计算目标检测和语义分割的PR-编程知识

需求描述

实际工作中，相比于mAP项目更加关心的是特定阈值下的precision和recall结果；
由于本次的GT中除了目标框之外还存在多边形标注，为此，计算IoU的方式从框与框之间变成了mask之间；
本文的代码适用于MMDetection下的预测结果和COCO格式之间来计算PR结果，具体的实现过程如下：

获取预测结果并保存到json文件中；
解析预测结果和GT；
根据image_id获取每张图的预测结果和GT；
基于mask计算预测结果和GT之间的iou矩阵；
根据iou矩阵得到对应的tp、fp和num_gt；
迭代所有的图像得到所有的tp、fp和num_gt累加，根据公式计算precision和recall；

具体实现

获取预测结果

在MMDetection框架下，通常使用如下的命令来评估模型的结果：

bash tools/dist_test.sh configs/aaaa/gaotie_cascade_rcnn_r50_fpn_1x.py work_dirs/gaotie_cascade_rcnn_r50_fpn_1x/epoch_20.pth 8 --eval bbox

此时能获取到类似下图的mAP结果。
mAP )
而我们需要在某个过程把预测结果保存下，用于后续得到PR结果，具体可以在mmdet/datasets/coco.py的438行位置添加如下代码：

 try:import shutilcocoDt = cocoGt.loadRes(result_files[metric])shutil.copyfile(result_files[metric], "results.bbox.json")

这样我们就可以得到results.bbox.json文件，里面包含的是模型的预测结果，如下图所示。
在这里插入图片描述 )

获取GT结果

由于标注时有两个格式：矩形框和多边形，因此在构建GT的coco格式文件时，对于矩形框会将其四个顶点作为多边形传入到segmentations字段，对于多边形会计算出外接矩形传入到bbox字段。
在这里插入图片描述 )
为此，获取GT信息的脚本实现如下：

def construct_gt_results(gt_json_path):results = dict()bbox_results = dict()cocoGt = COCO(annotation_file=gt_json_path)# cat_ids = cocoGt.getCatIds()img_ids = cocoGt.getImgIds()for id in img_ids:anno_ids = cocoGt.getAnnIds(imgIds=[id])annotations = cocoGt.loadAnns(ids=anno_ids)for info in annotations:img_id = info["image_id"]if img_id not in results:results[img_id] = list()bbox_results[img_id] = list()bbox = info["bbox"]x1, y1, x2, y2 = bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3]# results[img_id].append([x1, y1, x2, y2])# mask = _poly2mask(info["segmentation"], img_h=1544, img_w=2064)results[img_id].append(info["segmentation"])bbox_results[img_id].append([x1, y1, x2, y2])return results, img_ids, cocoGt, bbox_results

输入GT的json文件路径，返回所有图像的分割结果，image_id，COCO对象和目标框结果（用于后续的可视化结果）。

获取预测结果

模型预测出来的结果都是目标框的形式，与上面一样，将目标框的四个顶点作为多边形的分割结果。具体解析脚本如下：

def construct_det_results(det_json_path):results = dict()bbox_results = dict()scores  = dict()with open(det_json_path) as f:json_data = json.load(f)for info in json_data:img_id = info["image_id"]if img_id not in results:results[img_id] = list()scores[img_id] = list()bbox_results[img_id] = list()bbox = info["bbox"]x1, y1, x2, y2 = bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3]segm = [[x1, y1, x2, y1, x2, y2, x1, y2]]# mask = _poly2mask(segm, img_h=1544, img_w=2064)score = info["score"]# results[img_id].append([x1, y1, x2, y2, score])results[img_id].append(segm)bbox_results[img_id].append([x1, y1, x2, y2])scores[img_id].append(score)return results, scores, bbox_results

输入的是预测结果的json文件路径，输出是所有图像分割结果、得分和目标框结果。

根据image_id计算单个图像的TP、FP结果

本步骤的具体内容如下：

根据置信度阈值对预测框进行筛选；
将所有的多边形转换为mask，用于后续计算IoU；
得到tp和fp；
可视化fp和fn结果；

将多边形转换为mask

    if img_id in det_results:# for dt in det_results[img_id]:for idx, score in enumerate(det_scores[img_id]):# score = dt[-1]if score > conf_thrs:mask = _poly2mask(det_results[img_id][idx], img_h=1544, img_w=2064)det_bboxes.append(mask)det_thrs_scores.append(score)plot_det_bboxes.append(det_tmp_bboxes[img_id][idx])if img_id in gt_results:     for segm in gt_results[img_id]:mask = _poly2mask(segm, img_h=1544, img_w=2064)   gt_bboxes.append(mask)plot_gt_bboxes = gt_tmp_bboxes[img_id]

通过_poly2mask函数可以将多边形转换为mask，_poly2mask函数的实现如下。

def _poly2mask(mask_ann, img_h, img_w):"""Private function to convert masks represented with polygon tobitmaps.Args:mask_ann (list | dict): Polygon mask annotation input.img_h (int): The height of output mask.img_w (int): The width of output mask.Returns:numpy.ndarray: The decode bitmap mask of shape (img_h, img_w)."""if isinstance(mask_ann, list):# polygon -- a single object might consist of multiple parts# we merge all parts into one mask rle coderles = maskUtils.frPyObjects(mask_ann, img_h, img_w)rle = maskUtils.merge(rles)elif isinstance(mask_ann['counts'], list):# uncompressed RLErle = maskUtils.frPyObjects(mask_ann, img_h, img_w)else:# rlerle = mask_annmask = maskUtils.decode(rle)return mask

计算单张图像的TP和FP

本文中使用tpfp_default函数实现该功能，具体实现如下：

def tpfp_default(det_bboxes,gt_bboxes,gt_bboxes_ignore=None,det_thrs_scores=None,iou_thr=0.5,area_ranges=None):"""Check if detected bboxes are true positive or false positive.Args:det_bbox (ndarray): Detected bboxes of this image, of shape (m, 5).gt_bboxes (ndarray): GT bboxes of this image, of shape (n, 4).gt_bboxes_ignore (ndarray): Ignored gt bboxes of this image,of shape (k, 4). Default: Noneiou_thr (float): IoU threshold to be considered as matched.Default: 0.5.area_ranges (list[tuple] | None): Range of bbox areas to be evaluated,in the format [(min1, max1), (min2, max2), ...]. Default: None.Returns:tuple[np.ndarray]: (tp, fp) whose elements are 0 and 1. The shape ofeach array is (num_scales, m)."""# an indicator of ignored gtsgt_ignore_inds = np.concatenate((np.zeros(gt_bboxes.shape[0], dtype=np.bool),np.ones(gt_bboxes_ignore.shape[0], dtype=np.bool)))# stack gt_bboxes and gt_bboxes_ignore for convenience# gt_bboxes = np.vstack((gt_bboxes, gt_bboxes_ignore))num_dets = det_bboxes.shape[0]num_gts = gt_bboxes.shape[0]if area_ranges is None:area_ranges = [(None, None)]num_scales = len(area_ranges)# tp and fp are of shape (num_scales, num_gts), each row is tp or fp of# a certain scaletp = np.zeros((num_scales, num_dets), dtype=np.float32)fp = np.zeros((num_scales, num_dets), dtype=np.float32)# if there is no gt bboxes in this image, then all det bboxes# within area range are false positivesif gt_bboxes.shape[0] == 0:if area_ranges == [(None, None)]:fp[...] = 1else:det_areas = (det_bboxes[:, 2] - det_bboxes[:, 0] + 1) * (det_bboxes[:, 3] - det_bboxes[:, 1] + 1)for i, (min_area, max_area) in enumerate(area_ranges):fp[i, (det_areas >= min_area) & (det_areas < max_area)] = 1return tp, fp# ious = bbox_overlaps(det_bboxes, gt_bboxes)# ious = mask_overlaps(det_bboxes, gt_bboxes)ious = mask_wraper(det_bboxes, gt_bboxes)# for each det, the max iou with all gtsious_max = ious.max(axis=1)# for each det, which gt overlaps most with itious_argmax = ious.argmax(axis=1)# sort all dets in descending order by scores# sort_inds = np.argsort(-det_bboxes[:, -1])sort_inds = np.argsort(-det_thrs_scores)for k, (min_area, max_area) in enumerate(area_ranges):gt_covered = np.zeros(num_gts, dtype=bool)# if no area range is specified, gt_area_ignore is all Falseif min_area is None:gt_area_ignore = np.zeros_like(gt_ignore_inds, dtype=bool)else:gt_areas = (gt_bboxes[:, 2] - gt_bboxes[:, 0] + 1) * (gt_bboxes[:, 3] - gt_bboxes[:, 1] + 1)gt_area_ignore = (gt_areas < min_area) | (gt_areas >= max_area)for i in sort_inds:if ious_max[i] >= iou_thr:matched_gt = ious_argmax[i]     # 得到对应的GT索引if not (gt_ignore_inds[matched_gt]or gt_area_ignore[matched_gt]):if not gt_covered[matched_gt]:gt_covered[matched_gt] = True   # GT占位tp[k, i] = 1            else:fp[k, i] = 1# otherwise ignore this detected bbox, tp = 0, fp = 0elif min_area is None:fp[k, i] = 1else:bbox = det_bboxes[i, :4]area = (bbox[2] - bbox[0] + 1) * (bbox[3] - bbox[1] + 1)if area >= min_area and area < max_area:fp[k, i] = 1return tp, fp

过程是先获取预测框和GT框之间的IoU矩阵，然后按照置信度排序，将每个预测框分配给GT框得到tp和fp结果。

计算mask的IoU

IoU的定义都是一样的，计算公式如下：
在这里插入图片描述
基于mask计算IoU的实验也非常简单，代码如下：

def mask_overlaps(bboxes1, bboxes2, mode='iou'):assert mode in ['iou', 'iof']bboxes1 = bboxes1.astype(np.bool_)bboxes2 = bboxes2.astype(np.bool_)intersection = np.logical_and(bboxes1, bboxes2)union = np.logical_or(bboxes1, bboxes2)intersection_area = np.sum(intersection)union_area = np.sum(union)iou = intersection_area / union_areareturn iou

而计算预测框和GT之间的IoU矩阵实现如下：

def mask_wraper(bboxes1, bboxes2, mode='iou'):rows = bboxes1.shape[0]     # gtcols = bboxes2.shape[0]     # detious = np.zeros((rows, cols), dtype=np.float32)if rows * cols == 0:return iousfor i in range(rows):for j in range(cols):iou = mask_overlaps(bboxes1[i], bboxes2[j])ious[i, j] = ioureturn ious

至此，通过上述过程就能获取到单张图像的tp和fp结果。

可视化FP和FN结果

此外，我们需要分析模型的badcase，因此，可以将FP和FN的结果可视化出来，我这里是直接将存在问题的图像所有预测框和GT框都画出来了。

    if VIS and (fp > 0 or tp < gt):img_data, path = draw_bbox(img_id=img_id, cocoGt=cocoGt, det_bboxes=plot_det_bboxes, gt_bboxes=plot_gt_bboxes)if fp > 0:save_dir = os.path.join(VIS_ROOT, "tmp/FP/")os.makedirs(save_dir, exist_ok=True)cv2.imwrite(os.path.join(save_dir, os.path.basename(path)+".jpg"), img_data, [int(cv2.IMWRITE_JPEG_QUALITY), 30])if tp < gt:save_dir = os.path.join(VIS_ROOT, "tmp/FN/")os.makedirs(save_dir, exist_ok=True)cv2.imwrite(os.path.join(save_dir, os.path.basename(path)+".jpg"), img_data,[int(cv2.IMWRITE_JPEG_QUALITY), 30])

画框的实现如下：

def draw_bbox(img_id, cocoGt, det_bboxes, gt_bboxes):path = cocoGt.loadImgs(ids=[img_id])[0]["file_name"]img_path = os.path.join(IMG_ROOT, path)img_data = cv2.imread(img_path)for box in det_bboxes:# color_mask = (0, 0, 255)# color_mask = np.array([0, 0, 255], dtype=np.int8)# bbox_mask = box.astype(np.bool)cv2.rectangle(img_data, (int(box[0]), int(box[1])), (int(box[2]), int(box[3])), (0, 0, 255), 3)# img_data[bbox_mask] = img_data[bbox_mask] * 0.5 + color_mask * 0.5for box in gt_bboxes:# color_mask = np.array([0, 255, 0], dtype=np.int8)# bbox_mask = box.astype(np.bool)# img_data[bbox_mask] = img_data[bbox_mask] * 0.5 + color_mask * 0.5cv2.rectangle(img_data, (int(box[0]), int(box[1])), (int(box[2]), int(box[3])), (0, 255, 0), 3)return img_data, path

至此，我们实现了单张图像的所有业务逻辑。

多线程计算所有图像结果

通过multiprocessing启动一个进程池来加速结果计算。

def eval_multiprocessing(img_ids):from multiprocessing import Poolpool = Pool(processes=16)results = pool.map(eval_pr, img_ids)# 关闭进程池，表示不再接受新的任务pool.close()# 等待所有任务完成pool.join()return np.sum(np.array(results), axis=0)

计算PR结果

返回所有图像的TP和FP结果之后，就可以计算precision和recall值了。

gt, tp, fp = eval_multiprocessing(img_ids)
eps = np.finfo(np.float32).eps
recalls = tp / np.maximum(gt, eps)
precisions = tp / np.maximum((tp + fp), eps)print("conf_thrs:{:.3f} iou_thrs:{:.3f}, gt:{:d}, TP={:d}, FP={:d}, P={:.3f}, R={:.3f}".format(conf_thrs, iou_thrs, gt, tp, fp, precisions, recalls))

最后，也附上整个实现代码，方便后续复现或者参考。

from multiprocessing import Pool
import os
import numpy as np
import json
from pycocotools.coco import COCO
import cv2
from pycocotools import mask as maskUtilsdef bbox_overlaps(bboxes1, bboxes2, mode='iou'):"""Calculate the ious between each bbox of bboxes1 and bboxes2.Args:bboxes1(ndarray): shape (n, 4)bboxes2(ndarray): shape (k, 4)mode(str): iou (intersection over union) or iof (intersectionover foreground)Returns:ious(ndarray): shape (n, k)"""assert mode in ['iou', 'iof']bboxes1 = bboxes1.astype(np.float32)bboxes2 = bboxes2.astype(np.float32)rows = bboxes1.shape[0]cols = bboxes2.shape[0]ious = np.zeros((rows, cols), dtype=np.float32)if rows * cols == 0:return iousexchange = Falseif bboxes1.shape[0] > bboxes2.shape[0]:bboxes1, bboxes2 = bboxes2, bboxes1ious = np.zeros((cols, rows), dtype=np.float32)exchange = Truearea1 = (bboxes1[:, 2] - bboxes1[:, 0] + 1) * (bboxes1[:, 3] - bboxes1[:, 1] + 1)area2 = (bboxes2[:, 2] - bboxes2[:, 0] + 1) * (bboxes2[:, 3] - bboxes2[:, 1] + 1)for i in range(bboxes1.shape[0]):x_start = np.maximum(bboxes1[i, 0], bboxes2[:, 0])y_start = np.maximum(bboxes1[i, 1], bboxes2[:, 1])x_end = np.minimum(bboxes1[i, 2], bboxes2[:, 2])y_end = np.minimum(bboxes1[i, 3], bboxes2[:, 3])overlap = np.maximum(x_end - x_start + 1, 0) * np.maximum(y_end - y_start + 1, 0)if mode == 'iou':union = area1[i] + area2 - overlapelse:union = area1[i] if not exchange else area2ious[i, :] = overlap / unionif exchange:ious = ious.Treturn iousdef mask_wraper(bboxes1, bboxes2, mode='iou'):rows = bboxes1.shape[0]     # gtcols = bboxes2.shape[0]     # detious = np.zeros((rows, cols), dtype=np.float32)if rows * cols == 0:return iousfor i in range(rows):for j in range(cols):iou = mask_overlaps(bboxes1[i], bboxes2[j])ious[i, j] = ioureturn iousdef mask_overlaps(bboxes1, bboxes2, mode='iou'):assert mode in ['iou', 'iof']bboxes1 = bboxes1.astype(np.bool_)bboxes2 = bboxes2.astype(np.bool_)intersection = np.logical_and(bboxes1, bboxes2)union = np.logical_or(bboxes1, bboxes2)intersection_area = np.sum(intersection)union_area = np.sum(union)iou = intersection_area / union_areareturn ioudef tpfp_default(det_bboxes,gt_bboxes,gt_bboxes_ignore=None,det_thrs_scores=None,iou_thr=0.5,area_ranges=None):"""Check if detected bboxes are true positive or false positive.Args:det_bbox (ndarray): Detected bboxes of this image, of shape (m, 5).gt_bboxes (ndarray): GT bboxes of this image, of shape (n, 4).gt_bboxes_ignore (ndarray): Ignored gt bboxes of this image,of shape (k, 4). Default: Noneiou_thr (float): IoU threshold to be considered as matched.Default: 0.5.area_ranges (list[tuple] | None): Range of bbox areas to be evaluated,in the format [(min1, max1), (min2, max2), ...]. Default: None.Returns:tuple[np.ndarray]: (tp, fp) whose elements are 0 and 1. The shape ofeach array is (num_scales, m)."""# an indicator of ignored gtsgt_ignore_inds = np.concatenate((np.zeros(gt_bboxes.shape[0], dtype=np.bool),np.ones(gt_bboxes_ignore.shape[0], dtype=np.bool)))# stack gt_bboxes and gt_bboxes_ignore for convenience# gt_bboxes = np.vstack((gt_bboxes, gt_bboxes_ignore))num_dets = det_bboxes.shape[0]num_gts = gt_bboxes.shape[0]if area_ranges is None:area_ranges = [(None, None)]num_scales = len(area_ranges)# tp and fp are of shape (num_scales, num_gts), each row is tp or fp of# a certain scaletp = np.zeros((num_scales, num_dets), dtype=np.float32)fp = np.zeros((num_scales, num_dets), dtype=np.float32)# if there is no gt bboxes in this image, then all det bboxes# within area range are false positivesif gt_bboxes.shape[0] == 0:if area_ranges == [(None, None)]:fp[...] = 1else:det_areas = (det_bboxes[:, 2] - det_bboxes[:, 0] + 1) * (det_bboxes[:, 3] - det_bboxes[:, 1] + 1)for i, (min_area, max_area) in enumerate(area_ranges):fp[i, (det_areas >= min_area) & (det_areas < max_area)] = 1return tp, fp# ious = bbox_overlaps(det_bboxes, gt_bboxes)# ious = mask_overlaps(det_bboxes, gt_bboxes)ious = mask_wraper(det_bboxes, gt_bboxes)# for each det, the max iou with all gtsious_max = ious.max(axis=1)# for each det, which gt overlaps most with itious_argmax = ious.argmax(axis=1)# sort all dets in descending order by scores# sort_inds = np.argsort(-det_bboxes[:, -1])sort_inds = np.argsort(-det_thrs_scores)for k, (min_area, max_area) in enumerate(area_ranges):gt_covered = np.zeros(num_gts, dtype=bool)# if no area range is specified, gt_area_ignore is all Falseif min_area is None:gt_area_ignore = np.zeros_like(gt_ignore_inds, dtype=bool)else:gt_areas = (gt_bboxes[:, 2] - gt_bboxes[:, 0] + 1) * (gt_bboxes[:, 3] - gt_bboxes[:, 1] + 1)gt_area_ignore = (gt_areas < min_area) | (gt_areas >= max_area)for i in sort_inds:if ious_max[i] >= iou_thr:matched_gt = ious_argmax[i]     # 得到对应的GT索引if not (gt_ignore_inds[matched_gt]or gt_area_ignore[matched_gt]):if not gt_covered[matched_gt]:gt_covered[matched_gt] = True   # GT占位tp[k, i] = 1            else:fp[k, i] = 1# otherwise ignore this detected bbox, tp = 0, fp = 0elif min_area is None:fp[k, i] = 1else:bbox = det_bboxes[i, :4]area = (bbox[2] - bbox[0] + 1) * (bbox[3] - bbox[1] + 1)if area >= min_area and area < max_area:fp[k, i] = 1return tp, fpdef _poly2mask(mask_ann, img_h, img_w):"""Private function to convert masks represented with polygon tobitmaps.Args:mask_ann (list | dict): Polygon mask annotation input.img_h (int): The height of output mask.img_w (int): The width of output mask.Returns:numpy.ndarray: The decode bitmap mask of shape (img_h, img_w)."""if isinstance(mask_ann, list):# polygon -- a single object might consist of multiple parts# we merge all parts into one mask rle coderles = maskUtils.frPyObjects(mask_ann, img_h, img_w)rle = maskUtils.merge(rles)elif isinstance(mask_ann['counts'], list):# uncompressed RLErle = maskUtils.frPyObjects(mask_ann, img_h, img_w)else:# rlerle = mask_annmask = maskUtils.decode(rle)return maskdef construct_det_results(det_json_path):results = dict()bbox_results = dict()scores  = dict()with open(det_json_path) as f:json_data = json.load(f)for info in json_data:img_id = info["image_id"]if img_id not in results:results[img_id] = list()scores[img_id] = list()bbox_results[img_id] = list()bbox = info["bbox"]x1, y1, x2, y2 = bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3]segm = [[x1, y1, x2, y1, x2, y2, x1, y2]]# mask = _poly2mask(segm, img_h=1544, img_w=2064)score = info["score"]# results[img_id].append([x1, y1, x2, y2, score])results[img_id].append(segm)bbox_results[img_id].append([x1, y1, x2, y2])scores[img_id].append(score)return results, scores, bbox_resultsdef construct_gt_results(gt_json_path):results = dict()bbox_results = dict()cocoGt = COCO(annotation_file=gt_json_path)# cat_ids = cocoGt.getCatIds()img_ids = cocoGt.getImgIds()for id in img_ids:anno_ids = cocoGt.getAnnIds(imgIds=[id])annotations = cocoGt.loadAnns(ids=anno_ids)for info in annotations:img_id = info["image_id"]if img_id not in results:results[img_id] = list()bbox_results[img_id] = list()bbox = info["bbox"]x1, y1, x2, y2 = bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3]# results[img_id].append([x1, y1, x2, y2])# mask = _poly2mask(info["segmentation"], img_h=1544, img_w=2064)results[img_id].append(info["segmentation"])bbox_results[img_id].append([x1, y1, x2, y2])return results, img_ids, cocoGt, bbox_resultsdef draw_bbox(img_id, cocoGt, det_bboxes, gt_bboxes):path = cocoGt.loadImgs(ids=[img_id])[0]["file_name"]img_path = os.path.join(IMG_ROOT, path)img_data = cv2.imread(img_path)for box in det_bboxes:# color_mask = (0, 0, 255)# color_mask = np.array([0, 0, 255], dtype=np.int8)# bbox_mask = box.astype(np.bool)cv2.rectangle(img_data, (int(box[0]), int(box[1])), (int(box[2]), int(box[3])), (0, 0, 255), 3)# img_data[bbox_mask] = img_data[bbox_mask] * 0.5 + color_mask * 0.5for box in gt_bboxes:# color_mask = np.array([0, 255, 0], dtype=np.int8)# bbox_mask = box.astype(np.bool)# img_data[bbox_mask] = img_data[bbox_mask] * 0.5 + color_mask * 0.5cv2.rectangle(img_data, (int(box[0]), int(box[1])), (int(box[2]), int(box[3])), (0, 255, 0), 3)return img_data, pathdef eval_pr(img_id):tp, fp, gt = 0, 0, 0gt_bboxes, gt_ignore = [], []det_bboxes = list()gt_bboxes = list()det_thrs_scores = list()plot_det_bboxes = list()plot_gt_bboxes  = list()if img_id in det_results:# for dt in det_results[img_id]:for idx, score in enumerate(det_scores[img_id]):# score = dt[-1]if score > conf_thrs:mask = _poly2mask(det_results[img_id][idx], img_h=1544, img_w=2064)det_bboxes.append(mask)det_thrs_scores.append(score)plot_det_bboxes.append(det_tmp_bboxes[img_id][idx])if img_id in gt_results:     for segm in gt_results[img_id]:mask = _poly2mask(segm, img_h=1544, img_w=2064)   gt_bboxes.append(mask)plot_gt_bboxes = gt_tmp_bboxes[img_id]det_bboxes = np.array(det_bboxes)gt_bboxes = np.array(gt_bboxes)det_thrs_scores = np.array(det_thrs_scores)gt_ignore = np.array(gt_ignore).reshape(-1, 4)if len(gt_bboxes) > 0:if len(det_bboxes) == 0:tp, fp = 0, 0 else:tp, fp = tpfp_default(det_bboxes, gt_bboxes, gt_ignore, det_thrs_scores, iou_thrs)tp, fp = np.sum(tp == 1), np.sum(fp == 1)gt = len(gt_bboxes)else:fp = len(det_bboxes)if VIS and (fp > 0 or tp < gt):img_data, path = draw_bbox(img_id=img_id, cocoGt=cocoGt, det_bboxes=plot_det_bboxes, gt_bboxes=plot_gt_bboxes)if fp > 0:save_dir = os.path.join(VIS_ROOT, "tmp/FP/")os.makedirs(save_dir, exist_ok=True)cv2.imwrite(os.path.join(save_dir, os.path.basename(path)+".jpg"), img_data, [int(cv2.IMWRITE_JPEG_QUALITY), 30])if tp < gt:save_dir = os.path.join(VIS_ROOT, "tmp/FN/")os.makedirs(save_dir, exist_ok=True)cv2.imwrite(os.path.join(save_dir, os.path.basename(path)+".jpg"), img_data,[int(cv2.IMWRITE_JPEG_QUALITY), 30])return gt, tp, fpdef eval_multiprocessing(img_ids):from multiprocessing import Poolpool = Pool(processes=16)results = pool.map(eval_pr, img_ids)# 关闭进程池，表示不再接受新的任务pool.close()# 等待所有任务完成pool.join()return np.sum(np.array(results), axis=0)if __name__ == '__main__':VIS = 1IMG_ROOT = "gaotie_data"VIS_ROOT = 'badcase-vis-test-2/'conf_thrs = 0.5iou_thrs  = 0.001det_json_path = "results.bbox.json"gt_json_path  = "datasets/gaotie_test_data/annotations/test5_seg_removed.json"det_results, det_scores, det_tmp_bboxes = construct_det_results(det_json_path)gt_results, img_ids, cocoGt, gt_tmp_bboxes  = construct_gt_results(gt_json_path)gt, tp, fp = eval_multiprocessing(img_ids)eps = np.finfo(np.float32).epsrecalls = tp / np.maximum(gt, eps)precisions = tp / np.maximum((tp + fp), eps)print("conf_thrs:{:.3f} iou_thrs:{:.3f}, gt:{:d}, TP={:d}, FP={:d}, P={:.3f}, R={:.3f}".format(conf_thrs, iou_thrs, gt, tp, fp, precisions, recalls))