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))