python编写边缘检测应用（用Python实现实时目标检测）

python编写边缘检测应用（用Python实现实时目标检测）执行pip install TensorFlow安装最新版本TensorFlow版本在1.15.0或以上目标检测 (ObjectDetection) 也是计算机视觉最酷的应用之一，这是不容置疑的事实。现在的CV工具能够轻松地将目标检测应用于图片甚至是直播视频。本文将简单地展示如何用TensorFlow创建实时目标检测器。设置要求：

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来源：Pexels

从自动驾驶汽车检测路上的物体，到通过复杂的面部及身体语言识别发现可能的犯罪活动。多年来，研究人员一直在探索让机器通过视觉识别物体的可能性。

这一特殊领域被称为计算机视觉 (Computer Vision CV)，在现代生活中有着广泛的应用。

目标检测 (ObjectDetection) 也是计算机视觉最酷的应用之一，这是不容置疑的事实。

现在的CV工具能够轻松地将目标检测应用于图片甚至是直播视频。本文将简单地展示如何用TensorFlow创建实时目标检测器。

建立一个简单的目标检测器

设置要求：

TensorFlow版本在1.15.0或以上

执行pip install TensorFlow安装最新版本

一切就绪，现在开始吧！

设置环境

第一步：从Github上下载或复制TensorFlow目标检测的代码到本地计算机

在终端运行如下命令：

git clonehttps://github.com/tensorflow/models.git

第二步：安装依赖项

下一步是确定计算机上配备了运行目标检测器所需的库和组件。

下面列举了本项目所依赖的库。（大部分依赖都是TensorFlow自带的）

· Cython

· contextlib2

· pillow

· lxml

· matplotlib

若有遗漏的组件，在运行环境中执行pip install即可。

第三步：安装Protobuf编译器

谷歌的Protobuf，又称Protocol buffers，是一种语言无关、平台无关、可扩展的序列化结构数据的机制。Protobuf帮助程序员定义数据结构，轻松地在各种数据流中使用各种语言进行编写和读取结构数据。

Protobuf也是本项目的依赖之一。点击这里了解更多关于Protobufs的知识。接下来把Protobuf安装到计算机上。

打开终端或者打开命令提示符，将地址改为复制的代码仓库，在终端执行如下命令：

cd models/research \

wget -Oprotobuf.zip https://github.com/protocolbuffers/protobuf/releases/download/v3.9.1/protoc-3.9.1-osx-x86_64.zip\

unzipprotobuf.zip

注意：请务必在models/research目录解压protobuf.zip文件。

来源：Pexels

第四步：编辑Protobuf编译器

从research/ directory目录中执行如下命令编辑Protobuf编译器：

./bin/protoc object_detection/protos/*.proto--Python_out=.用Python实现目标检测

现在所有的依赖项都已经安装完毕，可以用Python实现目标检测了。

在下载的代码仓库中，将目录更改为：

models/research/object_detection

这个目录下有一个叫object_detection_tutorial.ipynb的ipython notebook。该文件是演示目标检测算法的demo，在执行时会用到指定的模型：

ssd_mobilenet_v1_coco_2017_11_17

这一测试会识别代码库中提供的两张测试图片。下面是测试结果之一：

要检测直播视频中的目标还需要一些微调。在同一文件夹中新建一个Jupyter notebook，按照下面的代码操作：

[1]:

import numpy as np

import os

import six.moves.urllib as urllib

import sys

import tarfile

import tensorflow as tf

import zipfile

from distutils.version import StrictVersion

from collections import defaultdict

from io import StringIO

from matplotlib import pyplot as plt

from PIL import Image

# This isneeded since the notebook is stored in the object_detection folder.

sys.path.append("..")

from utils import ops as utils_ops

if StrictVersion(tf.__version__) < StrictVersion('1.12.0'):

raise ImportError('Please upgrade your TensorFlow installation to v1.12.*.')

[2]:

# This isneeded to display the images.

get_ipython().run_line_magic('matplotlib' 'inline')

[3]:

# Objectdetection imports

# Here arethe imports from the object detection module.

from utils import label_map_util

from utils import visualization_utils as vis_util

[4]:

# Modelpreparation

# Anymodel exported using the `export_inference_graph.py` tool can be loaded heresimply by changing `PATH_TO_FROZEN_GRAPH` to point to a new .pb file.

# Bydefault we use an "SSD with Mobilenet" model here.

#See https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md

#for alist of other models that can be run out-of-the-box with varying speeds andaccuracies.

# Whatmodel to download.

MODEL_NAME= 'ssd_mobilenet_v1_coco_2017_11_17'

MODEL_FILE= MODEL_NAME '.tar.gz'

DOWNLOAD_BASE= 'http://download.tensorflow.org/models/object_detection/'

# Path tofrozen detection graph. This is the actual model that is used for the objectdetection.

PATH_TO_FROZEN_GRAPH= MODEL_NAME '/frozen_inference_graph.pb'

# List ofthe strings that is used to add correct label for each box.

PATH_TO_LABELS= os.path.join('data' 'mscoco_label_map.pbtxt')

[5]:

#DownloadModel

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())

[6]:

# Load a(frozen) Tensorflow model into memory.

detection_graph= tf.Graph()

with detection_graph.as_default():

od_graph_def= tf.GraphDef()

withtf.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='')

[7]:

# Loadinglabel map

# Labelmaps map indices to category names so that when our convolution networkpredicts `5`

#we knowthat this corresponds to `airplane`. Here we use internal utilityfunctions

#butanything that returns a dictionary mapping integers to appropriate stringlabels would be fine

category_index= label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS use_display_name=True)

[8]:

defrun_inference_for_single_image(image graph):

with graph.as_default():

with tf.Session() as sess:

# Get handles to input and output tensors

ops= 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 image

detection_boxes= tf.squeeze(tensor_dict['detection_boxes'] [0])

detection_masks= tf.squeeze(tensor_dict['detection_masks'] [0])

# Reframe is required to translate mask from boxcoordinates 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[1] image.shape[2])

detection_masks_reframed= tf.cast(

tf.greater(detection_masks_reframed 0.5) tf.uint8)

# Follow the convention by adding back the batchdimension

tensor_dict['detection_masks'] =tf.expand_dims(

detection_masks_reframed 0)

image_tensor= tf.get_default_graph().get_tensor_by_name('image_tensor:0')

# Run inference

output_dict= sess.run(tensor_dict feed_dict={image_tensor: image})

# all outputs are float32 numpy arrays so convert typesas appropriate

output_dict['num_detections'] =int(output_dict['num_detections'][0])

output_dict['detection_classes'] =output_dict[

'detection_classes'][0].astype(np.int64)

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

[9]:

import cv2

cam =cv2.cv2.VideoCapture(0)

rolling = True

while (rolling):

ret image_np = cam.read()

image_np_expanded= np.expand_dims(image_np axis=0)

# Actual detection.

output_dict= run_inference_for_single_image(image_np_expanded detection_graph)

# 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=8)

cv2.imshow('image' cv2.resize(image_np (1000 800)))

if cv2.waitKey(25) & 0xFF == ord('q'):

break

cv2.destroyAllWindows()

cam.release()

在运行Jupyter notebook时，网络摄影系统会开启并检测所有原始模型训练过的物品类别。

感谢阅读本文，如果有什么建议，欢迎在留言区积极发言哟~

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