s478839/wko-projekt
2
1
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity
b3724c49f4
wko-projekt/API/yolo.ipynb

1997 lines
1.9 MiB
Plaintext
Raw Normal View History

add miss deleted file
2023-02-01 00:34:29 +01:00
{
"cells": [
{
"cell_type": "markdown",
"id": "4b1d82bd",
"metadata": {},
"source": [
"# LICENSE PLATE DETECTION"
]
},
{
"cell_type": "markdown",
"id": "9b54ab67",
"metadata": {
"deletable": false,
"nbgrader": {
"cell_type": "code",
"checksum": "775443e2f8e6d780f7310e57d00701e7",
"grade": true,
"grade_id": "cell-53e7c09d33eaba20",
"locked": false,
"points": 6,
"schema_version": 3,
"solution": true,
"task": false
}
},
"source": [
"### YOLO V3"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "61ac9526",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cloning into 'keras-yolo3'...\n",
"remote: Enumerating objects: 169, done.\u001B[K\n",
"remote: Total 169 (delta 0), reused 0 (delta 0), pack-reused 169\u001B[K\n",
"Receiving objects: 100% (169/169), 172.74 KiB | 625.00 KiB/s, done.\n",
"Resolving deltas: 100% (80/80), done.\n"
]
}
],
"source": [
"!git clone https://github.com/roboflow-ai/keras-yolo3"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "5d7010e7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" % Total % Received % Xferd Average Speed Time Time Time Current\n",
" Dload Upload Total Spent Left Speed\n",
"100 897 100 897 0 0 269 0 0:00:03 0:00:03 --:--:-- 269 0\n",
"100 2120k 100 2120k 0 0 515k 0 0:00:04 0:00:04 --:--:-- 23.5M\n",
"Archive: roboflow.zip\n",
" extracting: README.dataset.txt \n",
" extracting: README.roboflow.txt \n",
" creating: test/\n",
" extracting: test/2images1_png.rf.5de47b3b58bc776388f9547915f46edf.jpg \n",
" extracting: test/2images41_png.rf.2f711be90f9f9e796139a02cb45fe9ba.jpg \n",
" extracting: test/2images45_png.rf.cbcc994c49d1a2ca5e7bc52cb9b2a1a3.jpg \n",
" extracting: test/3images22_png.rf.b139cdb6065c658e0c7acc2124854383.jpg \n",
" extracting: test/3images34_png.rf.9a25c14870c5acae15ee0f159a9707b4.jpg \n",
" extracting: test/3images4_png.rf.a3d6d0b11320142ada8e8347c918dc30.jpg \n",
" extracting: test/6images3_png.rf.8b1268f1823ea224077f537939c2ccba.jpg \n",
" extracting: test/7images0_png.rf.fb9d6e1e739e19321bdc7050f4a95798.jpg \n",
" extracting: test/_annotations.txt \n",
" extracting: test/_classes.txt \n",
" extracting: test/images6_png.rf.56641c848717baa02774239ac0039bd6.jpg \n",
" extracting: test/img105_png.rf.d69f400c7410b1e265136d01b1a2cc5e.jpg \n",
" extracting: test/img149_png.rf.c487d9bc6be853e23cc7a12359178b40.jpg \n",
" extracting: test/img14_png.rf.1a47d3748ad1566280dc8199d96430de.jpg \n",
" extracting: test/img35_png.rf.16e367a1ce2db4dc0b0b1491814e8c95.jpg \n",
" extracting: test/img89_png.rf.f0f546c24ed5d6a16a2cbf9389065678.jpg \n",
" creating: train/\n",
" extracting: train/20img2_png.rf.015a51172ce51d61531b54af5a144183.jpg \n",
" extracting: train/21img3_png.rf.c1601abdfd96ebfc6f13205c638364bc.jpg \n",
" extracting: train/22img34_png.rf.02ddffee2d6e8dc6ef169f89f622a933.jpg \n",
" extracting: train/23img46_png.rf.fd5a109b78b90ed3582888880b743303.jpg \n",
" extracting: train/24img50_png.rf.1f28fdcb1632f237fb0bf7be7d877351.jpg \n",
" extracting: train/25img73_png.rf.25d9c97db5c2c466bbe2692f9f69c869.jpg \n",
" extracting: train/26img74_png.rf.861f6c881709f3bb65637c7ea3871dca.jpg \n",
" extracting: train/2images0_png.rf.b8c8f0d2594f6bfaf8be2dca50416bb6.jpg \n",
" extracting: train/2images18_png.rf.951b35372d913193f0899fda6877cbee.jpg \n",
" extracting: train/2images22_png.rf.bb299b6d237016c2714b68aead8266d7.jpg \n",
" extracting: train/2images23_png.rf.cea092359f78eb1c22db6b50627790d6.jpg \n",
" extracting: train/2images29_png.rf.91d8be50c5d0f82577d74268153ac5fc.jpg \n",
" extracting: train/2images2_png.rf.62684ca2757500eaeac877d48e04c92f.jpg \n",
" extracting: train/2images34_png.rf.ffff2284b01426e5cd22ca8053450348.jpg \n",
" extracting: train/2images3_png.rf.c7b635e1dc54f5bb10aa338d78969c22.jpg \n",
" extracting: train/2images46_png.rf.d4143a5946da0d1bc8e540c239a648b8.jpg \n",
" extracting: train/2images4_png.rf.64541674b6b6df83b15534c2d8bf0030.jpg \n",
" extracting: train/30img11_png.rf.1a236b6935fd926336da07248a867a36.jpg \n",
" extracting: train/3images0_png.rf.ff30aaf2256dde95d2dc4893b7074098.jpg \n",
" extracting: train/3images11_png.rf.8a11e1eae3b52a369681843c7d7116d1.jpg \n",
" extracting: train/3images18_png.rf.0673ed9396fa1ae5a43ff44f10422ff5.jpg \n",
" extracting: train/3images29_png.rf.a96af5fe85f477adc0fce370e788f76c.jpg \n",
" extracting: train/3images2_png.rf.d7de4c45de845226a8391e8f332352d9.jpg \n",
" extracting: train/3images30_png.rf.dd0080eda6b7d8ff2e188c7e5590e7c6.jpg \n",
" extracting: train/3images33_png.rf.3abc75a93214fc0a497dd54cabd690a0.jpg \n",
" extracting: train/3images3_png.rf.e9771234c266dba02be2fd6f204aa66b.jpg \n",
" extracting: train/3images42_png.rf.b3b45a46d57ac11c2d546831ad52cceb.jpg \n",
" extracting: train/3images43_png.rf.0603c0f1b7a15be7449b6d46c621e7af.jpg \n",
" extracting: train/3images5_png.rf.6a53d28cdfade27885d25f8208f3028a.jpg \n",
" extracting: train/6images0_png.rf.1e11dd3d7f4e5a79ce207c7770185b0c.jpg \n",
" extracting: train/6images12_png.rf.d0d6b3319c39fdb6a9356047f5ddb8ee.jpg \n",
" extracting: train/6images1_png.rf.8c65b6bfe8d5b01a2a1545337de6c390.jpg \n",
" extracting: train/6images4_png.rf.2c77da3c85f4cb57ebe5d90ab8ed5e0c.jpg \n",
" extracting: train/6images5_png.rf.7033ded0e4684504365b5b0345529c5c.jpg \n",
" extracting: train/7images12_png.rf.c46a44810aea7edafc53b6b561c6cf6a.jpg \n",
" extracting: train/7images17_png.rf.ff8fc5bb0f84483dd914f5f2de524933.jpg \n",
" extracting: train/7images1_png.rf.cf5406f149f35ab24eda2c621f9298ed.jpg \n",
" extracting: train/7images2_png.rf.f84de676f7fb3de9d7789e1dafab8fa3.jpg \n",
" extracting: train/7images3_png.rf.14c5f2588d07e7234659792e20bd7fd8.jpg \n",
" extracting: train/7images4_png.rf.5e455f9a5c94b0a3b56043ef05d06854.jpg \n",
" extracting: train/_annotations.txt \n",
" extracting: train/_classes.txt \n",
" extracting: train/images0_png.rf.d1f446cd89662b7ccf994dc77f63ff56.jpg \n",
" extracting: train/images10_png.rf.bc421baf20b7cbf6af4ea822f259fcab.jpg \n",
" extracting: train/images13_png.rf.dff8711d203b47a3f8709c4cee5d6927.jpg \n",
" extracting: train/images15_png.rf.e1b904b94d5539da79117c3613ae5765.jpg \n",
" extracting: train/images1_png.rf.9c2cb373d7f4613a2735410f1fdb3043.jpg \n",
" extracting: train/images3_png.rf.e7cf0078d44c2571ebc5d607ffaacbc8.jpg \n",
" extracting: train/images4_png.rf.97f8f01f67adf77de50c99fd6ed7f879.jpg \n",
" extracting: train/images5_png.rf.d16b8c87a8a593e5971124648ba63736.jpg \n",
" extracting: train/img0_png.rf.fa065b68c3d51d65399f883f8713ccf2.jpg \n",
" extracting: train/img102_png.rf.3da7ec4deedfb6f15834e9a42aee4e7c.jpg \n",
" extracting: train/img103_png.rf.67216b08a719a9a9dba68f83c5460a74.jpg \n",
" extracting: train/img104_png.rf.db759d639a6b1ace6dc8e7442c86ba9a.jpg \n",
" extracting: train/img106_png.rf.d882268d61ac720e54c35110fb8bc4b0.jpg \n",
" extracting: train/img107_png.rf.a62231fc47913091ec76468e536d6f28.jpg \n",
" extracting: train/img10_png.rf.e7bba8322d47d623f71903aa50f48730.jpg \n",
" extracting: train/img113_png.rf.cb3afcbea4e7177a2ed703b4b1d94887.jpg \n",
" extracting: train/img116_png.rf.1f7034a069e5a888b00da9496e0df5ae.jpg \n",
" extracting: train/img118_png.rf.9e21a52ffda3719b2cc6deb0309efd7d.jpg \n",
" extracting: train/img11_png.rf.a9584dc2d254fd84ca6a30cc9b821bd5.jpg \n",
" extracting: train/img125_png.rf.4a0a9a2f74bd5127343124c4fb4d0670.jpg \n",
" extracting: train/img126_png.rf.0bad29364a3846287498838f6791cae8.jpg \n",
" extracting: train/img133_png.rf.e66c88015d6fb51921b20ad8008fc981.jpg \n",
" extracting: train/img146_png.rf.9811cc9a676e18c4cf2bce86398feb9d.jpg \n",
" extracting: train/img170_png.rf.1d04d991430ba0d672fabff684817dc6.jpg \n",
" extracting: train/img174_png.rf.4d01b9ebbdc8c1b434c61c945794a79e.jpg \n",
" extracting: train/img178_png.rf.b0e5b6547069d86483e91fc99356e5d9.jpg \n",
" extracting: train/img181_png.rf.363074a89b0325055d28f3794083e479.jpg \n",
" extracting: train/img189_png.rf.07aedf508ccbfc3e0244bd54bd76cbf8.jpg \n",
" extracting: train/img197_png.rf.36119ab11e392cfeded10c61aa97eac6.jpg \n",
" extracting: train/img1_png.rf.bf5b1060d3cb9959dc94b75d4fc78334.jpg \n",
" extracting: train/img202_png.rf.f6520c22d6c95c8e5a105b6ee48b8da1.jpg \n",
" extracting: train/img205_png.rf.98d121af5e0548a1402eb3e93560465d.jpg \n",
" extracting: train/img215_png.rf.6d29cfcf38f6a4b2165ba5ba110454d2.jpg \n",
" extracting: train/img270_png.rf.52541958250f2b45297faa1440d55d56.jpg \n",
" extracting: train/img278_png.rf.82173849dfde92f2a2ab2761e5679891.jpg \n",
" extracting: train/img283_png.rf.809b4e6edbe803fbcab887a40e59f526.jpg \n",
" extracting: train/img2_png.rf.23b2d7fe287627739888976776de8437.jpg \n",
" extracting: train/img306_png.rf.642a9812ecebfd9784d9eb593b78dcf2.jpg \n",
" extracting: train/img34_png.rf.f98b7fa7325ddb9ca373121c5c120f55.jpg \n",
" extracting: train/img38_png.rf.cd97a110e34ad869a4b79d8237d92a36.jpg \n",
" extracting: train/img39_png.rf.e9b1634ca400418b29839bad544e8634.jpg \n",
" extracting: train/img3_png.rf.3f382680461124ba2e19c1df51d895e7.jpg \n",
" extracting: train/img45_png.rf.870b550082c3da2c42e40017442c115b.jpg \n",
" extracting: train/img46_png.rf.2c1d961d3e61d1389c825f2aba32ab39.jpg \n",
" extracting: train/img4_png.rf.4f0ce3c02167bf3f8ae2454471c9c4fd.jpg \n",
" extracting: train/img57_png.rf.dc254e143fec0667ac462e303290e301.jpg \n",
" extracting: train/img58_png.rf.1a6e09bda52588bb7f3890768f0db5f2.jpg \n",
" extracting: train/img5_png.rf.542fe1bdd2a910b20f27ce55cf8689ff.jpg \n",
" extracting: train/img66_png.rf.534ec186146ae4409f8c875cf28dcb84.jpg \n",
" extracting: train/img6_png.rf.7aceac81d4a22f02ab0460ee5bd2227f.jpg \n",
" extracting: train/img77_png.rf.8f8e23567322fd7de129380c6a54bd01.jpg \n",
" extracting: train/img78_png.rf.eb48e94d48c04b3077d049cb8cd920bb.jpg \n",
" extracting: train/img7_png.rf.2dd95d826f13ab03805de7f7b842eb40.jpg \n",
" extracting: train/img85_png.rf.f7a4ae3bb16a8c3fe7f164e35f11ea65.jpg \n",
" extracting: train/img86_png.rf.3addc2b6c62b8d5098feba035bd6014d.jpg \n",
" extracting: train/img92_png.rf.5b79211320122e08554541c15fc041dd.jpg \n",
" extracting: train/img93_png.rf.7fbe9b0dcab1f063b154796d00ae669b.jpg \n",
" extracting: train/img95_png.rf.c97bb901c22e4f1519bac037ffbdbbf7.jpg \n",
" extracting: train/img97_png.rf.2e3f7205a9d122aa07906ebe643f1c04.jpg \n",
" extracting: train/img98_png.rf.c6da81320ec0c22868d84c2291b416f5.jpg \n",
" creating: valid/\n",
" extracting: valid/27img121_png.rf.6b1bbeee06ff52963c7b12c7bfb2aacc.jpg \n",
" extracting: valid/2images12_png.rf.ba715b76693ae62d01e142ba9859ffc9.jpg \n",
" extracting: valid/2images35_png.rf.81e0cc483a896440e148a5df5550d243.jpg \n",
" extracting: valid/2images40_png.rf.45e16e4d96b21eeb7b0e06556ca12291.jpg \n",
" extracting: valid/3images19_png.rf.aec1de41eff03d6e343427691b2a3029.jpg \n",
" extracting: valid/3images1_png.rf.f293d93f952977825a07613f23a55f70.jpg \n",
" extracting: valid/6images11_png.rf.a467d473bfa546de8e2c5ef4ef894802.jpg \n",
" extracting: valid/6images2_png.rf.386c9a11cef823c522619aefd9c7ca9d.jpg \n",
" extracting: valid/_annotations.txt \n",
" extracting: valid/_classes.txt \n",
" extracting: valid/images14_png.rf.f0a78b8df38e6394e9cc3d56d7677c87.jpg \n",
" extracting: valid/images2_png.rf.1f566a50352095712ec385ffc17b14c5.jpg \n",
" extracting: valid/img101_png.rf.aca3e688b7798ee456467954274733de.jpg \n",
" extracting: valid/img111_png.rf.4bc2a8d175d8bbe2a289ba9e0ed4c717.jpg \n",
" extracting: valid/img112_png.rf.aaadc30802c92e3c1196a96b859c8ebb.jpg \n",
" extracting: valid/img117_png.rf.76d5b2f35f4974cca3750f258af86101.jpg \n",
" extracting: valid/img121_png.rf.a11051677709f708036ca072d0725099.jpg \n",
" extracting: valid/img122_png.rf.f6c62a3f0290eae81ffc5c457f546adf.jpg \n",
" extracting: valid/img141_png.rf.9d9ff6b78c2940546bf364e662b1c813.jpg \n",
" extracting: valid/img165_png.rf.6bb45f3455f0340e377ec61e662d7846.jpg \n",
" extracting: valid/img177_png.rf.fd279311108df43a7d9225cc26c2542f.jpg \n",
" extracting: valid/img262_png.rf.cd066cf49feb976bf8cd8eca32dcf729.jpg \n",
" extracting: valid/img27_png.rf.09745a24cc36301e1eca5c3a9bab3853.jpg \n",
" extracting: valid/img304_png.rf.f91aa4dfe963c390a521fd748f1ab9f5.jpg \n",
" extracting: valid/img313_png.rf.8ea5815425e82f42c06715e0b98342f2.jpg \n",
" extracting: valid/img31_png.rf.3b72bf618de466d70ab487fe5e20ff70.jpg \n",
" extracting: valid/img40_png.rf.8389bb867a237cad805b4819dc788a98.jpg \n",
" extracting: valid/img41_png.rf.4f6f5b9dcbe9eb80f9913e223f321f66.jpg \n",
" extracting: valid/img69_png.rf.52cb5ea0d37bc73a2fcc1ee19de2b124.jpg \n",
" extracting: valid/img84_png.rf.c9700ee5dee2697886b497a2e17f1573.jpg \n"
]
}
],
"source": [
"!curl -L \"https://app.roboflow.com/ds/hTj8Pr7g7U?key=q9kdROYojM\" > roboflow.zip; unzip roboflow.zip; rm roboflow.zip"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "6989cf92",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--2023-01-18 12:01:19-- https://pjreddie.com/media/files/yolov3.weights\n",
"Translacja pjreddie.com (pjreddie.com)... 128.208.4.108\n",
"Łączenie się z pjreddie.com (pjreddie.com)|128.208.4.108|:443... połączono.\n",
"Żądanie HTTP wysłano, oczekiwanie na odpowiedź... 200 OK\n",
"Długość: 248007048 (237M) [application/octet-stream]\n",
"Zapis do: `yolov3.weights'\n",
"\n",
"yolov3.weights 100%[===================>] 236,52M 17,0MB/s w 15s \n",
"\n",
"2023-01-18 12:01:35 (15,4 MB/s) - zapisano `yolov3.weights' [248007048/248007048]\n",
"\n"
]
}
],
"source": [
"!wget https://pjreddie.com/media/files/yolov3.weights"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "e981c95d",
"metadata": {},
"outputs": [],
"source": [
"from keras.layers import ELU, PReLU, LeakyReLU"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "d8924aad",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loading weights.\n",
"Weights Header: 0 2 0 [32013312]\n",
"Parsing Darknet config.\n",
"Creating Keras model.\n",
"Parsing section net_0\n",
"Parsing section convolutional_0\n",
"conv2d bn leaky (3, 3, 3, 32)\n",
"Metal device set to: Apple M1\n",
"\n",
"systemMemory: 8.00 GB\n",
"maxCacheSize: 2.67 GB\n",
"\n",
"2023-01-18 12:03:25.001841: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:306] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
"2023-01-18 12:03:25.002402: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:272] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n",
"Parsing section convolutional_1\n",
"conv2d bn leaky (3, 3, 32, 64)\n",
"Parsing section convolutional_2\n",
"conv2d bn leaky (1, 1, 64, 32)\n",
"Parsing section convolutional_3\n",
"conv2d bn leaky (3, 3, 32, 64)\n",
"Parsing section shortcut_0\n",
"Parsing section convolutional_4\n",
"conv2d bn leaky (3, 3, 64, 128)\n",
"Parsing section convolutional_5\n",
"conv2d bn leaky (1, 1, 128, 64)\n",
"Parsing section convolutional_6\n",
"conv2d bn leaky (3, 3, 64, 128)\n",
"Parsing section shortcut_1\n",
"Parsing section convolutional_7\n",
"conv2d bn leaky (1, 1, 128, 64)\n",
"Parsing section convolutional_8\n",
"conv2d bn leaky (3, 3, 64, 128)\n",
"Parsing section shortcut_2\n",
"Parsing section convolutional_9\n",
"conv2d bn leaky (3, 3, 128, 256)\n",
"Parsing section convolutional_10\n",
"conv2d bn leaky (1, 1, 256, 128)\n",
"Parsing section convolutional_11\n",
"conv2d bn leaky (3, 3, 128, 256)\n",
"Parsing section shortcut_3\n",
"Parsing section convolutional_12\n",
"conv2d bn leaky (1, 1, 256, 128)\n",
"Parsing section convolutional_13\n",
"conv2d bn leaky (3, 3, 128, 256)\n",
"Parsing section shortcut_4\n",
"Parsing section convolutional_14\n",
"conv2d bn leaky (1, 1, 256, 128)\n",
"Parsing section convolutional_15\n",
"conv2d bn leaky (3, 3, 128, 256)\n",
"Parsing section shortcut_5\n",
"Parsing section convolutional_16\n",
"conv2d bn leaky (1, 1, 256, 128)\n",
"Parsing section convolutional_17\n",
"conv2d bn leaky (3, 3, 128, 256)\n",
"Parsing section shortcut_6\n",
"Parsing section convolutional_18\n",
"conv2d bn leaky (1, 1, 256, 128)\n",
"Parsing section convolutional_19\n",
"conv2d bn leaky (3, 3, 128, 256)\n",
"Parsing section shortcut_7\n",
"Parsing section convolutional_20\n",
"conv2d bn leaky (1, 1, 256, 128)\n",
"Parsing section convolutional_21\n",
"conv2d bn leaky (3, 3, 128, 256)\n",
"Parsing section shortcut_8\n",
"Parsing section convolutional_22\n",
"conv2d bn leaky (1, 1, 256, 128)\n",
"Parsing section convolutional_23\n",
"conv2d bn leaky (3, 3, 128, 256)\n",
"Parsing section shortcut_9\n",
"Parsing section convolutional_24\n",
"conv2d bn leaky (1, 1, 256, 128)\n",
"Parsing section convolutional_25\n",
"conv2d bn leaky (3, 3, 128, 256)\n",
"Parsing section shortcut_10\n",
"Parsing section convolutional_26\n",
"conv2d bn leaky (3, 3, 256, 512)\n",
"Parsing section convolutional_27\n",
"conv2d bn leaky (1, 1, 512, 256)\n",
"Parsing section convolutional_28\n",
"conv2d bn leaky (3, 3, 256, 512)\n",
"Parsing section shortcut_11\n",
"Parsing section convolutional_29\n",
"conv2d bn leaky (1, 1, 512, 256)\n",
"Parsing section convolutional_30\n",
"conv2d bn leaky (3, 3, 256, 512)\n",
"Parsing section shortcut_12\n",
"Parsing section convolutional_31\n",
"conv2d bn leaky (1, 1, 512, 256)\n",
"Parsing section convolutional_32\n",
"conv2d bn leaky (3, 3, 256, 512)\n",
"Parsing section shortcut_13\n",
"Parsing section convolutional_33\n",
"conv2d bn leaky (1, 1, 512, 256)\n",
"Parsing section convolutional_34\n",
"conv2d bn leaky (3, 3, 256, 512)\n",
"Parsing section shortcut_14\n",
"Parsing section convolutional_35\n",
"conv2d bn leaky (1, 1, 512, 256)\n",
"Parsing section convolutional_36\n",
"conv2d bn leaky (3, 3, 256, 512)\n",
"Parsing section shortcut_15\n",
"Parsing section convolutional_37\n",
"conv2d bn leaky (1, 1, 512, 256)\n",
"Parsing section convolutional_38\n",
"conv2d bn leaky (3, 3, 256, 512)\n",
"Parsing section shortcut_16\n",
"Parsing section convolutional_39\n",
"conv2d bn leaky (1, 1, 512, 256)\n",
"Parsing section convolutional_40\n",
"conv2d bn leaky (3, 3, 256, 512)\n",
"Parsing section shortcut_17\n",
"Parsing section convolutional_41\n",
"conv2d bn leaky (1, 1, 512, 256)\n",
"Parsing section convolutional_42\n",
"conv2d bn leaky (3, 3, 256, 512)\n",
"Parsing section shortcut_18\n",
"Parsing section convolutional_43\n",
"conv2d bn leaky (3, 3, 512, 1024)\n",
"Parsing section convolutional_44\n",
"conv2d bn leaky (1, 1, 1024, 512)\n",
"Parsing section convolutional_45\n",
"conv2d bn leaky (3, 3, 512, 1024)\n",
"Parsing section shortcut_19\n",
"Parsing section convolutional_46\n",
"conv2d bn leaky (1, 1, 1024, 512)\n",
"Parsing section convolutional_47\n",
"conv2d bn leaky (3, 3, 512, 1024)\n",
"Parsing section shortcut_20\n",
"Parsing section convolutional_48\n",
"conv2d bn leaky (1, 1, 1024, 512)\n",
"Parsing section convolutional_49\n",
"conv2d bn leaky (3, 3, 512, 1024)\n",
"Parsing section shortcut_21\n",
"Parsing section convolutional_50\n",
"conv2d bn leaky (1, 1, 1024, 512)\n",
"Parsing section convolutional_51\n",
"conv2d bn leaky (3, 3, 512, 1024)\n",
"Parsing section shortcut_22\n",
"Parsing section convolutional_52\n",
"conv2d bn leaky (1, 1, 1024, 512)\n",
"Parsing section convolutional_53\n",
"conv2d bn leaky (3, 3, 512, 1024)\n",
"Parsing section convolutional_54\n",
"conv2d bn leaky (1, 1, 1024, 512)\n",
"Parsing section convolutional_55\n",
"conv2d bn leaky (3, 3, 512, 1024)\n",
"Parsing section convolutional_56\n",
"conv2d bn leaky (1, 1, 1024, 512)\n",
"Parsing section convolutional_57\n",
"conv2d bn leaky (3, 3, 512, 1024)\n",
"Parsing section convolutional_58\n",
"conv2d linear (1, 1, 1024, 255)\n",
"Parsing section yolo_0\n",
"Parsing section route_0\n",
"Parsing section convolutional_59\n",
"conv2d bn leaky (1, 1, 512, 256)\n",
"Parsing section upsample_0\n",
"Parsing section route_1\n",
"Concatenating route layers: [<KerasTensor: shape=(None, None, None, 256) dtype=float32 (created by layer 'up_sampling2d')>, <KerasTensor: shape=(None, None, None, 512) dtype=float32 (created by layer 'add_18')>]\n",
"Parsing section convolutional_60\n",
"conv2d bn leaky (1, 1, 768, 256)\n",
"Parsing section convolutional_61\n",
"conv2d bn leaky (3, 3, 256, 512)\n",
"Parsing section convolutional_62\n",
"conv2d bn leaky (1, 1, 512, 256)\n",
"Parsing section convolutional_63\n",
"conv2d bn leaky (3, 3, 256, 512)\n",
"Parsing section convolutional_64\n",
"conv2d bn leaky (1, 1, 512, 256)\n",
"Parsing section convolutional_65\n",
"conv2d bn leaky (3, 3, 256, 512)\n",
"Parsing section convolutional_66\n",
"conv2d linear (1, 1, 512, 255)\n",
"Parsing section yolo_1\n",
"Parsing section route_2\n",
"Parsing section convolutional_67\n",
"conv2d bn leaky (1, 1, 256, 128)\n",
"Parsing section upsample_1\n",
"Parsing section route_3\n",
"Concatenating route layers: [<KerasTensor: shape=(None, None, None, 128) dtype=float32 (created by layer 'up_sampling2d_1')>, <KerasTensor: shape=(None, None, None, 256) dtype=float32 (created by layer 'add_10')>]\n",
"Parsing section convolutional_68\n",
"conv2d bn leaky (1, 1, 384, 128)\n",
"Parsing section convolutional_69\n",
"conv2d bn leaky (3, 3, 128, 256)\n",
"Parsing section convolutional_70\n",
"conv2d bn leaky (1, 1, 256, 128)\n",
"Parsing section convolutional_71\n",
"conv2d bn leaky (3, 3, 128, 256)\n",
"Parsing section convolutional_72\n",
"conv2d bn leaky (1, 1, 256, 128)\n",
"Parsing section convolutional_73\n",
"conv2d bn leaky (3, 3, 128, 256)\n",
"Parsing section convolutional_74\n",
"conv2d linear (1, 1, 256, 255)\n",
"Parsing section yolo_2\n",
"Model: \"model\"\n",
"__________________________________________________________________________________________________\n",
" Layer (type) Output Shape Param # Connected to \n",
"==================================================================================================\n",
" input_1 (InputLayer) [(None, None, None, 0 [] \n",
" 3)] \n",
" \n",
" conv2d (Conv2D) (None, None, None, 864 ['input_1[0][0]'] \n",
" 32) \n",
" \n",
" batch_normalization (BatchNorm (None, None, None, 128 ['conv2d[0][0]'] \n",
" alization) 32) \n",
" \n",
" leaky_re_lu (LeakyReLU) (None, None, None, 0 ['batch_normalization[0][0]'] \n",
" 32) \n",
" \n",
" zero_padding2d (ZeroPadding2D) (None, None, None, 0 ['leaky_re_lu[0][0]'] \n",
" 32) \n",
" \n",
" conv2d_1 (Conv2D) (None, None, None, 18432 ['zero_padding2d[0][0]'] \n",
" 64) \n",
" \n",
" batch_normalization_1 (BatchNo (None, None, None, 256 ['conv2d_1[0][0]'] \n",
" rmalization) 64) \n",
" \n",
" leaky_re_lu_1 (LeakyReLU) (None, None, None, 0 ['batch_normalization_1[0][0]'] \n",
" 64) \n",
" \n",
" conv2d_2 (Conv2D) (None, None, None, 2048 ['leaky_re_lu_1[0][0]'] \n",
" 32) \n",
" \n",
" batch_normalization_2 (BatchNo (None, None, None, 128 ['conv2d_2[0][0]'] \n",
" rmalization) 32) \n",
" \n",
" leaky_re_lu_2 (LeakyReLU) (None, None, None, 0 ['batch_normalization_2[0][0]'] \n",
" 32) \n",
" \n",
" conv2d_3 (Conv2D) (None, None, None, 18432 ['leaky_re_lu_2[0][0]'] \n",
" 64) \n",
" \n",
" batch_normalization_3 (BatchNo (None, None, None, 256 ['conv2d_3[0][0]'] \n",
" rmalization) 64) \n",
" \n",
" leaky_re_lu_3 (LeakyReLU) (None, None, None, 0 ['batch_normalization_3[0][0]'] \n",
" 64) \n",
" \n",
" add (Add) (None, None, None, 0 ['leaky_re_lu_1[0][0]', \n",
" 64) 'leaky_re_lu_3[0][0]'] \n",
" \n",
" zero_padding2d_1 (ZeroPadding2 (None, None, None, 0 ['add[0][0]'] \n",
" D) 64) \n",
" \n",
" conv2d_4 (Conv2D) (None, None, None, 73728 ['zero_padding2d_1[0][0]'] \n",
" 128) \n",
" \n",
" batch_normalization_4 (BatchNo (None, None, None, 512 ['conv2d_4[0][0]'] \n",
" rmalization) 128) \n",
" \n",
" leaky_re_lu_4 (LeakyReLU) (None, None, None, 0 ['batch_normalization_4[0][0]'] \n",
" 128) \n",
" \n",
" conv2d_5 (Conv2D) (None, None, None, 8192 ['leaky_re_lu_4[0][0]'] \n",
" 64) \n",
" \n",
" batch_normalization_5 (BatchNo (None, None, None, 256 ['conv2d_5[0][0]'] \n",
" rmalization) 64) \n",
" \n",
" leaky_re_lu_5 (LeakyReLU) (None, None, None, 0 ['batch_normalization_5[0][0]'] \n",
" 64) \n",
" \n",
" conv2d_6 (Conv2D) (None, None, None, 73728 ['leaky_re_lu_5[0][0]'] \n",
" 128) \n",
" \n",
" batch_normalization_6 (BatchNo (None, None, None, 512 ['conv2d_6[0][0]'] \n",
" rmalization) 128) \n",
" \n",
" leaky_re_lu_6 (LeakyReLU) (None, None, None, 0 ['batch_normalization_6[0][0]'] \n",
" 128) \n",
" \n",
" add_1 (Add) (None, None, None, 0 ['leaky_re_lu_4[0][0]', \n",
" 128) 'leaky_re_lu_6[0][0]'] \n",
" \n",
" conv2d_7 (Conv2D) (None, None, None, 8192 ['add_1[0][0]'] \n",
" 64) \n",
" \n",
" batch_normalization_7 (BatchNo (None, None, None, 256 ['conv2d_7[0][0]'] \n",
" rmalization) 64) \n",
" \n",
" leaky_re_lu_7 (LeakyReLU) (None, None, None, 0 ['batch_normalization_7[0][0]'] \n",
" 64) \n",
" \n",
" conv2d_8 (Conv2D) (None, None, None, 73728 ['leaky_re_lu_7[0][0]'] \n",
" 128) \n",
" \n",
" batch_normalization_8 (BatchNo (None, None, None, 512 ['conv2d_8[0][0]'] \n",
" rmalization) 128) \n",
" \n",
" leaky_re_lu_8 (LeakyReLU) (None, None, None, 0 ['batch_normalization_8[0][0]'] \n",
" 128) \n",
" \n",
" add_2 (Add) (None, None, None, 0 ['add_1[0][0]', \n",
" 128) 'leaky_re_lu_8[0][0]'] \n",
" \n",
" zero_padding2d_2 (ZeroPadding2 (None, None, None, 0 ['add_2[0][0]'] \n",
" D) 128) \n",
" \n",
" conv2d_9 (Conv2D) (None, None, None, 294912 ['zero_padding2d_2[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_9 (BatchNo (None, None, None, 1024 ['conv2d_9[0][0]'] \n",
" rmalization) 256) \n",
" \n",
" leaky_re_lu_9 (LeakyReLU) (None, None, None, 0 ['batch_normalization_9[0][0]'] \n",
" 256) \n",
" \n",
" conv2d_10 (Conv2D) (None, None, None, 32768 ['leaky_re_lu_9[0][0]'] \n",
" 128) \n",
" \n",
" batch_normalization_10 (BatchN (None, None, None, 512 ['conv2d_10[0][0]'] \n",
" ormalization) 128) \n",
" \n",
" leaky_re_lu_10 (LeakyReLU) (None, None, None, 0 ['batch_normalization_10[0][0]'] \n",
" 128) \n",
" \n",
" conv2d_11 (Conv2D) (None, None, None, 294912 ['leaky_re_lu_10[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_11 (BatchN (None, None, None, 1024 ['conv2d_11[0][0]'] \n",
" ormalization) 256) \n",
" \n",
" leaky_re_lu_11 (LeakyReLU) (None, None, None, 0 ['batch_normalization_11[0][0]'] \n",
" 256) \n",
" \n",
" add_3 (Add) (None, None, None, 0 ['leaky_re_lu_9[0][0]', \n",
" 256) 'leaky_re_lu_11[0][0]'] \n",
" \n",
" conv2d_12 (Conv2D) (None, None, None, 32768 ['add_3[0][0]'] \n",
" 128) \n",
" \n",
" batch_normalization_12 (BatchN (None, None, None, 512 ['conv2d_12[0][0]'] \n",
" ormalization) 128) \n",
" \n",
" leaky_re_lu_12 (LeakyReLU) (None, None, None, 0 ['batch_normalization_12[0][0]'] \n",
" 128) \n",
" \n",
" conv2d_13 (Conv2D) (None, None, None, 294912 ['leaky_re_lu_12[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_13 (BatchN (None, None, None, 1024 ['conv2d_13[0][0]'] \n",
" ormalization) 256) \n",
" \n",
" leaky_re_lu_13 (LeakyReLU) (None, None, None, 0 ['batch_normalization_13[0][0]'] \n",
" 256) \n",
" \n",
" add_4 (Add) (None, None, None, 0 ['add_3[0][0]', \n",
" 256) 'leaky_re_lu_13[0][0]'] \n",
" \n",
" conv2d_14 (Conv2D) (None, None, None, 32768 ['add_4[0][0]'] \n",
" 128) \n",
" \n",
" batch_normalization_14 (BatchN (None, None, None, 512 ['conv2d_14[0][0]'] \n",
" ormalization) 128) \n",
" \n",
" leaky_re_lu_14 (LeakyReLU) (None, None, None, 0 ['batch_normalization_14[0][0]'] \n",
" 128) \n",
" \n",
" conv2d_15 (Conv2D) (None, None, None, 294912 ['leaky_re_lu_14[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_15 (BatchN (None, None, None, 1024 ['conv2d_15[0][0]'] \n",
" ormalization) 256) \n",
" \n",
" leaky_re_lu_15 (LeakyReLU) (None, None, None, 0 ['batch_normalization_15[0][0]'] \n",
" 256) \n",
" \n",
" add_5 (Add) (None, None, None, 0 ['add_4[0][0]', \n",
" 256) 'leaky_re_lu_15[0][0]'] \n",
" \n",
" conv2d_16 (Conv2D) (None, None, None, 32768 ['add_5[0][0]'] \n",
" 128) \n",
" \n",
" batch_normalization_16 (BatchN (None, None, None, 512 ['conv2d_16[0][0]'] \n",
" ormalization) 128) \n",
" \n",
" leaky_re_lu_16 (LeakyReLU) (None, None, None, 0 ['batch_normalization_16[0][0]'] \n",
" 128) \n",
" \n",
" conv2d_17 (Conv2D) (None, None, None, 294912 ['leaky_re_lu_16[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_17 (BatchN (None, None, None, 1024 ['conv2d_17[0][0]'] \n",
" ormalization) 256) \n",
" \n",
" leaky_re_lu_17 (LeakyReLU) (None, None, None, 0 ['batch_normalization_17[0][0]'] \n",
" 256) \n",
" \n",
" add_6 (Add) (None, None, None, 0 ['add_5[0][0]', \n",
" 256) 'leaky_re_lu_17[0][0]'] \n",
" \n",
" conv2d_18 (Conv2D) (None, None, None, 32768 ['add_6[0][0]'] \n",
" 128) \n",
" \n",
" batch_normalization_18 (BatchN (None, None, None, 512 ['conv2d_18[0][0]'] \n",
" ormalization) 128) \n",
" \n",
" leaky_re_lu_18 (LeakyReLU) (None, None, None, 0 ['batch_normalization_18[0][0]'] \n",
" 128) \n",
" \n",
" conv2d_19 (Conv2D) (None, None, None, 294912 ['leaky_re_lu_18[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_19 (BatchN (None, None, None, 1024 ['conv2d_19[0][0]'] \n",
" ormalization) 256) \n",
" \n",
" leaky_re_lu_19 (LeakyReLU) (None, None, None, 0 ['batch_normalization_19[0][0]'] \n",
" 256) \n",
" \n",
" add_7 (Add) (None, None, None, 0 ['add_6[0][0]', \n",
" 256) 'leaky_re_lu_19[0][0]'] \n",
" \n",
" conv2d_20 (Conv2D) (None, None, None, 32768 ['add_7[0][0]'] \n",
" 128) \n",
" \n",
" batch_normalization_20 (BatchN (None, None, None, 512 ['conv2d_20[0][0]'] \n",
" ormalization) 128) \n",
" \n",
" leaky_re_lu_20 (LeakyReLU) (None, None, None, 0 ['batch_normalization_20[0][0]'] \n",
" 128) \n",
" \n",
" conv2d_21 (Conv2D) (None, None, None, 294912 ['leaky_re_lu_20[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_21 (BatchN (None, None, None, 1024 ['conv2d_21[0][0]'] \n",
" ormalization) 256) \n",
" \n",
" leaky_re_lu_21 (LeakyReLU) (None, None, None, 0 ['batch_normalization_21[0][0]'] \n",
" 256) \n",
" \n",
" add_8 (Add) (None, None, None, 0 ['add_7[0][0]', \n",
" 256) 'leaky_re_lu_21[0][0]'] \n",
" \n",
" conv2d_22 (Conv2D) (None, None, None, 32768 ['add_8[0][0]'] \n",
" 128) \n",
" \n",
" batch_normalization_22 (BatchN (None, None, None, 512 ['conv2d_22[0][0]'] \n",
" ormalization) 128) \n",
" \n",
" leaky_re_lu_22 (LeakyReLU) (None, None, None, 0 ['batch_normalization_22[0][0]'] \n",
" 128) \n",
" \n",
" conv2d_23 (Conv2D) (None, None, None, 294912 ['leaky_re_lu_22[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_23 (BatchN (None, None, None, 1024 ['conv2d_23[0][0]'] \n",
" ormalization) 256) \n",
" \n",
" leaky_re_lu_23 (LeakyReLU) (None, None, None, 0 ['batch_normalization_23[0][0]'] \n",
" 256) \n",
" \n",
" add_9 (Add) (None, None, None, 0 ['add_8[0][0]', \n",
" 256) 'leaky_re_lu_23[0][0]'] \n",
" \n",
" conv2d_24 (Conv2D) (None, None, None, 32768 ['add_9[0][0]'] \n",
" 128) \n",
" \n",
" batch_normalization_24 (BatchN (None, None, None, 512 ['conv2d_24[0][0]'] \n",
" ormalization) 128) \n",
" \n",
" leaky_re_lu_24 (LeakyReLU) (None, None, None, 0 ['batch_normalization_24[0][0]'] \n",
" 128) \n",
" \n",
" conv2d_25 (Conv2D) (None, None, None, 294912 ['leaky_re_lu_24[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_25 (BatchN (None, None, None, 1024 ['conv2d_25[0][0]'] \n",
" ormalization) 256) \n",
" \n",
" leaky_re_lu_25 (LeakyReLU) (None, None, None, 0 ['batch_normalization_25[0][0]'] \n",
" 256) \n",
" \n",
" add_10 (Add) (None, None, None, 0 ['add_9[0][0]', \n",
" 256) 'leaky_re_lu_25[0][0]'] \n",
" \n",
" zero_padding2d_3 (ZeroPadding2 (None, None, None, 0 ['add_10[0][0]'] \n",
" D) 256) \n",
" \n",
" conv2d_26 (Conv2D) (None, None, None, 1179648 ['zero_padding2d_3[0][0]'] \n",
" 512) \n",
" \n",
" batch_normalization_26 (BatchN (None, None, None, 2048 ['conv2d_26[0][0]'] \n",
" ormalization) 512) \n",
" \n",
" leaky_re_lu_26 (LeakyReLU) (None, None, None, 0 ['batch_normalization_26[0][0]'] \n",
" 512) \n",
" \n",
" conv2d_27 (Conv2D) (None, None, None, 131072 ['leaky_re_lu_26[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_27 (BatchN (None, None, None, 1024 ['conv2d_27[0][0]'] \n",
" ormalization) 256) \n",
" \n",
" leaky_re_lu_27 (LeakyReLU) (None, None, None, 0 ['batch_normalization_27[0][0]'] \n",
" 256) \n",
" \n",
" conv2d_28 (Conv2D) (None, None, None, 1179648 ['leaky_re_lu_27[0][0]'] \n",
" 512) \n",
" \n",
" batch_normalization_28 (BatchN (None, None, None, 2048 ['conv2d_28[0][0]'] \n",
" ormalization) 512) \n",
" \n",
" leaky_re_lu_28 (LeakyReLU) (None, None, None, 0 ['batch_normalization_28[0][0]'] \n",
" 512) \n",
" \n",
" add_11 (Add) (None, None, None, 0 ['leaky_re_lu_26[0][0]', \n",
" 512) 'leaky_re_lu_28[0][0]'] \n",
" \n",
" conv2d_29 (Conv2D) (None, None, None, 131072 ['add_11[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_29 (BatchN (None, None, None, 1024 ['conv2d_29[0][0]'] \n",
" ormalization) 256) \n",
" \n",
" leaky_re_lu_29 (LeakyReLU) (None, None, None, 0 ['batch_normalization_29[0][0]'] \n",
" 256) \n",
" \n",
" conv2d_30 (Conv2D) (None, None, None, 1179648 ['leaky_re_lu_29[0][0]'] \n",
" 512) \n",
" \n",
" batch_normalization_30 (BatchN (None, None, None, 2048 ['conv2d_30[0][0]'] \n",
" ormalization) 512) \n",
" \n",
" leaky_re_lu_30 (LeakyReLU) (None, None, None, 0 ['batch_normalization_30[0][0]'] \n",
" 512) \n",
" \n",
" add_12 (Add) (None, None, None, 0 ['add_11[0][0]', \n",
" 512) 'leaky_re_lu_30[0][0]'] \n",
" \n",
" conv2d_31 (Conv2D) (None, None, None, 131072 ['add_12[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_31 (BatchN (None, None, None, 1024 ['conv2d_31[0][0]'] \n",
" ormalization) 256) \n",
" \n",
" leaky_re_lu_31 (LeakyReLU) (None, None, None, 0 ['batch_normalization_31[0][0]'] \n",
" 256) \n",
" \n",
" conv2d_32 (Conv2D) (None, None, None, 1179648 ['leaky_re_lu_31[0][0]'] \n",
" 512) \n",
" \n",
" batch_normalization_32 (BatchN (None, None, None, 2048 ['conv2d_32[0][0]'] \n",
" ormalization) 512) \n",
" \n",
" leaky_re_lu_32 (LeakyReLU) (None, None, None, 0 ['batch_normalization_32[0][0]'] \n",
" 512) \n",
" \n",
" add_13 (Add) (None, None, None, 0 ['add_12[0][0]', \n",
" 512) 'leaky_re_lu_32[0][0]'] \n",
" \n",
" conv2d_33 (Conv2D) (None, None, None, 131072 ['add_13[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_33 (BatchN (None, None, None, 1024 ['conv2d_33[0][0]'] \n",
" ormalization) 256) \n",
" \n",
" leaky_re_lu_33 (LeakyReLU) (None, None, None, 0 ['batch_normalization_33[0][0]'] \n",
" 256) \n",
" \n",
" conv2d_34 (Conv2D) (None, None, None, 1179648 ['leaky_re_lu_33[0][0]'] \n",
" 512) \n",
" \n",
" batch_normalization_34 (BatchN (None, None, None, 2048 ['conv2d_34[0][0]'] \n",
" ormalization) 512) \n",
" \n",
" leaky_re_lu_34 (LeakyReLU) (None, None, None, 0 ['batch_normalization_34[0][0]'] \n",
" 512) \n",
" \n",
" add_14 (Add) (None, None, None, 0 ['add_13[0][0]', \n",
" 512) 'leaky_re_lu_34[0][0]'] \n",
" \n",
" conv2d_35 (Conv2D) (None, None, None, 131072 ['add_14[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_35 (BatchN (None, None, None, 1024 ['conv2d_35[0][0]'] \n",
" ormalization) 256) \n",
" \n",
" leaky_re_lu_35 (LeakyReLU) (None, None, None, 0 ['batch_normalization_35[0][0]'] \n",
" 256) \n",
" \n",
" conv2d_36 (Conv2D) (None, None, None, 1179648 ['leaky_re_lu_35[0][0]'] \n",
" 512) \n",
" \n",
" batch_normalization_36 (BatchN (None, None, None, 2048 ['conv2d_36[0][0]'] \n",
" ormalization) 512) \n",
" \n",
" leaky_re_lu_36 (LeakyReLU) (None, None, None, 0 ['batch_normalization_36[0][0]'] \n",
" 512) \n",
" \n",
" add_15 (Add) (None, None, None, 0 ['add_14[0][0]', \n",
" 512) 'leaky_re_lu_36[0][0]'] \n",
" \n",
" conv2d_37 (Conv2D) (None, None, None, 131072 ['add_15[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_37 (BatchN (None, None, None, 1024 ['conv2d_37[0][0]'] \n",
" ormalization) 256) \n",
" \n",
" leaky_re_lu_37 (LeakyReLU) (None, None, None, 0 ['batch_normalization_37[0][0]'] \n",
" 256) \n",
" \n",
" conv2d_38 (Conv2D) (None, None, None, 1179648 ['leaky_re_lu_37[0][0]'] \n",
" 512) \n",
" \n",
" batch_normalization_38 (BatchN (None, None, None, 2048 ['conv2d_38[0][0]'] \n",
" ormalization) 512) \n",
" \n",
" leaky_re_lu_38 (LeakyReLU) (None, None, None, 0 ['batch_normalization_38[0][0]'] \n",
" 512) \n",
" \n",
" add_16 (Add) (None, None, None, 0 ['add_15[0][0]', \n",
" 512) 'leaky_re_lu_38[0][0]'] \n",
" \n",
" conv2d_39 (Conv2D) (None, None, None, 131072 ['add_16[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_39 (BatchN (None, None, None, 1024 ['conv2d_39[0][0]'] \n",
" ormalization) 256) \n",
" \n",
" leaky_re_lu_39 (LeakyReLU) (None, None, None, 0 ['batch_normalization_39[0][0]'] \n",
" 256) \n",
" \n",
" conv2d_40 (Conv2D) (None, None, None, 1179648 ['leaky_re_lu_39[0][0]'] \n",
" 512) \n",
" \n",
" batch_normalization_40 (BatchN (None, None, None, 2048 ['conv2d_40[0][0]'] \n",
" ormalization) 512) \n",
" \n",
" leaky_re_lu_40 (LeakyReLU) (None, None, None, 0 ['batch_normalization_40[0][0]'] \n",
" 512) \n",
" \n",
" add_17 (Add) (None, None, None, 0 ['add_16[0][0]', \n",
" 512) 'leaky_re_lu_40[0][0]'] \n",
" \n",
" conv2d_41 (Conv2D) (None, None, None, 131072 ['add_17[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_41 (BatchN (None, None, None, 1024 ['conv2d_41[0][0]'] \n",
" ormalization) 256) \n",
" \n",
" leaky_re_lu_41 (LeakyReLU) (None, None, None, 0 ['batch_normalization_41[0][0]'] \n",
" 256) \n",
" \n",
" conv2d_42 (Conv2D) (None, None, None, 1179648 ['leaky_re_lu_41[0][0]'] \n",
" 512) \n",
" \n",
" batch_normalization_42 (BatchN (None, None, None, 2048 ['conv2d_42[0][0]'] \n",
" ormalization) 512) \n",
" \n",
" leaky_re_lu_42 (LeakyReLU) (None, None, None, 0 ['batch_normalization_42[0][0]'] \n",
" 512) \n",
" \n",
" add_18 (Add) (None, None, None, 0 ['add_17[0][0]', \n",
" 512) 'leaky_re_lu_42[0][0]'] \n",
" \n",
" zero_padding2d_4 (ZeroPadding2 (None, None, None, 0 ['add_18[0][0]'] \n",
" D) 512) \n",
" \n",
" conv2d_43 (Conv2D) (None, None, None, 4718592 ['zero_padding2d_4[0][0]'] \n",
" 1024) \n",
" \n",
" batch_normalization_43 (BatchN (None, None, None, 4096 ['conv2d_43[0][0]'] \n",
" ormalization) 1024) \n",
" \n",
" leaky_re_lu_43 (LeakyReLU) (None, None, None, 0 ['batch_normalization_43[0][0]'] \n",
" 1024) \n",
" \n",
" conv2d_44 (Conv2D) (None, None, None, 524288 ['leaky_re_lu_43[0][0]'] \n",
" 512) \n",
" \n",
" batch_normalization_44 (BatchN (None, None, None, 2048 ['conv2d_44[0][0]'] \n",
" ormalization) 512) \n",
" \n",
" leaky_re_lu_44 (LeakyReLU) (None, None, None, 0 ['batch_normalization_44[0][0]'] \n",
" 512) \n",
" \n",
" conv2d_45 (Conv2D) (None, None, None, 4718592 ['leaky_re_lu_44[0][0]'] \n",
" 1024) \n",
" \n",
" batch_normalization_45 (BatchN (None, None, None, 4096 ['conv2d_45[0][0]'] \n",
" ormalization) 1024) \n",
" \n",
" leaky_re_lu_45 (LeakyReLU) (None, None, None, 0 ['batch_normalization_45[0][0]'] \n",
" 1024) \n",
" \n",
" add_19 (Add) (None, None, None, 0 ['leaky_re_lu_43[0][0]', \n",
" 1024) 'leaky_re_lu_45[0][0]'] \n",
" \n",
" conv2d_46 (Conv2D) (None, None, None, 524288 ['add_19[0][0]'] \n",
" 512) \n",
" \n",
" batch_normalization_46 (BatchN (None, None, None, 2048 ['conv2d_46[0][0]'] \n",
" ormalization) 512) \n",
" \n",
" leaky_re_lu_46 (LeakyReLU) (None, None, None, 0 ['batch_normalization_46[0][0]'] \n",
" 512) \n",
" \n",
" conv2d_47 (Conv2D) (None, None, None, 4718592 ['leaky_re_lu_46[0][0]'] \n",
" 1024) \n",
" \n",
" batch_normalization_47 (BatchN (None, None, None, 4096 ['conv2d_47[0][0]'] \n",
" ormalization) 1024) \n",
" \n",
" leaky_re_lu_47 (LeakyReLU) (None, None, None, 0 ['batch_normalization_47[0][0]'] \n",
" 1024) \n",
" \n",
" add_20 (Add) (None, None, None, 0 ['add_19[0][0]', \n",
" 1024) 'leaky_re_lu_47[0][0]'] \n",
" \n",
" conv2d_48 (Conv2D) (None, None, None, 524288 ['add_20[0][0]'] \n",
" 512) \n",
" \n",
" batch_normalization_48 (BatchN (None, None, None, 2048 ['conv2d_48[0][0]'] \n",
" ormalization) 512) \n",
" \n",
" leaky_re_lu_48 (LeakyReLU) (None, None, None, 0 ['batch_normalization_48[0][0]'] \n",
" 512) \n",
" \n",
" conv2d_49 (Conv2D) (None, None, None, 4718592 ['leaky_re_lu_48[0][0]'] \n",
" 1024) \n",
" \n",
" batch_normalization_49 (BatchN (None, None, None, 4096 ['conv2d_49[0][0]'] \n",
" ormalization) 1024) \n",
" \n",
" leaky_re_lu_49 (LeakyReLU) (None, None, None, 0 ['batch_normalization_49[0][0]'] \n",
" 1024) \n",
" \n",
" add_21 (Add) (None, None, None, 0 ['add_20[0][0]', \n",
" 1024) 'leaky_re_lu_49[0][0]'] \n",
" \n",
" conv2d_50 (Conv2D) (None, None, None, 524288 ['add_21[0][0]'] \n",
" 512) \n",
" \n",
" batch_normalization_50 (BatchN (None, None, None, 2048 ['conv2d_50[0][0]'] \n",
" ormalization) 512) \n",
" \n",
" leaky_re_lu_50 (LeakyReLU) (None, None, None, 0 ['batch_normalization_50[0][0]'] \n",
" 512) \n",
" \n",
" conv2d_51 (Conv2D) (None, None, None, 4718592 ['leaky_re_lu_50[0][0]'] \n",
" 1024) \n",
" \n",
" batch_normalization_51 (BatchN (None, None, None, 4096 ['conv2d_51[0][0]'] \n",
" ormalization) 1024) \n",
" \n",
" leaky_re_lu_51 (LeakyReLU) (None, None, None, 0 ['batch_normalization_51[0][0]'] \n",
" 1024) \n",
" \n",
" add_22 (Add) (None, None, None, 0 ['add_21[0][0]', \n",
" 1024) 'leaky_re_lu_51[0][0]'] \n",
" \n",
" conv2d_52 (Conv2D) (None, None, None, 524288 ['add_22[0][0]'] \n",
" 512) \n",
" \n",
" batch_normalization_52 (BatchN (None, None, None, 2048 ['conv2d_52[0][0]'] \n",
" ormalization) 512) \n",
" \n",
" leaky_re_lu_52 (LeakyReLU) (None, None, None, 0 ['batch_normalization_52[0][0]'] \n",
" 512) \n",
" \n",
" conv2d_53 (Conv2D) (None, None, None, 4718592 ['leaky_re_lu_52[0][0]'] \n",
" 1024) \n",
" \n",
" batch_normalization_53 (BatchN (None, None, None, 4096 ['conv2d_53[0][0]'] \n",
" ormalization) 1024) \n",
" \n",
" leaky_re_lu_53 (LeakyReLU) (None, None, None, 0 ['batch_normalization_53[0][0]'] \n",
" 1024) \n",
" \n",
" conv2d_54 (Conv2D) (None, None, None, 524288 ['leaky_re_lu_53[0][0]'] \n",
" 512) \n",
" \n",
" batch_normalization_54 (BatchN (None, None, None, 2048 ['conv2d_54[0][0]'] \n",
" ormalization) 512) \n",
" \n",
" leaky_re_lu_54 (LeakyReLU) (None, None, None, 0 ['batch_normalization_54[0][0]'] \n",
" 512) \n",
" \n",
" conv2d_55 (Conv2D) (None, None, None, 4718592 ['leaky_re_lu_54[0][0]'] \n",
" 1024) \n",
" \n",
" batch_normalization_55 (BatchN (None, None, None, 4096 ['conv2d_55[0][0]'] \n",
" ormalization) 1024) \n",
" \n",
" leaky_re_lu_55 (LeakyReLU) (None, None, None, 0 ['batch_normalization_55[0][0]'] \n",
" 1024) \n",
" \n",
" conv2d_56 (Conv2D) (None, None, None, 524288 ['leaky_re_lu_55[0][0]'] \n",
" 512) \n",
" \n",
" batch_normalization_56 (BatchN (None, None, None, 2048 ['conv2d_56[0][0]'] \n",
" ormalization) 512) \n",
" \n",
" leaky_re_lu_56 (LeakyReLU) (None, None, None, 0 ['batch_normalization_56[0][0]'] \n",
" 512) \n",
" \n",
" conv2d_59 (Conv2D) (None, None, None, 131072 ['leaky_re_lu_56[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_58 (BatchN (None, None, None, 1024 ['conv2d_59[0][0]'] \n",
" ormalization) 256) \n",
" \n",
" leaky_re_lu_58 (LeakyReLU) (None, None, None, 0 ['batch_normalization_58[0][0]'] \n",
" 256) \n",
" \n",
" up_sampling2d (UpSampling2D) (None, None, None, 0 ['leaky_re_lu_58[0][0]'] \n",
" 256) \n",
" \n",
" concatenate (Concatenate) (None, None, None, 0 ['up_sampling2d[0][0]', \n",
" 768) 'add_18[0][0]'] \n",
" \n",
" conv2d_60 (Conv2D) (None, None, None, 196608 ['concatenate[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_59 (BatchN (None, None, None, 1024 ['conv2d_60[0][0]'] \n",
" ormalization) 256) \n",
" \n",
" leaky_re_lu_59 (LeakyReLU) (None, None, None, 0 ['batch_normalization_59[0][0]'] \n",
" 256) \n",
" \n",
" conv2d_61 (Conv2D) (None, None, None, 1179648 ['leaky_re_lu_59[0][0]'] \n",
" 512) \n",
" \n",
" batch_normalization_60 (BatchN (None, None, None, 2048 ['conv2d_61[0][0]'] \n",
" ormalization) 512) \n",
" \n",
" leaky_re_lu_60 (LeakyReLU) (None, None, None, 0 ['batch_normalization_60[0][0]'] \n",
" 512) \n",
" \n",
" conv2d_62 (Conv2D) (None, None, None, 131072 ['leaky_re_lu_60[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_61 (BatchN (None, None, None, 1024 ['conv2d_62[0][0]'] \n",
" ormalization) 256) \n",
" \n",
" leaky_re_lu_61 (LeakyReLU) (None, None, None, 0 ['batch_normalization_61[0][0]'] \n",
" 256) \n",
" \n",
" conv2d_63 (Conv2D) (None, None, None, 1179648 ['leaky_re_lu_61[0][0]'] \n",
" 512) \n",
" \n",
" batch_normalization_62 (BatchN (None, None, None, 2048 ['conv2d_63[0][0]'] \n",
" ormalization) 512) \n",
" \n",
" leaky_re_lu_62 (LeakyReLU) (None, None, None, 0 ['batch_normalization_62[0][0]'] \n",
" 512) \n",
" \n",
" conv2d_64 (Conv2D) (None, None, None, 131072 ['leaky_re_lu_62[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_63 (BatchN (None, None, None, 1024 ['conv2d_64[0][0]'] \n",
" ormalization) 256) \n",
" \n",
" leaky_re_lu_63 (LeakyReLU) (None, None, None, 0 ['batch_normalization_63[0][0]'] \n",
" 256) \n",
" \n",
" conv2d_67 (Conv2D) (None, None, None, 32768 ['leaky_re_lu_63[0][0]'] \n",
" 128) \n",
" \n",
" batch_normalization_65 (BatchN (None, None, None, 512 ['conv2d_67[0][0]'] \n",
" ormalization) 128) \n",
" \n",
" leaky_re_lu_65 (LeakyReLU) (None, None, None, 0 ['batch_normalization_65[0][0]'] \n",
" 128) \n",
" \n",
" up_sampling2d_1 (UpSampling2D) (None, None, None, 0 ['leaky_re_lu_65[0][0]'] \n",
" 128) \n",
" \n",
" concatenate_1 (Concatenate) (None, None, None, 0 ['up_sampling2d_1[0][0]', \n",
" 384) 'add_10[0][0]'] \n",
" \n",
" conv2d_68 (Conv2D) (None, None, None, 49152 ['concatenate_1[0][0]'] \n",
" 128) \n",
" \n",
" batch_normalization_66 (BatchN (None, None, None, 512 ['conv2d_68[0][0]'] \n",
" ormalization) 128) \n",
" \n",
" leaky_re_lu_66 (LeakyReLU) (None, None, None, 0 ['batch_normalization_66[0][0]'] \n",
" 128) \n",
" \n",
" conv2d_69 (Conv2D) (None, None, None, 294912 ['leaky_re_lu_66[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_67 (BatchN (None, None, None, 1024 ['conv2d_69[0][0]'] \n",
" ormalization) 256) \n",
" \n",
" leaky_re_lu_67 (LeakyReLU) (None, None, None, 0 ['batch_normalization_67[0][0]'] \n",
" 256) \n",
" \n",
" conv2d_70 (Conv2D) (None, None, None, 32768 ['leaky_re_lu_67[0][0]'] \n",
" 128) \n",
" \n",
" batch_normalization_68 (BatchN (None, None, None, 512 ['conv2d_70[0][0]'] \n",
" ormalization) 128) \n",
" \n",
" leaky_re_lu_68 (LeakyReLU) (None, None, None, 0 ['batch_normalization_68[0][0]'] \n",
" 128) \n",
" \n",
" conv2d_71 (Conv2D) (None, None, None, 294912 ['leaky_re_lu_68[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_69 (BatchN (None, None, None, 1024 ['conv2d_71[0][0]'] \n",
" ormalization) 256) \n",
" \n",
" leaky_re_lu_69 (LeakyReLU) (None, None, None, 0 ['batch_normalization_69[0][0]'] \n",
" 256) \n",
" \n",
" conv2d_72 (Conv2D) (None, None, None, 32768 ['leaky_re_lu_69[0][0]'] \n",
" 128) \n",
" \n",
" batch_normalization_70 (BatchN (None, None, None, 512 ['conv2d_72[0][0]'] \n",
" ormalization) 128) \n",
" \n",
" leaky_re_lu_70 (LeakyReLU) (None, None, None, 0 ['batch_normalization_70[0][0]'] \n",
" 128) \n",
" \n",
" conv2d_57 (Conv2D) (None, None, None, 4718592 ['leaky_re_lu_56[0][0]'] \n",
" 1024) \n",
" \n",
" conv2d_65 (Conv2D) (None, None, None, 1179648 ['leaky_re_lu_63[0][0]'] \n",
" 512) \n",
" \n",
" conv2d_73 (Conv2D) (None, None, None, 294912 ['leaky_re_lu_70[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_57 (BatchN (None, None, None, 4096 ['conv2d_57[0][0]'] \n",
" ormalization) 1024) \n",
" \n",
" batch_normalization_64 (BatchN (None, None, None, 2048 ['conv2d_65[0][0]'] \n",
" ormalization) 512) \n",
" \n",
" batch_normalization_71 (BatchN (None, None, None, 1024 ['conv2d_73[0][0]'] \n",
" ormalization) 256) \n",
" \n",
" leaky_re_lu_57 (LeakyReLU) (None, None, None, 0 ['batch_normalization_57[0][0]'] \n",
" 1024) \n",
" \n",
" leaky_re_lu_64 (LeakyReLU) (None, None, None, 0 ['batch_normalization_64[0][0]'] \n",
" 512) \n",
" \n",
" leaky_re_lu_71 (LeakyReLU) (None, None, None, 0 ['batch_normalization_71[0][0]'] \n",
" 256) \n",
" \n",
" conv2d_58 (Conv2D) (None, None, None, 261375 ['leaky_re_lu_57[0][0]'] \n",
" 255) \n",
" \n",
" conv2d_66 (Conv2D) (None, None, None, 130815 ['leaky_re_lu_64[0][0]'] \n",
" 255) \n",
" \n",
" conv2d_74 (Conv2D) (None, None, None, 65535 ['leaky_re_lu_71[0][0]'] \n",
" 255) \n",
" \n",
"==================================================================================================\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total params: 62,001,757\n",
"Trainable params: 61,949,149\n",
"Non-trainable params: 52,608\n",
"__________________________________________________________________________________________________\n",
"None\n",
"WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n",
"Saved Keras model to model_data/yolo.h5\n",
"Read 62001757 of 62001757.0 from Darknet weights.\n"
]
}
],
"source": [
"!python keras-yolo3/convert.py keras-yolo3/yolov3.cfg yolov3.weights model_data/yolo.h5"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4038756b",
"metadata": {
"pycharm": {
"is_executing": true
}
},
"outputs": [],
"source": [
"\"\"\"\n",
"Self-contained Python script to train YOLOv3 on your own dataset\n",
"\"\"\"\n",
"\n",
"import numpy as np\n",
"import keras.backend as K\n",
"from keras.layers import Input, Lambda\n",
"from keras.models import Model\n",
"from keras.optimizers import Adam\n",
"from keras.callbacks import TensorBoard, ModelCheckpoint, ReduceLROnPlateau, EarlyStopping\n",
"\n",
"from yolo3.model import preprocess_true_boxes, yolo_body, tiny_yolo_body, yolo_loss\n",
"from yolo3.utils import get_random_data\n",
"\n",
"\n",
"def _main():\n",
" annotation_path = './train/_annotations.txt' # path to Roboflow data annotations\n",
" log_dir = './logs/000/' # where we're storing our logs\n",
" classes_path = './train/_classes.txt' # path to Roboflow class names\n",
" anchors_path = './model_data/yolo_anchors.txt'\n",
" class_names = get_classes(classes_path)\n",
" print(\"-------------------CLASS NAMES-------------------\")\n",
" print(class_names)\n",
" print(\"-------------------CLASS NAMES-------------------\")\n",
" num_classes = len(class_names)\n",
" anchors = get_anchors(anchors_path)\n",
"\n",
" input_shape = (256,256) # multiple of 32, hw default = (416,416)\n",
"\n",
" is_tiny_version = len(anchors)==6 # default setting\n",
" if is_tiny_version:\n",
" model = create_tiny_model(input_shape, anchors, num_classes,\n",
" freeze_body=2, weights_path='./model_data/tiny_yolo_weights.h5')\n",
" else:\n",
" model = create_model(input_shape, anchors, num_classes,\n",
" freeze_body=2, weights_path='./model_data/yolo.h5') # make sure you know what you freeze\n",
"\n",
" logging = TensorBoard(log_dir=log_dir)\n",
" checkpoint = ModelCheckpoint(log_dir + 'ep{epoch:03d}-loss{loss:.3f}-val_loss{val_loss:.3f}.h5',\n",
" monitor='val_loss', save_weights_only=True, save_best_only=True, period=3)\n",
" reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=3, verbose=1)\n",
" early_stopping = EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=1)\n",
"\n",
" val_split = 0.2 # set the size of the validation set\n",
" with open(annotation_path) as f:\n",
" lines = f.readlines()\n",
" np.random.seed(10101)\n",
" np.random.shuffle(lines)\n",
" np.random.seed(None)\n",
" num_val = int(len(lines)*val_split)\n",
" num_train = len(lines) - num_val\n",
"\n",
" # Train with frozen layers first, to get a stable loss.\n",
" # Adjust num epochs to your dataset. This step is enough to obtain a not bad model.\n",
" if True:\n",
" model.compile(optimizer=Adam(lr=1e-3), loss={\n",
" # use custom yolo_loss Lambda layer.\n",
" 'yolo_loss': lambda y_true, y_pred: y_pred})\n",
"\n",
" batch_size = 16\n",
" print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size))\n",
" model.fit_generator(data_generator_wrapper(lines[:num_train], batch_size, input_shape, anchors, num_classes),\n",
" steps_per_epoch=max(1, num_train//batch_size),\n",
" validation_data=data_generator_wrapper(lines[num_train:], batch_size, input_shape, anchors, num_classes),\n",
" validation_steps=max(1, num_val//batch_size),\n",
" epochs=500,\n",
" initial_epoch=0,\n",
" callbacks=[logging, checkpoint])\n",
" model.save_weights(log_dir + 'trained_weights_stage_1.h5')\n",
"\n",
" # Unfreeze and continue training, to fine-tune.\n",
" # Train longer if the result is not good.\n",
" if True:\n",
" for i in range(len(model.layers)):\n",
" model.layers[i].trainable = True\n",
" model.compile(optimizer=Adam(lr=1e-4), loss={'yolo_loss': lambda y_true, y_pred: y_pred}) # recompile to apply the change\n",
" print('Unfreeze all of the layers.')\n",
"\n",
" batch_size = 16 # note that more GPU memory is required after unfreezing the body\n",
" print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size))\n",
" model.fit_generator(data_generator_wrapper(lines[:num_train], batch_size, input_shape, anchors, num_classes),\n",
" steps_per_epoch=max(1, num_train//batch_size),\n",
" validation_data=data_generator_wrapper(lines[num_train:], batch_size, input_shape, anchors, num_classes),\n",
" validation_steps=max(1, num_val//batch_size),\n",
" epochs=100,\n",
" initial_epoch=50,\n",
" callbacks=[logging, checkpoint, reduce_lr, early_stopping])\n",
" model.save_weights(log_dir + 'trained_weights_final.h5')\n",
"\n",
" # Further training if needed.\n",
"\n",
"\n",
"def get_classes(classes_path):\n",
" '''loads the classes'''\n",
" with open(classes_path) as f:\n",
" class_names = f.readlines()\n",
" class_names = [c.strip() for c in class_names]\n",
" return class_names\n",
"\n",
"def get_anchors(anchors_path):\n",
" '''loads the anchors from a file'''\n",
" with open(anchors_path) as f:\n",
" anchors = f.readline()\n",
" anchors = [float(x) for x in anchors.split(',')]\n",
" return np.array(anchors).reshape(-1, 2)\n",
"\n",
"\n",
"def create_model(input_shape, anchors, num_classes, load_pretrained=True, freeze_body=2,\n",
" weights_path='./model_data/yolo.h5'):\n",
" '''create the training model'''\n",
" K.clear_session() # get a new session\n",
" image_input = Input(shape=(None, None, 3))\n",
" h, w = input_shape\n",
" num_anchors = len(anchors)\n",
"\n",
" y_true = [Input(shape=(h//{0:32, 1:16, 2:8}[l], w//{0:32, 1:16, 2:8}[l], \\\n",
" num_anchors//3, num_classes+5)) for l in range(3)]\n",
"\n",
" model_body = yolo_body(image_input, num_anchors//3, num_classes)\n",
" print('Create YOLOv3 model with {} anchors and {} classes.'.format(num_anchors, num_classes))\n",
"\n",
" if load_pretrained:\n",
" model_body.load_weights(weights_path, by_name=True, skip_mismatch=True)\n",
" print('Load weights {}.'.format(weights_path))\n",
" if freeze_body in [1, 2]:\n",
" # Freeze darknet53 body or freeze all but 3 output layers.\n",
" num = (185, len(model_body.layers)-3)[freeze_body-1]\n",
" for i in range(num): model_body.layers[i].trainable = False\n",
" print('Freeze the first {} layers of total {} layers.'.format(num, len(model_body.layers)))\n",
"\n",
" model_loss = Lambda(yolo_loss, output_shape=(1,), name='yolo_loss',\n",
" arguments={'anchors': anchors, 'num_classes': num_classes, 'ignore_thresh': 0.5})(\n",
" [*model_body.output, *y_true])\n",
" model = Model([model_body.input, *y_true], model_loss)\n",
"\n",
" return model\n",
"\n",
"def create_tiny_model(input_shape, anchors, num_classes, load_pretrained=True, freeze_body=2,\n",
" weights_path='./model_data/tiny_yolo_weights.h5'):\n",
" '''create the training model, for Tiny YOLOv3'''\n",
" K.clear_session() # get a new session\n",
" image_input = Input(shape=(None, None, 3))\n",
" h, w = input_shape\n",
" num_anchors = len(anchors)\n",
"\n",
" y_true = [Input(shape=(h//{0:32, 1:16}[l], w//{0:32, 1:16}[l], \\\n",
" num_anchors//2, num_classes+5)) for l in range(2)]\n",
"\n",
" model_body = tiny_yolo_body(image_input, num_anchors//2, num_classes)\n",
" print('Create Tiny YOLOv3 model with {} anchors and {} classes.'.format(num_anchors, num_classes))\n",
"\n",
" if load_pretrained:\n",
" model_body.load_weights(weights_path, by_name=True, skip_mismatch=True)\n",
" print('Load weights {}.'.format(weights_path))\n",
" if freeze_body in [1, 2]:\n",
" # Freeze the darknet body or freeze all but 2 output layers.\n",
" num = (20, len(model_body.layers)-2)[freeze_body-1]\n",
" for i in range(num): model_body.layers[i].trainable = False\n",
" print('Freeze the first {} layers of total {} layers.'.format(num, len(model_body.layers)))\n",
"\n",
" model_loss = Lambda(yolo_loss, output_shape=(1,), name='yolo_loss',\n",
" arguments={'anchors': anchors, 'num_classes': num_classes, 'ignore_thresh': 0.7})(\n",
" [*model_body.output, *y_true])\n",
" model = Model([model_body.input, *y_true], model_loss)\n",
"\n",
" return model\n",
"\n",
"def data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes):\n",
" '''data generator for fit_generator'''\n",
" n = len(annotation_lines)\n",
" i = 0\n",
" while True:\n",
" image_data = []\n",
" box_data = []\n",
" for b in range(batch_size):\n",
" if i==0:\n",
" np.random.shuffle(annotation_lines)\n",
" image, box = get_random_data(annotation_lines[i], input_shape, random=True)\n",
" image_data.append(image)\n",
" box_data.append(box)\n",
" i = (i+1) % n\n",
" image_data = np.array(image_data)\n",
" box_data = np.array(box_data)\n",
" y_true = preprocess_true_boxes(box_data, input_shape, anchors, num_classes)\n",
" yield [image_data, *y_true], np.zeros(batch_size)\n",
"\n",
"def data_generator_wrapper(annotation_lines, batch_size, input_shape, anchors, num_classes):\n",
" n = len(annotation_lines)\n",
" if n==0 or batch_size<=0: return None\n",
" return data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes)\n",
"\n",
"if __name__ == '__main__':\n",
" _main()"
]
},
{
"cell_type": "markdown",
"id": "214e7684",
"metadata": {},
"source": [
"# Prepare image to ocr"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "abe450c6",
"metadata": {},
"outputs": [],
"source": [
"import cv2 as cv\n",
"from matplotlib import pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 218,
"id": "f650aacf",
"metadata": {},
"outputs": [],
"source": [
"def grayscale(image):\n",
" return cv.cvtColor(image, cv.COLOR_BGR2GRAY)\n",
"\n",
"def noise_removal(image):\n",
" import numpy as np\n",
" kernel = np.ones((1, 1), np.uint8)\n",
" image = cv.dilate(image, kernel, iterations=1)\n",
" kernel = np.ones((1, 1), np.uint8)\n",
" image = cv.erode(image, kernel, iterations=1)\n",
" image = cv.morphologyEx(image, cv.MORPH_CLOSE, kernel)\n",
" image = cv.medianBlur(image, 3)\n",
" return (image)\n",
"\n",
"def thin_font(image):\n",
" import numpy as np\n",
" image = cv.bitwise_not(image)\n",
" kernel = np.ones((2,2),np.uint8)\n",
" image = cv.erode(image, kernel, iterations=1)\n",
" image = cv.bitwise_not(image)\n",
" return (image)\n",
"\n",
"def thick_font(image):\n",
" import numpy as np\n",
" image = cv.bitwise_not(image)\n",
" kernel = np.ones((2,2),np.uint8)\n",
" image = cv.dilate(image, kernel, iterations=1)\n",
" image = cv.bitwise_not(image)\n",
" return (image)\n",
"\n",
"def remove_borders(image):\n",
" contours, heiarchy = cv.findContours(image, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)\n",
" cntsSorted = sorted(contours, key=lambda x:cv.contourArea(x))\n",
" cnt = cntsSorted[-1]\n",
" x, y, w, h = cv.boundingRect(cnt)\n",
" crop = image[y:y+h, x:x+w]\n",
" return (crop)"
]
},
{
"cell_type": "code",
"execution_count": 231,
"id": "9df02404",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 643.75x287.5 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"image_file = './img/img00.png'\n",
"img = cv.imread(image_file)\n",
"gray_image = grayscale(img)\n",
"thresh, im_bw = cv.threshold(gray_image, 100, 150, cv.THRESH_BINARY)\n",
"no_noise = noise_removal(im_bw)\n",
"# eroded_image = thin_font(no_noise)\n",
"# dilated_image = thick_font(eroded_image)\n",
"no_borders = remove_borders(no_noise)\n",
"cv.imwrite(\"temp/no_borders.jpg\", no_borders)\n",
"display('temp/no_borders.jpg')"
]
},
{
"cell_type": "code",
"execution_count": 169,
"id": "68bb5c6b",
"metadata": {},
"outputs": [],
"source": [
"def display(im_path):\n",
" dpi = 80\n",
" im_data = plt.imread(im_path)\n",
"\n",
" height, width = im_data.shape[:2]\n",
" \n",
" # What size does the figure need to be in inches to fit the image?\n",
" figsize = width / float(dpi), height / float(dpi)\n",
"\n",
" # Create a figure of the right size with one axes that takes up the full figure\n",
" fig = plt.figure(figsize=figsize)\n",
" ax = fig.add_axes([0, 0, 1, 1])\n",
"\n",
" # Hide spines, ticks, etc.\n",
" ax.axis('off')\n",
"\n",
" # Display the image.\n",
" ax.imshow(im_data, cmap='gray')\n",
"\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 170,
"id": "c57a2af5",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 673.75x375 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(image_file)"
]
},
{
"cell_type": "code",
"execution_count": 171,
"id": "d52ff256",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 673.75x375 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"inverted_image = cv.bitwise_not(img)\n",
"cv.imwrite(\"temp/inverted.jpg\", inverted_image)\n",
"display(\"temp/inverted.jpg\")"
]
},
{
"cell_type": "code",
"execution_count": 172,
"id": "e653440c",
"metadata": {},
"outputs": [],
"source": [
"def grayscale(image):\n",
" return cv.cvtColor(image, cv.COLOR_BGR2GRAY)"
]
},
{
"cell_type": "code",
"execution_count": 203,
"id": "0985148f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 203,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gray_image = grayscale(img)\n",
"cv.imwrite(\"temp/gray.jpg\", gray_image)"
]
},
{
"cell_type": "code",
"execution_count": 204,
"id": "b54b6db2",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 673.75x375 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(\"temp/gray.jpg\")"
]
},
{
"cell_type": "code",
"execution_count": 254,
"id": "90df13c4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 254,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"thresh, im_bw = cv.threshold(gray_image, 170, 210, cv.THRESH_BINARY)\n",
"cv.imwrite(\"temp/bw_image.jpg\", im_bw)"
]
},
{
"cell_type": "code",
"execution_count": 255,
"id": "d6f7feb2",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 673.75x375 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(\"temp/bw_image.jpg\")"
]
},
{
"cell_type": "code",
"execution_count": 207,
"id": "fe31b009",
"metadata": {},
"outputs": [],
"source": [
"def noise_removal(image):\n",
" import numpy as np\n",
" kernel = np.ones((1, 1), np.uint8)\n",
" image = cv.dilate(image, kernel, iterations=1)\n",
" kernel = np.ones((1, 1), np.uint8)\n",
" image = cv.erode(image, kernel, iterations=1)\n",
" image = cv.morphologyEx(image, cv.MORPH_CLOSE, kernel)\n",
" image = cv.medianBlur(image, 3)\n",
" return (image)"
]
},
{
"cell_type": "code",
"execution_count": 208,
"id": "4e873cc8",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 208,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"no_noise = noise_removal(im_bw)\n",
"cv.imwrite(\"temp/no_noise.jpg\", no_noise)"
]
},
{
"cell_type": "code",
"execution_count": 209,
"id": "a993d061",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 673.75x375 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(\"temp/no_noise.jpg\")"
]
},
{
"cell_type": "code",
"execution_count": 210,
"id": "e175dc1d",
"metadata": {},
"outputs": [],
"source": [
"def thin_font(image):\n",
" import numpy as np\n",
" image = cv.bitwise_not(image)\n",
" kernel = np.ones((2,2),np.uint8)\n",
" image = cv.erode(image, kernel, iterations=1)\n",
" image = cv.bitwise_not(image)\n",
" return (image)"
]
},
{
"cell_type": "code",
"execution_count": 211,
"id": "c77d5076",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 211,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"eroded_image = thin_font(no_noise)\n",
"cv.imwrite(\"temp/eroded_image.jpg\", eroded_image)"
]
},
{
"cell_type": "code",
"execution_count": 212,
"id": "0517c085",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 673.75x375 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(\"temp/eroded_image.jpg\")"
]
},
{
"cell_type": "code",
"execution_count": 213,
"id": "9b077539",
"metadata": {},
"outputs": [],
"source": [
"def thick_font(image):\n",
" import numpy as np\n",
" image = cv.bitwise_not(image)\n",
" kernel = np.ones((2,2),np.uint8)\n",
" image = cv.dilate(image, kernel, iterations=1)\n",
" image = cv.bitwise_not(image)\n",
" return (image)"
]
},
{
"cell_type": "code",
"execution_count": 214,
"id": "25bbdacd",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 214,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dilated_image = thick_font(no_noise)\n",
"cv.imwrite(\"temp/dilated_image.jpg\", dilated_image)"
]
},
{
"cell_type": "code",
"execution_count": 215,
"id": "cff53158",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 673.75x375 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(\"temp/dilated_image.jpg\")"
]
},
{
"cell_type": "code",
"execution_count": 216,
"id": "18cd2910",
"metadata": {},
"outputs": [],
"source": [
"def remove_borders(image):\n",
" contours, heiarchy = cv.findContours(image, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)\n",
" cntsSorted = sorted(contours, key=lambda x:cv.contourArea(x))\n",
" cnt = cntsSorted[-1]\n",
" x, y, w, h = cv.boundingRect(cnt)\n",
" crop = image[y:y+h, x:x+w]\n",
" return (crop)"
]
},
{
"cell_type": "code",
"execution_count": 217,
"id": "40842784",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 643.75x287.5 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"no_borders = remove_borders(no_noise)\n",
"cv.imwrite(\"temp/no_borders.jpg\", no_borders)\n",
"display('temp/no_borders.jpg')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.16"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,
"autoclose": false,
"autocomplete": true,
"bibliofile": "biblio.bib",
"cite_by": "apalike",
"current_citInitial": 1,
"eqLabelWithNumbers": true,
"eqNumInitial": 1,
"hotkeys": {
"equation": "Ctrl-E",
"itemize": "Ctrl-I"
},
"labels_anchors": false,
"latex_user_defs": false,
"report_style_numbering": false,
"user_envs_cfg": false
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": false,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": true,
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 5
}
Reference in New Issue Copy Permalink