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I had a similar issue when merging a sequence of detections <code class="notranslate

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Always tracker_id=None about supervision HOT 5 CLOSED

roboflow commented on May 22, 2024

Always tracker_id=None

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Comments (5)

SkalskiP commented on May 22, 2024 1

Hi @storm12t48 👋🏻! From what I see, you don't have probably the most important parameter of them all: stream=True. Could you add it and test it once again?

from supervision.

storm12t48 commented on May 22, 2024 1

Hi @storm12t48 👋🏻! From what I see, you don't have probably the most important parameter of them all: stream=True. Could you add it and test it once again?

thank you I didn't know that I still had these parameters in the loop

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github-actions commented on May 22, 2024

Hello there, thank you for opening an Issue ! 🙏🏻 The team was notified and they will get back to you asap.

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zburq commented on May 22, 2024

I had a similar issue when merging a sequence of detections det_1, det_2, ..., starting with an initial empty Detection:

dets_all = Detections.empty()
for det in ...:
    dets_all = Detections.merge([dets_all, det])

The empty detection doesn't have a tracker_id:

supervision/supervision/detection/core.py

Lines 345 to 364 in e16cca4

 @classmethod 

 def empty(cls) -> Detections: 

 """ 

  Create an empty Detections object with no bounding boxes, confidences, or class IDs. 

  Returns: 

  (Detections): An empty Detections object. 

  Example: 

  ```python 

  >>> from supervision import Detections 

  >>> empty_detections = Detections.empty() 

  ``` 

  """ 

 return cls( 

 xyxy=np.empty((0, 4), dtype=np.float32), 

 confidence=np.array([], dtype=np.float32), 

 class_id=np.array([], dtype=int), 

 )

For all subsequent detections, when merged (concatenated is a better name), tracker_id seems to be getting set to None here:

supervision/supervision/detection/core.py

Lines 366 to 413 in e16cca4

 @classmethod 

 def merge(cls, detections_list: List[Detections]) -> Detections: 

 """ 

  Merge a list of Detections objects into a single Detections object. 

  This method takes a list of Detections objects and combines their respective fields (`xyxy`, `mask`, 

  `confidence`, `class_id`, and `tracker_id`) into a single Detections object. If all elements in a field are not 

  `None`, the corresponding field will be stacked. Otherwise, the field will be set to `None`. 

  Args: 

  detections_list (List[Detections]): A list of Detections objects to merge. 

  Returns: 

  (Detections): A single Detections object containing the merged data from the input list. 

  Example: 

  ```python 

  >>> from supervision import Detections 

  >>> detections_1 = Detections(...) 

  >>> detections_2 = Detections(...) 

  >>> merged_detections = Detections.merge([detections_1, detections_2]) 

  ``` 

  """ 

 if len(detections_list) == 0: 

 return Detections.empty() 

 detections_tuples_list = [astuple(detection) for detection in detections_list] 

 xyxy, mask, confidence, class_id, tracker_id = [ 

 list(field) for field in zip(*detections_tuples_list) 

 ] 

 all_not_none = lambda l: all(x is not None for x in l) 

 xyxy = np.vstack(xyxy) 

 mask = np.vstack(mask) if all_not_none(mask) else None 

 confidence = np.hstack(confidence) if all_not_none(confidence) else None 

 class_id = np.hstack(class_id) if all_not_none(class_id) else None 

 tracker_id = np.hstack(tracker_id) if all_not_none(tracker_id) else None 

 return cls( 

 xyxy=xyxy, 

 mask=mask, 

 confidence=confidence, 

 class_id=class_id, 

 tracker_id=tracker_id, 

 )

from supervision.

SkalskiP commented on May 22, 2024

@zburq those are two separate issues. Lrt me move your sorry into a separate issue.

from supervision.

Always tracker_id=None about supervision HOT 5 CLOSED

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	@classmethod
	def empty(cls) -> Detections:
	"""
	Create an empty Detections object with no bounding boxes, confidences, or class IDs.

	Returns:
	(Detections): An empty Detections object.

	Example:
	```python
	>>> from supervision import Detections

	>>> empty_detections = Detections.empty()
	```
	"""
	return cls(
	xyxy=np.empty((0, 4), dtype=np.float32),
	confidence=np.array([], dtype=np.float32),
	class_id=np.array([], dtype=int),
	)

	@classmethod
	def merge(cls, detections_list: List[Detections]) -> Detections:
	"""
	Merge a list of Detections objects into a single Detections object.

	This method takes a list of Detections objects and combines their respective fields (`xyxy`, `mask`,
	`confidence`, `class_id`, and `tracker_id`) into a single Detections object. If all elements in a field are not
	`None`, the corresponding field will be stacked. Otherwise, the field will be set to `None`.

	Args:
	detections_list (List[Detections]): A list of Detections objects to merge.

	Returns:
	(Detections): A single Detections object containing the merged data from the input list.

	Example:
	```python
	>>> from supervision import Detections

	>>> detections_1 = Detections(...)
	>>> detections_2 = Detections(...)

	>>> merged_detections = Detections.merge([detections_1, detections_2])
	```
	"""
	if len(detections_list) == 0:
	return Detections.empty()

	detections_tuples_list = [astuple(detection) for detection in detections_list]
	xyxy, mask, confidence, class_id, tracker_id = [
	list(field) for field in zip(*detections_tuples_list)
	]

	all_not_none = lambda l: all(x is not None for x in l)

	xyxy = np.vstack(xyxy)
	mask = np.vstack(mask) if all_not_none(mask) else None
	confidence = np.hstack(confidence) if all_not_none(confidence) else None
	class_id = np.hstack(class_id) if all_not_none(class_id) else None
	tracker_id = np.hstack(tracker_id) if all_not_none(tracker_id) else None

	return cls(
	xyxy=xyxy,
	mask=mask,
	confidence=confidence,
	class_id=class_id,
	tracker_id=tracker_id,
	)