diff --git a/kraken/containers.py b/kraken/containers.py index e3614e650..d9c5f3880 100644 --- a/kraken/containers.py +++ b/kraken/containers.py @@ -104,7 +104,7 @@ class BBoxLine: Attributes: id: Unique identifier - bbox: Tuple in form `((x0, y0), (x1, y0), (x1, y1), (x0, y1))` defining + bbox: Tuple in form `(xmin, ymin, xmax, ymax)` defining the bounding box. text: Transcription of this line. base_dir: An optional string defining the base direction (also called @@ -120,10 +120,7 @@ class BBoxLine: reading direction (of the document). """ id: str - bbox: Tuple[Tuple[int, int], - Tuple[int, int], - Tuple[int, int], - Tuple[int, int]] + bbox: Tuple[int, int, int, int] text: Optional[str] = None base_dir: Optional[Literal['L', 'R']] = None type: str = 'bbox' diff --git a/kraken/lib/segmentation.py b/kraken/lib/segmentation.py index efc35be8b..0d0829fba 100644 --- a/kraken/lib/segmentation.py +++ b/kraken/lib/segmentation.py @@ -1139,7 +1139,8 @@ def extract_polygons(im: Image.Image, bounds: 'Segmentation') -> Image.Image: angle = 90 else: angle = 0 - for box in bounds.lines: + for line in bounds.lines: + box = line.bbox if isinstance(box, tuple): box = list(box) if (box < [0, 0, 0, 0] or box[::2] >= [im.size[0], im.size[0]] or diff --git a/kraken/rpred.py b/kraken/rpred.py index b75d163d9..dacdf0c83 100644 --- a/kraken/rpred.py +++ b/kraken/rpred.py @@ -91,13 +91,18 @@ def __init__(self, if not tags_ignore: tags_ignore = [] + if bounds.script_detection: + self.have_tags = True + else: + self.have_tags = False + if bounds.type not in seg_types or len(seg_types) > 1: logger.warning(f'Recognizers with segmentation types {seg_types} will be ' f'applied to segmentation of type {bounds.type}. ' f'This will likely result in severely degraded performace') one_channel_modes = set(recognizer.nn.one_channel_mode for recognizer in nets.values()) if '1' in one_channel_modes and len(one_channel_modes) > 1: - raise KrakenInputException('Mixing binary and non-binary recognition models is not supported.') + raise ValueError('Mixing binary and non-binary recognition models is not supported.') elif '1' in one_channel_modes and not is_bitonal(im): logger.warning('Running binary models on non-binary input image ' f'(mode {im.mode}). This will result in severely degraded ' @@ -113,32 +118,39 @@ def __init__(self, valid_norm = True self.next_iter = self._recognize_box_line - tags = set() - for x in bounds.lines: - tags.update(x.tags.items()) - - im_str = get_im_str(im) - logger.info(f'Running {len(nets)} multi-script recognizers on {im_str} with {self.len} lines') - - filtered_tags = [] - miss = [] - for tag in tags: - if not isinstance(nets, defaultdict) and (not nets.get(tag) and tag not in tags_ignore): - miss.append(tag) - elif tag not in tags_ignore: - filtered_tags.append(tag) - tags = filtered_tags - - if miss: - raise KrakenInputException(f'Missing models for tags {set(miss)}') - - # build dictionary for line preprocessing - self.ts = {} - for tag in tags: - logger.debug(f'Loading line transforms for {tag}') - network = nets[tag] + if self.have_tags: + tags = set() + for x in bounds.lines: + tags.update(x.tags.items()) + + im_str = get_im_str(im) + logger.info(f'Running {len(nets)} multi-script recognizers on {im_str} with {self.len} lines') + + filtered_tags = [] + miss = [] + for tag in tags: + if not isinstance(nets, defaultdict) and (not nets.get(tag) and tag not in tags_ignore): + miss.append(tag) + elif tag not in tags_ignore: + filtered_tags.append(tag) + tags = filtered_tags + + if miss: + raise KrakenInputException(f'Missing models for tags {set(miss)}') + + # build dictionary for line preprocessing + self.ts = {} + for tag in tags: + logger.debug(f'Loading line transforms for {tag}') + network = nets[tag] + batch, channels, height, width = network.nn.input + self.ts[tag] = ImageInputTransforms(batch, height, width, channels, (pad, 0), valid_norm) + elif isinstance(nets, defaultdict) and nets.default_factory: + network = nets.default_factory() batch, channels, height, width = network.nn.input - self.ts[tag] = ImageInputTransforms(batch, height, width, channels, (pad, 0), valid_norm) + self.ts = {('type', 'default'): ImageInputTransforms(batch, height, width, channels, (pad, 0), valid_norm)} + else: + raise ValueError('No tags in input data and no default model in mapping given.') self.im = im self.nets = nets @@ -148,23 +160,22 @@ def __init__(self, self.tags_ignore = tags_ignore def _recognize_box_line(self, line): - flat_box = [point for box in line.bbox for point in box] - xmin, xmax = min(flat_box[::2]), max(flat_box[::2]) - ymin, ymax = min(flat_box[1::2]), max(flat_box[1::2]) + xmin, ymin, xmax, ymax = line.bbox prediction = '' cuts = [] confidences = [] line.text_direction = self.bounds.text_direction - if self.tags_ignore is not None: - for tag in line.tags.values(): + if self.have_tags and self.tags_ignore: + for tag in line.tags.items(): if tag in self.tags_ignore: logger.info(f'Ignoring line segment with tags {line.tags} based on {tag}.') - return BaselineOCRRecord('', [], [], line) + return BBoxOCRRecord('', (), (), line) tag, net = self._resolve_tags_to_model(line.tags, self.nets) - box, coords = next(extract_polygons(self.im, line)) + seg = dataclasses.replace(self.bounds, lines=[line]) + box, coords = next(extract_polygons(self.im, seg)) self.box = box # check if boxes are non-zero in any dimension @@ -184,8 +195,6 @@ def _recognize_box_line(self, line): logger.warning('Empty run. Emitting empty record.') return BBoxOCRRecord('', (), (), line) - _, net = self._resolve_tags_to_model({'type': tag}, self.nets) - logger.debug(f'Forward pass with model {tag}.') preds = net.predict(ts_box.unsqueeze(0))[0] @@ -224,8 +233,8 @@ def _recognize_box_line(self, line): return rec.display_order(None) def _recognize_baseline_line(self, line): - if self.tags_ignore is not None: - for tag in line.tags.values(): + if self.have_tags and self.tags_ignore is not None: + for tag in line.tags.items(): if tag in self.tags_ignore: logger.info(f'Ignoring line segment with tags {line.tags} based on {tag}.') return BaselineOCRRecord('', [], [], line) @@ -319,15 +328,18 @@ def rpred(network: 'TorchSeqRecognizer', return mm_rpred(defaultdict(lambda: network), im, bounds, pad, bidi_reordering) -def _resolve_tags_to_model(tags: Sequence[Dict[str, str]], +def _resolve_tags_to_model(tags: Optional[Sequence[Dict[str, str]]], model_map: Dict[Tuple[str, str], 'TorchSeqRecognizer'], default: Optional['TorchSeqRecognizer'] = None) -> 'TorchSeqRecognizer': """ Resolves a sequence of tags """ - for tag in tags.items(): - if tag in model_map: - return tag, model_map[tag] - if default: + if not tags and default: + return ('type', 'default'), default + elif tags: + for tag in tags.items(): + if tag in model_map: + return tag, model_map[tag] + elif tags and default: return next(tags.values()), default raise KrakenInputException(f'No model for tags {tags}') diff --git a/kraken/serialization.py b/kraken/serialization.py index 6f378c8f4..94e490191 100644 --- a/kraken/serialization.py +++ b/kraken/serialization.py @@ -164,11 +164,14 @@ def serialize(results: 'Segmentation', # set field to indicate the availability of baseline segmentation in # addition to bounding boxes line = {'index': idx, - 'bbox': max_bbox([record.boundary] if record.type == 'baselines' else [record.bbox]), + 'bbox': max_bbox([record.boundary]) if record.type == 'baselines' else record.bbox, 'cuts': record.cuts, 'confidences': record.confidences, 'recognition': [], - 'boundary': [list(x) for x in record.boundary] if record.type == 'baselines' else record.bbox, + 'boundary': [list(x) for x in record.boundary] if record.type == 'baselines' else [[record.bbox[0], record.bbox[1]], + [record.bbox[2], record.bbox[1]], + [record.bbox[2], record.bbox[3]], + [record.bbox[0], record.bbox[3]]], 'type': 'line' } if record.tags is not None: diff --git a/tests/resources/records.json b/tests/resources/records.json index f8ca54e36..cf735d14b 100644 --- a/tests/resources/records.json +++ b/tests/resources/records.json @@ -1 +1 @@ -[{"prediction": "\u0645\u0627\u0644\u0633\u0644\u0627 \u0647\u064a\u0644\u0639 \u0647\u062a\u0627\u0632\u062c\u0639\u0645 \u064a\u0641", "cuts": [[[1437, 119], [1437, 256], [1449, 256], [1449, 119]], [[1484, 119], [1484, 256], [1496, 256], [1496, 119]], [[1508, 119], [1508, 256], [1520, 256], [1520, 119]], [[1568, 119], [1568, 256], [1568, 256], [1568, 119]], [[1603, 119], [1603, 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0.9998530149459839, 0.9993923902511597, 0.9804210662841797, 0.9999669790267944, 0.9681344628334045, 0.7007457613945007, 0.9999014139175415, 0.9938947558403015, 0.9991243481636047, 0.9994795918464661, 0.9906601309776306, 0.9998043179512024, 0.9999531507492065, 0.9996731281280518, 0.9904140830039978, 0.8484129309654236, 0.9998654127120972, 0.9998722076416016, 0.884601891040802, 0.9997289776802063, 0.5172210335731506, 0.9777674078941345, 0.9999169111251831, 0.49916383624076843, 0.9641693830490112, 0.9937703013420105, 0.7306916117668152, 0.9957754015922546, 0.9984592199325562, 0.943457841873169, 0.6241721510887146, 0.9954598546028137, 0.9971427321434021, 0.8888192176818848, 0.9960846900939941, 0.9893342852592468, 0.9664679169654846, 0.9914203882217407, 0.9974316954612732, 0.9991137385368347, 0.9997954964637756, 0.9750413298606873, 0.9770325422286987, 0.9967040419578552, 0.9981144666671753, 0.9363402724266052, 0.9950194358825684, 0.8760650753974915, 0.9999531507492065, 0.9947760105133057, 0.99937504529953, 0.9894686341285706, 0.9319698810577393, 0.9841182231903076, 0.998865008354187, 0.9998772144317627, 0.9999456405639648, 0.9940637946128845, 0.8951064348220825, 0.9981452226638794, 0.9989315867424011, 0.9663097262382507], "line": {"id": "47a85dad-e82c-41b6-9a41-f6a85da7f60b", "bbox": [297, 4427, 3128, 4585], "type": "bbox"}}] \ No newline at end of file diff --git a/tests/test_rpred.py b/tests/test_rpred.py index 9a3528b0a..55d026cc5 100644 --- a/tests/test_rpred.py +++ b/tests/test_rpred.py @@ -9,8 +9,9 @@ from pathlib import Path from collections import defaultdict +from kraken.containers import Segmentation, BBoxOCRRecord, BaselineOCRRecord, BaselineLine, BBoxLine from kraken.lib.models import load_any -from kraken.rpred import rpred, mm_rpred, BaselineOCRRecord, BBoxOCRRecord +from kraken.rpred import rpred, mm_rpred from kraken.lib.exceptions import KrakenInputException thisfile = Path(__file__).resolve().parent @@ -109,57 +110,78 @@ def test_bbox_record_slicing(self): self.assertEqual(cut, ((1484, 119), (1568, 119), (1568, 256), (1484, 256))) self.assertAlmostEqual(conf, 0.74411, places=4) + class TestBaselineRecords(unittest.TestCase): """ Tests the baseline OCR record. """ def setUp(self): - self.bidi_record = () - self.ltr_record = () + with open(resources / 'bl_records.json', 'r') as fp: + self.bl_records = json.load(fp) + self.ltr_record = self.bl_records[0] def test_baseline_record_cuts(self): """ Make sure that the cuts of a record are converted to absolute coordinates. """ - pass + record = BaselineOCRRecord(**self.ltr_record) def test_baseline_record_redisplay(self): """ Test that a display order record remains in display order when requesting a DO record. """ - pass + record = BaselineOCRRecord(**self.ltr_record, display_order=True) + self.assertEqual(record, record.display_order()) def test_baseline_record_relogical(self): """ Test that a logical order record remains in logical order when requesting a LO record. """ - pass + record = BaselineOCRRecord(**self.ltr_record, display_order=False) + self.assertEqual(record, record.logical_order()) def test_baseline_record_display(self): """ test display order conversion of record. """ - pass + record = BaselineOCRRecord(**self.ltr_record, display_order=False) + re_record = record.display_order() + self.assertEqual(re_record.prediction, 'في معجزاته عليه السلام') + self.assertEqual(re_record[:][1], ((1437, 119), (2173, 119), (2173, 256), (1437, 256))) + self.assertAlmostEqual(re_record[2:8][2], 0.9554762, places=4) def test_baseline_record_logical(self): """ Test logical order conversion of record. """ - pass + record = BaselineOCRRecord(**self.ltr_record, display_order=True) + re_record = record.logical_order() + self.assertEqual(re_record.prediction, 'في معجزاته عليه السلام') + self.assertEqual(re_record[:][1], ((1437, 119), (2173, 119), (2173, 256), (1437, 256))) + self.assertAlmostEqual(re_record[2:8][2], 0.9554762, places=4) def test_baseline_record_slicing(self): """ Tests simple slicing/aggregation of elements in record. """ - pass + record = BaselineOCRRecord(**self.ltr_record, display_order=True) + pred, cut, conf = record[1:8] + self.assertEqual(pred, 'السلا ه') + self.assertEqual(cut, ((1484, 119), (1674, 119), (1674, 256), (1484, 256))) + self.assertAlmostEqual(conf, 0.9259478, places=4) def test_baseline_record_slicing(self): """ Tests complex slicing/aggregation of elements in record. """ - pass + record = BaselineOCRRecord(**self.ltr_record, display_order=True) + pred, cut, conf = record[1:5:2] + self.assertEqual(pred, 'اس') + self.assertEqual(cut, ((1484, 119), (1568, 119), (1568, 256), (1484, 256))) + self.assertAlmostEqual(conf, 0.74411, places=4) + class TestRecognition(unittest.TestCase): @@ -170,16 +192,73 @@ def setUp(self): self.im = Image.open(resources / 'bw.png') self.overfit_line = Image.open(resources / '000236.png') self.model = load_any(resources / 'overfit.mlmodel') + self.invalid_box_seg = Segmentation(type='bbox', + imagename = resources / 'bw.png', + lines=[BBoxLine(id='foo', + bbox=[-1, -1, 10000, 10000])], + text_direction='horizontal-lr', + script_detection=False + ) + self.invalid_bl_seg = Segmentation(type='baselines', + imagename = resources / 'bw.png', + lines=[BaselineLine(id='bar', + tags={'type': 'default'}, + baseline=[[0,0], [10000, 0]], + boundary=[[-1, -1], [-1, 10000], [10000, 10000], [10000, -1]])], + text_direction='horizontal-lr', + script_detection=False + ) + + self.simple_box_seg = Segmentation(type='bbox', + imagename = resources / 'bw.png', + lines=[BBoxLine(id='foo', + bbox=[0, 0, 2544, 156])], + text_direction='horizontal-lr', + script_detection=False + ) + self.simple_bl_seg = Segmentation(type='baselines', + imagename = resources / 'bw.png', + lines=[BaselineLine(id='foo', + baseline=[[0, 10], [2543, 10]], + boundary=[[0, 0], [2543, 0], [2543, 155], [0, 155]])], + text_direction='horizontal-lr', + script_detection=False + ) + + self.tagged_box_seg = Segmentation(type='bbox', + imagename = resources / 'bw.png', + lines=[BBoxLine(id='foo', + bbox=[0, 0, 2544, 156], + tags={'type': 'foobar'}), + BBoxLine(id='bar', + bbox=[0, 0, 2544, 156], + tags={'type': 'default'}) + ], + text_direction='horizontal-lr', + script_detection=True + ) + self.tagged_bl_seg = Segmentation(type='baselines', + imagename = resources / 'bw.png', + lines=[BaselineLine(id='foo', + baseline=[[0, 10], [2543, 10]], + boundary=[[0, 0], [2543, 0], [2543, 155], [0, 155]], + tags={'type': 'foobar'}), + BaselineLine(id='bar', + baseline=[[0, 10], [2543, 10]], + boundary=[[0, 0], [2543, 0], [2543, 155], [0, 155]], + tags={'type': 'default'}), + ], + text_direction='horizontal-lr', + script_detection=True + ) - def tearDown(self): - self.im.close() def test_rpred_bbox_outbounds(self): """ Tests correct handling of invalid bbox line coordinates. """ with raises(KrakenInputException): - pred = rpred(self.model, self.im, {'boxes': [[-1, -1, 10000, 10000]], 'text_direction': 'horizontal'}, True) + pred = rpred(self.model, self.im, self.invalid_box_seg, True) next(pred) @pytest.mark.xfail @@ -188,18 +267,14 @@ def test_rpred_bl_outbounds(self): Tests correct handling of invalid baseline coordinates. """ with raises(KrakenInputException): - pred = rpred(self.model, self.im, {'lines': [{'tags': {'type': 'default'}, - 'baseline': [[0,0], [10000, 0]], - 'boundary': [[-1, -1], [-1, 10000], [10000, 10000], [10000, -1]]}], - 'text_direction': 'horizontal', - 'type': 'baselines'}, True) + pred = rpred(self.model, self.im, self.invalid_bl_seg, True) next(pred) def test_simple_bbox_rpred(self): """ Tests simple recognition without tags. """ - pred = rpred(self.model, self.overfit_line, {'boxes': [[0, 0, 2544, 156]], 'text_direction': 'horizontal'}, True) + pred = rpred(self.model, self.overfit_line, self.simple_box_seg, True) record = next(pred) self.assertEqual(record.prediction, 'ܡ ܘܡ ܗ ܡܕܐ ܐ ܐܐ ܡ ܗܗܐܐܐܕ') @@ -207,7 +282,7 @@ def test_simple_bl_rpred(self): """ Tests simple recognition without tags. """ - pred = rpred(self.model, self.overfit_line, {'boxes': [[0, 0, 2544, 156]], 'text_direction': 'horizontal'}, True) + pred = rpred(self.model, self.overfit_line, self.simple_bl_seg, True) record = next(pred) self.assertEqual(record.prediction, 'ܡ ܘܡ ܗ ܡܕܐ ܐ ܐܐ ܡ ܗܗܐܐܐܕ') @@ -215,44 +290,31 @@ def test_mm_rpred_bbox_missing_tags(self): """ Test that mm_rpred fails when tags are missing """ - with raises(KrakenInputException): - pred = mm_rpred({'default': self.model}, + with raises(ValueError): + pred = mm_rpred({('type', 'default'): self.model}, self.overfit_line, - {'boxes': [[('default', [0, 0, 2544, 156])], - [('foobar', [0, 0, 2544, 156])]], - 'text_direction': 'horizontal', - 'script_detection': True}, + self.simple_box_seg, True) def test_mm_rpred_bl_missing_tags(self): """ Test that mm_rpred fails when tags are missing """ - with raises(KrakenInputException): - pred = mm_rpred({'default': self.model}, + with raises(ValueError): + pred = mm_rpred({('type', 'default'): self.model}, self.overfit_line, - {'lines': [{'tags': {'type': 'default'}, - 'baseline': [[0,0], [10000, 0]], - 'boundary': [[-1, -1], [-1, 10000], [10000, 10000], [10000, -1]]}, - {'tags': {'type': 'foobar'}, - 'baseline': [[0,0], [10000, 0]], - 'boundary': [[-1, -1], [-1, 10000], [10000, 10000], [10000, -1]]}], - 'text_direction': 'horizontal', - 'type': 'baselines'}, + self.simple_bl_seg, True) def test_mm_rpred_bbox_ignore_tags(self): """ Tests mm_rpred recognition with ignore tags. """ - pred = mm_rpred({'default': self.model}, + pred = mm_rpred({('type', 'default'): self.model}, self.overfit_line, - {'boxes': [[('foobar', [0, 0, 2544, 156])], - [('default', [0, 0, 2544, 156])]], - 'text_direction': 'horizontal', - 'script_detection': True}, + self.tagged_box_seg, True, - tags_ignore=['foobar']) + tags_ignore=[('type', 'foobar')]) record = next(pred) self.assertEqual(record.prediction, '') record = next(pred) @@ -264,10 +326,7 @@ def test_mm_rpred_bbox_default_tags(self): """ pred = mm_rpred(defaultdict(lambda: self.model), self.overfit_line, - {'boxes': [[('foobar', [0, 0, 2544, 156])], - [('default', [0, 0, 2544, 156])]], - 'text_direction': 'horizontal', - 'script_detection': True}, + self.tagged_box_seg, True) record = next(pred) self.assertEqual(record.prediction, 'ܡ ܘܡ ܗ ܡܕܐ ܐ ܐܐ ܡ ܗܗܐܐܐܕ') @@ -278,18 +337,11 @@ def test_mm_rpred_bl_ignore_tags(self): """ Tests baseline recognition with ignore tags. """ - pred = mm_rpred({'default': self.model}, + pred = mm_rpred({('type', 'default'): self.model}, self.overfit_line, - {'lines': [{'tags': {'type': 'foobar'}, - 'baseline': [[0, 10], [2543, 10]], - 'boundary': [[0, 0], [2543, 0], [2543, 155], [0, 155]]}, - {'tags': {'type': 'default'}, - 'baseline': [[0, 10], [2543, 10]], - 'boundary': [[0, 0], [2543, 0], [2543, 155], [0, 155]]}], - 'script_detection': True, - 'type': 'baselines'}, + self.tagged_bl_seg, True, - tags_ignore=['foobar']) + tags_ignore=[('type', 'foobar')]) record = next(pred) self.assertEqual(record.prediction, '') record = next(pred) @@ -301,14 +353,7 @@ def test_mm_rpred_bl_default_tags(self): """ pred = mm_rpred(defaultdict(lambda: self.model), self.overfit_line, - {'lines': [{'tags': {'type': 'foobar'}, - 'baseline': [[0, 10], [2543, 10]], - 'boundary': [[0, 0], [2543, 0], [2543, 155], [0, 155]]}, - {'tags': {'type': 'default'}, - 'baseline': [[0, 10], [2543, 10]], - 'boundary': [[0, 0], [2543, 0], [2543, 155], [0, 155]]}], - 'script_detection': True, - 'type': 'baselines'}, + self.tagged_bl_seg, True) record = next(pred) self.assertEqual(record.prediction, '.ܗ ܣܗܐ ܕ ܣ ܗ ܕܗܗ ܟܕܗܣ ܠ ܐ .ܣܕܐܣ. ܗ ') @@ -321,11 +366,7 @@ def test_mm_rpred_bl_nobidi(self): """ pred = mm_rpred(defaultdict(lambda: self.model), self.overfit_line, - {'lines': [{'tags': {'type': 'default'}, - 'baseline': [[0, 10], [2543, 10]], - 'boundary': [[0, 0], [2543, 0], [2543, 155], [0, 155]]}], - 'script_detection': True, - 'type': 'baselines'}, + self.simple_bl_seg, bidi_reordering=False) record = next(pred) self.assertEqual(record.prediction, 'ܕܗ .ܣܐܗܗ.ܐ ܗܣ ܕ ܗܣ ܗ.ܗܝܣܗ ܣ ܗܢ ܪܗܗܕ ܐ ܗܠ') @@ -336,10 +377,7 @@ def test_mm_rpred_bbox_nobidi(self): """ pred = mm_rpred(defaultdict(lambda: self.model), self.overfit_line, - {'boxes': [[('foobar', [0, 0, 2544, 156])], - [('default', [0, 0, 2544, 156])]], - 'text_direction': 'horizontal', - 'script_detection': True}, + self.simple_box_seg, bidi_reordering=False) record = next(pred) self.assertEqual(record.prediction, 'ܕܗܣܐܕ ܪܝ .ܡܡ ܐܠܠ ܗܠ ܐܘܗ ܟܘܗܢ ܡܡ ܐܠ')