# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import absolute_import from __future__ import division from __future__ import print_function import sys import numpy as np from .map_utils import draw_pr_curve from .json_results import get_det_res, get_det_poly_res, get_seg_res, get_solov2_segm_res from . import fd_logging as logging import copy def loadRes(coco_obj, anns): """ Load result file and return a result api object. :param resFile (str) : file name of result file :return: res (obj) : result api object """ # This function has the same functionality as pycocotools.COCO.loadRes, # except that the input anns is list of results rather than a json file. # Refer to # https://github.com/cocodataset/cocoapi/blob/8c9bcc3cf640524c4c20a9c40e89cb6a2f2fa0e9/PythonAPI/pycocotools/coco.py#L305, # matplotlib.use() must be called *before* pylab, matplotlib.pyplot, # or matplotlib.backends is imported for the first time # pycocotools import matplotlib import matplotlib matplotlib.use('Agg') from pycocotools.coco import COCO import pycocotools.mask as maskUtils import time res = COCO() res.dataset['images'] = [img for img in coco_obj.dataset['images']] tic = time.time() assert type(anns) == list, 'results in not an array of objects' annsImgIds = [ann['image_id'] for ann in anns] assert set(annsImgIds) == (set(annsImgIds) & set(coco_obj.getImgIds())), \ 'Results do not correspond to current coco set' if 'caption' in anns[0]: imgIds = set([img['id'] for img in res.dataset['images']]) & set( [ann['image_id'] for ann in anns]) res.dataset['images'] = [ img for img in res.dataset['images'] if img['id'] in imgIds ] for id, ann in enumerate(anns): ann['id'] = id + 1 elif 'bbox' in anns[0] and not anns[0]['bbox'] == []: res.dataset['categories'] = copy.deepcopy(coco_obj.dataset[ 'categories']) for id, ann in enumerate(anns): bb = ann['bbox'] x1, x2, y1, y2 = [bb[0], bb[0] + bb[2], bb[1], bb[1] + bb[3]] if not 'segmentation' in ann: ann['segmentation'] = [[x1, y1, x1, y2, x2, y2, x2, y1]] ann['area'] = bb[2] * bb[3] ann['id'] = id + 1 ann['iscrowd'] = 0 elif 'segmentation' in anns[0]: res.dataset['categories'] = copy.deepcopy(coco_obj.dataset[ 'categories']) for id, ann in enumerate(anns): # now only support compressed RLE format as segmentation results ann['area'] = maskUtils.area(ann['segmentation']) if not 'bbox' in ann: ann['bbox'] = maskUtils.toBbox(ann['segmentation']) ann['id'] = id + 1 ann['iscrowd'] = 0 elif 'keypoints' in anns[0]: res.dataset['categories'] = copy.deepcopy(coco_obj.dataset[ 'categories']) for id, ann in enumerate(anns): s = ann['keypoints'] x = s[0::3] y = s[1::3] x0, x1, y0, y1 = np.min(x), np.max(x), np.min(y), np.max(y) ann['area'] = (x1 - x0) * (y1 - y0) ann['id'] = id + 1 ann['bbox'] = [x0, y0, x1 - x0, y1 - y0] res.dataset['annotations'] = anns res.createIndex() return res def get_infer_results(outs, catid, bias=0): """ Get result at the stage of inference. The output format is dictionary containing bbox or mask result. For example, bbox result is a list and each element contains image_id, category_id, bbox and score. """ if outs is None or len(outs) == 0: raise ValueError( 'The number of valid detection result if zero. Please use reasonable model and check input data.' ) im_id = outs['im_id'] infer_res = {} if 'bbox' in outs: if len(outs['bbox']) > 0 and len(outs['bbox'][0]) > 6: infer_res['bbox'] = get_det_poly_res( outs['bbox'], outs['bbox_num'], im_id, catid, bias=bias) else: infer_res['bbox'] = get_det_res( outs['bbox'], outs['bbox_num'], im_id, catid, bias=bias) if 'mask' in outs: # mask post process infer_res['mask'] = get_seg_res(outs['mask'], outs['bbox'], outs['bbox_num'], im_id, catid) if 'segm' in outs: infer_res['segm'] = get_solov2_segm_res(outs, im_id, catid) return infer_res def cocoapi_eval(anns, style, coco_gt=None, anno_file=None, max_dets=(100, 300, 1000), classwise=False): """ Args: anns: Evaluation result. style (str): COCOeval style, can be `bbox` , `segm` and `proposal`. coco_gt (str): Whether to load COCOAPI through anno_file, eg: coco_gt = COCO(anno_file) anno_file (str): COCO annotations file. max_dets (tuple): COCO evaluation maxDets. classwise (bool): Whether per-category AP and draw P-R Curve or not. """ assert coco_gt is not None or anno_file is not None from pycocotools.coco import COCO from pycocotools.cocoeval import COCOeval if coco_gt is None: coco_gt = COCO(anno_file) logging.info("Start evaluate...") coco_dt = loadRes(coco_gt, anns) if style == 'proposal': coco_eval = COCOeval(coco_gt, coco_dt, 'bbox') coco_eval.params.useCats = 0 coco_eval.params.maxDets = list(max_dets) else: coco_eval = COCOeval(coco_gt, coco_dt, style) coco_eval.evaluate() coco_eval.accumulate() coco_eval.summarize() if classwise: # Compute per-category AP and PR curve try: from terminaltables import AsciiTable except Exception as e: logging.error( 'terminaltables not found, plaese install terminaltables. ' 'for example: `pip install terminaltables`.') raise e precisions = coco_eval.eval['precision'] cat_ids = coco_gt.getCatIds() # precision: (iou, recall, cls, area range, max dets) assert len(cat_ids) == precisions.shape[2] results_per_category = [] for idx, catId in enumerate(cat_ids): # area range index 0: all area ranges # max dets index -1: typically 100 per image nm = coco_gt.loadCats(catId)[0] precision = precisions[:, :, idx, 0, -1] precision = precision[precision > -1] if precision.size: ap = np.mean(precision) else: ap = float('nan') results_per_category.append( (str(nm["name"]), '{:0.3f}'.format(float(ap)))) pr_array = precisions[0, :, idx, 0, 2] recall_array = np.arange(0.0, 1.01, 0.01) draw_pr_curve( pr_array, recall_array, out_dir=style + '_pr_curve', file_name='{}_precision_recall_curve.jpg'.format(nm["name"])) num_columns = min(6, len(results_per_category) * 2) import itertools results_flatten = list(itertools.chain(*results_per_category)) headers = ['category', 'AP'] * (num_columns // 2) results_2d = itertools.zip_longest( * [results_flatten[i::num_columns] for i in range(num_columns)]) table_data = [headers] table_data += [result for result in results_2d] table = AsciiTable(table_data) logging.info('Per-category of {} AP: \n{}'.format(style, table.table)) logging.info("per-category PR curve has output to {} folder.".format( style + '_pr_curve')) # flush coco evaluation result sys.stdout.flush() return coco_eval.stats