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Functions442 in github.com/LeonLok/Deep-SORT-YOLOv4

⨍ Functions 442◇ Types & classes 46
↓ 41 callersFunctionDarknetConv2D_BN_Leaky
Darknet Convolution2D followed by BatchNormalization and LeakyReLU.
tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/yolo4/model.py:55
↓ 41 callersFunctionDarknetConv2D_BN_Leaky
Darknet Convolution2D followed by BatchNormalization and LeakyReLU.
tensorflow1.14/deep-sort-yolov4/yolo4/model.py:55
↓ 41 callersFunctionDarknetConv2D_BN_Leaky
Darknet Convolution2D followed by BatchNormalization and LeakyReLU.
tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/yolo4/model.py:55
↓ 41 callersFunctionDarknetConv2D_BN_Leaky
Darknet Convolution2D followed by BatchNormalization and LeakyReLU.
tensorflow2.0/deep-sort-yolov4/yolo4/model.py:55
↓ 13 callersFunctionrand
(a=0, b=1)
tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/yolo4/utils.py:33
↓ 13 callersFunctionrand
(a=0, b=1)
tensorflow1.14/deep-sort-yolov4/yolo4/utils.py:33
↓ 13 callersFunctionrand
(a=0, b=1)
tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/yolo4/utils.py:33
↓ 13 callersFunctionrand
(a=0, b=1)
tensorflow2.0/deep-sort-yolov4/yolo4/utils.py:33
↓ 12 callersMethodread
(self)
tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/videocaptureasync.py:38
↓ 12 callersMethodread
(self)
tensorflow1.14/deep-sort-yolov4/videocaptureasync.py:38
↓ 12 callersMethodread
(self)
tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/videocaptureasync.py:38
↓ 12 callersMethodread
(self)
tensorflow2.0/deep-sort-yolov4/videocaptureasync.py:38
↓ 8 callersFunctionDarknetConv2D_BN_Mish
Darknet Convolution2D followed by BatchNormalization and LeakyReLU.
tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/yolo4/model.py:64
↓ 8 callersFunctionDarknetConv2D_BN_Mish
Darknet Convolution2D followed by BatchNormalization and LeakyReLU.
tensorflow1.14/deep-sort-yolov4/yolo4/model.py:64
↓ 8 callersFunctionDarknetConv2D_BN_Mish
Darknet Convolution2D followed by BatchNormalization and LeakyReLU.
tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/yolo4/model.py:64
↓ 8 callersFunctionDarknetConv2D_BN_Mish
Darknet Convolution2D followed by BatchNormalization and LeakyReLU.
tensorflow2.0/deep-sort-yolov4/yolo4/model.py:64
↓ 7 callersFunctioncompose
Compose arbitrarily many functions, evaluated left to right. Reference: https://mathieularose.com/function-composition-in-python/
tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/yolo4/utils.py:9
↓ 7 callersFunctioncompose
Compose arbitrarily many functions, evaluated left to right. Reference: https://mathieularose.com/function-composition-in-python/
tensorflow1.14/deep-sort-yolov4/yolo4/utils.py:9
↓ 7 callersFunctioncompose
Compose arbitrarily many functions, evaluated left to right. Reference: https://mathieularose.com/function-composition-in-python/
tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/yolo4/utils.py:9
↓ 7 callersFunctioncompose
Compose arbitrarily many functions, evaluated left to right. Reference: https://mathieularose.com/function-composition-in-python/
tensorflow2.0/deep-sort-yolov4/yolo4/utils.py:9
↓ 6 callersFunctionDarknetConv2D
Wrapper to set Darknet parameters for Convolution2D.
tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/yolo4/model.py:48
↓ 6 callersFunctionDarknetConv2D
Wrapper to set Darknet parameters for Convolution2D.
tensorflow1.14/deep-sort-yolov4/yolo4/model.py:48
↓ 6 callersFunctionDarknetConv2D
Wrapper to set Darknet parameters for Convolution2D.
tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/yolo4/model.py:48
↓ 6 callersFunctionDarknetConv2D
Wrapper to set Darknet parameters for Convolution2D.
tensorflow2.0/deep-sort-yolov4/yolo4/model.py:48
↓ 6 callersFunctionresidual_block
(incoming, scope, nonlinearity=tf.nn.elu, weights_initializer=tf.truncated_normal_initializ
tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/tools/freeze_model.py:72
↓ 6 callersFunctionresidual_block
(incoming, scope, nonlinearity=tf.nn.elu, weights_initializer=tf.truncated_normal_initializ
tensorflow1.14/deep-sort-yolov4/tools/freeze_model.py:72
↓ 6 callersFunctionresidual_block
(incoming, scope, nonlinearity=tf.nn.elu, weights_initializer=tf.truncated_normal_initializ
tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/tools/freeze_model.py:72
↓ 6 callersFunctionresidual_block
(incoming, scope, nonlinearity=tf.nn.elu, weights_initializer=tf.truncated_normal_initializ
tensorflow2.0/deep-sort-yolov4/tools/freeze_model.py:72
↓ 5 callersMethodget
(self, x)
tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/videocaptureasync.py:54
↓ 5 callersMethodget
(self, x)
tensorflow1.14/deep-sort-yolov4/videocaptureasync.py:54
↓ 5 callersMethodget
(self, x)
tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/videocaptureasync.py:54
↓ 5 callersMethodget
(self, x)
tensorflow2.0/deep-sort-yolov4/videocaptureasync.py:54
↓ 5 callersMethodis_confirmed
Returns True if this track is confirmed.
tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/deep_sort/track.py:174
↓ 5 callersMethodis_confirmed
Returns True if this track is confirmed.
tensorflow1.14/deep-sort-yolov4/deep_sort/track.py:160
↓ 5 callersMethodis_confirmed
Returns True if this track is confirmed.
tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/deep_sort/track.py:178
↓ 5 callersMethodis_confirmed
Returns True if this track is confirmed.
tensorflow2.0/deep-sort-yolov4/deep_sort/track.py:160
↓ 5 callersFunctionresblock_body
A series of resblocks starting with a downsampling Convolution2D
tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/yolo4/model.py:73
↓ 5 callersFunctionresblock_body
A series of resblocks starting with a downsampling Convolution2D
tensorflow1.14/deep-sort-yolov4/yolo4/model.py:73
↓ 5 callersFunctionresblock_body
A series of resblocks starting with a downsampling Convolution2D
tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/yolo4/model.py:73
↓ 5 callersFunctionresblock_body
A series of resblocks starting with a downsampling Convolution2D
tensorflow2.0/deep-sort-yolov4/yolo4/model.py:73
↓ 4 callersMethodupdate
Perform Kalman filter measurement update step and update the feature cache. This version creates tracks only when the average detecti
tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/deep_sort/track.py:132
↓ 4 callersMethodupdate
Perform Kalman filter measurement update step and update the feature cache. Parameters ---------- kf : kalman_filter.
tensorflow1.14/deep-sort-yolov4/deep_sort/track.py:126
↓ 4 callersMethodupdate
Perform Kalman filter measurement update step and update the feature cache. This version creates tracks only when the average detecti
tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/deep_sort/track.py:134
↓ 4 callersMethodupdate
Perform Kalman filter measurement update step and update the feature cache. Parameters ---------- kf : kalman_filter.
tensorflow2.0/deep-sort-yolov4/deep_sort/track.py:126
↓ 3 callersMethodrelease
(self)
tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/videocaptureasync.py:51
↓ 3 callersMethodrelease
(self)
tensorflow1.14/deep-sort-yolov4/videocaptureasync.py:51
↓ 3 callersMethodrelease
(self)
tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/videocaptureasync.py:51
↓ 3 callersMethodrelease
(self)
tensorflow2.0/deep-sort-yolov4/videocaptureasync.py:51
↓ 3 callersMethodto_xyah
Convert bounding box to format `(center x, center y, aspect ratio, height)`, where the aspect ratio is `width / height`.
tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/deep_sort/detection.py:43
↓ 3 callersMethodto_xyah
Convert bounding box to format `(center x, center y, aspect ratio, height)`, where the aspect ratio is `width / height`.
tensorflow1.14/deep-sort-yolov4/deep_sort/detection.py:43
↓ 3 callersMethodto_xyah
Convert bounding box to format `(center x, center y, aspect ratio, height)`, where the aspect ratio is `width / height`.
tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/deep_sort/detection.py:43
↓ 3 callersMethodto_xyah
Convert bounding box to format `(center x, center y, aspect ratio, height)`, where the aspect ratio is `width / height`.
tensorflow2.0/deep-sort-yolov4/deep_sort/detection.py:43
↓ 3 callersFunctionyolo_head
Convert final layer features to bounding box parameters.
tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/yolo4/model.py:186
↓ 3 callersFunctionyolo_head
Convert final layer features to bounding box parameters.
tensorflow1.14/deep-sort-yolov4/yolo4/model.py:186
↓ 3 callersFunctionyolo_head
Convert final layer features to bounding box parameters.
tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/yolo4/model.py:186
↓ 3 callersFunctionyolo_head
Convert final layer features to bounding box parameters.
tensorflow2.0/deep-sort-yolov4/yolo4/model.py:186
↓ 2 callersFunctionbox_iou
Return iou tensor Parameters ---------- b1: tensor, shape=(i1,...,iN, 4), xywh b2: tensor, shape=(j, 4), xywh Returns -
tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/yolo4/model.py:435
↓ 2 callersFunctionbox_iou
Return iou tensor Parameters ---------- b1: tensor, shape=(i1,...,iN, 4), xywh b2: tensor, shape=(j, 4), xywh Returns -
tensorflow1.14/deep-sort-yolov4/yolo4/model.py:435
↓ 2 callersFunctionbox_iou
Return iou tensor Parameters ---------- b1: tensor, shape=(i1,...,iN, 4), xywh b2: tensor, shape=(j, 4), xywh Returns -
tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/yolo4/model.py:435
↓ 2 callersFunctionbox_iou
Return iou tensor Parameters ---------- b1: tensor, shape=(i1,...,iN, 4), xywh b2: tensor, shape=(j, 4), xywh Returns -
tensorflow2.0/deep-sort-yolov4/yolo4/model.py:435
↓ 2 callersFunctionencoder
(image, boxes)
tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/tools/generate_detections.py:103
↓ 2 callersFunctionencoder
(image, boxes)
tensorflow1.14/deep-sort-yolov4/tools/generate_detections.py:103
↓ 2 callersFunctionencoder
(image, boxes)
tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/tools/generate_detections.py:104
↓ 2 callersFunctionencoder
(image, boxes)
tensorflow2.0/deep-sort-yolov4/tools/generate_detections.py:104
↓ 2 callersFunctionletterbox_image
resize image with unchanged aspect ratio using padding
tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/yolo4/utils.py:20
↓ 2 callersFunctionletterbox_image
resize image with unchanged aspect ratio using padding
tensorflow1.14/deep-sort-yolov4/yolo4/utils.py:20
↓ 2 callersFunctionletterbox_image
resize image with unchanged aspect ratio using padding
tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/yolo4/utils.py:20
↓ 2 callersFunctionletterbox_image
resize image with unchanged aspect ratio using padding
tensorflow2.0/deep-sort-yolov4/yolo4/utils.py:20
↓ 2 callersMethodproject
Project state distribution to measurement space. Parameters ---------- mean : ndarray The state's mean vector (8
tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/deep_sort/kalman_filter.py:125
↓ 2 callersMethodproject
Project state distribution to measurement space. Parameters ---------- mean : ndarray The state's mean vector (8
tensorflow1.14/deep-sort-yolov4/deep_sort/kalman_filter.py:125
↓ 2 callersMethodproject
Project state distribution to measurement space. Parameters ---------- mean : ndarray The state's mean vector (8
tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/deep_sort/kalman_filter.py:125
↓ 2 callersMethodproject
Project state distribution to measurement space. Parameters ---------- mean : ndarray The state's mean vector (8
tensorflow2.0/deep-sort-yolov4/deep_sort/kalman_filter.py:125
↓ 2 callersFunctionsigmoid_focal_loss
Compute sigmoid focal loss. Reference Paper: "Focal Loss for Dense Object Detection" https://arxiv.org/abs/1708.02002 #
tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/yolo4/model.py:404
↓ 2 callersFunctionsigmoid_focal_loss
Compute sigmoid focal loss. Reference Paper: "Focal Loss for Dense Object Detection" https://arxiv.org/abs/1708.02002 #
tensorflow1.14/deep-sort-yolov4/yolo4/model.py:404
↓ 2 callersFunctionsigmoid_focal_loss
Compute sigmoid focal loss. Reference Paper: "Focal Loss for Dense Object Detection" https://arxiv.org/abs/1708.02002 #
tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/yolo4/model.py:404
↓ 2 callersFunctionsigmoid_focal_loss
Compute sigmoid focal loss. Reference Paper: "Focal Loss for Dense Object Detection" https://arxiv.org/abs/1708.02002 #
tensorflow2.0/deep-sort-yolov4/yolo4/model.py:404
↓ 2 callersMethodstart
(self)
tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/videocaptureasync.py:21
↓ 2 callersMethodstart
(self)
tensorflow1.14/deep-sort-yolov4/videocaptureasync.py:21
↓ 2 callersMethodstart
(self)
tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/videocaptureasync.py:21
↓ 2 callersMethodstart
(self)
tensorflow2.0/deep-sort-yolov4/videocaptureasync.py:21
↓ 2 callersMethodstop
(self)
tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/videocaptureasync.py:47
↓ 2 callersMethodstop
(self)
tensorflow1.14/deep-sort-yolov4/videocaptureasync.py:47
↓ 2 callersMethodstop
(self)
tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/videocaptureasync.py:47
↓ 2 callersMethodstop
(self)
tensorflow2.0/deep-sort-yolov4/videocaptureasync.py:47
↓ 2 callersMethodto_tlbr
Get current position in bounding box format `(min x, miny, max x, max y)`. Returns ------- ndarray The bo
tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/deep_sort/track.py:104
↓ 2 callersMethodto_tlbr
Get current position in bounding box format `(min x, miny, max x, max y)`. Returns ------- ndarray The bo
tensorflow1.14/deep-sort-yolov4/deep_sort/track.py:98
↓ 2 callersMethodto_tlbr
Get current position in bounding box format `(min x, miny, max x, max y)`. Returns ------- ndarray The bo
tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/deep_sort/track.py:106
↓ 2 callersMethodto_tlbr
Get current position in bounding box format `(min x, miny, max x, max y)`. Returns ------- ndarray The bo
tensorflow2.0/deep-sort-yolov4/deep_sort/track.py:98
↓ 2 callersMethodto_tlwh
Get current position in bounding box format `(top left x, top left y, width, height)`. Returns ------- ndarray
tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/deep_sort/track.py:89
↓ 2 callersMethodto_tlwh
Get current position in bounding box format `(top left x, top left y, width, height)`. Returns ------- ndarray
tensorflow1.14/deep-sort-yolov4/deep_sort/track.py:83
↓ 2 callersMethodto_tlwh
Get current position in bounding box format `(top left x, top left y, width, height)`. Returns ------- ndarray
tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/deep_sort/track.py:91
↓ 2 callersMethodto_tlwh
Get current position in bounding box format `(top left x, top left y, width, height)`. Returns ------- ndarray
tensorflow2.0/deep-sort-yolov4/deep_sort/track.py:83
↓ 2 callersFunctionyolo_eval
Evaluate YOLO model on given input and return filtered boxes.
tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/yolo4/model.py:251
↓ 2 callersFunctionyolo_eval
Evaluate YOLO model on given input and return filtered boxes.
tensorflow1.14/deep-sort-yolov4/yolo4/model.py:251
↓ 2 callersFunctionyolo_eval
Evaluate YOLO model on given input and return filtered boxes.
tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/yolo4/model.py:251
↓ 2 callersFunctionyolo_eval
Evaluate YOLO model on given input and return filtered boxes.
tensorflow2.0/deep-sort-yolov4/yolo4/model.py:251
↓ 1 callersFunction_batch_norm_fn
(x, scope=None)
tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/tools/freeze_model.py:7
↓ 1 callersFunction_batch_norm_fn
(x, scope=None)
tensorflow1.14/deep-sort-yolov4/tools/freeze_model.py:7
↓ 1 callersFunction_batch_norm_fn
(x, scope=None)
tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/tools/freeze_model.py:7
↓ 1 callersFunction_batch_norm_fn
(x, scope=None)
tensorflow2.0/deep-sort-yolov4/tools/freeze_model.py:7
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