Object_Detection_API之Inference

2024-02-12 04:32
文章标签 object detection api inference

本文主要是介绍Object_Detection_API之Inference,希望对大家解决编程问题提供一定的参考价值,需要的开发者们随着小编来一起学习吧!

导入包

import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfilefrom distutils.version import StrictVersion
from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image# This is needed since the notebook is stored in the object_detection folder.
sys.path.append("../../")
from object_detection.utils import ops as utils_opsif StrictVersion(tf.__version__) < StrictVersion('1.9.0'):raise ImportError('Please upgrade your TensorFlow installation to v1.9.* or later!')%matplotlib inline
from utils import label_map_util
from utils import visualization_utils as vis_util

设置模型目录

MODEL_DIR = './model_from_200000_steps'
PATH_TO_FROZEN_GRAPH = MODEL_DIR + '/frozen_inference_graph.pb'
PATH_TO_LABELS = os.path.join('./dataset', 'traffic_light_label_map.pbtxt')

恢复计算图

detection_graph = tf.Graph()
with detection_graph.as_default():od_graph_def=tf.GraphDef()with tf.gfile.GFile(PATH_TO_FROZEN_GRAPH, 'rb') as fid:serialized_graph = fid.read()od_graph_def.ParseFromString(serialized_graph)tf.import_graph_def(od_graph_def, name='')ops = tf.get_default_graph().get_operations()all_tensor_names = {output.name for op in ops for output in op.outputs}
#         print(all_tensor_names)print(len(all_tensor_names))
for name in all_tensor_names:
#     if name.startswith('SecondStage'):
#         print(name)if name.find('bottleneck')==-1:passelse:print(name)

读取labelmap

category_index=label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS)
print(category_index)
def load_image_into_numpy_array(image):(im_width, im_height) = image.sizereturn np.array(image.getdata()).reshape((im_height, im_width, 3)).astype(np.uint8)

定义推理函数

def run_inference_for_single_image(image, graph):with graph.as_default():with tf.Session() as sess:# Get handles to input and output tensorsops = tf.get_default_graph().get_operations()all_tensor_names = {output.name for op in ops for output in op.outputs}tensor_dict = {}for key in ['num_detections', 'detection_boxes', 'detection_scores','detection_classes', 'detection_masks']:tensor_name = key + ':0'if tensor_name in all_tensor_names:tensor_dict[key] = tf.get_default_graph().get_tensor_by_name(tensor_name)if 'detection_masks' in tensor_dict:# The following processing is only for single imagedetection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0])detection_masks = tf.squeeze(tensor_dict['detection_masks'], [0])# Reframe is required to translate mask from box coordinates to image coordinates and fit the image size.real_num_detection = tf.cast(tensor_dict['num_detections'][0], tf.int32)detection_boxes = tf.slice(detection_boxes, [0, 0], [real_num_detection, -1])detection_masks = tf.slice(detection_masks, [0, 0, 0], [real_num_detection, -1, -1])detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(detection_masks, detection_boxes, image.shape[0], image.shape[1])detection_masks_reframed = tf.cast(tf.greater(detection_masks_reframed, 0.5), tf.uint8)# Follow the convention by adding back the batch dimensiontensor_dict['detection_masks'] = tf.expand_dims(detection_masks_reframed, 0)image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')# Run inferenceoutput_dict = sess.run(tensor_dict,feed_dict={image_tensor: np.expand_dims(image, 0)})# all outputs are float32 numpy arrays, so convert types as appropriateoutput_dict['num_detections'] = int(output_dict['num_detections'][0])output_dict['detection_classes'] = output_dict['detection_classes'][0].astype(np.uint8)output_dict['detection_boxes'] = output_dict['detection_boxes'][0]output_dict['detection_scores'] = output_dict['detection_scores'][0]if 'detection_masks' in output_dict:output_dict['detection_masks'] = output_dict['detection_masks'][0]return output_dict

进行推理

import random
import cv2
import glob
PATH_TO_TEST_IMAGES_DIR = './images_for_test'
TEST_IMAGE_PATHS = glob.glob(os.path.join(PATH_TO_TEST_IMAGES_DIR, "*.jpg"))
print(len(TEST_IMAGE_PATHS))
IMAGE_SIZE=(12,8)

推理

count=0
saved_dir_for_predicted_images="./images_predicted"
for count in range(10):chx = random.randint(0, len(TEST_IMAGE_PATHS)-1)image_path = TEST_IMAGE_PATHS[chx]image = Image.open(image_path)# the array based representation of the image will be used later in order to prepare the# result image with boxes and labels on it.image_np = load_image_into_numpy_array(image)# Expand dimensions since the model expects images to have shape: [1, None, None, 3]image_np_expanded = np.expand_dims(image_np, axis=0)# Actual detection.output_dict = run_inference_for_single_image(image_np, detection_graph)# print(output_dict)# Visualization of the results of a detection.vis_util.visualize_boxes_and_labels_on_image_array(image_np,output_dict['detection_boxes'],output_dict['detection_classes'],output_dict['detection_scores'],category_index,instance_masks=output_dict.get('detection_masks'),use_normalized_coordinates=True,line_thickness=4)im = Image.fromarray(image_np.astype('uint8'))saved_name = os.path.basename(image_path).split('.')[0]im.save(os.path.join(saved_dir_for_predicted_images, saved_name+'.jpg'))
#   image_np = cv2.cvtColor(image_np, cv2.COLOR_RGB2BGR)
#   cv2.imshow('image',image_np)
#   cv2.waitKey(10)
#   cv2.destroyAllWindows()
#   if cv2.waitKey(1000)&0xff == 113:
#     cv2.destroyAllWindows()
#   plt.figure(figsize=IMAGE_SIZE)
#   plt.imshow(image_np)
#   plt.show()

这篇关于Object_Detection_API之Inference的文章就介绍到这儿,希望我们推荐的文章对编程师们有所帮助!



http://www.chinasem.cn/article/701677

相关文章

Knife4j+Axios+Redis前后端分离架构下的 API 管理与会话方案(最新推荐)

《Knife4j+Axios+Redis前后端分离架构下的API管理与会话方案(最新推荐)》本文主要介绍了Swagger与Knife4j的配置要点、前后端对接方法以及分布式Session实现原理,... 目录一、Swagger 与 Knife4j 的深度理解及配置要点Knife4j 配置关键要点1.Spri

HTML5 getUserMedia API网页录音实现指南示例小结

《HTML5getUserMediaAPI网页录音实现指南示例小结》本教程将指导你如何利用这一API,结合WebAudioAPI,实现网页录音功能,从获取音频流到处理和保存录音,整个过程将逐步... 目录1. html5 getUserMedia API简介1.1 API概念与历史1.2 功能与优势1.3

使用Python实现调用API获取图片存储到本地的方法

《使用Python实现调用API获取图片存储到本地的方法》开发一个自动化工具,用于从JSON数据源中提取图像ID,通过调用指定API获取未经压缩的原始图像文件,并确保下载结果与Postman等工具直接... 目录使用python实现调用API获取图片存储到本地1、项目概述2、核心功能3、环境准备4、代码实现

无法启动此程序因为计算机丢失api-ms-win-core-path-l1-1-0.dll修复方案

《无法启动此程序因为计算机丢失api-ms-win-core-path-l1-1-0.dll修复方案》:本文主要介绍了无法启动此程序,详细内容请阅读本文,希望能对你有所帮助... 在计算机使用过程中,我们经常会遇到一些错误提示,其中之一就是"api-ms-win-core-path-l1-1-0.dll丢失

python通过curl实现访问deepseek的API

《python通过curl实现访问deepseek的API》这篇文章主要为大家详细介绍了python如何通过curl实现访问deepseek的API,文中的示例代码讲解详细,感兴趣的小伙伴可以跟随小编... API申请和充值下面是deepeek的API网站https://platform.deepsee

Java对接Dify API接口的完整流程

《Java对接DifyAPI接口的完整流程》Dify是一款AI应用开发平台,提供多种自然语言处理能力,通过调用Dify开放API,开发者可以快速集成智能对话、文本生成等功能到自己的Java应用中,本... 目录Java对接Dify API接口完整指南一、Dify API简介二、准备工作三、基础对接实现1.

一文详解如何在Vue3中封装API请求

《一文详解如何在Vue3中封装API请求》在现代前端开发中,API请求是不可避免的一部分,尤其是与后端交互时,下面我们来看看如何在Vue3项目中封装API请求,让你在实现功能时更加高效吧... 目录为什么要封装API请求1. vue 3项目结构2. 安装axIOS3. 创建API封装模块4. 封装API请求

springboot项目中常用的工具类和api详解

《springboot项目中常用的工具类和api详解》在SpringBoot项目中,开发者通常会依赖一些工具类和API来简化开发、提高效率,以下是一些常用的工具类及其典型应用场景,涵盖Spring原生... 目录1. Spring Framework 自带工具类(1) StringUtils(2) Coll

基于Flask框架添加多个AI模型的API并进行交互

《基于Flask框架添加多个AI模型的API并进行交互》:本文主要介绍如何基于Flask框架开发AI模型API管理系统,允许用户添加、删除不同AI模型的API密钥,感兴趣的可以了解下... 目录1. 概述2. 后端代码说明2.1 依赖库导入2.2 应用初始化2.3 API 存储字典2.4 路由函数2.5 应

C#集成DeepSeek模型实现AI私有化的流程步骤(本地部署与API调用教程)

《C#集成DeepSeek模型实现AI私有化的流程步骤(本地部署与API调用教程)》本文主要介绍了C#集成DeepSeek模型实现AI私有化的方法,包括搭建基础环境,如安装Ollama和下载DeepS... 目录前言搭建基础环境1、安装 Ollama2、下载 DeepSeek R1 模型客户端 ChatBo