示例#1
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 def setSegmentation(self, segmentation, cseg=0):
     """Set the line segmentation."""
     # reorder the labels by the x center of bounding box
     segmentation = common.renumber_labels_by_boxes(
         segmentation, key=lambda x: mean((x[1].start, x[1].stop)))
     # compute the bounding boxes in order
     boxes = [None] + measurements.find_objects(segmentation)
     n = len(boxes)
     # now consider groups of boxes
     groups = []
     for i in range(1, n):
         for r in range(1, self.maxrange + 1):
             box = None
             gap = 0
             labels = []
             for j in range(i, min(n, i + r)):
                 if box is not None:
                     gap = max(gap, boxes[j][1].start - box[1].stop)
                 box = sl.union(box, boxes[j])
                 labels.append(j)
             # skip if two constituent boxes have too large a gap between them
             if gap > self.maxdist: continue
             a = sl.aspect(box)
             # skip if the aspect ratio is wrong
             if 1.0 / a > self.maxaspect: continue
             groups.append((box, labels))
     # compute some statistics
     mw = median([sl.dim0(g[0]) for g in groups])
     # now select based on statistics
     groups = [g for g in groups if sl.dim1(g[0]) < self.maxwidth * mw]
     # now we have a list of candidate groups
     self.segmentation = segmentation
     self.groups = groups
     self.clearLattice()
     return len(self.groups)
示例#2
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def compute_lines(segmentation, scale, min_width):
    """Given a line segmentation map, computes a list
    of tuples consisting of 2D slices and masked images."""
    lobjects = morph.find_objects(segmentation)
    lines = []
    for i, o in enumerate(lobjects):
        if o is None: continue
        if sl.dim1(o) < min_width * scale or sl.dim0(o) < 0.5 * scale:
            print(sl.dim1(o), sl.dim0(o))
            continue
        mask = (segmentation[o] == i + 1)
        if amax(mask) == 0: continue
        result = record()
        result.label = i + 1
        result.bounds = o
        result.mask = mask
        lines.append(result)
    return lines
示例#3
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def compute_lines(segmentation,scale):
    """Given a line segmentation map, computes a list
    of tuples consisting of 2D slices and masked images."""
    lobjects = morph.find_objects(segmentation)
    lines = []
    for i,o in enumerate(lobjects):
        if o is None: continue
        if sl.dim1(o)<2*scale or sl.dim0(o)<scale: continue
        mask = (segmentation[o]==i+1)
        if amax(mask)==0: continue
        result = record()
        result.label = i+1
        result.bounds = o
        result.mask = mask
        lines.append(result)
    return lines
示例#4
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def show_lines(image, lines, lsort):
    """Overlays the computed lines on top of the image, for debugging
    purposes."""
    ys, xs = [], []
    clf()
    cla()
    imshow(image)
    for i in range(len(lines)):
        l = lines[lsort[i]]
        y, x = sl.center(l.bounds)
        xs.append(x)
        ys.append(y)
        o = l.bounds
        r = matplotlib.patches.Rectangle((o[1].start, o[0].start),
                                         edgecolor='r',
                                         fill=0,
                                         width=sl.dim1(o),
                                         height=sl.dim0(o))
        gca().add_patch(r)
    h, w = image.shape
    ylim(h, 0)
    xlim(0, w)
    plot(xs, ys)
示例#5
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def show_lines(image,lines,lsort):
    """Overlays the computed lines on top of the image, for debugging
    purposes."""
    ys,xs = [],[]
    clf(); cla()
    imshow(image)
    for i in range(len(lines)):
        l = lines[lsort[i]]
        y,x = sl.center(l.bounds)
        xs.append(x)
        ys.append(y)
        o = l.bounds
        r = matplotlib.patches.Rectangle((o[1].start,o[0].start),edgecolor='r',fill=0,width=sl.dim1(o),height=sl.dim0(o))
        gca().add_patch(r)
    h,w = image.shape
    ylim(h,0); xlim(0,w)
    plot(xs,ys)