File size: 5,001 Bytes
b837da3 1e9a6e5 b837da3 1e9a6e5 b837da3 1e9a6e5 b837da3 1e9a6e5 b837da3 1e9a6e5 b837da3 1e9a6e5 b837da3 1e9a6e5 b837da3 1e9a6e5 b837da3 1e9a6e5 b837da3 1e9a6e5 b837da3 1e9a6e5 b837da3 1e9a6e5 b837da3 1e9a6e5 b837da3 1e9a6e5 b837da3 1e9a6e5 b837da3 1e9a6e5 b837da3 1e9a6e5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 | from rapidfuzz import fuzz
from src.geometry import is_included
def _normalize_coords(coordinates):
"""Convert coordinates to list of [x, y] pairs.
Accepts either [{"x": x, "y": y}, ...] or [[x, y], ...].
"""
return [
[coord["x"], coord["y"]] if isinstance(coord, dict) else list(coord)
for coord in coordinates
]
def calc_nid(
gt_text : list,
pred_text : list,
) -> float:
"""Calculate the Normalized InDel score between the gt and pred text.
Args:
gt_text (str): The string of gt text to compare.
pred_text (str): The string of pred text to compare.
Returns:
float: The nid score between gt and pred text.
"""
# if gt and pred is empty, return 1
if len(gt_text) == 0 and len(pred_text) == 0:
score = 1
# if pred is empty while gt is not, return 0
elif len(gt_text) > 0 and len(pred_text) == 0:
score = 0
else:
score = fuzz.ratio(gt_text, pred_text)
return score
def extract_text(
gt_data : dict,
pred_data : dict,
ignore_classes : list = [],
strings_to_remove : list = ["\n"],
filter_by_gt_area : bool = True,
) -> tuple:
"""Extract text from both GT and prediction data, optionally filtering out
predictions that fall within GT ignored regions.
Args:
gt_data (dict): The GT data to extract text from.
pred_data (dict): The prediction data to extract text from.
ignore_classes (list): A list of classes to ignore during extraction.
strings_to_remove (list): A list of strings to remove from the extracted text.
filter_by_gt_area (bool): If True, filter out prediction text within GT ignored regions.
If False, only filter by category. Defaults to True.
Returns:
tuple: (gt_text, pred_text) - The concatenated text extracted from GT and predictions.
"""
ignore_classes = [x.lower() for x in ignore_classes]
# Collect GT ignored regions' coordinates (only if spatial filtering is enabled)
gt_ignored_regions = []
if filter_by_gt_area:
for elem in gt_data["elements"]:
if elem["category"].lower() in ignore_classes:
coords = _normalize_coords(elem["coordinates"])
gt_ignored_regions.append(coords)
# Extract GT text (excluding ignored classes)
gt_text = ""
for elem in gt_data["elements"]:
if elem["category"].lower() in ignore_classes:
continue
gt_text += elem["content"]["text"] + ' '
# Extract prediction text (excluding ignored classes AND optionally elements within GT ignored regions)
pred_text = ""
if pred_data is not None:
for elem in pred_data["elements"]:
if elem["category"].lower() in ignore_classes:
continue
# Check if this prediction element is included in any GT ignored region (only if enabled)
if filter_by_gt_area:
elem_coords = _normalize_coords(elem["coordinates"])
is_in_ignored_region = False
for ignored_region in gt_ignored_regions:
if is_included(ignored_region, elem_coords, soft=0.2):
is_in_ignored_region = True
break
if is_in_ignored_region:
continue
pred_text += elem["content"]["text"] + ' '
# Remove unwanted strings from both texts
for string in strings_to_remove:
gt_text = gt_text.replace(string, '')
pred_text = pred_text.replace(string, '')
return gt_text, pred_text
def evaluate_layout(
gt : dict,
pred : dict,
ignore_classes : list = [],
filter_by_gt_area : bool = True,
) -> tuple:
"""Evaluate the layout of the gt against the pred.
Args:
gt (dict): The gt layout to evaluate.
pred (dict): The pred layout to evaluate against.
ignore_classes (list): A list of classes to ignore during evaluation.
filter_by_gt_area (bool): If True, filter out prediction text within GT ignored regions.
If False, only filter by category. Defaults to True.
Returns:
tuple: (avg_score, per_image_scores) - The average layout evaluation score and per-image scores dict.
"""
scores = []
per_image_scores = {}
for image_key in gt.keys():
gt_data = gt.get(image_key)
pred_data = pred.get(image_key)
gt_text, pred_text = extract_text(gt_data, pred_data, ignore_classes, filter_by_gt_area=filter_by_gt_area)
score = calc_nid(gt_text, pred_text)
scores.append(score)
per_image_scores[image_key] = {
"nid_score": score / 100.0
}
if len(scores) > 0:
avg_score = sum(scores) / (len(scores) * 100)
else:
avg_score = 0
return avg_score, per_image_scores
|