Rectangular bounding box around blobs in monochrome using python

Question

I have several monochrome images (black and white, not gray) with several strange shaped objects. I am trying to extract each object using python27, PIL, scipy and numpy and the following method:

• Set a bounding box around each merged object
• “Extract” each object as an array - for each object / bounding block

I looked at http://www.scipy.org/Cookbook/Watershed and http://scikits-image.org/docs/dev/auto_examples/plot_contours.html and they work, but I am particularly interested in the bounding box was rectangular to ensure that any "slightly disabled" bits are included in the bounding box. Ideally, to deal with disabled bits (like lower left drops), I would have some kind of threshold control. Any ideas on which toolbox works best?

Answer 1

Here Joe Kington's find_paws function is used.

 import numpy as np import scipy.ndimage as ndimage import scipy.spatial as spatial import scipy.misc as misc import matplotlib.pyplot as plt import matplotlib.patches as patches class BBox(object): def __init__(self, x1, y1, x2, y2): ''' (x1, y1) is the upper left corner, (x2, y2) is the lower right corner, with (0, 0) being in the upper left corner. ''' if x1 > x2: x1, x2 = x2, x1 if y1 > y2: y1, y2 = y2, y1 self.x1 = x1 self.y1 = y1 self.x2 = x2 self.y2 = y2 def taxicab_diagonal(self): ''' Return the taxicab distance from (x1,y1) to (x2,y2) ''' return self.x2 - self.x1 + self.y2 - self.y1 def overlaps(self, other): ''' Return True iff self and other overlap. ''' return not ((self.x1 > other.x2) or (self.x2 < other.x1) or (self.y1 > other.y2) or (self.y2 < other.y1)) def __eq__(self, other): return (self.x1 == other.x1 and self.y1 == other.y1 and self.x2 == other.x2 and self.y2 == other.y2) def find_paws(data, smooth_radius = 5, threshold = 0.0001): # /questions/12426/how-can-i-improve-my-paw-detection """Detects and isolates contiguous regions in the input array""" # Blur the input data a bit so the paws have a continous footprint data = ndimage.uniform_filter(data, smooth_radius) # Threshold the blurred data (this needs to be a bit > 0 due to the blur) thresh = data > threshold # Fill any interior holes in the paws to get cleaner regions... filled = ndimage.morphology.binary_fill_holes(thresh) # Label each contiguous paw coded_paws, num_paws = ndimage.label(filled) # Isolate the extent of each paw # find_objects returns a list of 2-tuples: (slice(...), slice(...)) # which represents a rectangular box around the object data_slices = ndimage.find_objects(coded_paws) return data_slices def slice_to_bbox(slices): for s in slices: dy, dx = s[:2] yield BBox(dx.start, dy.start, dx.stop+1, dy.stop+1) def remove_overlaps(bboxes): ''' Return a set of BBoxes which contain the given BBoxes. When two BBoxes overlap, replace both with the minimal BBox that contains both. ''' # list upper left and lower right corners of the Bboxes corners = [] # list upper left corners of the Bboxes ulcorners = [] # dict mapping corners to Bboxes. bbox_map = {} for bbox in bboxes: ul = (bbox.x1, bbox.y1) lr = (bbox.x2, bbox.y2) bbox_map[ul] = bbox bbox_map[lr] = bbox ulcorners.append(ul) corners.append(ul) corners.append(lr) # Use a KDTree so we can find corners that are nearby efficiently. tree = spatial.KDTree(corners) new_corners = [] for corner in ulcorners: bbox = bbox_map[corner] # Find all points which are within a taxicab distance of corner indices = tree.query_ball_point( corner, bbox_map[corner].taxicab_diagonal(), p = 1) for near_corner in tree.data[indices]: near_bbox = bbox_map[tuple(near_corner)] if bbox != near_bbox and bbox.overlaps(near_bbox): # Expand both bboxes. # Since we mutate the bbox, all references to this bbox in # bbox_map are updated simultaneously. bbox.x1 = near_bbox.x1 = min(bbox.x1, near_bbox.x1) bbox.y1 = near_bbox.y1 = min(bbox.y1, near_bbox.y1) bbox.x2 = near_bbox.x2 = max(bbox.x2, near_bbox.x2) bbox.y2 = near_bbox.y2 = max(bbox.y2, near_bbox.y2) return set(bbox_map.values()) if __name__ == '__main__': fig = plt.figure() ax = fig.add_subplot(111) data = misc.imread('image.png') im = ax.imshow(data) data_slices = find_paws(255-data, smooth_radius = 20, threshold = 22) bboxes = remove_overlaps(slice_to_bbox(data_slices)) for bbox in bboxes: xwidth = bbox.x2 - bbox.x1 ywidth = bbox.y2 - bbox.y1 p = patches.Rectangle((bbox.x1, bbox.y1), xwidth, ywidth, fc = 'none', ec = 'red') ax.add_patch(p) plt.show() 

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