faster rcnn demo.py:在一个窗口显示所有类别标注

转载地址：http://blog.csdn.net/10km/article/details/68926498

方便自己随时看。

faster rcnn 的demo.py运行时，对于同一个图像，每个类别显示一个窗口，看起来不太方便，顺便小改一下，让一幅图像中检测到的所有类别物体都在一个窗口下标注，就方便多了。

代码改动也不复杂，就是把vis_detections函数中for循环前后三行代码移动到demo函数的for循环前后。

完整代码如下(顺便把标注框的线宽改成了1，以前是3.5太粗了，不好看)：

py-faster-rcnn/tools/demo.py (注意代码中本人添加的中文注释)

#!/usr/bin/env python
#coding=utf8
# 因为代码中我加了中文注释，所以 上面这行用于指定编码 ,否则python代码执行会报错
# --------------------------------------------------------
# Faster R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------

"""
Demo script showing detections in sample images.

See README.md for installation instructions before running.
"""

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__',
'aeroplane', 'bicycle', 'bird', 'boat',
'bottle', 'bus', 'car', 'cat', 'chair',
'cow', 'diningtable', 'dog', 'horse',
'motorbike', 'person', 'pottedplant',
'sheep', 'sofa', 'train', 'tvmonitor')

NETS = {'vgg16': ('VGG16',
'VGG16_faster_rcnn_final.caffemodel'),
'zf': ('ZF',
'ZF_faster_rcnn_final.caffemodel')}

#增加ax参数
def vis_detections(im, class_name, dets, ax, thresh=0.5):
"""Draw detected bounding boxes."""
inds = np.where(dets[:, -1] >= thresh)[0]
if len(inds) == 0:
return
# 删除这三行
#     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=1) # 矩形线宽从3.5改为1
)
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)
# 删除这三行
#     plt.axis('off')
#     plt.tight_layout()
#     plt.draw()

def demo(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(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
# 将vis_detections 函数中for 循环之前的3行代码移动到这里
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, :]
#将ax做为参数传入vis_detections
vis_detections(im, cls, dets, ax,thresh=CONF_THRESH)
# 将vis_detections 函数中for 循环之后的3行代码移动到这里
plt.axis('off')
plt.tight_layout()
plt.draw()

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')

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_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((300, 500, 3), dtype=np.uint8)
for i in xrange(2):
_, _= im_detect(net, im)

im_names = ['000456.jpg', '000542.jpg', '001150.jpg',
'001763.jpg', '004545.jpg']
for im_name in im_names:
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print 'Demo for data/demo/{}'.format(im_name)
demo(net, im_name)

plt.show()