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Improve batched yolo NMS
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6b2246cf08
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@ -148,27 +148,17 @@ def __post_process_multipart_yolo(
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bw = ((dw * 2.0) ** 2) * anchor_w
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bw = ((dw * 2.0) ** 2) * anchor_w
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bh = ((dh * 2.0) ** 2) * anchor_h
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bh = ((dh * 2.0) ** 2) * anchor_h
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x1 = max(0, bx - bw / 2) / width
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x1 = max(0, bx - bw / 2)
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y1 = max(0, by - bh / 2) / height
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y1 = max(0, by - bh / 2)
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x2 = min(width, bx + bw / 2) / width
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x2 = min(width, bx + bw / 2)
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y2 = min(height, by + bh / 2) / height
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y2 = min(height, by + bh / 2)
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all_boxes.append([x1, y1, x2, y2])
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all_boxes.append([x1, y1, x2, y2])
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all_scores.append(conf)
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all_scores.append(conf)
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all_class_ids.append(class_id)
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all_class_ids.append(class_id)
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formatted_boxes = [
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[
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int(x1 * width),
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int(y1 * height),
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int((x2 - x1) * width),
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int((y2 - y1) * height),
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]
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for x1, y1, x2, y2 in all_boxes
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]
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indices = cv2.dnn.NMSBoxes(
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indices = cv2.dnn.NMSBoxes(
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bboxes=formatted_boxes,
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bboxes=all_boxes,
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scores=all_scores,
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scores=all_scores,
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score_threshold=0.4,
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score_threshold=0.4,
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nms_threshold=0.4,
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nms_threshold=0.4,
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@ -181,7 +171,14 @@ def __post_process_multipart_yolo(
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class_id = all_class_ids[idx]
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class_id = all_class_ids[idx]
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conf = all_scores[idx]
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conf = all_scores[idx]
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x1, y1, x2, y2 = all_boxes[idx]
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x1, y1, x2, y2 = all_boxes[idx]
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results[i] = [class_id, conf, y1, x1, y2, x2]
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results[i] = [
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class_id,
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conf,
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y1 / height,
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x1 / width,
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y2 / height,
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x2 / width,
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]
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return np.array(results, dtype=np.float32)
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return np.array(results, dtype=np.float32)
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