门户网站建设推荐,长沙线上引流公司,桂林北站地址,企业网站备案要钱吗需求描述
实际工作中#xff0c;相比于mAP项目更加关心的是特定阈值下的precision和recall结果#xff1b;由于本次的GT中除了目标框之外还存在多边形标注#xff0c;为此#xff0c;计算IoU的方式从框与框之间变成了mask之间#xff1b; 本文的代码适用于MMDetection下的…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。 ) 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。 )
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。 ) 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_filegt_json_path)# cat_ids cocoGt.getCatIds()img_ids cocoGt.getImgIds()for id in img_ids:anno_ids cocoGt.getAnnIds(imgIds[id])annotations cocoGt.loadAnns(idsanno_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_h1544, img_w2064)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_idCOCO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_h1544, img_w2064)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_h1544, img_w2064)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_h1544, img_w2064) 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_ignoreNone,det_thrs_scoresNone,iou_thr0.5,area_rangesNone):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], dtypenp.bool),np.ones(gt_bboxes_ignore.shape[0], dtypenp.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), dtypenp.float32)fp np.zeros((num_scales, num_dets), dtypenp.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(axis1)# for each det, which gt overlaps most with itious_argmax ious.argmax(axis1)# 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, dtypebool)# 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, dtypebool)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 基于mask计算IoU的实验也非常简单代码如下
def mask_overlaps(bboxes1, bboxes2, modeiou):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, modeiou):rows bboxes1.shape[0] # gtcols bboxes2.shape[0] # detious np.zeros((rows, cols), dtypenp.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_idimg_id, cocoGtcocoGt, det_bboxesplot_det_bboxes, gt_bboxesplot_gt_bboxes)if fp 0:save_dir os.path.join(VIS_ROOT, tmp/FP/)os.makedirs(save_dir, exist_okTrue)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_okTrue)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], dtypenp.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], dtypenp.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(processes16)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), axis0)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, modeiou):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), dtypenp.float32)if rows * cols 0:return iousexchange Falseif bboxes1.shape[0] bboxes2.shape[0]:bboxes1, bboxes2 bboxes2, bboxes1ious np.zeros((cols, rows), dtypenp.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, modeiou):rows bboxes1.shape[0] # gtcols bboxes2.shape[0] # detious np.zeros((rows, cols), dtypenp.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, modeiou):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_ignoreNone,det_thrs_scoresNone,iou_thr0.5,area_rangesNone):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], dtypenp.bool),np.ones(gt_bboxes_ignore.shape[0], dtypenp.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), dtypenp.float32)fp np.zeros((num_scales, num_dets), dtypenp.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(axis1)# for each det, which gt overlaps most with itious_argmax ious.argmax(axis1)# 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, dtypebool)# 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, dtypebool)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_h1544, img_w2064)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_filegt_json_path)# cat_ids cocoGt.getCatIds()img_ids cocoGt.getImgIds()for id in img_ids:anno_ids cocoGt.getAnnIds(imgIds[id])annotations cocoGt.loadAnns(idsanno_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_h1544, img_w2064)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], dtypenp.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], dtypenp.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_h1544, img_w2064)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_h1544, img_w2064) 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_idimg_id, cocoGtcocoGt, det_bboxesplot_det_bboxes, gt_bboxesplot_gt_bboxes)if fp 0:save_dir os.path.join(VIS_ROOT, tmp/FP/)os.makedirs(save_dir, exist_okTrue)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_okTrue)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(processes16)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), axis0)if __name__ __main__:VIS 1IMG_ROOT gaotie_dataVIS_ROOT badcase-vis-test-2/conf_thrs 0.5iou_thrs 0.001det_json_path results.bbox.jsongt_json_path datasets/gaotie_test_data/annotations/test5_seg_removed.jsondet_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))
总结
本文针对目标检测任务中GT存在多边形情况下给出了如下计算数据集的PR结果基于mask来计算IoU与语义分割计算IoU的思路一致最后也给出了所有的实现代码作为参考。
