深度学习目标检测python代码
时间: 2023-07-31 21:12:56 浏览: 41
以下是使用Python和TensorFlow实现目标检测的示例代码:
1.导入必要的库和模块
```
import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile
from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image
```
2.添加TensorFlow模型库到系统路径
```
sys.path.append("..")
```
3.从TensorFlow模型库中导入目标检测API
```
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util
```
4.下载和解压缩预训练模型
```
MODEL_NAME = 'ssd_mobilenet_v1_coco_11_06_2017'
MODEL_FILE = MODEL_NAME + '.tar.gz'
DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'
PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'
opener = urllib.request.URLopener()
opener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE)
tar_file = tarfile.open(MODEL_FILE)
for file in tar_file.getmembers():
file_name = os.path.basename(file.name)
if 'frozen_inference_graph.pb' in file_name:
tar_file.extract(file, os.getcwd())
```
5.加载标签图和类别映射
```
PATH_TO_LABELS = os.path.join('object_detection', 'data', 'mscoco_label_map.pbtxt')
NUM_CLASSES = 90
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)
```
6.加载预训练模型
```
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='')
```
7.创建会话并运行目标检测
```
def run_inference_for_single_image(image, graph):
with graph.as_default():
with tf.Session() as sess:
# 输入和输出张量的名称
image_tensor = graph.get_tensor_by_name('image_tensor:0')
detection_boxes = graph.get_tensor_by_name('detection_boxes:0')
detection_scores = graph.get_tensor_by_name('detection_scores:0')
detection_classes = graph.get_tensor_by_name('detection_classes:0')
num_detections = graph.get_tensor_by_name('num_detections:0')
# 执行目标检测
(boxes, scores, classes, num) = sess.run(
[detection_boxes, detection_scores, detection_classes, num_detections],
feed_dict={image_tensor: np.expand_dims(image, 0)})
# 过滤掉分数低于阈值的目标
boxes = np.squeeze(boxes)
scores = np.squeeze(scores)
classes = np.squeeze(classes).astype(np.int32)
indices = np.where(scores > 0.5)[0]
boxes = boxes[indices]
scores = scores[indices]
classes = classes[indices]
# 返回检测结果
return boxes, scores, classes
# 加载测试图片
PATH_TO_TEST_IMAGE = 'test.jpg'
image = Image.open(PATH_TO_TEST_IMAGE)
image_np = np.array(image)
# 运行目标检测
boxes, scores, classes = run_inference_for_single_image(image_np, detection_graph)
# 可视化检测结果
vis_util.visualize_boxes_and_labels_on_image_array(
image_np,
boxes,
classes,
scores,
category_index,
use_normalized_coordinates=True,
line_thickness=8)
plt.figure(figsize=(12,8))
plt.imshow(image_np)
plt.show()
```
注意:上述代码中的“PATH_TO_TEST_IMAGE”需要替换为您的测试图像的路径。此外,还需要根据您的模型更改“MODEL_NAME”和“MODEL_FILE”。