def find_blobs(raw_img, args):
'''function performing two dimensional connected component analysis on an image.
Args:
img (ndarray): original image to be analyzed
args (Arguments instance): defined the threshold value to binarze the image
Returns:
an instance of the Components class, a stencil containing the final labels of components,
and a stencil containing the labels before eliminating equivalences
'''
# dimensions
height = raw_img.shape[0]
width = raw_img.shape[1]
img = processing.threshold(raw_img, args)
# adding column of zeros to prevent left and right most blob
# form being mistaken as one
zeros = np.zeros((height, 1))
img = np.concatenate((img, zeros), axis=1)
width += 1
size = height * width
img = img.reshape(size)
stencil = np.zeros(size, dtype=int)
labels = DisjointSet(n_labels=1)
# first pass
for i in range(size):
if img[i] != 0:
# if a neighboring pixel is labeled the investigated pixel is given the same label
# Note: when iterating from top left to bottom right indices to the right bottom of investigated
# pixel cannot be labeled before this pixel
for j in [i - 1, i - width, i - width - 1, i - width + 1]:
if j < 0 or j >= size:
continue
if stencil[j] != 0 and stencil[i] == 0: # connection
stencil[i] = stencil[j]
elif stencil[j] != 0 and stencil[j] != stencil[i]: # conflict
labels.unite(stencil[i], stencil[j])
else: # no connection nor conflict
continue
# if no neighboring pixel is labeled the investigated pixel is give a new label
if stencil[i] == 0:
new_label = labels.next()
stencil[i] = new_label
labels.add(new_label)
# uncomment to print show labels after first pass
# first_pass = deepcopy(stencil.reshape((height, width)))
# second pass to eliminate equivalences
eq = labels.get_equivalents()
for label in eq.keys():
stencil[stencil == label] = eq[label]
# reshaping stencil
stencil = stencil.reshape((height, width))
# SCIPY VARIANT
#stencil = measure.label(img, background=0)
# count pixels in blobs, calculate median to filter blobs
final_labels = np.arange(1, np.max(stencil) + 1)
pixel_counts = []
for label in final_labels:
pixel_counts.append(np.sum(stencil == label))
pixel_counts = np.array(pixel_counts)
min_allowed_pixels = np.median(
pixel_counts[pixel_counts > 0]) / 5 # arbitrary; seems to work well
# filter final lables and stencil
final_labels = np.array(final_labels)[pixel_counts >= min_allowed_pixels]
new_stencil = np.zeros_like(stencil)
for i, label in enumerate(final_labels):
new_stencil[stencil == label] = i + 1
stencil = new_stencil
# construct boxes around letters
bounding_boxes = get_bboxes(stencil)
# chars = get_chars_from_boxes(raw, bounding_boxes)
# extract characters from image in correct order
#chars = []
#bounding_boxes = []
#while boxes:
# box = heappop(boxes)
# chars.append(raw[box[2]:box[3], box[0]:box[1]])
# bounding_boxes.append(box)
return Components(boxes=bounding_boxes, img=raw_img, stencil=stencil)
