from numpy import *
from numpy import random
from scipy.ndimage import filters
import imtools
from PIL import Image
im = zeros((500,500))
im[100:400,100:400] = 128
im[200:300,200:300] = 255
im = im+ 30*random.standard_normal((500,500))
#im = array(Image.open('DSCF7481.JPG').convert('L'))
U,T = imtools.denoise(im,im)
G= filters.gaussian_filter(im,10)
import scipy.misc
scipy.misc.imsave('synth_rof.pdf',U)
scipy.misc.imsave('synth_ori.pdf',im)
def denoise(imgray): return imtools.denoise(imgray,imgray)
def denoise(im):
(im, _) = imtools.denoise(im, im)
# May need to convert to RGB
return im
def denoise(im):
(im, _) = imtools.denoise(im, im)
# May need to convert to RGB
return im
level = level.astype(int)
np.unique(level, return_counts=True)
levelimage = level.reshape(rows, cols)
imshow(levelimage)
testimgfile = r'O:\DataTeam\FindTheFish\Data\train\train\ALB\YVB\img_00003.jpg'
imgtest = array(Image.open(testimgfile).convert('L'))
figure()
gray()
imshow(imgtest)
imcorr = signal.correlate2d(imgtest, levelimage, boundary='wrap')
imshow(imcorr)
plot(imcorr[:, 1400])
imcolor = imtools.denoise(imgray, imgray)
## SIFT
image_rgb = scipy.misc.imread(imgname)
sift_ocl = sift.SiftPlan(template=image_rgb, device=GPU)
kp = sift_ocl.keypoints(image_rgb)
kp.sort(order=["scale", "angle", "x", "y"])
print kp
img = cv2.imread(imgname)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
sift = cv2.xfeatures2d.SIFT_create()
kp = sift.detect(imgray, None)
imsift = cv2.drawKeypoints(imgray, kp, imgray)
cv2.imwrite('sift_keypoints.jpg', imsift)
