python目标检测代码示例

当然，下面是一个简单的 Python 目标检测代码示例，使用了 OpenCV 和 TensorFlow 的目标检测 API：

import cv2
import numpy as np
import tensorflow as tf

# 加载模型
model_path = 'path_to_model/frozen_inference_graph.pb'
detection_graph = tf.Graph()
with detection_graph.as_default():
    od_graph_def = tf.GraphDef()
    with tf.gfile.GFile(model_path, 'rb') as fid:
        serialized_graph = fid.read()
        od_graph_def.ParseFromString(serialized_graph)
        tf.import_graph_def(od_graph_def, name='')

# 加载标签
label_map = 'path_to_labels/label_map.pbtxt'
categories = []
with open(label_map, 'r') as f:
    lines = f.readlines()
    for line in lines:
        if 'name' in line:
            category = line.split(':')[-1].strip().replace("'", "")
            categories.append(category)

# 进行目标检测
def detect_objects(image):
    with detection_graph.as_default():
        with tf.Session(graph=detection_graph) as sess:
            image_np_expanded = np.expand_dims(image, axis=0)
            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})

            # 显示检测结果
            for i in range(int(num_detections[0])):
                if scores[0][i] > 0.5:
                    class_index = int(classes[0][i])
                    class_name = categories[class_index - 1]
                    ymin, xmin, ymax, xmax = boxes[0][i]
                    xmin = int(xmin * image.shape[1])
                    xmax = int(xmax * image.shape[1])
                    ymin = int(ymin * image.shape[0])
                    ymax = int(ymax * image.shape[0])

                    cv2.rectangle(image, (xmin, ymin), (xmax, ymax), (0, 255, 0), 2)
                    cv2.putText(image, class_name, (xmin, ymin - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)

            return image

# 加载图像
image_path = 'path_to_image/image.jpg'
image = cv2.imread(image_path)

# 进行目标检测并显示结果
output_image = detect_objects(image)
cv2.imshow("Output", output_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

这个示例代码使用了一个已经训练好的目标检测模型（frozen_inference_graph.pb）和对应的标签文件（label_map.pbtxt）。你需要将模型路径和标签路径替换为你自己的路径，并提供一张待检测的图像（image.jpg）。

注意：这个示例代码仅仅是一个简单的演示，如果你想要在实际项目中进行目标检测，可能需要更复杂的模型和数据预处理。