Python exposure.rescale_intensity函数代码示例

def autolevels(image,minPercent=2,maxPercent=98,funcName='mean',perChannel=False):
    '''
    Rescale intensity of an image. For RGB images, the new limits are calculated 
    per channel and then mean or median of these limits are applied to the whole 
    image (if perChannel option is False).
    '''
    # dictionary of functions
    funcs = {'mean':np.mean,'median':np.median,'min':np.min,'max':np.max}
    
    # calculate percentiles (returns 3 values for RGB pictures or vectors, 1 for grayscale images)
    if image.shape[1] == 3:
        pMin,pMax = np.percentile(image,(minPercent, maxPercent),axis=0)
    else:
        pMin,pMax = np.percentile(image,(minPercent, maxPercent),axis=(0,1))

    # Apply normalisation
    if not perChannel: # finds new min and max using selected function applied to all channels
        newMin = funcs[funcName](pMin)
        newMax = funcs[funcName](pMax)
        auto = exposure.rescale_intensity(image,in_range=(newMin,newMax)) 

    else: # applies a rescale on each channel separately
        r_channel = exposure.rescale_intensity(image[:,:,0], in_range=(pMin[0],pMax[0])) 
        g_channel = exposure.rescale_intensity(image[:,:,1], in_range=(pMin[1],pMax[1])) 
        b_channel = exposure.rescale_intensity(image[:,:,2], in_range=(pMin[2],pMax[2])) 
        auto = np.stack((r_channel,g_channel,b_channel),axis=2)

    return auto 

def mod_zedge(composite, mod_id, algorithm, **kwargs):

    zedge_channel, zedge_channel_created = composite.channels.get_or_create(name="-zedge")

    for t in range(composite.series.ts):
        print("step02 | processing mod_zedge t{}/{}...".format(t + 1, composite.series.ts), end="\r")

        zdiff_mask = composite.masks.get(channel__name__contains=kwargs["channel_unique_override"], t=t).load()
        zbf = exposure.rescale_intensity(composite.gons.get(channel__name="-zbf", t=t).load() * 1.0)
        zedge = zbf.copy()

        binary_mask = zdiff_mask > 0
        outside_edge = distance_transform_edt(dilate(edge_image(binary_mask), iterations=4))
        outside_edge = 1.0 - exposure.rescale_intensity(outside_edge * 1.0)
        zedge *= outside_edge * outside_edge

        zedge_gon, zedge_gon_created = composite.gons.get_or_create(
            experiment=composite.experiment, series=composite.series, channel=zedge_channel, t=t
        )
        zedge_gon.set_origin(0, 0, 0, t)
        zedge_gon.set_extent(composite.series.rs, composite.series.cs, 1)

        zedge_gon.array = zedge.copy()
        zedge_gon.save_array(composite.series.experiment.composite_path, composite.templates.get(name="source"))
        zedge_gon.save()

def edge():

	#plt.switch_backend('MacOSX')
	image = io.imread(path + "bibme0.png")
	print type(image)
	print image.shape
#	edge_roberts = roberts(image)
#	edge_sobel = sobel(image)

	fig = plt.figure(figsize=(14, 7))
	ax_each = fig.add_subplot(121, adjustable='box-forced')
	ax_hsv = fig.add_subplot(122, sharex=ax_each, sharey=ax_each,
	                         adjustable='box-forced')

	# We use 1 - sobel_each(image)
	# but this will not work if image is not normalized
	ax_each.imshow(rescale_intensity(1 - sobel_gray(image)), cmap=plt.cm.gray)
	#ax_each.imshow(sobel_each(image))
	ax_each.set_xticks([]), ax_each.set_yticks([])
	ax_each.set_title("Sobel filter computed\n on individual RGB channels")
	
	
	# We use 1 - sobel_hsv(image) but this will not work if image is not normalized
	ax_hsv.imshow(rescale_intensity(1 - sobel_gray(image)), cmap=plt.cm.gray)
	ax_hsv.set_xticks([]), ax_hsv.set_yticks([])
	ax_hsv.set_title("Sobel filter computed\n on (V)alue converted image (HSV)")
	
	fig.savefig(out_path + 'sobel_gray.png')
	plt.show()

    def _write_image(self, img_data, filename, img_format=None, dtype=None):
        """
        Output image data to a file, in a given image format.
        Assumes that the output directory exists (must be checked before).

        @param img_data :: image data in the usual numpy representation
        @param filename :: file name, including directory and extension
        @param img_format :: image file format
        @param dtype :: can be used to force a pixel type, otherwise the type
                        of the input data is used

        Returns:: name of the file saved
        """
        if not img_format:
            img_format = self.default_out_format
        filename = filename + '.' + img_format

        if dtype and img_data.dtype != dtype:
            img_data = np.array(img_data, dtype=dtype)

        if img_format == 'tiff' and _USING_PLUGIN_TIFFFILE:
            img_data = exposure.rescale_intensity(img_data, out_range='uint16')
            skio.imsave(filename, img_data, plugin='tifffile')
        else:
            img_data = exposure.rescale_intensity(img_data, out_range='uint16')
            skio.imsave(filename, img_data)

        return filename

def rgb2he2(img):
    # This implementation follows http://web.hku.hk/~ccsigma/color-deconv/color-deconv.html

    assert (img.ndim == 3)
    assert (img.shape[2] == 3)

    height, width, _ = img.shape

    img = -np.log((img + 1.0) / img.max())

    # the following lines are replaced with the final result,
    # to speed up computations
    #
    # he = np.array([0.550, 0.758, 0.351]); he /= norm(he)
    # eo = np.array([0.398, 0.634, 0.600]); eo /= norm(eo)
    # bg = np.array([0.754, 0.077, 0.652]); bg /= norm(bg)
    #
    # M = np.hstack((he.reshape(3,1), eo.reshape(3,1), bg.reshape(3,1)))
    # D = alg.inv(M)
    #
    D = np.array([[ 1.92129515,  1.00941672, -2.34107612],
                  [-2.34500192,  0.47155124,  2.65616872],
                  [ 1.21495282, -0.99544467,  0.2459345 ]])

    rgb = img.swapaxes(2, 0).reshape((3, height*width))
    heb = np.dot(D, rgb)
    res_img = heb.reshape((3, width, height)).swapaxes(0, 2)

    return rescale_intensity(res_img[:,:,0], out_range=(0,1)), \
           rescale_intensity(res_img[:,:,1], out_range=(0,1)), \
           rescale_intensity(res_img[:,:,2], out_range=(0,1))

	def handle(self, *args, **options):
		# vars
		experiment_name = options['expt']
		series_name = options['series']
		t = options['t']

		if experiment_name!='' and series_name!='':
			experiment = Experiment.objects.get(name=experiment_name)
			series = experiment.series.get(name=series_name)

			# select composite
			composite = series.composites.get()

			zmean = exposure.rescale_intensity(composite.gons.get(channel__name='-zmean', t=t).load() * 1.0)
			zmod = exposure.rescale_intensity(composite.gons.get(channel__name='-zmod', t=t).load() * 1.0)

			zdiff = np.zeros(zmean.shape)
			for unique in np.unique(zmod):
				print(unique, len(np.unique(zmod)))
				zdiff[zmod==unique] = np.mean(zmean[zmod==unique]) / np.sum(zmean)

			plt.imshow(zdiff, cmap='Greys_r')
			plt.show()

			# imsave('zdiff.tiff', zdiff)

		else:
			print('Please enter an experiment')

def juntarcanais(c1, c2):


    h = exposure.rescale_intensity(c1, out_range=(0, 1))
    d = exposure.rescale_intensity(c2, out_range=(0, 1))
    zdh = np.dstack((np.zeros_like(h), d, h))

    return zdh

	def handle(self, *args, **options):
		# vars
		experiment_name = options['expt']
		series_name = options['series']
		t = options['t']

		R = 1
		delta_z = -8
		# sigma = 5

		if experiment_name!='' and series_name!='':
			experiment = Experiment.objects.get(name=experiment_name)
			series = experiment.series.get(name=series_name)

			# select composite
			composite = series.composites.get()

			# load gfp
			gfp_gon = composite.gons.get(t=t, channel__name='0')
			gfp_start = exposure.rescale_intensity(gfp_gon.load() * 1.0)
			print('loaded gfp...')

			# load bf
			bf_gon = composite.gons.get(t=t, channel__name='1')
			bf = exposure.rescale_intensity(bf_gon.load() * 1.0)
			print('loaded bf...')

			for sigma in [0, 5, 10, 20]:
				gfp = gf(gfp_start, sigma=sigma) # <<< SMOOTHING
				for level in range(gfp.shape[2]):
					print('level {} {}...'.format(R, level))
					gfp[:,:,level] = convolve(gfp[:,:,level], np.ones((R,R)))

				# initialise images
				Z = np.zeros(composite.series.shape(d=2), dtype=int)
				Zmean = np.zeros(composite.series.shape(d=2))
				Zbf = np.zeros(composite.series.shape(d=2))

				Z = np.argmax(gfp, axis=2) + delta_z

				# outliers
				Z[Z<0] = 0
				Z[Z>composite.series.zs-1] = composite.series.zs-1

				for level in range(bf.shape[2]):
					print('level {}...'.format(level))
					bf_level = bf[:,:,level]
					Zbf[Z==level] = bf_level[Z==level]

				Zmean = 1 - np.mean(gfp, axis=2) / np.max(gfp, axis=2)

				imsave('zbf_R-{}_sigma-{}_delta_z{}.png'.format(R, sigma, delta_z), Zbf)

			# plt.imshow(Zbf, cmap='Greys_r')
			# plt.show()

		else:
			print('Please enter an experiment')

def equalize_adapthist(image, ntiles_x=8, ntiles_y=8, clip_limit=0.01,
                       nbins=256):
    """Contrast Limited Adaptive Histogram Equalization.

    Parameters
    ----------
    image : array-like
        Input image.
    ntiles_x : int, optional
        Number of tile regions in the X direction.  Ranges between 2 and 16.
    ntiles_y : int, optional
        Number of tile regions in the Y direction.  Ranges between 2 and 16.
    clip_limit : float: optional
        Clipping limit, normalized between 0 and 1 (higher values give more
        contrast).
    nbins : int, optional
        Number of gray bins for histogram ("dynamic range").

    Returns
    -------
    out : ndarray
        Equalized image.

    Notes
    -----
    * The algorithm relies on an image whose rows and columns are even
      multiples of the number of tiles, so the extra rows and columns are left
      at their original values, thus  preserving the input image shape.
    * For color images, the following steps are performed:
       - The image is converted to LAB color space
       - The CLAHE algorithm is run on the L channel
       - The image is converted back to RGB space and returned
    * For RGBA images, the original alpha channel is removed.

    References
    ----------
    .. [1] http://tog.acm.org/resources/GraphicsGems/gems.html#gemsvi
    .. [2] https://en.wikipedia.org/wiki/CLAHE#CLAHE
    """
    args = [None, ntiles_x, ntiles_y, clip_limit * nbins, nbins]
    if image.ndim > 2:
        lab_img = color.rgb2lab(skimage.img_as_float(image))
        l_chan = lab_img[:, :, 0]
        l_chan /= np.max(np.abs(l_chan))
        l_chan = skimage.img_as_uint(l_chan)
        args[0] = rescale_intensity(l_chan, out_range=(0, NR_OF_GREY - 1))
        new_l = _clahe(*args).astype(float)
        new_l = rescale_intensity(new_l, out_range=(0, 100))
        lab_img[:new_l.shape[0], :new_l.shape[1], 0] = new_l
        image = color.lab2rgb(lab_img)
        image = rescale_intensity(image, out_range=(0, 1))
    else:
        image = skimage.img_as_uint(image)
        args[0] = rescale_intensity(image, out_range=(0, NR_OF_GREY - 1))
        out = _clahe(*args)
        image[:out.shape[0], :out.shape[1]] = out
        image = rescale_intensity(image)
    return image

 def _color_correction(self, band, band_id, low, coverage):
     self.output("Color correcting band %s" % band_id, normal=True, color='green', indent=1)
     p_low, cloud_cut_low = self._percent_cut(band, low, 100 - (coverage * 3 / 4))
     temp = numpy.zeros(numpy.shape(band), dtype=numpy.uint16)
     cloud_divide = 65000 - coverage * 100
     mask = numpy.logical_and(band < cloud_cut_low, band > 0)
     temp[mask] = rescale_intensity(band[mask], in_range=(p_low, cloud_cut_low), out_range=(256, cloud_divide))
     temp[band >= cloud_cut_low] = rescale_intensity(band[band >= cloud_cut_low], out_range=(cloud_divide, 65535))
     return temp

def equalize_adapthist(image, ntiles_x=8, ntiles_y=8, clip_limit=0.01,
                       nbins=256):
    args = [None, ntiles_x, ntiles_y, clip_limit * nbins, nbins]
    image = skimage.img_as_uint(image)
    args[0] = rescale_intensity(image, out_range=(0, NR_OF_GREY - 1))
    out = _clahe(*args)
    image[:out.shape[0], :out.shape[1]] = out
    image = rescale_intensity(image)
    return image

def equalize_adapthist(image, ntiles_x=8, ntiles_y=8, clip_limit=0.01,
                       nbins=256):
    """Contrast Limited Adaptive Histogram Equalization (CLAHE).

    An algorithm for local contrast enhancement, that uses histograms computed
    over different tile regions of the image. Local details can therefore be
    enhanced even in regions that are darker or lighter than most of the image.

    Parameters
    ----------
    image : array-like
        Input image.
    ntiles_x : int, optional
        Number of tile regions in the X direction.  Ranges between 1 and 16.
    ntiles_y : int, optional
        Number of tile regions in the Y direction.  Ranges between 1 and 16.
    clip_limit : float: optional
        Clipping limit, normalized between 0 and 1 (higher values give more
        contrast).
    nbins : int, optional
        Number of gray bins for histogram ("dynamic range").

    Returns
    -------
    out : ndarray
        Equalized image.

    See Also
    --------
    equalize_hist, rescale_intensity

    Notes
    -----
    * For color images, the following steps are performed:
       - The image is converted to HSV color space
       - The CLAHE algorithm is run on the V (Value) channel
       - The image is converted back to RGB space and returned
    * For RGBA images, the original alpha channel is removed.
    * The CLAHE algorithm relies on image blocks of equal size.  This may
      result in extra border pixels that would not be handled.  In that case,
      we pad the image with a repeat of the border pixels, apply the
      algorithm, and then trim the image to original size.

    References
    ----------
    .. [1] http://tog.acm.org/resources/GraphicsGems/gems.html#gemsvi
    .. [2] https://en.wikipedia.org/wiki/CLAHE#CLAHE
    """
    image = skimage.img_as_uint(image)
    image = rescale_intensity(image, out_range=(0, NR_OF_GREY - 1))
    out = _clahe(image, ntiles_x, ntiles_y, clip_limit * nbins, nbins)
    image[:out.shape[0], :out.shape[1]] = out
    image = skimage.img_as_float(image)
    return rescale_intensity(image)

 def _get_scalebar(self):
     """Get the length in pixels of the image scale bar"""
     box=(0,419,519,520) #row where scalebar exists
     im=self.crop_image(box=box, copy=True)
     im=skimage.img_as_float(im)
     im=exposure.rescale_intensity(im,in_range=(0.49,0.5)) #saturate black and white pixels
     im=exposure.rescale_intensity(im) #make sure they're black and white
     im=np.diff(im[0]) #1d numpy array, differences
     lim=[np.where(im>0.9)[0][0],
          np.where(im<-0.9)[0][0]] #first occurance of both cases
     assert len(lim)==2, 'Couldn\'t find scalebar'
     return lim[1]-lim[0]

def watershed(image):
    """ the watershed algorithm """
    if len(image.shape) != 2:
        raise TypeError("The input image must be gray-scale ")

    h, w = image.shape
    image = cv2.equalizeHist(image)
    image = denoise_bilateral(image, sigma_range=0.1, sigma_spatial=10)
    image = rescale_intensity(image)
    image = img_as_ubyte(image)
    image = rescale_intensity(image)
    # com.debug_im(image)

    _, thres = cv2.threshold(image, 80, 255, cv2.THRESH_BINARY_INV)

    distance = ndi.distance_transform_edt(thres)
    local_maxi = peak_local_max(distance, indices=False,
                                labels=thres,
                                min_distance=5)

    # com.debug_im(thres)
    # implt = plt.imshow(-distance, cmap=plt.cm.jet, interpolation='nearest')
    # plt.show()

    markers = ndi.label(local_maxi, np.ones((3, 3)))[0]
    labels = ws(-distance, markers, mask=thres)
    labels = np.uint8(labels)
    # result = np.round(255.0 / np.amax(labels) * labels).astype(np.uint8)
    # com.debug_im(result)

    segments = []
    for idx in range(1, np.amax(labels) + 1):

        indices = np.where(labels == idx)
        left = np.amin(indices[1])
        right = np.amax(indices[1])
        top = np.amin(indices[0])
        down = np.amax(indices[0])

        # region = labels[top:down, left:right]
        # m = (region > 0) & (region != idx)
        # region[m] = 0
        # region[region >= 1] = 1
        region = image[top:down, left:right]
        cont = Contour(mask=region)
        cont.lt = [left, top]
        cont.rb = [right, down]
        segments.append(cont)

    return segments

def proc_mbi(imgarray):
    # Normalize image:
    img = img_as_float(imgarray,force_copy=True)
    # Image equalization (Contrast stretching):
    p2,p98 = np.percentile(img, (2,98))
    img = exposure.rescale_intensity(img, in_range=(p2, p98), out_range=(0, 1))
    # Gamma correction:
    #img = exposure.adjust_gamma(img, 0.5)
    # Or Sigmoid correction:
    img = exposure.adjust_sigmoid(img)
    
    print "Init Morph Proc..."
    sizes = range(2,40,5)
    angles = [0,18,36,54,72,90,108,126,144,162]
    szimg = img.shape
    all_thr = np.zeros((len(sizes),szimg[0], szimg[1])).astype('float64')
    all_dmp = np.zeros((len(sizes) - 1,szimg[0], szimg[1])).astype('float64')
    
    idx = 0
    for sz in sizes:
        print sz
        builds_by_size = np.zeros(szimg).astype('float64')
        for ang in angles:
            print ang
            stel = ia870.iaseline(sz, ang)
            oprec = opening_by_reconstruction(img, stel)
            thr = np.absolute(img-oprec)
            builds_by_size += thr
        all_thr[idx,:,:] = (builds_by_size / len(angles))
        if idx>0:
            all_dmp[idx-1,:,:] = all_thr[idx,:,:] - all_thr[idx-1,:,:]
        idx += 1
    mbi = np.mean(all_dmp, axis=0)
    return mbi