DL之SSD：基于tensorflow利用SSD算法實現(xiàn)目標檢測(21類)

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輸出結(jié)果

SSD代碼

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輸出結(jié)果

VOC_LABELS = {'none': (0, 'Background'),'aeroplane': (1, 'Vehicle'),'bicycle': (2, 'Vehicle'),'bird': (3, 'Animal'),'boat': (4, 'Vehicle'),'bottle': (5, 'Indoor'),'bus': (6, 'Vehicle'),'car': (7, 'Vehicle'),'cat': (8, 'Animal'),'chair': (9, 'Indoor'),'cow': (10, 'Animal'),'diningtable': (11, 'Indoor'),'dog': (12, 'Animal'),'horse': (13, 'Animal'),'motorbike': (14, 'Vehicle'),'person': (15, 'Person'),'pottedplant': (16, 'Indoor'),'sheep': (17, 'Animal'),'sofa': (18, 'Indoor'),'train': (19, 'Vehicle'),'tvmonitor': (20, 'Indoor'), }

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SSD代碼

class SSDNet(object):"""Implementation of the SSD VGG-based 300 network.The default features layers with 300x300 image input are:conv4 ==> 38 x 38conv7 ==> 19 x 19conv8 ==> 10 x 10conv9 ==> 5 x 5conv10 ==> 3 x 3conv11 ==> 1 x 1The default image size used to train this network is 300x300."""default_params = SSDParams(img_shape=(300, 300),num_classes=21,no_annotation_label=21,feat_layers=['block4', 'block7', 'block8', 'block9', 'block10', 'block11'],feat_shapes=[(38, 38), (19, 19), (10, 10), (5, 5), (3, 3), (1, 1)],anchor_size_bounds=[0.15, 0.90],# anchor_size_bounds=[0.20, 0.90],anchor_sizes=[(21., 45.),(45., 99.),(99., 153.),(153., 207.),(207., 261.),(261., 315.)],# anchor_sizes=[(30., 60.),# (60., 111.),# (111., 162.),# (162., 213.),# (213., 264.),# (264., 315.)],anchor_ratios=[[2, .5],[2, .5, 3, 1./3],[2, .5, 3, 1./3],[2, .5, 3, 1./3],[2, .5],[2, .5]],anchor_steps=[8, 16, 32, 64, 100, 300],anchor_offset=0.5,normalizations=[20, -1, -1, -1, -1, -1],prior_scaling=[0.1, 0.1, 0.2, 0.2])def __init__(self, params=None):"""Init the SSD net with some parameters. Use the default onesif none provided."""if isinstance(params, SSDParams):self.params = paramselse:self.params = SSDNet.default_params# ======================================================================= #def net(self, inputs,is_training=True,update_feat_shapes=True,dropout_keep_prob=0.5,prediction_fn=slim.softmax,reuse=None,scope='ssd_300_vgg'):"""SSD network definition."""r = ssd_net(inputs,num_classes=self.params.num_classes,feat_layers=self.params.feat_layers,anchor_sizes=self.params.anchor_sizes,anchor_ratios=self.params.anchor_ratios,normalizations=self.params.normalizations,is_training=is_training,dropout_keep_prob=dropout_keep_prob,prediction_fn=prediction_fn,reuse=reuse,scope=scope)# Update feature shapes (try at least!)if update_feat_shapes:shapes = ssd_feat_shapes_from_net(r[0], self.params.feat_shapes)self.params = self.params._replace(feat_shapes=shapes)return rdef arg_scope(self, weight_decay=0.0005, data_format='NHWC'):"""Network arg_scope."""return ssd_arg_scope(weight_decay, data_format=data_format)def arg_scope_caffe(self, caffe_scope):"""Caffe arg_scope used for weights importing."""return ssd_arg_scope_caffe(caffe_scope)# ======================================================================= #def update_feature_shapes(self, predictions):"""Update feature shapes from predictions collection (Tensor or Numpyarray)."""shapes = ssd_feat_shapes_from_net(predictions, self.params.feat_shapes)self.params = self.params._replace(feat_shapes=shapes)def anchors(self, img_shape, dtype=np.float32):"""Compute the default anchor boxes, given an image shape."""return ssd_anchors_all_layers(img_shape,self.params.feat_shapes,self.params.anchor_sizes,self.params.anchor_ratios,self.params.anchor_steps,self.params.anchor_offset,dtype)def bboxes_encode(self, labels, bboxes, anchors,scope=None):"""Encode labels and bounding boxes."""return ssd_common.tf_ssd_bboxes_encode(labels, bboxes, anchors,self.params.num_classes,self.params.no_annotation_label,ignore_threshold=0.5,prior_scaling=self.params.prior_scaling,scope=scope)def bboxes_decode(self, feat_localizations, anchors,scope='ssd_bboxes_decode'):"""Encode labels and bounding boxes."""return ssd_common.tf_ssd_bboxes_decode(feat_localizations, anchors,prior_scaling=self.params.prior_scaling,scope=scope)def detected_bboxes(self, predictions, localisations,select_threshold=None, nms_threshold=0.5,clipping_bbox=None, top_k=400, keep_top_k=200):"""Get the detected bounding boxes from the SSD network output."""# Select top_k bboxes from predictions, and cliprscores, rbboxes = \ssd_common.tf_ssd_bboxes_select(predictions, localisations,select_threshold=select_threshold,num_classes=self.params.num_classes)rscores, rbboxes = \tfe.bboxes_sort(rscores, rbboxes, top_k=top_k)# Apply NMS algorithm.rscores, rbboxes = \tfe.bboxes_nms_batch(rscores, rbboxes,nms_threshold=nms_threshold,keep_top_k=keep_top_k)if clipping_bbox is not None:rbboxes = tfe.bboxes_clip(clipping_bbox, rbboxes)return rscores, rbboxesdef losses(self, logits, localisations,gclasses, glocalisations, gscores,match_threshold=0.5,negative_ratio=3.,alpha=1.,label_smoothing=0.,scope='ssd_losses'):"""Define the SSD network losses."""return ssd_losses(logits, localisations,gclasses, glocalisations, gscores,match_threshold=match_threshold,negative_ratio=negative_ratio,alpha=alpha,label_smoothing=label_smoothing,scope=scope)

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