Esempio n. 1
0
def boost_contrast_local(image, radius=3):
    """Returns a new image with contrast boosted locally.
    
    Args:
        image: assumed to be grayscale.
        radius: the size of the radial neighborhood to use.
    """
    return rank.enhance_contrast(image, disk(radius))
Esempio n. 2
0
def prepare_image(filename):
  img = io.imread(filename)
  img_out = rgb2gray(img)
  img_out = transform.resize(img_out, (IMAGE_DIM,IMAGE_DIM))

  img_out = enhance_contrast(img_out, disk(5))
  
  t = lambda x : 1.0  - x/255.
  t = np.vectorize(t)
  
  img_out = t(img_out)

  image = np.empty((1, 1, IMAGE_DIM,IMAGE_DIM), dtype=np.float32)
  for i in range(0,IMAGE_DIM):
    for j in range(0,IMAGE_DIM):
      image[:,:,i,j] = img_out[i,j]
  return image
Esempio n. 3
0
def prepare_image(filename):
    img = io.imread(filename)
    img_out = rgb2gray(img)
    img_out = transform.resize(img_out, (IMAGE_DIM, IMAGE_DIM))

    img_out = enhance_contrast(img_out, disk(5))

    t = lambda x: 1.0 - x / 255.
    t = np.vectorize(t)

    img_out = t(img_out)

    image = np.empty((1, 1, IMAGE_DIM, IMAGE_DIM), dtype=np.float32)
    for i in range(0, IMAGE_DIM):
        for j in range(0, IMAGE_DIM):
            image[:, :, i, j] = img_out[i, j]
    return image
Esempio n. 4
0
def label_from_thresh(frame, thresh, parameters):

    smooth = parameters['smooth']
    min_distance = np.int(parameters['nuc_distance'])
    image = rank.median(frame, disk(smooth))
    image = rank.enhance_contrast(image, disk(smooth))
    im_max = image.max()
    if im_max < thresh:
        return np.zeros(image.shape, dtype=np.int32)
    else:
        image = image > thresh
        distance = ndimage.distance_transform_edt(image)
        local_maxi = peak_local_max(distance,
                                    footprint=disk(min_distance),
                                    #min_distance=min_distance,
                                    indices=False, labels=image)
        markers = ndimage.label(local_maxi)[0]
        return watershed(-distance, markers, mask=image)
    ax.axis('off')
"""

.. image:: PLOT2RST.current_figure

The morphological contrast enhancement filter replaces the central pixel by the
local maximum if the original pixel value is closest to local maximum,
otherwise by the minimum local.

"""

from skimage.filter.rank import enhance_contrast

noisy_image = img_as_ubyte(data.camera())

enh = enhance_contrast(noisy_image, disk(5))

fig = plt.figure(figsize=[10, 7])
plt.subplot(2, 2, 1)
plt.imshow(noisy_image, cmap=plt.cm.gray)
plt.title('Original')
plt.axis('off')

plt.subplot(2, 2, 3)
plt.imshow(enh, cmap=plt.cm.gray)
plt.title('Local morphological contrast enhancement')
plt.axis('off')

plt.subplot(2, 2, 2)
plt.imshow(noisy_image[200:350, 350:450], cmap=plt.cm.gray)
plt.axis('off')
Esempio n. 6
0
File: a.py Progetto: ja999/sem5
from skimage import data
from skimage.morphology import disk
from skimage.filter.rank import autolevel, enhance_contrast
# Load test image
ima = data.camera()
# Stretch image contrast locally
auto = enhance_contrast(ima, disk(20))
"""

.. image:: PLOT2RST.current_figure

The morphological contrast enhancement filter replaces the central pixel by the
local maximum if the original pixel value is closest to local maximum,
otherwise by the minimum local.

"""

from skimage.filter.rank import enhance_contrast

noisy_image = img_as_ubyte(data.camera())

enh = enhance_contrast(noisy_image, disk(5))

fig = plt.figure(figsize=[10, 7])
plt.subplot(2, 2, 1)
plt.imshow(noisy_image, cmap=plt.cm.gray)
plt.title('Original')
plt.axis('off')

plt.subplot(2, 2, 3)
plt.imshow(enh, cmap=plt.cm.gray)
plt.title('Local morphological contrast enhancement')
plt.axis('off')

plt.subplot(2, 2, 2)
plt.imshow(noisy_image[200:350, 350:450], cmap=plt.cm.gray)
plt.axis('off')
def preprocessing(image, smooth_size, folder):
    """
    'The image low contrast and under segmentation
    problem is not yet addressed by most of the researchers'
    
    'Other researchers also proposed different method to
    remedy the problem of watershed.  Li, El-
    moataz, Fadili, and Ruan, S. (2003) proposed an improved
    image segmentation approach based 
    on level set and mathematical morphology'
    
    THE SPHERES MUST BE ALMOST ALONG THE SAME PLANES IN Z DIRECTION
    IF THEY ARE TOUCHING AND OVERLAP, WHILE BEING ALMOST MERGED
    IT IS IMPOSSIBLE TO RESOLVE THEM
    
    ONE IDEA MIGHT BE TO DETECT CENTRES ALONG ONE AXIS AND THEN ANOTHER
    AFTER ALL THE CENTRES WERE FOUND COMBINE THEM SOMEHOW... 
    """
    from skimage.restoration import denoise_tv_chambolle
    
    dim = int(image.shape[0] / 50.)
    smoothed = rank.median(image, disk(smooth_size))
    #smoothed = denoise_tv_chambolle(image, weight=0.002)
    smoothed = rank.enhance_contrast(smoothed, disk(smooth_size))
    
    pl.subplot(2, 3, 1)
    pl.title("after median")
    pl.imshow(smoothed)
    pl.gray()
    # If after smoothing the "dot" disappears
    # use the image value
    
    # TODO: wat do with thresh?
    try:
        im_max = smoothed.max()
        thresh = threshold_otsu(image)
    except:
        im_max = image.max()
        thresh = threshold_otsu(image)

    
    if im_max < thresh:
        labeled = np.zeros(smoothed.shape, dtype=np.int32)
        
    else:
        binary = smoothed > thresh
        
        # TODO: this array size is the fault of errors
        bin_open = binary_opening(binary, np.ones((dim, dim)), iterations=5)
        bin_close = binary_closing(bin_open, np.ones((5,5)), iterations=5)
        
        pl.subplot(2, 3, 2)
        pl.title("threshold")
        pl.imshow(binary, interpolation='nearest')
        pl.subplot(2, 3, 3)
        pl.title("opening")
        pl.imshow(bin_open, interpolation='nearest')
        pl.subplot(2, 3, 4)
        pl.title("closing")
        pl.imshow(bin_close, interpolation='nearest')
        
        distance = ndimage.distance_transform_edt(bin_open)
        local_maxi = peak_local_max(distance,
                                    indices=False, labels=bin_open)
        
        markers = ndimage.label(local_maxi)[0]
        
        labeled = watershed(-distance, markers, mask=bin_open)
        pl.subplot(2, 3, 5)
        pl.title("label")
        pl.imshow(labeled)
        #pl.show()
        pl.savefig(folder)
        pl.close('all')

        #misc.imsave(folder, labeled)
#         labels_rw = random_walker(bin_close, markers, mode='cg_mg')
#          
#         pl.imshow(labels_rw, interpolation='nearest')
#         pl.show()

    return labeled
def preprocessing(image, smooth_size, folder):
    """
    'The image low contrast and under segmentation
    problem is not yet addressed by most of the researchers'
    
    'Other researchers also proposed different method to
    remedy the problem of watershed.  Li, El-
    moataz, Fadili, and Ruan, S. (2003) proposed an improved
    image segmentation approach based 
    on level set and mathematical morphology'
    
    THE SPHERES MUST BE ALMOST ALONG THE SAME PLANES IN Z DIRECTION
    IF THEY ARE TOUCHING AND OVERLAP, WHILE BEING ALMOST MERGED
    IT IS IMPOSSIBLE TO RESOLVE THEM
    
    ONE IDEA MIGHT BE TO DETECT CENTRES ALONG ONE AXIS AND THEN ANOTHER
    AFTER ALL THE CENTRES WERE FOUND COMBINE THEM SOMEHOW... 
    """
    from skimage.restoration import denoise_tv_chambolle

    dim = int(image.shape[0] / 50.)
    smoothed = rank.median(image, disk(smooth_size))
    #smoothed = denoise_tv_chambolle(image, weight=0.002)
    smoothed = rank.enhance_contrast(smoothed, disk(smooth_size))

    pl.subplot(2, 3, 1)
    pl.title("after median")
    pl.imshow(smoothed)
    pl.gray()
    # If after smoothing the "dot" disappears
    # use the image value

    # TODO: wat do with thresh?
    try:
        im_max = smoothed.max()
        thresh = threshold_otsu(image)
    except:
        im_max = image.max()
        thresh = threshold_otsu(image)

    if im_max < thresh:
        labeled = np.zeros(smoothed.shape, dtype=np.int32)

    else:
        binary = smoothed > thresh

        # TODO: this array size is the fault of errors
        bin_open = binary_opening(binary, np.ones((dim, dim)), iterations=5)
        bin_close = binary_closing(bin_open, np.ones((5, 5)), iterations=5)

        pl.subplot(2, 3, 2)
        pl.title("threshold")
        pl.imshow(binary, interpolation='nearest')
        pl.subplot(2, 3, 3)
        pl.title("opening")
        pl.imshow(bin_open, interpolation='nearest')
        pl.subplot(2, 3, 4)
        pl.title("closing")
        pl.imshow(bin_close, interpolation='nearest')

        distance = ndimage.distance_transform_edt(bin_open)
        local_maxi = peak_local_max(distance, indices=False, labels=bin_open)

        markers = ndimage.label(local_maxi)[0]

        labeled = watershed(-distance, markers, mask=bin_open)
        pl.subplot(2, 3, 5)
        pl.title("label")
        pl.imshow(labeled)
        #pl.show()
        pl.savefig(folder)
        pl.close('all')

        #misc.imsave(folder, labeled)


#         labels_rw = random_walker(bin_close, markers, mode='cg_mg')
#
#         pl.imshow(labels_rw, interpolation='nearest')
#         pl.show()

    return labeled