faster-rcnn demo程式碼修改進行視訊實時性測試
阿新 • • 發佈：2018-12-18
參考部落格原址：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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# --------------------------------------------------------
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"""
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Demo script showing detections in sample images.
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See README.md for installation instructions before running.
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"""
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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
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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')
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NETS = {
'vgg16': (
'VGG16',
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'VGG16_faster_rcnn_final.caffemodel'),
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'zf': (
'ZF',
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'ZF_faster_rcnn_final.caffemodel'),
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'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):
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"""Draw detected bounding boxes."""
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inds = np.where(dets[:,
-1] >= thresh)[
0]
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if len(inds) ==
0:
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return
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im = im[:, :, (
2,
1,
0)]
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fig, ax = plt.subplots(figsize=(
12,
12))
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ax.imshow(im, aspect=
'equal')
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for i
in inds:
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bbox = dets[i, :
4]
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score = dets[i,
-1]
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ax.add_patch(
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plt.Rectangle((bbox[
0], bbox[
1]),
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bbox[
2] - bbox[
0],
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bbox[
3] - bbox[
1], fill=
False,
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edgecolor=
'red', linewidth=
3.5)
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)
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ax.text(bbox[
0], bbox[
1] -
2,
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'{:s} {:.3f}'.format(class_name, score),
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bbox=dict(facecolor=
'blue', alpha=
0.5),
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fontsize=
14, color=
'white')
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ax.set_title((
'{} detections with '
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'p({} | box) >= {:.1f}').format(class_name, class_name,
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thresh),
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fontsize=
14)
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plt.axis(
'off')
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plt.tight_layout()
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plt.draw()
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def vis_detections_video(im, class_name, dets, thresh=0.5):
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"""Draw detected bounding boxes."""
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global lastColor,frameRate
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inds = np.where(dets[:,
-1] >= thresh)[
0]
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if len(inds) ==
0:
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return im
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for i
in inds:
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bbox = dets[i, :
4]
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score = dets[i,
-1]
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cv2.rectangle(im,(bbox[
0],bbox[
1]),(bbox[
2],bbox[
3]),(
0,
0,
255),
2)
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cv2.rectangle(im,(int(bbox[
0]),int(bbox[
1]
-20)),(int(bbox[
0]+
200),int(bbox[
1])),(
10,
10,
10),
-1)
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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)
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return im
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def demo(net, im):
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"""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 '
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'{:d} object proposals').format(timer.total_time, boxes.shape[
0])
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frameRate =
1.0/timer.total_time
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print
"fps: " + str(frameRate)
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# Visualize detections for each class
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CONF_THRESH =
0.8
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NMS_THRESH =
0.3
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for cls_ind, cls
in enumerate(CLASSES[
1:]):
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cls_ind +=
1
# because we skipped background
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cls_boxes = boxes[:,
4*cls_ind:
4*(cls_ind +
1)]
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cls_scores = scores[:, cls_ind]
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dets = np.hstack((cls_boxes,
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cls_scores[:, np.newaxis])).astype(np.float32)
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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))
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cv2.imshow(videoFilePath.split(
'/')[len(videoFilePath.split(
'/'))
-1],im)
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cv2.waitKey(
20)
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def parse_args():
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"""Parse input arguments."""
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parser = argparse.ArgumentParser(description=
'Faster R-CNN demo')
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parser.add_argument(
'--gpu', dest=
'gpu_id', help=
'GPU device id to use [0]',
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default=
0, type=int)
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parser.add_argument(
'--cpu', dest=
'cpu_mode',
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help=
'Use CPU mode (overrides --gpu)',
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action=
'store_true')
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parser.add_argument(
'--net', dest=
'demo_net', help=
'Network to use [vgg16]',
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choices=NETS.keys(), default=
'vgg16')
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args = parser.parse_args()
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return args
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if __name__ ==
'__main__':
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cfg.TEST.HAS_RPN =
True
# Use RPN for proposals
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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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# print '\n\nok'
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if
not os.path.isfile(caffemodel):
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raise IOError((
'{:s} not found.\nDid you run ./data/script/'
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'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()
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else:
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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)
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print
'\n\nLoaded network {:s}'.format(caffemodel)
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# Warmup on a dummy image
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im =
128 * np.ones((
300,
500,
3), dtype=np.uint8)
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for i
in xrange(
2):
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_, _= im_detect(net, im)
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videoFilePath =
'/home/yexin/py-faster-rcnn/data/demo/test_1-3.mp4'
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videoCapture = cv2.VideoCapture(videoFilePath)
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#success, im = videoCapture.read()
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while
True :
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success, im = videoCapture.read()
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demo(net, im)
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if cv2.waitKey(
10) &
0xFF == ord(
'q'):
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break
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videoCapture.release()
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cv2.destroyAllWindows()
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#!/usr/bin/env python
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# --------------------------------------------------------
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# 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__',
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'ship')
-
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NETS = {
'vgg16': (
'VGG16',
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'VGG16_faster_rcnn_final.caffemodel'),
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'zf': (
'ZF',
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'ZF_faster_rcnn_final.caffemodel'),
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'wyx': (
'wyx',
'vgg_cnn_m_1024_faster_rcnn_iter_1000.caffemodel')}
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