{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/laurent_fainsin/Documents/unet/.venv/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
}
],
"source": [
"from PIL import Image\n",
"import numpy as np\n",
"\n",
"import albumentations as A\n",
"\n",
"%config InlineBackend.figure_formats = ['svg']\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"from utils import RandomPaste"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Drafts"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": "\n\n\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# paste_img = Image.open(\"/media/disk1/lfainsin/SPHERES/real/2022_SM/Face/2/DSC_0200.jpg\").convert(\"RGBA\")\n",
"# paste_mask = Image.open(\"/media/disk1/lfainsin/SPHERES/real/2022_SM/Face/2/MASK.PNG\").convert(\"LA\")\n",
"\n",
"paste_img = Image.open(\"/media/disk1/lfainsin/SPHERES/synth/void_specular/0035.png\").convert(\"RGBA\")\n",
"paste_mask = Image.open(\"/media/disk1/lfainsin/SPHERES/synth/void_specular/MASK.PNG\").convert(\"LA\")\n",
"\n",
"plt.figure(figsize=(18, 10))\n",
"\n",
"plt.subplot(1, 2, 1)\n",
"plt.imshow(paste_img)\n",
"\n",
"plt.subplot(1, 2, 2)\n",
"plt.imshow(paste_mask)\n",
"\n",
"ax = plt.gca()\n",
"ax.set_facecolor('xkcd:salmon')\n",
"\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": "\n\n\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"resampler = Image.Resampling.LANCZOS\n",
"\n",
"paste_image_resized = paste_img.resize((212, 212), resample=resampler)\n",
"paste_mask_resized = paste_mask.resize((212, 212), resample=resampler)\n",
"\n",
"plt.figure(figsize=(18, 10))\n",
"\n",
"plt.subplot(1, 2, 1)\n",
"plt.imshow(paste_image_resized)\n",
"\n",
"plt.subplot(1, 2, 2)\n",
"plt.imshow(paste_mask_resized)\n",
"\n",
"ax = plt.gca()\n",
"ax.set_facecolor('xkcd:salmon')\n",
"\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": "\n\n\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import cv2\n",
"\n",
"# transform = A.Compose([A.RandomScale((-0.95, -0.25), interpolation=cv2.INTER_LANCZOS4)])\n",
"transform = A.Compose([A.Resize(width=50, height=50, interpolation=cv2.INTER_LANCZOS4)])\n",
"\n",
"augmentations = transform(image=np.asarray(paste_img), mask=np.asarray(paste_mask))\n",
"paste_image_resized2 = augmentations[\"image\"]\n",
"paste_mask_resized2 = augmentations[\"mask\"]\n",
"\n",
"plt.figure(figsize=(18, 10))\n",
"\n",
"plt.subplot(1, 2, 1)\n",
"plt.imshow(Image.fromarray(paste_image_resized2))\n",
"\n",
"plt.subplot(1, 2, 2)\n",
"plt.imshow(Image.fromarray(paste_mask_resized2))\n",
"\n",
"ax = plt.gca()\n",
"ax.set_facecolor('xkcd:salmon')\n",
"\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": "\n\n\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import torchvision.transforms as T\n",
"\n",
"transform = T.Compose(\n",
" [\n",
" T.RandomAffine(0, shear=(-20, 20, -20, 20), interpolation=T.InterpolationMode.BILINEAR),\n",
" T.Resize((50, 50), T.InterpolationMode.BILINEAR),\n",
" ]\n",
")\n",
"\n",
"paste_image_resized3 = paste_img\n",
"paste_image_resized3 = T.functional.adjust_brightness(paste_image_resized3, 1.2)\n",
"paste_image_resized3 = T.functional.adjust_contrast(paste_image_resized3, 1.2)\n",
"paste_image_resized3 = T.functional.affine(paste_image_resized3, angle=0, translate=(0, 0), scale=0.95, shear=(5, 5), interpolation=T.InterpolationMode.BILINEAR)\n",
"paste_image_resized3 = T.functional.resize(paste_image_resized3, (50, 50), interpolation=T.InterpolationMode.BILINEAR)\n",
"\n",
"paste_mask_resized3 = paste_mask\n",
"paste_mask_resized3 = T.functional.affine(paste_mask_resized3, angle=0, translate=(0, 0), scale=0.95, shear=(5, 5), interpolation=T.InterpolationMode.BILINEAR)\n",
"paste_mask_resized3 = T.functional.resize(paste_mask_resized3, (50, 50), interpolation=T.InterpolationMode.BILINEAR)\n",
"\n",
"plt.figure(figsize=(18, 10))\n",
"\n",
"plt.subplot(1, 2, 1)\n",
"plt.imshow(paste_image_resized3)\n",
"\n",
"plt.subplot(1, 2, 2)\n",
"plt.imshow(paste_mask_resized3)\n",
"\n",
"ax = plt.gca()\n",
"ax.set_facecolor(\"xkcd:salmon\")\n",
"\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": "\n\n\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"target_img = Image.open(\"/home/laurent_fainsin/Images/bg.jpg\").convert(\"RGBA\").resize((500, 500))\n",
"\n",
"plt.figure(figsize=(25, 10))\n",
"\n",
"plt.subplot(1, 2, 1)\n",
"plt.imshow(target_img)\n",
"\n",
"target_img.paste(paste_image_resized3, (100, 100), paste_mask_resized3)\n",
"\n",
"plt.subplot(1, 2, 2)\n",
"plt.imshow(target_img)\n",
"\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Albumentations custom DualTransform"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": "\n\n\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"target_img = Image.open(\"/media/disk1/lfainsin/BACKGROUND/coco/000000000723.jpg\").convert(\"RGBA\")\n",
"target_img = np.asarray(target_img, dtype=np.uint8)\n",
"\n",
"target_mask = np.zeros((*target_img.shape[:2], 4), dtype=np.uint8)\n",
"\n",
"transforms = A.Compose([\n",
" RandomPaste(25, \"/media/disk1/lfainsin/SPHERES/\"),\n",
"])\n",
"\n",
"aug = transforms(image=target_img, mask=target_mask)\n",
"aug_image = aug[\"image\"]\n",
"aug_mask = aug[\"mask\"]\n",
"\n",
"plt.figure(figsize=(25, 10))\n",
"\n",
"plt.subplot(1, 2, 1)\n",
"plt.imshow(aug_image)\n",
"\n",
"plt.subplot(1, 2, 2)\n",
"plt.imshow(aug_mask)\n",
"\n",
"plt.show()\n"
]
}
],
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"kernelspec": {
"display_name": "Python 3.8.0 ('.venv': poetry)",
"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": "3.8.0"
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"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "dc80d2c03865715c8671359a6bf138f6c8ae4e26ae025f2543e0980b8db0ed7e"
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