Py-faster-rcnn: can py-faster-rcnn use video or webcam now?

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[rtgoring]

#!/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
import time

CLASSES = ('__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_video(im, class_name, dets, thresh=0.5):
    """Draw detected bounding boxes."""

    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])-10),(int(bbox[0]+200),int(bbox[1])+10),(10,10,10),-1)
        cv2.putText(im,'{:s} {:.3f}'.format(class_name, score),(int(bbox[0]),int(bbox[1]-2)),cv2.FONT_HERSHEY_SIMPLEX,.45,(255,255,255))#,cv2.CV_AA)
    return im

def demo_video(net, im):
    """Detect object classes in an image using pre-computed object proposals."""
    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])

    # Visualize detections for each class
    CONF_THRESH = 0.75

    NMS_THRESH = 0.2
    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, :]
        im=vis_detections_video(im, cls, dets, thresh=CONF_THRESH)
    cv2.imwrite(os.path.join('output',str(time.time())+'.jpg'),im)
    cv2.imshow('ret',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()
    cv2.namedWindow('ret',0)

    prototxt = os.path.join(cfg.MODELS_DIR, NETS[args.demo_net][0],
                            'faster_rcnn_alt_opt', 'faster_rcnn_test.pt')
    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 '\n\nLoaded network {:s}'.format(caffemodel)

    ### Load directory of images
    '''
    imagePath = 'C:\Users\RTGORING\Documents\Code\py-faster-rcnn\data\demo'
    imageFiles = []
    for f in os.listdir(imagePath):
        if f.endswith('jpg') or f.endswith('.jpeg'):
            imageFiles.append(f)
    imageFiles = sorted(imageFiles)
    for imageName in imageFiles:
        image = cv2.imread(os.path.join(imagePath,imageName))
        demo_video(net,image)
    '''

    ### Load Video File

    videoFilePath = "C:\Users\RTGORING\Documents\Data\Vid.avi"
    videoFile = cv2.VideoCapture(videoFilePath)
    while True:
        ret, image = videoFile.read()
        demo_video(net,image)