コード例 #1
0
def test_rcnn(imageset,
              year,
              root_path,
              devkit_path,
              prefix,
              epoch,
              ctx,
              vis=False,
              has_rpn=True,
              proposal='rpn',
              end2end=False):
    # load symbol and testing data
    if has_rpn:
        sym = get_vgg_test()
        config.TEST.HAS_RPN = True
        config.TEST.RPN_PRE_NMS_TOP_N = 6000
        config.TEST.RPN_POST_NMS_TOP_N = 300
        voc, roidb = load_gt_roidb(imageset, year, root_path, devkit_path)
    else:
        sym = get_vgg_rcnn_test()
        voc, roidb = eval('load_test_' + proposal + '_roidb')(imageset, year,
                                                              root_path,
                                                              devkit_path)

    # get test data iter
    test_data = ROIIter(roidb, batch_size=1, shuffle=False, mode='test')

    # load model
    args, auxs, _ = load_param(prefix, epoch, convert=True, ctx=ctx)

    # detect
    detector = Detector(sym, ctx, args, auxs)
    pred_eval(detector, test_data, voc, vis=vis)
コード例 #2
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def main():
    args = parse_args()
    ctx = mx.gpu(args.gpu)
    symbol = get_vgg_test(num_classes=config.NUM_CLASSES,
                          num_anchors=config.NUM_ANCHORS)
    predictor = get_net(symbol, args.prefix, args.epoch, ctx)
    demo_net(predictor, args.image, args.vis)
コード例 #3
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ファイル: demo.py プロジェクト: lkct/ADA-DD
def main():
    args = parse_args()
    ctx = mx.gpu(args.gpu)
    symbol = get_vgg_test(num_classes=config.NUM_CLASSES, num_anchors=config.NUM_ANCHORS)
    predictor = get_net(symbol, args.prefix, args.epoch, ctx)
    assert os.path.exists(args.image), args.image + ' not found'
    im = cv2.imread(args.image)
    demo_net(predictor, im, args.vis)
コード例 #4
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def main():
    color = cv2.imread(args.img)  # read image in b,g,r order
    img, scale = resize(color.copy(), 640, 1024)
    im_info = np.array([[img.shape[0], img.shape[1], scale]],
                       dtype=np.float32)  # (h, w, scale)
    img = np.swapaxes(img, 0, 2)
    img = np.swapaxes(img, 1, 2)  # change to r,g,b order
    img = img[np.newaxis, :]  # extend to (n, c, h, w)

    ctx = mx.gpu(args.gpu)
    _, arg_params, aux_params = mx.model.load_checkpoint(
        args.prefix, args.epoch)
    arg_params, aux_params = ch_dev(arg_params, aux_params, ctx)
    if 'resnet' in args.prefix:
        sym = resnet_50(num_class=2,
                        bn_mom=0.99,
                        bn_global=True,
                        is_train=False)
    else:
        sym = get_vgg_test(num_classes=2)
    arg_params["data"] = mx.nd.array(img, ctx)
    arg_params["im_info"] = mx.nd.array(im_info, ctx)

    exe = sym.bind(ctx,
                   arg_params,
                   args_grad=None,
                   grad_req="null",
                   aux_states=aux_params)
    exe.forward(is_train=False)
    output_dict = {
        name: nd
        for name, nd in zip(sym.list_outputs(), exe.outputs)
    }
    rois = output_dict['rpn_rois_output'].asnumpy(
    )[:, 1:]  # first column is index
    scores = output_dict['cls_prob_reshape_output'].asnumpy()[0]
    bbox_deltas = output_dict['bbox_pred_reshape_output'].asnumpy()[0]
    pred_boxes = bbox_pred(rois, bbox_deltas)
    pred_boxes = clip_boxes(pred_boxes, (im_info[0][0], im_info[0][1]))

    cls_boxes = pred_boxes[:, 4:8]
    cls_scores = scores[:, 1]
    keep = np.where(cls_scores >= args.thresh)[0]
    cls_boxes = cls_boxes[keep, :]
    cls_scores = cls_scores[keep]
    dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])).astype(np.float32)
    keep = nms(dets.astype(np.float32), args.nms_thresh)
    dets = dets[keep, :]
    keep = nest(dets, thresh=args.nest_thresh)
    dets = dets[keep, :]

    for i in range(dets.shape[0]):
        bbox = dets[i, :4]
        cv2.rectangle(
            color, (int(round(bbox[0] / scale)), int(round(bbox[1] / scale))),
            (int(round(bbox[2] / scale)), int(round(bbox[3] / scale))),
            (0, 255, 0), 2)
    cv2.imwrite("result.jpg", color)
コード例 #5
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def main():
    args = parse_args()
    ctx = mx.gpu(args.gpu)
    symbol = get_vgg_test(num_classes=config.NUM_CLASSES,
                          num_anchors=config.NUM_ANCHORS)
    viz = mx.viz.plot_network(symbol, shape={'data': (1, 3, 600, 800)})
    viz.view('faster-rcnn')
    predictor = get_net(symbol, args.prefix, args.epoch, ctx)
    demo_net(predictor, args.image, args.vis)
コード例 #6
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 def __init__(self,
              gpu=0,
              prefix=os.path.abspath(
                  os.path.join(os.path.dirname(__file__), '..', '..',
                               'data', 'final')),
              epoch=0):
     ctx = mx.gpu(gpu)
     symbol = get_vgg_test(num_classes=config.NUM_CLASSES,
                           num_anchors=config.NUM_ANCHORS)
     self.predictor = self.get_net(symbol, prefix, epoch, ctx)
コード例 #7
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def main():
    args = parse_args()
    ctx = mx.gpu(args.gpu)
    symbol = get_vgg_test(num_classes=config.NUM_CLASSES,
                          num_anchors=config.NUM_ANCHORS)

    df_load = pd.read_csv(args.rec)

    classes = ['blouse', 'outwear', 'skirt', 'dress', 'trousers']
    results = []
    for cls in classes:
        print('loading the model')
        prefix = args.prefix + cls
        df_pred = df_load[df_load['image_category'] == cls].copy()
        df_pred.reset_index(inplace=True)
        del df_pred['index']
        predictor = get_net(symbol, prefix, args.epoch, ctx)
        record = []
        for idx, row in df_pred.iterrows():
            image_id = row['image_id']
            cat = image_id.split('/')[1]
            if cat != cls:
                continue
            image = os.path.join(data_root, image_id)
            #cat = row['image_category']
            res = demo_net(predictor, image, cls, args.vis)
            print(res)

            datum = [
                image_id, cls,
                int(res[0]),
                int(res[1]),
                int(res[2]),
                int(res[3]), res[4]
            ]
            record.append(datum)
            #if idx >10:
            #   break
            if res[4] < 0.1:
                print(image_id)
        cloumns = [
            'image_id', 'image_category', 'xmin', 'ymin', 'xmax', 'ymax',
            'score'
        ]
        df_res = pd.DataFrame(record, columns=cloumns)
        results.append(df_res)
        df_res.to_csv(cls + '_res.csv', index=False)
    data = pd.concat(results, axis=0)
    data.to_csv('./rcnn_res.csv', index=None)
コード例 #8
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ファイル: det_batch.py プロジェクト: chenzx921020/faster-rcnn
def main():
    args = parse_args()
    ctx = mx.gpu(args.gpu)
    symbol = get_vgg_test(num_classes=config.NUM_CLASSES,
                          num_anchors=config.NUM_ANCHORS)
    predictor = get_net(symbol, args.prefix, args.epoch, ctx)
    txt_name = args.image_path[:-1]
    result_txt = open(txt_name + '.txt', 'wb')
    vis = False
    for directory in os.listdir(args.image_path):
        print directory[-4:]
        if ((directory[-4:] != '.txt') and
            (directory[-4:] != 'json')) and (directory != 'cache'):
            img_dir = args.image_path + directory
            for img in os.listdir(img_dir):
                demo_net(predictor, img_dir + '/' + img, result_txt, vis)
    result_txt.close()
コード例 #9
0
ファイル: predict.py プロジェクト: tornadomeet/mx-rcnn
def main():
    color = cv2.imread(args.img)  # read image in b,g,r order
    img, scale = resize(color.copy(), 640, 1024)
    im_info = np.array([[img.shape[0], img.shape[1], scale]], dtype=np.float32)  # (h, w, scale)
    img = np.swapaxes(img, 0, 2)
    img = np.swapaxes(img, 1, 2)  # change to r,g,b order
    img = img[np.newaxis, :]  # extend to (n, c, h, w)

    ctx = mx.gpu(args.gpu)
    _, arg_params, aux_params = mx.model.load_checkpoint(args.prefix, args.epoch)
    arg_params, aux_params = ch_dev(arg_params, aux_params, ctx)
    if 'resnet' in args.prefix:
        sym = resnet_50(num_class=2, bn_mom=0.99, bn_global=True, is_train=False)
    else:
        sym = get_vgg_test(num_classes=2)
    arg_params["data"] = mx.nd.array(img, ctx)
    arg_params["im_info"] = mx.nd.array(im_info, ctx)

    exe = sym.bind(ctx, arg_params, args_grad=None, grad_req="null", aux_states=aux_params)
    exe.forward(is_train=False)
    output_dict = {name: nd for name, nd in zip(sym.list_outputs(), exe.outputs)}
    rois = output_dict['rpn_rois_output'].asnumpy()[:, 1:]  # first column is index
    scores = output_dict['cls_prob_reshape_output'].asnumpy()[0]
    bbox_deltas = output_dict['bbox_pred_reshape_output'].asnumpy()[0]
    pred_boxes = bbox_pred(rois, bbox_deltas)
    pred_boxes = clip_boxes(pred_boxes, (im_info[0][0], im_info[0][1]))

    cls_boxes = pred_boxes[:, 4:8]
    cls_scores = scores[:, 1]
    keep = np.where(cls_scores >= args.thresh)[0]
    cls_boxes = cls_boxes[keep, :]
    cls_scores = cls_scores[keep]
    dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])).astype(np.float32)
    keep = nms(dets.astype(np.float32), args.nms_thresh)
    dets = dets[keep, :]
    keep = nest(dets, thresh=args.nest_thresh)
    dets = dets[keep, :]

    for i in range(dets.shape[0]):
        bbox = dets[i, :4]
        cv2.rectangle(color, (int(round(bbox[0]/scale)), int(round(bbox[1]/scale))),
                      (int(round(bbox[2]/scale)), int(round(bbox[3]/scale))),  (0, 255, 0), 2)
    cv2.imwrite("result.jpg", color)
コード例 #10
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ファイル: test_final.py プロジェクト: Alexbert1/mxnet
def test_net(imageset, year, root_path, devkit_path, prefix, epoch, ctx, vis):
    # set config
    config.TEST.HAS_RPN = True

    # set up logger
    logger = logging.getLogger()
    logger.setLevel(logging.INFO)

    # load testing data
    voc, roidb = load_gt_roidb(imageset, year, root_path, devkit_path)
    test_data = ROIIter(roidb, batch_size=1, shuffle=False, mode='test')

    # load model
    args, auxs = load_param(prefix, epoch, convert=True, ctx=ctx)

    # load symbol
    sym = get_vgg_test()

    # detect
    detector = Detector(sym, ctx, args, auxs)
    pred_eval(detector, test_data, voc, vis=vis)
コード例 #11
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ファイル: test_rcnn.py プロジェクト: 4ker/mxnet
def test_rcnn(imageset, year, root_path, devkit_path, prefix, epoch, ctx, vis=False, has_rpn=True, proposal='rpn'):
    # load symbol and testing data
    if has_rpn:
        sym = get_vgg_test()
        config.TEST.HAS_RPN = True
        config.TEST.RPN_PRE_NMS_TOP_N = 6000
        config.TEST.RPN_POST_NMS_TOP_N = 300
        voc, roidb = load_gt_roidb(imageset, year, root_path, devkit_path)
    else:
        sym = get_vgg_rcnn_test()
        voc, roidb = eval('load_test_' + proposal + '_roidb')(imageset, year, root_path, devkit_path)

    # get test data iter
    test_data = ROIIter(roidb, batch_size=1, shuffle=False, mode='test')

    # load model
    args, auxs, _ = load_param(prefix, epoch, convert=True, ctx=ctx)

    # detect
    detector = Detector(sym, ctx, args, auxs)
    pred_eval(detector, test_data, voc, vis=vis)
コード例 #12
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ファイル: test_final.py プロジェクト: izhaolei/mx-rcnn
def test_net(imageset, year, root_path, devkit_path, prefix, epoch, ctx, vis):
    # set config
    config.TEST.HAS_RPN = True

    # set up logger
    logger = logging.getLogger()
    logger.setLevel(logging.INFO)

    # load testing data
    voc, roidb = load_gt_roidb(imageset, year, root_path, devkit_path)
    test_data = ROIIter(roidb, batch_size=1, shuffle=False, mode='test')

    # load model
    args, auxs = load_param(prefix, epoch, convert=True, ctx=ctx)

    # load symbol
    sym = get_vgg_test()

    # detect
    detector = Detector(sym, ctx, args, auxs)
    pred_eval(detector, test_data, voc, vis=vis)
コード例 #13
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ファイル: demo.py プロジェクト: zhangxiaoqiang7/rcnn
def main():
    args = parse_args()
    ctx = mx.gpu(args.gpu)
    symbol = get_vgg_test(num_classes=config.NUM_CLASSES,
                          num_anchors=config.NUM_ANCHORS)
    predictor = get_net(symbol, args.prefix, args.epoch, ctx)
    if args.image:
        # single test image
        demo_net(predictor, args.image, args.vis)
    else:
        # a image dir for test
        # import pdb
        img_list = os.listdir(args.dir)
        num = len(img_list)
        for line in img_list:
            img_path = os.path.join(args.dir, line)
            if os.path.isfile(img_path) and os.path.splitext(img_path)[-1] in [
                    '.jpg'
            ]:
                # pdb.set_trace()
                logger.info('%s' % num)
                demo_net(predictor, img_path, args.vis)
                num = num - 1
コード例 #14
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ファイル: demo.py プロジェクト: kekedan/mx-rcnn
def get_net(prefix, epoch, ctx):
    args, auxs = load_param(prefix, epoch, convert=True, ctx=ctx)
    sym = get_vgg_test()
    detector = Detector(sym, ctx, args, auxs)
    return detector
コード例 #15
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ファイル: demo.py プロジェクト: CoderHHX/incubator-mxnet
def main():
    args = parse_args()
    ctx = mx.gpu(args.gpu)
    symbol = get_vgg_test(num_classes=config.NUM_CLASSES, num_anchors=config.NUM_ANCHORS)
    predictor = get_net(symbol, args.prefix, args.epoch, ctx)
    demo_net(predictor, args.image, args.vis)
コード例 #16
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def main():
    args = parse_args()
    ctx = mx.gpu(args.gpu)
    symbol = get_vgg_test()
    predictor = get_net(symbol, args.prefix, args.epoch, ctx)
    demo_net(predictor, args.image)
コード例 #17
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ファイル: demo.py プロジェクト: sxjscience/mxnet
def get_net(prefix, epoch, ctx):
    args, auxs, num_class = load_param(prefix, epoch, convert=True, ctx=ctx)
    sym = get_vgg_test(num_classes=num_class)
    detector = Detector(sym, ctx, args, auxs)
    return detector
コード例 #18
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ファイル: demo.py プロジェクト: ChuckTan123/FasterRCNN
def main():
    args = parse_args()
    ctx = mx.gpu(args.gpu)
    symbol = get_vgg_test(num_classes=21, num_anchors=9)
    predictor = get_net(symbol, args.prefix, args.epoch, ctx)
    demo_net(predictor, args.image, args.vis)
コード例 #19
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ファイル: result.py プロジェクト: luwei001/MXNet-Faster-RCNN
                        help='saved model prefix',
                        default='model/e2e',
                        type=str)
    parser.add_argument('--epoch',
                        help='epoch of pretrained model',
                        default=50,
                        type=int)
    parser.add_argument('--gpu', help='GPU device to use', default=0, type=int)
    parser.add_argument('--vis', help='display result', action='store_true')
    args = parser.parse_args()
    return args


if __name__ == '__main__':
    args = parse_args()
    ctx = mx.gpu(0)
    if (Global.prefix_value == 'model/e2e'):
        symbol = get_resnet_test(num_classes=Global.num_class_value,
                                 num_anchors=config.NUM_ANCHORS)
    if (Global.prefix_value == 'model/final'):
        symbol = get_vgg_test(num_classes=Global.num_class_value,
                              num_anchors=config.NUM_ANCHORS)
    predictor = get_net(symbol, args.prefix, ctx)
    from glob import glob
    res = glob("/root/mx-rcnn/testimage/bird/*.JPG")
    cnt = 0
    for i in res:
        demo_net(predictor, i, False)
        # if cnt>10:
# break