administrator/frigate
1
0
Fork 0
mirror of https://github.com/blakeblackshear/frigate.git synced 2026-05-05 04:57:42 +03:00
Code Issues Actions 2 Packages Projects Releases Wiki Activity

More debugging

Browse Source
This commit is contained in:
Nicolas Mowen 2025-04-17 16:43:05 -06:00
parent 722fe21287
commit c458214650
1 changed files with 19 additions and 11 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

30
frigate/util/model.py
View File

@ -257,10 +257,10 @@ def post_process_yolox(predictions: np.ndarray, width: int, height: int) -> np.n
scores = predictions[:, 4:5] * predictions[:, 5:] scores = predictions[:, 4:5] * predictions[:, 5:]
boxes_xyxy = np.ones_like(boxes) boxes_xyxy = np.ones_like(boxes)
boxes_xyxy[:, 0] = boxes[:, 0] - boxes[:, 2] / 2.0 boxes_xyxy[:, 0] = boxes[:, 0] - boxes[:, 2] / 2
boxes_xyxy[:, 1] = boxes[:, 1] - boxes[:, 3] / 2.0 boxes_xyxy[:, 1] = boxes[:, 1] - boxes[:, 3] / 2
boxes_xyxy[:, 2] = boxes[:, 0] + boxes[:, 2] / 2.0 boxes_xyxy[:, 2] = boxes[:, 0] + boxes[:, 2] / 2
boxes_xyxy[:, 3] = boxes[:, 1] + boxes[:, 3] / 2.0 boxes_xyxy[:, 3] = boxes[:, 1] + boxes[:, 3] / 2
cls_inds = scores.argmax(1) cls_inds = scores.argmax(1)
scores = scores[np.arange(len(cls_inds)), cls_inds] scores = scores[np.arange(len(cls_inds)), cls_inds]
@ -269,6 +269,15 @@ def post_process_yolox(predictions: np.ndarray, width: int, height: int) -> np.n
boxes_xyxy, scores, score_threshold=0.4, nms_threshold=0.4 boxes_xyxy, scores, score_threshold=0.4, nms_threshold=0.4
) )
final_boxes = boxes_xyxy[indices]
final_scores = scores[indices]
final_cls_inds = cls_inds[indices]
print(f"frig boxes: {final_boxes}")
print(f"frig cls: {final_cls_inds}")
print(f"frig scores: {final_scores}")
detections = np.zeros((20, 6), np.float32) detections = np.zeros((20, 6), np.float32)
for i, (bbox, confidence, class_id) in enumerate( for i, (bbox, confidence, class_id) in enumerate(
zip(boxes_xyxy[indices], scores[indices], cls_inds[indices]) zip(boxes_xyxy[indices], scores[indices], cls_inds[indices])
@ -278,14 +287,13 @@ def post_process_yolox(predictions: np.ndarray, width: int, height: int) -> np.n
detections[i] = [ detections[i] = [
class_id, class_id,
0.75, confidence,
bbox[1] / height, bbox[1],
bbox[0] / width, bbox[0],
bbox[3] / height, bbox[3],
bbox[2] / width, bbox[2],
] ]
print(f"got {detections[i]}")
# print(f"raw det is {detections[i]}")
return detections return detections