# 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 os import sys import numpy as np import itertools from paddlers.models.ppdet.metrics.json_results import get_det_res, get_det_poly_res, get_seg_res, get_solov2_segm_res, get_keypoint_res from paddlers.models.ppdet.metrics.map_utils import draw_pr_curve from paddlers.models.ppdet.utils.logger import setup_logger logger = setup_logger(__name__) 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) if 'keypoint' in outs: infer_res['keypoint'] = get_keypoint_res(outs, im_id) outs['bbox_num'] = [len(infer_res['keypoint'])] return infer_res def cocoapi_eval(jsonfile, style, coco_gt=None, anno_file=None, max_dets=(100, 300, 1000), classwise=False, sigmas=None, use_area=True): """ Args: jsonfile (str): Evaluation json file, eg: bbox.json, mask.json. style (str): COCOeval style, can be `bbox` , `segm` , `proposal`, `keypoints` and `keypoints_crowd`. 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. sigmas (nparray): keypoint labelling sigmas. use_area (bool): If gt annotations (eg. CrowdPose, AIC) do not have 'area', please set use_area=False. """ assert coco_gt != None or anno_file != None if style == 'keypoints_crowd': #please install xtcocotools==1.6 from xtcocotools.coco import COCO from xtcocotools.cocoeval import COCOeval else: from pycocotools.coco import COCO from pycocotools.cocoeval import COCOeval if coco_gt == None: coco_gt = COCO(anno_file) logger.info("Start evaluate...") coco_dt = coco_gt.loadRes(jsonfile) if style == 'proposal': coco_eval = COCOeval(coco_gt, coco_dt, 'bbox') coco_eval.params.useCats = 0 coco_eval.params.maxDets = list(max_dets) elif style == 'keypoints_crowd': coco_eval = COCOeval(coco_gt, coco_dt, style, sigmas, use_area) 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: logger.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) 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) logger.info('Per-category of {} AP: \n{}'.format(style, table.table)) logger.info("per-category PR curve has output to {} folder.".format( style + '_pr_curve')) # flush coco evaluation result sys.stdout.flush() return coco_eval.stats def json_eval_results(metric, json_directory, dataset): """ cocoapi eval with already exists proposal.json, bbox.json or mask.json """ assert metric == 'COCO' anno_file = dataset.get_anno() json_file_list = ['proposal.json', 'bbox.json', 'mask.json'] if json_directory: assert os.path.exists( json_directory), "The json directory:{} does not exist".format( json_directory) for k, v in enumerate(json_file_list): json_file_list[k] = os.path.join(str(json_directory), v) coco_eval_style = ['proposal', 'bbox', 'segm'] for i, v_json in enumerate(json_file_list): if os.path.exists(v_json): cocoapi_eval(v_json, coco_eval_style[i], anno_file=anno_file) else: logger.info("{} not exists!".format(v_json))