背景：实现相应的目标检测网络需要能够测试mAP
目的：实现mAP的测试。
参考代码：https://github.com/Cartucho/mAP#create-the-ground-truth-files
目录
一、mAP概览
1.1 mAP概览
mAP为目标检测领域的基础指标。
首先标签相同交并比IoU>0.5表示网络检测正确。
然后画出相应的查全率与查准率的曲线，积分得到的蓝色区域即为mAP。
各类的平均AP即mAP
1.2 测试需要的步骤
•Create a separate ground-truth text file for each image. In these files, each line should be in the following format:<class_name> <left> <top> <right> <bottom> [<difficult>]
生成相应的标注文档，无论是GroundTruth还是网络生成的预测，都需要按照上面的格式标注。
•Use matching names(e.g. image: "image_1.jpg", ground-truth: "image_1.txt"; "image_2.jpg", "image_2.txt"...).
然后图片放入相应的文件夹之中。
1.Create the ground-truth files创建相应的GroundTruth文件
2.Move the ground-truth files into the folderground-truth/ 放入相应文件夹
3.Create the predicted objects files 创建相应的预测的文件
4.Move the predictions files into the folderpredicted/ 放入相应文件夹
5.Run themAPcode 运行mAP的代码
二、GroundTruth文档的生成
按照要求将文件拷入文件夹与生成相应的标注。
<class_name> <left> <top> <right> <bottom> [<difficult>]标注需要与文件同名且每一行按照这种格式生成。
"""
author:xing xiangrui
time :2018.9.26 10:34
copy image and change name to the new directory
write image name and labels to the list.txt
"""
import os
import shutil
oldDir = "data"
newDir = "head_data"
width = 352
height = 288
fw = open('list.txt','w')
for root,dirs,files in os.walk(oldDir):
if 'images' in root:
continue
for file in files:
if 'jpg' in file:
# copy jpg
oldPic = root + os.path.sep + file
newPic = newDir + os.path.sep + root.split(os.path.sep)[-2] + "_" + root.split(os.path.sep)[-1] + "_" + file
shutil.copyfile(oldPic,newPic)
fw.write(newPic)
# write txt
oldTxt = root + os.path.sep + file.replace('jpg','txt')
fo = open(oldTxt,'r')
lines = fo.readlines()
for line in lines:
line = line.split()
cx, cy, w, h = float(line[1])*width, float(line[2])*height, float(line[3])*width, float(line[4])*height
#[x, y, w, h] = [float(line[i]) for i in range(1,5)]
x1 = round(cx - w/2, 2)
y1 = round(cy - h/2, 2)
x2 = round(cx + w/2, 2)
y2 = round(cy + h/2, 2)
print(x1,y1,x2,y2)
fw.write(" " + str(x1) + " " + str(y1) + " " + str(x2) + " " + str(y2))
fw.write("\n")
fo.close()
#print("fine")
print("done")
fw.close()此部分代码实现了
- 旧用于预测的图像重命名放入新目录下
- 旧标签按照mAP的要求格式生成新标签
三、网络预测结果生成
for tmp_file in jpg_list:
img=cv2.imread(tmp_file)
# add ROI region
ROI=img[ROI_idx[0]:ROI_idx[1],ROI_idx[2]:ROI_idx[3]]
ROI_temp=ROI.copy()
img[:,:,:]=0
img[ROI_idx[0]:ROI_idx[1],ROI_idx[2]:ROI_idx[3]]=ROI_temp
#create txt file
tmp_file=tmp_file.replace("jpg","txt")
txt_filename=tmp_file.replace("images","predicted")
print("LOACTION!!!predict:"+tmp_file)
# start_time = time.time()
#print("LOCATION!!!detect_face function start"+"\n")
rectangles, points = detect_face(img, args.minsize,
pnet_fun, rnet_fun, onet_fun,
args.threshold, args.factor)
#print("LOCATION!!!idetect_face function done"+"\n")
# duration = time.time() - start_time
# print("duration:"+str(duration))
#print(type(rectangles))
points = np.transpose(points)
#print("LOCATION!!!loop rectangles"+"\n")
with open(txt_filename,'w') as result_file:
for rectangle in rectangles:
result_file.write("head" + " " + str(rectangle[4]) + " " + str(rectangle[0]) + " " + str(rectangle[1]) + " " + str(rectangle[2]) + " " + str(rectangle[3])+"\n")
#print("LOCATION!!!Write done!"+"\n")
print(ROI_idx)
os.chdir("mAP/")
os.system("python main.py -na")根据网络预测将所有图片运行一遍，结果写入相应txt文件之中，然后调用mAP测试函数对结果进行预测。
四、预测mAP代码
4.1 运算IoU
# load prediction bounding-box
bb = [ float(x) for x in prediction["bbox"].split() ]
for obj in ground_truth_data:
# look for a class_name match
if obj["class_name"] == class_name:
bbgt = [ float(x) for x in obj["bbox"].split() ]
bi = [max(bb[0],bbgt[0]), max(bb[1],bbgt[1]), min(bb[2],bbgt[2]), min(bb[3],bbgt[3])]
iw = bi[2] - bi[0] + 1
ih = bi[3] - bi[1] + 1
if iw > 0 and ih > 0:
# compute overlap (IoU) = area of intersection / area of union
ua = (bb[2] - bb[0] + 1) * (bb[3] - bb[1] + 1) + (bbgt[2] - bbgt[0]
+ 1) * (bbgt[3] - bbgt[1] + 1) - iw * ih
ov = iw * ih / ua
if ov > ovmax:
ovmax = ov
gt_match = obj4.2 运算AP
先设置两个点，即precision为1的时候，recall为0；precision为0的时候，recall为1
def voc_ap(rec, prec):
"""
--- Official matlab code VOC2012---
mrec=[0 ; rec ; 1];
mpre=[0 ; prec ; 0];
for i=numel(mpre)-1:-1:1
mpre(i)=max(mpre(i),mpre(i+1));
end
i=find(mrec(2:end)~=mrec(1:end-1))+1;
ap=sum((mrec(i)-mrec(i-1)).*mpre(i));
"""
rec.insert(0, 0.0) # insert 0.0 at begining of list
rec.append(1.0) # insert 1.0 at end of list
mrec = rec[:]
prec.insert(0, 0.0) # insert 0.0 at begining of list
prec.append(0.0) # insert 0.0 at end of list
mpre = prec[:]precision随着recall的增减，逐渐减少
"""
This part makes the precision monotonically decreasing
(goes from the end to the beginning)
matlab: for i=numel(mpre)-1:-1:1
mpre(i)=max(mpre(i),mpre(i+1));
"""
# matlab indexes start in 1 but python in 0, so I have to do:
# range(start=(len(mpre) - 2), end=0, step=-1)
# also the python function range excludes the end, resulting in:
# range(start=(len(mpre) - 2), end=-1, step=-1)
for i in range(len(mpre)-2, -1, -1):
mpre[i] = max(mpre[i], mpre[i+1])创建recall的变化
"""
This part creates a list of indexes where the recall changes
matlab: i=find(mrec(2:end)~=mrec(1:end-1))+1;
"""
i_list = []
for i in range(1, len(mrec)):
if mrec[i] != mrec[i-1]:
i_list.append(i) # if it was matlab would be i + 1AP即为曲线下积分的面积
"""
The Average Precision (AP) is the area under the curve
(numerical integration)
matlab: ap=sum((mrec(i)-mrec(i-1)).*mpre(i));
"""
ap = 0.0
for i in i_list:
ap += ((mrec[i]-mrec[i-1])*mpre[i])
return ap, mrec, mpre4.3 mAP
根据每类的AP算出mAP
"""
Calculate the AP for each class
"""
sum_AP = 0.0
ap_dictionary = {}
# open file to store the results
with open(results_files_path + "/results.txt", 'w') as results_file:
results_file.write("# AP and precision/recall per class\n")
count_true_positives = {}。。。代码中有大量的代码用于画出曲线和显示出ground truth和图像上的prediction等，我们省去此部分。
results_file.write("\n# mAP of all classes\n")
mAP = sum_AP / n_classes
text = "mAP = {0:.2f}%".format(mAP*100)
results_file.write(text + "\n")
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