def compute_char_scores(self):
self.obj_classifier = CombinedClassifications()
cls = ImgObjClassifications({obj: obj.char_score for obj in self.objs})
self.obj_classifier.add(cls)
cls = ImgObjClassifications({obj: self.char_y_score(obj) for obj in self.objs})
self.obj_classifier.add(cls)
size_guess = self.character_size_guess()
cls = ImgObjClassifications({obj: self.char_size_score(obj, size_guess) for obj in self.objs})
self.obj_classifier.add(cls)
class Hierarchy(object):
def __init__(self, img):
self.img = img
self.binary_img = binary(img, invert=True)
knn_test = KNNTest(data_path)
self.char_test = Combiner([cont_test, mask_test, knn_test.test]).test
self.objs = [
ImgObj(x, self) for x in connected_components(self.binary_img) if self.good_component(x)
]
self.compute_char_scores()
self.group_words(score_threshold=0.2)
self.spell_corrector = SpellingCorrector('/usr/share/dict/words')
def good_component(self, com):
return (#0.5 < com.intensity and
10 < com.mask.rows and
5 < com.mask.cols and
0.5 <= float(com.mask.rows) / com.mask.cols <= 5.5)
def compute_char_scores(self):
self.obj_classifier = CombinedClassifications()
cls = ImgObjClassifications({obj: obj.char_score for obj in self.objs})
self.obj_classifier.add(cls)
cls = ImgObjClassifications({obj: self.char_y_score(obj) for obj in self.objs})
self.obj_classifier.add(cls)
size_guess = self.character_size_guess()
cls = ImgObjClassifications({obj: self.char_size_score(obj, size_guess) for obj in self.objs})
self.obj_classifier.add(cls)
def char_y_score(self, obj):
height = self.img.rows
m = height * height
score = 0.0
y = (obj.bounding_box[1] + obj.bounding_box[3]) / 2.0
for other in self.objs:
if obj is other:
continue
other_y = (other.bounding_box[1] + other.bounding_box[3]) / 2.0
score += (m - abs(y - other_y) ** 2) * self.obj_classifier.certainty(other)
return score / (m * (len(self.objs) - 1))
def character_size_guess(self):
r = self.obj_classifier.rankings()
n = int(.25 * len(r)) + 1
probable_chars = r[:n]
w, h = 0., 0.
for c in probable_chars:
w += c.bounding_box[2] - c.bounding_box[0] + 1
h += c.bounding_box[3] - c.bounding_box[1] + 1
return w / n, h / n
def char_size_score(self, obj, size_guess):
w_guess, h_guess = size_guess
w = obj.bounding_box[2] - obj.bounding_box[0] + 1
h = obj.bounding_box[3] - obj.bounding_box[1] + 1
w_score = interval_score(0.5 * w_guess, w, 2 * w_guess)
h_score = interval_score(0.5 * h_guess, h, 2 * h_guess)
return w_score * h_score
def group_words(self, score_threshold=0.5):
objs = sorted(self.objs, key=lambda c: c.bounding_box[0])
objs = [b for b in objs if score_threshold < self.obj_classifier.certainty(b)]
words = []
word = None
while objs:
if word is None:
c = objs.pop(0) # would be more efficient to do this backwards
h = c.bounding_box[3] - c.bounding_box[1]
y = (c.bounding_box[1] + c.bounding_box[3]) / 2.0
pos = (c.bounding_box[1], 1)
word = Word(pos=pos)
word.chars.append(c)
for n, d in enumerate(objs):
# Find vertical overlap to decide whether or not to
# break the line.
if c.bounding_box[1] < d.bounding_box[1]:
a0, a1 = c.bounding_box[1], c.bounding_box[3]
b0, b1 = d.bounding_box[1], d.bounding_box[3]
else:
a0, a1 = d.bounding_box[1], d.bounding_box[3]
b0, b1 = c.bounding_box[1], c.bounding_box[3]
overlap = max(0, a1 - b0) - max(0, a1 - b1)
overlap_percent = float(overlap) / min(a1 - a0, b1 - b0)
if 0.1 < overlap_percent:
if (c.bounding_box[2] - c.bounding_box[0] + d.bounding_box[2] - d.bounding_box[0]) / 4.0 \
< (d.bounding_box[0] - c.bounding_box[2]):
words.append(word)
pos = (pos[0], pos[1] + 1)
word = Word(pos)
c = d
h = c.bounding_box[3] - c.bounding_box[1]
y = (c.bounding_box[1] + c.bounding_box[3]) / 2.0
word.chars.append(c)
del objs[n]
break
else:
words.append(word)
word = None
if word:
words.append(word)
self.words = sorted(words, key=lambda w: w.pos)
def lines(self, show=False):
result = []
line = []
line_no = self.words[0].pos[0]
if show:
cpimg = cv.CloneMat(self.img)
for w in self.words:
if w.pos[0] != line_no:
line_no = w.pos[0]
result.append(line)
line = []
if show:
print '\n'
line.append(w)
if show:
print w
xmin = min([c.bounding_box[0] for c in w.chars])
xmax = max([c.bounding_box[2] for c in w.chars])
ymin = min([c.bounding_box[1] for c in w.chars])
ymax = max([c.bounding_box[3] for c in w.chars])
cv.Rectangle(cpimg, (xmin, ymin), (xmax, ymax), color=128, thickness=2)
showimg(cpimg)
result.append(line)
return result
def text(self, correct=True):
lines = self.lines()
if correct:
lines = [self.spell_corrector.correct_phrase(p) for p in lines]
text_lines = [' '.join(p) for p in lines]
return '\n'.join(text_lines)
def output(self):
print self.text()
