py-faster-rcnn demo.py解析

    对py-faster-rcnn/tools/demo.py文件的解析：
    运行该文件：先cd进入到py-faster-rcnn根目录，再在命令后窗口输入: ./tools/demo.py --net myvgg   即可运行

#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()