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import cv2
from numpy.core.fromnumeric import sort
import tqdm
import json
import copy
import Polygon
import numpy as np
from .scitsr.eval import json2Relations, eval_relations
def parse_layout(spans, num_rows, num_cols):
layout = np.full([num_rows, num_cols], -1, dtype=np.int)
cell_count = 0
for x1, y1, x2, y2 in spans:
layout[y1:y2+1, x1:x2+1] = cell_count
cell_count += 1
cells_id = list()
for row_idx in range(num_rows):
for col_idx in range(num_cols):
cell_id = layout[row_idx, col_idx]
if cell_id in cells_id:
layout[row_idx, col_idx] = cells_id.index(cell_id)
else:
layout[row_idx, col_idx] = len(cells_id)
cells_id.append(cell_id)
return layout
def parse_cells(layout, spans, row_segments, col_segments, lines):
cells = list()
num_cells = np.max(layout) + 1
for cell_id in range(num_cells):
cell_positions = np.argwhere(layout == cell_id)
y1 = np.min(cell_positions[:, 0])
y2 = np.max(cell_positions[:, 0])
x1 = np.min(cell_positions[:, 1])
x2 = np.max(cell_positions[:, 1])
assert np.all(layout[y1:y2, x1:x2] == cell_id)
x1 = col_segments[x1]
x2 = col_segments[x2+1]
y1 = row_segments[y1]
y2 = row_segments[y2+1]
cell = dict(
segmentation=[[[x1, y1], [x2, y1], [x2, y2], [x1, y2]]]
)
cells.append(cell)
extend_cell_lines(cells, lines)
return cells
def extend_cell_lines(cells, lines):
def segmentation_to_polygon(segmentation):
polygon = Polygon.Polygon()
for contour in segmentation:
polygon = polygon + Polygon.Polygon(contour)
return polygon
lines = copy.deepcopy(lines)
cells_poly = [segmentation_to_polygon(item['segmentation']) for item in cells]
lines_poly = [segmentation_to_polygon(item['segmentation']) for item in lines]
cells_lines = [[] for _ in range(len(cells))]
for line_idx, line_poly in enumerate(lines_poly):
if line_poly.area() == 0:
continue
line_area = line_poly.area()
max_overlap = 0
max_overlap_idx = None
for cell_idx, cell_poly in enumerate(cells_poly):
overlap = (cell_poly & line_poly).area() / line_area
if overlap > max_overlap:
max_overlap_idx = cell_idx
max_overlap = overlap
if max_overlap > 0:
cells_lines[max_overlap_idx].append(line_idx)
lines_y1 = [segmentation_to_bbox(item['segmentation'])[1] for item in lines]
cells_lines = [sorted(item, key=lambda idx: lines_y1[idx]) for item in cells_lines]
for cell, cell_lines in zip(cells, cells_lines):
transcript = []
for idx in cell_lines:
transcript.extend(lines[idx]['transcript'])
cell['transcript'] = transcript
def segmentation_to_bbox(segmentation):
x1 = min([min([pt[0] for pt in contour]) for contour in segmentation])
y1 = min([min([pt[1] for pt in contour]) for contour in segmentation])
x2 = max([max([pt[0] for pt in contour]) for contour in segmentation])
y2 = max([max([pt[1] for pt in contour]) for contour in segmentation])
return [x1, y1, x2, y2]
def cal_cell_spans(table):
layout = table['layout']
num_cells = len(table['cells'])
cells_span = list()
for cell_id in range(num_cells):
cell_positions = np.argwhere(layout == cell_id)
y1 = np.min(cell_positions[:, 0])
y2 = np.max(cell_positions[:, 0])
x1 = np.min(cell_positions[:, 1])
x2 = np.max(cell_positions[:, 1])
assert np.all(layout[y1:y2, x1:x2] == cell_id)
cells_span.append([x1, y1, x2, y2])
return cells_span
def pred_result_to_table(table, pred_result):
# gt ocr result
lines = [dict(segmentation=cell['segmentation'], transcript=cell['transcript']) for cell in table['cells'] if 'bbox' in cell.keys()]
row_segments, col_segments, divide, spans = pred_result
num_rows = len(row_segments) - 1
num_cols = len(col_segments) - 1
layout = parse_layout(spans, num_rows, num_cols)
cells = parse_cells(layout, spans, row_segments, col_segments, lines)
head_rows = list(range(0, divide))
body_rows = list(range(divide, num_rows))
table = dict(
layout=layout,
head_rows=head_rows,
body_rows=body_rows,
cells=cells
)
return table
def table_to_relations(table):
cell_spans = cal_cell_spans(table)
contents = [''.join(cell['transcript']).split() for cell in table['cells']]
relations = []
for span, content in zip(cell_spans, contents):
x1, y1, x2, y2 = span
relations.append(dict(start_row=y1, end_row=y2, start_col=x1, end_col=x2, content=content))
return dict(cells=relations)
def cal_f1(label, pred):
label = json2Relations(label, splitted_content=True)
pred = json2Relations(pred, splitted_content=True)
precision, recall = eval_relations(gt=[label], res=[pred], cmp_blank=True)
f1 = 2.0 * precision * recall / (precision + recall) if precision + recall > 0 else 0
return [precision, recall, f1]
def single_process(labels, preds):
scores = dict()
for key in tqdm.tqdm(labels.keys()):
pred = preds.get(key, '')
label = labels.get(key, '')
score = cal_f1(label, pred)
scores[key] = score
return scores
def _worker(labels, preds, keys, result_queue):
for key in keys:
label = labels.get(key, '')
pred = preds.get(key, '')
score = cal_f1(label, pred)
result_queue.put((key, score))
def multi_process(labels, preds, num_workers):
import multiprocessing
manager = multiprocessing.Manager()
result_queue = manager.Queue()
keys = list(labels.keys())
workers = list()
for worker_idx in range(num_workers):
worker = multiprocessing.Process(
target=_worker,
args=(
labels,
preds,
keys[worker_idx::num_workers],
result_queue
)
)
worker.daemon = True
worker.start()
workers.append(worker)
scores = dict()
tq = tqdm.tqdm(total=len(keys))
for _ in range(len(keys)):
key, val = result_queue.get()
scores[key] = val
P, R, F1 = (100 * np.array(list(scores.values()))).mean(0).tolist()
tq.set_description('P: %.2f, R: %.2f, F1: %.2f' % (P, R, F1), False)
tq.update()
return scores
def evaluate_f1(labels, preds, num_workers=0):
preds = {idx: pred for idx, pred in enumerate(preds)}
labels = {idx: label for idx, label in enumerate(labels)}
if num_workers == 0:
scores = single_process(labels, preds)
else:
scores = multi_process(labels, preds, num_workers)
sorted_idx = sorted(list(range(len(list(scores)))), key=lambda idx: list(scores.keys())[idx])
scores = [scores[idx] for idx in sorted_idx]
return scores
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