageArray1, imageArray2):
image1 = scipy.inner(numpy.asarray(imageArray1), [299, 587, 114]) / 1000.0
image2 = scipy.inner(numpy.asarray(imageArray2), [299, 587, 114]) / 1000.0
image1 = (image1 - image1.mean())/ image1.std()
image2 = (image2 - image2.mean())/ image2.std()
corelationimage1Withimage2 = c2d(image1, image2, mode = 'same')
return corelationimage1Withimage2.max()
def correlateKNN(choices, item):
reslst=np.zeros(10)
for i, d in enumerate(choices):
c11 = c2d(item, d, mode='full')
#print c11
reslst[i]=c11.max()
#print reslst
pred=np.argmax(reslst)
return pred
def compare(file1, file2, diff):
"""
Compares two files (JPEG, PNG or PDF)
"""
im1 = load_img(file1)
im2 = load_img(file2)
c11 = c2d(im1, im1, mode='same') # baseline
c22 = c2d(im2, im2, mode='same') # baseline
c12 = c2d(im1, im2, mode='same')
m = [c11.max(), c12.max(), c22.max()]
diff_ab = 100 * (1-m[1]/m[0])
diff_ba = 100 * (1-m[1]/m[2])
fail=max(diff_ab,diff_ba) > diff
if fail:
raise CompareImageException(c11.max(), c12.max(), c22.max())
return fail
def main():
if len(sys.argv)!=3:
print 'Usage: mirror.py <url1> <url2>'
sys.exit(-1)
outfile1=sys.argv[1].split('/')[-1]
urlretrieve(sys.argv[1],outfile1 )
outfile2=sys.argv[2].split('/')[-1]
urlretrieve(sys.argv[2],outfile2 )
print 'start'
im1 = get(outfile1)
print 'got img 1'
im2=get(outfile2)
ti=time.time()
print im1.shape, im2.shape
c11 = c2d(im1, im1, mode='same')
print c11.max(),time.time()-ti
ti=time.time()
c12 = c2d(im1, im2, mode='same')
print c12.max(),time.time()-ti
print 'Similarity :',((c12.max()/c11.max())*100)
def image2letter( image, digits_map ):
best_key = None
best_score = None
#h1 = misc.toimage( image ).histogram()
im1 = prepare_image( misc.fromimage( image ) )
for ( k, ir ) in digits_map.items():
#h2 = misc.toimage( ir ).histogram()
#rms = math.sqrt( reduce( operator.add, map( lambda a, b: ( a-b )**2, h1, h2 ) )/ len( h1 ) )
rms = c2d( im1, prepare_image( ir ), 'same' ).max()
if not best_score or best_score > rms:
best_key = k
best_score = rms
return best_key[0] if best_key else None, best_score
def captcha_solver(pid):
captcha=Image.open(resource_path('Captcha\\captcha%s.png'%pid))
captcha.convert('RGBA')
for letter in range (0,4):
#Slice captcha image into four 40*40 pieces
box=[letter*38,0,(letter+1)*38+1,40]
temp=captcha.crop(box).resize((19,20),Image.ANTIALIAS)
pixdata=temp.load()
#Map RGBA colour codes so reduces noises
for y in range(temp.size[1]):
for x in range(temp.size[0]):
if pixdata[x, y][0] < 95:
pixdata[x, y] = (0, 0, 0, 255)
if pixdata[x, y][1] < 95:
pixdata[x, y] = (0, 0, 0, 255)
if pixdata[x, y][2] > 150:
pixdata[x, y] = (255, 255, 255, 255)
temp.save(resource_path('Captcha\\captcha%s_%s.png'%(pid,letter)))
folders_list=['0','1','2','3','4','5','6','7','8','9']
answer_list=[]
for letter in range (0,4):
corr_list=[]
img1=get(resource_path('Captcha\\captcha%s_%s.png'%(pid,letter)))
baseline=c2d(img1,img1,mode='same').max()
for folder in folders_list:
sum_corr=0
counter=0
for img in os.listdir(resource_path('Iconset\\%s'%folder)):
img2=get(resource_path('Iconset\\%s\\%s'%(folder,img)))
corr=c2d(img1,img2,mode='same').max()
sum_corr+=corr
counter+=1
ave_corr=sum_corr/counter
corr_list.append(abs(ave_corr-baseline))
answer_list.append(folders_list[corr_list.index(min(corr_list))])
answer = "".join(str(x) for x in answer_list)
return answer
def compare(INPUTFILE,wire_loc):
global pics,piclen
test = imread(INPUTFILE)
test = imresize(test,0.4)
test = sp.inner(test, [299, 587, 114]) / 1000.0
test = (test - test.mean()) / test.std()
choose(wire_loc)
if not pics:
print "I cannot recognise what is in front of me"
return
for a in range (0,piclen):
for b in range(1,4):
im1 = get(pics[a],b)
com = c2d(im1, test, mode='same')
#print '%s%d' %(pics[a],b)
#print com.max()
if com.max() > 7000:
pics[a] = pics[a].replace("_", " ")
print '%s' %pics[a]
return
print "I cannot recognise what is in front of me"
def compare_images(orig_url, file_name_1, file_name_2):
file_path_1 = global_vars.img_filepath + file_name_1 + '-clipped'
file_path_2 = global_vars.img_filepath + file_name_2 + '-clipped'
hash_of_url = hashlib.md5(orig_url)
hash_link = global_vars.cache_url + hash_of_url.hexdigest() + '/'
f1_open = False
if os.path.isfile(file_path_1 + '.png'):
img1 = get(file_path_1)
f1_open = True
else:
f = open(global_vars.failed_pages, 'a')
f.write(file_name_1 + "\n")
f.close()
if os.path.isfile(file_path_2 + '.png'):
if f1_open == True:
img2 = get(file_path_2)
#c11 = c2d(img1, img1, mode='same')
c12 = c2d(img1, img2, mode='same')
diff = c12.max()
skew = c12.max() / 30000.
if skew < global_vars.quality_threshold:
# write results to results file
f = open(global_vars.comparison_results, 'a')
f.write(file_name_1 + ',' + file_name_2 + ',' + orig_url +
',' + hash_link + ',' + str(skew) + "\n")
f.close()
else:
f = open(global_vars.failed_pages, 'a')
f.write(file_name_2 + "\n")
f.close()
# get JPG image as Scipy array, RGB (3 layer)
# data = imread('base%s.jpg' % i)
data = io.imread('base%s.jpg' % i)
# convert to grey-scale using W3C luminance calc
data = sp.inner(data, [299, 587, 114]) / 1000.0
# data = numpy.inner(data, [299, 587, 114]) / 1000.0
# normalize per http://en.wikipedia.org/wiki/Cross-correlation
return (data - data.mean()) / data.std()
im1 = get(1)
im2 = get(2)
im3 = get(5)
im1.shape
print(im1.shape)
# (105, 401)
im2.shape
print(im2.shape)
# (109, 373)
im3.shape
print(im3.shape)
# (121, 457)
# cd2(COMPARA_ESSA_IMAGEM, COM_ESSA_IMAGEM)
c11 = c2d(im1, im1, mode='same') # baseline
c12 = c2d(im1, im2, mode='same')
c13 = c2d(im1, im3, mode='same')
c23 = c2d(im2, im3, mode='same')
c11.max(), c12.max(), c13.max(), c23.max()
# (42105.00000000259, 39898.103896795357, 16482.883608327804, 15873.465425120798)
# VALORES_GRANDES : PARECIDOS , PEQUENOS: DIFERENTES
def extractLetter(ref):
ref = rgb2gray(ref)
# a, b = compare_images(img, ref)
img1 = get(rgb2gray(imread('letters/a-new.crop.png')))
img2 = get(rgb2gray(imread('letters/b-new.crop.png')))
img3 = get(rgb2gray(imread('letters/c-new.crop.png')))
img4 = get(rgb2gray(imread('letters/d-new.jpg')))
c1 = c2d(img1, ref, mode='same') # baseline
c2 = c2d(img2, ref, mode='same')
c3 = c2d(img3, ref, mode='same')
c4 = c2d(img4, ref, mode='same')
result = np.array([c1.max(), c2.max(), c3.max(), c4.max()])
letters = ['a', 'b', 'c', 'd']
ridx = np.unravel_index(result.argmax(),result.shape) # finding max index
if (letters[ridx[0]] == 'd' and result[ridx[0]] < 190): # select next match
result[ridx[0]] = 0
ridx = np.unravel_index(result.argmax(),result.shape) # finding max index
return letters[ridx[0]], result[ridx[0]]
# refim = (imgBinarize(rgb2gray(imread('letters/d-new.jpg'))))
# im = (imgBinarize(rgb2gray(imread('letters/all-letters.jpg'))))
# # extractLetter(ref)
# table = buildRefTable(refim)
# acc = matchTable(im, table)
# val, ridx, cidx = findMaxima(acc)
# # find the half-width and height of template
# hheight = np.floor(refim.shape[0] / 2) + 1
# hwidth = np.floor(refim.shape[1] / 2) + 1
# cstart = max(cidx - hwidth, 1)
# cend = min(cidx + hwidth, im.shape[1] - 1)
# # mcq_col = im[0:im.shape[0]-1, cstart:cend] # column containing all mcq's
# # imsave('match-col-orig.jpg', mcq_col) # saving the matched component
# # convolving our reference image on mcq column
# # acc = matchTable(mcq_col, table)
# # val, ridx, cidx = findMaxima(acc)
# # find coordinates of the box
# rstart = max(ridx - hheight, 1)
# rend = min(ridx + hheight, im.shape[0] - 1)
# # cstart = max(cidx - hwidth, 1)
# # cend = min(cidx + hwidth, im.shape[1] - 1)
# # draw the box
# im[rstart:rend, cstart] = 255
# im[rstart:rend, cend] = 255
# im[rstart, cstart:cend] = 255
# im[rend, cstart:cend] = 255
# plt.imshow(im, cmap='gray')
# plt.show()
n_m, n_0 = compare_images(img1, img2)
diff = n_m/img1.size
maxDiff = max(maxDiff, diff)
#cutoff will be 9 (for now - just picking a value)
if maxDiff >= 9.:
#assume that there is an animal there
continue
#similarity test
similarity_l = []
for blank_pic,possible_pic in zip(blankFiles[0],photos):
p1 = get("day/"+blank_pic+".thumbnail")
p2 = get("day/"+possible_pic+".thumbnail")
similarity_l.append(c2d(p1, p2, mode='same').max())
overallSimilarity = min(similarity_l)
if photos in blankFiles:
blankDiff.append(overallSimilarity)
blankIndex.append(i)
else:
animalDiff.append(overallSimilarity)
animalIndex.append(i)
if (i == 7):
print(overallSimilarity)
print(daylight_photos[i])
assert(False)
def extractNumber(ref):
ref = rgb2gray(ref)
# a, b = compare_images(img, ref)
img1 = get(imgBinarize(rgb2gray(imread('numbers/1.crop.png'))))
img2 = get(imgBinarize(rgb2gray(imread('numbers/2.crop.png'))))
img3 = get(imgBinarize(rgb2gray(imread('numbers/3.crop.png'))))
img4 = get(imgBinarize(rgb2gray(imread('numbers/4.crop.png'))))
img5 = get(imgBinarize(rgb2gray(imread('numbers/5.crop.png'))))
img6 = get(imgBinarize(rgb2gray(imread('numbers/6.crop.png'))))
img7 = get(imgBinarize(rgb2gray(imread('numbers/7.crop.png'))))
img8 = get(imgBinarize(rgb2gray(imread('numbers/8.crop.png'))))
img9 = get(imgBinarize(rgb2gray(imread('numbers/9.crop.png'))))
img10 = get(imgBinarize(rgb2gray(imread('numbers/10.crop.png'))))
img11 = get(imgBinarize(rgb2gray(imread('numbers/11.crop.png'))))
img12 = get(imgBinarize(rgb2gray(imread('numbers/12.crop.png'))))
img13 = get(imgBinarize(rgb2gray(imread('numbers/13.crop.png'))))
img14 = get(imgBinarize(rgb2gray(imread('numbers/14.crop.png'))))
img15 = get(imgBinarize(rgb2gray(imread('numbers/15.crop.png'))))
c1 = c2d(img1, ref, mode='same') # baseline
c2 = c2d(img2, ref, mode='same')
c3 = c2d(img3, ref, mode='same')
c4 = c2d(img4, ref, mode='same')
c5 = c2d(img5, ref, mode='same')
c6 = c2d(img6, ref, mode='same')
c7 = c2d(img7, ref, mode='same')
c8 = c2d(img8, ref, mode='same')
c9 = c2d(img9, ref, mode='same')
c10 = c2d(img10, ref, mode='same')
c11 = c2d(img11, ref, mode='same')
c12 = c2d(img12, ref, mode='same')
c13 = c2d(img13, ref, mode='same')
c14 = c2d(img14, ref, mode='same')
c15 = c2d(img15, ref, mode='same')
result = np.array([
c1.max(),
c2.max(),
c3.max(),
c4.max(),
c5.max(),
c6.max(),
c7.max(),
c8.max(),
c9.max(),
c10.max(),
c11.max(),
c12.max(),
c13.max(),
c14.max(),
c15.max()
])
letters = [
'1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13',
'14', '15'
]
ridx = np.unravel_index(result.argmax(), result.shape) # finding max index
# if (letters[ridx[0]] == 'd' and result[ridx[0]] < 190): # select next match
result[ridx[0]] = 0
ridx = np.unravel_index(result.argmax(), result.shape) # finding max index
return letters[ridx[0]], result[ridx[0]]
import time
folder = "resized/"
images = {}
scores = {}
os.chdir(folder)
def get(image):
data = imread(image)
data = sp.inner(data, [299, 587, 114]) / 1000.0
return (data - data.mean()) / data.std()
start = time.clock()
search = get(sys.argv[1])
for image in glob.glob("*.jpg"):
images[image] = get(image)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
# This is to ignore a warning saying that imaginary part in complex numbers
# is being discarded
for image in images:
scores[image] = c2d(search, images[image], mode = "same").max()
stop = time.clock()
print "done"
for score in scores:
print score, scores[score]
print stop-start
# from: http://stackoverflow.com/questions/1819124/image-comparison-algorithm
import scipy as sp
from scipy.misc import imread
from scipy.signal.signaltools import correlate2d as c2d
def get(i):
# get JPG image as Scipy array, RGB (3 layer)
data = imread('im%s.jpg' % i)
# convert to grey-scale using W3C luminance calc
data = sp.inner(data, [299, 587, 114]) / 1000.0
# normalize per http://en.wikipedia.org/wiki/Cross-correlation
return (data - data.mean()) / data.std()
im1 = get(1)
im2 = get(2)
im3 = get(3)
c11 = c2d(im1, im1, mode='same') # baseline
c12 = c2d(im1, im2, mode='same')
c13 = c2d(im1, im3, mode='same')
c23 = c2d(im2, im3, mode='same')
print c11.max(), c12.max(), c13.max(), c23.max()
def correlate2d(file, output_folder, pairs):
logger = logging.getLogger('')
# load all submatrices and correlate them
all_arrays = defaultdict(list)
information_regions = defaultdict(list)
pairs_dict = defaultdict(str)
for pair_ix, reference_region, query_region in pairs:
pairs_dict[int(
pair_ix)] = str(reference_region) + '-' + str(query_region)
with open(file, 'r') as r:
for line in r:
region, position, x_min, x_max, y_min, y_max = line.split(',')[:6]
pair_id = pairs_dict[int(region)]
line_float = [float(x) for x in line.split(',')[6:]]
height, width = int(y_max) - int(y_min), int(x_max) - int(x_min)
mat = np.asanyarray(line_float).reshape(int(height), int(width))
all_arrays[int(position)].append(mat)
information_regions[int(position)].append((pair_id, int(position)))
logger.info(
'[MAIN]: All submatrices loaded, starting 2D cross-correlation')
tag = file.split('/')[-1].split('_')[0]
correlation_dataframe = pd.DataFrame(index=range(len(all_arrays.keys())),
columns=range(len(all_arrays.keys())))
for a, b in tqdm(itertools.combinations(range(len(all_arrays.keys())), 2)):
c11 = c2d(all_arrays[a][0], all_arrays[b][0], mode='same')
c112 = c2d(all_arrays[b][0], all_arrays[a][0], mode='same')
transp1 = c2d(np.fliplr(all_arrays[a][0]),
all_arrays[b][0],
mode='same')
transp2 = c2d(np.fliplr(all_arrays[b][0]),
all_arrays[a][0],
mode='same')
best = max(c11.max(), c112.max(), transp1.max(), transp2.max())
correlation_dataframe.loc[a, b] = best
correlation_dataframe.loc[b, a] = best
logger.info('[MAIN]: 2D cross-correlation done')
correlation_dataframe = correlation_dataframe.replace(np.Inf, np.nan)
correlation_dataframe = correlation_dataframe.replace(-np.Inf, np.nan)
scaled = scale(correlation_dataframe.fillna(0.))
# save dataframe ##
correlation_dataframe.to_csv(
path.join(output_folder, 'correlation_dataframe_%s.csv' % (tag)))
Sum_of_squared_distances = []
K = range(1, 15)
for k in K:
km = KMeans(n_clusters=k)
km = km.fit(scaled)
Sum_of_squared_distances.append(km.inertia_)
kn = KneeLocator(range(1,
len(Sum_of_squared_distances) + 1),
Sum_of_squared_distances,
curve='convex',
direction='decreasing')
optimal_number_clusters = kn.knee
plt.figure(figsize=(4, 3))
plt.xlabel('number of clusters k')
plt.ylabel('Sum of squared distances')
plt.plot(range(1,
len(Sum_of_squared_distances) + 1),
Sum_of_squared_distances, 'bx-')
plt.vlines(kn.knee, plt.ylim()[0], plt.ylim()[1], linestyles='dashed')
plt.savefig(path.join(output_folder, 'Elbow_index_%s.pdf' % tag))
# save positions and belonging cluster
logger.info('[MAIN]: Classification of the features')
kmeans = KMeans(n_clusters=optimal_number_clusters,
random_state=0,
precompute_distances=True).fit(scaled)
w = open(
path.join(
output_folder, 'subregions_%s_clusters_%s.tsv' %
(str(optimal_number_clusters), tag)), 'a+')
for n, i in enumerate(list(kmeans.labels_)):
t1, t2 = information_regions[n][0]
w.write('{}\t{}\t{}\n'.format('Cluster ' + str(i), t1, t2))
w.close()
def selfCorrelation(imageArray): #imageArray is the resut of Image.open()
image = scipy.inner(numpy.asarray(imageArray), [299, 587, 114]) / 1000.0
image = (image - image.mean())/ image.std()
selfcorelationimageWithimage = c2d(image, image, mode = 'same')
return selfcorelationimageWithimage.max()
# convert to grey-scale using W3C luminance calc
data = sp.inner(data, [299, 587, 114]) / 1000.0
# normalize per http://en.wikipedia.org/wiki/Cross-correlation
return (data - data.mean()) / data.std()
pics = os.listdir("D:\\Eigene Datein\\Python\\Image Similarity\\src2\\test_resize75")
index1 = 0
index2 = 0
f = open('out.txt', 'w')
for x in range(len(pics)):
im = "D:\\Eigene Datein\\Python\\Image Similarity\\src2\\test_resize75\\" + pics[index1]
#print pics
for pic in pics:
try:
im2 = "D:\\Eigene Datein\\Python\\Image Similarity\\src2\\test_resize75\\" + pics[index2]
data1 = get(im)
data2 = get(im2)
cc = c2d(data1, data2, mode='same')
f.write(pics[index1] + '\t' + pics[index2] + '\t' + str(cc.max()) + '\n')
print pics[index1], pics[index2], cc.max()
except IOError:
pass
index2 = index2 + 1
index1 = index1 + 1
index2 = 0
end = time.clock()
print end-start
f.close()
lum = [299, 587, 114]
if len(data[0][0]) > 3:
lum.append(0)
data = sp.inner(data, lum) / 1000.0
# normalize per http://en.wikipedia.org/wiki/Cross-correlation
return (data - data.mean()) / data.std()
im1 = load_img(sys.argv[1])
im2 = load_img(sys.argv[2])
diff_allowed = float(sys.argv[3])
#pprint([im1.shape])
#pprint([im2.shape])
c11 = c2d(im1, im1, mode='same') # baseline
c22 = c2d(im2, im2, mode='same') # baseline
c12 = c2d(im1, im2, mode='same')
m = [c11.max(), c12.max(), c22.max()]
# (42105.00000000259, 39898.103896795357, 16482.883608327804, 15873.465425120798)
# [7100.0000000003838, 7028.5659939232246, 7100.0000000000318]
diff_ab = 100 * (1 - m[1] / m[0])
diff_ba = 100 * (1 - m[1] / m[2])
print "diff a-b: %.2f%%" % (diff_ab)
print "diff b-a: %.2f%%" % (diff_ba)
fail = max(diff_ab, diff_ba) > diff_allowed
if fail: pprint([c11.max(), c12.max(), c22.max()])
print "limit: %.2f%% -> %s" % (diff_allowed, ("OK", "FAIL")[fail])
if fail:
sys.exit(1)
# This script a rough file to test the matching measure
# Check the file location properly.
#
import scipy as sp
from scipy.misc import imread
from scipy.signal.signaltools import correlate2d as c2d
import warnings
folder = "resized/"
def get(image):
data = imread(image)
data = sp.inner(data, [299, 587, 114]) / 1000.0
return (data - data.mean()) / data.std()
im1 = get(folder + 'capture1.jpg')
im2 = get(folder + 'capture2.jpg')
with warnings.catch_warnings():
warnings.simplefilter("ignore")
# This is to suppress a warning saying that imaginary part in complex numbers
# is being discarded
c11 = c2d(im1, im1, mode="same")
c22 = c2d(im2, im2, mode="same")
c12 = c2d(im1, im2, mode="same")
c21 = c2d(im2, im1, mode="same")
print c11.max(), c22.max(), c12.max(), c21.max()
## ValueError: matrices are not aligned, if alpha channel...
lum = [299, 587, 114]
if len(data[0][0]) > 3:
lum.append(0)
data = sp.inner(data, lum) / 1000.0
# normalize per http://en.wikipedia.org/wiki/Cross-correlation
return (data - data.mean()) / data.std()
im1 = load_img(sys.argv[1])
im2 = load_img(sys.argv[2])
diff_allowed = float(sys.argv[3])
#pprint([im1.shape])
#pprint([im2.shape])
c11 = c2d(im1, im1, mode='same') # baseline
c22 = c2d(im2, im2, mode='same') # baseline
c12 = c2d(im1, im2, mode='same')
m = [c11.max(), c12.max(), c22.max()]
# (42105.00000000259, 39898.103896795357, 16482.883608327804, 15873.465425120798)
# [7100.0000000003838, 7028.5659939232246, 7100.0000000000318]
diff_ab = 100 * (1-m[1]/m[0])
diff_ba = 100 * (1-m[1]/m[2])
print "diff a-b: %.2f%%" % (diff_ab)
print "diff b-a: %.2f%%" % (diff_ba)
fail = max(diff_ab,diff_ba) > diff_allowed
if fail: pprint([c11.max(), c12.max(), c22.max()])
print "limit: %.2f%% -> %s" % (diff_allowed, ("OK","FAIL")[fail])
if fail: sys.exit(1)
im2 = get(dirr + 'mri2.png', 1) print '' print '' print '' print '-----------------------------------------------------' print '' print 'ref image shape:', im1.shape print '' print '------------%%%%%%%%%%%%%%%%%%%%%%%------------------' print '' print 'moving image shape:', im2.shape print '' print '-----------------------------------------------------' c11 = c2d(im1, im1, mode='same', boundary='symm') # baseline imsave(dirr + 'c11.png', c11) c12 = c2d(im1, im2, mode='same', boundary='symm') imsave(dirr + 'c12.png', c12) # c13 = c2d(im1, im3, mode='same') # c23 = c2d(im2, im3, mode='same') print c11.max() print c12.max() # , c13.max(), c23.max() print '' print '' print '' perf = c12.max() * 100 / c11.max() print '-----------------------------------------------------' print '' print "perf", perf, "%"
import scipy as sp
from scipy.misc import imread
from scipy.signal.signaltools import correlate2d as c2d
def get(i):
data = imread('im%s.jpg' % i)
data = sp.inner(data, [299, 587, 114]) / 1000.0
return (data - data.mean()) / data.std()
im1 = get(1)
im2 = get(2)
im3 = get(3)
#c11 = c2d(im1, im1, mode='same')
c12 = c2d(im1, im2, mode='same')
#c13 = c2d(im1, im3, mode='same')
#c23 = c2d(im2, im3, mode='same')
#c11.max(), c12.max(), c13.max(), c23.max()
print c12.max()
data = cv2.imread('im%s.jpg' % i)
img = cv2.cvtColor(data, cv2.COLOR_BGR2RGB)
short_edge = min(img.shape[0], img.shape[1])
# print(short_edge)
yy = int((img.shape[0] - short_edge) // 2)
xx = int((img.shape[1] - short_edge) // 2)
img = img[yy: yy + short_edge, xx: xx + short_edge]
# 缩放图片统一尺寸
img = cv2.resize(img, (160, 160))
img = sp.inner(img, [299, 587, 114]) / 1000.0
print('done')
return (img - img.mean()) / img.std()
im1 = get(1)
im2 = get(2)
im3 = get(3)
print(im1.shape)
print(im2.shape)
print(im3.shape)
c11 = c2d(im1, im1, mode='same')
# print(c11)
c12 = c2d(im1, im2, mode='same')
# print(c12)
c13 = c2d(im1, im3, mode='same')
# print(c13)
# c11.max(), c12.max(), c13.max()
print(c11.max(), c12.max(), c13.max())
#!/usr/bin/env python
import scipy as sp
from scipy.misc import imread
from scipy.signal.signaltools import correlate2d as c2d
def get(fName):
# get JPG image as Scipy array, RGB (3 layer)
data = imread(fName)
# convert to grey-scale using W3C luminance calc
data = sp.inner(data, [299, 587, 114]) / 1000.0
# normalize per http://en.wikipedia.org/wiki/Cross-correlation
return (data - data.mean()) / data.std()
im1 = get("/home/ggdhines/Databases/serengeti/temp2/im2.jpg")
im2 = get("/home/ggdhines/Databases/serengeti/temp2/im3.jpg")
im3 = get("/home/ggdhines/Databases/serengeti/temp2/im1.jpg")
print "analyzing"
c11 = c2d(im1, im1, mode='same')
print c11.max()
c12 = c2d(im1, im2, mode='same')
print c12.max()
c13 = c2d(im1, im3, mode='same')
print c13.max()
