2023-02-01 00:34:29 +01:00
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "4b1d82bd",
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"metadata": {},
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"source": [
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"# LICENSE PLATE DETECTION"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9b54ab67",
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"metadata": {
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"deletable": false,
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"nbgrader": {
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"cell_type": "code",
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"checksum": "775443e2f8e6d780f7310e57d00701e7",
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"grade": true,
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"grade_id": "cell-53e7c09d33eaba20",
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"locked": false,
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"points": 6,
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"schema_version": 3,
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"solution": true,
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"task": false
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}
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},
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"source": [
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"### YOLO V3"
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]
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},
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{
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"cell_type": "code",
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2023-02-01 01:35:26 +01:00
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"execution_count": null,
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2023-02-01 00:34:29 +01:00
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"id": "61ac9526",
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2023-02-01 01:35:26 +01:00
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"metadata": {
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"pycharm": {
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"is_executing": true
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2023-02-01 00:34:29 +01:00
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}
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2023-02-01 01:35:26 +01:00
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},
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"outputs": [],
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2023-02-01 00:34:29 +01:00
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"source": [
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"!git clone https://github.com/roboflow-ai/keras-yolo3"
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]
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},
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{
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"cell_type": "code",
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2023-02-01 01:35:26 +01:00
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"execution_count": null,
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2023-02-01 00:34:29 +01:00
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"id": "5d7010e7",
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2023-02-01 01:35:26 +01:00
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"metadata": {
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"pycharm": {
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"is_executing": true
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2023-02-01 00:34:29 +01:00
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}
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2023-02-01 01:35:26 +01:00
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},
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"outputs": [],
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2023-02-01 00:34:29 +01:00
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"source": [
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"!curl -L \"https://app.roboflow.com/ds/hTj8Pr7g7U?key=q9kdROYojM\" > roboflow.zip; unzip roboflow.zip; rm roboflow.zip"
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]
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},
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{
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"cell_type": "code",
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2023-02-01 01:35:26 +01:00
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"execution_count": null,
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2023-02-01 00:34:29 +01:00
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"id": "6989cf92",
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2023-02-01 01:35:26 +01:00
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"metadata": {
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"pycharm": {
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"is_executing": true
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2023-02-01 00:34:29 +01:00
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}
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2023-02-01 01:35:26 +01:00
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},
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"outputs": [],
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2023-02-01 00:34:29 +01:00
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"source": [
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"!wget https://pjreddie.com/media/files/yolov3.weights"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "e981c95d",
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"metadata": {},
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"outputs": [],
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"source": [
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"from keras.layers import ELU, PReLU, LeakyReLU"
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]
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},
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{
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"cell_type": "code",
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2023-02-01 01:35:26 +01:00
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"execution_count": null,
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2023-02-01 00:34:29 +01:00
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"id": "d8924aad",
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2023-02-01 01:35:26 +01:00
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"metadata": {
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"pycharm": {
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"is_executing": true
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2023-02-01 00:34:29 +01:00
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}
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2023-02-01 01:35:26 +01:00
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},
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"outputs": [],
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2023-02-01 00:34:29 +01:00
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"source": [
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"!python keras-yolo3/convert.py keras-yolo3/yolov3.cfg yolov3.weights model_data/yolo.h5"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4038756b",
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"metadata": {
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"pycharm": {
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"is_executing": true
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}
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},
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"outputs": [],
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"source": [
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"\"\"\"\n",
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"Self-contained Python script to train YOLOv3 on your own dataset\n",
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"\"\"\"\n",
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"\n",
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"import numpy as np\n",
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"import keras.backend as K\n",
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"from keras.layers import Input, Lambda\n",
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"from keras.models import Model\n",
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"from keras.optimizers import Adam\n",
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"from keras.callbacks import TensorBoard, ModelCheckpoint, ReduceLROnPlateau, EarlyStopping\n",
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"\n",
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"from yolo3.model import preprocess_true_boxes, yolo_body, tiny_yolo_body, yolo_loss\n",
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"from yolo3.utils import get_random_data\n",
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"\n",
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"\n",
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"def _main():\n",
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" annotation_path = './train/_annotations.txt' # path to Roboflow data annotations\n",
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" log_dir = './logs/000/' # where we're storing our logs\n",
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" classes_path = './train/_classes.txt' # path to Roboflow class names\n",
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" anchors_path = './model_data/yolo_anchors.txt'\n",
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" class_names = get_classes(classes_path)\n",
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" print(\"-------------------CLASS NAMES-------------------\")\n",
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" print(class_names)\n",
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" print(\"-------------------CLASS NAMES-------------------\")\n",
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" num_classes = len(class_names)\n",
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" anchors = get_anchors(anchors_path)\n",
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"\n",
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" input_shape = (256,256) # multiple of 32, hw default = (416,416)\n",
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"\n",
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" is_tiny_version = len(anchors)==6 # default setting\n",
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" if is_tiny_version:\n",
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" model = create_tiny_model(input_shape, anchors, num_classes,\n",
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" freeze_body=2, weights_path='./model_data/tiny_yolo_weights.h5')\n",
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" else:\n",
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" model = create_model(input_shape, anchors, num_classes,\n",
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" freeze_body=2, weights_path='./model_data/yolo.h5') # make sure you know what you freeze\n",
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"\n",
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" logging = TensorBoard(log_dir=log_dir)\n",
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" checkpoint = ModelCheckpoint(log_dir + 'ep{epoch:03d}-loss{loss:.3f}-val_loss{val_loss:.3f}.h5',\n",
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" monitor='val_loss', save_weights_only=True, save_best_only=True, period=3)\n",
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" reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=3, verbose=1)\n",
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" early_stopping = EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=1)\n",
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"\n",
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" val_split = 0.2 # set the size of the validation set\n",
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" with open(annotation_path) as f:\n",
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" lines = f.readlines()\n",
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" np.random.seed(10101)\n",
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" np.random.shuffle(lines)\n",
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" np.random.seed(None)\n",
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" num_val = int(len(lines)*val_split)\n",
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" num_train = len(lines) - num_val\n",
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"\n",
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" # Train with frozen layers first, to get a stable loss.\n",
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" # Adjust num epochs to your dataset. This step is enough to obtain a not bad model.\n",
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" if True:\n",
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" model.compile(optimizer=Adam(lr=1e-3), loss={\n",
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" # use custom yolo_loss Lambda layer.\n",
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" 'yolo_loss': lambda y_true, y_pred: y_pred})\n",
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"\n",
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" batch_size = 16\n",
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" print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size))\n",
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" model.fit_generator(data_generator_wrapper(lines[:num_train], batch_size, input_shape, anchors, num_classes),\n",
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" steps_per_epoch=max(1, num_train//batch_size),\n",
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" validation_data=data_generator_wrapper(lines[num_train:], batch_size, input_shape, anchors, num_classes),\n",
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" validation_steps=max(1, num_val//batch_size),\n",
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" epochs=500,\n",
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" initial_epoch=0,\n",
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" callbacks=[logging, checkpoint])\n",
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" model.save_weights(log_dir + 'trained_weights_stage_1.h5')\n",
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"\n",
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" # Unfreeze and continue training, to fine-tune.\n",
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" # Train longer if the result is not good.\n",
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" if True:\n",
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" for i in range(len(model.layers)):\n",
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" model.layers[i].trainable = True\n",
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" model.compile(optimizer=Adam(lr=1e-4), loss={'yolo_loss': lambda y_true, y_pred: y_pred}) # recompile to apply the change\n",
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" print('Unfreeze all of the layers.')\n",
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"\n",
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" batch_size = 16 # note that more GPU memory is required after unfreezing the body\n",
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" print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size))\n",
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" model.fit_generator(data_generator_wrapper(lines[:num_train], batch_size, input_shape, anchors, num_classes),\n",
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" steps_per_epoch=max(1, num_train//batch_size),\n",
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" validation_data=data_generator_wrapper(lines[num_train:], batch_size, input_shape, anchors, num_classes),\n",
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" validation_steps=max(1, num_val//batch_size),\n",
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" epochs=100,\n",
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" initial_epoch=50,\n",
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" callbacks=[logging, checkpoint, reduce_lr, early_stopping])\n",
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" model.save_weights(log_dir + 'trained_weights_final.h5')\n",
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"\n",
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" # Further training if needed.\n",
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"\n",
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"\n",
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"def get_classes(classes_path):\n",
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" '''loads the classes'''\n",
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" with open(classes_path) as f:\n",
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" class_names = f.readlines()\n",
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" class_names = [c.strip() for c in class_names]\n",
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" return class_names\n",
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"\n",
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"def get_anchors(anchors_path):\n",
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" '''loads the anchors from a file'''\n",
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" with open(anchors_path) as f:\n",
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" anchors = f.readline()\n",
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" anchors = [float(x) for x in anchors.split(',')]\n",
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" return np.array(anchors).reshape(-1, 2)\n",
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"\n",
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"\n",
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"def create_model(input_shape, anchors, num_classes, load_pretrained=True, freeze_body=2,\n",
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" weights_path='./model_data/yolo.h5'):\n",
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" '''create the training model'''\n",
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" K.clear_session() # get a new session\n",
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" image_input = Input(shape=(None, None, 3))\n",
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" h, w = input_shape\n",
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" num_anchors = len(anchors)\n",
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"\n",
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" y_true = [Input(shape=(h//{0:32, 1:16, 2:8}[l], w//{0:32, 1:16, 2:8}[l], \\\n",
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" num_anchors//3, num_classes+5)) for l in range(3)]\n",
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"\n",
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" model_body = yolo_body(image_input, num_anchors//3, num_classes)\n",
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" print('Create YOLOv3 model with {} anchors and {} classes.'.format(num_anchors, num_classes))\n",
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"\n",
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" if load_pretrained:\n",
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" model_body.load_weights(weights_path, by_name=True, skip_mismatch=True)\n",
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" print('Load weights {}.'.format(weights_path))\n",
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" if freeze_body in [1, 2]:\n",
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" # Freeze darknet53 body or freeze all but 3 output layers.\n",
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" num = (185, len(model_body.layers)-3)[freeze_body-1]\n",
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" for i in range(num): model_body.layers[i].trainable = False\n",
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" print('Freeze the first {} layers of total {} layers.'.format(num, len(model_body.layers)))\n",
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"\n",
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" model_loss = Lambda(yolo_loss, output_shape=(1,), name='yolo_loss',\n",
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" arguments={'anchors': anchors, 'num_classes': num_classes, 'ignore_thresh': 0.5})(\n",
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" [*model_body.output, *y_true])\n",
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" model = Model([model_body.input, *y_true], model_loss)\n",
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"\n",
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" return model\n",
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"\n",
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"def create_tiny_model(input_shape, anchors, num_classes, load_pretrained=True, freeze_body=2,\n",
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" weights_path='./model_data/tiny_yolo_weights.h5'):\n",
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" '''create the training model, for Tiny YOLOv3'''\n",
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" K.clear_session() # get a new session\n",
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" image_input = Input(shape=(None, None, 3))\n",
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" h, w = input_shape\n",
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" num_anchors = len(anchors)\n",
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"\n",
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" y_true = [Input(shape=(h//{0:32, 1:16}[l], w//{0:32, 1:16}[l], \\\n",
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" num_anchors//2, num_classes+5)) for l in range(2)]\n",
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"\n",
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" model_body = tiny_yolo_body(image_input, num_anchors//2, num_classes)\n",
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" print('Create Tiny YOLOv3 model with {} anchors and {} classes.'.format(num_anchors, num_classes))\n",
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"\n",
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" if load_pretrained:\n",
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" model_body.load_weights(weights_path, by_name=True, skip_mismatch=True)\n",
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" print('Load weights {}.'.format(weights_path))\n",
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" if freeze_body in [1, 2]:\n",
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" # Freeze the darknet body or freeze all but 2 output layers.\n",
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" num = (20, len(model_body.layers)-2)[freeze_body-1]\n",
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" for i in range(num): model_body.layers[i].trainable = False\n",
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" print('Freeze the first {} layers of total {} layers.'.format(num, len(model_body.layers)))\n",
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"\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": "iVBORw0KGgoAAAANSUhEUgAAArUAAAGLCAYAAADZOGoBAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOz9WawuSZIeiH3m7hHx/2e5ee/NPbO2rL2K7H2pZldvQ5DDRcJohKE0EjkiMRAxxAygF0mApHnUAx8EQuCTpNkIjKSHIQYDaCQOG+R0c+lmL1XVVV3dXUtXd1XlnpV5M+96zvn/PyLc3fRgZu4ecU5W3RYfhAZOJE6ec/8lFl/MPjP7zIyYmXF9XB/Xx/VxfVwf18f1cX1cH3+KD/f/7xu4Pq6P6+P6uD6uj+vj+rg+ro9/3eMa1F4f18f1cX1cH9fH9XF9XB9/6o9rUHt9XB/Xx/VxfVwf18f1cX38qT+uQe31cX1cH9fH9XF9XB/Xx/Xxp/64BrXXx/VxfVwf18f1cX1cH9fHn/rjGtReH9fH9XF9XB/Xx/VxfVwff+qPa1B7fVwf18f1cX1cH9fH9XF9/Kk/rkHt9XF9XB/Xx/VxfVwf18f18af+uAa118f1cX1cH9fH9XF9XB/Xx5/6IzzuB9979ctwzoGIymsnR8fl30QE5xxAJK/p32AGckaMETklpJTAnEGOkVJEzhnMXM7DzMg5IaWIeZ4RY5TzQk4FyOdiTmDm8t0ca2M0ZgZnBpVPA+Dmbzg45+C9h3MO4zjKewzknJFSwjhP+r4HyIEcIQOIMZbnzeDm3hg5Z+SYQERgZoAbq6HzyDmXz6YYAQa89+XZ7VntNSICRxmzlJKcP2dkzkiYkRPKGJTv+VDupXwvydg458DMGMexPEO5V0cgnV/7PkAyh/pailyewb7vvQfgkMHlXmSiZJwcyRjOKaLrBuSUkRnIIOScMc0J3nfoug6+65BShu+CjG/OSDEBcBh8gPceXRfQdwF938M5h957nScCQ9aZrDGGc/LMCby4Z9axsLmoR10xMlZ1TcrSI1kP7fccwetrhDof7Vy3v23Mm22EPgTM84yUkiyVrkPOsZyLmTHPM8ZxRGrmUsYe5d82ZkdHR2VfASj3Ms8zACCEgBDCYi0755BzLue016ZpgnMO8zyX86SUEHxXPhNCQM4Zfd8jc4ZzhBBEtAzDgGmacDgcsNvtsNvtcHp6KvPtfflce01bg/K3A4HKs8nBujRtEDNySvIyEcgRLrVJVNmRU67rtBm39sfuxd5Hea3e2+LUej7Za6l8v/2czXm5hnfIulecnj/nDALgyF1an8wZOac6396BnINzJA/X7FmCrA1mXuwHH2zPzYhxRuaEruuw2Qz6vCKT9Y5he8HGIhMW6wqw/e10XcgYEHkwE7KOi/ceLpOcsegJgJiXc6q/yzpwjKzfz5A96D2p3OnKuJu8jDHqGPFiHbc6C5D175wrcy+/O3m2nEGc4ZxD13VwjkBu5f3hDM6myxL6vscwDLKWSfSdfEnnMGfwPBd9Ns8zpmnCPM/Y70eM41j+PcUZYMI8y+fkM3tg5uW4OweAkGJCihExRpU7UJld930/bOGbZw59BxdU3ntZK957+BDKnvTeA/pecB7e1TXZ6fumTzgmjDEhqo4C8lK3YLm3bS855xFjxPHx8UJmAsA0zmXf23Wvmk+RmVz2Vjs+pp+cc+B27Wa5pxhzeV57jvZatk7mbLpXcElKCYlR9KvtsfbeTK6ZXDc8ABgkyhUzZCDnWK5r57F7l+9UPd/+u/1bZJD+jXhp3ZvOED3aoet8kd3rfW2HJwfnASJf8FGMsazX8mxXzI/dT0oJxFU/2nfsmu+nI9vD3vt3/sbfvHSP6+OxQe12u5WF0n5ZB8RutNwsMzjnItTYwFgzeClPsFluN4ANWkq2UTOYaTHRADBHUbIi6Dw4LSeXiBCoKqScMziZ0s4APIgYKRGmaVKFUoVBCxIYCZQrcIECoxjjAsBCx8F7r+fjIqrTGJEzy/NkRsoJnLlsnFYALBZrrIC2XQgJSc9XgQszgTiW7+ackROXcYos4x85A1mUqV2XyINy1sVEopTABey3KMEEjXMCjsi7xdjLDcQCJAAAzlYOgTkjqQArQgdAzqKMRFA5/b5bnJfIwflQxg0KYOQZc7NRVFhA1qMoycbQWhwL8+eS0GTmAu4N2Bg4BoCUEzy5BZBqQVE7nyK4+dIttEJfBJfTz8vgE3k4FwBUQSBKPJfr2LUMHLeHCV1bKyaI7bDvr4Wbfaad3xgjvFuKDgO3oApKcs5FgU/TVBSv3QMRIaW0AOj2fr3fDCJR4LZWGQIU7LqCEX1jVMk9cTsj8qVmLTGYaaGMTL60e7G87x3KRiAqALqeX/YTr4Axc9a/sbgOyyDoXkZRNg71M7nsR1EqMl4ZRO16MiM+l1ujBoG1oCB4j0QRwdaggt+cGUR1z8iYL0EhEYGYQbB13z5LC14M7BBIZaJ3HolTMy+5OU8VLqRGo+BBAjkPxwzfdcicyrxlMKo5B6QUdVtnHWenc89wzsNB1oeB1HmeQQQE79D3vQDiKYGQBZQSoe8DhqGHD74BOgpWOQOcwCkDzAX05ihKPs4z4iRG6DRPmOeIw26HR2dnmKdJwF9iHA6HspYFtDdGc66OiRyTSOLWKCJCyozMqmchsg7eF+eEB9D3PbbHx+j6AUHf88Gj6zuQF2dNAfj6u+gjJ9cKzq90BS3lDMt9tHPZnseOIiOatdsalbIlRIZ3obsS7KwP55avrcGz3acPuq8zl2sAVUbpYyCYjMkZ8zgixhmj4gN7tpRkzFOuutnkmD1LK1tNjnO+DEJFFqdybnsGM0pbh187Fuvf67/Bq/WCKmPqTwCRh/cOROnyeLMCYecgYpXL+Vrn2xp3rUGu977IgtZ4MX21dmz+6x6PDWpTjMhUQS0R4ZAPZYG06HsNYIuibEGPy80i0JMWIJaQY0aOWQRV4wG2ARErTgRMnKMumDqBYk3FhZJwRIgpiTVO4hVNKZeF2QJZEy5EUBAaAQKYnDwHVBixCFNbk3Z/4jmqG4FRPRfteDHX6zpHYK4aSYC4AloFsFmBaeIWwMmze8+qfHUjpSyCL2eI07GCCDj5DpwoH6fgUkCbOBuyLe4G9IlSJXgf4INHCB1cc037TYjgTNXIATDPEUBj7KB6TjOLhyN0HdTZovcMxDnCAQIYUoRPodyHygmov2qx7hgMTpDdCRSPmIyZjfLSG6XLUN+vG9DAfvnWajObh6l9vz1s3uvrjYUKLASxKFE1jBRstEKwFRqtd6O91voz7eda67r1APR9Xz6/Pkd7fQDInOEb4BO6gK7vipKxz86Nh6qu+aUB1Cqgq+5fPoRidBqgrYJ0+dGcs3xOPmTDfem8AoTsNXflfdg8eycAW2SMAKey6PT+nKMCBu3SzLj8LAB4FWlyKs+cI91vl8cFqB7sYjDaZ/Ja8TuYQWCYjMiBnAe5DA+BhxJl4GIgczNeZIEaEBwcWN0trG8Wo1+NZoelUZBznQNfjAK7F663qWNavatAsaZJQGNo1hocyVSUNSCxGG/gq0y7RrDICahVeTX0PZwzxS6K1eUkgF6FTu8JnjIoA5xjkdlxnpGzRh1jVBkbG4+prPVpnDEeRszzVLyz+/1hsfemaVoays4hhK46WHQRd76TaBNR8V4SCOQYCKF4wGVNLgFq3/cYNhuELojBQA7Oe/ig650a2UOrdWfrujFsbMwXckcdJHB6D6gAqv3ewqgTqw6Av7Q/nHNwarZcJYvqwYvzL95ZATSL5Mr5BEgSGV7h4plvvejTNMmcW0Q5i07J5rBDu+cIXVc15awGvO3VtTxuZXk1fC+D1/Zodc5Vz3lJ1hRnoKsRIP2xe7bf3tueNUOlGuK2P9vrmYHSYr7WYbGW7bZGDWetI75rMPyvczw2qN3v9ovFaTe2BrXrhzQvLdisOHmAbgiywYoQ4mL5KlIEQbx35GwjV+AiigDITMgpllCCWdXmjSIWT0EIQSz+aZbQvz6HhWDlYLUecgGXxTMEkdAG88wjlDkXL4kBFWY
|
|
|
|
|
"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)"
|
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|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
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|
|
"cell_type": "code",
|
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|
|
|
"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",
|
2023-02-01 01:35:26 +01:00
|
|
|
"execution_count": null,
|
2023-02-01 00:34:29 +01:00
|
|
|
"id": "c77d5076",
|
2023-02-01 01:35:26 +01:00
|
|
|
"metadata": {
|
|
|
|
|
"pycharm": {
|
|
|
|
|
"is_executing": true
|
2023-02-01 00:34:29 +01:00
|
|
|
}
|
2023-02-01 01:35:26 +01:00
|
|
|
},
|
|
|
|
|
"outputs": [],
|
2023-02-01 00:34:29 +01:00
|
|
|
"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",
|
2023-02-01 01:35:26 +01:00
|
|
|
"execution_count": null,
|
2023-02-01 00:34:29 +01:00
|
|
|
"id": "25bbdacd",
|
2023-02-01 01:35:26 +01:00
|
|
|
"metadata": {
|
|
|
|
|
"pycharm": {
|
|
|
|
|
"is_executing": true
|
2023-02-01 00:34:29 +01:00
|
|
|
}
|
2023-02-01 01:35:26 +01:00
|
|
|
},
|
|
|
|
|
"outputs": [],
|
2023-02-01 00:34:29 +01:00
|
|
|
"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,
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"bibliofile": "biblio.bib",
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"cite_by": "apalike",
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"eqLabelWithNumbers": true,
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},
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"user_envs_cfg": false
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},
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"toc": {
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"nav_menu": {},
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"sideBar": true,
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"title_cell": "Table of Contents",
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"toc_section_display": true,
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"toc_window_display": false
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
|
