目标检测distancing函数

以下是一个基于TensorFlow Object Detection API的目标检测函数，可以用来检测人体并计算出人与人之间的距离，从而实现社交距离监测。

import cv2
import numpy as np
import tensorflow as tf
from object_detection.utils import visualization_utils as vis_util

# 加载模型
detection_graph = tf.Graph()
with detection_graph.as_default():
    od_graph_def = tf.compat.v1.GraphDef()
    with tf.compat.v2.io.gfile.GFile('path/to/frozen_inference_graph.pb', 'rb') as f:
        serialized_graph = f.read()
        od_graph_def.ParseFromString(serialized_graph)
        tf.import_graph_def(od_graph_def, name='')

# 设定输入和输出节点
input_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')

# 设定阈值
score_thresh = 0.5

# 设定目标类别编号（这里为人）
target_class = 1

# 设定相机参数（需要根据实际情况调整）
focal_length = 1000
sensor_width = 6.3
sensor_height = 4.7

def calculate_distance(box1, box2):
    # 计算两个框中心点之间的距离
    x1, y1 = (box1[0]+box1[2])/2, (box1[1]+box1[3])/2
    x2, y2 = (box2[0]+box2[2])/2, (box2[1]+box2[3])/2
    pixel_distance = np.sqrt((x1-x2)**2 + (y1-y2)**2)

    # 计算实际距离
    object_width = 0.4  # 人的平均肩宽
    pixel_width = max(box1[2]-box1[0], box2[2]-box2[0])
    distance = focal_length * object_width * sensor_width / (pixel_width * sensor_height)

    return distance, pixel_distance

def detect_distancing(frame):
    with detection_graph.as_default():
        with tf.compat.v1.Session(graph=detection_graph) as sess:
            # 预处理
            frame_expanded = np.expand_dims(frame, axis=0)

            # 目标检测
            (boxes, scores, classes, num) = sess.run(
                [detection_boxes, detection_scores, detection_classes, num_detections],
                feed_dict={input_tensor: frame_expanded})

            # 可视化
            vis_util.visualize_boxes_and_labels_on_image_array(
                frame,
                np.squeeze(boxes),
                np.squeeze(classes).astype(np.int32),
                np.squeeze(scores),
                {target_class: {'name': 'person'}},
                use_normalized_coordinates=True,
                line_thickness=2,
                min_score_thresh=score_thresh)

            # 计算距离
            boxes = np.squeeze(boxes)
            scores = np.squeeze(scores)
            classes = np.squeeze(classes).astype(np.int32)
            distances = []
            for i in range(len(scores)):
                if scores[i] > score_thresh and classes[i] == target_class:
                    for j in range(i+1, len(scores)):
                        if scores[j] > score_thresh and classes[j] == target_class:
                            distance, pixel_distance = calculate_distance(boxes[i], boxes[j])
                            if pixel_distance > 50:  # 防止计算错误
                                distances.append(distance)

            return frame, distances

该函数将返回处理后的帧以及检测出的人与人之间的距离列表。注意，在使用该函数前，需要先将模型文件`frozen_inference_graph.pb`下载并放在指定的路径下，并且需要根据相机的实际情况调整相机参数。