python类binary_closing()的实例源码

def get_segmented_lungs(im, plot=False):
    # Step 1: Convert into a binary image.
    binary = im < -400
    # Step 2: Remove the blobs connected to the border of the image.
    cleared = clear_border(binary)
    # Step 3: Label the image.
    label_image = label(cleared)
    # Step 4: Keep the labels with 2 largest areas.
    areas = [r.area for r in regionprops(label_image)]
    areas.sort()
    if len(areas) > 2:
        for region in regionprops(label_image):
            if region.area < areas[-2]:
                for coordinates in region.coords:
                       label_image[coordinates[0], coordinates[1]] = 0
    binary = label_image > 0
    # Step 5: Erosion operation with a disk of radius 2. This operation is seperate the lung nodules attached to the blood vessels.
    selem = disk(2)
    binary = binary_erosion(binary, selem)
    # Step 6: Closure operation with a disk of radius 10. This operation is    to keep nodules attached to the lung wall.
    selem = disk(10) # CHANGE BACK TO 10
    binary = binary_closing(binary, selem)
    # Step 7: Fill in the small holes inside the binary mask of lungs.
    edges = roberts(binary)
    binary = ndi.binary_fill_holes(edges)
    # Step 8: Superimpose the binary mask on the input image.
    get_high_vals = binary == 0
    im[get_high_vals] = -2000
    return im, binary

def image_filter(img):
  img2 = img.copy();
  img2[img2 < 30] = 100;
  img2 = exp.smooth_image(img2, sigma = 1.0);
  #plt.figure(6); plt.clf();
  #plt.imshow(img2);

  # threshold image and take zero smaller components..

  th = img2 < 92;

  th2 = morph.binary_closing(th, morph.diamond(1))

  label = meas.label(th2, background=0)
  #plt.imshow(mask)

  bs = meas.regionprops(label+1);
  area = np.array([prop.area for prop in bs]);
  if len(area) > 0:
    mask = np.logical_and(label > -1, label != np.argsort(area)[-1]);

    img2[mask] = 100;

  img2[:2,:] = 100; img2[-2:,:] = 100;
  img2[:,:2] = 100; img2[:,-2:] = 100;

  #plt.figure(6); plt.clf();
  #plt.subplot(1,2,1);
  #plt.imshow(img2, vmin = 84, vmax = 92, cmap = plt.cm.gray)
  #plt.subplot(1,2,2);
  #plt.imshow(img2);
  return img2;

def image_filter(img):
  img2 = img.copy();
  img2[img2 < 30] = 100;
  img2 = exp.smooth_image(img2, sigma = 1.0);
  #plt.figure(6); plt.clf();
  #plt.imshow(img2);

  # threshold image and take zero smaller components..

  th = img2 < 92;

  th2 = morph.binary_closing(th, morph.diamond(1))

  label = meas.label(th2, background=0)
  #plt.imshow(mask)

  bs = meas.regionprops(label+1);
  area = np.array([prop.area for prop in bs]);
  if len(area) > 0:
    mask = np.logical_and(label > -1, label != np.argsort(area)[-1]);

    img2[mask] = 100;

  img2[:2,:] = 100; img2[-2:,:] = 100;
  img2[:,:2] = 100; img2[:,-2:] = 100;

  #plt.figure(6); plt.clf();
  #plt.subplot(1,2,1);
  #plt.imshow(img2, vmin = 84, vmax = 92, cmap = plt.cm.gray)
  #plt.subplot(1,2,2);
  #plt.imshow(img2);
  return img2;

def get_segmented_lungs(im, plot=False):
    # Step 1: Convert into a binary image.
    binary = im < -400
    # Step 2: Remove the blobs connected to the border of the image.
    cleared = clear_border(binary)
    # Step 3: Label the image.
    label_image = label(cleared)
    # Step 4: Keep the labels with 2 largest areas.
    areas = [r.area for r in regionprops(label_image)]
    areas.sort()
    if len(areas) > 2:
        for region in regionprops(label_image):
            if region.area < areas[-2]:
                for coordinates in region.coords:
                       label_image[coordinates[0], coordinates[1]] = 0
    binary = label_image > 0
    # Step 5: Erosion operation with a disk of radius 2. This operation is seperate the lung nodules attached to the blood vessels.
    selem = disk(2)
    binary = binary_erosion(binary, selem)
    # Step 6: Closure operation with a disk of radius 10. This operation is    to keep nodules attached to the lung wall.
    selem = disk(10) # CHANGE BACK TO 10
    binary = binary_closing(binary, selem)
    # Step 7: Fill in the small holes inside the binary mask of lungs.
    edges = roberts(binary)
    binary = ndi.binary_fill_holes(edges)
    # Step 8: Superimpose the binary mask on the input image.
    get_high_vals = binary == 0
    im[get_high_vals] = -2000
    return im, binary

def compute_binary_mask_lasseck(spectrogram, threshold):
    # normalize to [0, 1)
    norm_spectrogram = normalize(spectrogram)

    # median clipping
    binary_image = median_clipping(norm_spectrogram, threshold)

    # closing binary image (dilation followed by erosion)
    binary_image = morphology.binary_closing(binary_image, selem=np.ones((4, 4)))

    # dialate binary image
    binary_image = morphology.binary_dilation(binary_image, selem=np.ones((4, 4)))

    # apply median filter
    binary_image = filters.median(binary_image, selem=np.ones((2, 2)))

    # remove small objects
    binary_image = morphology.remove_small_objects(binary_image, min_size=32, connectivity=1)

    mask = np.array([np.max(col) for col in binary_image.T])
    mask = smooth_mask(mask)

    return mask


# TODO: This method needs some real testing