2.0 MiB
2.0 MiB
LICENSE PLATE DETECTION
YOLO V3
!git clone https://github.com/roboflow-ai/keras-yolo3Cloning into 'keras-yolo3'... remote: Enumerating objects: 169, done.[K remote: Total 169 (delta 0), reused 0 (delta 0), pack-reused 169[K Receiving objects: 100% (169/169), 172.74 KiB | 625.00 KiB/s, done. Resolving deltas: 100% (80/80), done.
!curl -L "https://app.roboflow.com/ds/hTj8Pr7g7U?key=q9kdROYojM" > roboflow.zip; unzip roboflow.zip; rm roboflow.zip % Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 897 100 897 0 0 269 0 0:00:03 0:00:03 --:--:-- 269 0
100 2120k 100 2120k 0 0 515k 0 0:00:04 0:00:04 --:--:-- 23.5M
Archive: roboflow.zip
extracting: README.dataset.txt
extracting: README.roboflow.txt
creating: test/
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creating: valid/
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!wget https://pjreddie.com/media/files/yolov3.weights--2023-01-18 12:01:19-- https://pjreddie.com/media/files/yolov3.weights Translacja pjreddie.com (pjreddie.com)... 128.208.4.108 Łączenie się z pjreddie.com (pjreddie.com)|128.208.4.108|:443... połączono. Żądanie HTTP wysłano, oczekiwanie na odpowiedź... 200 OK Długość: 248007048 (237M) [application/octet-stream] Zapis do: `yolov3.weights' yolov3.weights 100%[===================>] 236,52M 17,0MB/s w 15s 2023-01-18 12:01:35 (15,4 MB/s) - zapisano `yolov3.weights' [248007048/248007048]
from keras.layers import ELU, PReLU, LeakyReLU!python keras-yolo3/convert.py keras-yolo3/yolov3.cfg yolov3.weights model_data/yolo.h5Loading weights.
Weights Header: 0 2 0 [32013312]
Parsing Darknet config.
Creating Keras model.
Parsing section net_0
Parsing section convolutional_0
conv2d bn leaky (3, 3, 3, 32)
Metal device set to: Apple M1
systemMemory: 8.00 GB
maxCacheSize: 2.67 GB
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.
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>)
Parsing section convolutional_1
conv2d bn leaky (3, 3, 32, 64)
Parsing section convolutional_2
conv2d bn leaky (1, 1, 64, 32)
Parsing section convolutional_3
conv2d bn leaky (3, 3, 32, 64)
Parsing section shortcut_0
Parsing section convolutional_4
conv2d bn leaky (3, 3, 64, 128)
Parsing section convolutional_5
conv2d bn leaky (1, 1, 128, 64)
Parsing section convolutional_6
conv2d bn leaky (3, 3, 64, 128)
Parsing section shortcut_1
Parsing section convolutional_7
conv2d bn leaky (1, 1, 128, 64)
Parsing section convolutional_8
conv2d bn leaky (3, 3, 64, 128)
Parsing section shortcut_2
Parsing section convolutional_9
conv2d bn leaky (3, 3, 128, 256)
Parsing section convolutional_10
conv2d bn leaky (1, 1, 256, 128)
Parsing section convolutional_11
conv2d bn leaky (3, 3, 128, 256)
Parsing section shortcut_3
Parsing section convolutional_12
conv2d bn leaky (1, 1, 256, 128)
Parsing section convolutional_13
conv2d bn leaky (3, 3, 128, 256)
Parsing section shortcut_4
Parsing section convolutional_14
conv2d bn leaky (1, 1, 256, 128)
Parsing section convolutional_15
conv2d bn leaky (3, 3, 128, 256)
Parsing section shortcut_5
Parsing section convolutional_16
conv2d bn leaky (1, 1, 256, 128)
Parsing section convolutional_17
conv2d bn leaky (3, 3, 128, 256)
Parsing section shortcut_6
Parsing section convolutional_18
conv2d bn leaky (1, 1, 256, 128)
Parsing section convolutional_19
conv2d bn leaky (3, 3, 128, 256)
Parsing section shortcut_7
Parsing section convolutional_20
conv2d bn leaky (1, 1, 256, 128)
Parsing section convolutional_21
conv2d bn leaky (3, 3, 128, 256)
Parsing section shortcut_8
Parsing section convolutional_22
conv2d bn leaky (1, 1, 256, 128)
Parsing section convolutional_23
conv2d bn leaky (3, 3, 128, 256)
Parsing section shortcut_9
Parsing section convolutional_24
conv2d bn leaky (1, 1, 256, 128)
Parsing section convolutional_25
conv2d bn leaky (3, 3, 128, 256)
Parsing section shortcut_10
Parsing section convolutional_26
conv2d bn leaky (3, 3, 256, 512)
Parsing section convolutional_27
conv2d bn leaky (1, 1, 512, 256)
Parsing section convolutional_28
conv2d bn leaky (3, 3, 256, 512)
Parsing section shortcut_11
Parsing section convolutional_29
conv2d bn leaky (1, 1, 512, 256)
Parsing section convolutional_30
conv2d bn leaky (3, 3, 256, 512)
Parsing section shortcut_12
Parsing section convolutional_31
conv2d bn leaky (1, 1, 512, 256)
Parsing section convolutional_32
conv2d bn leaky (3, 3, 256, 512)
Parsing section shortcut_13
Parsing section convolutional_33
conv2d bn leaky (1, 1, 512, 256)
Parsing section convolutional_34
conv2d bn leaky (3, 3, 256, 512)
Parsing section shortcut_14
Parsing section convolutional_35
conv2d bn leaky (1, 1, 512, 256)
Parsing section convolutional_36
conv2d bn leaky (3, 3, 256, 512)
Parsing section shortcut_15
Parsing section convolutional_37
conv2d bn leaky (1, 1, 512, 256)
Parsing section convolutional_38
conv2d bn leaky (3, 3, 256, 512)
Parsing section shortcut_16
Parsing section convolutional_39
conv2d bn leaky (1, 1, 512, 256)
Parsing section convolutional_40
conv2d bn leaky (3, 3, 256, 512)
Parsing section shortcut_17
Parsing section convolutional_41
conv2d bn leaky (1, 1, 512, 256)
Parsing section convolutional_42
conv2d bn leaky (3, 3, 256, 512)
Parsing section shortcut_18
Parsing section convolutional_43
conv2d bn leaky (3, 3, 512, 1024)
Parsing section convolutional_44
conv2d bn leaky (1, 1, 1024, 512)
Parsing section convolutional_45
conv2d bn leaky (3, 3, 512, 1024)
Parsing section shortcut_19
Parsing section convolutional_46
conv2d bn leaky (1, 1, 1024, 512)
Parsing section convolutional_47
conv2d bn leaky (3, 3, 512, 1024)
Parsing section shortcut_20
Parsing section convolutional_48
conv2d bn leaky (1, 1, 1024, 512)
Parsing section convolutional_49
conv2d bn leaky (3, 3, 512, 1024)
Parsing section shortcut_21
Parsing section convolutional_50
conv2d bn leaky (1, 1, 1024, 512)
Parsing section convolutional_51
conv2d bn leaky (3, 3, 512, 1024)
Parsing section shortcut_22
Parsing section convolutional_52
conv2d bn leaky (1, 1, 1024, 512)
Parsing section convolutional_53
conv2d bn leaky (3, 3, 512, 1024)
Parsing section convolutional_54
conv2d bn leaky (1, 1, 1024, 512)
Parsing section convolutional_55
conv2d bn leaky (3, 3, 512, 1024)
Parsing section convolutional_56
conv2d bn leaky (1, 1, 1024, 512)
Parsing section convolutional_57
conv2d bn leaky (3, 3, 512, 1024)
Parsing section convolutional_58
conv2d linear (1, 1, 1024, 255)
Parsing section yolo_0
Parsing section route_0
Parsing section convolutional_59
conv2d bn leaky (1, 1, 512, 256)
Parsing section upsample_0
Parsing section route_1
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')>]
Parsing section convolutional_60
conv2d bn leaky (1, 1, 768, 256)
Parsing section convolutional_61
conv2d bn leaky (3, 3, 256, 512)
Parsing section convolutional_62
conv2d bn leaky (1, 1, 512, 256)
Parsing section convolutional_63
conv2d bn leaky (3, 3, 256, 512)
Parsing section convolutional_64
conv2d bn leaky (1, 1, 512, 256)
Parsing section convolutional_65
conv2d bn leaky (3, 3, 256, 512)
Parsing section convolutional_66
conv2d linear (1, 1, 512, 255)
Parsing section yolo_1
Parsing section route_2
Parsing section convolutional_67
conv2d bn leaky (1, 1, 256, 128)
Parsing section upsample_1
Parsing section route_3
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')>]
Parsing section convolutional_68
conv2d bn leaky (1, 1, 384, 128)
Parsing section convolutional_69
conv2d bn leaky (3, 3, 128, 256)
Parsing section convolutional_70
conv2d bn leaky (1, 1, 256, 128)
Parsing section convolutional_71
conv2d bn leaky (3, 3, 128, 256)
Parsing section convolutional_72
conv2d bn leaky (1, 1, 256, 128)
Parsing section convolutional_73
conv2d bn leaky (3, 3, 128, 256)
Parsing section convolutional_74
conv2d linear (1, 1, 256, 255)
Parsing section yolo_2
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, None, None, 0 []
3)]
conv2d (Conv2D) (None, None, None, 864 ['input_1[0][0]']
32)
batch_normalization (BatchNorm (None, None, None, 128 ['conv2d[0][0]']
alization) 32)
leaky_re_lu (LeakyReLU) (None, None, None, 0 ['batch_normalization[0][0]']
32)
zero_padding2d (ZeroPadding2D) (None, None, None, 0 ['leaky_re_lu[0][0]']
32)
conv2d_1 (Conv2D) (None, None, None, 18432 ['zero_padding2d[0][0]']
64)
batch_normalization_1 (BatchNo (None, None, None, 256 ['conv2d_1[0][0]']
rmalization) 64)
leaky_re_lu_1 (LeakyReLU) (None, None, None, 0 ['batch_normalization_1[0][0]']
64)
conv2d_2 (Conv2D) (None, None, None, 2048 ['leaky_re_lu_1[0][0]']
32)
batch_normalization_2 (BatchNo (None, None, None, 128 ['conv2d_2[0][0]']
rmalization) 32)
leaky_re_lu_2 (LeakyReLU) (None, None, None, 0 ['batch_normalization_2[0][0]']
32)
conv2d_3 (Conv2D) (None, None, None, 18432 ['leaky_re_lu_2[0][0]']
64)
batch_normalization_3 (BatchNo (None, None, None, 256 ['conv2d_3[0][0]']
rmalization) 64)
leaky_re_lu_3 (LeakyReLU) (None, None, None, 0 ['batch_normalization_3[0][0]']
64)
add (Add) (None, None, None, 0 ['leaky_re_lu_1[0][0]',
64) 'leaky_re_lu_3[0][0]']
zero_padding2d_1 (ZeroPadding2 (None, None, None, 0 ['add[0][0]']
D) 64)
conv2d_4 (Conv2D) (None, None, None, 73728 ['zero_padding2d_1[0][0]']
128)
batch_normalization_4 (BatchNo (None, None, None, 512 ['conv2d_4[0][0]']
rmalization) 128)
leaky_re_lu_4 (LeakyReLU) (None, None, None, 0 ['batch_normalization_4[0][0]']
128)
conv2d_5 (Conv2D) (None, None, None, 8192 ['leaky_re_lu_4[0][0]']
64)
batch_normalization_5 (BatchNo (None, None, None, 256 ['conv2d_5[0][0]']
rmalization) 64)
leaky_re_lu_5 (LeakyReLU) (None, None, None, 0 ['batch_normalization_5[0][0]']
64)
conv2d_6 (Conv2D) (None, None, None, 73728 ['leaky_re_lu_5[0][0]']
128)
batch_normalization_6 (BatchNo (None, None, None, 512 ['conv2d_6[0][0]']
rmalization) 128)
leaky_re_lu_6 (LeakyReLU) (None, None, None, 0 ['batch_normalization_6[0][0]']
128)
add_1 (Add) (None, None, None, 0 ['leaky_re_lu_4[0][0]',
128) 'leaky_re_lu_6[0][0]']
conv2d_7 (Conv2D) (None, None, None, 8192 ['add_1[0][0]']
64)
batch_normalization_7 (BatchNo (None, None, None, 256 ['conv2d_7[0][0]']
rmalization) 64)
leaky_re_lu_7 (LeakyReLU) (None, None, None, 0 ['batch_normalization_7[0][0]']
64)
conv2d_8 (Conv2D) (None, None, None, 73728 ['leaky_re_lu_7[0][0]']
128)
batch_normalization_8 (BatchNo (None, None, None, 512 ['conv2d_8[0][0]']
rmalization) 128)
leaky_re_lu_8 (LeakyReLU) (None, None, None, 0 ['batch_normalization_8[0][0]']
128)
add_2 (Add) (None, None, None, 0 ['add_1[0][0]',
128) 'leaky_re_lu_8[0][0]']
zero_padding2d_2 (ZeroPadding2 (None, None, None, 0 ['add_2[0][0]']
D) 128)
conv2d_9 (Conv2D) (None, None, None, 294912 ['zero_padding2d_2[0][0]']
256)
batch_normalization_9 (BatchNo (None, None, None, 1024 ['conv2d_9[0][0]']
rmalization) 256)
leaky_re_lu_9 (LeakyReLU) (None, None, None, 0 ['batch_normalization_9[0][0]']
256)
conv2d_10 (Conv2D) (None, None, None, 32768 ['leaky_re_lu_9[0][0]']
128)
batch_normalization_10 (BatchN (None, None, None, 512 ['conv2d_10[0][0]']
ormalization) 128)
leaky_re_lu_10 (LeakyReLU) (None, None, None, 0 ['batch_normalization_10[0][0]']
128)
conv2d_11 (Conv2D) (None, None, None, 294912 ['leaky_re_lu_10[0][0]']
256)
batch_normalization_11 (BatchN (None, None, None, 1024 ['conv2d_11[0][0]']
ormalization) 256)
leaky_re_lu_11 (LeakyReLU) (None, None, None, 0 ['batch_normalization_11[0][0]']
256)
add_3 (Add) (None, None, None, 0 ['leaky_re_lu_9[0][0]',
256) 'leaky_re_lu_11[0][0]']
conv2d_12 (Conv2D) (None, None, None, 32768 ['add_3[0][0]']
128)
batch_normalization_12 (BatchN (None, None, None, 512 ['conv2d_12[0][0]']
ormalization) 128)
leaky_re_lu_12 (LeakyReLU) (None, None, None, 0 ['batch_normalization_12[0][0]']
128)
conv2d_13 (Conv2D) (None, None, None, 294912 ['leaky_re_lu_12[0][0]']
256)
batch_normalization_13 (BatchN (None, None, None, 1024 ['conv2d_13[0][0]']
ormalization) 256)
leaky_re_lu_13 (LeakyReLU) (None, None, None, 0 ['batch_normalization_13[0][0]']
256)
add_4 (Add) (None, None, None, 0 ['add_3[0][0]',
256) 'leaky_re_lu_13[0][0]']
conv2d_14 (Conv2D) (None, None, None, 32768 ['add_4[0][0]']
128)
batch_normalization_14 (BatchN (None, None, None, 512 ['conv2d_14[0][0]']
ormalization) 128)
leaky_re_lu_14 (LeakyReLU) (None, None, None, 0 ['batch_normalization_14[0][0]']
128)
conv2d_15 (Conv2D) (None, None, None, 294912 ['leaky_re_lu_14[0][0]']
256)
batch_normalization_15 (BatchN (None, None, None, 1024 ['conv2d_15[0][0]']
ormalization) 256)
leaky_re_lu_15 (LeakyReLU) (None, None, None, 0 ['batch_normalization_15[0][0]']
256)
add_5 (Add) (None, None, None, 0 ['add_4[0][0]',
256) 'leaky_re_lu_15[0][0]']
conv2d_16 (Conv2D) (None, None, None, 32768 ['add_5[0][0]']
128)
batch_normalization_16 (BatchN (None, None, None, 512 ['conv2d_16[0][0]']
ormalization) 128)
leaky_re_lu_16 (LeakyReLU) (None, None, None, 0 ['batch_normalization_16[0][0]']
128)
conv2d_17 (Conv2D) (None, None, None, 294912 ['leaky_re_lu_16[0][0]']
256)
batch_normalization_17 (BatchN (None, None, None, 1024 ['conv2d_17[0][0]']
ormalization) 256)
leaky_re_lu_17 (LeakyReLU) (None, None, None, 0 ['batch_normalization_17[0][0]']
256)
add_6 (Add) (None, None, None, 0 ['add_5[0][0]',
256) 'leaky_re_lu_17[0][0]']
conv2d_18 (Conv2D) (None, None, None, 32768 ['add_6[0][0]']
128)
batch_normalization_18 (BatchN (None, None, None, 512 ['conv2d_18[0][0]']
ormalization) 128)
leaky_re_lu_18 (LeakyReLU) (None, None, None, 0 ['batch_normalization_18[0][0]']
128)
conv2d_19 (Conv2D) (None, None, None, 294912 ['leaky_re_lu_18[0][0]']
256)
batch_normalization_19 (BatchN (None, None, None, 1024 ['conv2d_19[0][0]']
ormalization) 256)
leaky_re_lu_19 (LeakyReLU) (None, None, None, 0 ['batch_normalization_19[0][0]']
256)
add_7 (Add) (None, None, None, 0 ['add_6[0][0]',
256) 'leaky_re_lu_19[0][0]']
conv2d_20 (Conv2D) (None, None, None, 32768 ['add_7[0][0]']
128)
batch_normalization_20 (BatchN (None, None, None, 512 ['conv2d_20[0][0]']
ormalization) 128)
leaky_re_lu_20 (LeakyReLU) (None, None, None, 0 ['batch_normalization_20[0][0]']
128)
conv2d_21 (Conv2D) (None, None, None, 294912 ['leaky_re_lu_20[0][0]']
256)
batch_normalization_21 (BatchN (None, None, None, 1024 ['conv2d_21[0][0]']
ormalization) 256)
leaky_re_lu_21 (LeakyReLU) (None, None, None, 0 ['batch_normalization_21[0][0]']
256)
add_8 (Add) (None, None, None, 0 ['add_7[0][0]',
256) 'leaky_re_lu_21[0][0]']
conv2d_22 (Conv2D) (None, None, None, 32768 ['add_8[0][0]']
128)
batch_normalization_22 (BatchN (None, None, None, 512 ['conv2d_22[0][0]']
ormalization) 128)
leaky_re_lu_22 (LeakyReLU) (None, None, None, 0 ['batch_normalization_22[0][0]']
128)
conv2d_23 (Conv2D) (None, None, None, 294912 ['leaky_re_lu_22[0][0]']
256)
batch_normalization_23 (BatchN (None, None, None, 1024 ['conv2d_23[0][0]']
ormalization) 256)
leaky_re_lu_23 (LeakyReLU) (None, None, None, 0 ['batch_normalization_23[0][0]']
256)
add_9 (Add) (None, None, None, 0 ['add_8[0][0]',
256) 'leaky_re_lu_23[0][0]']
conv2d_24 (Conv2D) (None, None, None, 32768 ['add_9[0][0]']
128)
batch_normalization_24 (BatchN (None, None, None, 512 ['conv2d_24[0][0]']
ormalization) 128)
leaky_re_lu_24 (LeakyReLU) (None, None, None, 0 ['batch_normalization_24[0][0]']
128)
conv2d_25 (Conv2D) (None, None, None, 294912 ['leaky_re_lu_24[0][0]']
256)
batch_normalization_25 (BatchN (None, None, None, 1024 ['conv2d_25[0][0]']
ormalization) 256)
leaky_re_lu_25 (LeakyReLU) (None, None, None, 0 ['batch_normalization_25[0][0]']
256)
add_10 (Add) (None, None, None, 0 ['add_9[0][0]',
256) 'leaky_re_lu_25[0][0]']
zero_padding2d_3 (ZeroPadding2 (None, None, None, 0 ['add_10[0][0]']
D) 256)
conv2d_26 (Conv2D) (None, None, None, 1179648 ['zero_padding2d_3[0][0]']
512)
batch_normalization_26 (BatchN (None, None, None, 2048 ['conv2d_26[0][0]']
ormalization) 512)
leaky_re_lu_26 (LeakyReLU) (None, None, None, 0 ['batch_normalization_26[0][0]']
512)
conv2d_27 (Conv2D) (None, None, None, 131072 ['leaky_re_lu_26[0][0]']
256)
batch_normalization_27 (BatchN (None, None, None, 1024 ['conv2d_27[0][0]']
ormalization) 256)
leaky_re_lu_27 (LeakyReLU) (None, None, None, 0 ['batch_normalization_27[0][0]']
256)
conv2d_28 (Conv2D) (None, None, None, 1179648 ['leaky_re_lu_27[0][0]']
512)
batch_normalization_28 (BatchN (None, None, None, 2048 ['conv2d_28[0][0]']
ormalization) 512)
leaky_re_lu_28 (LeakyReLU) (None, None, None, 0 ['batch_normalization_28[0][0]']
512)
add_11 (Add) (None, None, None, 0 ['leaky_re_lu_26[0][0]',
512) 'leaky_re_lu_28[0][0]']
conv2d_29 (Conv2D) (None, None, None, 131072 ['add_11[0][0]']
256)
batch_normalization_29 (BatchN (None, None, None, 1024 ['conv2d_29[0][0]']
ormalization) 256)
leaky_re_lu_29 (LeakyReLU) (None, None, None, 0 ['batch_normalization_29[0][0]']
256)
conv2d_30 (Conv2D) (None, None, None, 1179648 ['leaky_re_lu_29[0][0]']
512)
batch_normalization_30 (BatchN (None, None, None, 2048 ['conv2d_30[0][0]']
ormalization) 512)
leaky_re_lu_30 (LeakyReLU) (None, None, None, 0 ['batch_normalization_30[0][0]']
512)
add_12 (Add) (None, None, None, 0 ['add_11[0][0]',
512) 'leaky_re_lu_30[0][0]']
conv2d_31 (Conv2D) (None, None, None, 131072 ['add_12[0][0]']
256)
batch_normalization_31 (BatchN (None, None, None, 1024 ['conv2d_31[0][0]']
ormalization) 256)
leaky_re_lu_31 (LeakyReLU) (None, None, None, 0 ['batch_normalization_31[0][0]']
256)
conv2d_32 (Conv2D) (None, None, None, 1179648 ['leaky_re_lu_31[0][0]']
512)
batch_normalization_32 (BatchN (None, None, None, 2048 ['conv2d_32[0][0]']
ormalization) 512)
leaky_re_lu_32 (LeakyReLU) (None, None, None, 0 ['batch_normalization_32[0][0]']
512)
add_13 (Add) (None, None, None, 0 ['add_12[0][0]',
512) 'leaky_re_lu_32[0][0]']
conv2d_33 (Conv2D) (None, None, None, 131072 ['add_13[0][0]']
256)
batch_normalization_33 (BatchN (None, None, None, 1024 ['conv2d_33[0][0]']
ormalization) 256)
leaky_re_lu_33 (LeakyReLU) (None, None, None, 0 ['batch_normalization_33[0][0]']
256)
conv2d_34 (Conv2D) (None, None, None, 1179648 ['leaky_re_lu_33[0][0]']
512)
batch_normalization_34 (BatchN (None, None, None, 2048 ['conv2d_34[0][0]']
ormalization) 512)
leaky_re_lu_34 (LeakyReLU) (None, None, None, 0 ['batch_normalization_34[0][0]']
512)
add_14 (Add) (None, None, None, 0 ['add_13[0][0]',
512) 'leaky_re_lu_34[0][0]']
conv2d_35 (Conv2D) (None, None, None, 131072 ['add_14[0][0]']
256)
batch_normalization_35 (BatchN (None, None, None, 1024 ['conv2d_35[0][0]']
ormalization) 256)
leaky_re_lu_35 (LeakyReLU) (None, None, None, 0 ['batch_normalization_35[0][0]']
256)
conv2d_36 (Conv2D) (None, None, None, 1179648 ['leaky_re_lu_35[0][0]']
512)
batch_normalization_36 (BatchN (None, None, None, 2048 ['conv2d_36[0][0]']
ormalization) 512)
leaky_re_lu_36 (LeakyReLU) (None, None, None, 0 ['batch_normalization_36[0][0]']
512)
add_15 (Add) (None, None, None, 0 ['add_14[0][0]',
512) 'leaky_re_lu_36[0][0]']
conv2d_37 (Conv2D) (None, None, None, 131072 ['add_15[0][0]']
256)
batch_normalization_37 (BatchN (None, None, None, 1024 ['conv2d_37[0][0]']
ormalization) 256)
leaky_re_lu_37 (LeakyReLU) (None, None, None, 0 ['batch_normalization_37[0][0]']
256)
conv2d_38 (Conv2D) (None, None, None, 1179648 ['leaky_re_lu_37[0][0]']
512)
batch_normalization_38 (BatchN (None, None, None, 2048 ['conv2d_38[0][0]']
ormalization) 512)
leaky_re_lu_38 (LeakyReLU) (None, None, None, 0 ['batch_normalization_38[0][0]']
512)
add_16 (Add) (None, None, None, 0 ['add_15[0][0]',
512) 'leaky_re_lu_38[0][0]']
conv2d_39 (Conv2D) (None, None, None, 131072 ['add_16[0][0]']
256)
batch_normalization_39 (BatchN (None, None, None, 1024 ['conv2d_39[0][0]']
ormalization) 256)
leaky_re_lu_39 (LeakyReLU) (None, None, None, 0 ['batch_normalization_39[0][0]']
256)
conv2d_40 (Conv2D) (None, None, None, 1179648 ['leaky_re_lu_39[0][0]']
512)
batch_normalization_40 (BatchN (None, None, None, 2048 ['conv2d_40[0][0]']
ormalization) 512)
leaky_re_lu_40 (LeakyReLU) (None, None, None, 0 ['batch_normalization_40[0][0]']
512)
add_17 (Add) (None, None, None, 0 ['add_16[0][0]',
512) 'leaky_re_lu_40[0][0]']
conv2d_41 (Conv2D) (None, None, None, 131072 ['add_17[0][0]']
256)
batch_normalization_41 (BatchN (None, None, None, 1024 ['conv2d_41[0][0]']
ormalization) 256)
leaky_re_lu_41 (LeakyReLU) (None, None, None, 0 ['batch_normalization_41[0][0]']
256)
conv2d_42 (Conv2D) (None, None, None, 1179648 ['leaky_re_lu_41[0][0]']
512)
batch_normalization_42 (BatchN (None, None, None, 2048 ['conv2d_42[0][0]']
ormalization) 512)
leaky_re_lu_42 (LeakyReLU) (None, None, None, 0 ['batch_normalization_42[0][0]']
512)
add_18 (Add) (None, None, None, 0 ['add_17[0][0]',
512) 'leaky_re_lu_42[0][0]']
zero_padding2d_4 (ZeroPadding2 (None, None, None, 0 ['add_18[0][0]']
D) 512)
conv2d_43 (Conv2D) (None, None, None, 4718592 ['zero_padding2d_4[0][0]']
1024)
batch_normalization_43 (BatchN (None, None, None, 4096 ['conv2d_43[0][0]']
ormalization) 1024)
leaky_re_lu_43 (LeakyReLU) (None, None, None, 0 ['batch_normalization_43[0][0]']
1024)
conv2d_44 (Conv2D) (None, None, None, 524288 ['leaky_re_lu_43[0][0]']
512)
batch_normalization_44 (BatchN (None, None, None, 2048 ['conv2d_44[0][0]']
ormalization) 512)
leaky_re_lu_44 (LeakyReLU) (None, None, None, 0 ['batch_normalization_44[0][0]']
512)
conv2d_45 (Conv2D) (None, None, None, 4718592 ['leaky_re_lu_44[0][0]']
1024)
batch_normalization_45 (BatchN (None, None, None, 4096 ['conv2d_45[0][0]']
ormalization) 1024)
leaky_re_lu_45 (LeakyReLU) (None, None, None, 0 ['batch_normalization_45[0][0]']
1024)
add_19 (Add) (None, None, None, 0 ['leaky_re_lu_43[0][0]',
1024) 'leaky_re_lu_45[0][0]']
conv2d_46 (Conv2D) (None, None, None, 524288 ['add_19[0][0]']
512)
batch_normalization_46 (BatchN (None, None, None, 2048 ['conv2d_46[0][0]']
ormalization) 512)
leaky_re_lu_46 (LeakyReLU) (None, None, None, 0 ['batch_normalization_46[0][0]']
512)
conv2d_47 (Conv2D) (None, None, None, 4718592 ['leaky_re_lu_46[0][0]']
1024)
batch_normalization_47 (BatchN (None, None, None, 4096 ['conv2d_47[0][0]']
ormalization) 1024)
leaky_re_lu_47 (LeakyReLU) (None, None, None, 0 ['batch_normalization_47[0][0]']
1024)
add_20 (Add) (None, None, None, 0 ['add_19[0][0]',
1024) 'leaky_re_lu_47[0][0]']
conv2d_48 (Conv2D) (None, None, None, 524288 ['add_20[0][0]']
512)
batch_normalization_48 (BatchN (None, None, None, 2048 ['conv2d_48[0][0]']
ormalization) 512)
leaky_re_lu_48 (LeakyReLU) (None, None, None, 0 ['batch_normalization_48[0][0]']
512)
conv2d_49 (Conv2D) (None, None, None, 4718592 ['leaky_re_lu_48[0][0]']
1024)
batch_normalization_49 (BatchN (None, None, None, 4096 ['conv2d_49[0][0]']
ormalization) 1024)
leaky_re_lu_49 (LeakyReLU) (None, None, None, 0 ['batch_normalization_49[0][0]']
1024)
add_21 (Add) (None, None, None, 0 ['add_20[0][0]',
1024) 'leaky_re_lu_49[0][0]']
conv2d_50 (Conv2D) (None, None, None, 524288 ['add_21[0][0]']
512)
batch_normalization_50 (BatchN (None, None, None, 2048 ['conv2d_50[0][0]']
ormalization) 512)
leaky_re_lu_50 (LeakyReLU) (None, None, None, 0 ['batch_normalization_50[0][0]']
512)
conv2d_51 (Conv2D) (None, None, None, 4718592 ['leaky_re_lu_50[0][0]']
1024)
batch_normalization_51 (BatchN (None, None, None, 4096 ['conv2d_51[0][0]']
ormalization) 1024)
leaky_re_lu_51 (LeakyReLU) (None, None, None, 0 ['batch_normalization_51[0][0]']
1024)
add_22 (Add) (None, None, None, 0 ['add_21[0][0]',
1024) 'leaky_re_lu_51[0][0]']
conv2d_52 (Conv2D) (None, None, None, 524288 ['add_22[0][0]']
512)
batch_normalization_52 (BatchN (None, None, None, 2048 ['conv2d_52[0][0]']
ormalization) 512)
leaky_re_lu_52 (LeakyReLU) (None, None, None, 0 ['batch_normalization_52[0][0]']
512)
conv2d_53 (Conv2D) (None, None, None, 4718592 ['leaky_re_lu_52[0][0]']
1024)
batch_normalization_53 (BatchN (None, None, None, 4096 ['conv2d_53[0][0]']
ormalization) 1024)
leaky_re_lu_53 (LeakyReLU) (None, None, None, 0 ['batch_normalization_53[0][0]']
1024)
conv2d_54 (Conv2D) (None, None, None, 524288 ['leaky_re_lu_53[0][0]']
512)
batch_normalization_54 (BatchN (None, None, None, 2048 ['conv2d_54[0][0]']
ormalization) 512)
leaky_re_lu_54 (LeakyReLU) (None, None, None, 0 ['batch_normalization_54[0][0]']
512)
conv2d_55 (Conv2D) (None, None, None, 4718592 ['leaky_re_lu_54[0][0]']
1024)
batch_normalization_55 (BatchN (None, None, None, 4096 ['conv2d_55[0][0]']
ormalization) 1024)
leaky_re_lu_55 (LeakyReLU) (None, None, None, 0 ['batch_normalization_55[0][0]']
1024)
conv2d_56 (Conv2D) (None, None, None, 524288 ['leaky_re_lu_55[0][0]']
512)
batch_normalization_56 (BatchN (None, None, None, 2048 ['conv2d_56[0][0]']
ormalization) 512)
leaky_re_lu_56 (LeakyReLU) (None, None, None, 0 ['batch_normalization_56[0][0]']
512)
conv2d_59 (Conv2D) (None, None, None, 131072 ['leaky_re_lu_56[0][0]']
256)
batch_normalization_58 (BatchN (None, None, None, 1024 ['conv2d_59[0][0]']
ormalization) 256)
leaky_re_lu_58 (LeakyReLU) (None, None, None, 0 ['batch_normalization_58[0][0]']
256)
up_sampling2d (UpSampling2D) (None, None, None, 0 ['leaky_re_lu_58[0][0]']
256)
concatenate (Concatenate) (None, None, None, 0 ['up_sampling2d[0][0]',
768) 'add_18[0][0]']
conv2d_60 (Conv2D) (None, None, None, 196608 ['concatenate[0][0]']
256)
batch_normalization_59 (BatchN (None, None, None, 1024 ['conv2d_60[0][0]']
ormalization) 256)
leaky_re_lu_59 (LeakyReLU) (None, None, None, 0 ['batch_normalization_59[0][0]']
256)
conv2d_61 (Conv2D) (None, None, None, 1179648 ['leaky_re_lu_59[0][0]']
512)
batch_normalization_60 (BatchN (None, None, None, 2048 ['conv2d_61[0][0]']
ormalization) 512)
leaky_re_lu_60 (LeakyReLU) (None, None, None, 0 ['batch_normalization_60[0][0]']
512)
conv2d_62 (Conv2D) (None, None, None, 131072 ['leaky_re_lu_60[0][0]']
256)
batch_normalization_61 (BatchN (None, None, None, 1024 ['conv2d_62[0][0]']
ormalization) 256)
leaky_re_lu_61 (LeakyReLU) (None, None, None, 0 ['batch_normalization_61[0][0]']
256)
conv2d_63 (Conv2D) (None, None, None, 1179648 ['leaky_re_lu_61[0][0]']
512)
batch_normalization_62 (BatchN (None, None, None, 2048 ['conv2d_63[0][0]']
ormalization) 512)
leaky_re_lu_62 (LeakyReLU) (None, None, None, 0 ['batch_normalization_62[0][0]']
512)
conv2d_64 (Conv2D) (None, None, None, 131072 ['leaky_re_lu_62[0][0]']
256)
batch_normalization_63 (BatchN (None, None, None, 1024 ['conv2d_64[0][0]']
ormalization) 256)
leaky_re_lu_63 (LeakyReLU) (None, None, None, 0 ['batch_normalization_63[0][0]']
256)
conv2d_67 (Conv2D) (None, None, None, 32768 ['leaky_re_lu_63[0][0]']
128)
batch_normalization_65 (BatchN (None, None, None, 512 ['conv2d_67[0][0]']
ormalization) 128)
leaky_re_lu_65 (LeakyReLU) (None, None, None, 0 ['batch_normalization_65[0][0]']
128)
up_sampling2d_1 (UpSampling2D) (None, None, None, 0 ['leaky_re_lu_65[0][0]']
128)
concatenate_1 (Concatenate) (None, None, None, 0 ['up_sampling2d_1[0][0]',
384) 'add_10[0][0]']
conv2d_68 (Conv2D) (None, None, None, 49152 ['concatenate_1[0][0]']
128)
batch_normalization_66 (BatchN (None, None, None, 512 ['conv2d_68[0][0]']
ormalization) 128)
leaky_re_lu_66 (LeakyReLU) (None, None, None, 0 ['batch_normalization_66[0][0]']
128)
conv2d_69 (Conv2D) (None, None, None, 294912 ['leaky_re_lu_66[0][0]']
256)
batch_normalization_67 (BatchN (None, None, None, 1024 ['conv2d_69[0][0]']
ormalization) 256)
leaky_re_lu_67 (LeakyReLU) (None, None, None, 0 ['batch_normalization_67[0][0]']
256)
conv2d_70 (Conv2D) (None, None, None, 32768 ['leaky_re_lu_67[0][0]']
128)
batch_normalization_68 (BatchN (None, None, None, 512 ['conv2d_70[0][0]']
ormalization) 128)
leaky_re_lu_68 (LeakyReLU) (None, None, None, 0 ['batch_normalization_68[0][0]']
128)
conv2d_71 (Conv2D) (None, None, None, 294912 ['leaky_re_lu_68[0][0]']
256)
batch_normalization_69 (BatchN (None, None, None, 1024 ['conv2d_71[0][0]']
ormalization) 256)
leaky_re_lu_69 (LeakyReLU) (None, None, None, 0 ['batch_normalization_69[0][0]']
256)
conv2d_72 (Conv2D) (None, None, None, 32768 ['leaky_re_lu_69[0][0]']
128)
batch_normalization_70 (BatchN (None, None, None, 512 ['conv2d_72[0][0]']
ormalization) 128)
leaky_re_lu_70 (LeakyReLU) (None, None, None, 0 ['batch_normalization_70[0][0]']
128)
conv2d_57 (Conv2D) (None, None, None, 4718592 ['leaky_re_lu_56[0][0]']
1024)
conv2d_65 (Conv2D) (None, None, None, 1179648 ['leaky_re_lu_63[0][0]']
512)
conv2d_73 (Conv2D) (None, None, None, 294912 ['leaky_re_lu_70[0][0]']
256)
batch_normalization_57 (BatchN (None, None, None, 4096 ['conv2d_57[0][0]']
ormalization) 1024)
batch_normalization_64 (BatchN (None, None, None, 2048 ['conv2d_65[0][0]']
ormalization) 512)
batch_normalization_71 (BatchN (None, None, None, 1024 ['conv2d_73[0][0]']
ormalization) 256)
leaky_re_lu_57 (LeakyReLU) (None, None, None, 0 ['batch_normalization_57[0][0]']
1024)
leaky_re_lu_64 (LeakyReLU) (None, None, None, 0 ['batch_normalization_64[0][0]']
512)
leaky_re_lu_71 (LeakyReLU) (None, None, None, 0 ['batch_normalization_71[0][0]']
256)
conv2d_58 (Conv2D) (None, None, None, 261375 ['leaky_re_lu_57[0][0]']
255)
conv2d_66 (Conv2D) (None, None, None, 130815 ['leaky_re_lu_64[0][0]']
255)
conv2d_74 (Conv2D) (None, None, None, 65535 ['leaky_re_lu_71[0][0]']
255)
==================================================================================================
Total params: 62,001,757
Trainable params: 61,949,149
Non-trainable params: 52,608
__________________________________________________________________________________________________
None
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.
Saved Keras model to model_data/yolo.h5
Read 62001757 of 62001757.0 from Darknet weights.
"""
Self-contained Python script to train YOLOv3 on your own dataset
"""
import numpy as np
import keras.backend as K
from keras.layers import Input, Lambda
from keras.models import Model
from keras.optimizers import Adam
from keras.callbacks import TensorBoard, ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
from yolo3.model import preprocess_true_boxes, yolo_body, tiny_yolo_body, yolo_loss
from yolo3.utils import get_random_data
def _main():
annotation_path = './train/_annotations.txt' # path to Roboflow data annotations
log_dir = './logs/000/' # where we're storing our logs
classes_path = './train/_classes.txt' # path to Roboflow class names
anchors_path = './model_data/yolo_anchors.txt'
class_names = get_classes(classes_path)
print("-------------------CLASS NAMES-------------------")
print(class_names)
print("-------------------CLASS NAMES-------------------")
num_classes = len(class_names)
anchors = get_anchors(anchors_path)
input_shape = (256,256) # multiple of 32, hw default = (416,416)
is_tiny_version = len(anchors)==6 # default setting
if is_tiny_version:
model = create_tiny_model(input_shape, anchors, num_classes,
freeze_body=2, weights_path='./model_data/tiny_yolo_weights.h5')
else:
model = create_model(input_shape, anchors, num_classes,
freeze_body=2, weights_path='./model_data/yolo.h5') # make sure you know what you freeze
logging = TensorBoard(log_dir=log_dir)
checkpoint = ModelCheckpoint(log_dir + 'ep{epoch:03d}-loss{loss:.3f}-val_loss{val_loss:.3f}.h5',
monitor='val_loss', save_weights_only=True, save_best_only=True, period=3)
reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=3, verbose=1)
early_stopping = EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=1)
val_split = 0.2 # set the size of the validation set
with open(annotation_path) as f:
lines = f.readlines()
np.random.seed(10101)
np.random.shuffle(lines)
np.random.seed(None)
num_val = int(len(lines)*val_split)
num_train = len(lines) - num_val
# Train with frozen layers first, to get a stable loss.
# Adjust num epochs to your dataset. This step is enough to obtain a not bad model.
if True:
model.compile(optimizer=Adam(lr=1e-3), loss={
# use custom yolo_loss Lambda layer.
'yolo_loss': lambda y_true, y_pred: y_pred})
batch_size = 16
print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size))
model.fit_generator(data_generator_wrapper(lines[:num_train], batch_size, input_shape, anchors, num_classes),
steps_per_epoch=max(1, num_train//batch_size),
validation_data=data_generator_wrapper(lines[num_train:], batch_size, input_shape, anchors, num_classes),
validation_steps=max(1, num_val//batch_size),
epochs=500,
initial_epoch=0,
callbacks=[logging, checkpoint])
model.save_weights(log_dir + 'trained_weights_stage_1.h5')
# Unfreeze and continue training, to fine-tune.
# Train longer if the result is not good.
if True:
for i in range(len(model.layers)):
model.layers[i].trainable = True
model.compile(optimizer=Adam(lr=1e-4), loss={'yolo_loss': lambda y_true, y_pred: y_pred}) # recompile to apply the change
print('Unfreeze all of the layers.')
batch_size = 16 # note that more GPU memory is required after unfreezing the body
print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size))
model.fit_generator(data_generator_wrapper(lines[:num_train], batch_size, input_shape, anchors, num_classes),
steps_per_epoch=max(1, num_train//batch_size),
validation_data=data_generator_wrapper(lines[num_train:], batch_size, input_shape, anchors, num_classes),
validation_steps=max(1, num_val//batch_size),
epochs=100,
initial_epoch=50,
callbacks=[logging, checkpoint, reduce_lr, early_stopping])
model.save_weights(log_dir + 'trained_weights_final.h5')
# Further training if needed.
def get_classes(classes_path):
'''loads the classes'''
with open(classes_path) as f:
class_names = f.readlines()
class_names = [c.strip() for c in class_names]
return class_names
def get_anchors(anchors_path):
'''loads the anchors from a file'''
with open(anchors_path) as f:
anchors = f.readline()
anchors = [float(x) for x in anchors.split(',')]
return np.array(anchors).reshape(-1, 2)
def create_model(input_shape, anchors, num_classes, load_pretrained=True, freeze_body=2,
weights_path='./model_data/yolo.h5'):
'''create the training model'''
K.clear_session() # get a new session
image_input = Input(shape=(None, None, 3))
h, w = input_shape
num_anchors = len(anchors)
y_true = [Input(shape=(h//{0:32, 1:16, 2:8}[l], w//{0:32, 1:16, 2:8}[l], \
num_anchors//3, num_classes+5)) for l in range(3)]
model_body = yolo_body(image_input, num_anchors//3, num_classes)
print('Create YOLOv3 model with {} anchors and {} classes.'.format(num_anchors, num_classes))
if load_pretrained:
model_body.load_weights(weights_path, by_name=True, skip_mismatch=True)
print('Load weights {}.'.format(weights_path))
if freeze_body in [1, 2]:
# Freeze darknet53 body or freeze all but 3 output layers.
num = (185, len(model_body.layers)-3)[freeze_body-1]
for i in range(num): model_body.layers[i].trainable = False
print('Freeze the first {} layers of total {} layers.'.format(num, len(model_body.layers)))
model_loss = Lambda(yolo_loss, output_shape=(1,), name='yolo_loss',
arguments={'anchors': anchors, 'num_classes': num_classes, 'ignore_thresh': 0.5})(
[*model_body.output, *y_true])
model = Model([model_body.input, *y_true], model_loss)
return model
def create_tiny_model(input_shape, anchors, num_classes, load_pretrained=True, freeze_body=2,
weights_path='./model_data/tiny_yolo_weights.h5'):
'''create the training model, for Tiny YOLOv3'''
K.clear_session() # get a new session
image_input = Input(shape=(None, None, 3))
h, w = input_shape
num_anchors = len(anchors)
y_true = [Input(shape=(h//{0:32, 1:16}[l], w//{0:32, 1:16}[l], \
num_anchors//2, num_classes+5)) for l in range(2)]
model_body = tiny_yolo_body(image_input, num_anchors//2, num_classes)
print('Create Tiny YOLOv3 model with {} anchors and {} classes.'.format(num_anchors, num_classes))
if load_pretrained:
model_body.load_weights(weights_path, by_name=True, skip_mismatch=True)
print('Load weights {}.'.format(weights_path))
if freeze_body in [1, 2]:
# Freeze the darknet body or freeze all but 2 output layers.
num = (20, len(model_body.layers)-2)[freeze_body-1]
for i in range(num): model_body.layers[i].trainable = False
print('Freeze the first {} layers of total {} layers.'.format(num, len(model_body.layers)))
model_loss = Lambda(yolo_loss, output_shape=(1,), name='yolo_loss',
arguments={'anchors': anchors, 'num_classes': num_classes, 'ignore_thresh': 0.7})(
[*model_body.output, *y_true])
model = Model([model_body.input, *y_true], model_loss)
return model
def data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes):
'''data generator for fit_generator'''
n = len(annotation_lines)
i = 0
while True:
image_data = []
box_data = []
for b in range(batch_size):
if i==0:
np.random.shuffle(annotation_lines)
image, box = get_random_data(annotation_lines[i], input_shape, random=True)
image_data.append(image)
box_data.append(box)
i = (i+1) % n
image_data = np.array(image_data)
box_data = np.array(box_data)
y_true = preprocess_true_boxes(box_data, input_shape, anchors, num_classes)
yield [image_data, *y_true], np.zeros(batch_size)
def data_generator_wrapper(annotation_lines, batch_size, input_shape, anchors, num_classes):
n = len(annotation_lines)
if n==0 or batch_size<=0: return None
return data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes)
if __name__ == '__main__':
_main()-------------------CLASS NAMES------------------- ['licence'] -------------------CLASS NAMES-------------------
2023-01-22 02:42:51.965089: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE4.1 SSE4.2 AVX AVX2 FMA To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
Create YOLOv3 model with 9 anchors and 1 classes. WARNING:tensorflow:Skipping loading weights for layer #249 (named conv2d_58) due to mismatch in shape for weight conv2d_58/kernel:0. Weight expects shape (1, 1, 1024, 18). Received saved weight with shape (255, 1024, 1, 1) WARNING:tensorflow:Skipping loading weights for layer #249 (named conv2d_58) due to mismatch in shape for weight conv2d_58/bias:0. Weight expects shape (18,). Received saved weight with shape (255,) WARNING:tensorflow:Skipping loading weights for layer #250 (named conv2d_66) due to mismatch in shape for weight conv2d_66/kernel:0. Weight expects shape (1, 1, 512, 18). Received saved weight with shape (255, 512, 1, 1) WARNING:tensorflow:Skipping loading weights for layer #250 (named conv2d_66) due to mismatch in shape for weight conv2d_66/bias:0. Weight expects shape (18,). Received saved weight with shape (255,) WARNING:tensorflow:Skipping loading weights for layer #251 (named conv2d_74) due to mismatch in shape for weight conv2d_74/kernel:0. Weight expects shape (1, 1, 256, 18). Received saved weight with shape (255, 256, 1, 1) WARNING:tensorflow:Skipping loading weights for layer #251 (named conv2d_74) due to mismatch in shape for weight conv2d_74/bias:0. Weight expects shape (18,). Received saved weight with shape (255,) Load weights ./model_data/yolo.h5. Freeze the first 249 layers of total 252 layers. WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen. Train on 488 samples, val on 121 samples, with batch size 16.
/Users/aczajka/miniconda3/envs/yolov3/lib/python3.9/site-packages/keras/optimizers/optimizer_v2/adam.py:117: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead. super().__init__(name, **kwargs) /var/folders/j_/grk4ythd0392dcw5z3gkgw5w0000gn/T/ipykernel_39692/4035785499.py:62: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators. model.fit_generator(data_generator_wrapper(lines[:num_train], batch_size, input_shape, anchors, num_classes),
Epoch 1/500 WARNING:tensorflow:From /Users/aczajka/miniconda3/envs/yolov3/lib/python3.9/site-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py:83: Analyzer.lamba_check (from tensorflow.python.autograph.pyct.static_analysis.liveness) is deprecated and will be removed after 2023-09-23. Instructions for updating: Lambda fuctions will be no more assumed to be used in the statement where they are used, or at least in the same block. https://github.com/tensorflow/tensorflow/issues/56089
2023-01-22 02:43:01.274999: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:954] layout failed: INVALID_ARGUMENT: Subshape must have computed start >= end since stride is negative, but is 0 and 2 (computed from start 0 and end 9223372036854775807 over shape with rank 2 and stride-1)
30/30 [==============================] - ETA: 0s - loss: 1092.7228
2023-01-22 02:43:54.290859: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:954] layout failed: INVALID_ARGUMENT: Subshape must have computed start >= end since stride is negative, but is 0 and 2 (computed from start 0 and end 9223372036854775807 over shape with rank 2 and stride-1)
30/30 [==============================] - 70s 2s/step - loss: 1092.7228 - val_loss: 216.0935 Epoch 2/500 30/30 [==============================] - 57s 2s/step - loss: 135.6953 - val_loss: 94.2604 Epoch 3/500 30/30 [==============================] - 52s 2s/step - loss: 79.2672 - val_loss: 68.8617 Epoch 4/500 30/30 [==============================] - 52s 2s/step - loss: 60.4469 - val_loss: 54.7572 Epoch 5/500 30/30 [==============================] - 50s 2s/step - loss: 50.0802 - val_loss: 47.2904 Epoch 6/500 30/30 [==============================] - 51s 2s/step - loss: 43.6335 - val_loss: 41.2742 Epoch 7/500 30/30 [==============================] - 51s 2s/step - loss: 39.3473 - val_loss: 38.5374 Epoch 8/500 30/30 [==============================] - 52s 2s/step - loss: 36.2422 - val_loss: 35.2012 Epoch 9/500 30/30 [==============================] - 51s 2s/step - loss: 33.6743 - val_loss: 33.0579 Epoch 10/500 30/30 [==============================] - 49s 2s/step - loss: 32.0283 - val_loss: 30.6336 Epoch 11/500 30/30 [==============================] - 49s 2s/step - loss: 30.3864 - val_loss: 29.2345 Epoch 12/500 30/30 [==============================] - 51s 2s/step - loss: 29.6261 - val_loss: 28.6320 Epoch 13/500 30/30 [==============================] - 51s 2s/step - loss: 28.1432 - val_loss: 27.8887 Epoch 14/500 30/30 [==============================] - 52s 2s/step - loss: 27.6032 - val_loss: 27.0226 Epoch 15/500 30/30 [==============================] - 52s 2s/step - loss: 26.9148 - val_loss: 26.3452 Epoch 16/500 30/30 [==============================] - 52s 2s/step - loss: 26.4210 - val_loss: 26.4830 Epoch 17/500 30/30 [==============================] - 53s 2s/step - loss: 25.6399 - val_loss: 25.2511 Epoch 18/500 30/30 [==============================] - 60s 2s/step - loss: 25.5443 - val_loss: 24.6174 Epoch 19/500 30/30 [==============================] - 52s 2s/step - loss: 25.2961 - val_loss: 24.7754 Epoch 20/500 30/30 [==============================] - 54s 2s/step - loss: 24.7307 - val_loss: 24.6782 Epoch 21/500 30/30 [==============================] - 58s 2s/step - loss: 24.2857 - val_loss: 24.3096 Epoch 22/500 30/30 [==============================] - 52s 2s/step - loss: 24.2008 - val_loss: 24.3196 Epoch 23/500 30/30 [==============================] - 51s 2s/step - loss: 23.5739 - val_loss: 23.3351 Epoch 24/500 30/30 [==============================] - 52s 2s/step - loss: 23.6946 - val_loss: 24.0281 Epoch 25/500 30/30 [==============================] - 52s 2s/step - loss: 23.7198 - val_loss: 23.4021 Epoch 26/500 30/30 [==============================] - 53s 2s/step - loss: 23.2751 - val_loss: 23.3185 Epoch 27/500 30/30 [==============================] - 53s 2s/step - loss: 23.2101 - val_loss: 22.7601 Epoch 28/500 30/30 [==============================] - 53s 2s/step - loss: 22.9937 - val_loss: 22.6282 Epoch 29/500 30/30 [==============================] - 53s 2s/step - loss: 22.8363 - val_loss: 22.1787 Epoch 30/500 30/30 [==============================] - 55s 2s/step - loss: 22.6890 - val_loss: 22.1749 Epoch 31/500 30/30 [==============================] - 56s 2s/step - loss: 22.4564 - val_loss: 22.6868 Epoch 32/500 30/30 [==============================] - 52s 2s/step - loss: 22.3397 - val_loss: 22.1918 Epoch 33/500 30/30 [==============================] - 53s 2s/step - loss: 22.8438 - val_loss: 22.4380 Epoch 34/500 30/30 [==============================] - 53s 2s/step - loss: 22.0734 - val_loss: 22.9481 Epoch 35/500 30/30 [==============================] - 53s 2s/step - loss: 21.9711 - val_loss: 22.8436 Epoch 36/500 30/30 [==============================] - 53s 2s/step - loss: 22.0127 - val_loss: 22.7770 Epoch 37/500 30/30 [==============================] - 53s 2s/step - loss: 22.6367 - val_loss: 21.8047 Epoch 38/500 30/30 [==============================] - 53s 2s/step - loss: 21.8459 - val_loss: 22.3148 Epoch 39/500 30/30 [==============================] - 54s 2s/step - loss: 21.9811 - val_loss: 21.6083 Epoch 40/500 30/30 [==============================] - 52s 2s/step - loss: 21.8194 - val_loss: 21.5877 Epoch 41/500 30/30 [==============================] - 53s 2s/step - loss: 21.6587 - val_loss: 21.3777 Epoch 42/500 30/30 [==============================] - 56s 2s/step - loss: 21.4056 - val_loss: 21.0999 Epoch 43/500 30/30 [==============================] - 52s 2s/step - loss: 21.4517 - val_loss: 20.9185 Epoch 44/500 30/30 [==============================] - 55s 2s/step - loss: 21.4323 - val_loss: 21.4888 Epoch 45/500 30/30 [==============================] - 57s 2s/step - loss: 21.4581 - val_loss: 20.9886 Epoch 46/500 30/30 [==============================] - 56s 2s/step - loss: 21.3487 - val_loss: 20.3990 Epoch 47/500 30/30 [==============================] - 56s 2s/step - loss: 20.9203 - val_loss: 20.3689 Epoch 48/500 30/30 [==============================] - 57s 2s/step - loss: 21.0719 - val_loss: 21.1066 Epoch 49/500 30/30 [==============================] - 57s 2s/step - loss: 21.3894 - val_loss: 21.2877 Epoch 50/500 30/30 [==============================] - 56s 2s/step - loss: 21.2891 - val_loss: 21.2323 Epoch 51/500 30/30 [==============================] - 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124s 4s/step - loss: 16.0947 - val_loss: 15.7753 Epoch 432/500 30/30 [==============================] - 127s 4s/step - loss: 15.8444 - val_loss: 15.5911 Epoch 433/500 30/30 [==============================] - 125s 4s/step - loss: 16.1289 - val_loss: 15.9490 Epoch 434/500 30/30 [==============================] - 125s 4s/step - loss: 15.9296 - val_loss: 15.6148 Epoch 435/500 30/30 [==============================] - 126s 4s/step - loss: 15.9802 - val_loss: 15.4892 Epoch 436/500 30/30 [==============================] - 123s 4s/step - loss: 16.0529 - val_loss: 15.2430 Epoch 437/500 30/30 [==============================] - 124s 4s/step - loss: 15.7882 - val_loss: 15.6371 Epoch 438/500 30/30 [==============================] - 119s 4s/step - loss: 16.0208 - val_loss: 15.5694 Epoch 439/500 30/30 [==============================] - 53s 2s/step - loss: 16.2243 - val_loss: 16.4516 Epoch 440/500 30/30 [==============================] - 69s 2s/step - loss: 15.8460 - val_loss: 15.2869 Epoch 441/500 30/30 [==============================] - 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122s 4s/step - loss: 15.7717 - val_loss: 16.1588 Epoch 452/500 30/30 [==============================] - 126s 4s/step - loss: 15.8032 - val_loss: 15.5423 Epoch 453/500 30/30 [==============================] - 125s 4s/step - loss: 16.0863 - val_loss: 16.2087 Epoch 454/500 30/30 [==============================] - 123s 4s/step - loss: 15.7231 - val_loss: 15.4152 Epoch 455/500 30/30 [==============================] - 123s 4s/step - loss: 15.9819 - val_loss: 15.6086 Epoch 456/500 30/30 [==============================] - 124s 4s/step - loss: 16.2392 - val_loss: 15.6546 Epoch 457/500 30/30 [==============================] - 125s 4s/step - loss: 15.9337 - val_loss: 15.5734 Epoch 458/500 30/30 [==============================] - 123s 4s/step - loss: 15.7483 - val_loss: 16.0871 Epoch 459/500 30/30 [==============================] - 125s 4s/step - loss: 15.9154 - val_loss: 15.7753 Epoch 460/500 30/30 [==============================] - 116s 4s/step - loss: 16.1634 - val_loss: 16.0291 Epoch 461/500 30/30 [==============================] - 52s 2s/step - loss: 16.0713 - val_loss: 15.6570 Epoch 462/500 30/30 [==============================] - 68s 2s/step - loss: 15.7077 - val_loss: 15.3641 Epoch 463/500 30/30 [==============================] - 59s 2s/step - loss: 16.0866 - val_loss: 15.8481 Epoch 464/500 30/30 [==============================] - 57s 2s/step - loss: 15.9679 - val_loss: 15.6844 Epoch 465/500 30/30 [==============================] - 59s 2s/step - loss: 15.9050 - val_loss: 15.2170 Epoch 466/500 30/30 [==============================] - 57s 2s/step - loss: 15.7928 - val_loss: 16.0792 Epoch 467/500 30/30 [==============================] - 57s 2s/step - loss: 15.9432 - val_loss: 15.4652 Epoch 468/500 30/30 [==============================] - 59s 2s/step - loss: 16.0087 - val_loss: 15.8910 Epoch 469/500 30/30 [==============================] - 64s 2s/step - loss: 15.9682 - val_loss: 15.9137 Epoch 470/500 30/30 [==============================] - 59s 2s/step - loss: 15.6714 - val_loss: 15.9395 Epoch 471/500 30/30 [==============================] - 88s 3s/step - loss: 16.0309 - val_loss: 15.9491 Epoch 472/500 30/30 [==============================] - 109s 4s/step - loss: 15.8227 - val_loss: 15.7770 Epoch 473/500 30/30 [==============================] - 117s 4s/step - loss: 16.0340 - val_loss: 15.3767 Epoch 474/500 30/30 [==============================] - 122s 4s/step - loss: 15.8100 - val_loss: 16.0189 Epoch 475/500 30/30 [==============================] - 129s 4s/step - loss: 15.8677 - val_loss: 15.8241 Epoch 476/500 30/30 [==============================] - 129s 4s/step - loss: 15.8201 - val_loss: 15.2546 Epoch 477/500 30/30 [==============================] - 123s 4s/step - loss: 16.1264 - val_loss: 16.2662 Epoch 478/500 30/30 [==============================] - 124s 4s/step - loss: 16.1311 - val_loss: 15.2587 Epoch 479/500 30/30 [==============================] - 125s 4s/step - loss: 16.2160 - val_loss: 15.7506 Epoch 480/500 30/30 [==============================] - 123s 4s/step - loss: 15.8996 - val_loss: 16.0202 Epoch 481/500 30/30 [==============================] - 127s 4s/step - loss: 15.9867 - val_loss: 15.5650 Epoch 482/500 30/30 [==============================] - 170s 6s/step - loss: 15.7489 - val_loss: 15.4263 Epoch 483/500 30/30 [==============================] - 63s 2s/step - loss: 16.0861 - val_loss: 15.6782 Epoch 484/500 30/30 [==============================] - 77s 3s/step - loss: 15.8524 - val_loss: 15.6728 Epoch 485/500 30/30 [==============================] - 68s 2s/step - loss: 15.9259 - val_loss: 15.5141 Epoch 486/500 30/30 [==============================] - 67s 2s/step - loss: 15.7106 - val_loss: 15.6335 Epoch 487/500 30/30 [==============================] - 63s 2s/step - loss: 15.9842 - val_loss: 15.1482 Epoch 488/500 30/30 [==============================] - 61s 2s/step - loss: 15.8998 - val_loss: 16.0844 Epoch 489/500 30/30 [==============================] - 59s 2s/step - loss: 15.8302 - val_loss: 16.6305 Epoch 490/500 30/30 [==============================] - 59s 2s/step - loss: 15.8365 - val_loss: 15.8551 Epoch 491/500 30/30 [==============================] - 57s 2s/step - loss: 16.0139 - val_loss: 15.3942 Epoch 492/500 30/30 [==============================] - 58s 2s/step - loss: 15.9906 - val_loss: 16.0351 Epoch 493/500 30/30 [==============================] - 59s 2s/step - loss: 15.7704 - val_loss: 15.5585 Epoch 494/500 30/30 [==============================] - 64s 2s/step - loss: 15.8734 - val_loss: 15.5017 Epoch 495/500 30/30 [==============================] - 86s 3s/step - loss: 15.8414 - val_loss: 16.0038 Epoch 496/500 30/30 [==============================] - 109s 4s/step - loss: 16.0293 - val_loss: 15.9147 Epoch 497/500 30/30 [==============================] - 119s 4s/step - loss: 15.7651 - val_loss: 15.6716 Epoch 498/500 30/30 [==============================] - 123s 4s/step - loss: 15.8485 - val_loss: 16.0082 Epoch 499/500 30/30 [==============================] - 126s 4s/step - loss: 15.8425 - val_loss: 14.8089 Epoch 500/500 30/30 [==============================] - 101s 3s/step - loss: 16.0761 - val_loss: 15.9947 Unfreeze all of the layers. Train on 488 samples, val on 121 samples, with batch size 16.
/var/folders/j_/grk4ythd0392dcw5z3gkgw5w0000gn/T/ipykernel_39692/4035785499.py:81: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators. model.fit_generator(data_generator_wrapper(lines[:num_train], batch_size, input_shape, anchors, num_classes),
Epoch 51/100
2023-01-22 13:37:37.110606: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:954] layout failed: INVALID_ARGUMENT: Subshape must have computed start >= end since stride is negative, but is 0 and 2 (computed from start 0 and end 9223372036854775807 over shape with rank 2 and stride-1)
30/30 [==============================] - ETA: 0s - loss: 16.2221
2023-01-22 13:43:00.326593: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:954] layout failed: INVALID_ARGUMENT: Subshape must have computed start >= end since stride is negative, but is 0 and 2 (computed from start 0 and end 9223372036854775807 over shape with rank 2 and stride-1)
30/30 [==============================] - 350s 11s/step - loss: 16.2221 - val_loss: 15.0134 - lr: 1.0000e-04 Epoch 52/100 30/30 [==============================] - 346s 12s/step - loss: 14.9261 - val_loss: 14.5771 - lr: 1.0000e-04 Epoch 53/100 30/30 [==============================] - 243s 8s/step - loss: 14.5103 - val_loss: 14.5714 - lr: 1.0000e-04 Epoch 54/100 30/30 [==============================] - 231s 8s/step - loss: 14.2489 - val_loss: 13.8991 - lr: 1.0000e-04 Epoch 55/100 30/30 [==============================] - 276s 9s/step - loss: 14.1362 - val_loss: 14.2145 - lr: 1.0000e-04 Epoch 56/100 30/30 [==============================] - 334s 11s/step - loss: 13.6959 - val_loss: 13.6794 - lr: 1.0000e-04 Epoch 57/100 30/30 [==============================] - 340s 11s/step - loss: 13.5898 - val_loss: 13.1452 - lr: 1.0000e-04 Epoch 58/100 30/30 [==============================] - 336s 11s/step - loss: 13.4866 - val_loss: 13.5824 - lr: 1.0000e-04 Epoch 59/100 30/30 [==============================] - 259s 9s/step - loss: 13.4531 - val_loss: 13.1278 - lr: 1.0000e-04 Epoch 60/100 30/30 [==============================] - 219s 7s/step - loss: 13.3503 - val_loss: 13.0499 - lr: 1.0000e-04 Epoch 61/100 30/30 [==============================] - 254s 8s/step - loss: 13.2267 - val_loss: 13.0210 - lr: 1.0000e-04 Epoch 62/100 30/30 [==============================] - 414s 14s/step - loss: 13.2120 - val_loss: 14.0383 - lr: 1.0000e-04 Epoch 63/100 30/30 [==============================] - 472s 16s/step - loss: 12.9336 - val_loss: 13.2708 - lr: 1.0000e-04 Epoch 64/100 30/30 [==============================] - ETA: 0s - loss: 13.1477 Epoch 64: ReduceLROnPlateau reducing learning rate to 9.999999747378752e-06. 30/30 [==============================] - 470s 16s/step - loss: 13.1477 - val_loss: 13.6016 - lr: 1.0000e-04 Epoch 65/100 30/30 [==============================] - 272s 9s/step - loss: 13.2001 - val_loss: 12.9789 - lr: 1.0000e-05 Epoch 66/100 30/30 [==============================] - 256s 9s/step - loss: 12.8699 - val_loss: 12.7537 - lr: 1.0000e-05 Epoch 67/100 30/30 [==============================] - 460s 15s/step - loss: 12.8529 - val_loss: 12.5797 - lr: 1.0000e-05 Epoch 68/100 30/30 [==============================] - 520s 17s/step - loss: 12.8881 - val_loss: 12.8464 - lr: 1.0000e-05 Epoch 69/100 30/30 [==============================] - 316s 10s/step - loss: 12.8289 - val_loss: 13.0487 - lr: 1.0000e-05 Epoch 70/100 30/30 [==============================] - ETA: 0s - loss: 12.7765 Epoch 70: ReduceLROnPlateau reducing learning rate to 9.999999747378752e-07. 30/30 [==============================] - 236s 8s/step - loss: 12.7765 - val_loss: 12.7764 - lr: 1.0000e-05 Epoch 71/100 30/30 [==============================] - 357s 12s/step - loss: 12.7222 - val_loss: 12.6030 - lr: 1.0000e-06 Epoch 72/100 30/30 [==============================] - 471s 16s/step - loss: 12.9312 - val_loss: 12.7407 - lr: 1.0000e-06 Epoch 73/100 30/30 [==============================] - ETA: 0s - loss: 12.7563 Epoch 73: ReduceLROnPlateau reducing learning rate to 9.999999974752428e-08. 30/30 [==============================] - 474s 16s/step - loss: 12.7563 - val_loss: 12.8981 - lr: 1.0000e-06 Epoch 74/100 30/30 [==============================] - 337s 11s/step - loss: 12.6372 - val_loss: 13.0085 - lr: 1.0000e-07 Epoch 75/100 30/30 [==============================] - 238s 8s/step - loss: 12.6892 - val_loss: 12.6015 - lr: 1.0000e-07 Epoch 76/100 30/30 [==============================] - ETA: 0s - loss: 12.7828 Epoch 76: ReduceLROnPlateau reducing learning rate to 1.0000000116860975e-08. 30/30 [==============================] - 308s 10s/step - loss: 12.7828 - val_loss: 13.2228 - lr: 1.0000e-07 Epoch 77/100 30/30 [==============================] - 336s 11s/step - loss: 12.7876 - val_loss: 12.4209 - lr: 1.0000e-08 Epoch 78/100 30/30 [==============================] - 337s 11s/step - loss: 12.5455 - val_loss: 12.7752 - lr: 1.0000e-08 Epoch 79/100 30/30 [==============================] - 258s 8s/step - loss: 12.7785 - val_loss: 12.7235 - lr: 1.0000e-08 Epoch 80/100 30/30 [==============================] - ETA: 0s - loss: 12.7194 Epoch 80: ReduceLROnPlateau reducing learning rate to 9.999999939225292e-10. 30/30 [==============================] - 222s 7s/step - loss: 12.7194 - val_loss: 12.8656 - lr: 1.0000e-08 Epoch 81/100 30/30 [==============================] - 255s 9s/step - loss: 13.0056 - val_loss: 12.7722 - lr: 1.0000e-09 Epoch 82/100 30/30 [==============================] - 275s 9s/step - loss: 12.6045 - val_loss: 12.7747 - lr: 1.0000e-09 Epoch 83/100 30/30 [==============================] - ETA: 0s - loss: 12.8213 Epoch 83: ReduceLROnPlateau reducing learning rate to 9.999999717180686e-11. 30/30 [==============================] - 371s 12s/step - loss: 12.8213 - val_loss: 12.7278 - lr: 1.0000e-09 Epoch 84/100 30/30 [==============================] - 476s 16s/step - loss: 12.8018 - val_loss: 12.7182 - lr: 1.0000e-10 Epoch 85/100 30/30 [==============================] - 395s 13s/step - loss: 12.6051 - val_loss: 12.9475 - lr: 1.0000e-10 Epoch 86/100 30/30 [==============================] - ETA: 0s - loss: 12.7930 Epoch 86: ReduceLROnPlateau reducing learning rate to 9.99999943962493e-12. 30/30 [==============================] - 221s 7s/step - loss: 12.7930 - val_loss: 12.5336 - lr: 1.0000e-10 Epoch 87/100 30/30 [==============================] - 239s 8s/step - loss: 12.6282 - val_loss: 12.7567 - lr: 1.0000e-11 Epoch 87: early stopping
Prepare image to ocr
import cv2 as cv
from matplotlib import pyplot as pltdef grayscale(image):
return cv.cvtColor(image, cv.COLOR_BGR2GRAY)
def noise_removal(image):
import numpy as np
kernel = np.ones((1, 1), np.uint8)
image = cv.dilate(image, kernel, iterations=1)
kernel = np.ones((1, 1), np.uint8)
image = cv.erode(image, kernel, iterations=1)
image = cv.morphologyEx(image, cv.MORPH_CLOSE, kernel)
image = cv.medianBlur(image, 3)
return (image)
def thin_font(image):
import numpy as np
image = cv.bitwise_not(image)
kernel = np.ones((2,2),np.uint8)
image = cv.erode(image, kernel, iterations=1)
image = cv.bitwise_not(image)
return (image)
def thick_font(image):
import numpy as np
image = cv.bitwise_not(image)
kernel = np.ones((2,2),np.uint8)
image = cv.dilate(image, kernel, iterations=1)
image = cv.bitwise_not(image)
return (image)
def remove_borders(image):
contours, heiarchy = cv.findContours(image, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
cntsSorted = sorted(contours, key=lambda x:cv.contourArea(x))
cnt = cntsSorted[-1]
x, y, w, h = cv.boundingRect(cnt)
crop = image[y:y+h, x:x+w]
return (crop)image_file = './img/img00.png'
img = cv.imread(image_file)
gray_image = grayscale(img)
thresh, im_bw = cv.threshold(gray_image, 100, 150, cv.THRESH_BINARY)
no_noise = noise_removal(im_bw)
# eroded_image = thin_font(no_noise)
# dilated_image = thick_font(eroded_image)
no_borders = remove_borders(no_noise)
cv.imwrite("temp/no_borders.jpg", no_borders)
display('temp/no_borders.jpg')def display(im_path):
dpi = 80
im_data = plt.imread(im_path)
height, width = im_data.shape[:2]
# What size does the figure need to be in inches to fit the image?
figsize = width / float(dpi), height / float(dpi)
# Create a figure of the right size with one axes that takes up the full figure
fig = plt.figure(figsize=figsize)
ax = fig.add_axes([0, 0, 1, 1])
# Hide spines, ticks, etc.
ax.axis('off')
# Display the image.
ax.imshow(im_data, cmap='gray')
plt.show()display(image_file)inverted_image = cv.bitwise_not(img)
cv.imwrite("temp/inverted.jpg", inverted_image)
display("temp/inverted.jpg")def grayscale(image):
return cv.cvtColor(image, cv.COLOR_BGR2GRAY)gray_image = grayscale(img)
cv.imwrite("temp/gray.jpg", gray_image)True
display("temp/gray.jpg")thresh, im_bw = cv.threshold(gray_image, 170, 210, cv.THRESH_BINARY)
cv.imwrite("temp/bw_image.jpg", im_bw)True
display("temp/bw_image.jpg")def noise_removal(image):
import numpy as np
kernel = np.ones((1, 1), np.uint8)
image = cv.dilate(image, kernel, iterations=1)
kernel = np.ones((1, 1), np.uint8)
image = cv.erode(image, kernel, iterations=1)
image = cv.morphologyEx(image, cv.MORPH_CLOSE, kernel)
image = cv.medianBlur(image, 3)
return (image)no_noise = noise_removal(im_bw)
cv.imwrite("temp/no_noise.jpg", no_noise)True
display("temp/no_noise.jpg")def thin_font(image):
import numpy as np
image = cv.bitwise_not(image)
kernel = np.ones((2,2),np.uint8)
image = cv.erode(image, kernel, iterations=1)
image = cv.bitwise_not(image)
return (image)eroded_image = thin_font(no_noise)
cv.imwrite("temp/eroded_image.jpg", eroded_image)True
display("temp/eroded_image.jpg")def thick_font(image):
import numpy as np
image = cv.bitwise_not(image)
kernel = np.ones((2,2),np.uint8)
image = cv.dilate(image, kernel, iterations=1)
image = cv.bitwise_not(image)
return (image)dilated_image = thick_font(no_noise)
cv.imwrite("temp/dilated_image.jpg", dilated_image)True
display("temp/dilated_image.jpg")def remove_borders(image):
contours, heiarchy = cv.findContours(image, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
cntsSorted = sorted(contours, key=lambda x:cv.contourArea(x))
cnt = cntsSorted[-1]
x, y, w, h = cv.boundingRect(cnt)
crop = image[y:y+h, x:x+w]
return (crop)no_borders = remove_borders(no_noise)
cv.imwrite("temp/no_borders.jpg", no_borders)
display('temp/no_borders.jpg')