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Code Issues Actions 2 Packages Projects Releases Wiki Activity

Cleanup detection (#17785)

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* Fix yolov9 NMS

* Improve batched yolo NMS

* Consolidate grids and strides calculation

* Use existing variable

* Remove

* Ensure init is called
This commit is contained in:
Nicolas Mowen
2025-04-18 10:26:34 -06:00
committed by GitHub
parent 14a32a6472
commit 68382d89b4
5 changed files with 116 additions and 121 deletions
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frigate/util/model.py
+24 -22
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@@ -148,27 +148,17 @@ def __post_process_multipart_yolo(
bw = ((dw * 2.0) ** 2) * anchor_w
bh = ((dh * 2.0) ** 2) * anchor_h
x1 = max(0, bx - bw / 2) / width
y1 = max(0, by - bh / 2) / height
x2 = min(width, bx + bw / 2) / width
y2 = min(height, by + bh / 2) / height
x1 = max(0, bx - bw / 2)
y1 = max(0, by - bh / 2)
x2 = min(width, bx + bw / 2)
y2 = min(height, by + bh / 2)
all_boxes.append([x1, y1, x2, y2])
all_scores.append(conf)
all_class_ids.append(class_id)
formatted_boxes = [
[
int(x1 * width),
int(y1 * height),
int((x2 - x1) * width),
int((y2 - y1) * height),
]
for x1, y1, x2, y2 in all_boxes
]
indices = cv2.dnn.NMSBoxes(
bboxes=formatted_boxes,
bboxes=all_boxes,
scores=all_scores,
score_threshold=0.4,
nms_threshold=0.4,
@@ -181,7 +171,14 @@ def __post_process_multipart_yolo(
class_id = all_class_ids[idx]
conf = all_scores[idx]
x1, y1, x2, y2 = all_boxes[idx]
results[i] = [class_id, conf, y1, x1, y2, x2]
results[i] = [
class_id,
conf,
y1 / height,
x1 / width,
y2 / height,
x2 / width,
]
return np.array(results, dtype=np.float32)
@@ -200,9 +197,14 @@ def __post_process_nms_yolo(predictions: np.ndarray, width, height) -> np.ndarra
# Rescale box
boxes = predictions[:, :4]
boxes_xyxy = np.ones_like(boxes)
boxes_xyxy[:, 0] = boxes[:, 0] - boxes[:, 2] / 2
boxes_xyxy[:, 1] = boxes[:, 1] - boxes[:, 3] / 2
boxes_xyxy[:, 2] = boxes[:, 0] + boxes[:, 2] / 2
boxes_xyxy[:, 3] = boxes[:, 1] + boxes[:, 3] / 2
boxes = boxes_xyxy
input_shape = np.array([width, height, width, height])
boxes = np.divide(boxes, input_shape, dtype=np.float32)
# run NMS
indices = cv2.dnn.NMSBoxes(boxes, scores, score_threshold=0.4, nms_threshold=0.4)
detections = np.zeros((20, 6), np.float32)
for i, (bbox, confidence, class_id) in enumerate(
@@ -214,10 +216,10 @@ def __post_process_nms_yolo(predictions: np.ndarray, width, height) -> np.ndarra
detections[i] = [
class_id,
confidence,
bbox[1] - bbox[3] / 2,
bbox[0] - bbox[2] / 2,
bbox[1] + bbox[3] / 2,
bbox[0] + bbox[2] / 2,
bbox[1] / height,
bbox[0] / width,
bbox[3] / height,
bbox[2] / width,
]
return detections