fixed minor issues

Pipfile

@@ -0,0 +1,47 @@
+[[source]]
+
+url = "https://pypi.python.org/simple"
+verify_ssl = true
+name = "pypi"
+
+
+[packages]
+
+Keras = "==2.1.5"
+tensorflow = "==1.6.0"
+"h5py" = "==2.7.1"
+scikit-image = "==0.13.1"
+scikit-learn = "==0.19.1"
+sk-video = "==1.1.10"
+tqdm = "==4.19.6"
+coloredlogs = "==9.0"
+absl-py = "==0.1.11"
+astor = "==0.6.2"
+bleach = "==1.5.0"
+Cycler = "==0.10.0"
+decorator = "==4.2.1"
+gast = "==0.2.0"
+grpcio = "==1.10.0"
+"html5lib" = "==0.9999999"
+humanfriendly = "==4.8"
+kiwisolver = "==1.0.1"
+Markdown = "==2.6.11"
+matplotlib = "==2.2.0"
+networkx = "==2.1"
+numpy = "==1.14.1"
+Pillow = "==5.0.0"
+protobuf = "==3.5.2"
+pyparsing = "==2.2.0"
+python-dateutil = "==2.6.1"
+pytz = "==2018.3"
+PyWavelets = "==0.5.2"
+PyYAML = "==3.12"
+scipy = "==1.0.0"
+six = "==1.11.0"
+tensorboard = "==1.6.0"
+termcolor = "==1.1.0"
+Werkzeug = "==0.14.1"
+
+
+[dev-packages]
+

Pipfile.lock

@@ -0,0 +1,572 @@
+{
+    "_meta": {
+        "hash": {
+            "sha256": "1e445b8f1ffe3f35f3ef67114b7c9567bc0226bec66033fc92da171fa2d281f8"
+        },
+        "pipfile-spec": 6,
+        "requires": {},
+        "sources": [
+            {
+                "name": "pypi",
+                "url": "https://pypi.python.org/simple",
+                "verify_ssl": true
+            }
+        ]
+    },
+    "default": {
+        "absl-py": {
+            "hashes": [
+                "sha256:ba6dc5510108fdfcf7176f8b38a5cc2482f2e902eb7211cbe6666998ea58daf9"
+            ],
+            "index": "pypi",
+            "version": "==0.1.11"
+        },
+        "astor": {
+            "hashes": [
+                "sha256:64c805f1ad6fbc505633416b6174fc23796eb164f371a7dc1f3951ea30560fb5",
+                "sha256:ff6d2e2962d834acb125cc4dcc80c54a8c17c253f4cc9d9c43b5102a560bb75d"
+            ],
+            "index": "pypi",
+            "version": "==0.6.2"
+        },
+        "bleach": {
+            "hashes": [
+                "sha256:978e758599b54cd3caa2e160d74102879b230ea8dc93871d0783721eef58bc65",
+                "sha256:e67f46adcec78dbc3c04462f3aba3213a673d5652eba2609ed1ef15492a44b8d"
+            ],
+            "index": "pypi",
+            "version": "==1.5.0"
+        },
+        "coloredlogs": {
+            "hashes": [
+                "sha256:6bd7ceac109c3f2e138db8578396664b1067f32aca55d3280a57dbf05f1ada6c",
+                "sha256:e3b19320bd21bde506444601a71397cf5215f040df06503013697c6261b05de9"
+            ],
+            "index": "pypi",
+            "version": "==9.0"
+        },
+        "cycler": {
+            "hashes": [
+                "sha256:1d8a5ae1ff6c5cf9b93e8811e581232ad8920aeec647c37316ceac982b08cb2d",
+                "sha256:cd7b2d1018258d7247a71425e9f26463dfb444d411c39569972f4ce586b0c9d8"
+            ],
+            "index": "pypi",
+            "version": "==0.10.0"
+        },
+        "decorator": {
+            "hashes": [
+                "sha256:7d46dd9f3ea1cf5f06ee0e4e1277ae618cf48dfb10ada7c8427cd46c42702a0e",
+                "sha256:94d1d8905f5010d74bbbd86c30471255661a14187c45f8d7f3e5aa8540fdb2e5"
+            ],
+            "index": "pypi",
+            "version": "==4.2.1"
+        },
+        "gast": {
+            "hashes": [
+                "sha256:7068908321ecd2774f145193c4b34a11305bd104b4551b09273dfd1d6a374930"
+            ],
+            "index": "pypi",
+            "version": "==0.2.0"
+        },
+        "grpcio": {
+            "hashes": [
+                "sha256:022dc2a6d1537a5a16af4ccc3355ad7b512f9c627a1d5d579cd7c18830378bb3",
+                "sha256:025a591606b0aca13bec3e019d6acec01a39421f01b915b98a3a93ea0a53b412",
+                "sha256:03265472d39bf26f124c3ef68446f7873c8260893e6ae65b323a5b51ed52e580",
+                "sha256:10efe2e016c3ca7a370771ffcf1de9baa3456d4bccefde0f4ce3be091d871c8f",
+                "sha256:14bca481188c3f19135012aaff9abefa4e15529c7e1aca3084183d78094d06bd",
+                "sha256:17240d672b5c1c9ff22e52236c1870413b7fb5af762b97ce5a747a55e0a57e98",
+                "sha256:1bc36e512741f82c1d73f42df536aa2ab75d840f0d35c149b5d0bee1aed16862",
+                "sha256:224c513fbe0c3ca546870e5c21b08a8a56cd25795b76b3192ee9702a3344764b",
+                "sha256:2894466c499d9752e0d49ee8adc5ee12c676d86211fc1b292bf713cc7cfe9853",
+                "sha256:435b3bab2e34814666854eec203c77b169df1cd56cf22fe449cf5510af416e7d",
+                "sha256:4765600467d7cdb8f62a591d4427ddbeefcf4dbbe46e2f1b10af555e815ecbcb",
+                "sha256:4fa658a7e1ba5727ca066b1c8bb64c6befb98f2b8007f04a16c7c84555bf11b9",
+                "sha256:87e52924a99ac5935a468b3fe49c4b0090bd9b05470b55ed1192308791e6d332",
+                "sha256:88afda198adb0a9da52a66152062027a57877b46f59ffcf55acc3cbfaff77160",
+                "sha256:982439a872d41f969724efc139e0416ba45e0d7446e9a41fd2ebe19351adff9a",
+                "sha256:a1bc37c9910d0fbf4d9e80d5822f92c6e01e28dd1eb01323636ed19666b537cb",
+                "sha256:aa473b8276de39eeccc4ad6cbb7fd7feab0868180d72c0c93226033c79fa69b7",
+                "sha256:ae82bf2f7ceac6ba956e816120b4f66bda035571350e46b61bbdde1808aed1dd",
+                "sha256:b56e4f355c2499bb0bf8f8f4d0362b618b06afdfd2c10722710596dc7e295c6c",
+                "sha256:d2accc8e354f0ed5b337865260a78b3c6851d2fe3c0e1b025d437122cc15dd31",
+                "sha256:d410835e7554d064c2d99cfa0dd393ffbb0ccf52145ab51c725a8472ed254a3c",
+                "sha256:d9e3105f6de6cb759b028702bdd21cb36d27e010227669e43c675b9957a3c180",
+                "sha256:da306c80d69801a3e4115c448ed4ad481957d723ec1e00b99497c6661573c3e5",
+                "sha256:e579e4124d2a0931ce39639c60e0711918d6659b933eb97e67f60f84666ea488",
+                "sha256:e86639989c03831912fd9924beda26f6e9ffcc267656cea035bde9d88cf793b2",
+                "sha256:ea9564f58144e2f07995d57fb8e636be5efb084cd59c8651391ada2bb75dc0ff",
+                "sha256:f4a38071dd27f140cfe774f56aecdf0e33de926c21289cc9c7521ce8dd91fc1c"
+            ],
+            "index": "pypi",
+            "version": "==1.10.0"
+        },
+        "h5py": {
+            "hashes": [
+                "sha256:07ddea6bb649a257fc57ccae359a36d691b2ef8b9617971ae7d6f74ef6f67cad",
+                "sha256:180a688311e826ff6ae6d3bda9b5c292b90b28787525ddfcb10a29d5ddcae2cc",
+                "sha256:1be9cd57e74b24f836d0d2c34ae376ff2df704f40aa8815aa9113b5a860d467f",
+                "sha256:1fad9aa32835230de77b31edd6980b7c202de7bb7d8384d1bcb47b5dd32c8c7c",
+                "sha256:2258fca3533a3276fd86e9196326786f408a95748ac707c010fff265edf60342",
+                "sha256:2b91c9117f2e7a2ef924bec41ac77e57567bec6731773373bf78eb4387b39a2a",
+                "sha256:2ccb4f405059314829ebad1859d2c68e133a9d13ca7c3cc7a298a76a438fd09c",
+                "sha256:2d137a1b2f529e58886b5865f6dec51cd96ea0671dd84cebc6dba5cd8c7d0a75",
+                "sha256:40dd37cbf24ca3b935a8d6eb8960ec5d0381219f82317bdc40aa9e08b3fcc143",
+                "sha256:478efa37b84a56061af5fcd286678331e873e216f6c5987cd31f9666edc2f157",
+                "sha256:4a6e6cd8668fa453864f4f9e243460dcc2d41e79d14516b84f4ba74ebcc5b222",
+                "sha256:52204972a02032d6a427addd37a24a22a2b97d4bce0850c84a6995db9c91926c",
+                "sha256:537a60879485e5ce484ab4350c7bd8b3da4b531f9f82ef0a18780beabde98c90",
+                "sha256:562045c57a2e47aca9c716ac8cd64448d4897c0f5fe456ab5a34b17c8b3907cb",
+                "sha256:66609c48f8841357ced4291b7c9009518bb6e6fec449d91eb46aa417b6f5f4cf",
+                "sha256:9d9fb861e10735c5c710fe18f34c69e470cf161a4ba38717b7dde21de2d33760",
+                "sha256:9e0537058efea7547d976f9c00067f7193727bb41ce6b4733c52de35beaa46f5",
+                "sha256:a314e5e98037ece52ad0b88b4e0d788ca554935268f3e9d293ca9bcd18611b42",
+                "sha256:b7e1c42367513108c3615cf1a24a9d366fd93eb9d2d92085bafb3011b785e8a9",
+                "sha256:bb990d8663dbeee22ce44135ffd65ab38bd23d6a689722a653cfbf2d18d46688",
+                "sha256:c050791989cd9979fe57a770d4e323b2e67ef95800e89e7dc6ad3652b8ccd86f",
+                "sha256:e1bfcfa2c425dc0f637d4edd858b94e400bbb5746dba324ace124d55fc21d3df",
+                "sha256:e78f09a44fc9256b84c9df98edf7b6ead3b3da2e12bf2d1e00384960a6a78a1a"
+            ],
+            "index": "pypi",
+            "version": "==2.7.1"
+        },
+        "html5lib": {
+            "hashes": [
+                "sha256:2612a191a8d5842bfa057e41ba50bbb9dcb722419d2408c78cff4758d0754868"
+            ],
+            "index": "pypi",
+            "version": "==0.9999999"
+        },
+        "humanfriendly": {
+            "hashes": [
+                "sha256:587b16ce804bec8e3cbb8c420decea051b38e3d895272b2c1e38fc69b4286b1c",
+                "sha256:d0e74171b87318a94b99520e4f0c5651e944b5f11d696c46be3330bb82b85300"
+            ],
+            "index": "pypi",
+            "version": "==4.8"
+        },
+        "keras": {
+            "hashes": [
+                "sha256:55e4598f6ff2a374a8abd8e7a4c65be95ac5d3a6ee81b08d7d2549f8c3e1d8d5",
+                "sha256:907ad29add1fff27342a9f4fe3e60003d450d3af41a38f22f629c7736fc8399d"
+            ],
+            "index": "pypi",
+            "version": "==2.1.5"
+        },
+        "kiwisolver": {
+            "hashes": [
+                "sha256:0ee4ed8b3ae8f5f712b0aa9ebd2858b5b232f1b9a96b0943dceb34df2a223bc3",
+                "sha256:0f7f532f3c94e99545a29f4c3f05637f4d2713e7fd91b4dd8abfc18340b86cd5",
+                "sha256:1a078f5dd7e99317098f0e0d490257fd0349d79363e8c923d5bb76428f318421",
+                "sha256:1aa0b55a0eb1bd3fa82e704f44fb8f16e26702af1a073cc5030eea399e617b56",
+                "sha256:2874060b91e131ceeff00574b7c2140749c9355817a4ed498e82a4ffa308ecbc",
+                "sha256:379d97783ba8d2934d52221c833407f20ca287b36d949b4bba6c75274bcf6363",
+                "sha256:3b791ddf2aefc56382aadc26ea5b352e86a2921e4e85c31c1f770f527eb06ce4",
+                "sha256:4329008a167fac233e398e8a600d1b91539dc33c5a3eadee84c0d4b04d4494fa",
+                "sha256:45813e0873bbb679334a161b28cb9606d9665e70561fd6caa8863e279b5e464b",
+                "sha256:53a5b27e6b5717bdc0125338a822605084054c80f382051fb945d2c0e6899a20",
+                "sha256:66f82819ff47fa67a11540da96966fb9245504b7f496034f534b81cacf333861",
+                "sha256:79e5fe3ccd5144ae80777e12973027bd2f4f5e3ae8eb286cabe787bed9780138",
+                "sha256:8b6a7b596ce1d2a6d93c3562f1178ebd3b7bb445b3b0dd33b09f9255e312a965",
+                "sha256:9576cb63897fbfa69df60f994082c3f4b8e6adb49cccb60efb2a80a208e6f996",
+                "sha256:95a25d9f3449046ecbe9065be8f8380c03c56081bc5d41fe0fb964aaa30b2195",
+                "sha256:aaec1cfd94f4f3e9a25e144d5b0ed1eb8a9596ec36d7318a504d813412563a85",
+                "sha256:acb673eecbae089ea3be3dcf75bfe45fc8d4dcdc951e27d8691887963cf421c7",
+                "sha256:b15bc8d2c2848a4a7c04f76c9b3dc3561e95d4dabc6b4f24bfabe5fd81a0b14f",
+                "sha256:b1c240d565e977d80c0083404c01e4d59c5772c977fae2c483f100567f50847b",
+                "sha256:ce3be5d520b4d2c3e5eeb4cd2ef62b9b9ab8ac6b6fedbaa0e39cdb6f50644278",
+                "sha256:e0f910f84b35c36a3513b96d816e6442ae138862257ae18a0019d2fc67b041dc",
+                "sha256:ea36e19ac0a483eea239320aef0bd40702404ff8c7e42179a2d9d36c5afcb55c",
+                "sha256:f923406e6b32c86309261b8195e24e18b6a8801df0cfc7814ac44017bfcb3939"
+            ],
+            "index": "pypi",
+            "version": "==1.0.1"
+        },
+        "markdown": {
+            "hashes": [
+                "sha256:9ba587db9daee7ec761cfc656272be6aabe2ed300fece21208e4aab2e457bc8f",
+                "sha256:a856869c7ff079ad84a3e19cd87a64998350c2b94e9e08e44270faef33400f81"
+            ],
+            "index": "pypi",
+            "version": "==2.6.11"
+        },
+        "matplotlib": {
+            "hashes": [
+                "sha256:08de895360bd5dc489514b395ff5da153931939145ae4608afb78799dc592dc0",
+                "sha256:3947b26e4ba51276df41167614a0fde9fba43740c7999fbd2b5de34fd75119cc",
+                "sha256:3ca7a2c17b5c4c21177f764abca197ff9ac0ac68352e24718d05929c8592ed0d",
+                "sha256:504c3a891fcfdb48e94983c50a5b8b626f25000d407ea2d9ab9ed5691762fbe8",
+                "sha256:5837e3dd76d43aa2fe0a90c79f6c1d4d38c205550e8ae685bda176af62c86bbd",
+                "sha256:6cbe2b9cdb61fdea331e1e1feebeb1d03c49cb84af43957767e5471b99ddb822",
+                "sha256:8917e89672b7668861f6db45561bd5fc1ecd240089ab0723e4ca96bd1e1f44ce",
+                "sha256:8df8c75938193cda5a041762fcc96f47658a6125fae07dc0678502dba3b62f4c",
+                "sha256:9819535c069d7207dd55f3992875e96a3ea6a787ddc37afea00cc95bba4125ee",
+                "sha256:b91ee711c7e6e4c43f106f223e9eac50e98f35153813f5fd70fd592be3616ade",
+                "sha256:c33cf0f2a2064b3ae60f9fb806a1da5addaf6be66ec3d106794955e3888d8442",
+                "sha256:c7d2524971ba0b9da1b04a9054749f8654298573243db6eb762d7f58daece1b4",
+                "sha256:d65879e7ddce62fc2e6339216a0c0273b3f0e4b2401f7edb512efcbd72d13e9b",
+                "sha256:d744e56eb6742a4a3a9dbbb22c71bd0d6262543a2817fb35c9ed21ff4d06e4ce",
+                "sha256:da2ccf36a7f09d07b64b8bb708f1389f8493a5326a12ba959483b9530c64fd23",
+                "sha256:f652a3f23ea347a57eb6743ba83cd91ac347bed08fb82acde0e015a9afacbbd9",
+                "sha256:f6c0fd70adbe1e41d11b3665409566a512ee037268654e558d79f669a6dd183f",
+                "sha256:fe816662169026f6d13063be05f7ccadb11d80e737351b778ceb3fa17ef23002"
+            ],
+            "index": "pypi",
+            "version": "==2.2.0"
+        },
+        "networkx": {
+            "hashes": [
+                "sha256:64272ca418972b70a196cb15d9c85a5a6041f09a2f32e0d30c0255f25d458bb1"
+            ],
+            "index": "pypi",
+            "version": "==2.1"
+        },
+        "numpy": {
+            "hashes": [
+                "sha256:12cf4b27039b88e407ad66894d99a957ef60fea0eeb442026af325add2ab264d",
+                "sha256:2da8dff91d489fea3e20155d41f4cd680de7d01d9a89fdd0ebb1bee6e72d3800",
+                "sha256:377def0873bbb1fbdedb14b3275b10a29b1b55619a3f7f775c4e7f9ce2461b9c",
+                "sha256:3d7ddd5bdfb12ec9668edf1aa49a4a3eddb0db4661b57ea431477eb9a2468894",
+                "sha256:49880b47d7272f902946dd995f346842c95fe275e2deb3082ef0495f0c718a69",
+                "sha256:4e2fc841c8c642f7fd44591ef856ca409cedba6aea27928df34004c533839eee",
+                "sha256:6b1011ffc87d7e2b1b7bcc6dc21bdf177163658746ef778dcd21bf0516b9126c",
+                "sha256:6b8c2daacbbffc83b4a2ba83a61aa3ce60c66340b07b962bd27b6c6bb175bee1",
+                "sha256:788e1757f8e409cd805a7cd82993cd9252fa19e334758a4c6eb5a8b334abb084",
+                "sha256:7d4c549e41507db4f04ec7cfab5597de8acf7871b16c9cf64cebcb9d39031ca6",
+                "sha256:81b9d8f6450e752bd82e7d9618fa053df8db1725747880e76fb09710b57f78d0",
+                "sha256:89b9419019c47ec87cf4cfca77d85da4611cc0be636ec87b5290346490b98450",
+                "sha256:9501c9ccd081977ca5579a3ec4009d6baff6bacb04bf07214aade3324734195a",
+                "sha256:9b762e78739b6e021124adbea07611682db99cd3fca7f3c3a8b98b8f74ea5699",
+                "sha256:a1f5173df8190ef9c6235d260d70ca70c6fb029683ceb66e244c5cc6e335947a",
+                "sha256:a3d5dd437112292c707e54f47141be2f1100221242f07eda7bd8477f3ddc2252",
+                "sha256:a8bc80f69570e11967763636db9b24c1e3e3689881d10ae793cec74cf7a627b6",
+                "sha256:b803306c4c201e7dcda0ce1b9a9c87f61a7c7ce43de2c60c8e56147b76849a1a",
+                "sha256:c8000a6cbc5140629be8c038c9c9cdb3a1c85ff90bd4180ec99f0f0c73050b5e",
+                "sha256:e2335d56d2fd9fc4e3a3f2d3148aafec4962682375f429f05c45a64dacf19436",
+                "sha256:e5ade7a69dccbd99c4fdbb95b6d091d941e62ffa588b0ed8fb0a2854118fef3f",
+                "sha256:e8522cad377cc2ef20fe13aae742cc265172910c98e8a0d6014b1a8d564019e2",
+                "sha256:fa0944650d5d3fb95869eaacd8eedbd2d83610c85e271bd9d3495ffa9bc4dc9c"
+            ],
+            "index": "pypi",
+            "version": "==1.14.1"
+        },
+        "pillow": {
+            "hashes": [
+                "sha256:0013f590a8f260df60bcfd65db19d18efc04e7f046c3c82a40e2e2b3292a937c",
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+                "sha256:bf36f3485e7b7291c36330a93bbfd4f5e8db23bbe4ea46c37b2839fef463f4e2",
+                "sha256:cd23894e1cc6eaa00e6807b6b12e4ca66d5ff092986c9c3eb01e97f24e2d6462",
+                "sha256:d84df0bc86bbdd49f0a6b6bad5cd62ccb02a3bfe546bf79263de44ae081bcd7b",
+                "sha256:e3a5673c105eab802fdecb77f102d877352e201df9328698a265b7f57546b34b",
+                "sha256:fa17be6c66985931d3a391f61a6ba97c902585cf26020aa3eb24604115732d22",
+                "sha256:ff8b6637d8d2c074ed67f3d57513e62f94747c6f1210f43e60ad3d8e93a424e4"
+            ],
+            "index": "pypi",
+            "version": "==1.0.0"
+        },
+        "six": {
+            "hashes": [
+                "sha256:70e8a77beed4562e7f14fe23a786b54f6296e34344c23bc42f07b15018ff98e9",
+                "sha256:832dc0e10feb1aa2c68dcc57dbb658f1c7e65b9b61af69048abc87a2db00a0eb"
+            ],
+            "index": "pypi",
+            "version": "==1.11.0"
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+        "sk-video": {
+            "hashes": [
+                "sha256:261aec6423a0188968771e532e6ccdbc53bcbd934d349280784210d9ca9ffe76",
+                "sha256:fb7125cfa2b31253dcf181d0bf285edd6bb25ffebf61dbd8ba11fadb783609a1"
+            ],
+            "index": "pypi",
+            "version": "==1.1.10"
+        },
+        "tensorboard": {
+            "hashes": [
+                "sha256:835ecbfee0ce505f8365435c23d1c7d6fd527fcad8a4829e4145b9f6f41d0ca2",
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+            "index": "pypi",
+            "version": "==1.6.0"
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+                "sha256:d9c628c857ccd9213d75e0747fcad6ef4d6866a79d1a3987d87a5957c3f0f819",
+                "sha256:eaa4af9eb161af500a5e3c65dcc434d63478801539f51cdd8c01572eda9dfa2d"
+            ],
+            "index": "pypi",
+            "version": "==1.6.0"
+        },
+        "termcolor": {
+            "hashes": [
+                "sha256:1d6d69ce66211143803fbc56652b41d73b4a400a2891d7bf7a1cdf4c02de613b"
+            ],
+            "index": "pypi",
+            "version": "==1.1.0"
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+        "tqdm": {
+            "hashes": [
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+                "sha256:f66468c14ccd011a627734c9b3fd72f20ce16f8faecc47384eb2507af5924fb9"
+            ],
+            "index": "pypi",
+            "version": "==4.19.6"
+        },
+        "werkzeug": {
+            "hashes": [
+                "sha256:c3fd7a7d41976d9f44db327260e263132466836cef6f91512889ed60ad26557c",
+                "sha256:d5da73735293558eb1651ee2fddc4d0dedcfa06538b8813a2e20011583c9e49b"
+            ],
+            "index": "pypi",
+            "version": "==0.14.1"
+        },
+        "wheel": {
+            "hashes": [
+                "sha256:9515fe0a94e823fd90b08d22de45d7bde57c90edce705b22f5e1ecf7e1b653c8",
+                "sha256:e721e53864f084f956f40f96124a74da0631ac13fbbd1ba99e8e2b5e9cafdf64"
+            ],
+            "version": "==0.30.0"
+        }
+    },
+    "develop": {}
+}

SETUP.md

@@ -0,0 +1,7 @@
+#### Process
+1. `$ ./setup.sh` downloads the avenue dataset to *./share/data/videos/avenue*
+2. `$ pipenv install` installs the dependencies
+3. `$ pipenv shell` activates the virtualenv
+4. `$ python convert_video_to_frame.py` to start preprocessing the Dataset
+5. `$ python start_train.py` to train the model.
+6. `$ python start_test.py` to text the trained model.

classifier.py

@@ -55,7 +55,7 @@ def compile_model(model, loss, optimizer):
     model.compile(loss=loss, optimizer=opt)
 
 
-def train(dataset, job_folder, logger, video_root_path='/share/data/videos'):
+def train(dataset, job_folder, logger, video_root_path='./share/data/videos'):
     """Build and train the model
     """
     import yaml
@@ -76,7 +76,7 @@ def train(dataset, job_folder, logger, video_root_path='/share/data/videos'):
     time_length = cfg['time_length']
     # shuffle = cfg['shuffle']
 
-    logger.info("Building model of type {} and activation {}".format(model_type, activation))
+    #logger.info("Building model of type {} and activation {}".format(model_type, activation))
     model = get_model(time_length)
     logger.info("Compiling model with {} and {} optimizer".format(loss, optimizer))
     compile_model(model, loss, optimizer)
@@ -198,7 +198,7 @@ def calc_auc_overall(logger, dataset, n_vid, save_path):
 
 
 def test(logger, dataset, t, job_uuid, epoch, val_loss, visualize_score=True, visualize_frame=False,
-         video_root_path='/share/data/videos'):
+         video_root_path='./share/data/videos'):
     import numpy as np
     from keras.models import load_model
     import os
@@ -306,4 +306,3 @@ def test(logger, dataset, t, job_uuid, epoch, val_loss, visualize_score=True, vi
 
     logger.info("{}: Calculating overall metrics".format(dataset))
     auc_overall, eer_overall = calc_auc_overall(logger, dataset, n_videos[dataset], save_path)
-

convert_video_to_frame.py

@@ -3,7 +3,7 @@ import skvideo.io
 from skimage.transform import resize
 from skimage.io import imsave
 
-video_root_path = '/share/data/videos'
+video_root_path = './share/data/videos'
 size = (224, 224)
 
 def video_to_frame(dataset, train_or_test):
@@ -28,17 +28,17 @@ video_to_frame('avenue', 'training')
 video_to_frame('avenue', 'testing')
 
 # ped1
-video_to_frame('ped1', 'training')
-video_to_frame('ped1', 'testing')
-
-# ped2
-video_to_frame('ped2', 'training')
-video_to_frame('ped2', 'testing')
-
-# enter
-video_to_frame('enter', 'training')
-video_to_frame('enter', 'testing')
-
-# exit
-video_to_frame('exit', 'training')
-video_to_frame('exit', 'testing')
+# video_to_frame('ped1', 'training')
+# video_to_frame('ped1', 'testing')
+#
+# # ped2
+# video_to_frame('ped2', 'training')
+# video_to_frame('ped2', 'testing')
+#
+# # enter
+# video_to_frame('enter', 'training')
+# video_to_frame('enter', 'testing')
+#
+# # exit
+# video_to_frame('exit', 'training')
+# video_to_frame('exit', 'testing')

dataset.py

@@ -1,5 +1,5 @@
 
-def calc_mean(dataset, video_root_path='/share/data/videos'):
+def calc_mean(dataset, video_root_path='./share/data/videos'):
     import os
     from skimage.io import imread
     import numpy as np
@@ -22,7 +22,7 @@ def calc_mean(dataset, video_root_path='/share/data/videos'):
     np.save(os.path.join(video_root_path, dataset, 'mean_frame_224.npy'), frame_mean)
 
 
-def subtract_mean(dataset, video_root_path='/share/data/videos'):
+def subtract_mean(dataset, video_root_path='./share/data/videos'):
     import os
     from skimage.io import imread
     import numpy as np
@@ -56,7 +56,7 @@ def subtract_mean(dataset, video_root_path='/share/data/videos'):
         np.save(os.path.join(video_root_path, dataset, 'testing_frames_{}.npy'.format(frame_folder)), testing_frames_vid)
 
 
-def build_h5(dataset, train_or_test, t, video_root_path='/share/data/videos'):
+def build_h5(dataset, train_or_test, t, video_root_path='./share/data/videos'):
     import h5py
     from tqdm import tqdm
     import os
@@ -87,7 +87,7 @@ def build_h5(dataset, train_or_test, t, video_root_path='/share/data/videos'):
     build_volume(train_or_test, num_videos, time_length=t)
 
 
-def combine_dataset(dataset, t, video_root_path='/share/data/videos'):
+def combine_dataset(dataset, t, video_root_path='./share/data/videos'):
     import h5py
     import os
     from tqdm import tqdm
@@ -121,7 +121,7 @@ def combine_dataset(dataset, t, video_root_path='/share/data/videos'):
     output_file.close()
 
 
-def preprocess_data(logger, dataset, t, video_root_path='/share/data/videos'):
+def preprocess_data(logger, dataset, t, video_root_path='./share/data/videos'):
     import os
 
     # Step 1: Calculate the mean frame of all training frames
@@ -134,6 +134,7 @@ def preprocess_data(logger, dataset, t, video_root_path='/share/data/videos'):
     testing_frame_path = os.path.join(video_root_path, dataset, 'testing_frames')
     if not os.path.isfile(mean_frame_file):
         # The frames must have already been extracted from training and testing videos
+        # import pdb; pdb.set_trace()
         assert(os.path.isdir(training_frame_path))
         assert(os.path.isdir(testing_frame_path))
         logger.info("Step 1/4: Calculating mean frame for {}".format(dataset))
@@ -182,4 +183,3 @@ def preprocess_data(logger, dataset, t, video_root_path='/share/data/videos'):
         combine_dataset(dataset, t, video_root_path)
 
     logger.info("Preprocessing is completed")
-

requirements.txt

@@ -0,0 +1,34 @@
+absl-py==0.1.11
+astor==0.6.2
+bleach==1.5.0
+coloredlogs==9.0
+cycler==0.10.0
+decorator==4.2.1
+gast==0.2.0
+grpcio==1.10.0
+h5py==2.7.1
+html5lib==0.9999999
+humanfriendly==4.8
+Keras==2.1.5
+kiwisolver==1.0.1
+Markdown==2.6.11
+matplotlib==2.2.0
+networkx==2.1
+numpy==1.14.1
+Pillow==5.0.0
+protobuf==3.5.2
+pyparsing==2.2.0
+python-dateutil==2.6.1
+pytz==2018.3
+PyWavelets==0.5.2
+PyYAML==3.12
+scikit-image==0.13.1
+scikit-learn==0.19.1
+scipy==1.0.0
+six==1.11.0
+sk-video==1.1.10
+tensorboard==1.6.0
+tensorflow==1.6.0
+termcolor==1.1.0
+tqdm==4.19.6
+Werkzeug==0.14.1

setup.sh

@@ -0,0 +1,6 @@
+#!/bin/sh
+
+wget http://www.cse.cuhk.edu.hk/leojia/projects/detectabnormal/Avenue_Dataset.zip
+unzip Avenue_Dataset.zip
+mkdir -p share/data/videos
+mv Avenue\ Dataset share/data/videos/avenue

start_test.py

@@ -13,7 +13,7 @@ epoch = 586
 val_loss = 0.001069
 time_length = 8
 
-job_folder = os.path.join('/share/clean/{}/jobs'.format(dataset), job_uuid)
+job_folder = os.path.join('./share/clean/{}/jobs'.format(dataset), job_uuid)
 log_path = os.path.join(job_folder, 'logs')
 os.makedirs(log_path, exist_ok=True)
 logging.basicConfig(filename=os.path.join(log_path, "test-{}.log".format(datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))),

start_train.py

@@ -7,11 +7,12 @@ from classifier import train
 import uuid
 from shutil import copyfile
 
-dataset = 'ped1'
+# dataset = 'ped1'
+dataset = 'avenue'
 device = 'gpu1'
 
 job_uuid = str(uuid.uuid4())
-job_folder = os.path.join('/share/clean/{}/jobs'.format(dataset), job_uuid)
+job_folder = os.path.join('./share/clean/{}/jobs'.format(dataset), job_uuid)
 os.makedirs(job_folder)
 copyfile('config.yml', os.path.join(job_folder, 'config.yml'))