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7d3be0edeb
Docling/docling/utils/layout_utils.py
Christoph Auer 7d3be0edeb
feat!: Docling v2 (#117) 
---------

Signed-off-by: Christoph Auer <cau@zurich.ibm.com>
Signed-off-by: Maxim Lysak <mly@zurich.ibm.com>
Signed-off-by: Michele Dolfi <dol@zurich.ibm.com>
Signed-off-by: Panos Vagenas <35837085+vagenas@users.noreply.github.com>
Co-authored-by: Maxim Lysak <mly@zurich.ibm.com>
Co-authored-by: Michele Dolfi <dol@zurich.ibm.com>
Co-authored-by: Panos Vagenas <35837085+vagenas@users.noreply.github.com>
2024-10-16 21:02:03 +02:00

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import copy
import logging
import networkx as nx
from docling_core.types.doc import DocItemLabel
logger = logging.getLogger("layout_utils")
## -------------------------------
## Geometric helper functions
## The coordinates grow left to right, and bottom to top.
## The bounding box list elements 0 to 3 are x_left, y_bottom, x_right, y_top.
def area(bbox):
return (bbox[2] - bbox[0]) * (bbox[3] - bbox[1])
def contains(bbox_i, bbox_j):
## Returns True if bbox_i contains bbox_j, else False
return (
bbox_i[0] <= bbox_j[0]
and bbox_i[1] <= bbox_j[1]
and bbox_i[2] >= bbox_j[2]
and bbox_i[3] >= bbox_j[3]
)
def is_intersecting(bbox_i, bbox_j):
return not (
bbox_i[2] < bbox_j[0]
or bbox_i[0] > bbox_j[2]
or bbox_i[3] < bbox_j[1]
or bbox_i[1] > bbox_j[3]
)
def bb_iou(boxA, boxB):
# determine the (x, y)-coordinates of the intersection rectangle
xA = max(boxA[0], boxB[0])
yA = max(boxA[1], boxB[1])
xB = min(boxA[2], boxB[2])
yB = min(boxA[3], boxB[3])
# compute the area of intersection rectangle
interArea = max(0, xB - xA + 1) * max(0, yB - yA + 1)
# compute the area of both the prediction and ground-truth
# rectangles
boxAArea = (boxA[2] - boxA[0] + 1) * (boxA[3] - boxA[1] + 1)
boxBArea = (boxB[2] - boxB[0] + 1) * (boxB[3] - boxB[1] + 1)
# compute the intersection over union by taking the intersection
# area and dividing it by the sum of prediction + ground-truth
# areas - the interesection area
iou = interArea / float(boxAArea + boxBArea - interArea)
# return the intersection over union value
return iou
def compute_intersection(bbox_i, bbox_j):
## Returns the size of the intersection area of the two boxes
if not is_intersecting(bbox_i, bbox_j):
return 0
## Determine the (x, y)-coordinates of the intersection rectangle:
xA = max(bbox_i[0], bbox_j[0])
yA = max(bbox_i[1], bbox_j[1])
xB = min(bbox_i[2], bbox_j[2])
yB = min(bbox_i[3], bbox_j[3])
## Compute the area of intersection rectangle:
interArea = (xB - xA) * (yB - yA)
if interArea < 0:
logger.debug("Warning: Negative intersection detected!")
return 0
return interArea
def surrounding(bbox_i, bbox_j):
## Computes minimal box that contains both input boxes
sbox = []
sbox.append(min(bbox_i[0], bbox_j[0]))
sbox.append(min(bbox_i[1], bbox_j[1]))
sbox.append(max(bbox_i[2], bbox_j[2]))
sbox.append(max(bbox_i[3], bbox_j[3]))
return sbox
def surrounding_list(bbox_list):
## Computes minimal box that contains all boxes in the input list
## The list should be non-empty, but just in case it's not:
if len(bbox_list) == 0:
sbox = [0, 0, 0, 0]
else:
sbox = []
sbox.append(min([bbox[0] for bbox in bbox_list]))
sbox.append(min([bbox[1] for bbox in bbox_list]))
sbox.append(max([bbox[2] for bbox in bbox_list]))
sbox.append(max([bbox[3] for bbox in bbox_list]))
return sbox
def vertical_overlap(bboxA, bboxB):
## bbox[1] is the lower bound, bbox[3] the upper bound (larger number)
if bboxB[3] < bboxA[1]: ## B below A
return False
elif bboxA[3] < bboxB[1]: ## A below B
return False
else:
return True
def vertical_overlap_fraction(bboxA, bboxB):
## Returns the vertical overlap as fraction of the lower bbox height.
## bbox[1] is the lower bound, bbox[3] the upper bound (larger number)
## Height 0 is permitted in the input.
heightA = bboxA[3] - bboxA[1]
heightB = bboxB[3] - bboxB[1]
min_height = min(heightA, heightB)
if bboxA[3] >= bboxB[3]: ## A starts higher or equal
if (
bboxA[1] <= bboxB[1]
): ## B is completely in A; this can include height of B = 0:
fraction = 1
else:
overlap = max(bboxB[3] - bboxA[1], 0)
fraction = overlap / max(min_height, 0.001)
else:
if (
bboxB[1] <= bboxA[1]
): ## A is completely in B; this can include height of A = 0:
fraction = 1
else:
overlap = max(bboxA[3] - bboxB[1], 0)
fraction = overlap / max(min_height, 0.001)
return fraction
## -------------------------------
## Cluster-and-cell relations
def compute_enclosed_cells(
cluster_bbox, raw_cells, min_cell_intersection_with_cluster=0.2
):
cells_in_cluster = []
cells_in_cluster_int = []
for ix, cell in enumerate(raw_cells):
cell_bbox = cell["bbox"]
intersection = compute_intersection(cell_bbox, cluster_bbox)
frac_area = area(cell_bbox) * min_cell_intersection_with_cluster
if (
intersection > frac_area and frac_area > 0
): # intersect > certain fraction of cell
cells_in_cluster.append(ix)
cells_in_cluster_int.append(intersection)
elif contains(
cluster_bbox,
[cell_bbox[0] + 3, cell_bbox[1] + 3, cell_bbox[2] - 3, cell_bbox[3] - 3],
):
cells_in_cluster.append(ix)
return cells_in_cluster, cells_in_cluster_int
def find_clusters_around_cells(cell_count, clusters):
## Per raw cell, find to which clusters it belongs.
## Return list of these indices in the raw-cell order.
clusters_around_cells = [[] for _ in range(cell_count)]
for cl_ix, cluster in enumerate(clusters):
for ix in cluster["cell_ids"]:
clusters_around_cells[ix].append(cl_ix)
return clusters_around_cells
def find_cell_index(raw_ix, cell_array):
## "raw_ix" is a rawcell_id.
## "cell_array" has the structure of an (annotation) cells array.
## Returns index of cell in cell_array that has this rawcell_id.
for ix, cell in enumerate(cell_array):
if cell["rawcell_id"] == raw_ix:
return ix
def find_cell_indices(cluster, cell_array):
## "cluster" must have the structure as in a clusters array in a prediction,
## "cell_array" that of a cells array.
## Returns list of indices of cells in cell_array that have the rawcell_ids as in the cluster,
## in the order of the rawcell_ids.
result = []
for raw_ix in sorted(cluster["cell_ids"]):
## Find the cell with this rawcell_id (if any)
for ix, cell in enumerate(cell_array):
if cell["rawcell_id"] == raw_ix:
result.append(ix)
return result
def find_first_cell_index(cluster, cell_array):
## "cluster" must be a dict with key "cell_ids"; it can also be a line.
## "cell_array" has the structure of a cells array in an annotation.
## Returns index of cell in cell_array that has the lowest rawcell_id from the cluster.
result = [] ## We keep it a list as it can be empty (picture without text cells)
if len(cluster["cell_ids"]) == 0:
return result
raw_ix = min(cluster["cell_ids"])
## Find the cell with this rawcell_id (if any)
for ix, cell in enumerate(cell_array):
if cell["rawcell_id"] == raw_ix:
result.append(ix)
break ## One is enough; should be only one anyway.
if result == []:
logger.debug(
" Warning: Raw cell " + str(raw_ix) + " not found in annotation cells"
)
return result
## -------------------------------
## Cluster labels and text
def relabel_cluster(cluster, cl_ix, new_label, target_pred):
## "cluster" must have the structure as in a clusters array in a prediction,
## "cl_ix" is its index in target_pred,
## "new_label" is the intended new label,
## "target_pred" is the entire current target prediction.
## Sets label on the cluster itself, and on the cells in the target_pred.
## Returns new_label so that also the cl_label variable in the main code is easily set.
target_pred["clusters"][cl_ix]["type"] = new_label
cluster_target_cells = find_cell_indices(cluster, target_pred["cells"])
for ix in cluster_target_cells:
target_pred["cells"][ix]["label"] = new_label
return new_label
def find_cluster_text(cluster, raw_cells):
## "cluster" must be a dict with "cell_ids"; it can also be a line.
## "raw_cells" must have the format of item["raw"]["cells"]
## Returns the text of the cluster, with blanks between the cell contents
## (which seem to be words or phrases without starting or trailing blanks).
## Note that in formulas, this may give a lot more blanks than originally
cluster_text = ""
for raw_ix in sorted(cluster["cell_ids"]):
cluster_text = cluster_text + raw_cells[raw_ix]["text"] + " "
return cluster_text.rstrip()
def find_cluster_text_without_blanks(cluster, raw_cells):
## "cluster" must be a dict with "cell_ids"; it can also be a line.
## "raw_cells" must have the format of item["raw"]["cells"]
## Returns the text of the cluster, without blanks between the cell contents
## Interesting in formula analysis.
cluster_text = ""
for raw_ix in sorted(cluster["cell_ids"]):
cluster_text = cluster_text + raw_cells[raw_ix]["text"]
return cluster_text.rstrip()
## -------------------------------
## Clusters and lines
## (Most line-oriented functions are only needed in TextAnalysisGivenClusters,
## but this one also in FormulaAnalysis)
def build_cluster_from_lines(lines, label, id):
## Lines must be a non-empty list of dicts (lines) with elements "cell_ids" and "bbox"
## (There is no condition that they are really geometrically lines)
## A cluster in standard format is returned with given label and id
local_lines = copy.deepcopy(
lines
) ## without this, it changes "lines" also outside this function
first_line = local_lines.pop(0)
cluster = {
"id": id,
"type": label,
"cell_ids": first_line["cell_ids"],
"bbox": first_line["bbox"],
"confidence": 0,
"created_by": "merged_cells",
}
confidence = 0
counter = 0
for line in local_lines:
new_cell_ids = cluster["cell_ids"] + line["cell_ids"]
cluster["cell_ids"] = new_cell_ids
cluster["bbox"] = surrounding(cluster["bbox"], line["bbox"])
counter += 1
confidence += line["confidence"]
confidence = confidence / counter
cluster["confidence"] = confidence
return cluster
## -------------------------------
## Reading order
def produce_reading_order(clusters, cluster_sort_type, cell_sort_type, sort_ids):
## In:
## Clusters: list as in predictions.
## cluster_sort_type: string, currently only "raw_cells".
## cell_sort_type: string, currently only "raw_cells".
## sort_ids: Boolean, whether the cluster ids should be adapted to their new position
## Out: Another clusters list, sorted according to the type.
logger.debug("---- Start cluster sorting ------")
if cell_sort_type == "raw_cell_ids":
for cl in clusters:
sorted_cell_ids = sorted(cl["cell_ids"])
cl["cell_ids"] = sorted_cell_ids
else:
logger.debug(
"Unknown cell_sort_type `"
+ cell_sort_type
+ "`, no cell sorting will happen."
)
if cluster_sort_type == "raw_cell_ids":
clusters_with_cells = [cl for cl in clusters if cl["cell_ids"] != []]
clusters_without_cells = [cl for cl in clusters if cl["cell_ids"] == []]
logger.debug(
"Clusters with cells: " + str([cl["id"] for cl in clusters_with_cells])
)
logger.debug(
" Their first cell ids: "
+ str([cl["cell_ids"][0] for cl in clusters_with_cells])
)
logger.debug(
"Clusters without cells: "
+ str([cl["id"] for cl in clusters_without_cells])
)
clusters_with_cells_sorted = sorted(
clusters_with_cells, key=lambda cluster: cluster["cell_ids"][0]
)
logger.debug(
" First cell ids after sorting: "
+ str([cl["cell_ids"][0] for cl in clusters_with_cells_sorted])
)
sorted_clusters = clusters_with_cells_sorted + clusters_without_cells
else:
logger.debug(
"Unknown cluster_sort_type: `"
+ cluster_sort_type
+ "`, no cluster sorting will happen."
)
if sort_ids:
for i, cl in enumerate(sorted_clusters):
cl["id"] = i
return sorted_clusters
## -------------------------------
## Line Splitting
def sort_cells_horizontal(line_cell_ids, raw_cells):
## "line_cells" should be a non-empty list of (raw) cell_ids
## "raw_cells" has the structure of item["raw"]["cells"].
## Sorts the cells in the line by x0 (left start).
new_line_cell_ids = sorted(
line_cell_ids, key=lambda cell_id: raw_cells[cell_id]["bbox"][0]
)
return new_line_cell_ids
def adapt_bboxes(raw_cells, clusters, orphan_cell_indices):
new_clusters = []
for ix, cluster in enumerate(clusters):
new_cluster = copy.deepcopy(cluster)
logger.debug(
"Treating cluster " + str(ix) + ", type " + str(new_cluster["type"])
)
logger.debug(" with cells: " + str(new_cluster["cell_ids"]))
if len(cluster["cell_ids"]) == 0 and cluster["type"] != DocItemLabel.PICTURE:
logger.debug(" Empty non-picture, removed")
continue ## Skip this former cluster, now without cells.
new_bbox = adapt_bbox(raw_cells, new_cluster, orphan_cell_indices)
new_cluster["bbox"] = new_bbox
new_clusters.append(new_cluster)
return new_clusters
def adapt_bbox(raw_cells, cluster, orphan_cell_indices):
if not (cluster["type"] in [DocItemLabel.TABLE, DocItemLabel.PICTURE]):
## A text-like cluster. The bbox only needs to be around the text cells:
logger.debug(" Initial bbox: " + str(cluster["bbox"]))
new_bbox = surrounding_list(
[raw_cells[cid]["bbox"] for cid in cluster["cell_ids"]]
)
logger.debug(" New bounding box:" + str(new_bbox))
if cluster["type"] == DocItemLabel.PICTURE:
## We only make the bbox completely comprise included text cells:
logger.debug(" Picture")
if len(cluster["cell_ids"]) != 0:
min_bbox = surrounding_list(
[raw_cells[cid]["bbox"] for cid in cluster["cell_ids"]]
)
logger.debug(" Minimum bbox: " + str(min_bbox))
logger.debug(" Initial bbox: " + str(cluster["bbox"]))
new_bbox = surrounding(min_bbox, cluster["bbox"])
logger.debug(" New bbox (initial and text cells): " + str(new_bbox))
else:
logger.debug(" without text cells, no change.")
new_bbox = cluster["bbox"]
else: ## A table
## At least we have to keep the included text cells, and we make the bbox completely comprise them
min_bbox = surrounding_list(
[raw_cells[cid]["bbox"] for cid in cluster["cell_ids"]]
)
logger.debug(" Minimum bbox: " + str(min_bbox))
logger.debug(" Initial bbox: " + str(cluster["bbox"]))
new_bbox = surrounding(min_bbox, cluster["bbox"])
logger.debug(" Possibly increased bbox: " + str(new_bbox))
## Now we look which non-belonging cells are covered.
## (To decrease dependencies, we don't make use of which cells we actually removed.)
## We don't worry about orphan cells, those could still be added to the table.
enclosed_cells = compute_enclosed_cells(
new_bbox, raw_cells, min_cell_intersection_with_cluster=0.3
)[0]
additional_cells = set(enclosed_cells) - set(cluster["cell_ids"])
logger.debug(
" Additional cells enclosed by Table bbox: " + str(additional_cells)
)
spurious_cells = additional_cells - set(orphan_cell_indices)
logger.debug(
" Spurious cells enclosed by Table bbox (additional minus orphans): "
+ str(spurious_cells)
)
if len(spurious_cells) == 0:
return new_bbox
## Else we want to keep as much as possible, e.g., grid lines, but not the spurious cells if we can.
## We initialize possible cuts with the current bbox.
left_cut = new_bbox[0]
right_cut = new_bbox[2]
upper_cut = new_bbox[3]
lower_cut = new_bbox[1]
for cell_ix in spurious_cells:
cell = raw_cells[cell_ix]
# logger.debug(" Spurious cell bbox: " + str(cell["bbox"]))
is_left = cell["bbox"][2] < min_bbox[0]
is_right = cell["bbox"][0] > min_bbox[2]
is_above = cell["bbox"][1] > min_bbox[3]
is_below = cell["bbox"][3] < min_bbox[1]
# logger.debug(" Left, right, above, below? " + str([is_left, is_right, is_above, is_below]))
if is_left:
if cell["bbox"][2] > left_cut:
## We move the left cut to exclude this cell:
left_cut = cell["bbox"][2]
if is_right:
if cell["bbox"][0] < right_cut:
## We move the right cut to exclude this cell:
right_cut = cell["bbox"][0]
if is_above:
if cell["bbox"][1] < upper_cut:
## We move the upper cut to exclude this cell:
upper_cut = cell["bbox"][1]
if is_below:
if cell["bbox"][3] > lower_cut:
## We move the left cut to exclude this cell:
lower_cut = cell["bbox"][3]
# logger.debug(" Current bbox: " + str([left_cut, lower_cut, right_cut, upper_cut]))
new_bbox = [left_cut, lower_cut, right_cut, upper_cut]
logger.debug(" Final bbox: " + str(new_bbox))
return new_bbox
def remove_cluster_duplicates_by_conf(cluster_predictions, threshold=0.5):
DuplicateDeletedClusterIDs = []
for cluster_1 in cluster_predictions:
for cluster_2 in cluster_predictions:
if cluster_1["id"] != cluster_2["id"]:
if_conf = False
if cluster_1["confidence"] > cluster_2["confidence"]:
if_conf = True
if if_conf == True:
if bb_iou(cluster_1["bbox"], cluster_2["bbox"]) > threshold:
DuplicateDeletedClusterIDs.append(cluster_2["id"])
elif contains(
cluster_1["bbox"],
[
cluster_2["bbox"][0] + 3,
cluster_2["bbox"][1] + 3,
cluster_2["bbox"][2] - 3,
cluster_2["bbox"][3] - 3,
],
):
DuplicateDeletedClusterIDs.append(cluster_2["id"])
DuplicateDeletedClusterIDs = list(set(DuplicateDeletedClusterIDs))
for cl_id in DuplicateDeletedClusterIDs:
for cluster in cluster_predictions:
if cl_id == cluster["id"]:
cluster_predictions.remove(cluster)
return cluster_predictions
# Assign orphan cells by a low confidence prediction that is below the assigned confidence
def assign_orphans_with_low_conf_pred(
cluster_predictions, cluster_predictions_low, raw_cells, orphan_cell_indices
):
for orph_id in orphan_cell_indices:
cluster_chosen = {}
iou_thresh = 0.05
confidence = 0.05
# Loop over all predictions, and find the one with the highest IOU, and confidence
for cluster in cluster_predictions_low:
calc_iou = bb_iou(cluster["bbox"], raw_cells[orph_id]["bbox"])
cluster_area = (cluster["bbox"][3] - cluster["bbox"][1]) * (
cluster["bbox"][2] - cluster["bbox"][0]
)
cell_area = (
raw_cells[orph_id]["bbox"][3] - raw_cells[orph_id]["bbox"][1]
) * (raw_cells[orph_id]["bbox"][2] - raw_cells[orph_id]["bbox"][0])
if (
(iou_thresh < calc_iou)
and (cluster["confidence"] > confidence)
and (cell_area * 3 > cluster_area)
):
cluster_chosen = cluster
iou_thresh = calc_iou
confidence = cluster["confidence"]
# If a candidate is found, assign to it the PDF cell ids, and tag that it was created by this function for tracking
if iou_thresh != 0.05 and confidence != 0.05:
cluster_chosen["cell_ids"].append(orph_id)
cluster_chosen["created_by"] = "orph_low_conf"
cluster_predictions.append(cluster_chosen)
orphan_cell_indices.remove(orph_id)
return cluster_predictions, orphan_cell_indices
def remove_ambigous_pdf_cell_by_conf(cluster_predictions, raw_cells, amb_cell_idxs):
for amb_cell_id in amb_cell_idxs:
highest_conf = 0
highest_bbox_iou = 0
cluster_chosen = None
problamatic_clusters = []
# Find clusters in question
for cluster in cluster_predictions:
if amb_cell_id in cluster["cell_ids"]:
problamatic_clusters.append(amb_cell_id)
# If the cell_id is in a cluster of high conf, and highest iou score, and smaller in area
bbox_iou_val = bb_iou(cluster["bbox"], raw_cells[amb_cell_id]["bbox"])
if (
cluster["confidence"] > highest_conf
and bbox_iou_val > highest_bbox_iou
):
cluster_chosen = cluster
highest_conf = cluster["confidence"]
highest_bbox_iou = bbox_iou_val
if cluster["id"] in problamatic_clusters:
problamatic_clusters.remove(cluster["id"])
# now remove the assigning of cell id from lower confidence, and threshold
for cluster in cluster_predictions:
for prob_amb_id in problamatic_clusters:
if prob_amb_id in cluster["cell_ids"]:
cluster["cell_ids"].remove(prob_amb_id)
amb_cell_idxs.remove(amb_cell_id)
return cluster_predictions, amb_cell_idxs
def ranges(nums):
# Find if consecutive numbers exist within pdf cells
# Used to remove line numbers for review manuscripts
nums = sorted(set(nums))
gaps = [[s, e] for s, e in zip(nums, nums[1:]) if s + 1 < e]
edges = iter(nums[:1] + sum(gaps, []) + nums[-1:])
return list(zip(edges, edges))
def set_orphan_as_text(
cluster_predictions, cluster_predictions_low, raw_cells, orphan_cell_indices
):
max_id = -1
figures = []
for cluster in cluster_predictions:
if cluster["type"] == DocItemLabel.PICTURE:
figures.append(cluster)
if cluster["id"] > max_id:
max_id = cluster["id"]
max_id += 1
lines_detector = False
content_of_orphans = []
for orph_id in orphan_cell_indices:
orph_cell = raw_cells[orph_id]
content_of_orphans.append(raw_cells[orph_id]["text"])
fil_content_of_orphans = []
for cell_content in content_of_orphans:
if cell_content.isnumeric():
try:
num = int(cell_content)
fil_content_of_orphans.append(num)
except ValueError: # ignore the cell
pass
# line_orphans = []
# Check if there are more than 2 pdf orphan cells, if there are more than 2,
# then check between the orphan cells if they are numeric
# and if they are a consecutive series of numbers (using ranges function) to decide
if len(fil_content_of_orphans) > 2:
out_ranges = ranges(fil_content_of_orphans)
if len(out_ranges) > 1:
cnt_range = 0
for ranges_ in out_ranges:
if ranges_[0] != ranges_[1]:
# If there are more than 75 (half the total line number of a review manuscript page)
# decide that there are line numbers on page to be ignored.
if len(list(range(ranges_[0], ranges_[1]))) > 75:
lines_detector = True
# line_orphans = line_orphans + list(range(ranges_[0], ranges_[1]))
for orph_id in orphan_cell_indices:
orph_cell = raw_cells[orph_id]
if bool(orph_cell["text"] and not orph_cell["text"].isspace()):
fig_flag = False
# Do not assign orphan cells if they are inside a figure
for fig in figures:
if contains(fig["bbox"], orph_cell["bbox"]):
fig_flag = True
# if fig_flag == False and raw_cells[orph_id]["text"] not in line_orphans:
if fig_flag == False and lines_detector == False:
# get class from low confidence detections if not set as text:
class_type = DocItemLabel.TEXT
for cluster in cluster_predictions_low:
intersection = compute_intersection(
orph_cell["bbox"], cluster["bbox"]
)
class_type = DocItemLabel.TEXT
if (
cluster["confidence"] > 0.1
and bb_iou(cluster["bbox"], orph_cell["bbox"]) > 0.4
):
class_type = cluster["type"]
elif contains(
cluster["bbox"],
[
orph_cell["bbox"][0] + 3,
orph_cell["bbox"][1] + 3,
orph_cell["bbox"][2] - 3,
orph_cell["bbox"][3] - 3,
],
):
class_type = cluster["type"]
elif intersection > area(orph_cell["bbox"]) * 0.2:
class_type = cluster["type"]
new_cluster = {
"id": max_id,
"bbox": orph_cell["bbox"],
"type": class_type,
"cell_ids": [orph_id],
"confidence": -1,
"created_by": "orphan_default",
}
max_id += 1
cluster_predictions.append(new_cluster)
return cluster_predictions, orphan_cell_indices
def merge_cells(cluster_predictions):
# Using graph component creates clusters if orphan cells are touching or too close.
G = nx.Graph()
for cluster in cluster_predictions:
if cluster["created_by"] == "orphan_default":
G.add_node(cluster["id"])
for cluster_1 in cluster_predictions:
for cluster_2 in cluster_predictions:
if (
cluster_1["id"] != cluster_2["id"]
and cluster_2["created_by"] == "orphan_default"
and cluster_1["created_by"] == "orphan_default"
):
cl1 = copy.deepcopy(cluster_1["bbox"])
cl2 = copy.deepcopy(cluster_2["bbox"])
cl1[0] = cl1[0] - 2
cl1[1] = cl1[1] - 2
cl1[2] = cl1[2] + 2
cl1[3] = cl1[3] + 2
cl2[0] = cl2[0] - 2
cl2[1] = cl2[1] - 2
cl2[2] = cl2[2] + 2
cl2[3] = cl2[3] + 2
if is_intersecting(cl1, cl2):
G.add_edge(cluster_1["id"], cluster_2["id"])
component = sorted(map(sorted, nx.k_edge_components(G, k=1)))
max_id = -1
for cluster_1 in cluster_predictions:
if cluster_1["id"] > max_id:
max_id = cluster_1["id"]
for nodes in component:
if len(nodes) > 1:
max_id += 1
lines = []
for node in nodes:
for cluster in cluster_predictions:
if cluster["id"] == node:
lines.append(cluster)
cluster_predictions.remove(cluster)
new_merged_cluster = build_cluster_from_lines(
lines, DocItemLabel.TEXT, max_id
)
cluster_predictions.append(new_merged_cluster)
return cluster_predictions
def clean_up_clusters(
cluster_predictions,
raw_cells,
merge_cells=False,
img_table=False,
one_cell_table=False,
):
DuplicateDeletedClusterIDs = []
for cluster_1 in cluster_predictions:
for cluster_2 in cluster_predictions:
if cluster_1["id"] != cluster_2["id"]:
# remove any artifcats created by merging clusters
if merge_cells == True:
if contains(
cluster_1["bbox"],
[
cluster_2["bbox"][0] + 3,
cluster_2["bbox"][1] + 3,
cluster_2["bbox"][2] - 3,
cluster_2["bbox"][3] - 3,
],
):
cluster_1["cell_ids"] = (
cluster_1["cell_ids"] + cluster_2["cell_ids"]
)
DuplicateDeletedClusterIDs.append(cluster_2["id"])
# remove clusters that might appear inside tables, or images (such as pdf cells in graphs)
elif img_table == True:
if (
cluster_1["type"] == DocItemLabel.TEXT
and cluster_2["type"] == DocItemLabel.PICTURE
or cluster_2["type"] == DocItemLabel.TABLE
):
if bb_iou(cluster_1["bbox"], cluster_2["bbox"]) > 0.5:
DuplicateDeletedClusterIDs.append(cluster_1["id"])
elif contains(
[
cluster_2["bbox"][0] - 3,
cluster_2["bbox"][1] - 3,
cluster_2["bbox"][2] + 3,
cluster_2["bbox"][3] + 3,
],
cluster_1["bbox"],
):
DuplicateDeletedClusterIDs.append(cluster_1["id"])
# remove tables that have one pdf cell
if one_cell_table == True:
if (
cluster_1["type"] == DocItemLabel.TABLE
and len(cluster_1["cell_ids"]) < 2
):
DuplicateDeletedClusterIDs.append(cluster_1["id"])
DuplicateDeletedClusterIDs = list(set(DuplicateDeletedClusterIDs))
for cl_id in DuplicateDeletedClusterIDs:
for cluster in cluster_predictions:
if cl_id == cluster["id"]:
cluster_predictions.remove(cluster)
return cluster_predictions
def assigning_cell_ids_to_clusters(clusters, raw_cells, threshold):
for cluster in clusters:
cells_in_cluster, _ = compute_enclosed_cells(
cluster["bbox"], raw_cells, min_cell_intersection_with_cluster=threshold
)
cluster["cell_ids"] = cells_in_cluster
## These cell_ids are ids of the raw cells.
## They are often, but not always, the same as the "id" or the index of the "cells" list in a prediction.
return clusters
# Creates a map of cell_id->cluster_id
def cell_id_state_map(clusters, cell_count):
clusters_around_cells = find_clusters_around_cells(cell_count, clusters)
orphan_cell_indices = [
ix for ix in range(cell_count) if len(clusters_around_cells[ix]) == 0
] # which cells are assigned no cluster?
ambiguous_cell_indices = [
ix for ix in range(cell_count) if len(clusters_around_cells[ix]) > 1
] # which cells are assigned > 1 clusters?
return clusters_around_cells, orphan_cell_indices, ambiguous_cell_indices
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