feat: add new Jupyter notebook for image transformation and dataset handling
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53
03.ipynb
53
03.ipynb
@ -1,63 +1,30 @@
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{
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"cells": [
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{
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"metadata": {},
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"cell_type": "code",
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"id": "initial_id",
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"metadata": {
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"collapsed": true,
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"ExecuteTime": {
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"end_time": "2025-06-11T07:39:14.997108Z",
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"start_time": "2025-06-11T07:39:14.986047Z"
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}
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},
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"outputs": [],
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"execution_count": null,
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"source": [
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"from PIL import Image\n",
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"\n",
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"im = Image.open('./data/jk.jpg')\n",
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"im = Image.open('data/images/jk.jpg')\n",
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"im.size"
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],
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(318, 116)"
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]
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},
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"execution_count": 73,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"execution_count": 73
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"id": "306b84c8b2d7bc36"
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-06-11T07:39:22.279332Z",
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"start_time": "2025-06-11T07:39:22.267501Z"
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}
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},
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"metadata": {},
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"cell_type": "code",
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"outputs": [],
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"execution_count": null,
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"source": [
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"import numpy as np\n",
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"\n",
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"im_pillow = np.asarray(im)\n",
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"im_pillow.shape"
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],
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"id": "e10b9e7640af00c9",
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(116, 318, 3)"
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]
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},
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"execution_count": 74,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"execution_count": 74
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"id": "627463cc80a9eea4"
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},
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{
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"metadata": {
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@ -99,7 +66,7 @@
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"source": [
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"import cv2\n",
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"\n",
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"im_cv2 = cv2.imread('./data/jk.jpg')\n",
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"im_cv2 = cv2.imread('./data/images/jk.jpg')\n",
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"print(type(im_cv2))\n",
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"\n",
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"print(im_cv2.shape)"
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