Python tifffile.imread函数代码示例

def remove_dark(A, folder):
    """
    This function will subtract the dark files from the data files
    Parameters
    ----------
    A : list
        list of tiff files
        
    Returns
    -------
    clean_data : array
        dark subtracted data , clean data
        shape (number of clean images, detectore shape 0, detecotor shape 1)
    """
    
    clean_data_arr = []  # save the cleaned data
    for name in A:
        if "dark" in name:   # check the dark files
            dark_data = imread(name)  
            print ("+++++ bad", name)
        else:
            arr = imread(name)
            print ("good", name)
            #  clean the data
            clean_data = arr - dark_data 
            #print (os.path.join(name))
            imsave(name, clean_data)
            clean_data_arr.append(clean_data)
    return np.asarray(clean_data_arr)

 def __init__(self, file_path):
     self.file_path = file_path
     if isinstance(file_path, str):
         self.data = tifffile.imread(self.file_path)
     elif any([isinstance(file_path, t) for t in [np.ndarray,list]]):
         data = [tifffile.imread(f) for f in self.file_path if 'tif' in f]
         self.data = np.concatenate([d if d.ndim==3 else [d] for d in data], axis=0)

 def get_new_images(self, temp_file_list):
     new_pics = []
     if temp_file_list is not None:
         for i in temp_file_list:
             self.pic_list.append(imread(self._directory_name + i))
             new_pics.append(imread(self._directory_name + i))
     return temp_file_list, new_pics

def imreadStack(filenameList):
    """Simple wrapper to read a list of image series tiffs into a stack.

    Note that this function assumes all images are the same size.

    We tend to work with single channel tiff files, and as such use tifffile's imread function.
    We've wrapped tifffiles read function to account for the differences in default
    image dimension ordering.  By convention, we use x,y,frame.  The major advantages of
    tifffile are 1) speed and 2) the ability to read multiframe tiffs.

    :param filenameList: list of strings representing the files to load
    :returns:  4d numpy array
    """

    firstImageSeries = tifffile.imread(filenameList[0])
    if len(firstImageSeries.shape) == 3:
	firstImageSeries=np.transpose(firstImageSeries, [1,2,0])

    imageStack = np.zeros((firstImageSeries.shape[0], firstImageSeries.shape[1], firstImageSeries.shape[2], len(filenameList)))

    for i, fileName in enumerate(filenameList):
	array=tifffile.imread(fileName)
	if len(array.shape) == 3:
	    array=np.transpose(array, [1,2,0])
        imageStack[:,:,:,i] = array
    return imageStack

def get_images(imageId, img_key = None):
    '''
    Load images correspoding to imageId

    Parameters
    ----------
    imageId : str
        imageId as used in grid_size.csv
    img_key : {None, '3', 'A', 'M', 'P'}, optional
        Specify this to load single image
        None loads all images and returns in a dict
        '3' loads image from three_band/
        'A' loads '_A' image from sixteen_band/
        'M' loads '_M' image from sixteen_band/
        'P' loads '_P' image from sixteen_band/

    Returns
    -------
    images : dict
        A dict of image data from TIFF files as numpy array
    '''
    img_names = get_image_names(imageId)
    images = dict()
    if img_key is None:
        for k in img_names.keys():
            images[k] = tiff.imread(img_names[k])
    else:
        images[img_key] = tiff.imread(img_names[img_key])
    return images

def main(vol_fname='', label_fname='', dataset_name='', percent_test=0,
	normalize_mean=False):

	print "Reading data..."
	vol = tifffile.imread(vol_fname)
	label = tifffile.imread(label_fname)

	if zero_mean:
		vol = zero_mean(vol)

	if len(label.shape) > 3 and label.shape[3] == 3:
		print "Converting label to binary..."
		label = rgb2bin(label)

	#Splitting into training and test sets
	train, label_train, test, label_test = split_data(vol, label, percent_test)

	#Transpose
	train = train.transpose(0,2,1)
	test = test.transpose(0,2,1)

	label_train = label_train.transpose(0,2,1)
	label_test = label_test.transpose(0,2,1)

	s_train = train.shape
	s_test = test.shape

	print "Saving data in znn format..."
	#Making the necessary directories
	os.makedirs('dataset/{}/data/'.format(dataset_name))
	os.makedirs('dataset/{}/spec/'.format(dataset_name))
		
	#Save as znn format
	train_outname = "dataset/{0}/data/batch{1}.image".format(dataset_name, 1)
	label_train_outname = "dataset/{0}/data/batch{1}.label".format(dataset_name, 1)

	if percent_test > 0:

		test_outname = "dataset/{0}/data/batch{1}.image".format(dataset_name, 2)
		label_test_outname = "dataset/{0}/data/batch{1}.label".format(dataset_name, 2)

	emirt.io.znn_img_save(train.astype('double'), train_outname)
	emirt.io.znn_img_save(label_train.astype('double'), label_train_outname)

	if percent_test > 0:

		emirt.io.znn_img_save(test.astype('double'), test_outname)
		emirt.io.znn_img_save(label_test.astype('double'), label_test_outname)

	#Prepare a spec file
	print "Writing spec file..."
	write_spec_file(dataset_name, 1, s_train)

	if percent_test > 0:

		write_spec_file(dataset_name, 2, s_test)

def readData(filename, x = all, y = all, z = all, **args):
    """Read data from a single tif image or stack
    
    Arguments:
        filename (str): file name as regular expression
        x,y,z (tuple): data range specifications
    
    Returns:
        array: image data
    """
    
    dsize = dataSize(filename);
    #print "dsize %s" % str(dsize);    
    
    if len(dsize) == 2:
        data = tiff.imread(filename, key = 0);
        #print "data.shape %s" % str(data.shape);        
        
        return io.dataToRange(data.transpose([1,0]), x = x, y = y);
        #return io.dataToRange(data, x = x, y = y);
        
    else:
        if z is all:
            data = tiff.imread(filename);
            if data.ndim == 2:
                # data = data
                data = data.transpose([1,0]);
            elif data.ndim == 3:
                #data = data.transpose([1,2,0]);
                data = data.transpose([2,1,0]);
            elif data.ndim == 4: # multi channel image
                #data = data.transpose([1,2,0,3]);
                data = data.transpose([2,1,0,3]);
            else:
                raise RuntimeError('readData: dimension %d not supproted!' % data.ndim)
            
            return io.dataToRange(data, x = x, y = y, z = all);
        
        else: #optimize for z ranges
            ds = io.dataSizeFromDataRange(dsize, x = x, y = y, z = z);
            t = tiff.TiffFile(filename);
            p = t.pages[0];
            data = numpy.zeros(ds, dtype = p.dtype);
            rz = io.toDataRange(dsize[2], r = z);
            
            #print "test"
            #print rz;
            #print dsize            
            
            for i in range(rz[0], rz[1]):
                xydata = t.pages[i].asarray();
                #data[:,:,i-rz[0]] = io.dataToRange(xydata, x = x, y = y);
                data[:,:,i-rz[0]] = io.dataToRange(xydata.transpose([1,0]), x = x, y = y);
            
            return data

  def __init__(self,infile,rawfile,verbosity=1):
    self.verbosity = verbosity;

    # read images
    self.IN   =tiff.imread(infile,verbosity=self.verbosity);
    self.INRAW=tiff.imread(rawfile,verbosity=self.verbosity);
  
    # image parameters (TODO: read scale from Tiff?)
    self.info = { 'desc':     infile.split('/')[-1],
                  'filename': infile,
                  'atoms'   : 'C' };

def baboon(size=512, dtype='float32'):
    """
    Load test baboon image array.

    Parameters
    ----------
    size : int or tuple of int, optional
        Size of the output image.
    dtype : str, optional
        The desired data-type for the array.

    Returns
    -------
    ndarray
        Output 3D test image.
    """
    size = _totuple(size, 2)
    fname = os.path.join(DATA_PATH, 'baboon.tif')
    im = tifffile.imread(fname)
    im = skimage.transform.resize(im, size, order=3,
                                  preserve_range=True, mode='constant',
                                  **resize_kwargs)
    im = np.expand_dims(im, 0)
    im = im.astype(dtype)
    return im

def singletiff2multidicom(in_files, dicomdir, plans, out_prefix):
    import DicomIO
    import numpy as np
    import os
    import shutil
    import warnings
    with warnings.catch_warnings():
        warnings.simplefilter("ignore")
        import tifffile as tiff
    outdir = experiment_dir + '/' + out_prefix + '/' + dicomdir
    if not os.path.exists(outdir):
        os.makedirs(outdir)
    else:
        shutil.rmtree(outdir)
        os.makedirs(outdir)

    # Resolve new frame list
    out_vols = plans
    for file_i in range(len(in_files)):
        print "Reading " + in_files[file_i]
        ds = tiff.imread(in_files[file_i])
        no_slices = ds.shape[0]
        for z_i in range(no_slices):
            out_vols[file_i][z_i].PixelData = ds[z_i].astype(np.uint16).tostring()

    dcmio = DicomIO.DicomIO()
    filenames = dcmio.WriteDICOM_frames(outdir, out_vols, 'IM')

    return outdir, filenames

def optimize_z(x,y,z,image,n=None):
	"""Optimize z for poly fit"""
	if type(image) == str:
		img = tf.imread(image)
	elif type(image) == np.ndarray:
		img = image

	data_z = img[:,y,x]

	if n is None:
		n = getn(data_z)

	x_opt_vals, y_opt_vals, z_opt_vals = [], [], []

	x_opt,y_opt,z_opt = x,y,z
	for i in range(5):
		try:
			print x_opt,y_opt,z_opt
			x_opt,y_opt,z_opt = int(round(x_opt)),int(round(y_opt)),int(round(z_opt))
			x_opt, y_opt = optimize_xy(x_opt,y_opt,z_opt,img,nx=None,ny=None)
			data_z = img[:,round(y_opt),round(x_opt)]
		except Exception as e:
			if clrmsg and debug is True: print clrmsg.ERROR
			print IndexError("Optimization failed, possibly due to low signal or low SNR. "+str(e))
			return [x],[y],['failed']
		n = getn(data_z)
		z_opt, data_z_yp_poly = parabolic.parabolic_polyfit(data_z, np.argmax(data_z), n)
		x_opt_vals.append(x_opt)
		y_opt_vals.append(y_opt)
		z_opt_vals.append(z_opt)

	return x_opt_vals, y_opt_vals, z_opt_vals

def relabel_volume(dir, outdir):


    files = sorted(os.listdir(dir))

    out = None
    out_is_there = False

    for f in files:

        i = tif.imread(os.path.join(dir,f))
        if (out_is_there):
            out = numpy.dstack([out, i])

        else:
            out = i
            out_is_there = True


    print '3d volume', out.shape

    import skimage
    from skimage.segmentation import relabel_sequential

    relabeled,fm,im = skimage.segmentation.relabel_sequential(out)

    print 'Max', relabeled.max()

    for z in range(relabeled.shape[2]):
        tif.imsave(os.path.join(outdir,str(z)+'.tif'),relabeled[:,:,z].astype(numpy.uint32))
        print 'stored', z

def find(args):
    from sys import stdout
    from tifffile import imread

    image = imread(str(args.image)).astype('float32')

    scale = asarray(args.scale) if args.scale else ones(image.ndim, dtype='int')
    blobs = findBlobs(image, range(*args.size), args.threshold)[:, 1:] # Remove scale
    blobs = blobs[peakEnclosed(blobs, shape=image.shape, size=args.edge)]
    blobs = blobs[:, ::-1] # Reverse to xyz order
    blobs = blobs * scale

    if args.format == "pickle":
        from pickle import dump, HIGHEST_PROTOCOL
        from functools import partial
        dump = partial(dump, protocol=HIGHEST_PROTOCOL)

        dump(blobs, stdout.buffer)
    else:
        import csv

        if args.format == 'txt':
            delimiter = ' '
        elif args.format == 'csv':
            delimiter = ','
        writer = csv.writer(stdout, delimiter=delimiter)
        for blob in blobs:
            writer.writerow(blob)

def loadTiffStack(fname,useLibTiff=False):
  """
  Read a TIFF stack.
  We're using tifflib by default as, right now, only this works when the application is compile on Windows. [17/08/15]
  Bugs: known to fail with tiffs produced by Icy [23/07/15]

  """
  if not os.path.exists(fname):
    print "imageStackLoader.loadTiffStack can not find %s" % fname
    return

  purePython = True
  if useLibTiff:
    from libtiff import TIFFfile
    import numpy as np
    tiff = TIFFfile(fname)
    samples, sample_names = tiff.get_samples() #we should have just one
    print "Loading:\n" + tiff.get_info() + " with libtiff\n"
    im = np.asarray(samples[0])
  else:
    print "Loading:\n" + fname + " with tifffile\n"
    from tifffile import imread 
    im = imread(fname)

  im=im.swapaxes(1,2) 
  print "read image of size: cols: %d, rows: %d, layers: %d" % (im.shape[1],im.shape[2],im.shape[0])
  return im

 def imread(_path):
     """ allow loading jpg, png as well as tif
     """
     if os.path.splitext(_path)[1] in ['.tiff', '.tif']:
         return tiff.imread(_path)
     else:
         return cv2.imread(_path,0)