File size: 11,948 Bytes
f31484b e55761e 998873a f31484b 9fbbef8 f31484b 9fbbef8 f31484b 9fbbef8 998873a f31484b e55761e f31484b e55761e f31484b e55761e f31484b 9fbbef8 f31484b 9fbbef8 f31484b 9fbbef8 f31484b e55761e f31484b e55761e f31484b e55761e f31484b e55761e f31484b 998873a f31484b e55761e f31484b e55761e f31484b 9fbbef8 f31484b 9fbbef8 f31484b 9fbbef8 f31484b 998873a f31484b 9fbbef8 f31484b 998873a f31484b 998873a f31484b 9fbbef8 f31484b 9fbbef8 f31484b 9fbbef8 f31484b 9fbbef8 f31484b 9fbbef8 f31484b 9fbbef8 f31484b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 |
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Importing all required libraries\n",
"\n",
"# these are needed for path processing \n",
"import os\n",
"import pathlib as pl\n",
"\n",
"#image processing and display\n",
"import numpy as np\n",
"import PIL\n",
"import PIL.Image as Image\n",
"import PIL.ImageDraw as ImageDraw\n",
"import matplotlib.pyplot as plt\n",
"\n",
"#these are needed for data processing\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"UsageError: Line magic function `%%script` not found.\n"
]
}
],
"source": [
"# This is the first step of the process. Once you have the images and csvs organized in folders with their names, you need to create the offset file that contains the offset. This code creates the offset file if it doesn't exist\n",
"\n",
"testset = os.listdir(\"secondleg\")[8]\n",
"print(testset)\n",
"tiff = Image.open(pl.Path(\n",
" rf'.\\secondleg\\{testset}\\{testset}.tiff')) \n",
"csv = pd.read_csv(pl.Path(\n",
" rf'.\\secondleg\\{testset}\\{testset}.csv')) \n",
"with open(pl.Path( \n",
" rf'.\\secondleg\\{testset}\\offset.txt'),\"+x\") as f: \n",
" offset = f.read() \n",
" if offset != '':\n",
" offset = int(offset)\n",
" else:\n",
" offset = 0\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# This is a helper method for chopping up a large glacial scope image into smaller chunks with a width of parameter length and a certain amount of overlap\n",
"# Length is the length of the desired chunk, overlap is how much overlap there should be\n",
"def window_with_remainder(length, overlap, input_size):\n",
" testarray = np.arange(0, input_size)\n",
" return np.vstack((testarray[0:length], np.lib.stride_tricks.sliding_window_view(testarray[len(testarray) % length:], length)[::overlap]))[:, [0, -1]] + [0, 1]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# This code draws a rectangle from (40,0) to (100, y_surface) in green, and from (40, y_surface) to (100, y_bed) in white.\n",
"# The y_surface and y_bed variables are read from the csv file, and the csv file is read in as a pandas dataframe.\n",
"# The first 5 rows of the csv file are also printed.\n",
"# this is done to help calibrate the offsets by allowing the user to manually calibrate the offset for an image and move through the dataset through altering the listdir line through changing the index\n",
"\n",
"testset = os.listdir(\"secondleg\")[10]\n",
"print(testset)\n",
"\n",
"# opens the images, csvs and offset files and reads the needed data\n",
"tiff = Image.open(pl.Path(\n",
" rf'.\\secondleg\\{testset}\\{testset}.tiff'))\n",
"csv = pd.read_csv(pl.Path(\n",
" rf'.\\secondleg\\{testset}\\{testset}.csv'))\n",
"with open(pl.Path(\n",
" rf'.\\secondleg\\{testset}\\offset.txt')) as f:\n",
" offset = f.read()\n",
" if offset == \"\":\n",
" offset = 0\n",
" else:\n",
" offset = int(offset)\n",
"\n",
"# prints the current offset\n",
"\n",
"print(offset)\n",
"\n",
"# There is no need to open up the entire image, so we make a copy and chop it up\n",
"img = tiff.copy()\n",
"img = img.crop((0,430,img.size[0],1790)) \n",
"\n",
"print(csv.head()) # prints first 5 rows of csv file\n",
"csv = csv[[\"x_surface\", \"y_surface\", \"x_bed\", \"y_bed\"]]+offset\n",
"# the CSV is backwards, so i am accouting for this and getting up the first mask data point\n",
"line = csv.iloc[-1] # gets last row of csv file\n",
"print(csv.head()) # prints first 5 rows of csv file to make sure that the offeset was applied properly\n",
"\n",
"# creates the image masks and shows the image for calibration\n",
"draw = ImageDraw.Draw(img)\n",
"draw.rectangle([(40, 0), (100, line[\"y_surface\"])], fill=\"green\") # draws rectangle from (40,0) to (100, y_surface) in green\n",
"draw.rectangle([(40, line[\"y_surface\"]),\n",
" (100, line[\"y_bed\"])], fill=\"white\") # draws rectangle from (40, y_surface) to (100, y_bed) in white\n",
"img.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# This code draws the segmentation masks for each scope from the csv file and saves them\n",
"\n",
"# Loop over all the files in the \"secondleg\" directory\n",
"for testset in os.listdir(\"secondleg\"):\n",
" # Print the name of the current file\n",
" print(testset)\n",
"\n",
" tiff = Image.open(pl.Path(\n",
" rf'.\\secondleg\\{testset}\\{testset}.tiff'))\n",
"\n",
" csv = pd.read_csv(pl.Path(\n",
" rf'.\\secondleg\\{testset}\\{testset}.csv'))\n",
"\n",
" with open(pl.Path(\n",
" rf'.\\secondleg\\{testset}\\offset.txt')) as f:\n",
" offset = f.read()\n",
" if offset == \"\":\n",
" offset = 0\n",
" else:\n",
" offset = int(offset)\n",
"\n",
" # Make a copy of the image and crop it to remove the unneeded parts\n",
" img = tiff.copy()\n",
" img = img.crop((0, 430, img.size[0], 1790))\n",
"\n",
" # Convert the image to float and then to grayscale\n",
" img_float = Image.fromarray(np.divide(np.array(img), 2**8-1))\n",
" img = img_float.convert(\"L\")\n",
"\n",
" # Save the cropped and converted image to the specified path\n",
" img.save(pl.Path(\n",
" rf'.\\secondleg\\{testset}\\cropped_img_{testset}.png'))\n",
"\n",
" # Add the offset to the specified columns of the csv file and reverse the order\n",
" csv = csv[[\"x_surface\", \"y_surface\", \"x_bed\", \"y_bed\"]]+offset\n",
" csv = csv[::-1].reset_index(drop=True)\n",
"\n",
" # Create new dataframes for the top and bottom of the image and concatenate them to the previous dataframe\n",
" top = pd.DataFrame(\n",
" {\"x_surface\": 0, \"y_surface\": csv.iloc[0][\"y_surface\"], \"x_bed\": 0, \"y_bed\": csv.iloc[0][\"y_bed\"]}, index=[0])\n",
" bottom = pd.DataFrame({\"x_surface\": tiff.size[0], \"y_surface\": csv.iloc[-1]\n",
" [\"y_surface\"], \"x_bed\": tiff.size[0], \"y_bed\": csv.iloc[-1][\"y_bed\"]}, index=[0])\n",
" csv = pd.concat([top, csv, bottom], ignore_index=True)\n",
"\n",
" # Create a draw object for the image for drawing the polygons\n",
" draw = ImageDraw.Draw(img)\n",
"\n",
" # Loop over the rows of the csv file\n",
" for i in range(len(csv)-1):\n",
" crow = csv.iloc[i]\n",
" nrow = csv.iloc[i+1]\n",
"\n",
" # Define the coordinates for the sky, bed, and bottom polygons\n",
" skycooords = [\n",
" (crow[\"x_surface\"], 0),\n",
" (nrow[\"x_surface\"], 0),\n",
" (nrow[\"x_surface\"], nrow[\"y_surface\"]),\n",
" (crow[\"x_surface\"], crow[\"y_surface\"])\n",
" ]\n",
" bedcoords = [\n",
" (crow[\"x_surface\"], crow[\"y_surface\"]),\n",
" (nrow[\"x_surface\"], nrow[\"y_surface\"]),\n",
" (nrow[\"x_bed\"], nrow[\"y_bed\"]),\n",
" (crow[\"x_bed\"], crow[\"y_bed\"])\n",
" ]\n",
" btmcoords = [\n",
" (crow[\"x_bed\"], crow[\"y_bed\"]),\n",
" (nrow[\"x_bed\"], nrow[\"y_bed\"]),\n",
" (nrow[\"x_bed\"], tiff.size[1]),\n",
" (crow[\"x_bed\"], tiff.size[1])\n",
" ]\n",
"\n",
" # Draw the polygons on the image\n",
" draw.polygon(skycooords, fill=\"#000000\")\n",
" draw.polygon(bedcoords, fill=\"#010101\")\n",
" draw.polygon(btmcoords, fill=\"#020202\")\n",
"\n",
" # Save the image with the drawn polygons to the specified path\n",
" img.save(pl.Path(\n",
" rf'.\\secondleg\\{testset}\\img_mask_{testset}.png'))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# This code is used to crop the images and masks in the second leg data set into 400x400 images.\n",
"\n",
"# Loop over all the files in the \"secondleg\" directory\n",
"for testset in os.listdir(\"secondleg\"):\n",
"\n",
" cimg = Image.open(pl.Path(\n",
" rf'.\\secondleg\\{testset}\\cropped_img_{testset}.png'))\n",
"\n",
" mask = Image.open(pl.Path(\n",
" rf'.\\secondleg\\{testset}\\img_mask_{testset}.png'))\n",
"\n",
" # Calculate the sections to crop the image into, with each section being 400 pixels wide and an overlap of 80 pixels\n",
" cropsection = window_with_remainder(400, 80, cimg.size[0])\n",
"\n",
" # Try to create directories for the cropped images and masks\n",
" try:\n",
" os.mkdir(pl.Path(\n",
" rf'.\\secondleg\\{testset}\\cropped_images'))\n",
"\n",
" os.mkdir(pl.Path(\n",
" rf'.\\secondleg\\{testset}\\cropped_masks'))\n",
" except:\n",
" pass\n",
"\n",
" for i in cropsection:\n",
" # Crop the image to the current section, resize it to 400x400, and save it to the specified path\n",
" cimg.crop((i[0], 0, i[1], cimg.size[1])).resize((400, 400)).save(pl.Path(\n",
" rf'\\secondleg\\{testset}\\cropped_images\\cimg-{testset}_{i[0]}_{i[1]}.png'))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from huggingface_hub import notebook_login\n",
"\n",
"from datasets import Dataset, DatasetDict, Image\n",
"\n",
"from glob import glob\n",
"\n",
"images = glob(\"secondleg/*/cropped_images/*.png\")\n",
"\n",
"masks = glob(\"secondleg/*/cropped_masks/*.png\")\n",
"\n",
"# Define a function to create a dataset from image and label paths\n",
"def create_dataset(image_paths, label_paths):\n",
" # Create a Dataset object from a dictionary of image and label paths\n",
" dataset = Dataset.from_dict({\"image\": sorted(image_paths),\n",
" \"label\": sorted(label_paths)})\n",
" dataset = dataset.cast_column(\"image\", Image())\n",
" dataset = dataset.cast_column(\"label\", Image())\n",
"\n",
" return dataset\n",
"\n",
"\n",
"dataset = create_dataset(images, masks)\n",
"\n",
"notebook_login()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Call the push_to_hub method on the dataset object, specifying the repository name and setting it to private\n",
"dataset.push_to_hub(\"aashraychegu/glacier_scopes\", private=True)\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.10.7"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
|