add for hw5
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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": 23,
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"id": "83d218b3",
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"metadata": {},
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"outputs": [],
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"source": [
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"from PIL import Image\n",
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"import matplotlib.pyplot as plt\n",
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"import matplotlib.font_manager as fm\n",
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"import numpy as np\n",
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"\n",
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"fm.fontManager.addfont(\"D2Coding.ttc\")\n",
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"\n",
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"plt.rcParams[\"font.family\"] = \"D2Coding\""
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]
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},
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{
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"cell_type": "markdown",
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"id": "18a0d48b",
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"metadata": {},
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"source": [
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"# Linear Algebra Homework 5: Image Resampling\n",
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"\n",
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"이미지를 읽고, 그걸 Bilinear Interpolation을 이용하여 Resampling 하면 된다.\n",
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"\n",
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"사용할 이미지는 아래와 같다. $(h, w) = (1000, 1000)$"
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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": "de8bc631",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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truncated
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"text/plain": [
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"<Figure size 640x480 with 1 Axes>"
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]
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},
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"metadata": {},
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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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"img = Image.open(\"image.png\")\n",
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"\n",
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"img_arr = np.array(img)\n",
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"\n",
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"def img_crop(arr: np.ndarray, from_h, to_h, from_w, to_w):\n",
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" h, w, _ = arr.shape\n",
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" return arr[int(h * from_h): int(h * to_h), int(w * from_w): int(w * to_w), :]\n",
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"\n",
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"plt.imshow(img)\n",
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"\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "284a3fd6",
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"metadata": {},
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"source": [
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"## Bilinear Interpolation Method\n",
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"\n",
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"두 방향으로 두번의 선형 보간을 거치는 방법이다.\n",
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"\n",
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"구현은 아래와 같이 하였다."
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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": 25,
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"id": "9ac0b16e",
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"metadata": {},
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"outputs": [],
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"source": [
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"def bilinear_interpolation(arr: np.ndarray, new_h, new_w):\n",
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" h, w, c = arr.shape\n",
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" resized_arr = np.zeros((new_h, new_w, c), dtype=arr.dtype)\n",
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"\n",
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" for i in range(new_h):\n",
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" for j in range(new_w):\n",
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" x = i * (h - 1) / (new_h - 1)\n",
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" y = j * (w - 1) / (new_w - 1)\n",
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"\n",
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" x1 = int(x)\n",
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" x2 = min(x1 + 1, h - 1)\n",
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" y1 = int(y)\n",
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" y2 = min(y1 + 1, w - 1)\n",
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"\n",
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" a = x - x1\n",
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" b = y - y1\n",
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"\n",
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" for k in range(c):\n",
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" resized_arr[i, j, k] = (\n",
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" (1 - a) * (1 - b) * arr[x1, y1, k]\n",
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" + a * (1 - b) * arr[x2, y1, k]\n",
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" + (1 - a) * b * arr[x1, y2, k]\n",
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" + a * b * arr[x2, y2, k]\n",
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" )\n",
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" return resized_arr"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e1da510d",
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"metadata": {},
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"source": [
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"위에 제시된 이미지를 2배 scale하여 $(h', w') = (2000, 2000)$이 되도록 리샘플링해주었다."
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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": "f26d84ae",
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"metadata": {},
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"outputs": [],
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"source": [
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"img_new_arr = bilinear_interpolation(img_arr, int(img_arr.shape[0] * 2), int(img_arr.shape[1] * 2))\n",
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"Image.fromarray(img_new_arr).save(\"image_bilinear.png\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "46906ed9",
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"metadata": {},
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"source": [
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"결과 이미지:"
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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": 40,
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"id": "2bd0f55e",
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"metadata": {},
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"outputs": [
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{
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"data": {
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||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 640x480 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"plt.imshow(img_new_arr)\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "012b87cf",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 결과"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 42,
|
||||
"id": "10657605",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1000x500 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fig, ax = plt.subplots(1, 2, figsize=(10, 5))\n",
|
||||
"\n",
|
||||
"ax[0].set_title(\"Original Image Crop\")\n",
|
||||
"ax[0].imshow(img_crop(img_arr, 0.2, 0.25, 0.45, 0.5))\n",
|
||||
"\n",
|
||||
"ax[1].set_title(\"Bilinear Interpolated Image Crop\")\n",
|
||||
"ax[1].imshow(img_crop(img_new_arr, 0.2, 0.25, 0.45, 0.5))\n",
|
||||
"\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 49,
|
||||
"id": "f912d7a8",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1000x500 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fig, ax = plt.subplots(1, 2, figsize=(10, 5))\n",
|
||||
"\n",
|
||||
"ax[0].set_title(\"Original Image Crop\")\n",
|
||||
"ax[0].imshow(img_crop(img_arr, 0.55, 0.60, 0.61, 0.66))\n",
|
||||
"\n",
|
||||
"ax[1].set_title(\"Bilinear Interpolated Image Crop\")\n",
|
||||
"ax[1].imshow(img_crop(img_new_arr, 0.55, 0.60, 0.61, 0.66))\n",
|
||||
"\n",
|
||||
"plt.show()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "3.12.11",
|
||||
"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": "3.12.11"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
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Reference in new issue
Block a user