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test-resnet.py
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test-resnet.py
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#!/sr/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 cv2
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from lib.config import config as cfg
from lib.utils.nms_wrapper import nms
from lib.utils.test import im_detect
from lib.nets.resnet_v1 import resnetv1
#from lib.nets.vgg16 import vgg16
from lib.utils.timer import Timer
#CLASSES = ('__background__', 'fake-rice', 'fake-huo_tui_mu_er_chao_dan', 'fake-qing_chao_si_ji_dou', #'fake-gong_bao_ji_ding',
# 'fake-fan_qie_chao_dan', 'fake-chao_xi_lan_hua', 'fake-hong_shao_rou', 'fake-suan_la_tu_dou_si', #'fake-xiao_long_bao',
# 'fake-suan_la_bai_cai', 'fake-ji_pai_fan', 'fake-niu_pai', 'fake-zhu_pa_fan',
# 'fake-jin_zhen_gu_xia', 'bread-pineapple', 'bread-mediumhorn', 'bread-largehorn',
# 'bread-pattern', 'bread-sausage', 'bread-prismatic',
# 'bread-hempflower', 'bread-caterpillar', 'bread-smallmeal', 'toast', 'donut', 'hamburger-opening')
pclslist = ['__background__']
path = r'E:\faster-rcnn\labels_10_JYsf.txt'
with open(path,'r') as frcls:
for line in frcls:
line = line.split()
#print ('line is : ', line[0])
pclslist.append(line[0])
#self._classes = ('__background__', # always index 0
# 'newcp-rectangle_small_plate')
classes_num = int(len(pclslist))
CLASSES = tuple(pclslist)
NETS = {'vgg16': ('vgg16_faster_rcnn_iter_70000.ckpt',), 'res50': ('res50_faster_rcnn_iter_110000.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)
# 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.FLAGS2["data_dir"], 'demo', image_name)
im_file = os.path.join(path1, 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 = 0.5
NMS_THRESH = 0.1
thresh = CONF_THRESH
im = im[:, :, (2, 1, 0)]
fig, ax = plt.subplots(figsize=(12, 12))
ax.imshow(im, aspect='equal')
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)
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=3.5)
)
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()
os.chdir(path2)
plt.savefig(im_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='res50')
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 path
demonet = args.demo_net
dataset = args.dataset
tfmodel = r'E:\faster-rcnn\default\voc_2007_trainval\default\res50_faster_rcnn_iter_30000.ckpt'
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 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 == 'res50':
net = resnetv1(batch_size=1, num_layers=50)
# elif demonet == 'res101':
# net = resnetv1(batch_size=1, num_layers=101)
else:
raise NotImplementedError
net.create_architecture(sess, "TEST", classes_num,
tag='default', anchor_scales=[8, 16, 32])
saver = tf.train.Saver()
saver.restore(sess, tfmodel)
print('Loaded network {:s}'.format(tfmodel))
#im_names = ['000001.jpg', '000002.jpg', '000003.jpg', '000004.jpg',
# '000005.jpg', '000006.jpg']
path1 = input("请输入测试图片的路径:")
im_names = os.listdir(path1)
path2 = r'E:\faster-rcnn\output'
if not os.path.exists(path2):
os.makedirs(path2)
for im_name in im_names:
print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
print('Demo for data/demo/{}'.format(im_name))
demo(sess, net, im_name)
#plt.show()