https://download.csdn.net/download/zefan7564/10148990
https://blog.csdn.net/qq_37124237/article/details/81087505
目标检测 Faster R-CNN运行及实时性DEMO测试
-
#!/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__',
-
'ship')
-
-
NETS = {'vgg16': ('VGG16',
-
'VGG16_faster_rcnn_final.caffemodel'),
-
'zf': ('ZF',
-
'ZF_faster_rcnn_final.caffemodel'),
-
'wyx': ('wyx','vgg_cnn_m_1024_faster_rcnn_iter_1000.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 vis_detections_video(im, class_name, dets, thresh=0.5):
-
"""Draw detected bounding boxes."""
-
global lastColor,frameRate
-
inds = np.where(dets[:, -1] >= thresh)[0]
-
if len(inds) == 0:
-
return im
-
-
for i in inds:
-
bbox = dets[i, :4]
-
score = dets[i, -1]
-
cv2.rectangle(im,(bbox[0],bbox[1]),(bbox[2],bbox[3]),(0,0,255),2)
-
cv2.rectangle(im,(int(bbox[0]),int(bbox[1]-20)),(int(bbox[0]+200),int(bbox[1])),(10,10,10),-1)
-
cv2.putText(im,'{:s} {:.3f}'.format(class_name, score),(int(bbox[0]),int(bbox[1]-2)),cv2.FONT_HERSHEY_SIMPLEX,.75,(255,255,255))#,cv2.CV_AA)
-
-
return im
-
-
-
-
def demo(net, im):
-
"""Detect object classes in an image using pre-computed object proposals."""
-
global frameRate
-
# 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)
-
timer.toc()
-
print ('Detection took {:.3f}s for '
-
'{:d} object proposals').format(timer.total_time, boxes.shape[0])
-
frameRate = 1.0/timer.total_time
-
print "fps: " + str(frameRate)
-
# 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_video(im, cls, dets, thresh=CONF_THRESH)
-
cv2.putText(im,'{:s} {:.2f}'.format("FPS:", frameRate),(1750,50),cv2.FONT_HERSHEY_SIMPLEX,1,(0,0,255))
-
cv2.imshow(videoFilePath.split('/')[len(videoFilePath.split('/'))-1],im)
-
cv2.waitKey(20)
-
-
-
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 = 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')
-
prototxt = '/home/yexin/py-faster-rcnn/models/pascal_voc/VGG_CNN_M_1024/faster_rcnn_end2end/test.prototxt'
-
# print 'see prototxt path{}'.format(prototxt)
-
-
-
# caffemodel = os.path.join(cfg.DATA_DIR, 'faster_rcnn_models',
-
# NETS[args.demo_net][1])
-
caffemodel = '/home/yexin/py-faster-rcnn/output/faster_rcnn_end2end/voc_2007_trainval/vgg_cnn_m_1024_faster_rcnn_iter_100.caffemodel'
-
-
-
# print ' ok'
-
-
if not os.path.isfile(caffemodel):
-
raise IOError(('{:s} not found. Did you run ./data/script/'
-
'fetch_faster_rcnn_models.sh?').format(caffemodel))
-
print ' ok'
-
-
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 ' Loaded 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)
-
-
videoFilePath = '/home/yexin/py-faster-rcnn/data/demo/test_1-3.mp4'
-
videoCapture = cv2.VideoCapture(videoFilePath)
-
#success, im = videoCapture.read()
-
while True :
-
success, im = videoCapture.read()
-
demo(net, im)
-
if cv2.waitKey(10) & 0xFF == ord('q'):
-
break
-
videoCapture.release()
-
cv2.destroyAllWindows()
-