84 lines
158 KiB
Plaintext
84 lines
158 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import cv2 as cv\n",
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"import matplotlib.pyplot as plt\n",
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"import matplotlib\n",
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"%matplotlib inline"
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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": 52,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<matplotlib.image.AxesImage at 0x7f90524e0cd0>"
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]
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},
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"execution_count": 52,
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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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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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"source": [
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"image = cv.imread(\"C_test_own.png\", cv.IMREAD_COLOR)\n",
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"\n",
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"image = cv.cvtColor(image, cv.COLOR_BGR2RGB)\n",
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"image = image[0:600, 0:600]\n",
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"\n",
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"image = cv.cvtColor(image, cv.COLOR_RGB2BGR)\n",
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"\n",
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"plt.imshow(image)\n",
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"# cv.imwrite('C_test_rect.png', image)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3.8.12 64-bit",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.12"
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},
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"orig_nbformat": 4,
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"vscode": {
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"interpreter": {
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"hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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