def test_extract_multiple(self):
im1 = np.asarray(Image.open('data/prnu1.jpg'))[:400, :500]
im2 = np.asarray(Image.open('data/prnu2.jpg'))[:400, :500]
imgs = [im1, im2]
k_st = prnu.extract_multiple_aligned(imgs, processes=1)
k_mt = prnu.extract_multiple_aligned(imgs, processes=2)
self.assertTrue(np.allclose(k_st, k_mt, atol=1e-6))
def get_fingerprint_from_img_list(img_path_list):
imgs = []
for img_path in img_path_list:
im = Image.open(img_path)
im_arr = np.asarray(im)
if im_arr.dtype != np.uint8:
print('Error while reading image: {}'.format(img_path))
continue
if im_arr.ndim != 3:
print('Image is not RGB: {}'.format(img_path))
continue
im_cut = prnu.cut_ctr(im_arr, (pix_size, pix_size, 3))
imgs += [im_cut]
fingerprint = prnu.extract_multiple_aligned(imgs)
return fingerprint
def main():
"""
Main example script. Load a subset of flatfield and natural images from Dresden.
For each device compute the fingerprint from all the flatfield images.
For each natural image compute the noise residual.
Check the detection performance obtained with cross-correlation and PCE
:return:
"""
ff_dirlist = np.array(sorted(glob('test/data/ff-jpg/*.JPG')))
ff_device = np.array(
[os.path.split(i)[1].rsplit('_', 1)[0] for i in ff_dirlist])
nat_dirlist = np.array(sorted(glob('test/data/nat-jpg/*.JPG')))
nat_device = np.array(
[os.path.split(i)[1].rsplit('_', 1)[0] for i in nat_dirlist])
print('Computing fingerprints')
fingerprint_device = sorted(np.unique(ff_device))
k = []
for device in fingerprint_device:
imgs = []
for img_path in ff_dirlist[ff_device == device]:
im = Image.open(img_path)
im_arr = np.asarray(im)
if im_arr.dtype != np.uint8:
print('Error while reading image: {}'.format(img_path))
continue
if im_arr.ndim != 3:
print('Image is not RGB: {}'.format(img_path))
continue
im_cut = prnu.cut_ctr(im_arr, (512, 512, 3))
imgs += [im_cut]
k += [prnu.extract_multiple_aligned(imgs, processes=cpu_count())]
k = np.stack(k, 0)
print('Computing residuals')
imgs = []
for img_path in nat_dirlist:
imgs += [prnu.cut_ctr(np.asarray(Image.open(img_path)), (512, 512, 3))]
pool = Pool(cpu_count())
w = pool.map(prnu.extract_single, imgs)
pool.close()
w = np.stack(w, 0)
# Computing Ground Truth
gt = prnu.gt(fingerprint_device, nat_device)
print('Computing cross correlation')
cc_aligned_rot = prnu.aligned_cc(k, w)['cc']
print('Computing statistics cross correlation')
stats_cc = prnu.stats(cc_aligned_rot, gt)
print('Computing PCE')
pce_rot = np.zeros((len(fingerprint_device), len(nat_device)))
for fingerprint_idx, fingerprint_k in enumerate(k):
for natural_idx, natural_w in enumerate(w):
cc2d = prnu.crosscorr_2d(fingerprint_k, natural_w)
pce_rot[fingerprint_idx, natural_idx] = prnu.pce(cc2d)['pce']
print('Computing statistics on PCE')
stats_pce = prnu.stats(pce_rot, gt)
print('AUC on CC {:.2f}, expected {:.2f}'.format(stats_cc['auc'], 0.98))
print('AUC on PCE {:.2f}, expected {:.2f}'.format(stats_pce['auc'], 0.81))