对Faster RCNN手动输入proposal regions，不使用自带的RPN
1、将demo.py中RPN标志位置为False
if __name__ == '__main__':
cfg.TEST.HAS_RPN = False2、对im_detect函数手动输入自己的proposal
proposal = np.array([[0,0,100,100],[100,100,200,200],[1069.62,239.235, 1735.65,1954.61]])
scores, boxes = im_detect(net, im, proposal)3、更改原demo使用的prototxt文件faster_rcnn_test.pt为
prototxt = '/home1/fastRCNN/faster-rcnn/models/pascal_voc/VGG16/fast_rcnn/test.prototxt'完整demo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_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__', 'person', 'sky', 'building', 'truck', 'bus', 'table', 'shirt', 'chair',
'car', 'train', 'glasses', 'tree', 'boat', 'hat', 'trees', 'grass', 'pants', 'road', 'motorcycle',
'jacket', 'monitor', 'wheel', 'umbrella', 'plate', 'bike', 'clock', 'bag', 'shoe', 'laptop', 'desk',
'cabinet', 'counter', 'bench', 'shoes', 'tower', 'bottle', 'helmet', 'stove', 'lamp', 'coat', 'bed',
'dog', 'mountain', 'horse', 'plane', 'roof', 'skateboard', 'traffic light', 'bush', 'phone', 'airplane',
'sofa', 'cup', 'sink', 'shelf', 'box', 'van', 'hand', 'shorts', 'post', 'jeans', 'cat', 'sunglasses',
'bowl', 'computer', 'pillow', 'pizza', 'basket', 'elephant', 'kite', 'sand', 'keyboard', 'plant', 'can',
'vase', 'refrigerator', 'cart', 'skis', 'pot', 'surfboard', 'paper', 'mouse', 'trash can', 'cone', 'camera',
'ball', 'bear', 'giraffe', 'tie', 'luggage', 'faucet', 'hydrant', 'snowboard', 'oven', 'engine', 'watch',
'face', 'street', 'ramp', 'suitcase')
NETS = {'vgg16': ('VGG16',
'VGG16_faster_rcnn_final.caffemodel'),
'zf': ('ZF',
'ZF_faster_rcnn_final.caffemodel')}
def vis_detections(im, class_name, dets, ax, thresh=0.5):
"""Draw detected bounding boxes."""
inds = np.where(dets[:, -1] >= thresh)[0]
# print np.size(inds)
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]
print bbox[0], bbox[1], bbox[2], bbox[3]
print class_name
ax.add_patch(
plt.Rectangle((bbox[0], bbox[1]),
bbox[2] - bbox[0],
bbox[3] - bbox[1], fill=False,
edgecolor='red', linewidth=2)
)
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):
"""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()
proposal = np.array([[0,0,100,100],[100,100,200,200],[1069.62,239.235, 1735.65,1954.61]])
print proposal
scores, boxes = im_detect(net, im, proposal)
# print scores
# 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 = 0.5
NMS_THRESH = 0.3
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, :]
vis_detections(im, cls, dets, ax, thresh=CONF_THRESH)
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 = False # 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')
prototxt = '/home1/fastRCNN/faster-rcnn/models/pascal_voc/VGG16/fast_rcnn/test.prototxt'
print prototxt
caffemodel = os.path.join(cfg.DATA_DIR, 'faster_rcnn_models',
NETS[args.demo_net][1])
print caffemodel
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 = ['005001.jpg', '004970.jpg', '004545.jpg']
for im_name in im_names:
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print 'Demo for data/demo/{}'.format(im_name)
demo(net, im_name)
# net.forward()
print [(k, v[0].data.shape) for k, v in net.params.items()]
print [(k, v.data.shape) for k, v in net.blobs.items()]
print net.blobs['fc7'].data
plt.show()