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demo.py
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demo.py
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#!/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 _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__',
'tree')
NETS = {'vgg16': ('vgg16_faster_rcnn_iter_20000.ckpt',), 'res101': ('res101_faster_rcnn_iter_190000.ckpt',)}
DATASETS = {'pascal_voc': ('voc_2007_trainval',), 'pascal_voc_0712': ('voc_2007_trainval+voc_2012_trainval',)}
def vis_detections(image_name, 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=2)
)
# 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()
##
plt.savefig('/tmp/infer_demo/' + os.path.split(image_name)[-1])
print("/tmp/infer_demo/{}".format(os.path.split(image_name)[-1]))
def demo(image_name, sess, net, vis, CONF_THRESH=0.8):
"""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(image_name)
# 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
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, :]
dets = dets[np.where(dets[:, -1] >= float(CONF_THRESH))]
if vis:
vis_detections(image_name, im, cls, dets, thresh=float(CONF_THRESH))
return dets
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]',
choices=DATASETS.keys(), default='pascal_voc')
parser.add_argument('--vis', action='store_true', help='is or not vis function')
parser.add_argument('--im_or_path', default='/home/data/tree/tile/0722/out/pic/', help='one image or image path')
# im_or_path = data/demo
parser.add_argument('--out_result_path', default='/tmp/tree.txt', help='out result file name')
parser.add_argument('--conf_thresh', default=0.1, help='confident thresh')
parser.add_argument('--tfmodel',
default='output/res101/voc_2007_trainval/default/res101_faster_rcnn_iter_190000.ckpt',
help='model path')
args = parser.parse_args()
return args
def save_result(detDict, out_path):
with open(out_path, 'w') as f:
for name, dets in detDict.items():
name = os.path.splitext(name)[0]
for det in dets:
ss = '{}\t{:.4f}\t{:.4f}\t{:.4f}\t{:.4f}\t{:.4f}\n'.format(name, det[-1], det[0], det[1], det[2],
det[3])
# print(ss)
f.write(ss)
f.flush()
if __name__ == '__main__':
cfg.TEST.HAS_RPN = True # Use RPN for proposals
args = parse_args()
# os.putenv("CUDA_VISIBLE_DEVICES", '1')
print(args)
# model path
demonet = args.demo_net
dataset = args.dataset
tfmodel = args.tfmodel
# 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
print(tfmodel)
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", 2,
tag='default', anchor_scales=[0.5, 1, 2, 3], anchor_ratios=[0.5, 1, 2])
saver = tf.train.Saver()
saver.restore(sess, tfmodel)
print('Loaded network {:s}'.format(tfmodel))
# im_names = ['000003.jpg', '000004.jpg', '000018.jpg', '000020.jpg',
# '000039.jpg', '000041.jpg', '000060.jpg', '000065.jpg']
im_names = os.listdir(args.im_or_path)
# map(mapfun,im_names)
detdict = {}
for im_name in im_names:
print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
im_name_path = os.path.join(args.im_or_path, im_name)
print('Demo for {}'.format(im_name_path))
dets = demo(im_name_path, sess, net, args.vis, args.conf_thresh)
detdict[im_name] = dets
save_result(detdict, args.out_result_path)
# plt.show()