#程序功能：调用caffemodel，画出检测到的人脸并显示
#用来指定用什么解释器运行脚本，以及解释器所在位置，这样就可以直接执行脚本
#!/usr/bin/env python
# --------------------------------------------------------
# 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 #导入“_init_paths.py”文件
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 #导入用来画图的工具
import numpy as np #numpg:矩阵计算模块
import scipy.io as sio #scipy.io：对matlab中mat文件进行读取操作
import caffe, os, sys, cv2
import argparse #argparse：是python用于解析命令行参数和选项的标准模块
#CLASSES = ('__background__', #背景 + 类
# 'aeroplane', 'bicycle', 'bird', 'boat',
# 'bottle', 'bus', 'car', 'cat', 'chair',
# 'cow', 'diningtable', 'dog', 'horse',
# 'motorbike', 'person', 'pottedplant',
# 'sheep', 'sofa', 'train', 'tvmonitor')
CLASSES = ('__background__','face') #只有一类：face
NETS = {'vgg16': ('VGG16', #网络
'VGG16_faster_rcnn_final.caffemodel'),
'myvgg': ('VGG_CNN_M_1024',
'VGG_CNN_M_1024_faster_rcnn_final.caffemodel'),
'zf': ('ZF',
'ZF_faster_rcnn_final.caffemodel'),
'myzf': ('ZF',
'zf_rpn_stage1_iter_80000.caffemodel'),
}
def vis_detections(im, class_name, dets, thresh=0.5):
"""Draw detected bounding boxes."""
inds = np.where(dets[:, -1] >= thresh)[0] #返回置信度大于阈值的窗口下标
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] #人脸坐标位置（Xmin,Ymin,Xmax,Ymax）
score = dets[i, -1] #置信度得分
ax.add_patch(
plt.Rectangle((bbox[0], bbox[1]), #bbox[0]:x, bbox[1]:y, bbox[2]:x+w, bbox[3]:y+h
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(net, image_name):
#检测目标类，在图片中提议窗口
"""Detect object classes in an image using pre-computed object proposals."""
# Load the demo image，得到图片绝对地址
im_file = os.path.join(cfg.DATA_DIR, 'demo', image_name) #拼接路径，返回'A/B/C'之类路径
im = cv2.imread(im_file) #读取图片
# Detect all object classes and regress object bounds
timer = Timer() #time.time()返回当前时间
timer.tic() #返回开始时间，见'time.py'中
scores, boxes = im_detect(net, im) #检测，返回得分和人脸区域所在位置
timer.toc() #返回平均时间，'time.py'文件中
print ('Detection took {:.3f}s for ' #输出
'{: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:]): #enumerate:用于遍历序列中元素及他们的下标
cls_ind += 1 # because we skipped background ，cls_ind:下标，cls:元素
cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)] #返回当前坐标
cls_scores = scores[:, cls_ind] #返回当前得分
dets = np.hstack((cls_boxes, #hstack:拷贝，合并参数
cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, NMS_THRESH)
dets = dets[keep, :]
vis_detections(im, cls, dets, thresh=CONF_THRESH) #画检测框
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__': #判断是否在直接运行该.py文件
cfg.TEST.HAS_RPN = True # Use RPN for proposals
args = parse_args() #模式设置
prototxt = os.path.join(cfg.MODELS_DIR, NETS[args.demo_net][0], #连接路径，设置prototxt文件
'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) #设置网络
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): #xrange是一个类，返回的是一个xrange对象
_, _= im_detect(net, im)
#用于演示的图片名
im_names = ['000456.jpg', '000542.jpg', '001150.jpg',
'001763.jpg', '004545.jpg', '001.jpg', '002.jpg', '003.jpg',
'004.jpg', '005.jpg', '006.jpg', '007.jpg', '008.jpg']
for im_name in im_names:
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print 'Demo for data/demo/{}'.format(im_name)
demo(net, im_name) #逐个跑demo
plt.show()
参考 https://blog.csdn.net/xzzppp/article/details/52073107