yolov8关键点检测推理后处理

### YOLOv8 关键点检测模型推理后处理方法

对于YOLOv8关键点检测模型，在完成推理之后，需要对输出数据进行一系列的后处理来获取最终的关键点位置和其他相关信息。具体来说：

#### 解析ONNX模型输出
当使用ONNX格式的YOLOv8-Pose模型进行推理时，其输出通常是一个形状为`[1, 56, 8400]`的张量[^3]。这个张量包含了预测框的位置信息以及对应于人体姿态估计中的各个关键点坐标。

#### 过滤低置信度预测
为了去除那些不准确或不必要的预测结果，可以设定一个阈值来过滤掉低于该阈值的概率得分。这一步骤能够有效减少误报并提高后续分析的质量。

import numpy as np

def filter_predictions(preds, conf_threshold=0.25):
    """
    Filters out predictions with confidence scores below the given threshold.
    
    Args:
        preds (numpy.ndarray): Prediction tensor from ONNX model output.
        conf_threshold (float): Confidence score threshold to apply.

    Returns:
        filtered_preds (list of dict): List containing dictionaries for each detected object,
            where keys are 'bbox', 'keypoints' and values represent bounding box coordinates 
            along with keypoints positions respectively.
    """
    # Assuming `preds` has shape [1, 56, 8400], reshape it into manageable form
    reshaped_preds = preds.reshape(-1, 56)

    bboxes = []
    kpts = []

    for pred in reshaped_preds:
        bbox = pred[:4]
        obj_confidence = pred[4]

        if obj_confidence >= conf_threshold:
            keypoints = pred[5:].reshape((-1, 3))[:, :2].tolist()
            
            bboxes.append(bbox.tolist())
            kpts.extend(keypoints)

    return {'bboxes': bboxes, 'keypoints': kpts}

#### 非极大抑制(NMS)
即使经过了上述筛选过程，仍然可能存在多个重叠严重的边界框指向同一个物体实例的情况。因此有必要实施非极大抑制算法以保留最有可能的那个候选框而丢弃其他相似者。

from itertools import product


def nms(bounding_boxes, scores, iou_threshold=0.45):
    """Performs Non-Maximum Suppression on a set of bounding boxes."""
    indices_to_keep = cv2.dnn.NMSBoxes(
        bounding_boxes=[tuple(map(int, bb)) for bb in bounding_boxes],
        scores=scores,
        score_threshold=iou_threshold,
        nms_threshold=iou_threshold
    )

    kept_bbs = [
        bounding_boxes[idx] for idx in range(len(bounding_boxes))
        if any([idx == keep_idx for keep_idx in indices_to_keep])
    ]

    return kept_bbs

#### 可视化结果
最后一步就是将这些处理后的数据可视化出来以便直观理解所获得的结果。可以通过绘制矩形边框围绕着被识别到的对象，并在其附近标注出相应的关节部位来进行展示。

import matplotlib.pyplot as plt
import matplotlib.patches as patches


def visualize_results(image_path, detections):
    fig, ax = plt.subplots(1)
    img = plt.imread(image_path)
    ax.imshow(img)

    colors = ['r', 'g', 'b']

    for det in detections['detections']:
        rect = patches.Rectangle((det['box'][0], det['box'][1]),
                                 det['box'][2]-det['box'][0],
                                 det['box'][3]-det['box'][1],
                                 linewidth=2,
                                 edgecolor='w',
                                 facecolor="none")

        ax.add_patch(rect)

        for pt_id, point in enumerate(det['keypoints']):
            circ = patches.Circle((point[0], point[1]), radius=3, color=colors[pt_id % len(colors)])
            ax.add_patch(circ)

    plt.show()

通过以上几个步骤就可以实现YOLOv8关键点检测模型的完整推理后处理流程[^2]。