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