Faster RCNN Tensorflow在测试时得到的result.txt文件

需要准备三个路径：

1、一个是进行测试时所使用的那些图片，找到其路径

2、result.txt所在的路径

3、生成图像的存放路径

#!/usr/bin/env python
 

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
 
import _init_paths
from model.config import cfg
from model.test import im_detect
from model.nms_wrapper import nms
 
from utils.timer import Timer
import tensorflow as tf
import matplotlib.pyplot as plt
from PIL import Image
import numpy as np
import os, cv2
import argparse
 
 
from nets.vgg16 import vgg16
from nets.resnet_v1 import resnetv1
 
CLASSES = ('__background__', 'dan','duo')
 
NETS = {'vgg16': ('vgg16_faster_rcnn_iter_2000.ckpt',),'res101': ('res101_faster_rcnn_iter_110000.ckpt',)}
DATASETS= {'pascal_voc': ('voc_2007_trainval',),'pascal_voc_0712': ('voc_2007_trainval+voc_2012_trainval',)}


def vis_detections(image_name, class_name, dets, thresh=0.7):
    """Draw detected bounding boxes."""
    inds = np.where(dets[:, -1] >= thresh)[0]
    if len(inds) == 0:
        return
    for i in inds:
        bbox = dets[i, :4]
        score = dets[i, -1]
        if(class_name == '__background__'):
            fw = open('/home/bioinfo/Documents/pathonwork/lzh/tfasterrcnn/data/VOCdevkit2007/VOC2007/ImageSets/Main/result.txt','a')   
            fw.write(str(image_name)+' '+class_name+' '+str(int(bbox[0]))+' '+str(int(bbox[1]))+' '+str(int(bbox[2]))+' '+str(int(bbox[3]))+'
')
            fw.close()
        elif(class_name == 'dan'):
            fw = open('/home/bioinfo/Documents/pathonwork/lzh/tfasterrcnn/data/VOCdevkit2007/VOC2007/ImageSets/Main/result.txt','a')
            fw.write(str(image_name)+' '+class_name+' '+str(int(bbox[0]))+' '+str(int(bbox[1]))+' '+str(int(bbox[2]))+' '+str(int(bbox[3]))+'
')
            fw.close()
        elif(class_name == 'duo'):
            fw = open('/home/bioinfo/Documents/pathonwork/lzh/tfasterrcnn/data/VOCdevkit2007/VOC2007/ImageSets/Main/result.txt','a')
            fw.write(str(image_name)+' '+class_name+' '+str(int(bbox[0]))+' '+str(int(bbox[1]))+' '+str(int(bbox[2]))+' '+str(int(bbox[3]))+'
')
            fw.close()
 
 
def demo(image_name, sess, net):
    im_file = os.path.join('/','home','bioinfo','Documents','pathonwork','lzh','tfasterrcnn', 'data', 'VOCdevkit2007', 'VOC2007', 'JPEGImages',image_name)
    #im_file = os.path.join(cfg.DATA_DIR, 'demo', image_name)
    im = cv2.imread(im_file)
    # Detect all object classes and regress object bounds
    timer = Timer()
    timer.tic()
    scores, boxes = im_detect(sess, net, im)
    timer.toc()
    print('Detection took {:.3f}s for {:d} object proposals'.format(timer.total_time, boxes.shape[0]))
    # Visualize detections for each class
    CONF_THRESH = 0.7
    thresh=0.7
    NMS_THRESH = 0.3
    im = im[:, :, (2, 1, 0)]
    fig, ax = plt.subplots(figsize=(12, 12))
    ax.imshow(im, aspect='equal', alpha=0.5)
    for cls_ind, cls in enumerate(CLASSES[1:]):
        cls_ind += 1 # because we skipped background
        cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]
        cls_scores = scores[:, cls_ind]
        dets = np.hstack((cls_boxes,
                          cls_scores[:, np.newaxis])).astype(np.float32)
        keep = nms(dets, NMS_THRESH)
        dets = dets[keep, :]
        vis_detections(image_name, cls, dets, thresh=CONF_THRESH)
        inds = np.where(dets[:, -1] >= thresh)[0]
        if len(inds) == 0:
            continue
        for i in inds:
            bbox = dets[i, :4]
            score = dets[i, -1]
            ax.add_patch(
                plt.Rectangle((bbox[0], bbox[1]),
                              bbox[2] - bbox[0],
                              bbox[3] - bbox[1], fill=False,
                              edgecolor='red', linewidth=1.5)
                )
            ax.text(bbox[0], bbox[1] - 2,
                '{:s} {:.3f}'.format(cls, score),
                bbox=dict(facecolor='blue', alpha=0.5),
                fontsize=14, color='white')
    
    plt.axis('off')
    plt.tight_layout()
    plt.draw()
    image_name=image_name.replace('jpg','jpg')
    plt.savefig('/home/bioinfo/Documents/pathonwork/lzh/tfasterrcnn/data/results/'+image_name)
    print("save image to /home/bioinfo/Documents/pathonwork/lzh/tfasterrcnn/data/results/{}".format(image_name))
 
def parse_args():
    """Parse input arguments."""
    parser = argparse.ArgumentParser(description='Tensorflow Faster R-CNN demo')
    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__':
    cfg.TEST.HAS_RPN = True # Use RPN for proposals
    args = parse_args()
    # model path
    demonet = args.demo_net
    dataset = args.dataset
    tfmodel = ('/home/bioinfo/Documents/pathonwork/lzh/tfasterrcnn/output/vgg16/voc_2007_trainval/default/vgg16_faster_rcnn_iter_2000.ckpt')
    
    if not os.path.isfile(tfmodel + '.meta'):
        raise IOError(('{:s} not found.
Did you download the proper networks from '
                      'our server and place them properly?').format(tfmodel + '.meta'))
    # set config
    tfconfig = tf.ConfigProto(allow_soft_placement=True)
    tfconfig.gpu_options.allow_growth=True
    # init session
    sess = tf.Session(config=tfconfig)
    # load network
    if demonet == 'vgg16':
        net = vgg16()
    elif demonet == 'res101':
        net = resnetv1(num_layers=101)
    else:
        raise NotImplementedError
    net.create_architecture("TEST", 3,
                          tag='default', anchor_scales=[8, 16, 32])
    saver = tf.train.Saver()
    saver.restore(sess, tfmodel)
    print('Loaded network {:s}'.format(tfmodel))
    
    fi=open('/home/bioinfo/Documents/pathonwork/lzh/tfasterrcnn/data/VOCdevkit2007/VOC2007/ImageSets/Main/test.txt','r')
    txt=fi.readlines()
    im_names = []
    for line in txt:
        line=line.strip('
')
        line=line.replace('
','')
        line=(line+'.jpg')
        im_names.append(line)
    print(im_names)
    fi.close()
    for im_name in im_names:
        print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
        print('Demo for data/demo/{}'.format(im_name))
        demo(im_name, sess, net)
    #plt.show()#

生成结果

参考文章：

1.https://blog.csdn.net/zxs0222/article/details/89605300

2.https://blog.csdn.net/gusui7202/article/details/83240212

作者：舟华520

出处：https://www.cnblogs.com/xfzh193/

本文以学习，分享，研究交流为主，欢迎转载，请标明作者出处！