Reference for ultralytics/trackers/utils/gmc.py
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ultralytics.trackers.utils.gmc.GMC
Generalized Motion Compensation (GMC) class for tracking and object detection in video frames.
This class provides methods for tracking and detecting objects based on several tracking algorithms including ORB, SIFT, ECC, and Sparse Optical Flow. It also supports downscaling of frames for computational efficiency.
Attributes:
Name | Type | Description |
---|---|---|
method |
str
|
The method used for tracking. Options include 'orb', 'sift', 'ecc', 'sparseOptFlow', 'none'. |
downscale |
int
|
Factor by which to downscale the frames for processing. |
prevFrame |
ndarray
|
Stores the previous frame for tracking. |
prevKeyPoints |
List
|
Stores the keypoints from the previous frame. |
prevDescriptors |
ndarray
|
Stores the descriptors from the previous frame. |
initializedFirstFrame |
bool
|
Flag to indicate if the first frame has been processed. |
Methods:
Name | Description |
---|---|
apply |
Applies the chosen method to a raw frame and optionally uses provided detections. |
applyEcc |
Applies the ECC algorithm to a raw frame. |
applyFeatures |
Applies feature-based methods like ORB or SIFT to a raw frame. |
applySparseOptFlow |
Applies the Sparse Optical Flow method to a raw frame. |
reset_params |
Resets the internal parameters of the GMC object. |
Examples:
Create a GMC object and apply it to a frame
>>> gmc = GMC(method="sparseOptFlow", downscale=2)
>>> frame = np.array([[1, 2, 3], [4, 5, 6]])
>>> processed_frame = gmc.apply(frame)
>>> print(processed_frame)
array([[1, 2, 3],
[4, 5, 6]])
Parameters:
Name | Type | Description | Default |
---|---|---|---|
method |
str
|
The method used for tracking. Options include 'orb', 'sift', 'ecc', 'sparseOptFlow', 'none'. |
'sparseOptFlow'
|
downscale |
int
|
Downscale factor for processing frames. |
2
|
Examples:
Initialize a GMC object with the 'sparseOptFlow' method and a downscale factor of 2
Source code in ultralytics/trackers/utils/gmc.py
apply
Apply object detection on a raw frame using the specified method.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
raw_frame |
ndarray
|
The raw frame to be processed, with shape (H, W, C). |
required |
detections |
List | None
|
List of detections to be used in the processing. |
None
|
Returns:
Type | Description |
---|---|
ndarray
|
Processed frame with applied object detection. |
Examples:
>>> gmc = GMC(method="sparseOptFlow")
>>> raw_frame = np.random.rand(480, 640, 3)
>>> processed_frame = gmc.apply(raw_frame)
>>> print(processed_frame.shape)
(480, 640, 3)
Source code in ultralytics/trackers/utils/gmc.py
applyEcc
Apply the ECC (Enhanced Correlation Coefficient) algorithm to a raw frame for motion compensation.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
raw_frame |
ndarray
|
The raw frame to be processed, with shape (H, W, C). |
required |
Returns:
Type | Description |
---|---|
ndarray
|
The processed frame with the applied ECC transformation. |
Examples:
>>> gmc = GMC(method="ecc")
>>> processed_frame = gmc.applyEcc(np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]))
>>> print(processed_frame)
[[1. 0. 0.]
[0. 1. 0.]]
Source code in ultralytics/trackers/utils/gmc.py
applyFeatures
Apply feature-based methods like ORB or SIFT to a raw frame.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
raw_frame |
ndarray
|
The raw frame to be processed, with shape (H, W, C). |
required |
detections |
List | None
|
List of detections to be used in the processing. |
None
|
Returns:
Type | Description |
---|---|
ndarray
|
Processed frame. |
Examples:
>>> gmc = GMC(method="orb")
>>> raw_frame = np.random.randint(0, 255, (480, 640, 3), dtype=np.uint8)
>>> processed_frame = gmc.applyFeatures(raw_frame)
>>> print(processed_frame.shape)
(2, 3)
Source code in ultralytics/trackers/utils/gmc.py
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|
applySparseOptFlow
Apply Sparse Optical Flow method to a raw frame.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
raw_frame |
ndarray
|
The raw frame to be processed, with shape (H, W, C). |
required |
Returns:
Type | Description |
---|---|
ndarray
|
Processed frame with shape (2, 3). |
Examples:
>>> gmc = GMC()
>>> result = gmc.applySparseOptFlow(np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]))
>>> print(result)
[[1. 0. 0.]
[0. 1. 0.]]
Source code in ultralytics/trackers/utils/gmc.py
reset_params
Reset the internal parameters including previous frame, keypoints, and descriptors.