The tools tool code of faster-rcnn imitates demo.py to output all the detection results, and at the same time draws on the picture to be detected while detecting.
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# --------------------------------------------------------
# Faster R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
"""
Draw the test results one by one
"""
import _init_paths
from fast_rcnn.config import cfg
from fast_rcnn.test import im_detect
from fast_rcnn.nms_wrapper import nms
from utils.timer import Timer
import matplotlib.pyplot as plt
import numpy as np
import scipy.io as sio
import caffe, os, sys, cv2
import argparse
CLASSES = ('__background__',
'plane', 'baseball-diamond', 'bridge', 'ground-track-field', 'small-vehicle', 'large-vehicle', 'ship', 'tennis-court',
'basketball-court', 'storage-tank', 'soccer-ball-field', 'roundabout', 'harbor', 'swimming-pool', 'helicopter')
NETS = {'vgg16': ('VGG16',
'vgg16_faster_rcnn_iter_20000.caffemodel'),
#'VGG16_faster_rcnn_final.caffemodel'),
'vggcp': ('VGGcp',
'vggcp_faster_rcnn_iter_30000.caffemodel'),
'zf': ('ZF',
'ZF_faster_rcnn_final.caffemodel')}
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 = its [i,: 4]
score = its [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=8, color='white')
ax.set_title(('{} detections with '
'p({} | box) >= {:.1f}').format(class_name, class_name,
thresh),
fontsize=14)
plt.axis ('! 321n4s.')
plt.tight_layout()
plt.draw()
###opencv draw
def vis_detections_cv(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
fname, extension=os.path.splitext(image_name)
#The path of the detection result txt
fid = open(os.path.join('/home/respectv/soft-nms-dota/data/VOCdevkit2007/testsplit_images_bbox2/','%s.txt' %fname),'w')
for i in inds:
bbox = its [i,: 4]
score = its [i, -1]
bbox_info = '%s %s %f %d %d %d %d\n' % (fname,class_name,score,int(bbox[0]),int(bbox[1]),int(bbox[2]),int(bbox[3]))
fid.writelines (bbox_info)
cv2.rectangle(im,(bbox[0],int(bbox[1])),(int(bbox[2]),int(bbox[3])),(255,255,0),2)
cv2.putText(im, '{:s}'.format(class_name), (int(bbox[0]), int(bbox[1] - 5)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,0,255), 2)
fid.close()
#The path where the detection result is drawn on the graph
cv2.imwrite(os.path.join('/home/respectv/soft-nms-dota/data/VOCdevkit2007/testsplit_image/demo','%s.txt.jpg' % image_name),im)
def demo(net, image_name):
"""Detect object classes in an image using pre-computed object proposals."""
im_file = os.path.join('/home/respectv/soft-nms-dota/data/VOCdevkit2007/testsplit_image/images', image_name)
im = cv2.imread(im_file)
# Detect all object classes and regress object bounds
timer = Timer ()
timer.tic()
scores, boxes = im_detect(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 = 0.6
#CONF_THRESH = 0.8
NMS_THRESH = 0.25
#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 = its [keep,:]
#vis_detections(im, cls, dets, thresh=CONF_THRESH)
vis_detections_cv(image_name,im, cls, dets, thresh=CONF_THRESH)
def parse_args():
"""Parse input arguments."""
parser = argparse.ArgumentParser(description='Faster R-CNN demo')
parser.add_argument('--gpu', dest='gpu_id', help='GPU device id to use [0]',
default=0, type=int)
parser.add_argument('--cpu', dest='cpu_mode',
help='Use CPU mode (overrides --gpu)',
action='store_true')
parser.add_argument('--net', dest='demo_net', help='Network to use [vgg16]',
choices=NETS.keys(), default='vgg16')
parser.add_argument('--nms', dest='soft_nms', help='wheather to use soft_nms',
default=1, type=int)
args = parser.parse_args()
return args
if __name__ == '__main__':
cfg.TEST.HAS_RPN = True # Use RPN for proposals
args = parse_args()
prototxt = os.path.join(cfg.MODELS_DIR, NETS[args.demo_net][0],
'faster_rcnn_end2end', 'test.prototxt')
#'faster_rcnn_alt_opt', 'faster_rcnn_test.pt')
caffemodel = os.path.join(cfg.DATA_DIR, 'faster_rcnn_models',
NETS[args.demo_net][1])
if not os.path.isfile(caffemodel):
raise IOError(('{:s} not found.\nDid you run ./data/script/'
'fetch_faster_rcnn_models.sh?').format(caffemodel))
if args.cpu_mode:
caffe.set_mode_cpu()
else:
caffe.set_mode_gpu()
caffe.set_device(args.gpu_id)
cfg.GPU_ID = args.gpu_id
net = caffe.Net(prototxt, caffemodel, caffe.TEST)
print '\n\nLoaded network {:s}'.format(caffemodel)
# Warmup on a dummy image
im = 128 * np.ones((1024, 1024, 3), dtype=np.uint8)
#im = 128 * np.ones((300, 500, 3), dtype=np.uint8)
for i in xrange(2):
_, _= im_detect(net, im)
#The path of the image to be detected
im_dir = '/home/respectv/soft-nms-dota/data/VOCdevkit2007/testsplit_image/images'
im_names = os.listdir(im_dir)
image_num = 0
for im_name in im_names:
if 'txt' in im_name:
continue
image_num += 1
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print 'Demo for %s/%s' % (im_dir,im_name)
demo(net, im_name)
#plt.show()
#cv2.destroyAllWindows()