def testRename(self): """ Check it's at least possible to open one DataArray, when the files are renamed """ # create a simple greyscale image size = (512, 256) white = (12, 52) # non symmetric position dtype = numpy.uint16 ldata = [] self.no_of_images = 2 metadata = [{ model.MD_IN_WL: (500e-9, 520e-9), # m model.MD_EXP_TIME: 0.2, # s }, { model.MD_EXP_TIME: 1.2, # s }, ] # Add metadata for i in range(self.no_of_images): a = model.DataArray(numpy.zeros(size[::-1], dtype), metadata[i]) a[white[::-1]] = 124 + i ldata.append(a) # export orig_name = "boo.0.tiff" stiff.export(orig_name, ldata) tokens = orig_name.split(".0.", 1) ntokens = FILENAME.split(".0.", 1) # Renaming the file for i in range(self.no_of_images): fname = tokens[0] + "." + str(i) + "." + tokens[1] new_fname = ntokens[0] + "." + str(i) + "." + ntokens[1] os.rename(fname, new_fname) # Iterate through the new files for i in range(self.no_of_images): fname = ntokens[0] + "." + str(i) + "." + ntokens[1] fmt_mng = dataio.find_fittest_converter(fname, mode=os.O_RDONLY) self.assertEqual(fmt_mng.FORMAT, "TIFF", "For '%s', expected format TIFF but got %s" % (fname, fmt_mng.FORMAT)) rdata = fmt_mng.read_data(fname) # Assert that at least one DA is there # In practice, currently, we expected precisely 1 self.assertGreaterEqual(len(rdata), 1) # Check the correct metadata is present for j in range(self.no_of_images): self.assertAlmostEqual(rdata[j].metadata[model.MD_EXP_TIME], ldata[j].metadata[model.MD_EXP_TIME]) rthumbnail = fmt_mng.read_thumbnail(fname) # No thumbnail handling for now, so assert that is empty self.assertEqual(rthumbnail, [])
def testMissing(self): """ Check it's at least possible to open one DataArray, when the other parts are missing. """ # create a simple greyscale image size = (512, 256) white = (12, 52) # non symmetric position dtype = numpy.uint16 ldata = [] self.no_of_images = 2 metadata = [ { model.MD_IN_WL: (500e-9, 520e-9), # m model.MD_EXP_TIME: 0.2, # s }, { model.MD_EXP_TIME: 1.2, # s }, ] # Add metadata for i in range(self.no_of_images): a = model.DataArray(numpy.zeros(size[::-1], dtype), metadata[i]) a[white[::-1]] = 124 + i ldata.append(a) # export stiff.export(FILENAME, ldata) # Ooops, the first file is gone => it should still be possible to open # the other files os.remove(FILENAME) tokens = FILENAME.split(".0.", 1) for i in range(1, self.no_of_images): fname = tokens[0] + "." + str(i) + "." + tokens[1] fmt_mng = dataio.find_fittest_converter(fname, mode=os.O_RDONLY) self.assertEqual( fmt_mng.FORMAT, "TIFF", "For '%s', expected format TIFF but got %s" % (fname, fmt_mng.FORMAT)) rdata = fmt_mng.read_data(fname) # Assert that at least one DA is there # In practice, currently, we expected precisely 1 self.assertGreaterEqual(len(rdata), 1) # Check the correct metadata is present self.assertAlmostEqual(rdata[0].metadata[model.MD_EXP_TIME], ldata[i].metadata[model.MD_EXP_TIME]) rthumbnail = fmt_mng.read_thumbnail(fname) # No thumbnail handling for now, so assert that is empty self.assertEqual(rthumbnail, [])
def testMissing(self): """ Check it's at least possible to open one DataArray, when the other parts are missing. """ # create a simple greyscale image size = (512, 256) white = (12, 52) # non symmetric position dtype = numpy.uint16 ldata = [] self.no_of_images = 2 metadata = [{ model.MD_IN_WL: (500e-9, 520e-9), # m model.MD_EXP_TIME: 0.2, # s }, { model.MD_EXP_TIME: 1.2, # s }, ] # Add metadata for i in range(self.no_of_images): a = model.DataArray(numpy.zeros(size[::-1], dtype), metadata[i]) a[white[::-1]] = 124 + i ldata.append(a) # export stiff.export(FILENAME, ldata) # Ooops, the first file is gone => it should still be possible to open # the other files os.remove(FILENAME) tokens = FILENAME.split(".0.", 1) for i in range(1, self.no_of_images): fname = tokens[0] + "." + str(i) + "." + tokens[1] fmt_mng = dataio.find_fittest_converter(fname, mode=os.O_RDONLY) self.assertEqual(fmt_mng.FORMAT, "TIFF", "For '%s', expected format TIFF but got %s" % (fname, fmt_mng.FORMAT)) rdata = fmt_mng.read_data(fname) # Assert that at least one DA is there # In practice, currently, we expected precisely 1 self.assertGreaterEqual(len(rdata), 1) # Check the correct metadata is present self.assertAlmostEqual(rdata[0].metadata[model.MD_EXP_TIME], ldata[i].metadata[model.MD_EXP_TIME]) rthumbnail = fmt_mng.read_thumbnail(fname) # No thumbnail handling for now, so assert that is empty self.assertEqual(rthumbnail, [])
def testExportRead(self): """ Checks that we can read back an image """ # create 2 simple greyscale images sizes = [(512, 256), (500, 400) ] # different sizes to ensure different acquisitions dtype = numpy.dtype("uint16") white = (12, 52) # non symmetric position ldata = [] num = 2 # TODO: check support for combining channels when same data shape for i in range(num): a = model.DataArray(numpy.zeros(sizes[i][-1:-3:-1], dtype)) a[white[-1:-3:-1]] = 1027 ldata.append(a) # thumbnail : small RGB completely red tshape = (sizes[0][1] // 8, sizes[0][0] // 8, 3) tdtype = numpy.uint8 thumbnail = model.DataArray(numpy.zeros(tshape, tdtype)) thumbnail[:, :, 0] += 255 # red blue = (12, 22) # non symmetric position thumbnail[blue[-1:-3:-1]] = [0, 0, 255] # export stiff.export(FILENAME, ldata, thumbnail) tokens = FILENAME.split(".0.", 1) # Iterate through the files generated for file_index in range(num): fname = tokens[0] + "." + str(file_index) + "." + tokens[1] # check it's here st = os.stat(fname) # this test also that the file is created self.assertGreater(st.st_size, 0) # check data rdata = tiff.read_data(fname) self.assertEqual(len(rdata), num) for i, im in enumerate(rdata): if len(im.shape) > 2: subim = im[0, 0, 0] # remove C,T,Z dimensions else: subim = im # TODO: should it always be 5 dim? self.assertEqual(subim.shape, sizes[i][-1::-1]) self.assertEqual(subim[white[-1:-3:-1]], ldata[i][white[-1:-3:-1]])
def testExportReadPyramidal(self): """ Checks that we can read back a pyramidal image """ # create 2 simple greyscale images sizes = [(512, 256), (500, 400)] # different sizes to ensure different acquisitions dtype = numpy.dtype("uint16") white = (12, 52) # non symmetric position ldata = [] num = 2 # TODO: check support for combining channels when same data shape for i in range(num): a = model.DataArray(numpy.zeros(sizes[i][-1:-3:-1], dtype)) a[white[-1:-3:-1]] = 1027 ldata.append(a) # thumbnail : small RGB completely red tshape = (sizes[0][1] // 8, sizes[0][0] // 8, 3) tdtype = numpy.uint8 thumbnail = model.DataArray(numpy.zeros(tshape, tdtype)) thumbnail[:, :, 0] += 255 # red blue = (12, 22) # non symmetric position thumbnail[blue[-1:-3:-1]] = [0, 0, 255] # export stiff.export(FILENAME, ldata, thumbnail, pyramid=True) tokens = FILENAME.split(".0.", 1) # Iterate through the files generated for file_index in range(num): fname = tokens[0] + "." + str(file_index) + "." + tokens[1] # check it's here st = os.stat(fname) # this test also that the file is created self.assertGreater(st.st_size, 0) # check data rdata = tiff.read_data(fname) self.assertEqual(len(rdata), num) for i, im in enumerate(rdata): if len(im.shape) > 2: subim = im[0, 0, 0] # remove C,T,Z dimensions else: subim = im # TODO: should it always be 5 dim? self.assertEqual(subim.shape, sizes[i][-1::-1]) self.assertEqual(subim[white[-1:-3:-1]], ldata[i][white[-1:-3:-1]])
def testExportOpener(self): # create a simple greyscale image size = (512, 256) white = (12, 52) # non symmetric position dtype = numpy.uint16 ldata = [] self.no_of_images = 2 metadata = [ { model.MD_IN_WL: (500e-9, 520e-9), # m }, { model.MD_EXP_TIME: 1.2, # s }, ] # Add wavelength metadata just to group them for i in range(self.no_of_images): a = model.DataArray(numpy.zeros(size[::-1], dtype), metadata[i]) a[white[::-1]] = 124 ldata.append(a) # export stiff.export(FILENAME, ldata) tokens = FILENAME.split(".0.", 1) # Iterate through the files generated. Opening any of them should be # returning _all_ the DAs for file_index in range(self.no_of_images): fname = tokens[0] + "." + str(file_index) + "." + tokens[1] fmt_mng = dataio.find_fittest_converter(fname, mode=os.O_RDONLY) self.assertEqual( fmt_mng.FORMAT, "TIFF", "For '%s', expected format TIFF but got %s" % (fname, fmt_mng.FORMAT)) rdata = fmt_mng.read_data(fname) # Assert all the DAs are there self.assertEqual(len(rdata), len(ldata)) for da in rdata: self.assertEqual(da[white[::-1]], 124) rthumbnail = fmt_mng.read_thumbnail(fname) # No thumbnail handling for now, so assert that is empty self.assertEqual(rthumbnail, [])
def testExportOpener(self): # create a simple greyscale image size = (512, 256) white = (12, 52) # non symmetric position dtype = numpy.uint16 ldata = [] self.no_of_images = 2 metadata = [{ model.MD_IN_WL: (500e-9, 520e-9), # m }, { model.MD_EXP_TIME: 1.2, # s }, ] # Add wavelength metadata just to group them for i in range(self.no_of_images): a = model.DataArray(numpy.zeros(size[::-1], dtype), metadata[i]) a[white[::-1]] = 124 ldata.append(a) # export stiff.export(FILENAME, ldata) tokens = FILENAME.split(".0.", 1) # Iterate through the files generated. Opening any of them should be # returning _all_ the DAs for file_index in range(self.no_of_images): fname = tokens[0] + "." + str(file_index) + "." + tokens[1] fmt_mng = dataio.find_fittest_converter(fname, mode=os.O_RDONLY) self.assertEqual(fmt_mng.FORMAT, "TIFF", "For '%s', expected format TIFF but got %s" % (fname, fmt_mng.FORMAT)) rdata = fmt_mng.read_data(fname) # Assert all the DAs are there self.assertEqual(len(rdata), len(ldata)) for da in rdata: self.assertEqual(da[white[::-1]], 124) rthumbnail = fmt_mng.read_thumbnail(fname) # No thumbnail handling for now, so assert that is empty self.assertEqual(rthumbnail, [])
def testExportMultiPage(self): # create a simple greyscale image size = (512, 256) white = (12, 52) # non symmetric position dtype = numpy.uint16 ldata = [] num = 2 metadata = { model.MD_IN_WL: (500e-9, 520e-9), # m } # Add wavelength metadata just to group them for i in range(num): a = model.DataArray(numpy.zeros(size[::-1], dtype), metadata) a[white[::-1]] = 124 ldata.append(a) # export stiff.export(FILENAME, ldata) tokens = FILENAME.split(".0.", 1) self.no_of_images = 1 # Iterate through the files generated for file_index in range(self.no_of_images): fname = tokens[0] + "." + str(file_index) + "." + tokens[1] # check it's here st = os.stat(fname) # this test also that the file is created self.assertGreater(st.st_size, 0) im = Image.open(fname) self.assertEqual(im.format, "TIFF") # check the number of pages for i in range(num): im.seek(i) self.assertEqual(im.size, size) self.assertEqual(im.getpixel(white), 124) del im for file_index in range(self.no_of_images): fname = tokens[0] + "." + str(file_index) + "." + tokens[1] os.remove(fname)
def testExportOpener(self): # create a simple greyscale image size = (512, 256) white = (12, 52) # non symmetric position dtype = numpy.uint16 ldata = [] num = 2 metadata = { model.MD_IN_WL: (500e-9, 520e-9), # m } for i in range(num): a = model.DataArray(numpy.zeros(size[::-1], dtype), metadata) a[white[::-1]] = 124 ldata.append(a) # export stiff.export(FILENAME, ldata) tokens = FILENAME.split(".0.", 1) self.no_of_images = 1 # Iterate through the files generated for file_index in range(self.no_of_images): fname = tokens[0] + "." + str(file_index) + "." + tokens[1] fmt_mng = dataio.find_fittest_converter(fname, mode=os.O_RDONLY) self.assertEqual( fmt_mng.FORMAT, "TIFF", "For '%s', expected format TIFF but got %s" % (fname, fmt_mng.FORMAT)) rdata = fmt_mng.read_data(fname) # Assert all the DAs are there self.assertEqual(len(rdata[file_index]), len(ldata)) rthumbnail = fmt_mng.read_thumbnail(fname) # No thumbnail handling for now, so assert that is empty self.assertEqual(rthumbnail, []) self.no_of_images = 1 # Iterate through the files generated for file_index in range(self.no_of_images): fname = tokens[0] + "." + str(file_index) + "." + tokens[1] os.remove(fname)
def testExportMultiPage(self): # create a simple greyscale image size = (512, 256) white = (12, 52) # non symmetric position dtype = numpy.uint16 ldata = [] num = 2 metadata = { model.MD_IN_WL: (500e-9, 520e-9), # m } # Add wavelength metadata just to group them for i in range(num): a = model.DataArray(numpy.zeros(size[::-1], dtype), metadata) a[white[::-1]] = 124 ldata.append(a) # export stiff.export(FILENAME, ldata) tokens = FILENAME.split(".0.", 1) self.no_of_images = 1 # Iterate through the files generated for file_index in range(self.no_of_images): fname = tokens[0] + "." + str(file_index) + "." + tokens[1] # check it's here st = os.stat(fname) # this test also that the file is created self.assertGreater(st.st_size, 0) im = Image.open(fname) self.assertEqual(im.format, "TIFF") # check the number of pages for i in range(num): im.seek(i) self.assertEqual(im.size, size) self.assertEqual(im.getpixel(white), 124) del im for file_index in range(self.no_of_images): fname = tokens[0] + "." + str(file_index) + "." + tokens[1] os.remove(fname)
def testExportOpener(self): # create a simple greyscale image size = (512, 256) white = (12, 52) # non symmetric position dtype = numpy.uint16 ldata = [] num = 2 metadata = { model.MD_IN_WL: (500e-9, 520e-9), # m } for i in range(num): a = model.DataArray(numpy.zeros(size[::-1], dtype), metadata) a[white[::-1]] = 124 ldata.append(a) # export stiff.export(FILENAME, ldata) tokens = FILENAME.split(".0.", 1) self.no_of_images = 1 # Iterate through the files generated for file_index in range(self.no_of_images): fname = tokens[0] + "." + str(file_index) + "." + tokens[1] fmt_mng = dataio.find_fittest_converter(fname, mode=os.O_RDONLY) self.assertEqual(fmt_mng.FORMAT, "TIFF", "For '%s', expected format TIFF but got %s" % (fname, fmt_mng.FORMAT)) rdata = fmt_mng.read_data(fname) # Assert all the DAs are there self.assertEqual(len(rdata[file_index]), len(ldata)) rthumbnail = fmt_mng.read_thumbnail(fname) # No thumbnail handling for now, so assert that is empty self.assertEqual(rthumbnail, []) self.no_of_images = 1 # Iterate through the files generated for file_index in range(self.no_of_images): fname = tokens[0] + "." + str(file_index) + "." + tokens[1] os.remove(fname)
def testExportMultiArrayPyramid(self): """ Checks that we can export and read back the metadata and data of 1 SEM image, 2 optical images, 1 RGB imagem and a RGB thumnail """ metadata = [{model.MD_SW_VERSION: "1.0-test", model.MD_HW_NAME: "fake hw", model.MD_DESCRIPTION: "brightfield", model.MD_ACQ_DATE: time.time(), model.MD_BPP: 12, }, {model.MD_SW_VERSION: "1.0-test", model.MD_HW_NAME: "fake hw", model.MD_DESCRIPTION: "blue dye", model.MD_ACQ_DATE: time.time() + 1, model.MD_BPP: 12, }, {model.MD_SW_VERSION: "1.0-test", model.MD_HW_NAME: "fake hw", model.MD_DESCRIPTION: "green dye", model.MD_ACQ_DATE: time.time() + 2, model.MD_BPP: 12, }, {model.MD_SW_VERSION: "1.0-test", model.MD_HW_NAME: "fake hw", model.MD_DESCRIPTION: "green dye", model.MD_ACQ_DATE: time.time() + 2, model.MD_BPP: 12, model.MD_DIMS: "YXC", # In order to test shear is applied even without rotation # provided. And also check that *_COR is merged into its # normal metadata brother. # model.MD_SHEAR: 0.03, model.MD_SHEAR_COR: 0.003, }, ] # create 3 greyscale images of same size size = (5120, 7680) dtype = numpy.dtype("uint16") ldata = [] # iterate on the first 3 metadata items for i, md in enumerate(metadata[:-1]): nparray = numpy.zeros(size[::-1], dtype) a = model.DataArray(nparray, md.copy()) a[i, i + 10] = i # "watermark" it ldata.append(a) # write a RGB image a = model.DataArray(numpy.zeros((514, 516, 3), dtype), metadata[3].copy()) a[8:24, 24:40] = [5, 8, 13] # "watermark" a square ldata.append(a) # thumbnail : small RGB completely green tshape = (size[1] // 8, size[0] // 8, 3) tdtype = numpy.uint8 thumbnail = model.DataArray(numpy.zeros(tshape, tdtype)) thumbnail.metadata[model.MD_DIMS] = "YXC" thumbnail.metadata[model.MD_POS] = (13.7e-3, -30e-3) thumbnail[:, :, 1] += 255 # green # export stiff.export(FILENAME, ldata, thumbnail, pyramid=True) tokens = FILENAME.split(".0.", 1) self.no_of_images = 4 # Iterate through the files generated for file_index in range(self.no_of_images): fname = tokens[0] + "." + str(file_index) + "." + tokens[1] # check it's here st = os.stat(fname) # this test also that the file is created self.assertGreater(st.st_size, 0) f = libtiff.TIFF.open(FILENAME) # read all images and subimages and store in main_images main_images = [] count = 0 for im in f.iter_images(): zoom_level_images = [] zoom_level_images.append(im) # get an array of offsets, one for each subimage sub_ifds = f.GetField(T.TIFFTAG_SUBIFD) if not sub_ifds: main_images.append(zoom_level_images) f.SetDirectory(count) count += 1 continue for n in xrange(len(sub_ifds)): # set the offset of the current subimage f.SetSubDirectory(sub_ifds[n]) # read the subimage subim = f.read_image() zoom_level_images.append(subim) f.SetDirectory(count) count += 1 main_images.append(zoom_level_images) # check the total number of main images self.assertEqual(len(main_images), 1) # check the sizes of each grayscale pyramidal image for main_image in main_images: self.assertEqual(len(main_image), 6) self.assertEqual(main_image[0].shape, (7680, 5120)) self.assertEqual(main_image[1].shape, (3840, 2560)) self.assertEqual(main_image[2].shape, (1920, 1280)) self.assertEqual(main_image[3].shape, (960, 640)) self.assertEqual(main_image[4].shape, (480, 320)) self.assertEqual(main_image[5].shape, (240, 160))
def testReadMDFluo(self): """ Checks that we can read back the metadata of a fluoresence image The OME-TIFF file will contain just one big array, but three arrays should be read back with the right data. """ metadata = [{model.MD_SW_VERSION: "1.0-test", model.MD_HW_NAME: "fake hw", model.MD_DESCRIPTION: "brightfield", model.MD_ACQ_DATE: time.time(), model.MD_BPP: 12, model.MD_BINNING: (1, 1), # px, px model.MD_PIXEL_SIZE: (1e-6, 1e-6), # m/px model.MD_POS: (13.7e-3, -30e-3), # m model.MD_EXP_TIME: 1.2, # s model.MD_IN_WL: (400e-9, 630e-9), # m model.MD_OUT_WL: (400e-9, 630e-9), # m # correction metadata model.MD_POS_COR: (-1e-6, 3e-6), # m model.MD_PIXEL_SIZE_COR: (1.2, 1.2), model.MD_ROTATION_COR: 6.27, # rad model.MD_SHEAR_COR: 0.005, }, {model.MD_SW_VERSION: "1.0-test", model.MD_HW_NAME: "fake hw", model.MD_DESCRIPTION: "blue dye", model.MD_ACQ_DATE: time.time() + 1, model.MD_BPP: 12, model.MD_BINNING: (1, 1), # px, px model.MD_PIXEL_SIZE: (1e-6, 1e-6), # m/px model.MD_POS: (13.7e-3, -30e-3), # m model.MD_EXP_TIME: 1.2, # s model.MD_IN_WL: (500e-9, 522e-9), # m model.MD_OUT_WL: (650e-9, 660e-9, 675e-9, 678e-9, 680e-9), # m model.MD_USER_TINT: (255, 0, 65), # purple model.MD_LIGHT_POWER: 100e-3 # W }, {model.MD_SW_VERSION: "1.0-test", model.MD_HW_NAME: "fake hw", model.MD_DESCRIPTION: "green dye", model.MD_ACQ_DATE: time.time() + 2, model.MD_BPP: 12, model.MD_BINNING: (1, 1), # px, px model.MD_PIXEL_SIZE: (1e-6, 1e-6), # m/px model.MD_POS: (13.7e-3, -30e-3), # m model.MD_EXP_TIME: 1, # s model.MD_IN_WL: (590e-9, 620e-9), # m model.MD_OUT_WL: (620e-9, 650e-9), # m model.MD_ROTATION: 0.1, # rad model.MD_SHEAR: 0, model.MD_BASELINE: 400.0 }, {model.MD_SW_VERSION: "1.0-test", model.MD_HW_NAME: "fake hw", model.MD_DESCRIPTION: "green dye", model.MD_ACQ_DATE: time.time() + 2, model.MD_BPP: 12, model.MD_BINNING: (1, 1), # px, px model.MD_PIXEL_SIZE: (1e-6, 1e-6), # m/px model.MD_POS: (13.7e-3, -30e-3), # m model.MD_EXP_TIME: 1, # s model.MD_IN_WL: (600e-9, 630e-9), # m model.MD_OUT_WL: (620e-9, 650e-9), # m # In order to test shear is applied even without rotation # provided. And also check that *_COR is merged into its # normal metadata brother. # model.MD_SHEAR: 0.03, model.MD_SHEAR_COR: 0.003, }, ] # create 3 greyscale images of same size size = (512, 256) dtype = numpy.dtype("uint16") ldata = [] for i, md in enumerate(metadata): a = model.DataArray(numpy.zeros(size[::-1], dtype), md.copy()) a[i, i] = i # "watermark" it ldata.append(a) # thumbnail : small RGB completely red tshape = (size[1] // 8, size[0] // 8, 3) tdtype = numpy.uint8 thumbnail = model.DataArray(numpy.zeros(tshape, tdtype)) thumbnail[:, :, 1] += 255 # green # export stiff.export(FILENAME, ldata, thumbnail) tokens = FILENAME.split(".0.", 1) self.no_of_images = 4 # Iterate through the files generated for file_index in range(self.no_of_images): fname = tokens[0] + "." + str(file_index) + "." + tokens[1] # check it's here st = os.stat(fname) # this test also that the file is created self.assertGreater(st.st_size, 0) # check data rdata = tiff.read_data(fname) self.assertEqual(len(rdata), len(ldata)) # TODO: rdata and ldata don't have to be in the same order for i, im in enumerate(rdata): md = metadata[i].copy() img.mergeMetadata(md) self.assertEqual(im.metadata[model.MD_DESCRIPTION], md[model.MD_DESCRIPTION]) numpy.testing.assert_allclose(im.metadata[model.MD_POS], md[model.MD_POS], rtol=1e-4) numpy.testing.assert_allclose(im.metadata[model.MD_PIXEL_SIZE], md[model.MD_PIXEL_SIZE]) self.assertAlmostEqual(im.metadata[model.MD_ACQ_DATE], md[model.MD_ACQ_DATE], delta=1) self.assertEqual(im.metadata[model.MD_BPP], md[model.MD_BPP]) self.assertEqual(im.metadata[model.MD_BINNING], md[model.MD_BINNING]) if model.MD_USER_TINT in md: self.assertEqual(im.metadata[model.MD_USER_TINT], md[model.MD_USER_TINT]) iwl = im.metadata[model.MD_IN_WL] # nm self.assertTrue((md[model.MD_IN_WL][0] <= iwl[0] and iwl[1] <= md[model.MD_IN_WL][-1])) owl = im.metadata[model.MD_OUT_WL] # nm self.assertTrue((md[model.MD_OUT_WL][0] <= owl[0] and owl[1] <= md[model.MD_OUT_WL][-1])) if model.MD_LIGHT_POWER in md: self.assertEqual(im.metadata[model.MD_LIGHT_POWER], md[model.MD_LIGHT_POWER]) self.assertAlmostEqual(im.metadata.get(model.MD_ROTATION, 0), md.get(model.MD_ROTATION, 0)) self.assertAlmostEqual(im.metadata.get(model.MD_SHEAR, 0), md.get(model.MD_SHEAR, 0))
def testReadMDAR(self): """ Checks that we can read back the metadata of an Angular Resolved image """ metadata = [{model.MD_SW_VERSION: "1.0-test", model.MD_HW_NAME: "fake hw", model.MD_DESCRIPTION: "sem survey", model.MD_ACQ_DATE: time.time(), model.MD_BPP: 12, model.MD_BINNING: (1, 2), # px, px model.MD_PIXEL_SIZE: (1e-6, 2e-5), # m/px model.MD_POS: (1e-3, -30e-3), # m model.MD_EXP_TIME: 1.2, # s model.MD_LENS_MAG: 1200, # ratio }, {model.MD_SW_VERSION: "1.0-test", model.MD_HW_NAME: "fake ccd", model.MD_DESCRIPTION: "AR", model.MD_ACQ_DATE: time.time(), model.MD_BPP: 12, model.MD_BINNING: (1, 1), # px, px model.MD_SENSOR_PIXEL_SIZE: (13e-6, 13e-6), # m/px model.MD_PIXEL_SIZE: (1e-6, 2e-5), # m/px model.MD_POS: (1.2e-3, -30e-3), # m model.MD_EXP_TIME: 1.2, # s model.MD_AR_POLE: (253.1, 65.1), # px model.MD_AR_XMAX: 12e-3, model.MD_AR_HOLE_DIAMETER: 0.6e-3, model.MD_AR_FOCUS_DISTANCE: 0.5e-3, model.MD_AR_PARABOLA_F: 2e-3, model.MD_LENS_MAG: 60, # ratio }, {model.MD_SW_VERSION: "1.0-test", model.MD_HW_NAME: "fake ccd", model.MD_DESCRIPTION: "AR", model.MD_ACQ_DATE: time.time(), model.MD_BPP: 12, model.MD_BINNING: (1, 1), # px, px model.MD_SENSOR_PIXEL_SIZE: (13e-6, 13e-6), # m/px model.MD_PIXEL_SIZE: (1e-6, 2e-5), # m/px model.MD_POS: (1e-3, -30e-3), # m model.MD_EXP_TIME: 1.2, # s model.MD_AR_POLE: (253.1, 65.1), # px model.MD_AR_XMAX: 12e-3, model.MD_AR_HOLE_DIAMETER: 0.6e-3, model.MD_AR_FOCUS_DISTANCE: 0.5e-3, model.MD_AR_PARABOLA_F: 2e-3, model.MD_LENS_MAG: 60, # ratio }, ] # create 2 simple greyscale images sizes = [(512, 256), (500, 400), (500, 400)] # different sizes to ensure different acquisitions dtype = numpy.dtype("uint16") ldata = [] for s, md in zip(sizes, metadata): a = model.DataArray(numpy.zeros(s[::-1], dtype), md) ldata.append(a) # thumbnail : small RGB completely red tshape = (sizes[0][1] // 8, sizes[0][0] // 8, 3) tdtype = numpy.uint8 thumbnail = model.DataArray(numpy.zeros(tshape, tdtype)) thumbnail[:, :, 1] += 255 # green # export stiff.export(FILENAME, ldata, thumbnail) tokens = FILENAME.split(".0.", 1) self.no_of_images = 2 # Iterate through the files generated for file_index in range(self.no_of_images): fname = tokens[0] + "." + str(file_index) + "." + tokens[1] # check it's here st = os.stat(fname) # this test also that the file is created self.assertGreater(st.st_size, 0) # check data rdata = tiff.read_data(fname) self.assertEqual(len(rdata), len(ldata)) for im, md in zip(rdata, metadata): self.assertEqual(im.metadata[model.MD_DESCRIPTION], md[model.MD_DESCRIPTION]) numpy.testing.assert_allclose(im.metadata[model.MD_POS], md[model.MD_POS], rtol=1e-4) numpy.testing.assert_allclose(im.metadata[model.MD_PIXEL_SIZE], md[model.MD_PIXEL_SIZE]) self.assertAlmostEqual(im.metadata[model.MD_ACQ_DATE], md[model.MD_ACQ_DATE], delta=1) if model.MD_AR_POLE in md: numpy.testing.assert_allclose(im.metadata[model.MD_AR_POLE], md[model.MD_AR_POLE]) if model.MD_AR_XMAX in md: self.assertAlmostEqual(im.metadata[model.MD_AR_XMAX], md[model.MD_AR_XMAX]) if model.MD_AR_HOLE_DIAMETER in md: self.assertAlmostEqual(im.metadata[model.MD_AR_HOLE_DIAMETER], md[model.MD_AR_HOLE_DIAMETER]) if model.MD_AR_FOCUS_DISTANCE in md: self.assertAlmostEqual(im.metadata[model.MD_AR_FOCUS_DISTANCE], md[model.MD_AR_FOCUS_DISTANCE]) if model.MD_AR_PARABOLA_F in md: self.assertAlmostEqual(im.metadata[model.MD_AR_PARABOLA_F], md[model.MD_AR_PARABOLA_F]) if model.MD_LENS_MAG in md: self.assertAlmostEqual(im.metadata[model.MD_LENS_MAG], md[model.MD_LENS_MAG])
def testReadMDSpec(self): """ Checks that we can read back the metadata of a spectrum image """ metadata = [{model.MD_SW_VERSION: "1.0-test", model.MD_HW_NAME: "fake hw", model.MD_DESCRIPTION: "test", model.MD_ACQ_DATE: time.time(), model.MD_BPP: 12, model.MD_BINNING: (1, 2), # px, px model.MD_PIXEL_SIZE: (1e-6, 2e-5), # m/px model.MD_POS: (13.7e-3, -30e-3), # m model.MD_EXP_TIME: 1.2, # s model.MD_IN_WL: (500e-9, 520e-9), # m model.MD_OUT_WL: (650e-9, 660e-9, 675e-9, 678e-9, 680e-9), # m }, {model.MD_SW_VERSION: "1.0-test", model.MD_HW_NAME: "fake spec", model.MD_DESCRIPTION: "test3d", model.MD_ACQ_DATE: time.time(), model.MD_BPP: 12, model.MD_BINNING: (1, 1), # px, px model.MD_PIXEL_SIZE: (1e-6, 2e-5), # m/px model.MD_WL_POLYNOMIAL: [500e-9, 1e-9], # m, m/px: wl polynomial model.MD_POS: (13.7e-3, -30e-3), # m model.MD_EXP_TIME: 1.2, # s }, ] # create 2 simple greyscale images sizes = [(512, 256), (500, 400, 1, 1, 220)] # different sizes to ensure different acquisitions dtype = numpy.dtype("uint8") ldata = [] for i, s in enumerate(sizes): a = model.DataArray(numpy.zeros(s[::-1], dtype), metadata[i]) ldata.append(a) # thumbnail : small RGB completely red tshape = (sizes[0][1] // 8, sizes[0][0] // 8, 3) tdtype = numpy.uint8 thumbnail = model.DataArray(numpy.zeros(tshape, tdtype)) thumbnail[:, :, 1] += 255 # green # export stiff.export(FILENAME, ldata, thumbnail) tokens = FILENAME.split(".0.", 1) self.no_of_images = 2 # Iterate through the files generated for file_index in range(self.no_of_images): fname = tokens[0] + "." + str(file_index) + "." + tokens[1] # check it's here st = os.stat(fname) # this test also that the file is created self.assertGreater(st.st_size, 0) # check data rdata = tiff.read_data(fname) self.assertEqual(len(rdata), len(ldata)) for i, im in enumerate(rdata): md = metadata[i] self.assertEqual(im.metadata[model.MD_DESCRIPTION], md[model.MD_DESCRIPTION]) numpy.testing.assert_allclose(im.metadata[model.MD_POS], md[model.MD_POS], rtol=1e-4) numpy.testing.assert_allclose(im.metadata[model.MD_PIXEL_SIZE], md[model.MD_PIXEL_SIZE]) self.assertAlmostEqual(im.metadata[model.MD_ACQ_DATE], md[model.MD_ACQ_DATE], delta=1) self.assertEqual(im.metadata[model.MD_BPP], md[model.MD_BPP]) self.assertEqual(im.metadata[model.MD_BINNING], md[model.MD_BINNING]) if model.MD_WL_POLYNOMIAL in md: pn = md[model.MD_WL_POLYNOMIAL] # 2 formats possible if model.MD_WL_LIST in im.metadata: l = ldata[i].shape[0] npn = polynomial.Polynomial(pn, domain=[0, l - 1], window=[0, l - 1]) wl = npn.linspace(l)[1] numpy.testing.assert_allclose(im.metadata[model.MD_WL_LIST], wl) else: numpy.testing.assert_allclose(im.metadata[model.MD_WL_POLYNOMIAL], pn)
def testExportNoWL(self): """ Check it's possible to export/import a spectrum with missing wavelength info """ dtype = numpy.dtype("uint16") size3d = (512, 256, 220) # X, Y, C size = (512, 256) metadata = [{model.MD_SW_VERSION: "1.0-test", model.MD_HW_NAME: "bad spec", model.MD_DESCRIPTION: "test3d", model.MD_ACQ_DATE: time.time(), model.MD_BPP: 12, model.MD_BINNING: (1, 1), # px, px model.MD_PIXEL_SIZE: (1e-6, 2e-5), # m/px model.MD_WL_POLYNOMIAL: [0], # m, m/px: missing polynomial model.MD_POS: (1e-3, -30e-3), # m model.MD_EXP_TIME: 1.2, #s }, {model.MD_SW_VERSION: "1.0-test", model.MD_HW_NAME: u"", # check empty unicode strings model.MD_DESCRIPTION: u"tÉst", # tiff doesn't support É (but XML does) model.MD_ACQ_DATE: time.time(), model.MD_BPP: 12, model.MD_BINNING: (1, 2), # px, px model.MD_PIXEL_SIZE: (1e-6, 2e-5), # m/px model.MD_POS: (1e-3, -30e-3), # m model.MD_EXP_TIME: 1.2, #s model.MD_IN_WL: (500e-9, 520e-9), #m }] ldata = [] # 3D data generation (+ metadata): gradient along the wavelength data3d = numpy.empty(size3d[::-1], dtype=dtype) end = 2 ** metadata[0][model.MD_BPP] step = end // size3d[2] lin = numpy.arange(0, end, step, dtype=dtype)[:size3d[2]] lin.shape = (size3d[2], 1, 1) # to be able to copy it on the first dim data3d[:] = lin # introduce Time and Z dimension to state the 3rd dim is channel data3d = data3d[:, numpy.newaxis, numpy.newaxis, :, :] ldata.append(model.DataArray(data3d, metadata[0])) # an additional 2D data, for the sake of it ldata.append(model.DataArray(numpy.zeros(size[::-1], dtype), metadata[1])) # export stiff.export(FILENAME, ldata) # check 3D data tokens = FILENAME.split(".0.", 1) self.no_of_images = 2 # Iterate through the files generated for file_index in range(self.no_of_images): fname = tokens[0] + "." + str(file_index) + "." + tokens[1] # check it's here st = os.stat(fname) # this test also that the file is created self.assertGreater(st.st_size, 0) rdata = tiff.read_data(fname) self.assertEqual(len(rdata), len(ldata)) for i, im in enumerate(rdata): md = metadata[i] self.assertEqual(im.metadata[model.MD_DESCRIPTION], md[model.MD_DESCRIPTION]) numpy.testing.assert_allclose(im.metadata[model.MD_POS], md[model.MD_POS], rtol=1e-4) numpy.testing.assert_allclose(im.metadata[model.MD_PIXEL_SIZE], md[model.MD_PIXEL_SIZE]) self.assertAlmostEqual(im.metadata[model.MD_ACQ_DATE], md[model.MD_ACQ_DATE], delta=1) self.assertEqual(im.metadata[model.MD_BPP], md[model.MD_BPP]) self.assertEqual(im.metadata[model.MD_BINNING], md[model.MD_BINNING]) if model.MD_WL_POLYNOMIAL in md: pn = md[model.MD_WL_POLYNOMIAL] # either identical, or nothing at all if model.MD_WL_POLYNOMIAL in im.metadata: numpy.testing.assert_allclose(im.metadata[model.MD_WL_POLYNOMIAL], pn) else: self.assertNotIn(model.MD_WL_LIST, im.metadata)
def testExportCube(self): """ Check it's possible to export a 3D data (typically: 2D area with full spectrum for each point) """ dtype = numpy.dtype("uint16") size3d = (512, 256, 220) # X, Y, C size = (512, 256) metadata3d = {model.MD_SW_VERSION: "1.0-test", model.MD_HW_NAME: "fake spec", model.MD_HW_VERSION: "1.23", model.MD_DESCRIPTION: "test3d", model.MD_ACQ_DATE: time.time(), model.MD_BPP: 12, model.MD_BINNING: (1, 1), # px, px model.MD_PIXEL_SIZE: (1e-6, 2e-5), # m/px model.MD_WL_POLYNOMIAL: [500e-9, 1e-9], # m, m/px: wl polynomial model.MD_POS: (1e-3, -30e-3), # m model.MD_EXP_TIME: 1.2, #s model.MD_IN_WL: (500e-9, 520e-9), #m } metadata = {model.MD_SW_VERSION: "1.0-test", model.MD_HW_NAME: u"", # check empty unicode strings model.MD_DESCRIPTION: u"tÉst", # tiff doesn't support É (but XML does) model.MD_ACQ_DATE: time.time(), model.MD_BPP: 12, model.MD_BINNING: (1, 2), # px, px model.MD_PIXEL_SIZE: (1e-6, 2e-5), # m/px model.MD_POS: (1e-3, -30e-3), # m model.MD_EXP_TIME: 1.2, #s model.MD_IN_WL: (500e-9, 520e-9), #m } ldata = [] # 3D data generation (+ metadata): gradient along the wavelength data3d = numpy.empty(size3d[-1::-1], dtype=dtype) end = 2**metadata3d[model.MD_BPP] step = end // size3d[2] lin = numpy.arange(0, end, step, dtype=dtype)[:size3d[2]] lin.shape = (size3d[2], 1, 1) # to be able to copy it on the first dim data3d[:] = lin # introduce Time and Z dimension to state the 3rd dim is channel data3d = data3d[:, numpy.newaxis, numpy.newaxis,:,:] ldata.append(model.DataArray(data3d, metadata3d)) # an additional 2D data, for the sake of it ldata.append(model.DataArray(numpy.zeros(size[-1::-1], dtype), metadata)) # export stiff.export(FILENAME, ldata) tokens = FILENAME.split(".0.", 1) fname = tokens[0] + "." + str(0) + "." + tokens[1] # check it's here st = os.stat(fname) # this test also that the file is created self.assertGreater(st.st_size, 0) im = Image.open(fname) self.assertEqual(im.format, "TIFF") # check the 3D data (one image per channel) for i in range(size3d[2]): im.seek(i) self.assertEqual(im.size, size3d[0:2]) self.assertEqual(im.getpixel((1, 1)), i * step) del im fname = tokens[0] + "." + str(1) + "." + tokens[1] # check it's here st = os.stat(fname) # this test also that the file is created self.assertGreater(st.st_size, 0) im = Image.open(fname) self.assertEqual(im.format, "TIFF") # check the 2D data self.assertEqual(im.size, size) self.assertEqual(im.getpixel((1, 1)), 0)