faster rcnn修改demo.py保存网络中间结果
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faster rcnn用python版本https://github.com/rbgirshick/py-faster-rcnn
以demo.py中默认网络VGG16.
原本demo.py地址https://github.com/rbgirshick/py-faster-rcnn/blob/master/tools/demo.py
图有点多,贴一个图的本分结果出来:
上图是原图,下面第一张是网络中命名为“conv1_1”的结果图;第二张是命名为“rpn_cls_prob_reshape”的结果图;第三张是“rpnoutput”的结果图
看一下我修改后的代码:
#!/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_pathsfrom fast_rcnn.config import cfgfrom fast_rcnn.test import im_detectfrom fast_rcnn.nms_wrapper import nmsfrom utils.timer import Timerimport matplotlib.pyplot as pltimport numpy as npimport scipy.io as sioimport caffe, os, sys, cv2import argparseimport 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: 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() #plt.draw()def save_feature_picture(data, name, image_name=None, padsize = 1, padval = 1): data = data[0] #print "data.shape1: ", data.shape 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) #print "padding: ", padding data = np.pad(data, padding, mode='constant', constant_values=(padval, padval)) #print "data.shape2: ", data.shape data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1))) #print "data.shape3: ", data.shape, n data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:]) #print "data.shape4: ", data.shape plt.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 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) 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 class CONF_THRESH = 0.8 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 = 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 proposals args = parse_args() prototxt = os.path.join(cfg.MODELS_DIR, NETS[args.demo_net][0], 'faster_rcnn_alt_opt', 'faster_rcnn_test.pt') #print "prototxt: ", prototxt 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_param(net) 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()1.在data下手动创建“feature_picture”文件夹就可以替换原来的demo使用了。
2.上面代码主要添加方法是:save_feature_picture,它会对网络测试的某些阶段的数据处理然后保存。
3.某些阶段是因为:if k.find("conv")>-1 or k.find("pool")>-1 or k.find("rpn")>-1这行代码(110行),保证网络层name有这三个词的才会被保存,因为其他层无法用图片
保存,如全连接(参数已经是二维的了)等层。
4.放开174行print_param(net)的注释,就可以看到网络参数的输出。
5.执行的最终结果 是在data/feature_picture产生以图片名字为文件夹名字的文件夹,文件夹下有以网络每层name为名字的图片。
6.另外部分网络的层name中有非法字符不能作为图片名字,我在代码的111行只是把‘字符/’剔除掉了,所以建议网络名字不要又其他字符。
图片下载和代码下载方式:
git clone https://github.com/meihuakaile/faster-rcnn.git
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