StegaStamp_train_model.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"StegaStamp_train_model.ipynb","provenance":[],"collapsed_sections":["rwGX0a55OFkx"],"toc_visible":true,"authorship_tag":"ABX9TyOWdf4gISaaHQN3CnkHDS96"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","metadata":{"id":"xSc4MUyYM1y3"},"source":["# StegaStamp"]},{"cell_type":"markdown","metadata":{"id":"Dbrzsxx0Oyrx"},"source":["> A good exploit is one that is delivered in style.  -- Saumil Shah\n","\n","Improved the original repo, <i>Invisible Hyperlinks in Physical Photographs</i> @tancik, which without datasets and training parameters.\n","\n","<br>\n","\n","<p align=\"center\">\n","  <img src=\"https://github.com/Charmve/StegaStamp-plus/raw/main/profile.png\" alt=\"Folders in Ubuntu\">\n","</p>\n","\n","<br>\n","\n","Made by [![Follow](https://img.shields.io/twitter/follow/psyth91?style=social)](https://www.zhihu.com/zvideo/1302252124579733504)  &nbsp;&nbsp;&nbsp;  <a href=\"https://github.com/Charmve\"><img src=\"https://img.shields.io/badge/👓-Charmve-blue\" alt=\"GitHub\"></a>\n","\n","To see how the model works, visit the project repository.\n","\n","[![GitHub stars](https://img.shields.io/github/stars/Charmve/StegaStamp-plus?style=social)](https://github.com/Charmve/StegaStamp-plus)"]},{"cell_type":"markdown","metadata":{"id":"rwGX0a55OFkx"},"source":["## mount google drive folder"]},{"cell_type":"code","metadata":{"id":"Be4GiY63M6ZX","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1618717984409,"user_tz":-480,"elapsed":22992,"user":{"displayName":"Charmve","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj9zT4vUzycNuqCVAvPYhbS-GWoSOslxHjcLtv4=s64","userId":"07530046818488914519"}},"outputId":"12c0d47b-ecf6-4956-df89-5a65f065b3b8"},"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"nCOfIEmEOW1P","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1618717986581,"user_tz":-480,"elapsed":824,"user":{"displayName":"Charmve","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj9zT4vUzycNuqCVAvPYhbS-GWoSOslxHjcLtv4=s64","userId":"07530046818488914519"}},"outputId":"f5fe8ee8-2a7a-4bf1-a487-5772dc3803ca"},"source":["%cd '/content/drive/MyDrive/StegaStamp'\n","%ls"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/content/drive/MyDrive/StegaStamp\n","\u001b[0m\u001b[01;34mcheckpoints\u001b[0m/      encode_image.py               README.md\n","\u001b[01;34mdatasets\u001b[0m/         encode_img.sh                 requirements.txt\n","decode_image.py   LICENSE                       \u001b[01;34msaved_models\u001b[0m/\n","decode_img.sh     \u001b[01;34mlogs\u001b[0m/                         \u001b[01;34mscripts\u001b[0m/\n","detector_cam.py   \u001b[01;34mlpips\u001b[0m/                        StegaStamp_train_model.ipynb\n","detector_cam.sh   models.py                     train.py\n","\u001b[01;34mdetector_models\u001b[0m/  ngrok                         utils.py\n","detector.py       ngrok-stable-linux-amd64.zip  video_cam.py\n","detector.sh       \u001b[01;34mout\u001b[0m/\n","\u001b[01;34mdocs\u001b[0m/             \u001b[01;34m__pycache__\u001b[0m/\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"C59p2sKLODc5"},"source":["## Installation"]},{"cell_type":"markdown","metadata":{"id":"gUPg8VSeQSeF"},"source":["- <b>[Skip]</b> Clone repo and install submodules (<b>SPECIAL</b> for Chamrve)\n"]},{"cell_type":"code","metadata":{"id":"YI3HCraEzWxL"},"source":["!git pull https://github.com/Charmve/StegaStamp-plus.git\n","%cd '/content/drive/MyDrive/StegaStamp'"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"rxsY6sp-Q3bw"},"source":["- Install tensorflow (tested with tf 1.13)"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JQ8Gkcj1PV04","executionInfo":{"status":"ok","timestamp":1618571902066,"user_tz":-480,"elapsed":3483,"user":{"displayName":"Charmve","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj9zT4vUzycNuqCVAvPYhbS-GWoSOslxHjcLtv4=s64","userId":"07530046818488914519"}},"outputId":"2d4bda09-3df9-4f11-8b0b-72734025efb8"},"source":["!pip show tensorflow"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Name: tensorflow\n","Version: 2.4.1\n","Summary: TensorFlow is an open source machine learning framework for everyone.\n","Home-page: https://www.tensorflow.org/\n","Author: Google Inc.\n","Author-email: packages@tensorflow.org\n","License: Apache 2.0\n","Location: /usr/local/lib/python3.7/dist-packages\n","Requires: h5py, tensorflow-estimator, flatbuffers, absl-py, opt-einsum, wrapt, protobuf, google-pasta, keras-preprocessing, numpy, six, astunparse, termcolor, typing-extensions, wheel, gast, tensorboard, grpcio\n","Required-by: fancyimpute\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Vna6pvyTPldI","executionInfo":{"status":"ok","timestamp":1618718042986,"user_tz":-480,"elapsed":50044,"user":{"displayName":"Charmve","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj9zT4vUzycNuqCVAvPYhbS-GWoSOslxHjcLtv4=s64","userId":"07530046818488914519"}},"outputId":"2b3e5ad8-213b-4e35-ee3b-1736249f2e7c"},"source":["!pip install tensorflow==1.13.1"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Collecting tensorflow==1.13.1\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d4/29/6b4f1e02417c3a1ccc85380f093556ffd0b35dc354078074c5195c8447f2/tensorflow-1.13.1-cp37-cp37m-manylinux1_x86_64.whl (92.6MB)\n","\u001b[K     |████████████████████████████████| 92.6MB 61kB/s \n","\u001b[?25hCollecting tensorboard<1.14.0,>=1.13.0\n","\u001b[?25l  Downloading 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python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard<1.14.0,>=1.13.0->tensorflow==1.13.1) (3.10.1)\n","Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard<1.14.0,>=1.13.0->tensorflow==1.13.1) (3.4.1)\n","Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard<1.14.0,>=1.13.0->tensorflow==1.13.1) (3.7.4.3)\n","Installing collected packages: tensorboard, keras-applications, mock, tensorflow-estimator, tensorflow\n","  Found existing installation: tensorboard 2.4.1\n","    Uninstalling tensorboard-2.4.1:\n","      Successfully uninstalled tensorboard-2.4.1\n","  Found existing installation: tensorflow-estimator 2.4.0\n","    Uninstalling tensorflow-estimator-2.4.0:\n","      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Downloading https://files.pythonhosted.org/packages/a5/2b/bd5839080debb778c6217df20eec0f7d27d61c4e86fc2b5d93923d6b67ae/bchlib-0.14.0-cp37-cp37m-manylinux1_x86_64.whl (55kB)\n","\r\u001b[K     |█████▉                          | 10kB 14.5MB/s eta 0:00:01\r\u001b[K     |███████████▊                    | 20kB 12.6MB/s eta 0:00:01\r\u001b[K     |█████████████████▋              | 30kB 9.5MB/s eta 0:00:01\r\u001b[K     |███████████████████████▌        | 40kB 7.0MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▎  | 51kB 5.1MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 61kB 3.1MB/s \n","\u001b[?25hRequirement already satisfied: opencv-python>=3.4.3.18 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 2)) (4.1.2.30)\n","Requirement already satisfied: numpy>=1.16.2 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 3)) (1.19.5)\n","Requirement already satisfied: pillow>=5.4.1 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 4)) (7.1.2)\n","Collecting stn>=1.0.1\n","  Downloading https://files.pythonhosted.org/packages/a5/56/c4931082eba2a672495521c1b91d23092fbb8c275cd41831171501104610/stn-1.0.1-py3-none-any.whl\n","Installing collected packages: bchlib, stn\n","Successfully installed bchlib-0.14.0 stn-1.0.1\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"KicPBYuwRbTi"},"source":["- Python 3 required"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qfsOUDEORR-y","executionInfo":{"status":"ok","timestamp":1618718105114,"user_tz":-480,"elapsed":755,"user":{"displayName":"Charmve","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj9zT4vUzycNuqCVAvPYhbS-GWoSOslxHjcLtv4=s64","userId":"07530046818488914519"}},"outputId":"1bb8136e-4df7-4afa-a62d-d1280ea17dbc"},"source":["!python --version"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Python 3.7.10\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"ryMHr6O_CdYW"},"source":["## Training"]},{"cell_type":"markdown","metadata":{"id":"y7Ml31LXR-8K"},"source":["\n","### Encoder / Decoder\n","- Set dataset path in train.py\n","\n","`TRAIN_PATH = DIR_OF_DATASET_IMAGES`\n","\n","- Train model\n","\n","`bash scripts/base.sh EXP_NAME`\n","\n","The training is performed in train.py. There are a number of hyperparameters, many corresponding to the augmentation parameters. scripts/bash.sh provides a good starting place."]},{"cell_type":"markdown","metadata":{"id":"pX_1rW2YdrFH"},"source":["Make sure that directory is ``/StegaStamp``"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FzoK4CiXhxNI","executionInfo":{"status":"ok","timestamp":1618653040941,"user_tz":-480,"elapsed":884,"user":{"displayName":"Charmve","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj9zT4vUzycNuqCVAvPYhbS-GWoSOslxHjcLtv4=s64","userId":"07530046818488914519"}},"outputId":"7b063f6f-a984-4445-fac8-c485b890b21c"},"source":["%cd '/content/drive/MyDrive/StegaStamp'"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/content/drive/MyDrive/StegaStamp\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"TgwAg4XTSWbc","outputId":"39a9052c-a84a-407f-a32c-6f2804604040"},"source":["!bash scripts/base.sh MyModel"],"execution_count":null,"outputs":[{"output_type":"stream","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","        [0.90588236, 0.9019608 , 0.8862745 ]]], dtype=float32), array([[[0.14901961, 0.08627451, 0.02745098],\n","        [0.14901961, 0.08627451, 0.02745098],\n","        [0.14901961, 0.08627451, 0.02745098],\n","        ...,\n","        [0.1254902 , 0.07450981, 0.04313726],\n","        [0.13725491, 0.10196079, 0.04313726],\n","        [0.12941177, 0.09803922, 0.02352941]],\n","\n","       [[0.14901961, 0.08627451, 0.02352941],\n","        [0.14901961, 0.08627451, 0.02352941],\n","        [0.14901961, 0.08627451, 0.02352941],\n","        ...,\n","        [0.14509805, 0.10196079, 0.02352941],\n","        [0.14117648, 0.10980392, 0.01960784],\n","        [0.11764706, 0.09019608, 0.        ]],\n","\n","       [[0.14117648, 0.09019608, 0.02352941],\n","        [0.14117648, 0.09019608, 0.02352941],\n","        [0.14901961, 0.08627451, 0.02352941],\n","        ...,\n","        [0.09803922, 0.05098039, 0.05098039],\n","        [0.12156863, 0.0627451 , 0.03529412],\n","        [0.14509805, 0.07450981, 0.01960784]],\n","\n","       ...,\n","\n","       [[0.27058825, 0.16470589, 0.09803922],\n","        [0.27450982, 0.16470589, 0.10980392],\n","        [0.27450982, 0.16470589, 0.10980392],\n","        ...,\n","        [0.02745098, 0.02745098, 0.01960784],\n","        [0.05098039, 0.05098039, 0.04313726],\n","        [0.09019608, 0.09019608, 0.08235294]],\n","\n","       [[0.31764707, 0.19215687, 0.10980392],\n","        [0.32156864, 0.2       , 0.12156863],\n","        [0.30980393, 0.1882353 , 0.11764706],\n","        ...,\n","        [0.02352941, 0.02352941, 0.01568628],\n","        [0.05098039, 0.05098039, 0.05098039],\n","        [0.09019608, 0.09019608, 0.09019608]],\n","\n","       [[0.3254902 , 0.20392157, 0.13333334],\n","        [0.31764707, 0.19607843, 0.1254902 ],\n","        [0.29411766, 0.17254902, 0.10196079],\n","        ...,\n","        [0.01960784, 0.01960784, 0.01176471],\n","        [0.04313726, 0.04313726, 0.04313726],\n","        [0.09019608, 0.09019608, 0.09019608]]], dtype=float32)], \n","\n","./datasets/train/mirflickr/im24784.jpg\n","./datasets/train/mirflickr/im25.jpg\n","./datasets/train/mirflickr/im15787.jpg\n","./datasets/train/mirflickr/im21370.jpg\n","batch_cover: [array([[[0.49411765, 0.23137255, 0.10980392],\n","        [0.5019608 , 0.23529412, 0.12156863],\n","        [0.52156866, 0.2509804 , 0.13725491],\n","        ...,\n","        [0.9529412 , 0.68235296, 0.3764706 ],\n","        [0.972549  , 0.6509804 , 0.37254903],\n","        [0.99607843, 0.6627451 , 0.36078432]],\n","\n","       [[0.49803922, 0.22745098, 0.10588235],\n","        [0.50980395, 0.23921569, 0.12156863],\n","        [0.5294118 , 0.2509804 , 0.13725491],\n","        ...,\n","        [0.95686275, 0.7921569 , 0.5254902 ],\n","        [0.9529412 , 0.6901961 , 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0.47058824, 0.31764707],\n","        [0.20392157, 0.4745098 , 0.3254902 ],\n","        [0.19215687, 0.47843137, 0.33333334]],\n","\n","       [[0.02745098, 0.22352941, 0.10980392],\n","        [0.03921569, 0.23137255, 0.11372549],\n","        [0.05882353, 0.23529412, 0.11372549],\n","        ...,\n","        [0.21568628, 0.4745098 , 0.3254902 ],\n","        [0.21960784, 0.47058824, 0.32941177],\n","        [0.18039216, 0.47058824, 0.32156864]],\n","\n","       [[0.04705882, 0.23529412, 0.11764706],\n","        [0.04705882, 0.24705882, 0.12156863],\n","        [0.05098039, 0.25490198, 0.11764706],\n","        ...,\n","        [0.19215687, 0.4627451 , 0.31764707],\n","        [0.23529412, 0.4862745 , 0.34901962],\n","        [0.20392157, 0.47843137, 0.3372549 ]],\n","\n","       ...,\n","\n","       [[0.        , 0.        , 0.        ],\n","        [0.        , 0.        , 0.        ],\n","        [0.        , 0.        , 0.        ],\n","        ...,\n","        [0.03921569, 0.02745098, 0.        ],\n","        [0.05098039, 0.03921569, 0.        ],\n","        [0.0627451 , 0.05490196, 0.        ]],\n","\n","       [[0.        , 0.        , 0.        ],\n","        [0.        , 0.        , 0.        ],\n","        [0.        , 0.        , 0.        ],\n","        ...,\n","        [0.        , 0.00784314, 0.        ],\n","        [0.        , 0.00392157, 0.        ],\n","        [0.        , 0.        , 0.        ]],\n","\n","       [[0.        , 0.        , 0.        ],\n","        [0.        , 0.        , 0.        ],\n","        [0.        , 0.        , 0.        ],\n","        ...,\n","        [0.        , 0.00392157, 0.        ],\n","        [0.00392157, 0.00784314, 0.        ],\n","        [0.00392157, 0.00784314, 0.        ]]], dtype=float32)], \n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"mkZQ7LXSdkPg"},"source":["### Tensorboard\n","To visualize the training run the following command and then navigate to https://8c5d2393526a.ngrok.io in your browser."]},{"cell_type":"markdown","metadata":{"id":"_IJikYVeKmyq"},"source":["#### Open ``tensorboard`` in Colab"]},{"cell_type":"markdown","metadata":{"id":"mpQ7l8ynKn_l"},"source":["**Method 1**"]},{"cell_type":"markdown","metadata":{"id":"cUewu-iR_rF2"},"source":["\n","\n","1.install ``ngrok`` （<b>had installed</b>, which can be seen at ``\"drive/MyDrive/StegaStamp/ngrok\"``）\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vGsaznJzAF7N","executionInfo":{"status":"ok","timestamp":1618650898595,"user_tz":-480,"elapsed":9765,"user":{"displayName":"Charmve","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj9zT4vUzycNuqCVAvPYhbS-GWoSOslxHjcLtv4=s64","userId":"07530046818488914519"}},"outputId":"5a4d500f-2903-4bd1-afaa-543121c71a6a"},"source":["# !wget https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip\n","!unzip ngrok-stable-linux-amd64.zip"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Archive:  ngrok-stable-linux-amd64.zip\n","replace ngrok? [y]es, [n]o, [A]ll, [N]one, [r]ename: n\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"XKl56DeEAFpO"},"source":["2.run TensorBoard on Colab\n"]},{"cell_type":"markdown","metadata":{"id":"e0h36MC8As1d"},"source":["- add tensorboard's directory"]},{"cell_type":"code","metadata":{"id":"qN6TQGCaAIs1"},"source":["LOG_DIR = './logs'\n","get_ipython().system_raw(\n","    'tensorboard --logdir {} --host 0.0.0.0 --port 6006 &'\n","    .format(LOG_DIR)\n",")"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"1GtNBuXrA6Ge"},"source":["- start ngrok service, and band port 6006(tensorboard)"]},{"cell_type":"code","metadata":{"id":"X3Wl6MzLALoX"},"source":["get_ipython().system_raw('./ngrok http 6006 &')"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"w6HScO-cA8qH"},"source":["- generate website, click the link to visit tensorboard"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jjnjmq9mAPQ9","executionInfo":{"status":"ok","timestamp":1618718662455,"user_tz":-480,"elapsed":807,"user":{"displayName":"Charmve","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj9zT4vUzycNuqCVAvPYhbS-GWoSOslxHjcLtv4=s64","userId":"07530046818488914519"}},"outputId":"b919deb9-06c1-4cfd-d774-347e5a117111"},"source":["!curl -s http://localhost:4040/api/tunnels | python3 -c \\\n","    \"import sys, json; print(json.load(sys.stdin)['tunnels'][0]['public_url'])\""],"execution_count":null,"outputs":[{"output_type":"stream","text":["Traceback (most recent call last):\n","  File \"<string>\", line 1, in <module>\n","  File \"/usr/lib/python3.7/json/__init__.py\", line 296, in load\n","    parse_constant=parse_constant, object_pairs_hook=object_pairs_hook, **kw)\n","  File \"/usr/lib/python3.7/json/__init__.py\", line 348, in loads\n","    return _default_decoder.decode(s)\n","  File \"/usr/lib/python3.7/json/decoder.py\", line 337, in decode\n","    obj, end = self.raw_decode(s, idx=_w(s, 0).end())\n","  File \"/usr/lib/python3.7/json/decoder.py\", line 355, in raw_decode\n","    raise JSONDecodeError(\"Expecting value\", s, err.value) from None\n","json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"TPKSLo-7KwEF"},"source":["**Method 2**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qqGOCiGMKCCk","executionInfo":{"status":"ok","timestamp":1618718780525,"user_tz":-480,"elapsed":7126,"user":{"displayName":"Charmve","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj9zT4vUzycNuqCVAvPYhbS-GWoSOslxHjcLtv4=s64","userId":"07530046818488914519"}},"outputId":"e662696c-e79a-4486-b930-c792a17152d8"},"source":["LOG_DIR = './logs'\n","get_ipython().system_raw(\n","    'tensorboard --logdir {} --host 0.0.0.0 --port 6006 &'\n","    .format(LOG_DIR)\n",")\n","# Install\n","! npm install -g localtunnel\n","\n","# Tunnel port 6006 (TensorBoard assumed running)\n","get_ipython().system_raw('lt --port 6006 >> url.txt 2>&1 &')\n","\n","# Get url\n","! cat url.txt"],"execution_count":null,"outputs":[{"output_type":"stream","text":["\u001b[K\u001b[?25h/tools/node/bin/lt -> /tools/node/lib/node_modules/localtunnel/bin/lt.js\n","+ localtunnel@2.0.1\n","added 22 packages from 22 contributors in 4.218s\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"S9_qqLjyKPIN","executionInfo":{"status":"ok","timestamp":1618718817506,"user_tz":-480,"elapsed":755,"user":{"displayName":"Charmve","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj9zT4vUzycNuqCVAvPYhbS-GWoSOslxHjcLtv4=s64","userId":"07530046818488914519"}},"outputId":"89431dda-6e6d-41fb-8d43-7892cc8174fb"},"source":["! cat url.txt"],"execution_count":null,"outputs":[{"output_type":"stream","text":["your url is: https://brown-fly-97.loca.lt\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"TJLgCUMCDx4J"},"source":["## Encoding a Message\n","The script `encode_image.py` can be used to encode a message into an image or a directory of images. The default model expects a utf-8 encoded secret that is <= 7 characters (100 bit message -> 56 bits after ECC).\n","\n","### Encode a message into an image:"]},{"cell_type":"code","metadata":{"id":"JJn0OkaoEYLh","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1618647587733,"user_tz":-480,"elapsed":5479,"user":{"displayName":"Charmve","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj9zT4vUzycNuqCVAvPYhbS-GWoSOslxHjcLtv4=s64","userId":"07530046818488914519"}},"outputId":"9796dc9a-65df-466a-b1c5-4ab552905ea8"},"source":["!python encode_image.py \\\n","  saved_models/stegastamp_pretrained \\\n","  --image datasets/test/test_img1.png  \\\n","  --save_dir out \\\n","  --secret Hello"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/dtypes.py:526: 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'.\n","  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n","/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/dtypes.py:527: 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'.\n","  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n","/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/dtypes.py:528: 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'.\n","  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n","/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/dtypes.py:529: 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'.\n","  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n","/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/dtypes.py:530: 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'.\n","  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n","/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/dtypes.py:535: 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'.\n","  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n","2021-04-17 08:19:45.026442: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA\n","2021-04-17 08:19:45.029791: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2199995000 Hz\n","2021-04-17 08:19:45.029958: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x5572955f4100 executing computations on platform Host. Devices:\n","2021-04-17 08:19:45.029986: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): <undefined>, <undefined>\n","WARNING:tensorflow:From encode_image.py:70: load (from tensorflow.python.saved_model.loader_impl) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.loader.load or tf.compat.v1.saved_model.load. There will be a new function for importing SavedModels in Tensorflow 2.0.\n","WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/training/saver.py:1266: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Use standard file APIs to check for files with this prefix.\n","执行开始，祈祷不报错\n","100%[*->]0.10s\n","执行结束，万幸\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"HA6cbbU210sX"},"source":["### Show result\n","\n","-- image datasets/test/test_img1.png  \\\n","-- save_dir out \\"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":162},"id":"RSURRwHGzXAP","executionInfo":{"status":"ok","timestamp":1618646178523,"user_tz":-480,"elapsed":1975,"user":{"displayName":"Charmve","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj9zT4vUzycNuqCVAvPYhbS-GWoSOslxHjcLtv4=s64","userId":"07530046818488914519"}},"outputId":"03a8a147-c7c2-44b2-f6c4-3d3c0ab631b5"},"source":["from PIL import Image,ImageOps\n","import matplotlib.pyplot as plt\n","import matplotlib.image as mpimg\n","\n","def show_result(filename, hidden_img, residual_img):\n","    # show these three img encoded\n","    # sess.run(tf.global_variables_initializer())\n","    \n","    fig = plt.figure()\n","    \n","    ax = fig.add_subplot(131)\n","    ax.set_title('source image')\n","    src_img = mpimg.imread(filename)\n","    ax.imshow(src_img)\n","    \n","    ax = fig.add_subplot(132)\n","    ax.set_title('hidden image')\n","    hid_img = mpimg.imread(hidden_img)\n","    ax.imshow(hid_img)\n","    \n","    ax = fig.add_subplot(133)\n","    ax.set_title('residual image')\n","    res_img = mpimg.imread(residual_img)\n","    ax.imshow(res_img)\n","\n","if __name__ == \"__main__\":\n","  filename, hidden_img, residual_img = 'datasets/test/test_img1.png', './out/test_img1_hidden.png', './out/test_img1_residual.png'\n","  show_result(filename, hidden_img, residual_img)\n"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 3 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"XJFLHBJcLpa4"},"source":["<b>Note:</b> \n","\n","- if you use the modle you trained above, plese use ``saved_models/MyModel`` instead of ``saved_models/stegastamp_pretrained``\n","\n","- This will save both the StegaStamp and the residual that was applied to the original image."]},{"cell_type":"markdown","metadata":{"id":"MMlg6QkfEaQZ"},"source":["Or run the bash code directly:\n","```bash=\n","! bash encode_image.sh\n","```"]},{"cell_type":"markdown","metadata":{"id":"HbLkYdaLDiNo"},"source":["## Decoding a Message\n","The script `decode_image.py` can be used to decode a message from a StegaStamp.\n","\n","Example usage:\n"]},{"cell_type":"code","metadata":{"id":"Jq_MUif8EwUG"},"source":["!python decode_image.py \\\n","  saved_models/stegastamp_pretrained \\\n","  --image out/test_img1_hidden.png"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"80nabjV4ExtJ"},"source":["Samely, or run the bash code directly:\n","```bash=\n","!bash decode_image.sh\n","```\n","\n","And if you use the modle you trained above, plese use ``saved_models/MyModel`` instead of ``saved_models/stegastamp_pretrained``"]},{"cell_type":"markdown","metadata":{"id":"C13J4QFZEssZ"},"source":["## Detecting and Decoding\n","The script `detector.py` can be used to detect and decode StegaStamps in an image. This is useful in cases where there are multiple StegaStamps are present or the StegaStamp does not fill the frame of the image.\n","\n","To use the detector, make sure to download the detector model as described in the installation section. The recomended input video resolution is 1920x1080."]},{"cell_type":"code","metadata":{"id":"S6Pvv_5MFBcS"},"source":["!python detector.py \\\n","  --detector_model detector_models/stegastamp_detector \\\n","  --decoder_model saved_models/stegastamp_pretrained \\\n","  --video test_vid.mp4"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"TVUNfU5MFDPr"},"source":["<strong>Discription:</strong>\n","- Add the `--save_video FILENAME` flag to save out the results.\n","\n","- The `--visualize_detector` flag can be used to visualize the output of the detector network. The mask corresponds to the segmentation mask, the colored polygons are fit to this segmentation mask using a set of heuristics. The detector outputs can noisy and are sensitive to size of the stegastamp. Further optimization of the detection network is not explored in this paper.\n","\n","Samely, or run the bash code directly: ```bash= bash detector.sh``` in the terminal.\n","\n","\n"]},{"cell_type":"markdown","metadata":{"id":"TNb8f-SePnIP"},"source":["### Detecting and Decoding from Webcam\n","In browser \n","\n","```bash=\n","localhost:8080/detecdecode.html\n","```"]},{"cell_type":"markdown","metadata":{"id":"A04OFK1WC2r2"},"source":["## Update to GitHub"]},{"cell_type":"code","metadata":{"id":"DLl99OvuC-of"},"source":["%cd '/content/drive/MyDrive/StegaStamp'\n","!git pull\n","!git status"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"XDxSaG-BDND_"},"source":["!git add .\n","!git status"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"TyYTBrsEDO5X"},"source":["!git commit -m \"update saved_mode and .ipynb\"\n","!git status"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"bHQEZvvYDcbX"},"source":["!git push"],"execution_count":null,"outputs":[]}]}