FasterRcnn⼀个⽬标被识别成多个label（重复框问题）
在使⽤faster-rcnn检测⽬标时，出现这种问题很正常，对于作者的数据集来说，主要是识别⼀些物体，所以出现下⾯这种图的结果很正常。
但是当我们进⾏检测，有可能检测的是⼀个物体的状态，由于模型的拟合的不够好，⼀个物体的状态有可能会被检测成两种不同的状态，这样就需要我们对两种状态取⼀个最⼤值，然后保存。例如下图
该label标注的是normal，但在识别的时候出现了normal和tilt。只能说明模型的拟合程度不够好，并不是哪⾥出错了
解决⽅法其实很简单：
将所有的 box都保存起来，然后作nms，nms之后再进⾏可视化操作（就是画框框，上图相当于nms后把normal去掉了）
下⾯是我demo.py的代码。
在此之前，已经在原作者的基础上进⾏修改了，主要修改的⽅⾯有： 、  、还有⼀个是计数⽅便数据统计
#!/usr/bin/env python
# ._*_.coding:utf-8
# --------------------------------------------------------
# Tensorflow Faster R-CNN
# Licensed under The MIT License [see LICENSE for details]
# 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 xml.dom.minidom as xmldom
import _init_paths
fig import cfg
st import im_detect
s_wrapper import nms
from utils.timer import Timer
import tensorflow as tf
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import os, cv2
import argparse
from nets.vgg16 import vgg16
snet_v1 import resnetv1
CLASSES = ('__background__',
'tilt', 'miss', 'normal')
NETS = {'vgg16': ('vgg16_faster_rcnn_iter_70000.ckpt',), 'res101': ('res101_faster_rcnn_iter_100000.ckpt',)} DATASETS = {'pascal_voc': ('voc_2007_trainval',), 'pascal_voc_0712': ('voc_2007_trainval+voc_2012_trainval',)}
# 计算IOU交并⽐
def compute_IOU(rec1, rec2):
"""
计算两个矩形框的交并⽐。
:param rec1: (x0,y0,x1,y1)      (x0,y0)代表矩形左上的顶点，（x1,y1）代表矩形右下的顶点。下同。
:param rec2: (x0,y0,x1,y1)
:return: 交并⽐IOU.
"""
left_column_max = max(rec1[0], rec2[0])
right_column_min = min(rec1[2], rec2[2])
up_row_max = max(rec1[1], rec2[1])
down_row_min = min(rec1[3], rec2[3])
# 两矩形⽆相交区域的情况
if left_column_max >= right_column_min or down_row_min <= up_row_max:
return 0
# 两矩形有相交区域的情况
else:
S1 = (rec1[2] - rec1[0]) * (rec1[3] - rec1[1])
S2 = (rec2[2] - rec2[0]) * (rec2[3] - rec2[1])
S_cross = (down_row_min - up_row_max) * (right_column_min - left_column_max)
return S_cross / (S1 + S2 - S_cross)
global count
count = {
"tilt": 0,
"normal": 0,
"miss": 0
}
}
global count1
count1 = {
"tilt": 0,
"normal": 0,
"miss": 0
}
global iou_sum
iou_sum = {
"sum": 0.0,
"time": 0.0
}
global error
error = {
"cuowu": '000'
}
def vis_detections(im, class_name, dets, image_name, ax, im_file, thresh=0.5):
"""Draw detected bounding boxes."""
inds = np.where(dets[:, -1] >= thresh)[0]
if len(inds) == 0:
return
# 查的图⽚地址
# 怎么获取图⽚的GB呢
# print(im_file)
inpath = '/home/share/liubo/tf-faster-rcnn1/data/VOCdevkit2007/VOC2007/Annotations/' + image_name[0:-4] + '.xml'    # inpath = inpath.decode('GB2312').encode('utf-8')
# uipath = unicode(inpath, "GB2312")
domobj = xmldom.parse(inpath)
elementobj = domobj.documentElement
name = ElementsByTagName("name")
xmin = ElementsByTagName("xmin")
ymin = ElementsByTagName("ymin")
xmax = ElementsByTagName("xmax")
ymax = ElementsByTagName("ymax")
size = len(name)
if im_name == '300313.JPG':
print(name[0].firstChild.data)
for i in range(size):
ax.add_patch(plt.Rectangle((int(xmin[i].firstChild.data), int(ymin[i].firstChild.data)),
int(xmax[i].firstChild.data) - int(xmin[i].firstChild.data),
int(ymax[i].firstChild.data) - int(ymin[i].firstChild.data), fill=False,
edgecolor='yellow', linewidth=3.5)
)
<(int(xmax[i].firstChild.data), int(ymin[i].firstChild.data) - 2,
'{:s}'.format(name[i].firstChild.data),
bbox=dict(facecolor='white', alpha=0.5),
fontsize=20, color='black')
# count1[name[i].firstChild.data] += 1
# im = im[:, :, (2, 1, 0)]
# fig, ax = plt.subplots(figsize=(40, 40))
# ax.imshow(im, aspect='equal')
for i in inds:
bbox = dets[i, :4]
score = dets[i, -1]
print(class_name, score)
ax.add_patch(
plt.Rectangle((bbox[0], bbox[1]),
bbox[2] - bbox[0],
bbox[3] - bbox[1], fill=False,
edgecolor='red', linewidth=3.5)
edgecolor='red', linewidth=3.5)
)
<(bbox[0], bbox[1] - 2,
'{:s} {:.3f}'.format(class_name, score),
bbox=dict(facecolor='blue', alpha=0.5),
fontsize=14, color='white')
r1 = (bbox[0], bbox[1], bbox[2], bbox[3])
for j in range(size):
parse error怎么解决r2 = (int(xmin[j].firstChild.data), int(ymin[j].firstChild.data), int(xmax[j].firstChild.data),
int(ymax[j].firstChild.data))
IOU = compute_IOU(r1, r2)
if IOU >= 0.5 and name[j].firstChild.data == class_name:
count[class_name] += 1
iou_sum['sum'] += IOU
if IOU >= 0.5 and name[j].firstChild.data != class_name:
error['cuowu'] = error['cuowu'] + '\n' + image_name[0:-4]
# iou_sum += IOU
# plt.axis('off')
#  plt.tight_layout()
#  plt.draw()
def demo(sess, net, image_name):
"""Detect object classes in an image using pre-computed object proposals."""
# Load the demo image
im_file = os.path.join(cfg.DATA_DIR, 'demo', image_name)
im = cv2.imread(im_file)
# print("------------------------------")
# print(im_file)
# Detect all object classes and regress object bounds
timer = Timer()
timer.tic()
scores, boxes = im_detect(sess, net, im)
<()
iou_sum['time'] += al_time
print('Detection took {:.3f}s for {:d} object proposals'.al_time, boxes.shape[0]))
# Visualize detections for each class
CONF_THRESH = 0.8
NMS_THRESH = 0.0
im = im[:, :, (2, 1, 0)]
fig, ax = plt.subplots(figsize=(40, 40))
ax.imshow(im, aspect='equal')
all_dets = np.empty((0, 5), np.float32)
all_cls = np.empty((0, 6))
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, :]
inds = np.where(dets[:, -1] >= 0.8)[0]
dets = dets[inds, :]
if len(dets) == 0:
continue
all_dets = np.append(all_dets, dets, axis=0)
for i in dets:
all_cls = np.vstack((all_cls, np.hstack((i, np.array([cls_ind])))))
# all_cls = np.hstack((all_dets, np.array([[cls_ind]])))
# all_cls = np.hstack((all_dets, np.array([[cls_ind]])))
# all_cls = np.append(all_cls, np.array([[cls_ind]]), axis=0)
# print('pre nms ************************')
# for i in all_dets:
#    print(i)
keep1 = nms(all_dets, NMS_THRESH)
all_dets = all_dets[keep1, :]
all_cls = shape(-1, 6)
all_cls = all_cls[keep1, :]
for i in np.arange(len(all_cls)):
cls_index = int(all_cls[i][5])
vis_detections(im, CLASSES[cls_index], all_dets[i].reshape(-1, 5), image_name, ax, im_file,
thresh=CONF_THRESH)
plt.axis('off')
plt.tight_layout()
plt.draw()
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='res101')
parser.add_argument('--dataset', dest='dataset', help='Trained dataset [pascal_voc pascal_voc_0712]',                        choices=DATASETS.keys(), default='pascal_voc_0712')
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 = os.path.join('output', demonet, 'voc_2007_trainval', 'default',
NETS[demonet][0])
if not os.path.isfile(tfmodel + '.meta'):
raise IOError(('{:s} not found.\nDid 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
tag='default', anchor_scales=[8, 16, 32])
saver = tf.train.Saver()
print('Loaded network {:s}'.format(tfmodel))
# im_names = ['000456.jpg', '000542.jpg', '001150.jpg',
#            '001763.jpg', '004545.jpg']

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