参考博客原址：https://blog.csdn.net/qq_37124237/article/details/81087505
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#!/usr/bin/env python
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-
# --------------------------------------------------------
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# Faster R-CNN
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# Copyright (c) 2015 Microsoft
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# Licensed under The MIT License [see LICENSE for details]
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# Written by Ross Girshick
-
# --------------------------------------------------------
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-
"""
-
Demo script showing detections in sample images.
-
-
See README.md for installation instructions before running.
-
"""
-
-
import _init_paths
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from fast_rcnn.config
import cfg
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from fast_rcnn.test
import im_detect
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from fast_rcnn.nms_wrapper
import nms
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from utils.timer
import Timer
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import matplotlib.pyplot
as plt
-
import numpy
as np
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import scipy.io
as sio
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import caffe, os, sys, cv2
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import argparse
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-
CLASSES = (
'__background__',
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'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')}
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-
-
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]),
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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')
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plt.tight_layout()
-
plt.draw()
-
-
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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."""
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global frameRate
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# Load the demo image
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#im_file = os.path.join(cfg.DATA_DIR, 'demo', image_name)
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#im = cv2.imread(im_file)
-
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# Detect all object classes and regress object bounds
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timer = Timer()
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timer.tic()
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scores, boxes = im_detect(net, im)
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timer.toc()
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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)
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dets = dets[keep, :]
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vis_detections_video(im, cls, dets, thresh=CONF_THRESH)
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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()
-
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return args
-
-
-
-
-
if __name__ ==
'__main__':
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cfg.TEST.HAS_RPN =
True
# Use RPN for proposals
-
-
args = parse_args()
-
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# prototxt = os.path.join(cfg.MODELS_DIR, NETS[args.demo_net][0],
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# 'faster_rcnn_alt_opt', 'faster_rcnn_test.pt')
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prototxt =
'/home/yexin/py-faster-rcnn/models/pascal_voc/VGG_CNN_M_1024/faster_rcnn_end2end/test.prototxt'
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# print 'see prototxt path{}'.format(prototxt)
-
-
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# caffemodel = os.path.join(cfg.DATA_DIR, 'faster_rcnn_models',
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# NETS[args.demo_net][1])
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caffemodel =
'/home/yexin/py-faster-rcnn/output/faster_rcnn_end2end/voc_2007_trainval/vgg_cnn_m_1024_faster_rcnn_iter_100.caffemodel'
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-
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# print '\n\nok'
-
-
if
not os.path.isfile(caffemodel):
-
raise IOError((
'{:s} not found.\nDid you run ./data/script/'
-
'fetch_faster_rcnn_models.sh?').format(caffemodel))
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print
'\n\nok'
-
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if args.cpu_mode:
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caffe.set_mode_cpu()
-
else:
-
caffe.set_mode_gpu()
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caffe.set_device(args.gpu_id)
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cfg.GPU_ID = args.gpu_id
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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)
-
-
videoFilePath =
'/home/yexin/py-faster-rcnn/data/demo/test_1-3.mp4'
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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()
-
-
#!/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 '\n\nok'
-
-
if
not os.path.isfile(caffemodel):
-
raise IOError((
'{:s} not found.\nDid you run ./data/script/'
-
'fetch_faster_rcnn_models.sh?').format(caffemodel))
-
print
'\n\nok'
-
-
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)
-
-
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()
-