diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml
index 2425967..25dec71 100644
--- a/.github/workflows/ci.yaml
+++ b/.github/workflows/ci.yaml
@@ -3,7 +3,7 @@ name: CI
on:
push:
branches:
- - 'develop'
+ - 'dev'
pull_request:
branches:
- '*'
@@ -32,4 +32,7 @@ jobs:
poetry install
- name: Run Pre commit hooks
- run: make format-code
\ No newline at end of file
+ run: make format-code
+
+ - name: Run Tests
+ run: poetry run pytest tests
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
index 7953eb3..f09c445 100644
--- a/.gitignore
+++ b/.gitignore
@@ -129,4 +129,13 @@ dmypy.json
.pyre/
# Poetry
-poetry.lock
\ No newline at end of file
+poetry.lock
+
+# model
+*.pt
+
+# images
+*.png
+
+# videos
+*.mp4
\ No newline at end of file
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
new file mode 100644
index 0000000..815cc20
--- /dev/null
+++ b/CONTRIBUTING.md
@@ -0,0 +1,77 @@
+# 🧑💻 Contributing to bytetrack
+
+## How to contribute
+
+### Issues, Pull Requests and Code Reviews.
+
+Issues and Pull requests templates are mandatory.
+
+At least one code review is needed to merge. Please merge your feature branches on `develop`.
+
+We try to rebase as much as possible and use squash and merge to keep a linear and condensed git history.
+
+### Getting started
+
+This project uses [Poetry](https://python-poetry.org/) for dependency management. Poetry's doc is really good, so you should check it out if you have any questions.
+
+To install poetry:
+
+```bash
+make download-poetry
+```
+
+You can start by creating a virtual environment (conda or other) or use poetry venv(please check the Makefile first if so, as poetry venv is deactivated there). Then, to install the project dependencies, run the following command:
+
+```bash
+make install
+```
+
+To develop, you will need dev requirements too. Run:
+
+```bash
+make install-dev-requirements
+```
+
+!!! note "About poetry.lock"
+`poetry.lock` is not committed deliberately, as recommended by Poetry's doc. You can read more about it [here](https://python-poetry.org/docs/basic-usage/#as-a-library-developer).
+
+### Codestyle
+
+This projects uses [Black](https://black.readthedocs.io/en/stable/), isort, ruff for codestyle. You can run the following command to format your code. It uses Pre-commit hooks to run the formatters and linters.
+
+```bash
+make format-code
+```
+
+### Docstring convention
+
+This project uses [Google docstring convention](https://google.github.io/styleguide/pyguide.html#38-comments-and-docstrings).
+
+A full example is available in [here](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html).
+
+## How to release
+
+This project uses [Python Semantic Versioning](https://python-semantic-release.readthedocs.io/en/latest/automatic-releases/github-actions.html)
+and [Poetry](https://python-poetry.org/docs/cli/#build) to create releases and tags.
+
+The release process is automated through GitHub Actions. Here is the process:
+
+- Create a Pull Request from `develop` to `main`.
+- Merge the Pull Request. This must create a merge commit.
+- The merge will trigger the Release GitHub Action defined in [this workflow](.github/workflows/release.yaml).
+
+The Release GitHub Action does the following:
+
+- Checks out the code.
+- Runs the CI GitHub Action, which runs the tests and linters.
+- Runs Python Semantic Release, which takes care of version update, tag creation, and release creation.
+
+The action is triggered by any push to main.
+
+!!! tip
+The release action will be triggered by any push to `main` only if the 'CI' job in the 'release.yaml' workflow succeeds.
+Python Semantic Release will take care of version number update, tag creation and release creation.
+
+When it's done, rebase develop to keep it up to date with main.
+
+And you're done ! 🎉
diff --git a/MANIFEST.in b/MANIFEST.in
deleted file mode 100644
index f9bd145..0000000
--- a/MANIFEST.in
+++ /dev/null
@@ -1 +0,0 @@
-include requirements.txt
diff --git a/README.md b/README.md
index 3b7e621..6825e18 100644
--- a/README.md
+++ b/README.md
@@ -1,38 +1,50 @@
-
-
- ByteTrack-Pip: Packaged version of the ByteTrack repository
-
+# Bytetrack starter guide
+
+This repo is a packaged version of the [ByteTrack](https://github.com/ifzhang/ByteTrack) algorithm.
+
+ByteTrack is a multi-object tracking computer vision model. Using ByteTrack, you can allocate IDs for unique objects in a video for use in tracking objects.
+
-
+
-
-
-
-
-
-
-## Overview
-This repo is a packaged version of the [ByteTrack](https://github.com/ifzhang/ByteTrack) algorithm.
### Installation
+
+To install the library, run the following command:
+
+```bash
+pip install git+https://github.com/artefactory-fr/bytetrack.git@main
```
-pip install bytetracker
+
+To install a specific version, run the following command:
+
+```bash
+pip install git+https://github.com/artefactory-fr/bytetrack.git@x.y.z
```
### Detection Model + ByteTrack
+
```python
from bytetracker import BYTETracker
-
tracker = BYTETracker(args)
-for image in images:
- dets = detector(image)
- online_targets = tracker.update(dets)
+for frame_id, image_filename in enumerate(frames):
+ img = cv2.imread(image_filename)
+ detections = your_model.predict(img)
+ tracked_objects = tracker.update(detections, frame_id)
```
-### Reference:
- - [Yolov5-Pip](https://github.com/fcakyon/yolov5-pip)
- - [ByteTrack](https://github.com/ifzhang/ByteTrack)
+
+## Copyright
+
+Copyright (c) 2022 Kadir Nar
+
+## ByteTrack License
+
+ByteTrack is licensed under the MIT License. See the [LICENSE](LICENSE) file and the [ByteTrack repository](https://github.com/bytedance/ByteTrack) for more information.
### Citation
+
+This is a fork of [bytetrack-pip](https://github.com/kadirnar/bytetrack-pip).
+
```bibtex
@article{zhang2022bytetrack,
title={ByteTrack: Multi-Object Tracking by Associating Every Detection Box},
diff --git a/assets/demo.gif b/assets/demo.gif
deleted file mode 100644
index 5bc9720..0000000
Binary files a/assets/demo.gif and /dev/null differ
diff --git a/assets/traffic.gif b/assets/traffic.gif
new file mode 100644
index 0000000..80d7679
Binary files /dev/null and b/assets/traffic.gif differ
diff --git a/bytetracker/basetrack.py b/bytetracker/basetrack.py
index b65032e..3e577bb 100644
--- a/bytetracker/basetrack.py
+++ b/bytetracker/basetrack.py
@@ -1,8 +1,3 @@
-from collections import OrderedDict
-
-import numpy as np
-
-
class TrackState(object):
New = 0
Tracked = 1
@@ -17,16 +12,9 @@ class BaseTrack(object):
is_activated = False
state = TrackState.New
- history = OrderedDict()
- features = []
- curr_feature = None
score = 0
start_frame = 0
frame_id = 0
- time_since_update = 0
-
- # multi-camera
- location = (np.inf, np.inf)
@property
def end_frame(self):
diff --git a/bytetracker/byte_tracker.py b/bytetracker/byte_tracker.py
index 26c323e..f9c0d3c 100644
--- a/bytetracker/byte_tracker.py
+++ b/bytetracker/byte_tracker.py
@@ -5,8 +5,17 @@
from bytetracker.kalman_filter import KalmanFilter
-def xywh2xyxy(x):
- # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
+def xywh2xyxy(x: np.ndarray):
+ """
+ Converts bounding boxes from [x, y, w, h] format to [x1, y1, x2, y2] format
+
+ Parameters
+ ----------
+ x: Array at [x, y, w, h] format
+ Returns
+ -------
+ y: Array [x1, y1, x2, y2] format
+ """
y = np.copy(x)
y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x
y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y
@@ -15,8 +24,18 @@ def xywh2xyxy(x):
return y
-def xyxy2xywh(x):
- # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right
+def xyxy2xywh(x: np.ndarray):
+ """
+ Converts bounding boxes from [x1, y1, x2, y2] format to [x, y, w, h] format
+
+ Parameters
+ ----------
+ x: Array at [x1, y1, x2, y2] format
+
+ Returns
+ -------
+ y: Array at [x, y, w, h] format
+ """
y = np.copy(x)
y[:, 0] = (x[:, 0] + x[:, 2]) / 2 # x center
y[:, 1] = (x[:, 1] + x[:, 3]) / 2 # y center
@@ -36,17 +55,28 @@ def __init__(self, tlwh, score, cls):
self.is_activated = False
self.score = score
- self.tracklet_len = 0
self.cls = cls
def predict(self):
+ """
+ updates the mean and covariance using a Kalman filter prediction, with a condition
+ based on the state of the track.
+ """
mean_state = self.mean.copy()
if self.state != TrackState.Tracked:
mean_state[7] = 0
self.mean, self.covariance = self.kalman_filter.predict(mean_state, self.covariance)
@staticmethod
- def multi_predict(stracks):
+ def multi_predict(stracks: list["STrack"]):
+ """
+ takes a list of tracks, updates their mean and covariance values, and
+ performs a Kalman filter prediction step.
+
+ Parameters
+ ----------
+ stracks (list): list of STrack objects
+ """
if len(stracks) > 0:
multi_mean = np.asarray([st.mean.copy() for st in stracks])
multi_covariance = np.asarray([st.covariance for st in stracks])
@@ -60,13 +90,20 @@ def multi_predict(stracks):
stracks[i].mean = mean
stracks[i].covariance = cov
- def activate(self, kalman_filter, frame_id):
- """Start a new tracklet"""
+ def activate(self, kalman_filter: KalmanFilter, frame_id: int):
+ """
+ initializes a new tracklet with a Kalman filter and assigns a track ID and
+ state based on the frame ID.
+
+ Parameters
+ ----------
+ kalman_filter: Kalman filter object
+ frame_id (int): The `frame_id` parameter in the `activate` method.
+ """
self.kalman_filter = kalman_filter
self.track_id = self.next_id()
self.mean, self.covariance = self.kalman_filter.initiate(self.tlwh_to_xyah(self._tlwh))
- self.tracklet_len = 0
self.state = TrackState.Tracked
if frame_id == 1:
self.is_activated = True
@@ -74,11 +111,22 @@ def activate(self, kalman_filter, frame_id):
self.frame_id = frame_id
self.start_frame = frame_id
- def re_activate(self, new_track, frame_id, new_id=False):
+ def re_activate(self, new_track: "STrack", frame_id: int, new_id: bool = False):
+ """
+ Updates a track using Kalman filtering
+
+ Parameters
+ ----------
+ new_track : STrack
+ The new track object to update.
+ frame_id : int
+ The frame ID.
+ new_id : bool
+ Whether to assign a new ID to the track, by default False.
+ """
self.mean, self.covariance = self.kalman_filter.update(
self.mean, self.covariance, self.tlwh_to_xyah(new_track.tlwh)
)
- self.tracklet_len = 0
self.state = TrackState.Tracked
self.is_activated = True
self.frame_id = frame_id
@@ -87,16 +135,18 @@ def re_activate(self, new_track, frame_id, new_id=False):
self.score = new_track.score
self.cls = new_track.cls
- def update(self, new_track, frame_id):
+ def update(self, new_track: "STrack", frame_id: int):
"""
- Update a matched track
- :type new_track: STrack
- :type frame_id: int
- :type update_feature: bool
- :return:
+ Update a matched track.
+
+ Parameters
+ ----------
+ new_track : STrack
+ The new track object to update.
+ frame_id : int
+ The frame ID.
"""
self.frame_id = frame_id
- self.tracklet_len += 1
self.cls = new_track.cls
new_tlwh = new_track.tlwh
@@ -124,7 +174,8 @@ def tlwh(self):
@property
# @jit(nopython=True)
def tlbr(self):
- """Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,
+ """
+ Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,
`(top left, bottom right)`.
"""
ret = self.tlwh.copy()
@@ -134,7 +185,8 @@ def tlbr(self):
@staticmethod
# @jit(nopython=True)
def tlwh_to_xyah(tlwh):
- """Convert bounding box to format `(center x, center y, aspect ratio,
+ """
+ Convert bounding box to format `(center x, center y, aspect ratio,
height)`, where the aspect ratio is `width / height`.
"""
ret = np.asarray(tlwh).copy()
@@ -142,23 +194,6 @@ def tlwh_to_xyah(tlwh):
ret[2] /= ret[3]
return ret
- def to_xyah(self):
- return self.tlwh_to_xyah(self.tlwh)
-
- @staticmethod
- # @jit(nopython=True)
- def tlbr_to_tlwh(tlbr):
- ret = np.asarray(tlbr).copy()
- ret[2:] -= ret[:2]
- return ret
-
- @staticmethod
- # @jit(nopython=True)
- def tlwh_to_tlbr(tlwh):
- ret = np.asarray(tlwh).copy()
- ret[2:] += ret[:2]
- return ret
-
def __repr__(self):
return "OT_{}_({}-{})".format(self.track_id, self.start_frame, self.end_frame)
@@ -188,7 +223,23 @@ def reset(self):
self.kalman_filter = KalmanFilter()
BaseTrack._count = 0
- def update(self, dets, frame_id):
+ def update(self, dets: np.ndarray, frame_id: int):
+ """
+ Performs object tracking by associating detections with existing tracks and updating their states accordingly.
+
+ Parameters
+ ----------
+ dets : np.ndarray
+ Detection boxes of objects in the format (n x 6), where each row contains (x1, y1, x2, y2, score, class).
+ frame_id : int
+ The ID of the current frame in the video.
+
+ Returns
+ -------
+ np.ndarray
+ An array of outputs containing bounding box coordinates, track ID, class label, and
+ score for each tracked object.
+ """
self.frame_id = frame_id
activated_starcks = []
refind_stracks = []
@@ -262,7 +313,7 @@ def update(self, dets, frame_id):
strack_pool[i] for i in u_track if strack_pool[i].state == TrackState.Tracked
]
dists = matching.iou_distance(r_tracked_stracks, detections_second)
- matches, u_track, u_detection_second = matching.linear_assignment(dists, thresh=0.5)
+ matches, u_track, _ = matching.linear_assignment(dists, thresh=0.5)
for itracked, idet in matches:
track = r_tracked_stracks[itracked]
det = detections_second[idet]
@@ -339,7 +390,23 @@ def update(self, dets, frame_id):
return outputs
-def joint_stracks(tlista, tlistb):
+def joint_stracks(tlista: list["STrack"], tlistb: list["STrack"]):
+ """
+ Merges two lists of objects based on a specific attribute while
+ ensuring no duplicates are added.
+
+ Parameters
+ ----------
+ tlista : List[STrack]
+ list of STrack objects.
+ tlistb : List[STrack]
+ list of STrack objects.
+
+ Returns
+ -------
+ List[STrack]
+ A list containing all unique elements from both input lists.
+ """
exists = {}
res = []
for t in tlista:
@@ -353,7 +420,22 @@ def joint_stracks(tlista, tlistb):
return res
-def sub_stracks(tlista, tlistb):
+def sub_stracks(tlista: list["STrack"], tlistb: list["STrack"]):
+ """
+ Returns a list of STrack objects that are present in tlista but not in tlistb.
+
+ Parameters
+ ----------
+ tlista : List[STrack]
+ list of STrack objects.
+ tlistb : List[STrack]
+ list of STrack objects.
+
+ Returns
+ -------
+ List[STrack]
+ A list containing STrack objects present in tlista but not in tlistb.
+ """
stracks = {}
for t in tlista:
stracks[t.track_id] = t
@@ -364,7 +446,22 @@ def sub_stracks(tlista, tlistb):
return list(stracks.values())
-def remove_duplicate_stracks(stracksa, stracksb):
+def remove_duplicate_stracks(stracksa: list["STrack"], stracksb: list["STrack"]):
+ """
+ Removes duplicate STrack objects from the input lists based on their frame IDs.
+
+ Parameters
+ ----------
+ stracksa : List[STrack]
+ list of STrack objects.
+ stracksb : List[STrack]
+ list of STrack objects.
+
+ Returns
+ -------
+ Tuple[List[STrack], List[STrack]]
+ Two lists containing unique STrack objects after removing duplicates.
+ """
pdist = matching.iou_distance(stracksa, stracksb)
pairs = np.where(pdist < 0.15)
dupa, dupb = list(), list()
diff --git a/bytetracker/kalman_filter.py b/bytetracker/kalman_filter.py
index f3b9182..06f920f 100644
--- a/bytetracker/kalman_filter.py
+++ b/bytetracker/kalman_filter.py
@@ -1,23 +1,6 @@
import numpy as np
import scipy.linalg
-"""
-Table for the 0.95 quantile of the chi-square distribution with N degrees of
-freedom (contains values for N=1, ..., 9). Taken from MATLAB/Octave's chi2inv
-function and used as Mahalanobis gating threshold.
-"""
-chi2inv95 = {
- 1: 3.8415,
- 2: 5.9915,
- 3: 7.8147,
- 4: 9.4877,
- 5: 11.070,
- 6: 12.592,
- 7: 14.067,
- 8: 15.507,
- 9: 16.919,
-}
-
class KalmanFilter(object):
"""
@@ -85,7 +68,7 @@ def initiate(self, measurement):
covariance = np.diag(np.square(std))
return mean, covariance
- def predict(self, mean, covariance):
+ def predict(self, mean: np.ndarray, covariance: np.ndarray):
"""Run Kalman filter prediction step.
Parameters
@@ -126,7 +109,7 @@ def predict(self, mean, covariance):
return mean, covariance
- def project(self, mean, covariance):
+ def project(self, mean: np.ndarray, covariance: np.ndarray):
"""Project state distribution to measurement space.
Parameters
@@ -155,7 +138,7 @@ def project(self, mean, covariance):
covariance = np.linalg.multi_dot((self._update_mat, covariance, self._update_mat.T))
return mean, covariance + innovation_cov
- def multi_predict(self, mean, covariance):
+ def multi_predict(self, mean: np.ndarray, covariance: np.ndarray):
"""Run Kalman filter prediction step (Vectorized version).
Parameters
----------
@@ -196,7 +179,7 @@ def multi_predict(self, mean, covariance):
return mean, covariance
- def update(self, mean, covariance, measurement):
+ def update(self, mean: np.ndarray, covariance: np.ndarray, measurement: np.ndarray):
"""Run Kalman filter correction step.
Parameters
@@ -229,46 +212,3 @@ def update(self, mean, covariance, measurement):
(kalman_gain, projected_cov, kalman_gain.T)
)
return new_mean, new_covariance
-
- def gating_distance(self, mean, covariance, measurements, only_position=False, metric="maha"):
- """Compute gating distance between state distribution and measurements.
- A suitable distance threshold can be obtained from `chi2inv95`. If
- `only_position` is False, the chi-square distribution has 4 degrees of
- freedom, otherwise 2.
- Parameters
- ----------
- mean : ndarray
- Mean vector over the state distribution (8 dimensional).
- covariance : ndarray
- Covariance of the state distribution (8x8 dimensional).
- measurements : ndarray
- An Nx4 dimensional matrix of N measurements, each in
- format (x, y, a, h) where (x, y) is the bounding box center
- position, a the aspect ratio, and h the height.
- only_position : Optional[bool]
- If True, distance computation is done with respect to the bounding
- box center position only.
- Returns
- -------
- ndarray
- Returns an array of length N, where the i-th element contains the
- squared Mahalanobis distance between (mean, covariance) and
- `measurements[i]`.
- """
- mean, covariance = self.project(mean, covariance)
- if only_position:
- mean, covariance = mean[:2], covariance[:2, :2]
- measurements = measurements[:, :2]
-
- d = measurements - mean
- if metric == "gaussian":
- return np.sum(d * d, axis=1)
- elif metric == "maha":
- cholesky_factor = np.linalg.cholesky(covariance)
- z = scipy.linalg.solve_triangular(
- cholesky_factor, d.T, lower=True, check_finite=False, overwrite_b=True
- )
- squared_maha = np.sum(z * z, axis=0)
- return squared_maha
- else:
- raise ValueError("invalid distance metric")
diff --git a/bytetracker/matching.py b/bytetracker/matching.py
index d7384c9..753fbc9 100644
--- a/bytetracker/matching.py
+++ b/bytetracker/matching.py
@@ -1,40 +1,22 @@
import lap
import numpy as np
-import scipy
-from scipy.spatial.distance import cdist
-from bytetracker import kalman_filter
+def linear_assignment(cost_matrix: np.ndarray, thresh: float):
+ """
+ Assigns detections to existing tracks based on a cost matrix using linear assignment.
-def merge_matches(m1, m2, shape):
- O, P, Q = shape
- m1 = np.asarray(m1)
- m2 = np.asarray(m2)
-
- M1 = scipy.sparse.coo_matrix((np.ones(len(m1)), (m1[:, 0], m1[:, 1])), shape=(O, P))
- M2 = scipy.sparse.coo_matrix((np.ones(len(m2)), (m2[:, 0], m2[:, 1])), shape=(P, Q))
-
- mask = M1 * M2
- match = mask.nonzero()
- match = list(zip(match[0], match[1]))
- unmatched_O = tuple(set(range(O)) - set([i for i, j in match]))
- unmatched_Q = tuple(set(range(Q)) - set([j for i, j in match]))
-
- return match, unmatched_O, unmatched_Q
-
-
-def _indices_to_matches(cost_matrix, indices, thresh):
- matched_cost = cost_matrix[tuple(zip(*indices))]
- matched_mask = matched_cost <= thresh
-
- matches = indices[matched_mask]
- unmatched_a = tuple(set(range(cost_matrix.shape[0])) - set(matches[:, 0]))
- unmatched_b = tuple(set(range(cost_matrix.shape[1])) - set(matches[:, 1]))
-
- return matches, unmatched_a, unmatched_b
-
+ Parameters
+ ----------
+ cost_matrix : np.ndarray
+ The cost matrix representing the association cost between detections and tracks.
+ thresh : float
+ The threshold for cost matching.
-def linear_assignment(cost_matrix, thresh):
+ Returns
+ -------
+ Tuple containing matches, unmatched detections, and unmatched tracks.
+ """
if cost_matrix.size == 0:
return (
np.empty((0, 2), dtype=int),
@@ -42,7 +24,7 @@ def linear_assignment(cost_matrix, thresh):
tuple(range(cost_matrix.shape[1])),
)
matches, unmatched_a, unmatched_b = [], [], []
- cost, x, y = lap.lapjv(cost_matrix, extend_cost=True, cost_limit=thresh)
+ _, x, y = lap.lapjv(cost_matrix, extend_cost=True, cost_limit=thresh)
for ix, mx in enumerate(x):
if mx >= 0:
matches.append([ix, mx])
@@ -54,11 +36,19 @@ def linear_assignment(cost_matrix, thresh):
def ious(atlbrs, btlbrs):
"""
- Compute cost based on IoU
- :type atlbrs: list[tlbr] | np.ndarray
- :type atlbrs: list[tlbr] | np.ndarray
+ Compute cost over Union (IoU) between bounding box pairs
- :rtype ious np.ndarray
+ Parameters
+ ----------
+ atlbrs : Union[list, np.ndarray]
+ The bounding boxes of the first set in (min x, min y, max x, max y) format.
+ btlbrs : Union[list, np.ndarray]
+ The bounding boxes of the second set in (min x, min y, max x, max y) format.
+
+ Returns
+ -------
+ np.ndarray
+ An array containing IoU values for each pair of bounding boxes.
"""
ious = np.zeros((len(atlbrs), len(btlbrs)), dtype=np.float32)
if ious.size == 0:
@@ -95,92 +85,22 @@ def iou_distance(atracks, btracks):
return cost_matrix
-def v_iou_distance(atracks, btracks):
- """
- Compute cost based on IoU
- :type atracks: list[STrack]
- :type btracks: list[STrack]
-
- :rtype cost_matrix np.ndarray
+def fuse_score(cost_matrix: np.ndarray, detections: np.ndarray):
"""
+ Fuse detection scores with similarity scores from a cost matrix.
- if (len(atracks) > 0 and isinstance(atracks[0], np.ndarray)) or (
- len(btracks) > 0 and isinstance(btracks[0], np.ndarray)
- ):
- atlbrs = atracks
- btlbrs = btracks
- else:
- atlbrs = [track.tlwh_to_tlbr(track.pred_bbox) for track in atracks]
- btlbrs = [track.tlwh_to_tlbr(track.pred_bbox) for track in btracks]
- _ious = ious(atlbrs, btlbrs)
- cost_matrix = 1 - _ious
-
- return cost_matrix
-
+ Parameters
+ ----------
+ cost_matrix : np.ndarray
+ The cost matrix representing the dissimilarity between tracks and detections.
+ detections : np.ndarray
+ The array of detections, each containing a score.
-def embedding_distance(tracks, detections, metric="cosine"):
- """
- :param tracks: list[STrack]
- :param detections: list[BaseTrack]
- :param metric:
- :return: cost_matrix np.ndarray
+ Returns
+ -------
+ np.ndarray
+ The fused cost matrix, incorporating both similarity scores and detection scores.
"""
-
- cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float32)
- if cost_matrix.size == 0:
- return cost_matrix
- det_features = np.asarray([track.curr_feat for track in detections], dtype=np.float32)
- # for i, track in enumerate(tracks):
- # cost_matrix[i, :] = np.maximum(0.0, cdist(track.smooth_feat.reshape(1,-1), det_features, metric))
- track_features = np.asarray([track.smooth_feat for track in tracks], dtype=np.float32)
- cost_matrix = np.maximum(0.0, cdist(track_features, det_features, metric)) # Nomalized features
- return cost_matrix
-
-
-def gate_cost_matrix(kf, cost_matrix, tracks, detections, only_position=False):
- if cost_matrix.size == 0:
- return cost_matrix
- gating_dim = 2 if only_position else 4
- gating_threshold = kalman_filter.chi2inv95[gating_dim]
- measurements = np.asarray([det.to_xyah() for det in detections])
- for row, track in enumerate(tracks):
- gating_distance = kf.gating_distance(
- track.mean, track.covariance, measurements, only_position
- )
- cost_matrix[row, gating_distance > gating_threshold] = np.inf
- return cost_matrix
-
-
-def fuse_motion(kf, cost_matrix, tracks, detections, only_position=False, lambda_=0.98):
- if cost_matrix.size == 0:
- return cost_matrix
- gating_dim = 2 if only_position else 4
- gating_threshold = kalman_filter.chi2inv95[gating_dim]
- measurements = np.asarray([det.to_xyah() for det in detections])
- for row, track in enumerate(tracks):
- gating_distance = kf.gating_distance(
- track.mean, track.covariance, measurements, only_position, metric="maha"
- )
- cost_matrix[row, gating_distance > gating_threshold] = np.inf
- cost_matrix[row] = lambda_ * cost_matrix[row] + (1 - lambda_) * gating_distance
- return cost_matrix
-
-
-def fuse_iou(cost_matrix, tracks, detections):
- if cost_matrix.size == 0:
- return cost_matrix
- reid_sim = 1 - cost_matrix
- iou_dist = iou_distance(tracks, detections)
- iou_sim = 1 - iou_dist
- fuse_sim = reid_sim * (1 + iou_sim) / 2
- det_scores = np.array([det.score for det in detections])
- det_scores = np.expand_dims(det_scores, axis=0).repeat(cost_matrix.shape[0], axis=0)
- # fuse_sim = fuse_sim * (1 + det_scores) / 2
- fuse_cost = 1 - fuse_sim
- return fuse_cost
-
-
-def fuse_score(cost_matrix, detections):
if cost_matrix.size == 0:
return cost_matrix
iou_sim = 1 - cost_matrix
@@ -191,7 +111,7 @@ def fuse_score(cost_matrix, detections):
return fuse_cost
-def bbox_ious(boxes, query_boxes):
+def bbox_ious(boxes: np.ndarray, query_boxes: np.ndarray):
"""
Parameters
----------
diff --git a/examples/requirements.txt b/examples/requirements.txt
new file mode 100644
index 0000000..e3a5a00
--- /dev/null
+++ b/examples/requirements.txt
@@ -0,0 +1,4 @@
+git+https://github.com/artefactory-fr/bytetrack.git@main
+opencv-python==4.8.1.78
+ultralytics==8.0.216
+matplotlib==3.8.2
diff --git a/examples/test_bytetrack_car.ipynb b/examples/test_bytetrack_car.ipynb
new file mode 100644
index 0000000..8cc6d83
--- /dev/null
+++ b/examples/test_bytetrack_car.ipynb
@@ -0,0 +1,294 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Executive Summary: Car Detection with ByteTrack - An Introductory Guide\n",
+ "\n",
+ "This guide is designed to provide a beginner-friendly introduction to the application of ByteTrack for car detection in video footage. ByteTrack is an advanced algorithm that leverages the capabilities of the YOLO (You Only Look Once) model for object detection, specifically focusing on tracking objects across video frames.\n",
+ "\n",
+ "For more information on YOLO and ultralytics, visit [this link](https://github.com/ultralytics/ultralytics).\n",
+ "\n",
+ "For more information on ByteTrack, visit [this link](https://github.com/ifzhang/ByteTrack).\n",
+ "\n",
+ "1. **Frame Extraction**: \n",
+ " This video is decomposed into frames, transforming continuous video into discrete snapshots for analysis.\n",
+ "\n",
+ "2. **Detection and tracking**: \n",
+ " We initialize the ByteTracker object and load the pre-trained Yolo model, indicating its parameters. Going through all the frames of the video, the YOLO model enables object detection. Tracking is handled by the ByteTrack algorithm, using the bounding boxes and assigning each of it an ID that enables to track its movement.\n",
+ "\n",
+ "3. **Visualization of Tracking**: \n",
+ " Recomposing the video from the frames with object detected, writing it in a MP4 format to same folder.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%load_ext autoreload\n",
+ "%autoreload 2\n",
+ "import glob\n",
+ "import matplotlib.pyplot as plt\n",
+ "import cv2\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "\n",
+ "# YOLO and video packages \n",
+ "from ultralytics import YOLO\n",
+ "from bytetracker import BYTETracker\n",
+ "from bytetracker.basetrack import BaseTrack\n",
+ "from utils import draw_all_bbox_on_image, yolo_results_to_bytetrack_format, scale_bbox_as_xyxy\n",
+ "from IPython.display import Video"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Download the video\n",
+ "VIDEO_PATH = 'videos/traffic.mp4'\n",
+ "!if [ ! -f $VIDEO_PATH ]; then mkdir -p videos && wget https://storage.googleapis.com/bytetrack-data-public/traffic.mp4 -O $VIDEO_PATH; fi"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Reading video"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "Video(VIDEO_PATH, width=800,embed=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### 1. Frame Extraction "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# You can run this only once:\n",
+ "# Transform this VIDEO_PATH into a list of frames in this folder under frames/\n",
+ "!mkdir -p frames && ffmpeg -i $VIDEO_PATH -vf fps=12 frames/%d.png -hide_banner -loglevel panic"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# - list and sort PNG frames in the 'frames' directory, ensuring they are ordered numerically for subsequent processing.\n",
+ "# - usinglob to find all PNG files and sorts them based on the numeric part of their filenames, avoiding lexicographic order issues"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "available_frames = glob.glob(\"frames/*.png\")\n",
+ "available_frames = sorted(available_frames, key=lambda x: int(x.split(\"/\")[-1].split(\".\")[0]))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib inline\n",
+ "\n",
+ "MODEL_WEIGHTS = \"yolov8m.pt\"\n",
+ "\n",
+ "model = YOLO(MODEL_WEIGHTS)\n",
+ "results = model(available_frames[0])[0]\n",
+ "\n",
+ "plt.imshow(cv2.cvtColor(results.plot(), cv2.COLOR_BGR2RGB))\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Classes for prediction, indicating which object to detect\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "### We will track only car \n",
+ "CAR_CLASS_ID = 2"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ " #### BYTETracker Parameters\n",
+ " - `track_thresh`: Threshold for considering a detection as a potential object to track.\n",
+ " - `track_buffer`: Number of frames to keep tracking information for an object before discarding it.\n",
+ " - `match_thresh`: Threshold for matching detections between consecutive frames.\n",
+ " - `frame_rate`: Frame rate of the video or sequence being processed."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tracker = BYTETracker(track_thresh= 0.15, track_buffer = 3, match_thresh = 0.85, frame_rate= 12)\n",
+ "BaseTrack._count = 0"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model = YOLO(MODEL_WEIGHTS, task=\"detect\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### 2. Detection and tracking"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "all_tracked_objects = []\n",
+ "for frame_id, image_filename in enumerate(available_frames):\n",
+ " img = cv2.imread(image_filename)\n",
+ " detections = model.predict(img, classes=[CAR_CLASS_ID], conf=0.15, verbose=False)[0]\n",
+ " detections_bytetrack_format = yolo_results_to_bytetrack_format(detections)\n",
+ " tracked_objects = tracker.update(detections_bytetrack_format, frame_id)\n",
+ " if len(tracked_objects) > 0:\n",
+ " tracked_objects = np.insert(tracked_objects, 0, frame_id, axis=1)\n",
+ " all_tracked_objects.append(tracked_objects)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Scaling the bounding boxes to match with original image size "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_tracked = pd.DataFrame(np.concatenate(all_tracked_objects), columns=[\"frame_id\", \"x1\", \"y1\", \"x2\", \"y2\", \"track_id\", \"class\", \"confidence\"])\n",
+ "df_tracked[[\"x1\", \"y1\", \"x2\", \"y2\"]] = df_tracked[[\"x1\", \"y1\", \"x2\", \"y2\"]].apply(\n",
+ " lambda x: scale_bbox_as_xyxy(x[0:4], detections.orig_shape), axis=1, result_type=\"expand\"\n",
+ " )\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### 3. Visualization of Tracking"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fourcc = cv2.VideoWriter_fourcc(*'H264')\n",
+ "OUTPUT_WITH_BBOX = \"videos/traffic_tracked.mp4\"\n",
+ "out = cv2.VideoWriter(OUTPUT_WITH_BBOX, fourcc, 12, (1280, 720))\n",
+ "for frame_id, image_filename in enumerate(available_frames):\n",
+ " image = cv2.imread(image_filename)\n",
+ " if frame_id in df_tracked.frame_id.astype('int').values:\n",
+ " df_current_frame = df_tracked[df_tracked.frame_id == frame_id][[\"x1\", \"y1\", \"x2\", \"y2\", \"track_id\", \"class\", \"confidence\"]].to_numpy()\n",
+ " image = draw_all_bbox_on_image(image, df_current_frame)\n",
+ " out.write(image)\n",
+ "out.release()\n",
+ "print(\"Video with bounding box is saved at:\", OUTPUT_WITH_BBOX)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(\"Number of detected objects: \", len(df_tracked.track_id.unique()))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "video_path = \"videos/traffic_tracked.mp4\"\n",
+ "display(Video(video_path, embed=True, width=800))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "3.8",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.13"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/utils.py b/examples/utils.py
new file mode 100644
index 0000000..0d45b6b
--- /dev/null
+++ b/examples/utils.py
@@ -0,0 +1,70 @@
+import cv2
+import numpy as np
+
+
+def draw_all_bbox_on_image(image, tracking_objects: np.ndarray):
+ """
+ A list of of detections with track id, class id and confidence.
+ [
+ [x, y, x, y, track_id, class_id, conf],
+ [x, y, x, y, track_id, class_id, conf],
+ ...
+ ]
+
+ Plot this on the image with the track id, class id and confidence.
+ """
+ for detection in tracking_objects:
+ x1, y1, x2, y2, track_id, _, conf = detection
+ x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
+ cv2.rectangle(image, (x1, y1), (x2, y2), (255, 0, 0), 2)
+ cv2.putText(
+ image,
+ f"{int(track_id)} ({conf:.2f})",
+ (x1, y1 - 10),
+ 0,
+ 1,
+ (0, 255, 0),
+ 2,
+ )
+ return image
+
+
+def yolo_results_to_bytetrack_format(detections):
+ """Transforms YOLO detections into the bytetrack format.
+
+ Args:
+ detections: A list of YOLO detections.
+
+ Returns:
+ A list of bytetrack detections.
+ """
+ boxes = detections.numpy().boxes.xyxyn
+ scores = detections.numpy().boxes.conf
+ classes = detections.numpy().boxes.cls
+ return np.stack(
+ [
+ boxes[:, 0],
+ boxes[:, 1],
+ boxes[:, 2],
+ boxes[:, 3],
+ scores,
+ classes,
+ ],
+ axis=1,
+ )
+
+
+def scale_bbox_as_xyxy(bbox: np.ndarray, target_img_size: tuple):
+ """Scales a bounding box to a target image size.
+
+ Args:
+ bbox: A bounding box in the format [x, y, x, y].
+ target_img_size: The target image size as a tuple (h, W).
+
+ Returns:
+ The scaled bounding box.
+ """
+ x1, y1, x2, y2 = bbox
+ h, w = target_img_size
+ scaled_bbox = np.array([x1 * w, y1 * h, x2 * w, y2 * h])
+ return scaled_bbox
diff --git a/requirements.txt b/requirements.txt
deleted file mode 100644
index 4f47e74..0000000
--- a/requirements.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-lapx>=0.5.5
-scipy==1.9.3
-torch>=1.13
diff --git a/tests/expected_output/objects_detected_and_tracked_video1.txt b/tests/expected_output/objects_detected_and_tracked_video1.txt
new file mode 100644
index 0000000..0c137d0
--- /dev/null
+++ b/tests/expected_output/objects_detected_and_tracked_video1.txt
@@ -0,0 +1,1782 @@
+1 0.43556426884883115 0.77328302 0.5555945999999999 0.933616134 1 2 0.8920609
+1 0.5097041794815526 0.4710114960000001 0.5574228520000001 0.5331648560000001 2 2 0.87958884
+1 0.30967665687427137 0.515491712 0.368265234 0.588124726 3 2 0.87676245
+1 0.20932472635232796 0.6073970599999998 0.319006622 0.7888608799999999 4 2 0.8569984
+1 0.43064804095509635 0.40286853 0.46754843 0.453162686 5 2 0.8008579
+1 0.49973847842413416 0.39702042 0.53537594 0.437601128 6 2 0.83086425
+1 0.7886013982269225 0.299308234 0.807044934 0.320431396 7 2 0.6858464
+1 0.39118222710900574 0.37134256 0.42421318399999997 0.413057714 8 2 0.6306694
+1 0.8701450097580157 0.3399176000000001 0.9076027200000001 0.38101248000000004 9 2 0.7219576
+1 0.8092601660633973 0.30876634999999997 0.83809088 0.349479144 12 2 0.38684392
+1 0.423969592100573 0.321943424 0.45593904 0.350395562 13 2 0.31593144
+1 0.48005837000000007 0.29037353 0.49557677 0.30770555 15 2 0.2673088
+2 0.43446934963818784 0.7800244164167186 0.5565387917209875 0.9432298960380675 1 2 0.89139915
+2 0.5100957796923421 0.47235451680478024 0.558109731347528 0.5348896636894789 2 2 0.87843114
+2 0.30881118835385013 0.5173628581386166 0.3678770365185283 0.5905844815155824 3 2 0.87684804
+2 0.2070695853680926 0.6110799205330216 0.3180549861940387 0.7947095177626325 4 2 0.8544163
+2 0.4304205866881114 0.40369751046823577 0.467470798212068 0.4542131324738795 5 2 0.80048555
+2 0.5000370430182745 0.3977960991019721 0.535761932404 0.43846324898221267 6 2 0.8275519
+2 0.7882574299438495 0.2993240752482507 0.8065062449780754 0.3202253117972536 7 2 0.69196576
+2 0.3908830930711261 0.3720901238348118 0.42395136668352884 0.4138437381705426 8 2 0.6281891
+2 0.8690550785691576 0.3396742913759221 0.9063659993859895 0.3806154827725119 9 2 0.7188587
+2 0.808298718311558 0.3075815939048854 0.8375057039609526 0.3489771735508074 12 2 0.38245678
+2 0.4241068226727336 0.32200566405499925 0.45582759225855696 0.3504504416625069 13 2 0.32340264
+2 0.47973808482716646 0.2902383316528926 0.4954560894214876 0.30780398099173556 15 2 0.26988575
+3 0.46305630986843 0.8460161979260311 0.5690370822992061 0.9862081625146936 1 2 0.84967417
+3 0.5120576786813239 0.4861647504212477 0.5634869399804708 0.553172614285874 2 2 0.8513546
+3 0.2990945326843665 0.5344579498735058 0.36307381328028665 0.613821699954433 3 2 0.878007
+3 0.1828543238040704 0.6452930216391701 0.30787132943719936 0.8523791799696974 4 2 0.7862277
+3 0.42807842274616326 0.41001635654831686 0.4668571379404781 0.46293038292868277 5 2 0.80004036
+3 0.5005758954400974 0.40380887249053393 0.5378430447990568 0.44621690837043787 6 2 0.81224805
+3 0.7851468199477869 0.29758088304129726 0.8030060451821117 0.31803610783472036 7 2 0.64156467
+3 0.38736053619765115 0.377392561824352 0.42068954699538946 0.41945785443488415 8 2 0.735354
+3 0.8609843353104356 0.3371558893368453 0.8966689515774058 0.3762965592118422 9 2 0.63407296
+3 0.8041732373153357 0.3049024541551971 0.8330302963239912 0.3460296585670395 12 2 0.46897697
+3 0.4231106880302257 0.3240282849219125 0.45455684810062186 0.35249900601861495 13 2 0.3221926
+3 0.47812930396595293 0.29132705301331013 0.49354736579576924 0.30856464848197723 15 2 0.3084651
+4 0.4942251840716074 0.8932109917244956 0.5772916752717989 0.9995003221546322 1 2 0.82778764
+4 0.5140351286966588 0.49523853090844594 0.566817065579868 0.5639927536885427 2 2 0.84572405
+4 0.293184842643466 0.545903972996657 0.3603122770647582 0.6292876924621813 3 2 0.87811077
+4 0.1678330558926387 0.6678814386484097 0.3014324524739503 0.8895998267710014 4 2 0.68566775
+4 0.4266488667484329 0.41420097661022576 0.4666256683765811 0.4688131787409872 5 2 0.79346776
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diff --git a/tests/expected_output/objects_detected_and_tracked_video2.txt b/tests/expected_output/objects_detected_and_tracked_video2.txt
new file mode 100644
index 0000000..cf1f372
--- /dev/null
+++ b/tests/expected_output/objects_detected_and_tracked_video2.txt
@@ -0,0 +1,19 @@
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diff --git a/tests/test_input/objects_detected_video1.txt b/tests/test_input/objects_detected_video1.txt
new file mode 100644
index 0000000..0af4d6d
--- /dev/null
+++ b/tests/test_input/objects_detected_video1.txt
@@ -0,0 +1,2379 @@
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diff --git a/tests/test_input/objects_detected_video2.txt b/tests/test_input/objects_detected_video2.txt
new file mode 100644
index 0000000..d6a3d8c
--- /dev/null
+++ b/tests/test_input/objects_detected_video2.txt
@@ -0,0 +1,21 @@
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+132 0.31567994 0.5045963 0.4752435 0.7699328 0.9226942 49
+133 0.26709083 0.5050746 0.4275921 0.7702349 0.9281332 49
+134 0.22100373 0.5047798 0.3819117 0.77039224 0.92945176 49
+135 0.17729278 0.5041295 0.3402054 0.77022386 0.92946494 49
diff --git a/tests/test_model_regression.py b/tests/test_model_regression.py
new file mode 100644
index 0000000..304363c
--- /dev/null
+++ b/tests/test_model_regression.py
@@ -0,0 +1,102 @@
+from pathlib import Path
+
+import numpy as np
+import pytest
+
+# bytetracker
+from bytetracker import BYTETracker
+
+# static paths
+OUTPUT_FOLDER = Path("tests/output")
+TEST_INPUT_FOLDER = Path("tests/test_input")
+EXPECTED_OUTPUT_FOLDER = Path("tests/expected_output")
+OUTPUT_FOLDER = Path("tests/output")
+
+OUTPUT_FOLDER.mkdir(parents=True, exist_ok=True)
+
+
+def reading_detections_file(video_number):
+ """Read the detections object file from a specific format
+
+ Args:
+ video_number (string): string representing the video name in folder
+
+ Returns:
+ df_detection (ndarray): A list of array of the tuples.
+ """
+ df_detection = np.loadtxt(
+ TEST_INPUT_FOLDER / f"objects_detected_{video_number}.txt", delimiter=" "
+ )
+
+ return df_detection
+
+
+def reading_expected_results_from_txt(video_number):
+ """Read the detections and tracked objects frames from Yolo and Bytetrack models
+
+ Args:
+ video_number (str): Video title to import
+
+ Returns:
+ expected_results_df (ndarray): array of frames detection and tracking imported from expected results.
+ """
+ expected_results_df = np.loadtxt(
+ EXPECTED_OUTPUT_FOLDER / f"objects_detected_and_tracked_{video_number}.txt"
+ )
+
+ return expected_results_df
+
+
+@pytest.mark.parametrize(
+ "expected_results, test_input, video_number",
+ [
+ (
+ reading_expected_results_from_txt("video1"),
+ reading_detections_file("video1"),
+ "video1",
+ ),
+ (
+ reading_expected_results_from_txt("video2"),
+ reading_detections_file("video2"),
+ "video2",
+ ),
+ ],
+)
+def test_video_prediction_tracking(expected_results, test_input, video_number):
+ """Execute the integration test: performing the tracking of test_input,
+ whose output is to compare with expected_results
+
+ Args:
+ expected_result (DataFrame): the dataframe of the results expected to match the test result
+ test_input (array): dataframe that is the import of prediction frames in test_input folder
+ video_number (string): string component specific to a video
+
+ Returns:
+ Test assertion results
+ """
+ tracker = BYTETracker(track_thresh=0.15, track_buffer=3, match_thresh=0.85, frame_rate=12)
+ # first column of test input is the frame id
+ frame_idx = sorted(np.unique(test_input[:, 0]))
+ test_results = []
+
+ for frame_id in frame_idx:
+ detections = test_input[test_input[:, 0] == frame_id][:, 1:]
+ tracked_objects = tracker.update(detections, frame_id)
+ if len(tracked_objects) > 0:
+ tracked_objects = np.insert(
+ tracked_objects, 0, np.full(len(tracked_objects), frame_id), axis=1
+ )
+ test_results.append(tracked_objects)
+
+ if test_results: # Ensure test_results is not empty to avoid errors
+ combined_array = np.concatenate(test_results)
+ else:
+ combined_array = np.array([]) # Or handle the empty case as needed
+
+ output_file_path = OUTPUT_FOLDER / f"{video_number}_test_results.txt"
+ np.savetxt(output_file_path, combined_array, delimiter=" ", fmt="%s")
+
+ assert np.allclose(expected_results, combined_array)
+
+ # Remove the file if the test is successful
+ output_file_path.unlink()