Testing byte track through example in notebook with car video

CHANGELOG.md

@@ -1,5 +1,24 @@
 # CHANGELOG
 
+
+
+## v0.2.0 (2023-12-22)
+
+### Feature
+
+* feat: allow python 3.11 (#17) ([`2515015`](https://github.com/artefactory-fr/bytetrack/commit/25150157967432f9986103aeb7ab03671653ffb1))
+
+* feat: allow python 3.11 (#16)
+
+Co-authored-by: TomDarmon <[email protected]>
+Co-authored-by: github-actions <[email protected]> ([`c9442e3`](https://github.com/artefactory-fr/bytetrack/commit/c9442e30811b5479520ad8695706d0da8ce09397))
+
+* feat: allow python 3.11 (#14)
+
+Co-authored-by: TomDarmon <[email protected]>
+Co-authored-by: github-actions <[email protected]> ([`e8c77a0`](https://github.com/artefactory-fr/bytetrack/commit/e8c77a04138214022a3823637869f58851a5f8ef))
+
+
 ## v0.1.0 (2023-12-19)
 
 ### Feature
@@ -16,6 +35,7 @@
 
 * Tp/add reset (#10) ([`074bb18`](https://github.com/artefactory-fr/bytetrack/commit/074bb18424f7670b0d26d945fa1fefc97faed041))
 
+
 ## v0.0.3 (2023-11-24)
 
 ### Fix
@@ -26,20 +46,22 @@
 
 * Hotfix/frame id in update (#8) ([`1aaeb78`](https://github.com/artefactory-fr/bytetrack/commit/1aaeb780420c84857b14597083afc8692b16962f))
 
-* fix frame_id in update instead of_ ([`aafc3b7`](https://github.com/artefactory-fr/bytetrack/commit/aafc3b7d7be40534a6e88628302b9478991f533c))
+* fix frame_id in update instead of _ ([`aafc3b7`](https://github.com/artefactory-fr/bytetrack/commit/aafc3b7d7be40534a6e88628302b9478991f533c))
+
 
 ## v0.0.2 (2023-11-13)
 
 ### Fix
 
 * fix: cython (#6)
 
-Co-authored-by: TomDarmon &lt;<[email protected]>&gt; ([`553d5c1`](https://github.com/artefactory-fr/bytetrack/commit/553d5c192b6d1414e30dcd5701ea95cd761205bb))
+Co-authored-by: TomDarmon &lt;[email protected]&gt; ([`553d5c1`](https://github.com/artefactory-fr/bytetrack/commit/553d5c192b6d1414e30dcd5701ea95cd761205bb))
 
 ### Unknown
 
 * Fix: cython release (#7) ([`d145ef6`](https://github.com/artefactory-fr/bytetrack/commit/d145ef6511c63b9b6fc6e0c6073249320e0d5861))
 
+
 ## v0.0.1 (2023-11-13)
 
 ### Fix

examples/requirements.txt

@@ -0,0 +1,5 @@
+git+https://github.com/artefactory-fr/bytetrack.git@main
+git+https://github.com/artefactory-fr/track-reid.git@main
+opencv-python==4.8.1.78
+ultralytics==8.0.216
+matplotlib==3.8.2

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