def nlbin(im, threshold=0.5, zoom=0.5, escale=1.0, border=0.1, perc=80, range=20, low=5, high=90): """ Performs binarization using non-linear processing. Args: im (PIL.Image): threshold (float): zoom (float): Zoom for background page estimation escale (float): Scale for estimating a mask over the text region border (float): Ignore this much of the border perc (int): Percentage for filters range (int): Range for filters low (int): Percentile for black estimation high (int): Percentile for white estimation Returns: PIL.Image containing the binarized image """ if im.mode == '1': return im raw = pil2array(im) # rescale image to between -1 or 0 and 1 raw = raw/np.float(np.iinfo(raw.dtype).max) if raw.ndim == 3: raw = np.mean(raw, 2) # perform image normalization if np.amax(raw) == np.amin(raw): raise KrakenInputException('Image is empty') image = raw-np.amin(raw) image /= np.amax(image) m = interpolation.zoom(image, zoom) m = filters.percentile_filter(m, perc, size=(range, 2)) m = filters.percentile_filter(m, perc, size=(2, range)) m = interpolation.zoom(m, 1.0/zoom) w, h = np.minimum(np.array(image.shape), np.array(m.shape)) flat = np.clip(image[:w, :h]-m[:w, :h]+1, 0, 1) # estimate low and high thresholds d0, d1 = flat.shape o0, o1 = int(border*d0), int(border*d1) est = flat[o0:d0-o0, o1:d1-o1] # by default, we use only regions that contain # significant variance; this makes the percentile # based low and high estimates more reliable v = est-filters.gaussian_filter(est, escale*20.0) v = filters.gaussian_filter(v**2, escale*20.0)**0.5 v = (v > 0.3*np.amax(v)) v = morphology.binary_dilation(v, structure=np.ones((escale*50, 1))) v = morphology.binary_dilation(v, structure=np.ones((1, escale*50))) est = est[v] lo = np.percentile(est.ravel(), low) hi = np.percentile(est.ravel(), high) flat -= lo flat /= (hi-lo) flat = np.clip(flat, 0, 1) bin = np.array(255*(flat > threshold), 'B') return array2pil(bin)
def degrade_line(im, eta=0, alpha=1.7, beta=1.7, alpha_0=1, beta_0=1): """ Degrades a line image by adding noise Args: im (PIL.Image): Input image Returns: PIL.Image in mode 'L' """ im = pil2array(im) im = np.amax(im) - im im = im * 1.0 / np.amax(im) # foreground distance transform and flipping to white probability fg_dist = distance_transform_cdt(im, metric='taxicab') fg_prob = alpha_0 * np.exp(-alpha * (fg_dist**2)) + eta fg_prob[im == 0] = 0 fg_flip = np.random.binomial(1, fg_prob) # background distance transform and flipping to black probability bg_dist = distance_transform_cdt(1 - im, metric='taxicab') bg_prob = beta_0 * np.exp(-beta * (bg_dist**2)) + eta bg_prob[im == 1] = 0 bg_flip = np.random.binomial(1, bg_prob) # flip im -= fg_flip im += bg_flip # use a circular kernel of size 3 sel = np.array([[1, 1], [1, 1]]) im = binary_closing(im, sel) return array2pil(255 - im.astype('B') * 255)
def compute_segmentation_map(im, mask: Optional[np.array] = None, model=None, device: str = 'cpu'): """ """ im_str = get_im_str(im) logger.info(f'Segmenting {im_str}') if model.input[ 1] == 1 and model.one_channel_mode == '1' and not is_bitonal(im): logger.warning('Running binary model on non-binary input image ' '(mode {}). This will result in severely degraded ' 'performance'.format(im.mode)) model.eval() model.to(device) if mask: if mask.mode != '1' and not is_bitonal(mask): logger.error('Mask is not bitonal') raise KrakenInputException('Mask is not bitonal') mask = mask.convert('1') if mask.size != im.size: logger.error( 'Mask size {mask.size} doesn\'t match image size {im.size}') raise KrakenInputException( 'Mask size {mask.size} doesn\'t match image size {im.size}') logger.info('Masking enabled in segmenter.') mask = pil2array(mask) batch, channels, height, width = model.input transforms = dataset.generate_input_transforms(batch, height, width, channels, 0, valid_norm=False) res_tf = tf.Compose(transforms.transforms[:3]) scal_im = res_tf(im).convert('L') with torch.no_grad(): logger.debug('Running network forward pass') o, _ = model.nn(transforms(im).unsqueeze(0).to(device)) logger.debug('Upsampling network output') o = F.interpolate(o, size=scal_im.size[::-1]) o = o.squeeze().cpu().numpy() scale = np.divide(im.size, o.shape[:0:-1]) bounding_regions = model.user_metadata[ 'bounding_regions'] if 'bounding_regions' in model.user_metadata else None return { 'heatmap': o, 'cls_map': model.user_metadata['class_mapping'], 'bounding_regions': bounding_regions, 'scale': scale, 'scal_im': scal_im }
def distort_line(im, distort=3.0, sigma=10, eps=0.03, delta=0.3): """ Distorts a line image. Run BEFORE degrade_line as a white border of 5 pixels will be added. Args: im (PIL.Image): Input image distort (float): sigma (float): eps (float): delta (float): Returns: PIL.Image in mode 'L' """ w, h = im.size # XXX: determine correct output shape from transformation matrices instead # of guesstimating. logger.debug('Pasting source image into canvas') image = Image.new('L', (int(1.5 * w), 4 * h), 255) image.paste(im, (int((image.size[0] - w) / 2), int( (image.size[1] - h) / 2))) line = pil2array(image.convert('L')) # shear in y direction with factor eps * randn(), scaling with 1 + eps * # randn() in x/y axis (all offset at d) logger.debug('Performing affine transformation') m = np.array([[1 + eps * np.random.randn(), 0.0], [eps * np.random.randn(), 1.0 + eps * np.random.randn()]]) c = np.array([w / 2.0, h / 2]) d = c - np.dot(m, c) + np.array( [np.random.randn() * delta, np.random.randn() * delta]) line = affine_transform(line, m, offset=d, order=1, mode='constant', cval=255) hs = gaussian_filter(np.random.randn(4 * h, int(1.5 * w)), sigma) ws = gaussian_filter(np.random.randn(4 * h, int(1.5 * w)), sigma) hs *= distort / np.amax(hs) ws *= distort / np.amax(ws) def _f(p): return (p[0] + hs[p[0], p[1]], p[1] + ws[p[0], p[1]]) logger.debug('Performing geometric transformation') im = array2pil(geometric_transform(line, _f, order=1, mode='nearest')) logger.debug('Cropping canvas to content box') im = im.crop(ImageOps.invert(im).getbbox()) return im
def ocropy_degrade(im, distort=1.0, dsigma=20.0, eps=0.03, delta=0.3, degradations=[(0.5, 0.0, 0.5, 0.0)]): """ Degrades and distorts a line using the same noise model used by ocropus. Args: im (PIL.Image): Input image distort (float): dsigma (float): eps (float): delta (float): degradations (list): list returning 4-tuples corresponding to the degradations argument of ocropus-linegen. Returns: PIL.Image in mode 'L' """ w, h = im.size # XXX: determine correct output shape from transformation matrices instead # of guesstimating. image = Image.new('L', (int(1.5*w), 4*h), 255) image.paste(im, (int((image.size[0] - w) / 2), int((image.size[1] - h) / 2))) a = pil2array(image.convert('L')) (sigma,ssigma,threshold,sthreshold) = degradations[np.random.choice(len(degradations))] sigma += (2*np.random.rand()-1)*ssigma threshold += (2*np.random.rand()-1)*sthreshold a = a*1.0/np.amax(a) if sigma>0.0: a = gaussian_filter(a,sigma) a += np.clip(np.random.randn(*a.shape)*0.2,-0.25,0.25) m = np.array([[1+eps*np.random.randn(),0.0],[eps*np.random.randn(),1.0+eps*np.random.randn()]]) w,h = a.shape c = np.array([w/2.0,h/2]) d = c-np.dot(m, c)+np.array([np.random.randn()*delta, np.random.randn()*delta]) a = affine_transform(a, m, offset=d, order=1, mode='constant', cval=a[0,0]) a = np.array(a>threshold,'f') [[r,c]] = find_objects(np.array(a==0,'i')) r0 = r.start r1 = r.stop c0 = c.start c1 = c.stop a = a[r0-5:r1+5,c0-5:c1+5] if distort > 0: h,w = a.shape hs = np.random.randn(h,w) ws = np.random.randn(h,w) hs = gaussian_filter(hs, dsigma) ws = gaussian_filter(ws, dsigma) hs *= distort/np.amax(hs) ws *= distort/np.amax(ws) def f(p): return (p[0]+hs[p[0],p[1]],p[1]+ws[p[0],p[1]]) a = geometric_transform(a, f, output_shape=(h,w), order=1, mode='constant', cval=np.amax(a)) im = array2pil(a).convert('L') return im
def segment(im, scale=None, black_colseps=False): """ Segments a page into text lines. Segments a page into text lines and returns the absolute coordinates of each line in reading order. Args: im (PIL.Image): A bi-level page of mode '1' or 'L' scale (float): Scale of the image black_colseps (bool): Whether column separators are assumed to be vertical black lines or not Returns: [(x1, y1, x2, y2),...]: A list of tuples containing the bounding boxes of the segmented lines in reading order. Raises: KrakenInputException if the input image is not binarized """ if im.mode != '1' and not is_bitonal(im): raise KrakenInputException('Image is not bi-level') # honestly I've got no idea what's going on here. In theory a simple # np.array(im, 'i') should suffice here but for some reason the # tostring/fromstring magic in pil2array alters the array in a way that is # needed for the algorithm to work correctly. a = pil2array(im) binary = np.array(a > 0.5 * (np.amin(a) + np.amax(a)), 'i') binary = 1 - binary if not scale: scale = estimate_scale(binary) binary = remove_hlines(binary, scale) if black_colseps: colseps, binary = compute_black_colseps(binary, scale) else: colseps = compute_white_colseps(binary, scale) bottom, top, boxmap = compute_gradmaps(binary, scale) seeds = compute_line_seeds(binary, bottom, top, colseps, scale) llabels = morph.propagate_labels(boxmap, seeds, conflict=0) spread = morph.spread_labels(seeds, maxdist=scale) llabels = np.where(llabels > 0, llabels, spread * binary) segmentation = llabels * binary lines = compute_lines(segmentation, scale) order = reading_order([l.bounds for l in lines]) lsort = topsort(order) lines = [lines[i].bounds for i in lsort] return [(s2.start, s1.start, s2.stop, s1.stop) for s1, s2 in lines]
def segment(im, scale=None, black_colseps=False): """ Segments a page into text lines. Segments a page into text lines and returns the absolute coordinates of each line in reading order. Args: im (PIL.Image): A bi-level page of mode '1' or 'L' scale (float): Scale of the image black_colseps (bool): Whether column separators are assumed to be vertical black lines or not Returns: [(x1, y1, x2, y2),...]: A list of tuples containing the bounding boxes of the segmented lines in reading order. Raises: KrakenInputException if the input image is not binarized """ if im.mode != '1' and im.histogram().count(0) != 254: raise KrakenInputException('Image is not bi-level') # honestly I've got no idea what's going on here. In theory a simple # np.array(im, 'i') should suffice here but for some reason the # tostring/fromstring magic in pil2array alters the array in a way that is # needed for the algorithm to work correctly. a = pil2array(im) binary = np.array(a > 0.5*(np.amin(a) + np.amax(a)), 'i') binary = 1 - binary if not scale: scale = estimate_scale(binary) binary = remove_hlines(binary, scale) if black_colseps: colseps, binary = compute_black_colseps(binary, scale) else: colseps = compute_white_colseps(binary, scale) bottom, top, boxmap = compute_gradmaps(binary, scale) seeds = compute_line_seeds(binary, bottom, top, colseps, scale) llabels = morph.propagate_labels(boxmap, seeds, conflict=0) spread = morph.spread_labels(seeds, maxdist=scale) llabels = np.where(llabels > 0, llabels, spread*binary) segmentation = llabels*binary lines = compute_lines(segmentation, scale) order = reading_order([l.bounds for l in lines]) lsort = topsort(order) lines = [lines[i].bounds for i in lsort] return [(s2.start, s1.start, s2.stop, s1.stop) for s1, s2 in lines]
def degrade_line(im, eta=0.0, alpha=1.5, beta=1.5, alpha_0=1.0, beta_0=1.0): """ Degrades a line image by adding noise. For parameter meanings consult [1]. Args: im (PIL.Image): Input image eta (float): alpha (float): beta (float): alpha_0 (float): beta_0 (float): Returns: PIL.Image in mode '1' """ logger.debug('Inverting and normalizing input image') im = pil2array(im) im = np.amax(im) - im im = im * 1.0 / np.amax(im) logger.debug('Calculating foreground distance transform') fg_dist = distance_transform_cdt(1 - im, metric='taxicab') logger.debug('Calculating flip to white probability') fg_prob = alpha_0 * np.exp(-alpha * (fg_dist**2)) + eta fg_prob[im == 1] = 0 fg_flip = np.random.binomial(1, fg_prob) logger.debug('Calculating background distance transform') bg_dist = distance_transform_cdt(im, metric='taxicab') logger.debug('Calculating flip to black probability') bg_prob = beta_0 * np.exp(-beta * (bg_dist**2)) + eta bg_prob[im == 0] = 0 bg_flip = np.random.binomial(1, bg_prob) # flip logger.debug('Flipping') im -= bg_flip im += fg_flip logger.debug('Binary closing') sel = np.array([[1, 1], [1, 1]]) im = binary_closing(im, sel) logger.debug('Converting to image') return array2pil(255 - im.astype('B') * 255)
def degrade_line(im, eta=0.0, alpha=1.5, beta=1.5, alpha_0=1.0, beta_0=1.0): """ Degrades a line image by adding noise. For parameter meanings consult [1]. Args: im (PIL.Image): Input image eta (float): alpha (float): beta (float): alpha_0 (float): beta_0 (float): Returns: PIL.Image in mode '1' """ logger.debug(u'Inverting and normalizing input image') im = pil2array(im) im = np.amax(im)-im im = im*1.0/np.amax(im) logger.debug(u'Calculating foreground distance transform') fg_dist = distance_transform_cdt(1-im, metric='taxicab') logger.debug(u'Calculating flip to white probability') fg_prob = alpha_0 * np.exp(-alpha * (fg_dist**2)) + eta fg_prob[im == 1] = 0 fg_flip = np.random.binomial(1, fg_prob) logger.debug(u'Calculating background distance transform') bg_dist = distance_transform_cdt(im, metric='taxicab') logger.debug(u'Calculating flip to black probability') bg_prob = beta_0 * np.exp(-beta * (bg_dist**2)) + eta bg_prob[im == 0] = 0 bg_flip = np.random.binomial(1, bg_prob) # flip logger.debug(u'Flipping') im -= bg_flip im += fg_flip logger.debug(u'Binary closing') sel = np.array([[1, 1], [1, 1]]) im = binary_closing(im, sel) logger.debug(u'Converting to image') return array2pil(255-im.astype('B')*255)
def distort_line(im, distort=3.0, sigma=10, eps=0.03, delta=0.3): """ Distorts a line image. Run BEFORE degrade_line as a white border of 5 pixels will be added. Args: im (PIL.Image): Input image distort (float): sigma (float): eps (float): delta (float): Returns: PIL.Image in mode 'L' """ w, h = im.size # XXX: determine correct output shape from transformation matrices instead # of guesstimating. logger.debug(u'Pasting source image into canvas') image = Image.new('L', (int(1.5*w), 4*h), 255) image.paste(im, (int((image.size[0] - w) / 2), int((image.size[1] - h) / 2))) line = pil2array(image.convert('L')) # shear in y direction with factor eps * randn(), scaling with 1 + eps * # randn() in x/y axis (all offset at d) logger.debug(u'Performing affine transformation') m = np.array([[1 + eps * np.random.randn(), 0.0], [eps * np.random.randn(), 1.0 + eps * np.random.randn()]]) c = np.array([w/2.0, h/2]) d = c - np.dot(m, c) + np.array([np.random.randn() * delta, np.random.randn() * delta]) line = affine_transform(line, m, offset=d, order=1, mode='constant', cval=255) hs = gaussian_filter(np.random.randn(4*h, int(1.5*w)), sigma) ws = gaussian_filter(np.random.randn(4*h, int(1.5*w)), sigma) hs *= distort/np.amax(hs) ws *= distort/np.amax(ws) def _f(p): return (p[0] + hs[p[0], p[1]], p[1] + ws[p[0], p[1]]) logger.debug(u'Performing geometric transformation') im = array2pil(geometric_transform(line, _f, order=1, mode='nearest')) logger.debug(u'Cropping canvas to content box') im = im.crop(ImageOps.invert(im).getbbox()) return im
def add(self, image, split=lambda x: os.path.splitext(x)[0], suffix='.gt.txt', normalization=None, reorder=True, pad=16): """ Adds a single image to the training set. """ with click.open_file(split(image) + suffix, 'r', encoding='utf-8') as fp: gt = fp.read() if normalization: gt = unicodedata.normalize(normalization, gt) if reorder: gt = bd.get_display(gt) im = Image.open(image) im = rpred.dewarp(self.lnorm, im) im = pil2array(im) im = lstm.prepare_line(im, pad) self.training_set.append((im, gt))
def dewarp(normalizer, im): """ Dewarps an image of a line using a kraken.lib.lineest.CenterNormalizer instance. Args: normalizer (kraken.lib.lineest.CenterNormalizer): A line normalizer instance im (PIL.Image): Image to dewarp Returns: PIL.Image containing the dewarped image. """ line = pil2array(im) temp = np.amax(line) - line temp = temp * 1.0 / np.amax(temp) normalizer.measure(temp) line = normalizer.normalize(line, cval=np.amax(line)) return array2pil(line)
def dewarp(normalizer: CenterNormalizer, im: Image.Image) -> Image.Image: """ Dewarps an image of a line using a kraken.lib.lineest.CenterNormalizer instance. Args: normalizer (kraken.lib.lineest.CenterNormalizer): A line normalizer instance im (PIL.Image.Image): Image to dewarp Returns: PIL.Image containing the dewarped image. """ line = pil2array(im) temp = np.amax(line)-line temp = temp*1.0/np.amax(temp) normalizer.measure(temp) line = normalizer.normalize(line, cval=np.amax(line)) return array2pil(line)
def degrade_line(im, mean=0.0, sigma=0.001, density=0.002): """ Degrades a line image by adding several kinds of noise. Args: im (PIL.Image): Input image mean (float): Mean of distribution for Gaussian noise sigma (float): Standard deviation for Gaussian noise density (float): Noise density for Salt and Pepper noiase Returns: PIL.Image in mode 'L' """ im = pil2array(im) m = np.amax(im) im = gaussian_filter(im.astype('f') / m, 0.5) im += np.random.normal(mean, sigma, im.shape) flipped = np.ceil(density / 2 * im.size) coords = [np.random.randint(0, i - 1, int(flipped)) for i in im.shape] im[coords] = 255 coords = [np.random.randint(0, i - 1, int(flipped)) for i in im.shape] im[coords] = 0 return array2pil(np.clip(im * m, 0, 255).astype('uint8'))
def degrade_line(im, mean=0.0, sigma=0.001, density=0.002): """ Degrades a line image by adding several kinds of noise. Args: im (PIL.Image): Input image mean (float): Mean of distribution for Gaussian noise sigma (float): Standard deviation for Gaussian noise density (float): Noise density for Salt and Pepper noise Returns: PIL.Image in mode 'L' """ im = pil2array(im) m = np.amax(im) im = gaussian_filter(im.astype('f')/m, 0.5) im += np.random.normal(mean, sigma, im.shape) flipped = np.ceil(density/2 * im.size) coords = [np.random.randint(0, i - 1, int(flipped)) for i in im.shape] im[coords] = 255 coords = [np.random.randint(0, i - 1, int(flipped)) for i in im.shape] im[coords] = 0 return array2pil(np.clip(im * m, 0, 255).astype('uint8'))
def rpred(network, im, bounds, pad=16, line_normalization=True, bidi_reordering=True): """ Uses a RNN to recognize text Args: network (kraken.lib.lstm.SegRecognizer): A SegRecognizer object im (PIL.Image): Image to extract text from bounds (dict): A dictionary containing a 'boxes' entry with a list of coordinates (x0, y0, x1, y1) of a text line in the image and an entry 'text_direction' containing 'horizontal-lr/rl/vertical-lr/rl'. pad (int): Extra blank padding to the left and right of text line line_normalization (bool): Dewarp line using the line estimator contained in the network. If no normalizer is available one using the default parameters is created. By aware that you may have to scale lines manually to the target line height if disabled. bidi_reordering (bool): Reorder classes in the ocr_record according to the Unicode bidirectional algorithm for correct display. Yields: An ocr_record containing the recognized text, absolute character positions, and confidence values for each character. """ lnorm = getattr(network, 'lnorm', CenterNormalizer()) for box, coords in extract_boxes(im, bounds): # check if boxes are non-zero in any dimension if sum(coords[::2]) == 0 or coords[3] - coords[1] == 0: yield ocr_record('', [], []) continue raw_line = pil2array(box) # check if line is non-zero if np.amax(raw_line) == np.amin(raw_line): yield ocr_record('', [], []) continue if line_normalization: # fail gracefully and return no recognition result in case the # input line can not be normalized. try: box = dewarp(lnorm, box) except: yield ocr_record('', [], []) continue line = pil2array(box) line = lstm.prepare_line(line, pad) pred = network.predictString(line) # calculate recognized LSTM locations of characters scale = len(raw_line.T) / (len(network.outputs) - 2 * pad) result = lstm.translate_back_locations(network.outputs) pos = [] conf = [] for _, start, end, c in result: if bounds['text_direction'].startswith('horizontal'): pos.append((coords[0] + int( (start - pad) * scale), coords[1], coords[0] + int( (end - pad / 2) * scale), coords[3])) else: pos.append((coords[0], coords[1] + int( (start - pad) * scale), coords[2], coords[1] + int( (end - pad / 2) * scale))) conf.append(c) if bidi_reordering: yield bidi_record(ocr_record(pred, pos, conf)) else: yield ocr_record(pred, pos, conf)
def mm_rpred(nets, im, bounds, pad=16, line_normalization=True, bidi_reordering=True): """ Multi-model version of kraken.rpred.rpred. Takes a dictionary of ISO15924 script identifiers->models and an script-annotated segmentation to dynamically select appropriate models for these lines. Args: nets (dict): A dict mapping ISO15924 identifiers to SegRecognizer objects. Recommended to be an defaultdict. im (PIL.Image): Image to extract text from bounds (dict): A dictionary containing a 'boxes' entry with a list of lists of coordinates (script, (x0, y0, x1, y1)) of a text line in the image and an entry 'text_direction' containing 'horizontal-lr/rl/vertical-lr/rl'. pad (int): Extra blank padding to the left and right of text line line_normalization (bool): Dewarp line using the line estimator contained in the network. If no normalizer is available one using the default parameters is created. By aware that you may have to scale lines manually to the target line height if disabled. bidi_reordering (bool): Reorder classes in the ocr_record according to the Unicode bidirectional algorithm for correct display. Yields: An ocr_record containing the recognized text, absolute character positions, and confidence values for each character. """ for line in bounds['boxes']: rec = ocr_record('', [], []) for script, (box, coords) in zip( map(lambda x: x[0], line), extract_boxes( im, { 'text_direction': bounds['text_direction'], 'boxes': map(lambda x: x[1], line) })): # check if boxes are non-zero in any dimension if sum(coords[::2]) == 0 or coords[3] - coords[1] == 0: continue raw_line = pil2array(box) # check if line is non-zero if np.amax(raw_line) == np.amin(raw_line): continue if line_normalization: # fail gracefully and return no recognition result in case the # input line can not be normalized. try: lnorm = getattr(nets[script], 'lnorm', CenterNormalizer()) box = dewarp(lnorm, box) except Exception as e: continue line = pil2array(box) line = lstm.prepare_line(line, pad) pred = nets[script].predictString(line) # calculate recognized LSTM locations of characters scale = len(raw_line.T) / (len(nets[script].outputs) - 2 * pad) result = lstm.translate_back_locations(nets[script].outputs) pos = [] conf = [] for _, start, end, c in result: if bounds['text_direction'].startswith('horizontal'): pos.append((coords[0] + int( (start - pad) * scale), coords[1], coords[0] + int( (end - pad / 2) * scale), coords[3])) else: pos.append((coords[0], coords[1] + int( (start - pad) * scale), coords[2], coords[1] + int( (end - pad / 2) * scale))) conf.append(c) rec.prediction += pred rec.cuts.extend(pos) rec.confidences.extend(conf) if bidi_reordering: yield bidi_record(rec) else: yield rec
def segment(im, text_direction='horizontal-lr', scale=None, maxcolseps=2, black_colseps=False, no_hlines=True, pad=0, mask=None): """ Segments a page into text lines. Segments a page into text lines and returns the absolute coordinates of each line in reading order. Args: im (PIL.Image): A bi-level page of mode '1' or 'L' text_direction (str): Principal direction of the text (horizontal-lr/rl/vertical-lr/rl) scale (float): Scale of the image maxcolseps (int): Maximum number of whitespace column separators black_colseps (bool): Whether column separators are assumed to be vertical black lines or not no_hlines (bool): Switch for horizontal line removal pad (int or tuple): Padding to add to line bounding boxes. If int the same padding is used both left and right. If a 2-tuple, uses (padding_left, padding_right). mask (PIL.Image): A bi-level mask image of the same size as `im` where 0-valued regions are ignored for segmentation purposes. Disables column detection. Returns: {'text_direction': '$dir', 'boxes': [(x1, y1, x2, y2),...]}: A dictionary containing the text direction and a list of reading order sorted bounding boxes under the key 'boxes'. Raises: KrakenInputException if the input image is not binarized or the text direction is invalid. """ im_str = get_im_str(im) logger.info('Segmenting {}'.format(im_str)) if im.mode != '1' and not is_bitonal(im): logger.error('Image {} is not bi-level'.format(im_str)) raise KrakenInputException('Image {} is not bi-level'.format(im_str)) # rotate input image for vertical lines if text_direction.startswith('horizontal'): angle = 0 offset = (0, 0) elif text_direction == 'vertical-lr': angle = 270 offset = (0, im.size[1]) elif text_direction == 'vertical-rl': angle = 90 offset = (im.size[0], 0) else: logger.error('Invalid text direction \'{}\''.format(text_direction)) raise KrakenInputException('Invalid text direction {}'.format(text_direction)) logger.debug('Rotating input image by {} degrees'.format(angle)) im = im.rotate(angle, expand=True) # honestly I've got no idea what's going on here. In theory a simple # np.array(im, 'i') should suffice here but for some reason the # tostring/fromstring magic in pil2array alters the array in a way that is # needed for the algorithm to work correctly. a = pil2array(im) binary = np.array(a > 0.5*(np.amin(a) + np.amax(a)), 'i') binary = 1 - binary if not scale: scale = estimate_scale(binary) if no_hlines: binary = remove_hlines(binary, scale) # emptyish images wll cause exceptions here. try: if mask: if mask.mode != '1' and not is_bitonal(mask): logger.error('Mask is not bitonal') raise KrakenInputException('Mask is not bitonal') mask = mask.convert('1') if mask.size != im.size: logger.error('Mask size {} doesn\'t match image size {}'.format(mask.size, im.size)) raise KrakenInputException('Mask size {} doesn\'t match image size {}'.format(mask.size, im.size)) logger.info('Masking enabled in segmenter. Disabling column detection.') mask = mask.rotate(angle, expand=True) colseps = pil2array(mask) elif black_colseps: colseps, binary = compute_black_colseps(binary, scale, maxcolseps) else: colseps = compute_white_colseps(binary, scale, maxcolseps) except ValueError: logger.warning('Exception in column finder (probably empty image) for {}.'.format(im_str)) return {'text_direction': text_direction, 'boxes': []} bottom, top, boxmap = compute_gradmaps(binary, scale) seeds = compute_line_seeds(binary, bottom, top, colseps, scale) llabels = morph.propagate_labels(boxmap, seeds, conflict=0) spread = morph.spread_labels(seeds, maxdist=scale) llabels = np.where(llabels > 0, llabels, spread*binary) segmentation = llabels*binary lines = compute_lines(segmentation, scale) order = reading_order([l.bounds for l in lines], text_direction[-2:]) lsort = topsort(order) lines = [lines[i].bounds for i in lsort] lines = [(s2.start, s1.start, s2.stop, s1.stop) for s1, s2 in lines] if isinstance(pad, int): pad = (pad, pad) lines = [(max(x[0]-pad[0], 0), x[1], min(x[2]+pad[1], im.size[0]), x[3]) for x in lines] return {'text_direction': text_direction, 'boxes': rotate_lines(lines, 360-angle, offset).tolist(), 'script_detection': False}
def rpred(network, im, bounds, pad=16, line_normalization=True, bidi_reordering=True): """ Uses a RNN to recognize text Args: network (kraken.lib.lstm.SegRecognizer): A SegRecognizer object im (PIL.Image): Image to extract text from bounds (iterable): An iterable returning a tuple defining the absolute coordinates (x0, y0, x1, y1) of a text line in the Image. pad (int): Extra blank padding to the left and right of text line line_normalization (bool): Dewarp line using the line estimator contained in the network. If no normalizer is available one using the default parameters is created. By aware that you may have to scale lines manually to the target line height if disabled. bidi_reordering (bool): Reorder classes in the ocr_record according to the Unicode bidirectional algorithm for correct display. Yields: An ocr_record containing the recognized text, absolute character positions, and confidence values for each character. """ lnorm = getattr(network, 'lnorm', CenterNormalizer()) for box, coords in extract_boxes(im, bounds): # check if boxes are non-zero in any dimension if sum(coords[::2]) == False or coords[3] - coords[1] == False: yield ocr_record('', [], []) continue raw_line = pil2array(box) # check if line is non-zero if np.amax(raw_line) == np.amin(raw_line): yield ocr_record('', [], []) continue if line_normalization: # fail gracefully and return no recognition result in case the # input line can not be normalized. try: box = dewarp(lnorm, box) except: yield ocr_record('', [], []) continue line = pil2array(box) line = lstm.prepare_line(line, pad) pred = network.predictString(line) # calculate recognized LSTM locations of characters scale = len(raw_line.T)/(len(network.outputs)-2 * pad) result = lstm.translate_back_locations(network.outputs) pos = [] conf = [] for _, start, end, c in result: pos.append((coords[0] + int((start-pad)*scale), coords[1], coords[0] + int((end-pad/2)*scale), coords[3])) conf.append(c) if bidi_reordering: yield bidi_record(ocr_record(pred, pos, conf)) else: yield ocr_record(pred, pos, conf)
def rpred(network, im, bounds, pad=16, line_normalization=True, bidi_reordering=True): """ Uses a RNN to recognize text Args: network (kraken.lib.lstm.SegRecognizer): A SegRecognizer object im (PIL.Image): Image to extract text from bounds (dict): A dictionary containing a 'boxes' entry with a list of coordinates (x0, y0, x1, y1) of a text line in the image and an entry 'text_direction' containing 'horizontal-lr/rl/vertical-lr/rl'. pad (int): Extra blank padding to the left and right of text line line_normalization (bool): Dewarp line using the line estimator contained in the network. If no normalizer is available one using the default parameters is created. By aware that you may have to scale lines manually to the target line height if disabled. bidi_reordering (bool): Reorder classes in the ocr_record according to the Unicode bidirectional algorithm for correct display. Yields: An ocr_record containing the recognized text, absolute character positions, and confidence values for each character. """ im_str = get_im_str(im) logger.info(u'Running recognizer on {} with {} lines'.format( im_str, len(bounds['boxes']))) logger.debug(u'Loading line normalizer') lnorm = getattr(network, 'lnorm', CenterNormalizer()) if not is_bitonal(im): logger.info(u'Image is grayscale. Adjusting normalizer parameters') lnorm.range = 2 for box, coords in extract_boxes(im, bounds): # check if boxes are non-zero in any dimension if sum(coords[::2]) == 0 or coords[3] - coords[1] == 0: logger.warning( u'bbox {} with zero dimension. Emitting empty record.'.format( coords)) yield ocr_record('', [], []) continue raw_line = pil2array(box) # check if line is non-zero if np.amax(raw_line) == np.amin(raw_line): logger.warning( u'Empty line {}. Emitting empty record.'.format(coords)) yield ocr_record('', [], []) continue if line_normalization: # fail gracefully and return no recognition result in case the # input line can not be normalized. try: box = dewarp(lnorm, box) except: logger.warning( u'Dewarping for bbox {} failed. Emitting empty record.'. format(coords)) yield ocr_record('', [], []) continue line = pil2array(box) logger.debug(u'Preparing line.') line = lstm.prepare_line(line, pad) logger.debug(u'Performing forward pass.') pred = network.predictString(line) logger.info(u'Prediction: {}'.format(pred)) # calculate recognized LSTM locations of characters scale = len(raw_line.T) / (len(network.outputs) - 2 * pad) logger.debug(u'Extracting labels.') result = lstm.translate_back_locations(network.outputs) pos = [] conf = [] for _, start, end, c in result: if bounds['text_direction'].startswith('horizontal'): xmin = coords[0] + int(max((start - pad) * scale, 0)) xmax = coords[0] + max( int(min((end - pad) * scale, coords[2] - coords[0])), 1) pos.append((xmin, coords[1], xmax, coords[3])) else: ymin = coords[1] + int(max((start - pad) * scale, 0)) ymax = coords[1] + max( int(min((end - pad) * scale, coords[3] - coords[1])), 1) pos.append((coords[0], ymin, coords[2], ymax)) conf.append(c) if bidi_reordering: logger.debug(u'BiDi reordering record.') yield bidi_record(ocr_record(pred, pos, conf)) else: yield ocr_record(pred, pos, conf)
def segment(im, text_direction: str = 'horizontal-lr', mask: Optional[np.array] = None, reading_order_fn: Callable = polygonal_reading_order, model=None, device: str = 'cpu'): """ Segments a page into text lines using the baseline segmenter. Segments a page into text lines and returns the polyline formed by each baseline and their estimated environment. Args: im (PIL.Image): An RGB image. text_direction (str): Ignored by the segmenter but kept for serialization. mask (PIL.Image): A bi-level mask image of the same size as `im` where 0-valued regions are ignored for segmentation purposes. Disables column detection. reading_order_fn (function): Function to determine the reading order. Has to accept a list of tuples (baselines, polygon) and a text direction (`lr` or `rl`). model (vgsl.TorchVGSLModel): A TorchVGSLModel containing a segmentation model. If none is given a default model will be loaded. device (str or torch.Device): The target device to run the neural network on. Returns: {'text_direction': '$dir', 'type': 'baseline', 'lines': [ {'baseline': [[x0, y0], [x1, y1], ..., [x_n, y_n]], 'boundary': [[x0, y0, x1, y1], ... [x_m, y_m]]}, {'baseline': [[x0, ...]], 'boundary': [[x0, ...]]} ] 'regions': [ {'region': [[x0, y0], [x1, y1], ..., [x_n, y_n]], 'type': 'image'}, {'region': [[x0, ...]], 'type': 'text'} ] }: A dictionary containing the text direction and under the key 'lines' a list of reading order sorted baselines (polylines) and their respective polygonal boundaries. The last and first point of each boundary polygon is connected. Raises: KrakenInputException if the input image is not binarized or the text direction is invalid. """ im_str = get_im_str(im) logger.info(f'Segmenting {im_str}') if model is None: logger.info('No segmentation model given. Loading default model.') model = vgsl.TorchVGSLModel.load_model(pkg_resources.resource_filename(__name__, 'blla.mlmodel')) if model.one_channel_mode == '1' and not is_bitonal(im): logger.warning('Running binary model on non-binary input image ' '(mode {}). This will result in severely degraded ' 'performance'.format(im.mode)) model.eval() model.to(device) if mask: if mask.mode != '1' and not is_bitonal(mask): logger.error('Mask is not bitonal') raise KrakenInputException('Mask is not bitonal') mask = mask.convert('1') if mask.size != im.size: logger.error('Mask size {mask.size} doesn\'t match image size {im.size}') raise KrakenInputException('Mask size {mask.size} doesn\'t match image size {im.size}') logger.info('Masking enabled in segmenter.') mask = pil2array(mask) batch, channels, height, width = model.input transforms = dataset.generate_input_transforms(batch, height, width, channels, 0, valid_norm=False) res_tf = tf.Compose(transforms.transforms[:3]) scal_im = res_tf(im).convert('L') with torch.no_grad(): logger.debug('Running network forward pass') o = model.nn(transforms(im).unsqueeze(0).to(device)) logger.debug('Upsampling network output') o = F.interpolate(o, size=scal_im.size[::-1]) o = o.squeeze().cpu().numpy() scale = np.divide(im.size, o.shape[:0:-1]) # postprocessing cls_map = model.user_metadata['class_mapping'] st_sep = cls_map['aux']['_start_separator'] end_sep = cls_map['aux']['_end_separator'] logger.info('Vectorizing baselines') baselines = [] regions = {} for bl_type, idx in cls_map['baselines'].items(): logger.debug(f'Vectorizing lines of type {bl_type}') baselines.extend([(bl_type,x) for x in vectorize_lines(o[(st_sep, end_sep, idx), :, :])]) logger.info('Vectorizing regions') for region_type, idx in cls_map['regions'].items(): logger.debug(f'Vectorizing lines of type {bl_type}') regions[region_type] = vectorize_regions(o[idx]) logger.debug('Polygonizing lines') lines = list(filter(lambda x: x[2] is not None, zip([x[0] for x in baselines], [x[1] for x in baselines], calculate_polygonal_environment(scal_im, [x[1] for x in baselines])))) logger.debug('Scaling vectorized lines') sc = scale_polygonal_lines([x[1:] for x in lines], scale) lines = list(zip([x[0] for x in lines], [x[0] for x in sc], [x[1] for x in sc])) logger.debug('Scaling vectorized regions') for reg_id, regs in regions.items(): regions[reg_id] = scale_regions(regs, scale) logger.debug('Reordering baselines') order_regs = [] for regs in regions.values(): order_regs.extend(regs) lines = reading_order_fn(lines=lines, regions=order_regs, text_direction=text_direction[-2:]) if 'class_mapping' in model.user_metadata and len(model.user_metadata['class_mapping']['baselines']) > 1: script_detection = True else: script_detection = False return {'text_direction': text_direction, 'type': 'baselines', 'lines': [{'script': bl_type, 'baseline': bl, 'boundary': pl} for bl_type, bl, pl in lines], 'regions': regions, 'script_detection': script_detection}
def nlbin(im: Image.Image, threshold: float = 0.5, zoom: float = 0.5, escale: float = 1.0, border: float = 0.1, perc: int = 80, range: int = 20, low: int = 5, high: int = 90) -> Image: """ Performs binarization using non-linear processing. Args: im (PIL.Image.Image): threshold (float): zoom (float): Zoom for background page estimation escale (float): Scale for estimating a mask over the text region border (float): Ignore this much of the border perc (int): Percentage for filters range (int): Range for filters low (int): Percentile for black estimation high (int): Percentile for white estimation Returns: PIL.Image containing the binarized image Raises: KrakenInputException when trying to binarize an empty image. """ im_str = get_im_str(im) logger.info(f'Binarizing {im_str}') if is_bitonal(im): logger.info(f'Skipping binarization because {im_str} is bitonal.') return im # convert to grayscale first logger.debug(f'Converting {im_str} to grayscale') im = im.convert('L') raw = pil2array(im) logger.debug('Scaling and normalizing') # rescale image to between -1 or 0 and 1 raw = raw / np.float(np.iinfo(raw.dtype).max) # perform image normalization if np.amax(raw) == np.amin(raw): logger.warning(f'Trying to binarize empty image {im_str}') raise KrakenInputException('Image is empty') image = raw - np.amin(raw) image /= np.amax(image) logger.debug('Interpolation and percentile filtering') with warnings.catch_warnings(): warnings.simplefilter('ignore', UserWarning) m = interpolation.zoom(image, zoom) m = filters.percentile_filter(m, perc, size=(range, 2)) m = filters.percentile_filter(m, perc, size=(2, range)) mh, mw = m.shape oh, ow = image.shape scale = np.diag([mh * 1.0 / oh, mw * 1.0 / ow]) m = affine_transform(m, scale, output_shape=image.shape) w, h = np.minimum(np.array(image.shape), np.array(m.shape)) flat = np.clip(image[:w, :h] - m[:w, :h] + 1, 0, 1) # estimate low and high thresholds d0, d1 = flat.shape o0, o1 = int(border * d0), int(border * d1) est = flat[o0:d0 - o0, o1:d1 - o1] logger.debug('Threshold estimates {}'.format(est)) # by default, we use only regions that contain # significant variance; this makes the percentile # based low and high estimates more reliable logger.debug('Refine estimates') v = est - filters.gaussian_filter(est, escale * 20.0) v = filters.gaussian_filter(v**2, escale * 20.0)**0.5 v = (v > 0.3 * np.amax(v)) v = morphology.binary_dilation(v, structure=np.ones((int(escale * 50), 1))) v = morphology.binary_dilation(v, structure=np.ones((1, int(escale * 50)))) est = est[v] lo = np.percentile(est.ravel(), low) hi = np.percentile(est.ravel(), high) flat -= lo flat /= (hi - lo) flat = np.clip(flat, 0, 1) logger.debug(f'Thresholding at {threshold}') bin = np.array(255 * (flat > threshold), 'B') return array2pil(bin)
def nlbin(im, threshold=0.5, zoom=0.5, escale=1.0, border=0.1, perc=80, range=20, low=5, high=90): """ Performs binarization using non-linear processing. Args: im (PIL.Image): threshold (float): zoom (float): Zoom for background page estimation escale (float): Scale for estimating a mask over the text region border (float): Ignore this much of the border perc (int): Percentage for filters range (int): Range for filters low (int): Percentile for black estimation high (int): Percentile for white estimation Returns: PIL.Image containing the binarized image """ if im.mode == '1': return im raw = pil2array(im) # rescale image to between -1 or 0 and 1 raw = raw / np.float(np.iinfo(raw.dtype).max) if raw.ndim == 3: raw = np.mean(raw, 2) # perform image normalization if np.amax(raw) == np.amin(raw): raise KrakenInputException('Image is empty') image = raw - np.amin(raw) image /= np.amax(image) with warnings.catch_warnings(): warnings.simplefilter('ignore', UserWarning) m = interpolation.zoom(image, zoom) m = filters.percentile_filter(m, perc, size=(range, 2)) m = filters.percentile_filter(m, perc, size=(2, range)) m = interpolation.zoom(m, 1.0 / zoom) w, h = np.minimum(np.array(image.shape), np.array(m.shape)) flat = np.clip(image[:w, :h] - m[:w, :h] + 1, 0, 1) # estimate low and high thresholds d0, d1 = flat.shape o0, o1 = int(border * d0), int(border * d1) est = flat[o0:d0 - o0, o1:d1 - o1] # by default, we use only regions that contain # significant variance; this makes the percentile # based low and high estimates more reliable v = est - filters.gaussian_filter(est, escale * 20.0) v = filters.gaussian_filter(v**2, escale * 20.0)**0.5 v = (v > 0.3 * np.amax(v)) v = morphology.binary_dilation(v, structure=np.ones((int(escale * 50), 1))) v = morphology.binary_dilation(v, structure=np.ones((1, int(escale * 50)))) est = est[v] lo = np.percentile(est.ravel(), low) hi = np.percentile(est.ravel(), high) flat -= lo flat /= (hi - lo) flat = np.clip(flat, 0, 1) bin = np.array(255 * (flat > threshold), 'B') return array2pil(bin)
def rpred(network, im, bounds, pad=16, line_normalization=True): """ Uses a RNN to recognize text Args: network (kraken.lib.lstm.SegRecognizer): A SegRecognizer object im (PIL.Image): Image to extract text from bounds (iterable): An iterable returning a tuple defining the absolute coordinates (x0, y0, x1, y1) of a text line in the Image. pad (int): Extra blank padding to the left and right of text line line_normalization (bool): Dewarp line using the line estimator contained in the network. If no normalizer is available one using the default parameters is created. By aware that you may have to scale lines manually to the target line height if disabled. Yields: A tuple containing the recognized text (0), absolute character positions in the image (1), and confidence values for each character(2). """ lnorm = getattr(network, "lnorm", CenterNormalizer()) for box, coords in extract_boxes(im, bounds): # check if boxes are non-zero in any dimension if sum(coords[::2]) == False or coords[3] - coords[1] == False: yield ocr_record("", [], []) continue raw_line = pil2array(box) # check if line is non-zero if np.amax(raw_line) == np.amin(raw_line): yield ocr_record("", [], []) continue if line_normalization: # fail gracefully and return no recognition result in case the # input line can not be normalized. try: box = dewarp(lnorm, box) except: yield ocr_record("", [], []) continue line = pil2array(box) line = lstm.prepare_line(line, pad) pred = network.predictString(line) # calculate recognized LSTM locations of characters scale = len(raw_line.T) / (len(network.outputs) - 2 * pad) result = lstm.translate_back(network.outputs, pos=1) conf = [network.outputs[r, c] for r, c in result if c != 0] cuts = [(int((r - pad) * scale), c) for (r, c) in result] # append last offset to end of line cuts.append((coords[2] - coords[0], 0)) pos = [] lx = 0 for i, d in enumerate(cuts): if d[1] == 0: lx = d[0] continue try: pos.append((coords[0] + lx, coords[1], coords[0] + d[0], coords[3])) except: break lx = d[0] yield ocr_record(pred, pos, conf)
def read(self, page): """Perfoms OCR with Kraken.""" stages = page.stages scan = stages.get("clean", None) if scan is None: return None nonLetter = self.nonLetter model = self.ensureLoaded() blocks = page.blocks ocrChars = [] ocrWords = [] ocrLines = [] stages["char"] = ocrChars stages["word"] = ocrWords stages["line"] = ocrLines binary = pil2array(nlbin(array2pil(scan))) for ((stripe, block), data) in blocks.items(): (left, top, right, bottom) = data["inner"] thisBinary = binary[top:bottom, left:right] lines = data["bands"]["main"]["lines"] for (ln, (up, lo)) in enumerate(lines): lln = ln + 1 roi = thisBinary[up : lo + 1] (b, e, roi) = removeMargins(roi, keep=16) ocrLines.append((stripe, block, lln, left + b, top + up, left + e, top + lo)) (roiH, roiW) = roi.shape[0:2] roi = array2pil(roi) bounds = dict(boxes=([0, 0, roiW, roiH],), text_direction=RL) # adapt the boxes, because they corresponds to peaks of recognition, # not to character extends # # See https://github.com/mittagessen/kraken/issues/184 adaptedPreds = [] for (c, (le, to, ri, bo), conf) in chain.from_iterable( rpred(model, roi, bounds, pad=0, bidi_reordering=True) ): if adaptedPreds: prevPred = adaptedPreds[-1] prevEdge = prevPred[1][0] else: prevEdge = roiW correction = int(round((prevEdge - ri) / 2)) thisRi = ri + correction if adaptedPreds: adaptedPreds[-1][1][0] -= correction adaptedPreds.append([c, [le, to, thisRi, bo], conf]) if adaptedPreds: adaptedPreds[-1][1][0] = 0 # divide into words, not only on spaces, but also on punctuation curWord = [[], []] inWord = True for (c, (le, to, ri, bo), conf) in adaptedPreds: offsetW = left + b offsetH = top + up pos = (le + offsetW, to + offsetH, ri + offsetW, bo + offsetH) conf = int(round(conf * 100)) ocrChars.append((stripe, block, lln, *pos, conf, c)) spaceSeen = c == " " changeWord = not inWord and c not in nonLetter element = (c, pos, conf) if spaceSeen: curWord[1].append(element) if spaceSeen or changeWord: if curWord[0] or curWord[1]: ocrWords.append((stripe, block, lln, *addWord(curWord))) curWord = [[], []] inWord = True continue if inWord: if c in nonLetter: inWord = False dest = 0 if inWord else 1 curWord[dest].append(element) if curWord[0] or curWord[1]: ocrWords.append((stripe, block, lln, *addWord(curWord))) page.write(stage="line,word,char")
def ocropy_degrade(im, distort=1.0, dsigma=20.0, eps=0.03, delta=0.3, degradations=((0.5, 0.0, 0.5, 0.0), )): """ Degrades and distorts a line using the same noise model used by ocropus. Args: im (PIL.Image): Input image distort (float): dsigma (float): eps (float): delta (float): degradations (list): list returning 4-tuples corresponding to the degradations argument of ocropus-linegen. Returns: PIL.Image in mode 'L' """ w, h = im.size # XXX: determine correct output shape from transformation matrices instead # of guesstimating. logger.debug('Pasting source image into canvas') image = Image.new('L', (int(1.5 * w), 4 * h), 255) image.paste(im, (int((image.size[0] - w) / 2), int( (image.size[1] - h) / 2))) a = pil2array(image.convert('L')) logger.debug('Selecting degradations') (sigma, ssigma, threshold, sthreshold) = degradations[np.random.choice(len(degradations))] sigma += (2 * np.random.rand() - 1) * ssigma threshold += (2 * np.random.rand() - 1) * sthreshold a = a * 1.0 / np.amax(a) if sigma > 0.0: logger.debug('Apply Gaussian filter') a = gaussian_filter(a, sigma) logger.debug('Adding noise') a += np.clip(np.random.randn(*a.shape) * 0.2, -0.25, 0.25) logger.debug('Perform affine transformation and resize') m = np.array([[1 + eps * np.random.randn(), 0.0], [eps * np.random.randn(), 1.0 + eps * np.random.randn()]]) w, h = a.shape c = np.array([w / 2.0, h / 2]) d = c - np.dot(m, c) + np.array( [np.random.randn() * delta, np.random.randn() * delta]) a = affine_transform(a, m, offset=d, order=1, mode='constant', cval=a[0, 0]) a = np.array(a > threshold, 'f') [[r, c]] = find_objects(np.array(a == 0, 'i')) r0 = r.start r1 = r.stop c0 = c.start c1 = c.stop a = a[r0 - 5:r1 + 5, c0 - 5:c1 + 5] if distort > 0: logger.debug('Perform geometric transformation') h, w = a.shape hs = np.random.randn(h, w) ws = np.random.randn(h, w) hs = gaussian_filter(hs, dsigma) ws = gaussian_filter(ws, dsigma) hs *= distort / np.amax(hs) ws *= distort / np.amax(ws) def _f(p): return (p[0] + hs[p[0], p[1]], p[1] + ws[p[0], p[1]]) a = geometric_transform(a, _f, output_shape=(h, w), order=1, mode='constant', cval=np.amax(a)) im = array2pil(a).convert('L') return im
def segment(im, text_direction='horizontal-lr', scale=None, maxcolseps=2, black_colseps=False): """ Segments a page into text lines. Segments a page into text lines and returns the absolute coordinates of each line in reading order. Args: im (PIL.Image): A bi-level page of mode '1' or 'L' text_direction (str): Principal direction of the text (horizontal-lr/rl/vertical-lr/rl) scale (float): Scale of the image maxcolseps (int): Maximum number of whitespace column separators black_colseps (bool): Whether column separators are assumed to be vertical black lines or not Returns: {'text_direction': '$dir', 'boxes': [(x1, y1, x2, y2),...]}: A dictionary containing the text direction and a list of reading order sorted bounding boxes under the key 'boxes'. Raises: KrakenInputException if the input image is not binarized or the text direction is invalid. """ if im.mode != '1' and not is_bitonal(im): raise KrakenInputException('Image is not bi-level') # rotate input image for vertical lines if text_direction.startswith('horizontal'): angle = 0 offset = (0, 0) elif text_direction == 'vertical-lr': angle = 270 offset = (0, im.size[1]) elif text_direction == 'vertical-rl': angle = 90 offset = (im.size[0], 0) else: raise KrakenInputException('Invalid text direction') im = im.rotate(angle, expand=True) # honestly I've got no idea what's going on here. In theory a simple # np.array(im, 'i') should suffice here but for some reason the # tostring/fromstring magic in pil2array alters the array in a way that is # needed for the algorithm to work correctly. a = pil2array(im) binary = np.array(a > 0.5 * (np.amin(a) + np.amax(a)), 'i') binary = 1 - binary if not scale: scale = estimate_scale(binary) binary = remove_hlines(binary, scale) # emptyish images wll cause exceptions here. try: if black_colseps: colseps, binary = compute_black_colseps(binary, scale, maxcolseps) else: colseps = compute_white_colseps(binary, scale, maxcolseps) except ValueError: return {'text_direction': text_direction, 'boxes': []} bottom, top, boxmap = compute_gradmaps(binary, scale) seeds = compute_line_seeds(binary, bottom, top, colseps, scale) llabels = morph.propagate_labels(boxmap, seeds, conflict=0) spread = morph.spread_labels(seeds, maxdist=scale) llabels = np.where(llabels > 0, llabels, spread * binary) segmentation = llabels * binary lines = compute_lines(segmentation, scale) order = reading_order([l.bounds for l in lines], text_direction[-2:]) lsort = topsort(order) lines = [lines[i].bounds for i in lsort] lines = [(s2.start, s1.start, s2.stop, s1.stop) for s1, s2 in lines] return { 'text_direction': text_direction, 'boxes': rotate_lines(lines, 360 - angle, offset).tolist(), 'script_detection': False }
def segment(im, text_direction: str = 'horizontal-lr', scale: Optional[float] = None, maxcolseps: float = 2, black_colseps: bool = False, no_hlines: bool = True, pad: int = 0, mask: Optional[np.array] = None, reading_order_fn: Callable = reading_order) -> Dict[str, Any]: """ Segments a page into text lines. Segments a page into text lines and returns the absolute coordinates of each line in reading order. Args: im (PIL.Image): A bi-level page of mode '1' or 'L' text_direction (str): Principal direction of the text (horizontal-lr/rl/vertical-lr/rl) scale (float): Scale of the image maxcolseps (int): Maximum number of whitespace column separators black_colseps (bool): Whether column separators are assumed to be vertical black lines or not no_hlines (bool): Switch for horizontal line removal pad (int or tuple): Padding to add to line bounding boxes. If int the same padding is used both left and right. If a 2-tuple, uses (padding_left, padding_right). mask (PIL.Image): A bi-level mask image of the same size as `im` where 0-valued regions are ignored for segmentation purposes. Disables column detection. reading_order_fn (Callable): Function to call to order line output. Callable accepting a list of slices (y, x) and a text direction in (`rl`, `lr`). Returns: {'text_direction': '$dir', 'boxes': [(x1, y1, x2, y2),...]}: A dictionary containing the text direction and a list of reading order sorted bounding boxes under the key 'boxes'. Raises: KrakenInputException if the input image is not binarized or the text direction is invalid. """ im_str = get_im_str(im) logger.info(f'Segmenting {im_str}') if im.mode != '1' and not is_bitonal(im): logger.error(f'Image {im_str} is not bi-level') raise KrakenInputException(f'Image {im_str} is not bi-level') # rotate input image for vertical lines if text_direction.startswith('horizontal'): angle = 0 offset = (0, 0) elif text_direction == 'vertical-lr': angle = 270 offset = (0, im.size[1]) elif text_direction == 'vertical-rl': angle = 90 offset = (im.size[0], 0) else: logger.error(f'Invalid text direction \'{text_direction}\'') raise KrakenInputException(f'Invalid text direction {text_direction}') logger.debug(f'Rotating input image by {angle} degrees') im = im.rotate(angle, expand=True) a = pil2array(im) binary = np.array(a > 0.5 * (np.amin(a) + np.amax(a)), 'i') binary = 1 - binary if not scale: scale = estimate_scale(binary) if no_hlines: binary = remove_hlines(binary, scale) # emptyish images wll cause exceptions here. try: if mask: if mask.mode != '1' and not is_bitonal(mask): logger.error('Mask is not bitonal') raise KrakenInputException('Mask is not bitonal') mask = mask.convert('1') if mask.size != im.size: logger.error( f'Mask size {mask.size} doesn\'t match image size {im.size}' ) raise KrakenInputException( f'Mask size {mask.size} doesn\'t match image size {im.size}' ) logger.info( 'Masking enabled in segmenter. Disabling column detection.') mask = mask.rotate(angle, expand=True) colseps = pil2array(mask) elif black_colseps: colseps, binary = compute_black_colseps(binary, scale, maxcolseps) else: colseps = compute_white_colseps(binary, scale, maxcolseps) except ValueError: logger.warning( f'Exception in column finder (probably empty image) for {im_str}') return {'text_direction': text_direction, 'boxes': []} bottom, top, boxmap = compute_gradmaps(binary, scale) seeds = compute_line_seeds(binary, bottom, top, colseps, scale) llabels = morph.propagate_labels(boxmap, seeds, conflict=0) spread = morph.spread_labels(seeds, maxdist=scale) llabels = np.where(llabels > 0, llabels, spread * binary) segmentation = llabels * binary lines = compute_lines(segmentation, scale) order = reading_order_fn([l.bounds for l in lines], text_direction[-2:]) lsort = topsort(order) lines = [lines[i].bounds for i in lsort] lines = [(s2.start, s1.start, s2.stop, s1.stop) for s1, s2 in lines] if isinstance(pad, int): pad = (pad, pad) lines = [(max(x[0] - pad[0], 0), x[1], min(x[2] + pad[1], im.size[0]), x[3]) for x in lines] return { 'text_direction': text_direction, 'boxes': rotate_lines(lines, 360 - angle, offset).tolist(), 'script_detection': False }
def nlbin(im: Image.Image, threshold: float = 0.5, zoom: float = 0.5, escale: float = 1.0, border: float = 0.1, perc: int = 80, range: int = 20, low: int = 5, high: int = 90) -> Image: """ Performs binarization using non-linear processing. Args: im (PIL.Image.Image): threshold (float): zoom (float): Zoom for background page estimation escale (float): Scale for estimating a mask over the text region border (float): Ignore this much of the border perc (int): Percentage for filters range (int): Range for filters low (int): Percentile for black estimation high (int): Percentile for white estimation Returns: PIL.Image containing the binarized image Raises: KrakenInputException when trying to binarize an empty image. """ im_str = get_im_str(im) logger.info('Binarizing {}'.format(im_str)) if is_bitonal(im): logger.info('Skipping binarization because {} is bitonal.'.format(im_str)) return im # convert to grayscale first logger.debug('Converting {} to grayscale'.format(im_str)) im = im.convert('L') raw = pil2array(im) logger.debug('Scaling and normalizing') # rescale image to between -1 or 0 and 1 raw = raw/np.float(np.iinfo(raw.dtype).max) # perform image normalization if np.amax(raw) == np.amin(raw): logger.warning('Trying to binarize empty image {}'.format(im_str)) raise KrakenInputException('Image is empty') image = raw-np.amin(raw) image /= np.amax(image) logger.debug('Interpolation and percentile filtering') with warnings.catch_warnings(): warnings.simplefilter('ignore', UserWarning) m = interpolation.zoom(image, zoom) m = filters.percentile_filter(m, perc, size=(range, 2)) m = filters.percentile_filter(m, perc, size=(2, range)) mh, mw = m.shape oh, ow = image.shape scale = np.diag([mh * 1.0/oh, mw * 1.0/ow]) m = affine_transform(m, scale, output_shape=image.shape) w, h = np.minimum(np.array(image.shape), np.array(m.shape)) flat = np.clip(image[:w, :h]-m[:w, :h]+1, 0, 1) # estimate low and high thresholds d0, d1 = flat.shape o0, o1 = int(border*d0), int(border*d1) est = flat[o0:d0-o0, o1:d1-o1] logger.debug('Threshold estimates {}'.format(est)) # by default, we use only regions that contain # significant variance; this makes the percentile # based low and high estimates more reliable logger.debug('Refine estimates') v = est-filters.gaussian_filter(est, escale*20.0) v = filters.gaussian_filter(v**2, escale*20.0)**0.5 v = (v > 0.3*np.amax(v)) v = morphology.binary_dilation(v, structure=np.ones((int(escale * 50), 1))) v = morphology.binary_dilation(v, structure=np.ones((1, int(escale * 50)))) est = est[v] lo = np.percentile(est.ravel(), low) hi = np.percentile(est.ravel(), high) flat -= lo flat /= (hi-lo) flat = np.clip(flat, 0, 1) logger.debug('Thresholding at {}'.format(threshold)) bin = np.array(255*(flat > threshold), 'B') return array2pil(bin)