用python写安全帽检测的程序

要用Python编写安全帽检测程序，可以使用深度学习框架中的目标检测模型，例如YOLOv3、Faster R-CNN或SSD等。以下是一个使用TensorFlow Object Detection API实现安全帽检测的示例代码：

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

# 载入模型
PATH_TO_CKPT = 'path/to/frozen_inference_graph.pb'
PATH_TO_LABELS = 'path/to/label_map.pbtxt'
NUM_CLASSES = 1

detection_graph = tf.Graph()
with detection_graph.as_default():
    od_graph_def = tf.GraphDef()
    with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
        serialized_graph = fid.read()
        od_graph_def.ParseFromString(serialized_graph)
        tf.import_graph_def(od_graph_def, name='')

# 载入标签映射
label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)

# 初始化摄像头
cap = cv2.VideoCapture(0)

with detection_graph.as_default():
    with tf.Session(graph=detection_graph) as sess:
        while True:
            # 获取图像帧
            ret, image_np = cap.read()
            image_np_expanded = np.expand_dims(image_np, axis=0)

            # 获取检测结果
            image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
            boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
            scores = detection_graph.get_tensor_by_name('detection_scores:0')
            classes = detection_graph.get_tensor_by_name('detection_classes:0')
            num_detections = detection_graph.get_tensor_by_name('num_detections:0')
            (boxes, scores, classes, num_detections) = sess.run(
                [boxes, scores, classes, num_detections],
                feed_dict={image_tensor: image_np_expanded})

            # 可视化检测结果
            vis_util.visualize_boxes_and_labels_on_image_array(
                image_np,
                np.squeeze(boxes),
                np.squeeze(classes).astype(np.int32),
                np.squeeze(scores),
                category_index,
                use_normalized_coordinates=True,
                line_thickness=2)

            # 显示图像
            cv2.imshow('object detection', cv2.resize(image_np, (800, 600)))
            if cv2.waitKey(25) & 0xFF == ord('q'):
                cap.release()
                cv2.destroyAllWindows()
                break

在代码中，首先载入已经训练好的目标检测模型和标签映射。然后使用OpenCV库初始化摄像头，并在每个图像帧上运行检测模型，将检测结果可视化后显示在屏幕上。