Faster R-CNN 修改demo.py为测试批量图片
修改:1.多张图片放入/data/demo/文件夹中(无需在demo.py中列出图片的名字)2.将识别的图片结果保存到其他文件夹中(这里是在主目录下建了testfigs文件夹)#!/usr/bin/env python# --------------------------------------------------------# Tensorflow Faster R-CNN# Licens
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修改:
1.多张图片放入/data/demo/文件夹中(无需在demo.py中列出图片的名字)
2.将识别的图片结果保存到其他文件夹中(这里是在主目录下建了testfigs文件夹)
#!/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 matplotlib
matplotlib.use('Agg')
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
import numpy as np
import os, cv2
import argparse
from nets.vgg16 import vgg16
from nets.resnet_v1 import resnetv1
CLASSES = ('__background__', # always index 0
'类别1',
'类别2',
'类别3',
'类别4')
NETS = {'vgg16': ('vgg16_faster_rcnn_iter_5000.ckpt',),'res50': ('res50_faster_rcnn_iter_5000.ckpt',),'res101': ('res101_faster_rcnn_iter_5000.ckpt',),'res152': ('res152_faster_rcnn_iter_5000.ckpt',)}
DATASETS= {'pascal_voc': ('voc_2007_trainval',),'pascal_voc_0712': ('voc_2007_trainval+voc_2012_trainval',)}
def vis_detections(im, class_name, dets, thresh=0.5):
"""Draw detected bounding boxes."""
inds = np.where(dets[:, -1] >= thresh)[0]
if len(inds) == 0:
return
im = im[:, :, (2, 1, 0)]
fig, ax = plt.subplots(figsize=(12, 12))
ax.imshow(im, aspect='equal')
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=3.5)
)
ax.text(bbox[0], bbox[1] - 2,
'{:s} {:.3f}'.format(class_name, score),
bbox=dict(facecolor='blue', alpha=0.5),
fontsize=14, color='white')
ax.set_title(('{} detections with '
'p({} | box) >= {:.1f}').format(class_name, class_name,
thresh),
fontsize=14)
print("类别" + class_name) #控制台输出了识别出的类别
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)
# 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 = 0.8
NMS_THRESH = 0.3
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(im, cls, dets, thresh=CONF_THRESH)
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 res50 res101 res152]',
choices=NETS.keys(), default='vgg16') #需要更改的地方default
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 = os.path.join('output', demonet, DATASETS[dataset][0], '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)
elif demonet == 'res50':
net = resnetv1(num_layers=50)
elif demonet == 'res152':
net = resnetv1(num_layers=152)
else:
raise NotImplementedError
net.create_architecture("TEST", 5,
tag='default', anchor_scales=[8, 16, 32])
saver = tf.train.Saver()
saver.restore(sess, tfmodel)
print('Loaded network {:s}'.format(tfmodel))
root = "/content/drive/My Drive/tf-faster-rcnn/data/demo/"
#从路径中读取图片
for dirpath,dirnames,im_names in os.walk(root):
for im_name in im_names:
print ('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
print ('Demo for data/demo/{}'.format(im_name))
demo(sess, net, im_name)
plt.savefig("/content/drive/My Drive/tf-faster-rcnn/testfigs/" + im_name)
#保存路径
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