def test_read_tiff(self):
"""Test of TIFFReaderWriter reading"""
srw = em2d.SpiderImageReaderWriter()
trw = em2d.TIFFImageReaderWriter()
fn_tif_img = self.get_input_file_name("lena-256x256.tif")
tif_img=em2d.Image(fn_tif_img,trw)
fn_spider_img = self.get_input_file_name("lena-256x256.spi")
spider_img=em2d.Image(fn_spider_img,srw)
rows =int( tif_img.get_header().get_number_of_rows())
cols = int(tif_img.get_header().get_number_of_columns())
self.assertEqual(spider_img.get_header().get_number_of_rows(),rows)
self.assertEqual(spider_img.get_header().get_number_of_columns(),cols)
ccc=em2d.get_cross_correlation_coefficient(tif_img.get_data(),
spider_img.get_data())
self.assertAlmostEqual(ccc,1,delta=0.01,msg="ccc ins not 1")
def test_read_tiff(self):
"""Test of TIFFReaderWriter reading"""
srw = em2d.SpiderImageReaderWriter()
trw = em2d.TIFFImageReaderWriter()
fn_tif_img = self.get_input_file_name("lena-256x256.tif")
tif_img = em2d.Image(fn_tif_img, trw)
fn_spider_img = self.get_input_file_name("lena-256x256.spi")
spider_img = em2d.Image(fn_spider_img, srw)
rows = int(tif_img.get_header().get_number_of_rows())
cols = int(tif_img.get_header().get_number_of_columns())
self.assertEqual(spider_img.get_header().get_number_of_rows(), rows)
self.assertEqual(spider_img.get_header().get_number_of_columns(), cols)
ccc = em2d.get_cross_correlation_coefficient(tif_img.get_data(),
spider_img.get_data())
self.assertAlmostEqual(ccc, 1, delta=0.01, msg="ccc ins not 1")
def test_write_jpg(self):
"""Test of JPGReaderWriter writing"""
jrw = em2d.JPGImageReaderWriter()
fn_img1 = self.get_input_file_name("lena-256x256.jpg")
img1=em2d.Image(fn_img1,jrw)
fn_img2 = self.get_input_file_name("temp.jpg")
img1.write(fn_img2,jrw)
img2 = em2d.Image(fn_img2,jrw)
# Use the ccc for testing instead of the pixel values. The matrix
# in img2 is transformed from floats to ints son it can be written.
# Values can change, but the ccc has to be very close to 1.
ccc= em2d.get_cross_correlation_coefficient(img1.get_data(),
img2.get_data())
self.assertAlmostEqual(ccc,1,delta=0.05,
msg="Written JPG image is not equal to read ")
os.remove(fn_img2)
def test_write_jpg(self):
"""Test of JPGReaderWriter writing"""
jrw = em2d.JPGImageReaderWriter()
fn_img1 = self.get_input_file_name("lena-256x256.jpg")
img1 = em2d.Image(fn_img1, jrw)
fn_img2 = "temp.jpg"
img1.write(fn_img2, jrw)
img2 = em2d.Image(fn_img2, jrw)
# Use the ccc for testing instead of the pixel values. The matrix
# in img2 is transformed from floats to ints son it can be written.
# Values can change, but the ccc has to be very close to 1.
ccc = em2d.get_cross_correlation_coefficient(img1.get_data(),
img2.get_data())
self.assertAlmostEqual(ccc, 1, delta=0.05,
msg="Written JPG image is not equal to read ")
os.remove(fn_img2)
def test_write_tiff(self):
"""Test of TIFFReaderWriter writing"""
trw = em2d.TIFFImageReaderWriter()
srw = em2d.SpiderImageReaderWriter()
fn_img1 = self.get_input_file_name("lena-256x256.tif")
img1=em2d.Image(fn_img1,trw)
fn_img2 = "temp.tif"
img1.write(fn_img2,trw)
img2 = em2d.Image(fn_img2,trw)
# Use the ccc for testing instead of the pixel values. The matrix
# in img2 is transformed from floats to ints son it can be written.
# Values can change, but the ccc has to be very close to 1.
ccc= em2d.get_cross_correlation_coefficient(img1.get_data(),
img2.get_data())
print ccc
self.assertAlmostEqual(ccc,1,delta=0.01,
msg="Written TIFF image is not equal to read ")
os.remove(fn_img2)
def test_noisy_projections(self):
""" Test the generation of noisy projections"""
smodel = IMP.kernel.Model()
ssel = IMP.atom.ATOMPDBSelector()
fn_model = self.get_input_file_name("1e6v.pdb")
prot = IMP.atom.read_pdb(fn_model, smodel, ssel)
particles = IMP.core.get_leaves(prot)
n_projections = 16
rows = 100
cols = 100
resolution = 1
apix = 1.5
noise_SNR = 0.5
# read the stored noisy images
stored_names = []
srw = em2d.SpiderImageReaderWriter()
for i in range(0, n_projections):
fn_subject = "1e6v-subject-%d-set-%d-%s-apix" \
"-%s-SNR.spi" % (i, n_projections, str(apix), str(noise_SNR))
stored_names.append(self.get_input_file_name(fn_subject))
stored_images = em2d.read_images(stored_names, srw)
# Read registration parameters and generate new images
fn_regs = self.get_input_file_name('1e6v-subjects-0.5.params')
Regs = em2d.read_registration_results(fn_regs)
options = em2d.ProjectingOptions(apix, resolution)
projections = em2d.get_projections(particles, Regs, rows, cols,
options)
# Add noise
for i in range(0, n_projections):
em2d.do_normalize(projections[i], True)
em2d.add_noise(projections[i], 0.0, 1. / (noise_SNR**0.5),
"gaussian", 3)
# theoretical ccc for same images at a level of noise
theoretical_ccc = noise_SNR / (noise_SNR + 1)
for n in range(0, n_projections):
ccc = em2d.get_cross_correlation_coefficient(
projections[n], stored_images[n])
# allow 3% difference in cross-correlation
self.assertAlmostEqual(
theoretical_ccc,
ccc,
delta=0.03,
msg="Noisy projections generated and stored are different")
def test_noisy_projections(self):
""" Test the generation of noisy projections"""
smodel = IMP.kernel.Model()
ssel = IMP.atom.ATOMPDBSelector()
fn_model = self.get_input_file_name("1e6v.pdb")
prot = IMP.atom.read_pdb(fn_model, smodel, ssel)
particles = IMP.core.get_leaves(prot)
n_projections = 16
rows = 100
cols = 100
resolution = 1
apix = 1.5
noise_SNR = 0.5
# read the stored noisy images
stored_names = []
srw = em2d.SpiderImageReaderWriter()
for i in range(0, n_projections):
fn_subject = "1e6v-subject-%d-set-%d-%s-apix" \
"-%s-SNR.spi" % (i, n_projections, str(apix), str(noise_SNR))
stored_names.append(self.get_input_file_name(fn_subject))
stored_images = em2d.read_images(stored_names, srw)
# Read registration parameters and generate new images
fn_regs = self.get_input_file_name('1e6v-subjects-0.5.params')
Regs = em2d.read_registration_results(fn_regs)
options = em2d.ProjectingOptions(apix, resolution)
projections = em2d.get_projections(particles, Regs,
rows, cols, options)
# Add noise
for i in range(0, n_projections):
em2d.do_normalize(projections[i], True)
em2d.add_noise(projections[i],
0.0, 1. / (noise_SNR ** 0.5), "gaussian", 3)
# theoretical ccc for same images at a level of noise
theoretical_ccc = noise_SNR / (noise_SNR + 1)
for n in range(0, n_projections):
ccc = em2d.get_cross_correlation_coefficient(projections[n],
stored_images[n])
# allow 3% difference in cross-correlation
self.assertAlmostEqual(theoretical_ccc, ccc, delta=0.03,
msg="Noisy projections generated and stored are different")
def test_registration(self):
"""Test the registration of 3 subjects from 1gyt.pdb at 0.5 SNR"""
# Get model from PDB file
smodel = IMP.Model()
ssel = IMP.atom.ATOMPDBSelector()
fn_model = self.get_input_file_name("1gyt.pdb")
prot = IMP.atom.read_pdb(fn_model, smodel, ssel)
particles = IMP.core.get_leaves(prot)
# Read subject images
srw = em2d.SpiderImageReaderWriter()
selection_file = self.get_input_file_name("1gyt-subjects-0.5-SNR.sel")
images_to_read_names = em2d.read_selection_file(selection_file)
for i in range(0, len(images_to_read_names)):
images_to_read_names[i] = self.get_input_file_name(
images_to_read_names[i])
subjects = em2d.read_images(images_to_read_names, srw)
self.assertEqual(len(subjects), 3, "Problem reading subject images")
# Generate 20 evenly distributed projections from the PDB file
n_projections = 20
proj_params = em2d.get_evenly_distributed_registration_results(
n_projections)
rows = 128
cols = 128
pixel_size = 1.5
# for generating projections, use a very high resolution
resolution = 8.5
options = em2d.ProjectingOptions(pixel_size, resolution)
projections = em2d.get_projections(particles, proj_params, rows, cols,
options)
self.assertEqual(len(projections), n_projections,
"Problem generating projections")
# Prepare registration
# IMP.set_log_level(IMP.VERBOSE)
finder = em2d.ProjectionFinder()
score_function = em2d.EM2DScore()
params = em2d.Em2DRestraintParameters(pixel_size, resolution,
n_projections)
params.save_match_images = False
params.coarse_registration_method = em2d.ALIGN2D_PREPROCESSING
params.optimization_steps = 30
params.simplex_initial_length = 0.1
params.simplex_minimum_size = 0.01
finder.setup(score_function, params)
finder.set_model_particles(particles)
finder.set_subjects(subjects)
finder.set_projections(projections)
finder.set_fast_mode(2)
finder.get_complete_registration()
# Recover the registration results:
registration_parameters = finder.get_registration_results()
fn_registration_results = "my_1gyt_registration.params"
em2d.write_registration_results(fn_registration_results,
registration_parameters)
# Read the correct registration results:
correct_parameters = em2d.read_registration_results(
self.get_input_file_name("1gyt-subjects-0.5-SNR.params"))
print("determined: ")
for r in registration_parameters:
print(r.get_rotation(), r.get_shift())
print("correct: ")
for r in correct_parameters:
print(r.get_rotation(), r.get_shift())
for i in range(0, len(registration_parameters)):
# Generate the registered projection
imgx = em2d.Image()
imgx.set_size(rows, cols)
em2d.get_projection(imgx, particles, registration_parameters[i],
options)
ccc = em2d.get_cross_correlation_coefficient(
subjects[i].get_data(), imgx.get_data())
print(i, "ccc", ccc)
snr = 0.5
theoretical_ccc = (snr / (1. + snr))**.5
self.assertAlmostEqual(
ccc,
theoretical_ccc,
delta=0.02,
msg="Error in registration of subject %d: ccc %8.3f "
"theoretical_ccc %8.3f " % (i, ccc, theoretical_ccc))
os.remove(fn_registration_results)
def test_registration(self):
"""Test the registration of 3 subjects from 1gyt.pdb at 0.5 SNR"""
# Get model from PDB file
smodel = IMP.Model()
ssel = IMP.atom.ATOMPDBSelector()
fn_model = self.get_input_file_name("1gyt.pdb")
prot = IMP.atom.read_pdb(fn_model, smodel, ssel)
particles = IMP.core.get_leaves(prot)
# Read subject images
srw = em2d.SpiderImageReaderWriter()
selection_file = self.get_input_file_name("1gyt-subjects-0.5-SNR.sel")
images_to_read_names = em2d.read_selection_file(selection_file)
for i in range(0, len(images_to_read_names)):
images_to_read_names[i] = self.get_input_file_name(
images_to_read_names[i])
subjects = em2d.read_images(images_to_read_names, srw)
self.assertEqual(len(subjects), 3, "Problem reading subject images")
# Generate 20 evenly distributed projections from the PDB file
n_projections = 20
proj_params = em2d.get_evenly_distributed_registration_results(
n_projections)
rows = 128
cols = 128
pixel_size = 1.5
# for generating projections, use a very high resolution
resolution = 1
options = em2d.ProjectingOptions(pixel_size, resolution)
projections = em2d.get_projections(particles, proj_params,
rows, cols, options)
self.assertEqual(len(projections), n_projections,
"Problem generating projections")
# Prepare registration
# IMP.set_log_level(IMP.VERBOSE)
finder = em2d.ProjectionFinder()
score_function = em2d.EM2DScore()
params = em2d.Em2DRestraintParameters(pixel_size,
resolution, n_projections)
params.save_match_images = False
params.coarse_registration_method = em2d.ALIGN2D_PREPROCESSING
params.optimization_steps = 30
params.simplex_initial_length = 0.1
params.simplex_minimum_size = 0.01
finder.setup(score_function, params)
finder.set_model_particles(particles)
finder.set_subjects(subjects)
finder.set_projections(projections)
finder.set_fast_mode(2)
finder.get_complete_registration()
# Recover the registration results:
registration_parameters = finder.get_registration_results()
fn_registration_results = "my_1gyt_registration.params"
em2d.write_registration_results(fn_registration_results,
registration_parameters)
# Read the correct registration results:
correct_parameters = em2d.read_registration_results(
self.get_input_file_name("1gyt-subjects-0.5-SNR.params"))
print("determined: ")
for r in registration_parameters:
print(r.get_rotation(), r.get_shift())
print("correct: ")
for r in correct_parameters:
print(r.get_rotation(), r.get_shift())
for i in range(0, len(registration_parameters)):
# Generate the registered projection
imgx = em2d.Image()
imgx.set_size(rows, cols)
em2d.get_projection(imgx, particles,
registration_parameters[i], options)
ccc = em2d.get_cross_correlation_coefficient(
subjects[i].get_data(),
imgx.get_data())
print(i, "ccc", ccc)
snr = 0.5
theoretical_ccc = (snr / (1. + snr)) ** .5
self.assertAlmostEqual(ccc, theoretical_ccc, delta=0.02,
msg="Error in registration of subject %d: ccc %8.3f "
"theoretical_ccc %8.3f " % (i, ccc, theoretical_ccc))
os.remove(fn_registration_results)
