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Yilei JIANG
208
UIED/result_processing/evaluation.py
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208
UIED/result_processing/evaluation.py
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import json
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import numpy as np
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import cv2
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from glob import glob
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from os.path import join as pjoin
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from tqdm import tqdm
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def resize_label(bboxes, d_height, gt_height, bias=0):
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bboxes_new = []
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scale = gt_height / d_height
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for bbox in bboxes:
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bbox = [int(b * scale + bias) for b in bbox]
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bboxes_new.append(bbox)
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return bboxes_new
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def draw_bounding_box(org, corners, color=(0, 255, 0), line=2, show=False):
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board = org.copy()
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for i in range(len(corners)):
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board = cv2.rectangle(board, (corners[i][0], corners[i][1]), (corners[i][2], corners[i][3]), color, line)
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if show:
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cv2.imshow('a', cv2.resize(board, (500, 1000)))
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cv2.waitKey(0)
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return board
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def load_detect_result_json(reslut_file_root, shrink=4):
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def is_bottom_or_top(corner):
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column_min, row_min, column_max, row_max = corner
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if row_max < 36 or row_min > 725:
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return True
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return False
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result_files = glob(pjoin(reslut_file_root, '*.json'))
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compos_reform = {}
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print('Loading %d detection results' % len(result_files))
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for reslut_file in tqdm(result_files):
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img_name = reslut_file.split('\\')[-1].split('.')[0]
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compos = json.load(open(reslut_file, 'r'))['compos']
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for compo in compos:
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if compo['column_max'] - compo['column_min'] < 10 or compo['row_max'] - compo['row_min'] < 10:
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continue
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if is_bottom_or_top((compo['column_min'], compo['row_min'], compo['column_max'], compo['row_max'])):
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continue
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if img_name not in compos_reform:
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compos_reform[img_name] = {'bboxes': [[compo['column_min'] + shrink, compo['row_min'] + shrink, compo['column_max'] - shrink, compo['row_max'] - shrink]],
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'categories': [compo['category']]}
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else:
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compos_reform[img_name]['bboxes'].append([compo['column_min'] + shrink, compo['row_min'] + shrink, compo['column_max'] - shrink, compo['row_max'] - shrink])
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compos_reform[img_name]['categories'].append(compo['category'])
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return compos_reform
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def load_ground_truth_json(gt_file):
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def get_img_by_id(img_id):
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for image in images:
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if image['id'] == img_id:
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return image['file_name'].split('/')[-1][:-4], (image['height'], image['width'])
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def cvt_bbox(bbox):
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'''
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:param bbox: [x,y,width,height]
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:return: [col_min, row_min, col_max, row_max]
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'''
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bbox = [int(b) for b in bbox]
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return [bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3]]
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data = json.load(open(gt_file, 'r'))
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images = data['images']
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annots = data['annotations']
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compos = {}
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print('Loading %d ground truth' % len(annots))
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for annot in tqdm(annots):
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img_name, size = get_img_by_id(annot['image_id'])
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if img_name not in compos:
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compos[img_name] = {'bboxes': [cvt_bbox(annot['bbox'])], 'categories': [annot['category_id']], 'size': size}
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else:
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compos[img_name]['bboxes'].append(cvt_bbox(annot['bbox']))
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compos[img_name]['categories'].append(annot['category_id'])
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return compos
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def eval(detection, ground_truth, img_root, show=True, no_text=False, only_text=False):
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def compo_filter(compos, flag):
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if not no_text and not only_text:
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return compos
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compos_new = {'bboxes': [], 'categories': []}
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for k, category in enumerate(compos['categories']):
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if only_text:
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if flag == 'det' and category != 'TextView':
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continue
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if flag == 'gt' and int(category) != 14:
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continue
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elif no_text:
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if flag == 'det' and category == 'TextView':
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continue
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if flag == 'gt' and int(category) == 14:
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continue
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compos_new['bboxes'].append(compos['bboxes'][k])
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compos_new['categories'].append(category)
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return compos_new
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def match(org, d_bbox, gt_bboxes, matched):
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'''
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:param matched: mark if the ground truth component is matched
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:param d_bbox: [col_min, row_min, col_max, row_max]
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:param gt_bboxes: list of ground truth [[col_min, row_min, col_max, row_max]]
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:return: Boolean: if IOU large enough or detected box is contained by ground truth
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'''
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area_d = (d_bbox[2] - d_bbox[0]) * (d_bbox[3] - d_bbox[1])
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for i, gt_bbox in enumerate(gt_bboxes):
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if matched[i] == 0:
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continue
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area_gt = (gt_bbox[2] - gt_bbox[0]) * (gt_bbox[3] - gt_bbox[1])
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col_min = max(d_bbox[0], gt_bbox[0])
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row_min = max(d_bbox[1], gt_bbox[1])
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col_max = min(d_bbox[2], gt_bbox[2])
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row_max = min(d_bbox[3], gt_bbox[3])
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# if not intersected, area intersection should be 0
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w = max(0, col_max - col_min)
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h = max(0, row_max - row_min)
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area_inter = w * h
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if area_inter == 0:
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continue
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iod = area_inter / area_d
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iou = area_inter / (area_d + area_gt - area_inter)
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# if show:
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# cv2.putText(org, (str(round(iou, 2)) + ',' + str(round(iod, 2))), (d_bbox[0], d_bbox[1]),
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# cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
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if iou > 0.9 or iod == 1:
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matched[i] = 0
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return True
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return False
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amount = len(detection)
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TP, FP, FN = 0, 0, 0
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pres, recalls, f1s = [], [], []
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for i, image_id in enumerate(detection):
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TP_this, FP_this, FN_this = 0, 0, 0
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img = cv2.imread(pjoin(img_root, image_id + '.jpg'))
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d_compos = detection[image_id]
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if image_id not in ground_truth:
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continue
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gt_compos = ground_truth[image_id]
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org_height = gt_compos['size'][0]
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d_compos = compo_filter(d_compos, 'det')
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gt_compos = compo_filter(gt_compos, 'gt')
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d_compos['bboxes'] = resize_label(d_compos['bboxes'], 800, org_height)
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matched = np.ones(len(gt_compos['bboxes']), dtype=int)
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for d_bbox in d_compos['bboxes']:
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if match(img, d_bbox, gt_compos['bboxes'], matched):
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TP += 1
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TP_this += 1
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else:
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FP += 1
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FP_this += 1
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FN += sum(matched)
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FN_this = sum(matched)
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try:
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pre_this = TP_this / (TP_this + FP_this)
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recall_this = TP_this / (TP_this + FN_this)
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f1_this = 2 * (pre_this * recall_this) / (pre_this + recall_this)
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except:
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print('empty')
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continue
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pres.append(pre_this)
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recalls.append(recall_this)
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f1s.append(f1_this)
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if show:
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print(image_id + '.jpg')
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print('[%d/%d] TP:%d, FP:%d, FN:%d, Precesion:%.3f, Recall:%.3f' % (
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i, amount, TP_this, FP_this, FN_this, pre_this, recall_this))
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# cv2.imshow('org', cv2.resize(img, (500, 1000)))
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broad = draw_bounding_box(img, d_compos['bboxes'], color=(255, 0, 0), line=3)
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draw_bounding_box(broad, gt_compos['bboxes'], color=(0, 0, 255), show=True, line=2)
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if i % 200 == 0:
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precision = TP / (TP + FP)
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recall = TP / (TP + FN)
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f1 = 2 * (precision * recall) / (precision + recall)
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print(
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'[%d/%d] TP:%d, FP:%d, FN:%d, Precesion:%.3f, Recall:%.3f, F1:%.3f' % (i, amount, TP, FP, FN, precision, recall, f1))
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precision = TP / (TP + FP)
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recall = TP / (TP + FN)
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print('[%d/%d] TP:%d, FP:%d, FN:%d, Precesion:%.3f, Recall:%.3f, F1:%.3f' % (i, amount, TP, FP, FN, precision, recall, f1))
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# print("Average precision:%.4f; Average recall:%.3f" % (sum(pres)/len(pres), sum(recalls)/len(recalls)))
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return pres, recalls, f1s
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no_text = True
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only_text = False
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# detect = load_detect_result_json('E:\\Mulong\\Result\\rico\\rico_uied\\rico_new_uied_cls\\ip')
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detect = load_detect_result_json('E:\\Mulong\\Result\\rico\\rico_uied\\rico_new_uied_cls\\merge')
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# detect = load_detect_result_json('E:\\Mulong\\Result\\rico\\rico_uied\\rico_new_uied_v3\\merge')
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# detect = load_detect_result_json('E:\\Mulong\\Result\\rico\\rico_uied\\rico_new_uied_v3\\ocr')
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gt = load_ground_truth_json('E:\\Mulong\\Datasets\\rico\\instances_test.json')
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eval(detect, gt, 'E:\\Mulong\\Datasets\\rico\\combined', show=False, no_text=no_text, only_text=only_text)
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