ZT台式机 Tensorflow配置

1、安装Anaconda （最好不要安装在C盘）

安装参考：https://blog.csdn.net/weixin_50888378/article/details/109022585

2、安装Protoc

①解压protoc-3.11.4-win64.zip

②配置环境，在桌面上选中“此电脑”，单击右键，在弹出菜单中选择 “属性”，电脑自动打开系统属性面板，在面板左侧菜单栏中选择 “高级系统设置”菜单选项，系统自动打开系统属性配置对话框，点击下面的“配置环境变量(N)...”按钮，在系统变量面板下点击“新建(W)...”按钮，

变量名：Protoc

变量值：E:\Program Files (x86)\protoc-3.11.4-win64

然后点击“确定”按钮。

在系统变量里面选中变量名为 Path 的选项，双击，系统自动打开编辑环境变量面板，在最下面空白行点双击，输入：%Protoc%\bin，然后点击“确定”按钮。

依次点击确定按钮关闭刚才打开的窗口，Protoc环境变量配置完毕。

解压目录下有

bin

include

readme.txt

上面两步配置完毕以后，在操作系统开始菜单中打开： Anaconda Prompt

(base) C:\Users\zzt>
(base) C:\Users\zzt>
(base) C:\Users\zzt>
(base) C:\Users\zzt>
(base) C:\Users\zzt>E:

(base) E:\>
(base) E:\>
(base) E:\>
(base) E:\>
(base) E:\>
(base) E:\>
(base) E:\>
(base) E:\>cd Anaconda3

(base) E:\Anaconda3>
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(base) E:\Anaconda3>
(base) E:\Anaconda3>
(base) E:\Anaconda3>
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(base) E:\Anaconda3>
(base) E:\Anaconda3>
(base) E:\Anaconda3>
(base) E:\Anaconda3>conda create -n tf_2021 python=3.7
Solving environment: done


==> WARNING: A newer version of conda exists. <==
  current version: 4.5.11
  latest version: 4.10.1

Please update conda by running

    $ conda update -n base -c defaults conda



## Package Plan ##

  environment location: C:\Users\zzt\AppData\Local\conda\conda\envs\tf_2021

  added / updated specs:
    - python=3.7


The following packages will be downloaded:

    package                    |            build
    ---------------------------|-----------------
    certifi-2020.12.5          |   py37haa95532_0         144 KB
    openssl-1.1.1k             |       h2bbff1b_0         5.7 MB
    python-3.7.10              |       h6244533_0        17.4 MB
    vc-14.2                    |       h21ff451_1           8 KB
    setuptools-52.0.0          |   py37haa95532_0         936 KB
    wheel-0.36.2               |     pyhd3eb1b0_0          31 KB
    vs2015_runtime-14.27.29016 |       h5e58377_2         2.2 MB
    ca-certificates-2021.4.13  |       haa95532_1         150 KB
    sqlite-3.35.4              |       h2bbff1b_0         1.2 MB
    pip-21.0.1                 |   py37haa95532_0         2.0 MB
    ------------------------------------------------------------
                                           Total:        29.8 MB

The following NEW packages will be INSTALLED:

    ca-certificates: 2021.4.13-haa95532_1
    certifi:         2020.12.5-py37haa95532_0
    openssl:         1.1.1k-h2bbff1b_0
    pip:             21.0.1-py37haa95532_0
    python:          3.7.10-h6244533_0
    setuptools:      52.0.0-py37haa95532_0
    sqlite:          3.35.4-h2bbff1b_0
    vc:              14.2-h21ff451_1
    vs2015_runtime:  14.27.29016-h5e58377_2
    wheel:           0.36.2-pyhd3eb1b0_0
    wincertstore:    0.2-py37_0

Proceed ([y]/n)? y


Downloading and Extracting Packages
certifi-2020.12.5    | 144 KB    | ############################################################################ | 100%
openssl-1.1.1k       | 5.7 MB    | ############################################################################ | 100%
python-3.7.10        | 17.4 MB   | ############################################################################ | 100%
vc-14.2              | 8 KB      | ############################################################################ | 100%
setuptools-52.0.0    | 936 KB    | ############################################################################ | 100%
wheel-0.36.2         | 31 KB     | ############################################################################ | 100%
vs2015_runtime-14.27 | 2.2 MB    | ############################################################################ | 100%
ca-certificates-2021 | 150 KB    | ############################################################################ | 100%
sqlite-3.35.4        | 1.2 MB    | ############################################################################ | 100%
pip-21.0.1           | 2.0 MB    | ############################################################################ | 100%
Preparing transaction: done
Verifying transaction: done
Executing transaction: done
#
# To activate this environment, use
#
#     $ conda activate tf_2021
#
# To deactivate an active environment, use
#
#     $ conda deactivate


(base) E:\Anaconda3>
(base) E:\Anaconda3>
(base) E:\Anaconda3>
(base) E:\Anaconda3>
(base) E:\Anaconda3>
(base) E:\Anaconda3>

pip install tensorflow

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(base) E:\Anaconda3>conda activate tf_2021

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(tf_2021) E:\Anaconda3>pip install tensorflow==1.14.0
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Installing collected packages: zipp, typing-extensions, six, numpy, importlib-metadata, cached-property, werkzeug, protobuf, markdown, h5py, grpcio, absl-py, wrapt, termcolor, tensorflow-estimator, tensorboard, keras-preprocessing, keras-applications, google-pasta, gast, astor, tensorflow
Successfully installed absl-py-0.12.0 astor-0.8.1 cached-property-1.5.2 gast-0.4.0 google-pasta-0.2.0 grpcio-1.37.0 h5py-3.2.1 importlib-metadata-3.10.1 keras-applications-1.0.8 keras-preprocessing-1.1.2 markdown-3.3.4 numpy-1.20.2 protobuf-3.15.8 six-1.15.0 tensorboard-1.14.0 tensorflow-1.14.0 tensorflow-estimator-1.14.0 termcolor-1.1.0 typing-extensions-3.7.4.3 werkzeug-1.0.1 wrapt-1.12.1 zipp-3.4.1

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(tf_2021) E:\Anaconda3>pip install protobuf-compiler
Collecting protobuf-compiler
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(tf_2021) E:\Anaconda3>pip install Cython
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(tf_2021) E:\Anaconda3>pip install contextlib2
Collecting contextlib2
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Building wheels for collected packages: pyrsistent, pandocfilters
  Building wheel for pyrsistent (setup.py) ... done
  Created wheel for pyrsistent: filename=pyrsistent-0.17.3-cp37-cp37m-win_amd64.whl size=55871 sha256=ca43d3b92456d91bd189bad6e48f27eef7908cee0572d03154a591933a4d9702
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  Building wheel for pandocfilters (setup.py) ... done
  Created wheel for pandocfilters: filename=pandocfilters-1.4.3-py3-none-any.whl size=7992 sha256=9033ec8aa079b66ed1671e688b32a7d50ca91af50deb2c21ac27d3b170592227
  Stored in directory: c:\users\zzt\appdata\local\pip\cache\wheels\42\81\34\545dc2fbf0e9137811e901108d37fc04650e81d48f97078000
Successfully built pyrsistent pandocfilters
Installing collected packages: ipython-genutils, traitlets, pywin32, pyrsistent, attrs, wcwidth, tornado, pyzmq, python-dateutil, parso, jupyter-core, jsonschema, pygments, pycparser, prompt-toolkit, pickleshare, nest-asyncio, nbformat, MarkupSafe, jupyter-client, jedi, decorator, backcall, async-generator, testpath, pywinpty, pandocfilters, nbclient, mistune, jupyterlab-pygments, jinja2, ipython, entrypoints, defusedxml, cffi, terminado, Send2Trash, prometheus-client, nbconvert, ipykernel, argon2-cffi, notebook, widgetsnbextension, qtpy, jupyterlab-widgets, qtconsole, jupyter-console, ipywidgets, jupyter
Successfully installed MarkupSafe-1.1.1 Send2Trash-1.5.0 argon2-cffi-20.1.0 async-generator-1.10 attrs-20.3.0 backcall-0.2.0 cffi-1.14.5 decorator-5.0.7 defusedxml-0.7.1 entrypoints-0.3 ipykernel-5.5.3 ipython-7.22.0 ipython-genutils-0.2.0 ipywidgets-7.6.3 jedi-0.18.0 jinja2-2.11.3 jsonschema-3.2.0 jupyter-1.0.0 jupyter-client-6.1.12 jupyter-console-6.4.0 jupyter-core-4.7.1 jupyterlab-pygments-0.1.2 jupyterlab-widgets-1.0.0 mistune-0.8.4 nbclient-0.5.3 nbconvert-6.0.7 nbformat-5.1.3 nest-asyncio-1.5.1 notebook-6.3.0 pandocfilters-1.4.3 parso-0.8.2 pickleshare-0.7.5 prometheus-client-0.10.1 prompt-toolkit-3.0.18 pycparser-2.20 pygments-2.8.1 pyrsistent-0.17.3 python-dateutil-2.8.1 pywin32-300 pywinpty-0.5.7 pyzmq-22.0.3 qtconsole-5.0.3 qtpy-1.9.0 terminado-0.9.4 testpath-0.4.4 tornado-6.1 traitlets-5.0.5 wcwidth-0.2.5 widgetsnbextension-3.5.1

(tf_2021) E:\Anaconda3>
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(tf_2021) E:\Anaconda3>
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(tf_2021) E:\Anaconda3>
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(tf_2021) E:\Anaconda3>

(tf_2021) E:\Anaconda3>
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(tf_2021) E:\Anaconda3>
(tf_2021) E:\Anaconda3>
(tf_2021) E:\Anaconda3>
(tf_2021) E:\Anaconda3>
(tf_2021) E:\Anaconda3>pip install matplotlib
Collecting matplotlib
  Downloading matplotlib-3.4.1-cp37-cp37m-win_amd64.whl (7.1 MB)
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Requirement already satisfied: pillow>=6.2.0 in c:\users\zzt\appdata\local\conda\conda\envs\tf_2021\lib\site-packages (from matplotlib) (8.2.0)
Collecting pyparsing>=2.2.1
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Requirement already satisfied: python-dateutil>=2.7 in c:\users\zzt\appdata\local\conda\conda\envs\tf_2021\lib\site-packages (from matplotlib) (2.8.1)
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Requirement already satisfied: numpy>=1.16 in c:\users\zzt\appdata\local\conda\conda\envs\tf_2021\lib\site-packages (from matplotlib) (1.20.2)
Collecting kiwisolver>=1.0.1
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Requirement already satisfied: six in c:\users\zzt\appdata\local\conda\conda\envs\tf_2021\lib\site-packages (from cycler>=0.10->matplotlib) (1.15.0)
Installing collected packages: pyparsing, kiwisolver, cycler, matplotlib
Successfully installed cycler-0.10.0 kiwisolver-1.3.1 matplotlib-3.4.1 pyparsing-2.4.7

(tf_2021) E:\Anaconda3>
(tf_2021) E:\Anaconda3>
(tf_2021) E:\Anaconda3>
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(tf_2021) E:\Anaconda3>
(tf_2021) E:\Anaconda3>

(tf_2021) E:\Anaconda3\models-1.13.0\research>
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(tf_2021) E:\Anaconda3\models-1.13.0\research>
(tf_2021) E:\Anaconda3\models-1.13.0\research>pip install opencv-python
Collecting opencv-python
  Downloading opencv_python-4.5.1.48-cp37-cp37m-win_amd64.whl (34.9 MB)
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Installing collected packages: opencv-python
Successfully installed opencv-python-4.5.1.48

(tf_2021) E:\Anaconda3\models-1.13.0\research>
(tf_2021) E:\Anaconda3\models-1.13.0\research>
(tf_2021) E:\Anaconda3\models-1.13.0\research>

(tf_2021) E:\Anaconda3\models-1.13.0\research>
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(tf_2021) E:\Anaconda3\models-1.13.0\research>
(tf_2021) E:\Anaconda3\models-1.13.0\research>
(tf_2021) E:\Anaconda3\models-1.13.0\research>protoc object_detection/protos/*.proto python_out=.
Missing output directives.

(tf_2021) E:\Anaconda3\models-1.13.0\research>
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(tf_2021) E:\Anaconda3\models-1.13.0\research>
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(tf_2021) E:\Anaconda3\models-1.13.0\research>
(tf_2021) E:\Anaconda3\models-1.13.0\research>
(tf_2021) E:\Anaconda3\models-1.13.0\research>for /f %i in ('dir /b object_detection\protos\*.proto') do protoc object_detection\protos\%i --python_out=.

(tf_2021) E:\Anaconda3\models-1.13.0\research>protoc object_detection\protos\anchor_generator.proto --python_out=.

(tf_2021) E:\Anaconda3\models-1.13.0\research>protoc object_detection\protos\argmax_matcher.proto --python_out=.

(tf_2021) E:\Anaconda3\models-1.13.0\research>protoc object_detection\protos\bipartite_matcher.proto --python_out=.

(tf_2021) E:\Anaconda3\models-1.13.0\research>protoc object_detection\protos\box_coder.proto --python_out=.

(tf_2021) E:\Anaconda3\models-1.13.0\research>protoc object_detection\protos\box_predictor.proto --python_out=.

(tf_2021) E:\Anaconda3\models-1.13.0\research>protoc object_detection\protos\eval.proto --python_out=.

(tf_2021) E:\Anaconda3\models-1.13.0\research>protoc object_detection\protos\faster_rcnn.proto --python_out=.

(tf_2021) E:\Anaconda3\models-1.13.0\research>protoc object_detection\protos\faster_rcnn_box_coder.proto --python_out=.

(tf_2021) E:\Anaconda3\models-1.13.0\research>protoc object_detection\protos\graph_rewriter.proto --python_out=.

(tf_2021) E:\Anaconda3\models-1.13.0\research>protoc object_detection\protos\grid_anchor_generator.proto --python_out=.

(tf_2021) E:\Anaconda3\models-1.13.0\research>protoc object_detection\protos\hyperparams.proto --python_out=.

(tf_2021) E:\Anaconda3\models-1.13.0\research>protoc object_detection\protos\image_resizer.proto --python_out=.

(tf_2021) E:\Anaconda3\models-1.13.0\research>protoc object_detection\protos\input_reader.proto --python_out=.

(tf_2021) E:\Anaconda3\models-1.13.0\research>protoc object_detection\protos\keypoint_box_coder.proto --python_out=.

(tf_2021) E:\Anaconda3\models-1.13.0\research>protoc object_detection\protos\losses.proto --python_out=.

(tf_2021) E:\Anaconda3\models-1.13.0\research>protoc object_detection\protos\matcher.proto --python_out=.

(tf_2021) E:\Anaconda3\models-1.13.0\research>protoc object_detection\protos\mean_stddev_box_coder.proto --python_out=.

(tf_2021) E:\Anaconda3\models-1.13.0\research>protoc object_detection\protos\model.proto --python_out=.

(tf_2021) E:\Anaconda3\models-1.13.0\research>protoc object_detection\protos\multiscale_anchor_generator.proto --python_out=.

(tf_2021) E:\Anaconda3\models-1.13.0\research>protoc object_detection\protos\optimizer.proto --python_out=.

(tf_2021) E:\Anaconda3\models-1.13.0\research>protoc object_detection\protos\pipeline.proto --python_out=.

(tf_2021) E:\Anaconda3\models-1.13.0\research>protoc object_detection\protos\post_processing.proto --python_out=.

(tf_2021) E:\Anaconda3\models-1.13.0\research>protoc object_detection\protos\preprocessor.proto --python_out=.

(tf_2021) E:\Anaconda3\models-1.13.0\research>protoc object_detection\protos\region_similarity_calculator.proto --python_out=.

(tf_2021) E:\Anaconda3\models-1.13.0\research>protoc object_detection\protos\square_box_coder.proto --python_out=.

(tf_2021) E:\Anaconda3\models-1.13.0\research>protoc object_detection\protos\ssd.proto --python_out=.

(tf_2021) E:\Anaconda3\models-1.13.0\research>protoc object_detection\protos\ssd_anchor_generator.proto --python_out=.

(tf_2021) E:\Anaconda3\models-1.13.0\research>protoc object_detection\protos\string_int_label_map.proto --python_out=.

(tf_2021) E:\Anaconda3\models-1.13.0\research>protoc object_detection\protos\train.proto --python_out=.

(tf_2021) E:\Anaconda3\models-1.13.0\research>
(tf_2021) E:\Anaconda3\models-1.13.0\research>
(tf_2021) E:\Anaconda3\models-1.13.0\research>
(tf_2021) E:\Anaconda3\models-1.13.0\research>
(tf_2021) E:\Anaconda3\models-1.13.0\research>
(tf_2021) E:\Anaconda3\models-1.13.0\research>
(tf_2021) E:\Anaconda3\models-1.13.0\research>

(tf_2021) E:\Anaconda3\models-1.13.0\research>
(tf_2021) E:\Anaconda3\models-1.13.0\research>
(tf_2021) E:\Anaconda3\models-1.13.0\research>
(tf_2021) E:\Anaconda3\models-1.13.0\research>
(tf_2021) E:\Anaconda3\models-1.13.0\research>SET PYTHONPATH=%cd%;%cd%\slim

(tf_2021) E:\Anaconda3\models-1.13.0\research>
(tf_2021) E:\Anaconda3\models-1.13.0\research>
(tf_2021) E:\Anaconda3\models-1.13.0\research>
(tf_2021) E:\Anaconda3\models-1.13.0\research>

(tf_2021) E:\Anaconda3\models-1.13.0\research>
(tf_2021) E:\Anaconda3\models-1.13.0\research>
(tf_2021) E:\Anaconda3\models-1.13.0\research>
(tf_2021) E:\Anaconda3\models-1.13.0\research>python object_detection\builders\model_builder_test.py
C:\Users\zzt\AppData\Local\conda\conda\envs\tf_2021\lib\site-packages\tensorflow\python\framework\dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint8 = np.dtype([("qint8", np.int8, 1)])
C:\Users\zzt\AppData\Local\conda\conda\envs\tf_2021\lib\site-packages\tensorflow\python\framework\dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint8 = np.dtype([("quint8", np.uint8, 1)])
C:\Users\zzt\AppData\Local\conda\conda\envs\tf_2021\lib\site-packages\tensorflow\python\framework\dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint16 = np.dtype([("qint16", np.int16, 1)])
C:\Users\zzt\AppData\Local\conda\conda\envs\tf_2021\lib\site-packages\tensorflow\python\framework\dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint16 = np.dtype([("quint16", np.uint16, 1)])
C:\Users\zzt\AppData\Local\conda\conda\envs\tf_2021\lib\site-packages\tensorflow\python\framework\dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint32 = np.dtype([("qint32", np.int32, 1)])
C:\Users\zzt\AppData\Local\conda\conda\envs\tf_2021\lib\site-packages\tensorflow\python\framework\dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  np_resource = np.dtype([("resource", np.ubyte, 1)])
C:\Users\zzt\AppData\Local\conda\conda\envs\tf_2021\lib\site-packages\tensorboard\compat\tensorflow_stub\dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint8 = np.dtype([("qint8", np.int8, 1)])
C:\Users\zzt\AppData\Local\conda\conda\envs\tf_2021\lib\site-packages\tensorboard\compat\tensorflow_stub\dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint8 = np.dtype([("quint8", np.uint8, 1)])
C:\Users\zzt\AppData\Local\conda\conda\envs\tf_2021\lib\site-packages\tensorboard\compat\tensorflow_stub\dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint16 = np.dtype([("qint16", np.int16, 1)])
C:\Users\zzt\AppData\Local\conda\conda\envs\tf_2021\lib\site-packages\tensorboard\compat\tensorflow_stub\dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint16 = np.dtype([("quint16", np.uint16, 1)])
C:\Users\zzt\AppData\Local\conda\conda\envs\tf_2021\lib\site-packages\tensorboard\compat\tensorflow_stub\dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint32 = np.dtype([("qint32", np.int32, 1)])
C:\Users\zzt\AppData\Local\conda\conda\envs\tf_2021\lib\site-packages\tensorboard\compat\tensorflow_stub\dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  np_resource = np.dtype([("resource", np.ubyte, 1)])
WARNING:tensorflow:
The TensorFlow contrib module will not be included in TensorFlow 2.0.
For more information, please see:
  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md
  * https://github.com/tensorflow/addons
  * https://github.com/tensorflow/io (for I/O related ops)
If you depend on functionality not listed there, please file an issue.

WARNING:tensorflow:From E:\Anaconda3\models-1.13.0\research\slim\nets\inception_resnet_v2.py:373: The name tf.GraphKeys is deprecated. Please use tf.compat.v1.GraphKeys instead.

WARNING:tensorflow:From E:\Anaconda3\models-1.13.0\research\slim\nets\mobilenet\mobilenet.py:389: The name tf.nn.avg_pool is deprecated. Please use tf.nn.avg_pool2d instead.

Running tests under Python 3.7.10: C:\Users\zzt\AppData\Local\conda\conda\envs\tf_2021\python.exe
[ RUN      ] ModelBuilderTest.test_create_embedded_ssd_mobilenet_v1_model_from_config
C:\Users\zzt\AppData\Local\conda\conda\envs\tf_2021\lib\site-packages\tensorflow\python\framework\tensor_util.py:538: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.
  tensor_proto.tensor_content = nparray.tostring()
[       OK ] ModelBuilderTest.test_create_embedded_ssd_mobilenet_v1_model_from_config
[ RUN      ] ModelBuilderTest.test_create_faster_rcnn_inception_resnet_v2_model_from_config
WARNING:tensorflow:From E:\Anaconda3\models-1.13.0\research\object_detection\anchor_generators\grid_anchor_generator.py:59: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
W0415 08:32:15.916748  1152 deprecation.py:323] From E:\Anaconda3\models-1.13.0\research\object_detection\anchor_generators\grid_anchor_generator.py:59: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
[       OK ] ModelBuilderTest.test_create_faster_rcnn_inception_resnet_v2_model_from_config
[ RUN      ] ModelBuilderTest.test_create_faster_rcnn_inception_v2_model_from_config
[       OK ] ModelBuilderTest.test_create_faster_rcnn_inception_v2_model_from_config
[ RUN      ] ModelBuilderTest.test_create_faster_rcnn_model_from_config_with_example_miner
[       OK ] ModelBuilderTest.test_create_faster_rcnn_model_from_config_with_example_miner
[ RUN      ] ModelBuilderTest.test_create_faster_rcnn_nas_model_from_config
[       OK ] ModelBuilderTest.test_create_faster_rcnn_nas_model_from_config
[ RUN      ] ModelBuilderTest.test_create_faster_rcnn_pnas_model_from_config
[       OK ] ModelBuilderTest.test_create_faster_rcnn_pnas_model_from_config
[ RUN      ] ModelBuilderTest.test_create_faster_rcnn_resnet101_with_mask_prediction_enabled0 (use_matmul_crop_and_resize=False)
[       OK ] ModelBuilderTest.test_create_faster_rcnn_resnet101_with_mask_prediction_enabled0 (use_matmul_crop_and_resize=False)
[ RUN      ] ModelBuilderTest.test_create_faster_rcnn_resnet101_with_mask_prediction_enabled1 (use_matmul_crop_and_resize=True)
[       OK ] ModelBuilderTest.test_create_faster_rcnn_resnet101_with_mask_prediction_enabled1 (use_matmul_crop_and_resize=True)
[ RUN      ] ModelBuilderTest.test_create_faster_rcnn_resnet_v1_models_from_config
[       OK ] ModelBuilderTest.test_create_faster_rcnn_resnet_v1_models_from_config
[ RUN      ] ModelBuilderTest.test_create_rfcn_resnet_v1_model_from_config
[       OK ] ModelBuilderTest.test_create_rfcn_resnet_v1_model_from_config
[ RUN      ] ModelBuilderTest.test_create_ssd_inception_v2_model_from_config
[       OK ] ModelBuilderTest.test_create_ssd_inception_v2_model_from_config
[ RUN      ] ModelBuilderTest.test_create_ssd_inception_v3_model_from_config
[       OK ] ModelBuilderTest.test_create_ssd_inception_v3_model_from_config
[ RUN      ] ModelBuilderTest.test_create_ssd_mobilenet_v1_fpn_model_from_config
[       OK ] ModelBuilderTest.test_create_ssd_mobilenet_v1_fpn_model_from_config
[ RUN      ] ModelBuilderTest.test_create_ssd_mobilenet_v1_model_from_config
[       OK ] ModelBuilderTest.test_create_ssd_mobilenet_v1_model_from_config
[ RUN      ] ModelBuilderTest.test_create_ssd_mobilenet_v1_ppn_model_from_config
[       OK ] ModelBuilderTest.test_create_ssd_mobilenet_v1_ppn_model_from_config
[ RUN      ] ModelBuilderTest.test_create_ssd_mobilenet_v2_fpn_model_from_config
[       OK ] ModelBuilderTest.test_create_ssd_mobilenet_v2_fpn_model_from_config
[ RUN      ] ModelBuilderTest.test_create_ssd_mobilenet_v2_fpnlite_model_from_config
[       OK ] ModelBuilderTest.test_create_ssd_mobilenet_v2_fpnlite_model_from_config
[ RUN      ] ModelBuilderTest.test_create_ssd_mobilenet_v2_keras_model_from_config
[       OK ] ModelBuilderTest.test_create_ssd_mobilenet_v2_keras_model_from_config
[ RUN      ] ModelBuilderTest.test_create_ssd_mobilenet_v2_model_from_config
[       OK ] ModelBuilderTest.test_create_ssd_mobilenet_v2_model_from_config
[ RUN      ] ModelBuilderTest.test_create_ssd_resnet_v1_fpn_model_from_config
[       OK ] ModelBuilderTest.test_create_ssd_resnet_v1_fpn_model_from_config
[ RUN      ] ModelBuilderTest.test_create_ssd_resnet_v1_ppn_model_from_config
[       OK ] ModelBuilderTest.test_create_ssd_resnet_v1_ppn_model_from_config
[ RUN      ] ModelBuilderTest.test_session
[  SKIPPED ] ModelBuilderTest.test_session
----------------------------------------------------------------------
Ran 22 tests in 0.078s

OK (skipped=1)

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#!/usr/bin/env python
# coding: utf-8

# # Object Detection Demo
# Welcome to the object detection inference walkthrough!  This notebook will walk you step by step through the process of using a pre-trained model to detect objects in an image. Make sure to follow the [installation instructions](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md) before you start.

# # Imports

# In[ ]:


import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile

import cv2

from 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_ops

if StrictVersion(tf.__version__) < StrictVersion('1.9.0'):
  raise ImportError('Please upgrade your TensorFlow installation to v1.9.* or later!')


# ## Env setup

# In[ ]:


# This is needed to display the images.
#get_ipython().run_line_magic('matplotlib', 'inline')


# ## Object detection imports
# Here are the imports from the object detection module.

# In[ ]:


from utils import label_map_util

from utils import visualization_utils as vis_util


# # Model preparation 

# ## Variables
# 
# Any model exported using the `export_inference_graph.py` tool can be loaded here simply by changing `PATH_TO_FROZEN_GRAPH` to point to a new .pb file.  
# 
# By default we use an "SSD with Mobilenet" model here. See the [detection model zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md) for a list of other models that can be run out-of-the-box with varying speeds and accuracies.

# In[ ]:


# What model 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 to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_FROZEN_GRAPH = MODEL_NAME + '/frozen_inference_graph.pb'

# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt')


# ## Download Model

# In[ ]:


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())


# ## Load a (frozen) Tensorflow model into memory.

# In[ ]:


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='')


# ## Loading label map
# Label maps map indices to category names, so that when our convolution network predicts `5`, we know that this corresponds to `airplane`.  Here we use internal utility functions, but anything that returns a dictionary mapping integers to appropriate string labels would be fine

# In[ ]:


category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)


# ## Helper code

# In[ ]:


def load_image_into_numpy_array(image):
  (im_width, im_height) = image.size
  return np.array(image.getdata()).reshape(
      (im_height, im_width, 3)).astype(np.uint8)


# # Detection

# In[ ]:


# For the sake of simplicity we will use only 2 images:
# image1.jpg
# image2.jpg
# If you want to test the code with your images, just add path to the images to the TEST_IMAGE_PATHS.
PATH_TO_TEST_IMAGES_DIR = 'test_images'
PATH_TO_OUT_TEST_IMAGES_DIR = 'test_images_out'
TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(1, 3) ]

# Size, in inches, of the output images.
IMAGE_SIZE = (12, 8)


# In[ ]:


def run_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 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 dimension
        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: np.expand_dims(image, 0)})

      # all outputs are float32 numpy arrays, so convert types as appropriate
      output_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


# In[ ]:


for image_path in TEST_IMAGE_PATHS:
  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)
  # 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)
  #plt.figure(figsize=IMAGE_SIZE)



  #plt.imshow(image_np)
  #OUT_TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_OUT_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(1, 3) ]
  image_path_out = image_path.replace("test_images","test_images_out")
  print(image_path_out)
  cv2.imwrite(image_path_out,image_np)


# In[ ]:

安装 labelme

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(wind_202104) F:\TensorflowProject\maks_rcnn_2018>pip install labelme
Collecting labelme
  Downloading labelme-4.5.7.tar.gz (1.5 MB)
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Requirement already satisfied: numpy in e:\anaconda3\install\envs\wind_202104\lib\site-packages (from labelme) (1.20.2)
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Requirement already satisfied: qtpy in c:\users\bim\appdata\roaming\python\python37\site-packages (from labelme) (1.9.0)
Requirement already satisfied: termcolor in e:\anaconda3\install\envs\wind_202104\lib\site-packages (from labelme) (1.1.0)
Collecting PyQt5
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Collecting PyQt5-Qt5>=5.15
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Collecting PyQt5-sip<13,>=12.8
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Building wheels for collected packages: labelme, imgviz
  Building wheel for labelme (setup.py) ... done
  Created wheel for labelme: filename=labelme-4.5.7-py3-none-any.whl size=1464688 sha256=60187add8acd7a5d1ccf80309ee30594778e0ba32dcdd826d9f5b7c5f2108fcd
  Stored in directory: c:\users\bim\appdata\local\pip\cache\wheels\20\74\58\c8c6dacbe2504c3cf738ca1d4587fdb4885792548d4f7b1eba
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  Stored in directory: c:\users\bim\appdata\local\pip\cache\wheels\e6\64\f9\a28eca2133ece5f072f51282577f2f9b7d6d0492eb3d2104dd
Successfully built labelme imgviz
Installing collected packages: PyQt5-sip, PyQt5-Qt5, matplotlib, PyQt5, imgviz, labelme
  Attempting uninstall: matplotlib
    Found existing installation: matplotlib 3.4.1
    Uninstalling matplotlib-3.4.1:
      Successfully uninstalled matplotlib-3.4.1
Successfully installed PyQt5-5.15.4 PyQt5-Qt5-5.15.2 PyQt5-sip-12.8.1 imgviz-1.2.6 labelme-4.5.7 matplotlib-3.2.2

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https://blog.csdn.net/zhangzc12409/article/details/90512044

https://github.com/tensorflow/models/blob/v1.13.0/research/object_detection/g3doc/installation.md

############################

posted @ 2021-04-15 07:47 西北逍遥阅读(232) 评论(0) 编辑收藏举报

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