Functions — github.com/LeonLok/Deep-SORT-YOLOv4

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

Compute pair-wise cosine distance between points in `a` and `b`. Parameters ---------- a : array_like An NxM matrix of N samples

tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/deep_sort/nn_matching.py:31

↓ 1 callersFunction_cosine_distance

Compute pair-wise cosine distance between points in `a` and `b`. Parameters ---------- a : array_like An NxM matrix of N samples

tensorflow1.14/deep-sort-yolov4/deep_sort/nn_matching.py:31

↓ 1 callersFunction_cosine_distance

Compute pair-wise cosine distance between points in `a` and `b`. Parameters ---------- a : array_like An NxM matrix of N samples

tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/deep_sort/nn_matching.py:31

↓ 1 callersFunction_cosine_distance

Compute pair-wise cosine distance between points in `a` and `b`. Parameters ---------- a : array_like An NxM matrix of N samples

tensorflow2.0/deep-sort-yolov4/deep_sort/nn_matching.py:31

↓ 1 callersFunction_create_network

(incoming, reuse=None, weight_decay=1e-8)

tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/tools/freeze_model.py:88

↓ 1 callersFunction_create_network

(incoming, reuse=None, weight_decay=1e-8)

tensorflow1.14/deep-sort-yolov4/tools/freeze_model.py:88

↓ 1 callersFunction_create_network

(incoming, reuse=None, weight_decay=1e-8)

tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/tools/freeze_model.py:88

↓ 1 callersFunction_create_network

(incoming, reuse=None, weight_decay=1e-8)

tensorflow2.0/deep-sort-yolov4/tools/freeze_model.py:88

↓ 1 callersMethod_get_anchors

(self)

tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/yolo.py:39

↓ 1 callersMethod_get_anchors

(self)

tensorflow1.14/deep-sort-yolov4/yolo.py:39

↓ 1 callersMethod_get_anchors

(self)

tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/yolo.py:41

↓ 1 callersMethod_get_anchors

(self)

tensorflow2.0/deep-sort-yolov4/yolo.py:41

↓ 1 callersMethod_get_class

(self)

tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/yolo.py:32

↓ 1 callersMethod_get_class

(self)

tensorflow1.14/deep-sort-yolov4/yolo.py:32

↓ 1 callersMethod_get_class

(self)

tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/yolo.py:34

↓ 1 callersMethod_get_class

(self)

tensorflow2.0/deep-sort-yolov4/yolo.py:34

↓ 1 callersMethod_initiate_track

(self, detection)

tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/deep_sort/tracker.py:134

↓ 1 callersMethod_initiate_track

(self, detection)

tensorflow1.14/deep-sort-yolov4/deep_sort/tracker.py:133

↓ 1 callersMethod_initiate_track

(self, detection)

tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/deep_sort/tracker.py:134

↓ 1 callersMethod_initiate_track

(self, detection)

tensorflow2.0/deep-sort-yolov4/deep_sort/tracker.py:133

↓ 1 callersMethod_match

(self, detections)

tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/deep_sort/tracker.py:94

↓ 1 callersMethod_match

(self, detections)

tensorflow1.14/deep-sort-yolov4/deep_sort/tracker.py:93

↓ 1 callersMethod_match

(self, detections)

tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/deep_sort/tracker.py:94

↓ 1 callersMethod_match

(self, detections)

tensorflow2.0/deep-sort-yolov4/deep_sort/tracker.py:93

↓ 1 callersFunction_network_factory

(weight_decay=1e-8)

tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/tools/freeze_model.py:160

↓ 1 callersFunction_network_factory

(weight_decay=1e-8)

tensorflow1.14/deep-sort-yolov4/tools/freeze_model.py:160

↓ 1 callersFunction_network_factory

(weight_decay=1e-8)

tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/tools/freeze_model.py:160

↓ 1 callersFunction_network_factory

(weight_decay=1e-8)

tensorflow2.0/deep-sort-yolov4/tools/freeze_model.py:160

↓ 1 callersFunction_pdist

Compute pair-wise squared distance between points in `a` and `b`. Parameters ---------- a : array_like An NxM matrix of N samples

tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/deep_sort/nn_matching.py:5

↓ 1 callersFunction_pdist

Compute pair-wise squared distance between points in `a` and `b`. Parameters ---------- a : array_like An NxM matrix of N samples

tensorflow1.14/deep-sort-yolov4/deep_sort/nn_matching.py:5

↓ 1 callersFunction_pdist

Compute pair-wise squared distance between points in `a` and `b`. Parameters ---------- a : array_like An NxM matrix of N samples

tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/deep_sort/nn_matching.py:5

↓ 1 callersFunction_pdist

Compute pair-wise squared distance between points in `a` and `b`. Parameters ---------- a : array_like An NxM matrix of N samples

tensorflow2.0/deep-sort-yolov4/deep_sort/nn_matching.py:5

↓ 1 callersFunction_preprocess

(image)

tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/tools/freeze_model.py:175

↓ 1 callersFunction_preprocess

(image)

tensorflow1.14/deep-sort-yolov4/tools/freeze_model.py:175

↓ 1 callersFunction_preprocess

(image)

tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/tools/freeze_model.py:175

↓ 1 callersFunction_preprocess

(image)

tensorflow2.0/deep-sort-yolov4/tools/freeze_model.py:175

↓ 1 callersFunction_run_in_batches

(f, data_dict, out, batch_size)

tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/tools/generate_detections.py:10

↓ 1 callersFunction_run_in_batches

(f, data_dict, out, batch_size)

tensorflow1.14/deep-sort-yolov4/tools/generate_detections.py:10

↓ 1 callersFunction_run_in_batches

(f, data_dict, out, batch_size)

tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/tools/generate_detections.py:11

↓ 1 callersFunction_run_in_batches

(f, data_dict, out, batch_size)

tensorflow2.0/deep-sort-yolov4/tools/generate_detections.py:11

↓ 1 callersFunction_smooth_labels

(y_true, label_smoothing)

tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/yolo4/model.py:583

↓ 1 callersFunction_smooth_labels

(y_true, label_smoothing)

tensorflow1.14/deep-sort-yolov4/yolo4/model.py:583

↓ 1 callersFunction_smooth_labels

(y_true, label_smoothing)

tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/yolo4/model.py:583

↓ 1 callersFunction_smooth_labels

(y_true, label_smoothing)

tensorflow2.0/deep-sort-yolov4/yolo4/model.py:583

↓ 1 callersFunctionbox_diou

Calculate DIoU loss on anchor boxes Reference Paper: "Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression"

tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/yolo4/model.py:525

↓ 1 callersFunctionbox_diou

Calculate DIoU loss on anchor boxes Reference Paper: "Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression"

tensorflow1.14/deep-sort-yolov4/yolo4/model.py:525

↓ 1 callersFunctionbox_diou

Calculate DIoU loss on anchor boxes Reference Paper: "Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression"

tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/yolo4/model.py:525

↓ 1 callersFunctionbox_diou

Calculate DIoU loss on anchor boxes Reference Paper: "Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression"

tensorflow2.0/deep-sort-yolov4/yolo4/model.py:525

↓ 1 callersFunctionbox_giou

Calculate GIoU loss on anchor boxes Reference Paper: "Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regre

tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/yolo4/model.py:475

↓ 1 callersFunctionbox_giou

Calculate GIoU loss on anchor boxes Reference Paper: "Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regre

tensorflow1.14/deep-sort-yolov4/yolo4/model.py:475

↓ 1 callersFunctionbox_giou

Calculate GIoU loss on anchor boxes Reference Paper: "Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regre

tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/yolo4/model.py:475

↓ 1 callersFunctionbox_giou

Calculate GIoU loss on anchor boxes Reference Paper: "Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regre

tensorflow2.0/deep-sort-yolov4/yolo4/model.py:475

↓ 1 callersMethodclose_session

(self)

tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/convert.py:159

↓ 1 callersMethodclose_session

(self)

tensorflow1.14/deep-sort-yolov4/convert.py:159

↓ 1 callersMethodclose_session

(self)

tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/convert.py:160

↓ 1 callersMethodclose_session

(self)

tensorflow2.0/deep-sort-yolov4/convert.py:166

↓ 1 callersFunctioncreate_box_encoder

(model_filename, input_name="images", output_name="features", batch_size=32)

tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/tools/generate_detections.py:98

↓ 1 callersFunctioncreate_box_encoder

(model_filename, input_name="images", output_name="features", batch_size=32)

tensorflow1.14/deep-sort-yolov4/tools/generate_detections.py:98

↓ 1 callersFunctioncreate_box_encoder

(model_filename, input_name="images", output_name="features", batch_size=32)

tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/tools/generate_detections.py:99

↓ 1 callersFunctioncreate_box_encoder

(model_filename, input_name="images", output_name="features", batch_size=32)

tensorflow2.0/deep-sort-yolov4/tools/generate_detections.py:99

↓ 1 callersFunctioncreate_inner_block

( incoming, scope, nonlinearity=tf.nn.elu, weights_initializer=tf.truncated_normal_initializer

tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/tools/freeze_model.py:43

↓ 1 callersFunctioncreate_inner_block

( incoming, scope, nonlinearity=tf.nn.elu, weights_initializer=tf.truncated_normal_initializer

tensorflow1.14/deep-sort-yolov4/tools/freeze_model.py:43

↓ 1 callersFunctioncreate_inner_block

( incoming, scope, nonlinearity=tf.nn.elu, weights_initializer=tf.truncated_normal_initializer

tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/tools/freeze_model.py:43

↓ 1 callersFunctioncreate_inner_block

( incoming, scope, nonlinearity=tf.nn.elu, weights_initializer=tf.truncated_normal_initializer

tensorflow2.0/deep-sort-yolov4/tools/freeze_model.py:43

↓ 1 callersFunctioncreate_link

( incoming, network_builder, scope, nonlinearity=tf.nn.elu, weights_initializer=tf.truncated_n

tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/tools/freeze_model.py:13

↓ 1 callersFunctioncreate_link

( incoming, network_builder, scope, nonlinearity=tf.nn.elu, weights_initializer=tf.truncated_n

tensorflow1.14/deep-sort-yolov4/tools/freeze_model.py:13

↓ 1 callersFunctioncreate_link

( incoming, network_builder, scope, nonlinearity=tf.nn.elu, weights_initializer=tf.truncated_n

tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/tools/freeze_model.py:13

↓ 1 callersFunctioncreate_link

( incoming, network_builder, scope, nonlinearity=tf.nn.elu, weights_initializer=tf.truncated_n

tensorflow2.0/deep-sort-yolov4/tools/freeze_model.py:13

↓ 1 callersFunctiondarknet_body

Darknent body having 52 Convolution2D layers

tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/yolo4/model.py:89

↓ 1 callersFunctiondarknet_body

Darknent body having 52 Convolution2D layers

tensorflow1.14/deep-sort-yolov4/yolo4/model.py:89

↓ 1 callersFunctiondarknet_body

Darknent body having 52 Convolution2D layers

tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/yolo4/model.py:89

↓ 1 callersFunctiondarknet_body

Darknent body having 52 Convolution2D layers

tensorflow2.0/deep-sort-yolov4/yolo4/model.py:89

↓ 1 callersMethoddetect_image

(self, image)

tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/yolo.py:75

↓ 1 callersMethoddetect_image

(self, image)

tensorflow1.14/deep-sort-yolov4/yolo.py:75

↓ 1 callersMethoddetect_image

(self, image)

tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/yolo.py:77

↓ 1 callersMethoddetect_image

(self, image)

tensorflow2.0/deep-sort-yolov4/yolo.py:77

↓ 1 callersMethoddistance

Compute distance between features and targets. Parameters ---------- features : ndarray An NxM matrix of N featur

tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/deep_sort/nn_matching.py:156

↓ 1 callersMethoddistance

Compute distance between features and targets. Parameters ---------- features : ndarray An NxM matrix of N featur

tensorflow1.14/deep-sort-yolov4/deep_sort/nn_matching.py:156

↓ 1 callersMethoddistance

Compute distance between features and targets. Parameters ---------- features : ndarray An NxM matrix of N featur

tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/deep_sort/nn_matching.py:156

↓ 1 callersMethoddistance

Compute distance between features and targets. Parameters ---------- features : ndarray An NxM matrix of N featur

tensorflow2.0/deep-sort-yolov4/deep_sort/nn_matching.py:156

↓ 1 callersFunctionextract_image_patch

Extract image patch from bounding box. Parameters ---------- image : ndarray The full image. bbox : array_like The bo

tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/tools/generate_detections.py:24

↓ 1 callersFunctionextract_image_patch

Extract image patch from bounding box. Parameters ---------- image : ndarray The full image. bbox : array_like The bo

tensorflow1.14/deep-sort-yolov4/tools/generate_detections.py:24

↓ 1 callersFunctionextract_image_patch

Extract image patch from bounding box. Parameters ---------- image : ndarray The full image. bbox : array_like The bo

tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/tools/generate_detections.py:25

↓ 1 callersFunctionextract_image_patch

Extract image patch from bounding box. Parameters ---------- image : ndarray The full image. bbox : array_like The bo

tensorflow2.0/deep-sort-yolov4/tools/generate_detections.py:25

↓ 1 callersFunctionfactory_fn

(image, reuse)

tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/tools/freeze_model.py:162

↓ 1 callersFunctionfactory_fn

(image, reuse)

tensorflow1.14/deep-sort-yolov4/tools/freeze_model.py:162

↓ 1 callersFunctionfactory_fn

(image, reuse)

tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/tools/freeze_model.py:162

↓ 1 callersFunctionfactory_fn

(image, reuse)

tensorflow2.0/deep-sort-yolov4/tools/freeze_model.py:162

↓ 1 callersMethodgating_distance

Compute gating distance between state distribution and measurements. A suitable distance threshold can be obtained from `chi2inv95`. If

tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/deep_sort/kalman_filter.py:188

↓ 1 callersMethodgating_distance

Compute gating distance between state distribution and measurements. A suitable distance threshold can be obtained from `chi2inv95`. If

tensorflow1.14/deep-sort-yolov4/deep_sort/kalman_filter.py:188

↓ 1 callersMethodgating_distance

Compute gating distance between state distribution and measurements. A suitable distance threshold can be obtained from `chi2inv95`. If

tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/deep_sort/kalman_filter.py:188

↓ 1 callersMethodgating_distance

Compute gating distance between state distribution and measurements. A suitable distance threshold can be obtained from `chi2inv95`. If

tensorflow2.0/deep-sort-yolov4/deep_sort/kalman_filter.py:188

↓ 1 callersMethodgenerate

(self)

tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/yolo.py:47

↓ 1 callersMethodgenerate

(self)

tensorflow1.14/deep-sort-yolov4/yolo.py:47

↓ 1 callersMethodgenerate

(self)

tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/yolo.py:49

↓ 1 callersMethodgenerate

(self)

tensorflow2.0/deep-sort-yolov4/yolo.py:49

↓ 1 callersFunctiongenerate_detections

Generate detections with features. Parameters ---------- encoder : Callable[image, ndarray] -> ndarray The encoder function takes

tensorflow1.14/deep-sort-yolov4-low-confidence-track-filtering/tools/generate_detections.py:118

↓ 1 callersFunctiongenerate_detections

Generate detections with features. Parameters ---------- encoder : Callable[image, ndarray] -> ndarray The encoder function takes

tensorflow1.14/deep-sort-yolov4/tools/generate_detections.py:118

↓ 1 callersFunctiongenerate_detections

Generate detections with features. Parameters ---------- encoder : Callable[image, ndarray] -> ndarray The encoder function takes

tensorflow2.0/deep-sort-yolov4-low-confidence-track-filtering/tools/generate_detections.py:119

↓ 1 callersFunctiongenerate_detections

Generate detections with features. Parameters ---------- encoder : Callable[image, ndarray] -> ndarray The encoder function takes

tensorflow2.0/deep-sort-yolov4/tools/generate_detections.py:119

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