| import json |
| import numpy as np |
| import cv2 |
| from glob import glob |
| from os.path import join as pjoin |
| from tqdm import tqdm |
|
|
| class_map = {'0':'Button', '1':'CheckBox', '2':'Chronometer', '3':'EditText', '4':'ImageButton', '5':'ImageView', |
| '6':'ProgressBar', '7':'RadioButton', '8':'RatingBar', '9':'SeekBar', '10':'Spinner', '11':'Switch', |
| '12':'ToggleButton', '13':'VideoView', '14':'TextView'} |
|
|
|
|
| def resize_label(bboxes, d_height, gt_height, bias=0): |
| bboxes_new = [] |
| scale = gt_height / d_height |
| for bbox in bboxes: |
| bbox = [int(b * scale + bias) for b in bbox] |
| bboxes_new.append(bbox) |
| return bboxes_new |
|
|
|
|
| def draw_bounding_box(org, corners, color=(0, 255, 0), line=2, show=False): |
| board = org.copy() |
| for i in range(len(corners)): |
| board = cv2.rectangle(board, (corners[i][0], corners[i][1]), (corners[i][2], corners[i][3]), color, line) |
| if show: |
| cv2.imshow('a', cv2.resize(board, (500, 1000))) |
| cv2.waitKey(0) |
| return board |
|
|
|
|
| def load_detect_result_json(reslut_file_root, shrink=4): |
| def is_bottom_or_top(corner): |
| column_min, row_min, column_max, row_max = corner |
| if row_max < 36 or row_min > 725: |
| return True |
| return False |
|
|
| result_files = glob(pjoin(reslut_file_root, '*.json')) |
| compos_reform = {} |
| print('Loading %d detection results' % len(result_files)) |
| for reslut_file in tqdm(result_files): |
| img_name = reslut_file.split('\\')[-1].split('.')[0] |
| compos = json.load(open(reslut_file, 'r'))['compos'] |
| for compo in compos: |
| if compo['column_max'] - compo['column_min'] < 10 or compo['row_max'] - compo['row_min'] < 10: |
| continue |
| if is_bottom_or_top((compo['column_min'], compo['row_min'], compo['column_max'], compo['row_max'])): |
| continue |
| if img_name not in compos_reform: |
| compos_reform[img_name] = {'bboxes': [[compo['column_min'] + shrink, compo['row_min'] + shrink, compo['column_max'] - shrink, compo['row_max'] - shrink]], |
| 'categories': [compo['category']]} |
| else: |
| compos_reform[img_name]['bboxes'].append([compo['column_min'] + shrink, compo['row_min'] + shrink, compo['column_max'] - shrink, compo['row_max'] - shrink]) |
| compos_reform[img_name]['categories'].append(compo['category']) |
| return compos_reform |
|
|
|
|
| def load_ground_truth_json(gt_file): |
| def get_img_by_id(img_id): |
| for image in images: |
| if image['id'] == img_id: |
| return image['file_name'].split('/')[-1][:-4], (image['height'], image['width']) |
|
|
| def cvt_bbox(bbox): |
| ''' |
| :param bbox: [x,y,width,height] |
| :return: [col_min, row_min, col_max, row_max] |
| ''' |
| bbox = [int(b) for b in bbox] |
| return [bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3]] |
|
|
| data = json.load(open(gt_file, 'r')) |
| images = data['images'] |
| annots = data['annotations'] |
| compos = {} |
| print('Loading %d ground truth' % len(annots)) |
| for annot in tqdm(annots): |
| img_name, size = get_img_by_id(annot['image_id']) |
| if img_name not in compos: |
| compos[img_name] = {'bboxes': [cvt_bbox(annot['bbox'])], 'categories': [class_map[str(annot['category_id'])]], 'size': size} |
| else: |
| compos[img_name]['bboxes'].append(cvt_bbox(annot['bbox'])) |
| compos[img_name]['categories'].append(class_map[str(annot['category_id'])]) |
| return compos |
|
|
|
|
| def eval(detection, ground_truth, img_root, show=True, no_text=False, only_text=False): |
| def compo_filter(compos, flag): |
| if not no_text and not only_text: |
| return compos |
| compos_new = {'bboxes': [], 'categories': []} |
| for k, category in enumerate(compos['categories']): |
| if only_text: |
| if flag == 'det' and category != 'TextView': |
| continue |
| if flag == 'gt' and category != 'TextView': |
| continue |
| elif no_text: |
| if flag == 'det' and category == 'TextView': |
| continue |
| if flag == 'gt' and category == 'TextView': |
| continue |
|
|
| compos_new['bboxes'].append(compos['bboxes'][k]) |
| compos_new['categories'].append(category) |
| return compos_new |
|
|
| def match(org, d_bbox, d_category, gt_compos, matched): |
| ''' |
| :param matched: mark if the ground truth component is matched |
| :param d_bbox: [col_min, row_min, col_max, row_max] |
| :param gt_bboxes: list of ground truth [[col_min, row_min, col_max, row_max]] |
| :return: Boolean: if IOU large enough or detected box is contained by ground truth |
| ''' |
| area_d = (d_bbox[2] - d_bbox[0]) * (d_bbox[3] - d_bbox[1]) |
| gt_bboxes = gt_compos['bboxes'] |
| gt_categories = gt_compos['categories'] |
| for i, gt_bbox in enumerate(gt_bboxes): |
| if matched[i] == 0: |
| continue |
| area_gt = (gt_bbox[2] - gt_bbox[0]) * (gt_bbox[3] - gt_bbox[1]) |
| col_min = max(d_bbox[0], gt_bbox[0]) |
| row_min = max(d_bbox[1], gt_bbox[1]) |
| col_max = min(d_bbox[2], gt_bbox[2]) |
| row_max = min(d_bbox[3], gt_bbox[3]) |
| |
| w = max(0, col_max - col_min) |
| h = max(0, row_max - row_min) |
| area_inter = w * h |
| if area_inter == 0: |
| continue |
| iod = area_inter / area_d |
| iou = area_inter / (area_d + area_gt - area_inter) |
| |
| |
| |
|
|
| if iou > 0.9 or iod == 1: |
| if d_category == gt_categories[i]: |
| matched[i] = 0 |
| return True |
| return False |
|
|
| amount = len(detection) |
| TP, FP, FN = 0, 0, 0 |
| pres, recalls, f1s = [], [], [] |
| for i, image_id in enumerate(detection): |
| TP_this, FP_this, FN_this = 0, 0, 0 |
| img = cv2.imread(pjoin(img_root, image_id + '.jpg')) |
| d_compos = detection[image_id] |
| if image_id not in ground_truth: |
| continue |
| gt_compos = ground_truth[image_id] |
|
|
| org_height = gt_compos['size'][0] |
|
|
| d_compos = compo_filter(d_compos, 'det') |
| gt_compos = compo_filter(gt_compos, 'gt') |
|
|
| d_compos['bboxes'] = resize_label(d_compos['bboxes'], 800, org_height) |
| matched = np.ones(len(gt_compos['bboxes']), dtype=int) |
| for j, d_bbox in enumerate(d_compos['bboxes']): |
| if match(img, d_bbox, d_compos['categories'][j], gt_compos, matched): |
| TP += 1 |
| TP_this += 1 |
| else: |
| FP += 1 |
| FP_this += 1 |
| FN += sum(matched) |
| FN_this = sum(matched) |
|
|
| try: |
| pre_this = TP_this / (TP_this + FP_this) |
| recall_this = TP_this / (TP_this + FN_this) |
| f1_this = 2 * (pre_this * recall_this) / (pre_this + recall_this) |
| except: |
| print('empty') |
| continue |
|
|
| pres.append(pre_this) |
| recalls.append(recall_this) |
| f1s.append(f1_this) |
| if show: |
| print(image_id + '.jpg') |
| print('[%d/%d] TP:%d, FP:%d, FN:%d, Precesion:%.3f, Recall:%.3f' % ( |
| i, amount, TP_this, FP_this, FN_this, pre_this, recall_this)) |
| |
| broad = draw_bounding_box(img, d_compos['bboxes'], color=(255, 0, 0), line=3) |
| draw_bounding_box(broad, gt_compos['bboxes'], color=(0, 0, 255), show=True, line=2) |
|
|
| if i % 200 == 0: |
| precision = TP / (TP + FP) |
| recall = TP / (TP + FN) |
| f1 = 2 * (precision * recall) / (precision + recall) |
| print( |
| '[%d/%d] TP:%d, FP:%d, FN:%d, Precesion:%.3f, Recall:%.3f, F1:%.3f' % (i, amount, TP, FP, FN, precision, recall, f1)) |
|
|
| precision = TP / (TP + FP) |
| recall = TP / (TP + FN) |
| print('[%d/%d] TP:%d, FP:%d, FN:%d, Precesion:%.3f, Recall:%.3f, F1:%.3f' % (i, amount, TP, FP, FN, precision, recall, f1)) |
| |
|
|
| return pres, recalls, f1s |
|
|
|
|
| no_text = True |
| only_text = False |
|
|
| |
| |
| detect = load_detect_result_json('E:\\Mulong\\Result\\rico\\rico_uied\\rico_new_uied_v3\\merge') |
| |
| gt = load_ground_truth_json('E:\\Mulong\\Datasets\\rico\\instances_test.json') |
| eval(detect, gt, 'E:\\Mulong\\Datasets\\rico\\combined', show=False, no_text=no_text, only_text=only_text) |
|
|
