mAP concept reference https://blog.csdn.net/u013066730/article/details/82707559
import os
import numpy as np
from os import mkdir
import xml.etree.ElementTree as ET
from easydict import EasyDict as edict
cfgs = edict()
cfgs.EVALUATE_DIR = r"E:\data\yolov3\test\evaluate"
cfgs.VERSION = r"cls_dir"
cfgs.USE_07_METRIC = False
NAME_LABEL_MAP = {"person": 0, "car": 1}
def write_voc_results_file(all_boxes, test_imgid_list, det_save_dir):
for cls, cls_id in NAME_LABEL_MAP.items():
if cls == 'back_ground':
continue
print("Writing {} VOC resutls file".format(cls))
if not os.path.exists(det_save_dir):
mkdir(det_save_dir)
det_save_path = os.path.join(det_save_dir, "det_" + cls + ".txt")
with open(det_save_path, 'wt') as f:
for index, img_name in enumerate(test_imgid_list):
this_img_detections = np.array(all_boxes[index])
if this_img_detections.shape[0] == 0:
continue
this_cls_detections = this_img_detections[this_img_detections[:, 0] == cls_id]
if this_cls_detections.shape[0] == 0:
continue # this cls has none detections in this img
for a_det in this_cls_detections:
f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'.
format(img_name, a_det[1],
a_det[2], a_det[3],
a_det[4], a_det[5])) # that is [img_name, score, xmin, ymin, xmax, ymax]
def parse_rec(filename):
""" Parse a PASCAL VOC xml file """
tree = ET.parse(filename)
objects = []
for obj in tree.findall('object'):
obj_struct = {}
obj_struct['name'] = obj.find('name').text #####################这里可能需要调整
obj_struct['pose'] = obj.find('pose').text
obj_struct['truncated'] = int(obj.find('truncated').text)
obj_struct['difficult'] = int(obj.find('difficult').text)
bbox = obj.find('bndbox')
obj_struct['bbox'] = [int(bbox.find('xmin').text),
int(bbox.find('ymin').text),
int(bbox.find('xmax').text),
int(bbox.find('ymax').text)]
objects.append(obj_struct)
return objects
def voc_ap(rec, prec, use_07_metric=False):
if use_07_metric:
# 11 point metric
ap = 0.
for t in np.arange(0., 1.1, 0.1):
if np.sum(rec >= t) == 0:
p = 0
else:
p = np.max(prec[rec >= t])
ap = ap + p / 11.
else:
# correct AP calculation
# first append sentinel values at the end
mrec = np.concatenate(([0.], rec, [1.]))
mpre = np.concatenate(([0.], prec, [0.]))
# compute the precision envelope
for i in range(mpre.size - 1, 0, -1):
mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])
# to calculate area under PR curve, look for points
# where X axis (recall) changes value
i = np.where(mrec[1:] != mrec[:-1])[0]
# and sum (\Delta recall) * prec
ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
return ap
def voc_eval(detpath, annopath, test_imgid_list, cls_name, ovthresh=0.5,
use_07_metric=False, use_diff=False):
# 1. parse xml to get gtboxes
# read list of images
imagenames = test_imgid_list
recs = {}
for i, imagename in enumerate(imagenames):
recs[imagename] = parse_rec(os.path.join(annopath, imagename + '.xml'))
# if i % 100 == 0:
# print('Reading annotation for {:d}/{:d}'.format(
# i + 1, len(imagenames)))
# 2. get gtboxes for this class.
class_recs = {}
num_pos = 0
# if cls_name == 'person':
# print ("aaa")
for imagename in imagenames:
R = [obj for obj in recs[imagename] if obj['name'] == cls_name]
bbox = np.array([x['bbox'] for x in R])
if use_diff:
difficult = np.array([False for x in R]).astype(np.bool)
else:
difficult = np.array([x['difficult'] for x in R]).astype(np.bool)
det = [False] * len(R)
num_pos = num_pos + sum(~difficult) # ignored the diffcult boxes
class_recs[imagename] = {'bbox': bbox,
'difficult': difficult,
'det': det} # det means that gtboxes has already been detected
# 3. read the detection file
detfile = os.path.join(detpath, "det_" + cls_name + ".txt")
with open(detfile, 'r') as f:
lines = f.readlines()
# for a line. that is [img_name, confidence, xmin, ymin, xmax, ymax]
splitlines = [x.strip().split(' ') for x in lines] # a list that include a list
image_ids = [x[0] for x in splitlines] # img_id is img_name
confidence = np.array([float(x[1]) for x in splitlines])
BB = np.array([[float(z) for z in x[2:]] for x in splitlines])
nd = len(image_ids) # num of detections. That, a line is a det_box.
tp = np.zeros(nd)
fp = np.zeros(nd)
if BB.shape[0] > 0:
# sort by confidence
sorted_ind = np.argsort(-confidence)
sorted_scores = np.sort(-confidence)
BB = BB[sorted_ind, :]
image_ids = [image_ids[x] for x in sorted_ind] # reorder the img_name
# go down dets and mark TPs and FPs
for d in range(nd):
R = class_recs[image_ids[d]] # img_id is img_name
bb = BB[d, :].astype(float)
ovmax = -np.inf
BBGT = R['bbox'].astype(float)
if BBGT.size > 0:
# compute overlaps
# intersection
ixmin = np.maximum(BBGT[:, 0], bb[0])
iymin = np.maximum(BBGT[:, 1], bb[1])
ixmax = np.minimum(BBGT[:, 2], bb[2])
iymax = np.minimum(BBGT[:, 3], bb[3])
iw = np.maximum(ixmax - ixmin + 1., 0.)
ih = np.maximum(iymax - iymin + 1., 0.)
inters = iw * ih
# union
uni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) +
(BBGT[:, 2] - BBGT[:, 0] + 1.) *
(BBGT[:, 3] - BBGT[:, 1] + 1.) - inters)
overlaps = inters / uni
ovmax = np.max(overlaps)
jmax = np.argmax(overlaps)
if ovmax > ovthresh:
if not R['difficult'][jmax]:
if not R['det'][jmax]:
tp[d] = 1.
R['det'][jmax] = 1
else:
fp[d] = 1.
else:
fp[d] = 1.
# 4. get recall, precison and AP
fp = np.cumsum(fp)
tp = np.cumsum(tp)
rec = tp / float(num_pos)
# avoid divide by zero in case the first detection matches a difficult
# ground truth
prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)
ap = voc_ap(rec, prec, use_07_metric)
return rec, prec, ap
def do_python_eval(test_imgid_list, test_annotation_path):
AP_list = []
# import matplotlib.pyplot as plt
# import matplotlib.colors as colors
# color_list = colors.cnames.keys()[::6]
for cls, index in NAME_LABEL_MAP.items():
if cls == 'back_ground':
continue
recall, precision, AP = voc_eval(detpath=os.path.join(cfgs.EVALUATE_DIR, cfgs.VERSION),
test_imgid_list=test_imgid_list,
cls_name=cls,
annopath=test_annotation_path,
use_07_metric=cfgs.USE_07_METRIC)
AP_list += [AP]
print("cls : {}|| Recall: {} || Precison: {}|| AP: {}".format(cls, recall[-1], precision[-1], AP))
# plt.plot(recall, precision, label=cls, color=color_list[index])
# plt.legend(loc='upper right')
# print(10*"__")
# plt.show()
# plt.savefig(cfgs.VERSION+'.jpg')
print("mAP is : {}".format(np.mean(AP_list)))
def voc_evaluate_detections(all_boxes, test_annotation_path, test_imgid_list):
'''
all_boxes:形状为(n,m,6)n表示有多少张图像，m表示一张图像有多少个检测物体，6表示 [category, score, xmin, ymin, xmax, ymax]
test_imgid_list：表示评估图片的图片名列表，其形状为（n,）
test_annotation_path：标签xml所在的文件夹
:param all_boxes: is a list. each item reprensent the detections of a img.The detections is a array. shape is [-1, 6]. [category, score, xmin, ymin, xmax, ymax].Note that: if none detections in this img. that the detetions is : []
:return:
'''
test_imgid_list = [item.split('.')[0] for item in test_imgid_list]
write_voc_results_file(all_boxes, test_imgid_list=test_imgid_list,
det_save_dir=os.path.join(cfgs.EVALUATE_DIR, cfgs.VERSION))
do_python_eval(test_imgid_list, test_annotation_path=test_annotation_path)
test_annotation_path = r"E:\data\yolov3\annotations"
import pickle
with open(r"E:\data\yolov3\test\evaluate\all_boxes.pkl", 'rb') as opened_file: # 读出文件
all_boxes = pickle.load(opened_file)
test_imgid_list = []
with open(r'E:\data\yolov3\test\evaluate\test_imgid_list.txt') as f:
lines = f.readlines()
for line in lines:
test_imgid_list.append(line.split("\n")[0])
voc_evaluate_detections(all_boxes, test_annotation_path, test_imgid_list)The main parameters
The first part of the parameters
This is mainly for storing temporary files, you can set it up by yourself, it does n’t matter if you set it up
cfgs.EVALUATE_DIR = r"E:\data\yolov3\test\evaluate"
cfgs.VERSION = r"cls_dir"
The second part of the parameters
Here is the folder where you store the xml file. The specific xml format is similar to voc.
test_annotation_path = r"E:\data\yolov3\annotations"
The third part of the parameters
with open(r"E:\data\yolov3\test\evaluate\all_boxes.pkl", 'rb') as opened_file
This all_boxes.pkl holds a bunch of arrays, the specific shape is (n, m, 6), where n represents how many images, m represents how many detection objects an image has, 6 represents [category, score, xmin , ymin, xmax, ymax].
The fourth part of the parameters
with open(r'E:\data\yolov3\test\evaluate\test_imgid_list.txt') as f
It is mainly the test_imgid_list.txt file, which mainly saves the name of the image, without .png and other suffixes, and without the path, it is simply
00001
00002
00003
Wait.
note
The third part parameters and the fourth part parameters need one-to-one correspondence
