def test_read_jpg(self):
"""Test of JPGReaderWriter reading"""
srw = em2d.SpiderImageReaderWriter()
jrw = em2d.JPGImageReaderWriter()
fn_jpg_img = self.get_input_file_name("lena-256x256.jpg")
jpg_img = em2d.Image(fn_jpg_img, jrw)
fn_spider_img = self.get_input_file_name("lena-256x256.spi")
spider_img = em2d.Image(fn_spider_img, srw)
rows = int(jpg_img.get_header().get_number_of_rows())
cols = int(jpg_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)
for i in range(0, rows):
for j in range(0, cols):
# due to rounding, integer numbers in the jpg file can vary
# to the next integer. Allow delta 1
self.assertAlmostEqual(
abs(spider_img(i, j) - jpg_img(i, j)),
0,
delta=1,
msg="JPG image is not equal to spider image "
"at pixel (%d,%d)" % (i, j))
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_random_projection_generation(self):
"""Generation of random projection from a PDB file with em2d images"""
testfile = "opencv_test.spi"
if os.path.isfile(testfile):
# delete the file to check
os.remove(testfile)
smodel = IMP.Model()
ssel = IMP.atom.ATOMPDBSelector()
prot = IMP.atom.read_pdb(
self.get_input_file_name("1z5s.pdb"),
smodel,
ssel)
IMP.atom.add_radii(prot)
particles = IMP.core.get_leaves(prot)
rows = 80
cols = 80
resolution = 1
apix = 1.5
img = em2d.Image()
img.set_size(rows, cols)
srw = em2d.SpiderImageReaderWriter()
rr = em2d.RegistrationResult()
rr.set_random_registration(0, 5)
options = em2d.ProjectingOptions(apix, resolution)
options.srw = srw
em2d.get_projection(img, particles, rr, options)
img.write(testfile, srw)
self.assertTrue(os.path.isfile(testfile),
"Projection image not generated")
os.remove(testfile)
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_polar_resampling(self):
"""Test of polar resampling of images"""
srw = em2d.SpiderImageReaderWriter()
fn_input = self.get_input_file_name("1gyt-subject-1-0.5-SNR.spi")
img = em2d.Image(fn_input, srw)
polar_params = em2d.PolarResamplingParameters()
polar = em2d.Image()
em2d.do_resample_polar(img, polar, polar_params)
fn_saved = self.get_input_file_name("1gyt-subject-1-0.5-SNR-polar.spi")
saved = em2d.Image(fn_saved, srw)
rows = int(polar.get_header().get_number_of_rows())
cols = int(polar.get_header().get_number_of_columns())
for i in range(0, rows):
for j in range(0, cols):
self.assertAlmostEqual(saved(i, j), polar(i, j), delta=0.001,
msg="Generated polar image is different from stored"
" row %d col %d" % (i, j))
def test_substract(self):
"""Test subtracting images"""
srw = em2d.SpiderImageReaderWriter()
rows = int(10)
cols = int(5)
img1 = em2d.Image(rows, cols)
img2 = em2d.Image(rows, cols)
result = em2d.Image(rows, cols)
for i in range(0, rows):
for j in range(0, cols):
img1.set_value(i, j, random.uniform(-1, 1))
img2.set_value(i, j, img1(i, j))
em2d.do_subtract_images(img1, img2, result)
for i in range(0, rows):
for j in range(0, cols):
self.assertAlmostEqual(abs(result(i, j)), 0, delta=0.001,
msg="Subtract images error")
def test_write_error_jpg(self):
"""Test that writing with JPGReaderWriter fails with bad extension"""
jrw = em2d.JPGImageReaderWriter()
fn_img1 = self.get_input_file_name("lena-256x256.jpg")
img1 = em2d.Image(fn_img1, jrw)
self.assertRaises(IOError, img1.write, "temp.xxx", jrw)
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_rigid_body_image_fit_restraint(self):
"""Test scoring with RigidBodiesImageFitRestraint"""
m = IMP.kernel.Model()
# read full complex
fn = self.get_input_file_name("1z5s.pdb")
prot = atom.read_pdb(fn, m, IMP.atom.ATOMPDBSelector())
# read components
names = ["1z5sA", "1z5sB", "1z5sC", "1z5sD"]
fn_pdbs = [self.get_input_file_name(name + ".pdb") for name in names]
components = [
atom.read_pdb(fn, m, IMP.atom.ATOMPDBSelector()) for fn in fn_pdbs
]
components_rbs = [atom.create_rigid_body(c) for c in components]
# img
R = alg.get_identity_rotation_3d()
reg = em2d.RegistrationResult(R)
img = em2d.Image()
img.set_size(80, 80)
srw = em2d.SpiderImageReaderWriter()
resolution = 5
pixel_size = 1.5
options = em2d.ProjectingOptions(pixel_size, resolution)
ls = core.get_leaves(prot)
em2d.get_projection(img, ls, reg, options)
# img.write("rbfit_test_image.spi",srw)
# set restraint
score_function = em2d.EM2DScore()
rb_fit = em2d.RigidBodiesImageFitRestraint(score_function,
components_rbs, img)
pp = em2d.ProjectingParameters(pixel_size, resolution)
rb_fit.set_projecting_parameters(pp)
# set the trivial case:
n_masks = 1
for rb in components_rbs:
# set as the only possible orientation the one that the rigid
# body already has
rb_fit.set_orientations(rb, [
rb.get_reference_frame().get_transformation_to().get_rotation(
)
])
self.assertEqual(rb_fit.get_number_of_masks(rb), n_masks,
"Incorrect number rigid body masks")
# Calculate the positions of the rigid bodies respect to the centroid
# of the entire molecule
ls = core.get_leaves(prot)
xyzs = core.XYZs(ls)
centroid = core.get_centroid(xyzs)
coords = [rb.get_coordinates() - centroid for rb in components_rbs]
for rb, coord in zip(components_rbs, coords):
rb.set_coordinates(coord)
# Check that the value is a perfect registration
m.add_restraint(rb_fit)
score = rb_fit.evaluate(False)
# print "score ...", score
# It seems that projecting with the masks is slightly less accurate
# I have to establish a tolerance of 0.03
self.assertAlmostEqual(score,
0,
delta=0.03,
msg="Wrong value for the score %f " % (score))
