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初心者なもので、、、コードをちゃんと書いてませんでした。どうぞよろしくお願いします。

2019/12/05 14:20

投稿

Griffon

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1	- python~~コードを~~2クラス検出にしたいですがどこを変えればよいかわかりません。
1	+ python　他クラスから2クラス検出にしたいですがどこをどう変えればよいかわかりません。

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-ここに質問の内容を詳しく書いてください。
 pythonを使ってディープラーニングをやっているのですが、２クラスだけ（Backgroundを含めずに）の検出にしたい場合、

初心者なもので、、、コードをちゃんと書いてませんでした。どうぞよろしくお願いします。

2019/12/05 14:19

投稿

Griffon

スコア4

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@@ -17,3 +17,435 @@
 https://github.com/guoruoqian/DetNet_pytorch/blob/master/demo.py
+```ここに言語を入力
+    for target_size in cfg.TEST.SCALES:
+        im_scale = float(target_size) / float(im_size_min)
+        # Prevent the biggest axis from being more than MAX_SIZE
+        if np.round(im_scale * im_size_max) > cfg.TEST.MAX_SIZE:
+            im_scale = float(cfg.TEST.MAX_SIZE) / float(im_size_max)
+        im = cv2.resize(im_orig, None, None, fx=im_scale, fy=im_scale,
+                        interpolation=cv2.INTER_LINEAR)
+        im_scale_factors.append(im_scale)
+        processed_ims.append(im)
+    # Create a blob to hold the input images
+    blob = im_list_to_blob(processed_ims)
+    return blob, np.array(im_scale_factors)
+if __name__ == '__main__':
+    args = parse_args()
+    print('Called with args:')
+    print(args)
+    args.cfg_file = "cfgs/{}.yml".format(args.net)
+    if args.cfg_file is not None:
+        cfg_from_file(args.cfg_file)
+    if args.set_cfgs is not None:
+        cfg_from_list(args.set_cfgs)
+    if not os.path.exists(args.result_dir):
+        os.mkdir(args.result_dir)
+    print('Using config:')
+    pprint.pprint(cfg)
+    np.random.seed(cfg.RNG_SEED)
+    # train set
+    # -- Note: Use validation set and disable the flipped to enable faster loading.
+    if args.exp_name is not None:
+        input_dir = args.load_dir + "/" + args.net + "/" + args.dataset + '/' + args.exp_name
+    else:
+        input_dir = args.load_dir + "/" + args.net + "/" + args.dataset
+    if not os.path.exists(input_dir):
+        raise Exception('There is no input directory for loading network from ' + input_dir)
+    load_name = os.path.join(input_dir,
+                             'fpn_{}_{}_{}.pth'.format(args.checksession, args.checkepoch, args.checkpoint))
+    classes = np.asarray(['__background__',
+                          'aeroplane', 'bicycle', 'bird', 'boat',
+                          'bottle', 'bus', 'car', 'cat', 'chair',
+                          'cow', 'diningtable', 'dog', 'horse',
+                          'motorbike', 'person', 'pottedplant',
+                          'sheep', 'sofa', 'train', 'tvmonitor'])
+    if args.net == 'detnet59':
+        fpn = detnet(classes, 59, pretrained=False, class_agnostic=args.class_agnostic)
+    else:
+        print("network is not defined")
+        pdb.set_trace()
+    fpn.create_architecture()
+    checkpoint = torch.load(load_name)
+    fpn.load_state_dict(checkpoint['model'])
+    if 'pooling_mode' in checkpoint.keys():
+        cfg.POOLING_MODE = checkpoint['pooling_mode']
+    print('load model successfully!')
+    # pdb.set_trace()
+    print("load checkpoint %s" % (load_name))
+    # initilize the tensor holder here.
+    im_data = torch.FloatTensor(1)
+    im_info = torch.FloatTensor(1)
+    num_boxes = torch.LongTensor(1)
+    gt_boxes = torch.FloatTensor(1)
+    # ship to cuda
+    if args.cuda:
+        im_data = im_data.cuda()
+        im_info = im_info.cuda()
+        num_boxes = num_boxes.cuda()
+        gt_boxes = gt_boxes.cuda()
+    # make variable
+    im_data = Variable(im_data, volatile=True)
+    im_info = Variable(im_info, volatile=True)
+    num_boxes = Variable(num_boxes, volatile=True)
+    gt_boxes = Variable(gt_boxes, volatile=True)
+    if args.cuda:
+        cfg.CUDA = True
+    if args.cuda:
+        fpn.cuda()
+    fpn.eval()
+    start = time.time()
+    max_per_image = 100
+    thresh = 0.05
+    vis = True
+    imglist = os.listdir(args.image_dir)
+    num_images = len(imglist)
+    print('Loaded Photo: {} images.'.format(num_images))
+    for i in range(num_images):
+        # Load the demo image
+        im_file = os.path.join(args.image_dir, imglist[i])
+        # im = cv2.imread(im_file)
+        im = np.array(Image.open(im_file))
+        if len(im.shape) == 2:
+            im = im[:, :, np.newaxis]
+            im = np.concatenate((im, im, im), axis=2)
+        blobs, im_scales = _get_image_blob(im)
+        assert len(im_scales) == 1, "Only single-image batch implemented"
+        im_blob = blobs
+        im_info_np = np.array([[im_blob.shape[1], im_blob.shape[2], im_scales[0]]], dtype=np.float32)
+        im_data_pt = torch.from_numpy(im_blob)
+        im_data_pt = im_data_pt.permute(0, 3, 1, 2)
+        im_info_pt = torch.from_numpy(im_info_np)
+        im_data.data.resize_(im_data_pt.size()).copy_(im_data_pt)
+        im_info.data.resize_(im_info_pt.size()).copy_(im_info_pt)
+        gt_boxes.data.resize_(1, 1, 5).zero_()
+        num_boxes.data.resize_(1).zero_()
+        # pdb.set_trace()
+        det_tic = time.time()
+        rois, cls_prob, bbox_pred, \
+        _, _, _, _, _ = fpn(im_data, im_info, gt_boxes, num_boxes)
+        scores = cls_prob.data
+        boxes = (rois[:, :, 1:5] / im_scales[0]).data
+        if cfg.TEST.BBOX_REG:
+            # Apply bounding-box regression deltas
+            box_deltas = bbox_pred.data
+            if cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED:
+                # Optionally normalize targets by a precomputed mean and stdev
+                if args.class_agnostic:
+                    box_deltas = box_deltas.view(-1, 4) * torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_STDS).cuda() \
+                                 + torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_MEANS).cuda()
+                    box_deltas = box_deltas.view(1, -1, 4)
+                else:
+                    box_deltas = box_deltas.view(-1, 4) * torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_STDS).cuda() \
+                                 + torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_MEANS).cuda()
+                    box_deltas = box_deltas.view(1, -1, 4 * len(classes))
+            pred_boxes = bbox_transform_inv(boxes, box_deltas, 1)
+            pred_boxes = clip_boxes(pred_boxes, im_info.data, 1)
+        else:
+            # Simply repeat the boxes, once for each class
+            pred_boxes = np.tile(boxes, (1, scores.size[1]))
+        scores = scores.squeeze()
+        pred_boxes = pred_boxes.squeeze()
+        # _t['im_detect'].tic()
+        det_toc = time.time()
+        detect_time = det_toc - det_tic
+        misc_tic = time.time()
+        if vis:
+            im2show = np.copy(im[:, :, ::-1])
+        for j in xrange(1, len(classes)):
+            inds = torch.nonzero(scores[:, j] > thresh).view(-1)
+            if inds.numel() > 0:
+                cls_scores = scores[:, j][inds]
+                _, order = torch.sort(cls_scores, 0, True)
+                if args.class_agnostic:
+                    cls_boxes = pred_boxes[inds, :]
+                else:
+                    cls_boxes = pred_boxes[inds][:, j * 4:(j + 1) * 4]
+                cls_dets = torch.cat((cls_boxes, cls_scores.unsqueeze(1)), 1)
+                cls_dets = cls_dets[order]
+                if args.soft_nms:
+                    np_dets = cls_dets.cpu().numpy().astype(np.float32)
+                    keep = soft_nms(np_dets, method=cfg.TEST.SOFT_NMS_METHOD)  # np_dets will be changed
+                    keep = torch.from_numpy(keep).type_as(cls_dets).int()
+                    cls_dets = torch.from_numpy(np_dets).type_as(cls_dets)
+                else:
+                    keep = nms(cls_dets, cfg.TEST.NMS)
+                cls_dets = cls_dets[keep.view(-1).long()]
+                cls_dets = cls_dets.cpu().numpy()
+            else:
+                cls_dets = np.array([])
+            if vis:
+                im2show = vis_detections(im2show, classes[j], cls_dets, thresh=0.5)
+        misc_toc = time.time()
+        nms_time = misc_toc - misc_tic
+        sys.stdout.write('im_detect: {:d}/{:d} {:.3f}s {:.3f}s   \r' \
+                         .format(i + 1, num_images, detect_time, nms_time))
+        sys.stdout.flush()
+        if vis:
+            # cv2.imshow('test', im2show)
+            # cv2.waitKey(0)
+            result_path = os.path.join(args.result_dir, imglist[i][:-4] + "_det.jpg")
+            cv2.imwrite(result_path, im2show)
+```