轉(zhuǎn)載自：http://blog.csdn.net/u010668907/article/details/51439503
faster rcnn用Python版本https://github.com/rbgirshick/py-faster-rcnn
以demo.py中默認網(wǎng)絡VGG16.
原本demo.py地址https://github.com/rbgirshick/py-faster-rcnn/blob/master/tools/demo.py
圖有點多，貼一個圖的本分結(jié)果出來：
上圖是原圖，下面第一張是網(wǎng)絡中命名為“conv1_1”的結(jié)果圖；第二張是命名為“rpn_cls_prob_reshape”的結(jié)果圖；第三張是“rpnoutput”的結(jié)果圖
看一下我修改后的代碼：
[python] view plaincopyprint?
??????????????????????????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??import?math????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')}??????def?vis_detections(im,?class_name,?dets,?thresh=0.5):????????????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=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()????????def?save_feature_picture(data,?name,?image_name=None,?padsize?=?1,?padval?=?1):??????data?=?data[0]????????????n?=?int(np.ceil(np.sqrt(data.shape[0])))??????padding?=?((0,?n?**?2?-?data.shape[0]),?(0,?0),?(0,?padsize))?+?((0,?0),)?*?(data.ndim?-?3)????????????data?=?np.pad(data,?padding,?mode='constant',?constant_values=(padval,?padval))??????????????????data?=?data.reshape((n,?n)?+?data.shape[1:]).transpose((0,?2,?1,?3)?+?tuple(range(4,?data.ndim?+?1)))????????????data?=?data.reshape((n?*?data.shape[1],?n?*?data.shape[3])?+?data.shape[4:])????????????plt.figure()??????plt.imshow(data,cmap='gray')??????plt.axis('off')????????????if?image_name?==?None:??????????img_path?=?'./data/feature_picture/'???????else:??????????img_path?=?'./data/feature_picture/'?+?image_name?+?"/"??????????check_file(img_path)??????plt.savefig(img_path?+?name?+?".jpg",?dpi?=?400,?bbox_inches?=?"tight")??def?check_file(path):??????if?not?os.path.exists(path):??????????os.mkdir(path)??def?demo(net,?image_name):????????????????????im_file?=?os.path.join(cfg.DATA_DIR,?'demo',?image_name)??????im?=?cv2.imread(im_file)??????????????timer?=?Timer()??????timer.tic()??????scores,?boxes?=?im_detect(net,?im)??????for?k,?v?in?net.blobs.items():??????????if?k.find("conv")>-1?or?k.find("pool")>-1?or?k.find("rpn")>-1:??????????????save_feature_picture(v.data,?k.replace("/",?""),?image_name)#net.blobs["conv1_1"].data,?"conv1_1")???????timer.toc()??????print?('Detection?took?{:.3f}s?for?'?????????????'{:d}?object?proposals').format(timer.total_time,?boxes.shape[0])??????????????CONF_THRESH?=?0.8??????NMS_THRESH?=?0.3??????for?cls_ind,?cls?in?enumerate(CLASSES[1:]):??????????cls_ind?+=?1???????????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,?:]??????????vis_detections(im,?cls,?dets,?thresh=CONF_THRESH)????def?parse_args():????????????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????def?print_param(net):??????for?k,?v?in?net.blobs.items():??????print?(k,?v.data.shape)??????print?""??????for?k,?v?in?net.params.items():??????print?(k,?v[0].data.shape)??????if?__name__?==?'__main__':??????cfg.TEST.HAS_RPN?=?True??????????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)??????????????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)??????????
#!/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
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
import mathCLASSES = ('__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')}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:returnim = 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=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 save_feature_picture(data, name, image_name=None, padsize = 1, padval = 1):data = data[0]#print "data.shape1: ", data.shapen = int(np.ceil(np.sqrt(data.shape[0])))padding = ((0, n ** 2 - data.shape[0]), (0, 0), (0, padsize)) + ((0, 0),) * (data.ndim - 3)#print "padding: ", paddingdata = np.pad(data, padding, mode='constant', constant_values=(padval, padval))#print "data.shape2: ", data.shapedata = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))#print "data.shape3: ", data.shape, ndata = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])#print "data.shape4: ", data.shapeplt.figure()plt.imshow(data,cmap='gray')plt.axis('off')#plt.show()if image_name == None:img_path = './data/feature_picture/' else:img_path = './data/feature_picture/' + image_name + "/"check_file(img_path)plt.savefig(img_path + name + ".jpg", dpi = 400, bbox_inches = "tight")
def check_file(path):if not os.path.exists(path):os.mkdir(path)
def demo(net, image_name):"""Detect object classes in an image using pre-computed object proposals."""# Load the demo imageim_file = os.path.join(cfg.DATA_DIR, 'demo', image_name)im = cv2.imread(im_file)# Detect all object classes and regress object boundstimer = Timer()timer.tic()scores, boxes = im_detect(net, im)for k, v in net.blobs.items():if k.find("conv")>-1 or k.find("pool")>-1 or k.find("rpn")>-1:save_feature_picture(v.data, k.replace("/", ""), image_name)#net.blobs["conv1_1"].data, "conv1_1") timer.toc()print ('Detection took {:.3f}s for ''{:d} object proposals').format(timer.total_time, boxes.shape[0])# Visualize detections for each classCONF_THRESH = 0.8NMS_THRESH = 0.3for cls_ind, cls in enumerate(CLASSES[1:]):cls_ind += 1 # because we skipped backgroundcls_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, :]vis_detections(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')args = parser.parse_args()return argsdef print_param(net):for k, v in net.blobs.items():print (k, v.data.shape)print ""for k, v in net.params.items():print (k, v[0].data.shape) if __name__ == '__main__':cfg.TEST.HAS_RPN = True # Use RPN for proposalsargs = parse_args()prototxt = os.path.join(cfg.MODELS_DIR, NETS[args.demo_net][0],'faster_rcnn_alt_opt', 'faster_rcnn_test.pt')#print "prototxt: ", prototxtcaffemodel = 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_idnet = caffe.Net(prototxt, caffemodel, caffe.TEST)#print_param(net)print '\n\nLoaded network {:s}'.format(caffemodel)# Warmup on a dummy imageim = 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()1.在data下手動創(chuàng)建“feature_picture”文件夾就可以替換原來的demo使用了。
2.上面代碼主要添加方法是：save_feature_picture，它會對網(wǎng)絡測試的某些階段的數(shù)據(jù)處理然后保存。
3.某些階段是因為：if k.find("conv")>-1 or k.find("pool")>-1 or k.find("rpn")>-1這行代碼（110行），保證網(wǎng)絡層name有這三個詞的才會被保存，因為其他層無法用圖片
保存，如全連接（參數(shù)已經(jīng)是二維的了）等層。
4.放開174行print_param(net)的注釋，就可以看到網(wǎng)絡參數(shù)的輸出。
5.執(zhí)行的最終結(jié)果 是在data/feature_picture產(chǎn)生以圖片名字為文件夾名字的文件夾，文件夾下有以網(wǎng)絡每層name為名字的圖片。
6.另外部分網(wǎng)絡的層name中有非法字符不能作為圖片名字，我在代碼的111行只是把‘字符/’剔除掉了，所以建議網(wǎng)絡名字不要又其他字符。
圖片下載和代碼下載方式：
[plain] view plaincopyprint?
git?clone?https://github.com/meihuakaile/faster-rcnn.git ?
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