行人检测一般用FPPI而不是mAP作为评估指标，但是网上找了好久都没找到Python版本的代码，原来FPPI官方的代码是matlab的，这里记录一下用官方的代码来绘制FPPI曲线有哪些需要注意的
有两个方法:
感谢这篇文章提供思路：FPPI曲线绘制记录
matlab官方文档地址：evaluateDetectionMissRate
测试数据下载地址：matlab绘制FPPI测试数据
load fppi.mat
[am, fppi, missRate] = evaluateDetectionMissRate(results, a);
%%
% Plot log average miss rate - FPPI.
figure
loglog(fppi, missRate);
grid on
title(sprintf('log Average Miss Rate = %.5f',am))
成功绘制后图片长这个样子
如果要改成自己数据的话：
预测结果的格式（即代码中的result）:
fppi.mat看下）label的格式（即代码中的a，名字我随便取的哈哈哈）：
;隔开详情可以matlab源码中的注释：
function [logAverageMissRate, fppi, missRate] = evaluateDetectionMissRate(...
detectionResults, groundTruthData, varargin)
%evaluateDetectionMissRate Evaluate the miss rate metric for object detection.
% logAverageMissRate = evaluateDetectionMissRate(detectionResults,
% groundTruthData) returns log-average miss rate to measure the detection
% performance. For a multi-class detector, logAverageMissRate is a vector
% of log-average miss rates for each object class. The class order
% follows the same column order as the groundTruthData table.
%
% Inputs:
% -------
% detectionResults - a table that has two columns for single-class
% detector, or three columns for multi-class
% detector. The first column contains M-by-4 matrices
% of [x, y, width, height] bounding boxes specifying
% object locations. The second column contains scores
% for each detection. For multi-class detector, the
% third column contains the predicted label for each
% detection. The label must be categorical type
% defined by the variable names of groundTruthData
% table.
%
% groundTruthData - a table that has one column for single-class, or
% multiple columns for multi-class. Each column
% contains M-by-4 matrices of [x, y, width, height]
% bounding boxes specifying object locations. The
% column name specifies the class label.
%
% [..., fppi, missRate] = evaluateDetectionMissRate(...) returns data
% points for plotting the log-miss-rate/false-positives-per-image(FPPI)
% curve. You can visualize the performance curve using loglog(fppi,missRate).
% For multi-class detector, recall and precision are cell arrays, where
% each cell contains the data points for each object class.
%
% [...] = evaluateDetectionMissRate(..., threshold) specifies the
% overlap threshold for assigning a detection to a ground truth box. The
% overlap ratio is computed as the intersection over union. The default
% value is 0.5.
%
% Example : Evaluate stop sign detector
% -------------------------------------
% % Load the ground truth table
% load('stopSignsAndCars.mat')
% stopSigns = stopSignsAndCars(:, 1:2);
% stopSigns.imageFilename = fullfile(toolboxdir('vision'),'visiondata', ...
% stopSigns.imageFilename);
%
% % Train an ACF based detector
% detector = trainACFObjectDetector(stopSigns,'NegativeSamplesFactor',2);
%
% % Create a struct array to store the results
% numImages = height(stopSigns);
% results(numImages) = struct('Boxes', [], 'Scores', []);
%
% % Run the detector on the training images
% for i = 1 : numImages
% I = imread(stopSigns.imageFilename{i});
% [bboxes, scores] = detect(detector, I);
% results(i).Boxes = bboxes;
% results(i).Scores = scores;
% end
%
% results = struct2table(results);
%
% % Evaluate the results against the ground truth data
% [am, fppi, missRate] = evaluateDetectionMissRate(results, stopSigns(:, 2));
%
% % Plot log-miss-rate/FPPI curve
% figure
% loglog(fppi, missRate);
% grid on
% title(sprintf('log Average Miss Rate = %.1f', am))
%
% See also evaluateDetectionPrecision, acfObjectDetector,
% rcnnObjectDetector, trainACFObjectDetector, trainRCNNObjectDetector.
% Copyright 2016 The MathWorks, Inc.
%
% References
% ----------
% [1] C. D. Manning, P. Raghavan, and H. Schutze. An Introduction to
% Information Retrieval. Cambridge University Press, 2008.
%
% [2] D. Hoiem, Y. Chodpathumwan, and Q. Dai. Diagnosing error in
% object detectors. In Proc. ECCV, 2012.
%
% [3] Dollar, Piotr, et al. "Pedestrian Detection: An Evaluation of the
% State of the Art." Pattern Analysis and Machine Intelligence, IEEE
% Transactions on 34.4 (2012): 743 - 761.
非常感谢这篇文章：Caltech评估方法
评估的方法按照上面那篇文章可以跑通代码，需要注意的是：
../code3.2.1/data-USA中../code3.2.1/data-USA/res中（res文件夹需要自己你新建）文件放置如图所示：
annotations文件内如图所示：
res文件内如图所示（ABC是我自己的算法名字）：
3. 修改dbEval.m中的algs变量（在第57行），保留要你对比的算法，他的的注释掉（名字要与你res文件夹中的算法名字相同）。同时，在最后面加上你自己算法的名字，前面的布尔变量表示是否将每个box的高度调整为100/128，clrs表示颜色，'-'表示线条类型
4. 然后运行dbEval.m即可。图会闪一下，因为最终图片会保存到pdf里面，所以需要安装Ghostscript和pdfcrop。当然也可以在savefig(fName1,1,'pdf','-r300','-fonts'); close(1); f1=[fName1 '.pdf'];设一个断点，然后手动保存
5. 自己的算法一定要放在前面，不然绘图的时候会显示不出来，这个坑以后再填
6. 自己的数据格式摹仿别人的预测就好了，第一列是图片id，然后是xmin、ymin、w、h，最后不知道是什么待填坑