add OCRBench v2
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99Franklin
184
OCRBench_v2/eval_scripts/spotting_metric.py
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184
OCRBench_v2/eval_scripts/spotting_metric.py
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import re
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import os
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import ast
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import ipdb
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import shutil
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import zipfile
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import subprocess
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import spotting_eval.rrc_evaluation_funcs_1_1 as rrc_evaluation_funcs
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from spotting_eval.script import default_evaluation_params,validate_data,evaluate_method
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def extract_bounding_boxes_robust(predict_str):
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"""
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Extract coordinates and text content from the given prediction string,
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handling potential format issues.
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Args:
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predict_str (str): Model prediction output as a string.
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Returns:
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list: Extracted data in the format [[x1, y1, x2, y2, text_content], ...].
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Returns None if no valid data is extracted.
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"""
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results = []
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seen = set()
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# try parsing with ast.literal_eval
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try:
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data = ast.literal_eval(predict_str)
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except Exception:
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data = None
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if data is not None:
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if isinstance(data, (list, tuple)):
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for item in data:
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if isinstance(item, (list, tuple)) and len(item) >= 5:
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x1_str, y1_str, x2_str, y2_str = item[:4]
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text_content = item[4]
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x1_str = str(x1_str).strip()
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y1_str = str(y1_str).strip()
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x2_str = str(x2_str).strip()
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y2_str = str(y2_str).strip()
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text_content = str(text_content).replace("\n", "").strip().strip('"').strip("'")
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try:
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x1 = int(x1_str)
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y1 = int(y1_str)
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x2 = int(x2_str)
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y2 = int(y2_str)
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if not (0 <= x1 <= 1000 and 0 <= y1 <= 1000 and 0 <= x2 <= 1000 and 0 <= y2 <= 1000):
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continue
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key = (x1, y1, x2, y2, text_content)
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if key in seen:
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continue
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seen.add(key)
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results.append([x1, y1, x2, y2, text_content])
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except ValueError:
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continue
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else:
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# try parsing with regular expression
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list_content = predict_str
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items = re.findall(r'[\[\(]\s*([^\[\]\(\)]*?)\s*[\]\)]', list_content)
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if not items:
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return None
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for item in items:
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parts = item.split(',', 4)
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if len(parts) < 5:
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continue
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x1_str, y1_str, x2_str, y2_str, text_content = parts
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x1_str = x1_str.strip()
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y1_str = y1_str.strip()
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x2_str = x2_str.strip()
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y2_str = y2_str.strip()
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text_content = text_content.replace("\n", "").strip().strip('"').strip("'")
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try:
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x1 = int(x1_str)
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y1 = int(y1_str)
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x2 = int(x2_str)
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y2 = int(y2_str)
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if not (0 <= x1 <= 1000 and 0 <= y1 <= 1000 and 0 <= x2 <= 1000 and 0 <= y2 <= 1000):
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continue
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key = (x1, y1, x2, y2, text_content)
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if key in seen:
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continue
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seen.add(key)
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results.append([x1, y1, x2, y2, text_content])
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except ValueError:
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continue
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if not results:
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return None
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return results
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def zip_folder(source_folder, destination_zip):
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abs_source = os.path.abspath(source_folder)
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abs_destination = os.path.abspath(destination_zip)
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with zipfile.ZipFile(abs_destination, 'w', zipfile.ZIP_DEFLATED) as zf:
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for root, _, files in os.walk(abs_source):
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for file in files:
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abs_file_path = os.path.join(root, file)
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relative_path = os.path.relpath(abs_file_path, abs_source)
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zf.write(abs_file_path, relative_path)
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def spotting_evaluation(prediction_list, img_metas):
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score = 0
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submit_path = "./eval_scripts/spotting_eval/submit"
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gt_path = "./eval_scripts/spotting_eval/gt"
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submit_zip_path = "./eval_scripts/spotting_eval/submit.zip"
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gt_zip_path = "./eval_scripts/spotting_eval/gt.zip"
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for file_path in [submit_path, gt_path, submit_zip_path, gt_zip_path]:
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if "zip" in file_path:
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if os.path.exists(file_path):
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os.remove(file_path)
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else:
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if os.path.exists(file_path):
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shutil.rmtree(file_path)
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os.makedirs(file_path)
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res_submit_list = []
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for item in prediction_list:
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if len(item) != 5:
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ipdb.set_trace()
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x1, y1, x2, y2, rec = item
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if x1 >= x2 or y1 >= y2:
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continue
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res_submit_list.append(",".join([str(x1),str(y1),str(x2),str(y1),str(x2),str(y2),str(x1),str(y2),rec]))
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res_gt_list = []
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for bbox, rec in zip(img_metas["bbox"], img_metas["content"]):
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x_coords = bbox[0::2]
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y_coords = bbox[1::2]
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x1, y1 = min(x_coords), min(y_coords)
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x2, y2 = max(x_coords), max(y_coords)
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res_gt_list.append(",".join([str(x1),str(y1),str(x2),str(y1),str(x2),str(y2),str(x1),str(y2),rec]))
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if len(res_submit_list) == 0 or len(res_gt_list) == 0:
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return 0
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with open(os.path.join(submit_path,"res_img_0.txt"), "w") as f:
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for item in res_submit_list[:-1]:
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f.write(item + "\n")
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f.write(res_submit_list[-1])
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with open(os.path.join(gt_path,"gt_img_0.txt"), "w") as f:
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for item in res_gt_list[:-1]:
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f.write(item + "\n")
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f.write(res_gt_list[-1])
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zip_folder(submit_path, submit_zip_path)
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zip_folder(gt_path, gt_zip_path)
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command = {
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'g': gt_zip_path,
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's': submit_zip_path,
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'o': './',
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'p': '{"IOU_CONSTRAINT":0.5}'
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}
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# run rrc_evaluation_funcs
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result = rrc_evaluation_funcs.main_evaluation(command,default_evaluation_params,validate_data,evaluate_method)
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score = result["method"]["hmean"]
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return score
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