177 lines
229 KiB
Plaintext
177 lines
229 KiB
Plaintext
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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": "08a9cccb-e107-4062-80a5-5cd53bf609e7",
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"metadata": {},
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"source": [
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"## Ćwiczenie 1\n",
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"\n",
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"Wczytaj plik [`img/soyjaks.jpg`](https://knowyourmeme.com/memes/two-soyjaks-pointing) i spróbuj odtworzyć poniższy obrazek."
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]
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},
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{
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"cell_type": "markdown",
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"id": "8f2c7557-6200-4d7a-8276-82f40aa08eba",
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"metadata": {},
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"source": [
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""
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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": 399,
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"id": "fe514d4d",
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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": 400,
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"id": "ae6c6ab1",
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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 0x7faec3255bb0>"
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]
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},
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"execution_count": 400,
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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(\"img/soyjaks.jpg\", 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[0:520, 45:490]\n",
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"cv.circle(image, (276, 226), 50, (205, 100, 229), 8)\n",
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"\n",
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"cv.putText(image, 'OMG not chikenz!', (10, 100), cv.FONT_HERSHEY_PLAIN , 3, (255,0,0), 5, cv.FILLED)\n",
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"plt.imshow(image)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "209ce6e2",
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"metadata": {},
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"source": [
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"## Ćwiczenie 2\n",
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"\n",
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"Załaduj obrazy `img/pipe.png` oraz `img/man-without-pipe.png` i wykonaj operacje tak, aby uzyskać poniższy obraz.\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f9c71d3f",
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"metadata": {},
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"source": [
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""
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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": 401,
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"id": "745da0ae",
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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 0x7faec31ce1f0>"
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]
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},
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"execution_count": 401,
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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": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"\n",
|
||
|
|
"pipe = cv.imread(\"img/pipe.png\", cv.IMREAD_UNCHANGED)\n",
|
||
|
|
"pipe = cv.cvtColor(pipe, cv.COLOR_BGRA2RGBA)\n",
|
||
|
|
"\n",
|
||
|
|
"pipe = cv.resize(pipe, None, fx=0.55, fy=0.55)\n",
|
||
|
|
"pipe = cv.flip(pipe, 1)\n",
|
||
|
|
"\n",
|
||
|
|
"man = cv.imread(\"img/man-without-pipe.png\", cv.IMREAD_UNCHANGED)\n",
|
||
|
|
"\n",
|
||
|
|
"man = cv.cvtColor(man, cv.COLOR_BGR2RGB)\n",
|
||
|
|
"\n",
|
||
|
|
"alpha_s = pipe[:, :, 3] / 255.0\n",
|
||
|
|
"\n",
|
||
|
|
"x_offset=70\n",
|
||
|
|
"y_offset=280\n",
|
||
|
|
"y1, y2 = y_offset, y_offset + pipe.shape[0]\n",
|
||
|
|
"x1, x2 = x_offset, x_offset + pipe.shape[1]\n",
|
||
|
|
"\n",
|
||
|
|
"alpha_s = pipe[:, :, 3] / 255.0\n",
|
||
|
|
"alpha_l = 1.0 - alpha_s\n",
|
||
|
|
"\n",
|
||
|
|
"for c in range(0, 3):\n",
|
||
|
|
" man[y1:y2, x1:x2, c] = (alpha_s * pipe[:, :, c] +\n",
|
||
|
|
" alpha_l * man[y1:y2, x1:x2, c])\n",
|
||
|
|
"plt.imshow(man)"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3.8.12 64-bit",
|
||
|
|
"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": "3.8.12"
|
||
|
|
},
|
||
|
|
"vscode": {
|
||
|
|
"interpreter": {
|
||
|
|
"hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 5
|
||
|
|
}
|