File size: 2,535 Bytes
4b4ea7f | 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 | from rapidfuzz import fuzz
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(
data : dict,
ignore_classes : list = [],
strings_to_remove : list = ["\n"],
) -> str:
"""Extract text from the dictionary data.
Args:
data (dict): The 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.
Returns:
str: The concatenated text extracted from the data.
"""
ignore_classes = [x.lower() for x in ignore_classes]
concatenated_text = ""
for elem in data["elements"]:
if elem["category"].lower() in ignore_classes:
continue
concatenated_text += str(elem["content"]["text"]) + ' '
# remove unwanted strings
for string in strings_to_remove:
concatenated_text = concatenated_text.replace(string, '')
return concatenated_text
def evaluate_layout(
gt : dict,
pred : dict,
ignore_classes : list = [],
) -> float:
"""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.
Returns:
float: The layout evaluation score.
"""
scores = []
for image_key in gt.keys():
gt_data = gt.get(image_key)
pred_data = pred.get(image_key)
gt_text = extract_text(gt_data, ignore_classes)
pred_text = extract_text(pred_data, ignore_classes)
score = calc_nid(gt_text, pred_text)
scores.append(score)
if len(scores) > 0:
avg_score = sum(scores) / (len(scores) * 100)
else:
avg_score = 0
return avg_score
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