Frasterrcnn-tensorflow-python3.5-master生成预测坐标位置并存储到xlsl表格中，并生成pr曲线

1.首先在根目录新建test_net.py

# !/usr/bin/env python
# --------------------------------------------------------
# Tensorflow Faster R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Xinlei Chen, based on code from Ross Girshick
# --------------------------------------------------------
"""
Demo script showing detections in sample images.
See README.md for installation instructions before running.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import os
import tensorflow as tf
from lib.nets.vgg16 import vgg16
from lib.datasets.factory import get_imdb
from lib.utils.test import test_net
# NETS = {'vgg16': ('vgg16_faster_rcnn_iter_70000.ckpt',), 'res101': ('res101_faster_rcnn_iter_110000.ckpt',)}
NETS = {'vgg16': ('vgg16_faster_rcnn_iter_5000.ckpt',)}  # 训练输出模型
DATASETS = {'pascal_voc': ('voc_2007_trainval',), 'pascal_voc_0712': ('voc_2007_trainval+voc_2012_trainval',)}
def parse_args():"""Parse input arguments."""parser = argparse.ArgumentParser(description='Tensorflow Faster R-CNN test')parser.add_argument('--net', dest='demo_net', help='Network to use [vgg16 res101]',choices=NETS.keys(), default='vgg16')parser.add_argument('--dataset', dest='dataset', help='Trained dataset [pascal_voc pascal_voc_0712]',choices=DATASETS.keys(), default='pascal_voc')args = parser.parse_args()return args
if __name__ == '__main__':args = parse_args()# model pathdemonet = args.demo_netdataset = args.datasettfmodel = os.path.join('output', demonet, DATASETS[dataset][0], 'default', NETS[demonet][0])  # 模型路径# 获得模型文件名称filename = (os.path.splitext(tfmodel)[0]).split('\\')[-1]filename = 'default' + '/' + filenameimdb = get_imdb("voc_2007_test")  # 得到imdb.competition_mode('competition mode')if not os.path.isfile(tfmodel + '.meta'):print(tfmodel)raise IOError(('{:s} not found.\nDid you download the proper networks from ''our server and place them properly?').format(tfmodel + '.meta'))# set configtfconfig = tf.ConfigProto(allow_soft_placement=True)tfconfig.gpu_options.allow_growth = True# init sessionsess = tf.Session(config=tfconfig)# load networkif demonet == 'vgg16':net = vgg16(batch_size=1)# elif demonet == 'res101':# net = resnetv1(batch_size=1, num_layers=101)else:raise NotImplementedErrornet.create_architecture(sess, "TEST", 8,  # 记得修改第3个参数为：类别数量+1tag='default', anchor_scales=[8, 16, 32])saver = tf.train.Saver()saver.restore(sess, tfmodel)print('Loaded network {:s}'.format(tfmodel))print(filename)test_net(sess, net, imdb, filename, max_per_image=100)sess.close()

2.更改passcal_voc.py如下

提前在conda中按照好openpyxl包

# --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick and Xinlei Chen
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import pandas as pd
import matplotlib.pyplot as plt
import pylab as pl
import csv
from sklearn.metrics import precision_recall_curve
from itertools import cycle
import os
import pickle
import subprocess
import uuid
import xml.etree.ElementTree as ET
import numpy as np
import scipy.sparse
from lib.config import config2 as cfg
from lib.datasets.imdb import imdb
from lib.datasets.voc_eval import voc_eval
import openpyxl
def write_excel_xlsx(path, sheet_name,value):index = len(value)workbook = openpyxl.Workbook()sheet = workbook.activesheet.title = sheet_namefor i in range(index):#写入行，0，1，2......for j in range(len(value[i])):#写入该行的几列:第0行的1，2，3，4，5，6列然后第1行，2行等等sheet.cell(row=i + 1, column=j + 1, value=str(value[i][j]))workbook.save(path)print("xlsx格式表格写入数据成功！")
class pascal_voc(imdb):def __init__(self, image_set, year, devkit_path=None):imdb.__init__(self, 'voc_' + year + '_' + image_set)self._year = yearself._image_set = image_setself._devkit_path = self._get_default_path() if devkit_path is None \else devkit_pathself._data_path = os.path.join(self._devkit_path, 'VOC' + self._year)self._classes = ('__background__',  # always index 0'r','l','ipr','lf','c','ic','bt')#自己类别self._class_to_ind = dict(list(zip(self.classes, list(range(self.num_classes)))))self._image_ext = '.bmp'self._image_index = self._load_image_set_index()# Default to roidb handlerself._roidb_handler = self.gt_roidbself._salt = str(uuid.uuid4())self._comp_id = 'comp4'# PASCAL specific config optionsself.config = {'cleanup': True,'use_salt': True,'use_diff': False,'matlab_eval': False,'rpn_file': None}assert os.path.exists(self._devkit_path), \'VOCdevkit path does not exist: {}'.format(self._devkit_path)assert os.path.exists(self._data_path), \'Path does not exist: {}'.format(self._data_path)def image_path_at(self, i):"""Return the absolute path to image i in the image sequence."""return self.image_path_from_index(self._image_index[i])def image_path_from_index(self, index):"""Construct an image path from the image's "index" identifier."""image_path = os.path.join(self._data_path, 'JPEGImages',index + self._image_ext)assert os.path.exists(image_path), \'Path does not exist: {}'.format(image_path)return image_pathdef _load_image_set_index(self):"""Load the indexes listed in this dataset's image set file."""# Example path to image set file:# self._devkit_path + /VOCdevkit2007/VOC2007/ImageSets/Main/val.txtimage_set_file = os.path.join(self._data_path, 'ImageSets', 'Main',self._image_set + '.txt')assert os.path.exists(image_set_file), \'Path does not exist: {}'.format(image_set_file)with open(image_set_file) as f:image_index = [x.strip() for x in f.readlines()]return image_indexdef _get_default_path(self):"""Return the default path where PASCAL VOC is expected to be installed."""return os.path.join(cfg.FLAGS2["data_dir"], 'VOCdevkit' + self._year)def gt_roidb(self):"""Return the database of ground-truth regions of interest.This function loads/saves from/to a cache file to speed up future calls."""cache_file = os.path.join(self.cache_path, self.name + '_gt_roidb.pkl')if os.path.exists(cache_file):with open(cache_file, 'rb') as fid:try:roidb = pickle.load(fid)except:roidb = pickle.load(fid, encoding='bytes')print('{} gt roidb loaded from {}'.format(self.name, cache_file))return roidbgt_roidb = [self._load_pascal_annotation(index)for index in self.image_index]with open(cache_file, 'wb') as fid:pickle.dump(gt_roidb, fid, pickle.HIGHEST_PROTOCOL)print('wrote gt roidb to {}'.format(cache_file))return gt_roidbdef rpn_roidb(self):if int(self._year) == 2007 or self._image_set != 'test':gt_roidb = self.gt_roidb()rpn_roidb = self._load_rpn_roidb(gt_roidb)roidb = imdb.merge_roidbs(gt_roidb, rpn_roidb)else:roidb = self._load_rpn_roidb(None)return roidbdef _load_rpn_roidb(self, gt_roidb):filename = self.config['rpn_file']print('loading {}'.format(filename))assert os.path.exists(filename), \'rpn data not found at: {}'.format(filename)with open(filename, 'rb') as f:box_list = pickle.load(f)return self.create_roidb_from_box_list(box_list, gt_roidb)def _load_pascal_annotation(self, index):"""Load image and bounding boxes info from XML file in the PASCAL VOCformat."""filename = os.path.join(self._data_path, 'Annotations', index + '.xml')tree = ET.parse(filename)objs = tree.findall('object')if not self.config['use_diff']:# Exclude the samples labeled as difficultnon_diff_objs = [obj for obj in objs if int(obj.find('difficult').text) == 0]# if len(non_diff_objs) != len(objs):#     print 'Removed {} difficult objects'.format(#         len(objs) - len(non_diff_objs))objs = non_diff_objsnum_objs = len(objs)#图片物体数量boxes = np.zeros((num_objs, 4), dtype=np.uint16)gt_classes = np.zeros((num_objs), dtype=np.int32)#num_objs个元素的一维矩阵，代表overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)# "Seg" area for pascal is just the box areaseg_areas = np.zeros((num_objs), dtype=np.float32)# Load object bounding boxes into a data frame.for ix, obj in enumerate(objs):bbox = obj.find('bndbox')# Make pixel indexes 0-basedx1 = float(bbox.find('xmin').text) - 1y1 = float(bbox.find('ymin').text) - 1x2 = float(bbox.find('xmax').text) - 1y2 = float(bbox.find('ymax').text) - 1cls = self._class_to_ind[obj.find('name').text.lower().strip()]boxes[ix, :] = [x1, y1, x2, y2]gt_classes[ix] = clsoverlaps[ix, cls] = 1.0seg_areas[ix] = (x2 - x1 + 1) * (y2 - y1 + 1)overlaps = scipy.sparse.csr_matrix(overlaps)return {'boxes': boxes,#'gt_classes': gt_classes,'gt_overlaps': overlaps,#'flipped': False,'seg_areas': seg_areas}def _get_comp_id(self):comp_id = (self._comp_id + '_' + self._salt if self.config['use_salt']else self._comp_id)return comp_id'''def _get_voc_results_file_template(self):##生成txt文本路路径# VOCdevkit/results/VOC2007/Main/<comp_id>_det_test_aeroplane.txtfilename = self._get_comp_id() + '_det_' + self._image_set + '_{:s}.txt'#待检测图片文件名path = os.path.join(self._devkit_path,'results','VOC' + self._year,'Main',filename)return pathdef _write_voc_results_file(self, all_boxes):###写入文本文件for cls_ind, cls in enumerate(self.classes):if cls == '__background__':continue#继续循环print('Writing {} VOC results file'.format(cls))#cls为类别filename = self._get_voc_results_file_template().format(cls)with open(filename, 'w') as f:for im_ind, index in enumerate(self.image_index):dets = all_boxes[cls_ind][im_ind]if dets == []:continue# the VOCdevkit expects 1-based indicesfor k in range(dets.shape[0]):f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\n'.format(index, dets[k, -1],dets[k, 0] + 1, dets[k, 1] + 1,dets[k, 2] + 1, dets[k, 3] + 1))'''#改为生成表格文档def _get_voc_results_file_template(self):  ##生成txt文本路路径# VOCdevkit/results/VOC2007/Main/<comp_id>_det_test_aeroplane.txtfilename = self._get_comp_id() + '_det_' + self._image_set + '_{:s}.xlsx'  # 待检测图片文件名path = os.path.join(self._devkit_path,'results','VOC' + self._year,'Main',filename)return pathdef _write_voc_results_file(self, all_boxes):  ###写入文本文件for cls_ind, cls in enumerate(self.classes):predict=[]if cls == '__background__':continue  # 继续循环print('Writing {} VOC results file'.format(cls))  # cls为类别filename = self._get_voc_results_file_template().format(cls)for im_ind, index in enumerate(self.image_index):dets = all_boxes[cls_ind][im_ind]if dets == []:continue# the VOCdevkit expects 1-based indices###############for k in range(dets.shape[0]):pred=[]pred.append(index)pred.append(str(round(dets[k, -1], 3)))pred.append(str(round(dets[k, 0] + 1, 1)))pred.append(str(round(dets[k, 1] + 1, 1)))pred.append(str(round(dets[k, 2] + 1, 1)))pred.append(str(round(dets[k, 3] + 1, 1)))predict.append(pred)print(predict)if os.path.exists(filename):#判断是否存在该文件存在则删除os.remove(filename)write_excel_xlsx(filename, 'name',predict)def _do_python_eval(self, output_dir='output'):annopath = self._devkit_path + '\\VOC' + self._year + '\\Annotations\\' + '{:s}.xml'imagesetfile = os.path.join(self._devkit_path,'VOC' + self._year,'ImageSets','Main',self._image_set + '.txt')cachedir = os.path.join(self._devkit_path, 'annotations_cache')aps = []#加recs=[]precs=[]#结束# The PASCAL VOC metric changed in 2010use_07_metric = True if int(self._year) < 2010 else Falseprint('VOC07 metric? ' + ('Yes' if use_07_metric else 'No'))if not os.path.isdir(output_dir):os.mkdir(output_dir)##加#######for i, cls in enumerate(self._classes):if cls == '__background__':continuefilename = self._get_voc_results_file_template().format(cls)rec, prec, ap = voc_eval(filename, annopath, imagesetfile, cls, cachedir, ovthresh=0.5,use_07_metric=use_07_metric)aps += [ap]#加'''recs += [rec[-1]]precs += [prec[-1]]print('AP for {} = {:.4f}'.format(cls, ap))print('recall for {} = {:.4f}'.format(cls, rec[-1]))print('precision for {} = {:.4f}'.format(cls, prec[-1]))with open(os.path.join(output_dir, cls + '_pr.pkl'), 'w') as f:pickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)print('Mean AP = {:.4f}'.format(np.mean(aps)))print('~~~~~~~~')print('Results:')'''pl.plot(rec, prec, lw=2,label='Precision-recall curve of class {} (area = {:.4f})'''.format(cls, ap))#cls代表类别，ap面积print(('AP for {} = {:.4f}'.format(cls, ap)))#加#file.writelines('AP for {} = {:.4f}'.format(cls, ap))#file.write('\n')#file.close()with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:pickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)#加pkl中存储的是每个类的rec,prec从小到大的值以及ap的值##加#with open(os.path.join(output_dir,cls+'.txt'),'w') as f:#f.writelines(ap)####结束pl.xlabel('Recall')pl.ylabel('Precision')plt.grid(True)pl.ylim([0.0, 1.2])pl.xlim([0.0, 1.0])pl.title('Precision-Recall')pl.legend(loc="upper right")plt.show()print(('Mean AP = {:.4f}'.format(np.mean(aps))))print('~~~~~~~~')print('Results:')for ap in aps:print(('{:.3f}'.format(ap)))print(('{:.3f}'.format(np.mean(aps))))print('~~~~~~~~')print('')print('--------------------------------------------------------------')print('Results computed with the **unofficial** Python eval code.')print('Results should be very close to the official MATLAB eval code.')print('Recompute with `./tools/reval.py --matlab ...` for your paper.')print('-- Thanks, The Management')print('--------------------------------------------------------------')def _do_matlab_eval(self, output_dir='output'):print('-----------------------------------------------------')print('Computing results with the official MATLAB eval code.')print('-----------------------------------------------------')path = os.path.join(cfg.FLAGS2["root_dir"], 'lib', 'datasets','VOCdevkit-matlab-wrapper')cmd = 'cd {} && '.format(path)cmd += '{:s} -nodisplay -nodesktop '.format('matlab')cmd += '-r "dbstop if error; 'cmd += 'voc_eval(\'{:s}\',\'{:s}\',\'{:s}\',\'{:s}\'); quit;"' \.format(self._devkit_path, self._get_comp_id(),self._image_set, output_dir)print(('Running:\n{}'.format(cmd)))status = subprocess.call(cmd, shell=True)def evaluate_detections(self, all_boxes, output_dir):self._write_voc_results_file(all_boxes)self._do_python_eval(output_dir)if self.config['matlab_eval']:self._do_matlab_eval(output_dir)#if self.config['cleanup']:#    for cls in self._classes:#        if cls == '__background__':#            continue#        filename = self._get_voc_results_file_template().format(cls)#       os.remove(filename)def competition_mode(self, on):if on:self.config['use_salt'] = Falseself.config['cleanup'] = Falseelse:self.config['use_salt'] = Trueself.config['cleanup'] = True
if __name__ == '__main__':from lib.datasets.pascal_voc import pascal_vocd = pascal_voc('trainval', '2007')res = d.roidbfrom IPython import embed;embed()

3.更改voc_eval.py如下

# --------------------------------------------------------
# Fast/er R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Bharath Hariharan
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import pickle
import xml.etree.ElementTree as ET
import numpy as np
import openpyxl
def read_excel_xlsx(path, sheet_name):workbook = openpyxl.load_workbook(path)sheet = workbook[sheet_name]print(sheet,sheet_name)spitlines=[]for row in sheet.rows:T=[]for cell in row:T.append(str(cell.value))spitlines.append(T)return spitlines
def parse_rec(filename):#读取标注xml文件""" Parse a PASCAL VOC xml file """tree = ET.parse(filename)objects = []#./data/VOCdevkit2007/VOC2007/Annotations/for obj in tree.findall('object'):obj_struct = {}obj_struct['name'] = obj.find('name').textobj_struct['pose'] = obj.find('pose').textobj_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):#rec=召回率，prec=精确度""" ap = voc_ap(rec, prec, [use_07_metric])Compute VOC AP given precision and recall.If use_07_metric is true, uses theVOC 07 11 point method (default:False).计算AP值，若use_07_metric=true,则用11个点采样的方法，将rec从0-1分成11个点，这些点prec值求平均近似表示AP若use_07_metric=false,则采用更为精确的逐点积分方法"""if use_07_metric:# 11 point metricap = 0.for t in np.arange(0., 1.1, 0.1):if np.sum(rec >= t) == 0:p = 0else:p = np.max(prec[rec >= t])ap = ap + p / 11.else:# correct AP calculation# first append sentinel values at the endmrec = np.concatenate(([0.], rec, [1.]))mpre = np.concatenate(([0.], prec, [0.]))# compute the precision envelopefor 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 valuei = np.where(mrec[1:] != mrec[:-1])[0]# and sum (\Delta recall) * precap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])return ap
def voc_eval(detpath,#主函数，计算当前类别的recall和precisionannopath,#标注路径imagesetfile,#图片路径classname,#类别名称cachedir,ovthresh=0.5,use_07_metric=False):"""rec, prec, ap = voc_eval(detpath,annopath,imagesetfile,classname,[ovthresh],[use_07_metric])
该文件格式：imagename1 confidence xmin ymin xmax ymax  (图像1的第一个结果)imagename1 confidence xmin ymin xmax ymax  (图像1的第二个结果)imagename1 confidence xmin ymin xmax ymax  (图像2的第一个结果)每个结果占一行，检测到多少个BBox就有多少行，这里假设有20000个检测结果Top level function that does the PASCAL VOC evaluation.detpath: Path to detectionsdetpath.format(classname) should produce the detection results file.annopath: Path to annotationsannopath.format(imagename) should be the xml annotations file.imagesetfile: Text file containing the list of images, one image per line.classname: Category name (duh)cachedir: Directory for caching the annotations[ovthresh]: Overlap threshold (default = 0.5)[use_07_metric]: Whether to use VOC07's 11 point AP computation(default False)"""# assumes detections are in detpath.format(classname)# assumes annotations are in annopath.format(imagename)# assumes imagesetfile is a text file with each line an image name# cachedir caches the annotations in a pickle file# first load gtif not os.path.isdir(cachedir):os.mkdir(cachedir)cachefile = os.path.join(cachedir, 'annots.pkl')#cachdir=data/vocdevkit2007# read list of imageswith open(imagesetfile, 'r') as f:lines = f.readlines()#读取所有待检测图像名字imagenames = [x.strip() for x in lines]#待检测图像文件名字存在于数组imagenames长度1000if not os.path.isfile(cachefile):#如果只读文件不存在，则只好从原始数据集中重新加载数据# load annotsrecs = {}for i, imagename in enumerate(imagenames):recs[imagename] = parse_rec(annopath.format(imagename))if i % 100 == 0:#parse_rec函数读取当前图像标注文件，返回当前图像标注，存于recs字典（key是图像名字，values是gt）print('Reading annotation for {:d}/{:d}'.format(i + 1, len(imagenames)))# saveprint('Saving cached annotations to {:s}'.format(cachefile))with open(cachefile, 'wb') as f:pickle.dump(recs, f)else:# loadwith open(cachefile, 'rb') as f:try:recs = pickle.load(f)except:recs = pickle.load(f, encoding='bytes')# extract gt objects for this classclass_recs = {}npos = 0for imagename in imagenames:R = [obj for obj in recs[imagename] if obj['name'] == classname]bbox = np.array([x['bbox'] for x in R])#抽取bboxdifficult = np.array([x['difficult'] for x in R]).astype(np.bool)det = [False] * len(R)npos = npos + sum(~difficult)class_recs[imagename] = {'bbox': bbox,'difficult': difficult,'det': det}# read dets读取检测结果detfile = detpath.format(classname)#detfilew为预测的文本文件位置，每一类的都有#with open(detfile, 'r') as f:#lines = f.readlines()#splitlines = [x.strip().split(' ') for x in lines]splitlines=read_excel_xlsx(detfile,'name')image_ids = [x[0] for x in splitlines]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)tp = np.zeros(nd)fp = np.zeros(nd)if BB.shape[0] > 0:# sort by confidencesorted_ind = np.argsort(-confidence)sorted_scores = np.sort(-confidence)BB = BB[sorted_ind, :]image_ids = [image_ids[x] for x in sorted_ind]# go down dets and mark TPs and FPsfor d in range(nd):R = class_recs[image_ids[d]]bb = BB[d, :].astype(float)ovmax = -np.infBBGT = R['bbox'].astype(float)if BBGT.size > 0:# compute overlaps# intersectionixmin = 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# unionuni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) +(BBGT[:, 2] - BBGT[:, 0] + 1.) *(BBGT[:, 3] - BBGT[:, 1] + 1.) - inters)overlaps = inters / uniovmax = np.max(overlaps)#最大重合率jmax = np.argmax(overlaps)if ovmax > ovthresh:#如果当前检测结果与真实标注最大重合率满足阈值if not R['difficult'][jmax]:if not R['det'][jmax]:tp[d] = 1.#正检数目+1R['det'][jmax] = 1else:#相反，认为检测到一个虚警fp[d] = 1.else:fp[d] = 1.# compute precision recallfp = np.cumsum(fp)#积分图，在当前节点前的虚警数量，fp长度tp = np.cumsum(tp)#积分图，在当前节点前的正检数量rec = tp / float(npos) #召回率，长度20000，从0到1# avoid divide by zero in case the first detection matches a difficult# ground truthprec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)ap = voc_ap(rec, prec, use_07_metric)##tpr=recreturn rec, prec, ap

效果：

Frasterrcnn-tensorflow-python3.5-master生成预测坐标位置并存储到xlsl表格中，并生成pr曲线相关推荐

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1.首先在根目录新建test_net.py

2.更改passcal_voc.py如下

3.更改voc_eval.py如下

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