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292 lines
8.6 KiB
Plaintext
292 lines
8.6 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "4fdb98fc-65bb-467e-be0c-168fee9b0fca",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"cuda:0\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"<contextlib.ExitStack at 0x76adb5fa4260>"
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]
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},
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"execution_count": 3,
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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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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import time\n",
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"import io\n",
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"import cv2\n",
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"import copy\n",
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"import os\n",
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"from tqdm import tqdm\n",
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"import torch.nn as nn\n",
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"import torch\n",
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"import torchvision\n",
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"from torch.utils.data import Dataset\n",
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"from torch import default_generator, randperm\n",
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"from PIL import Image\n",
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"#from torch._utils import _accumulate\n",
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"import csv\n",
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"from torch.utils.data.dataset import Subset\n",
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"from scipy import ndimage\n",
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"device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
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"print(device)\n",
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"batch_size = 16\n",
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"momentum=0.9\n",
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"lr = 1e-3\n",
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"import random\n",
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"sub_sample = 0.5\n",
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"import matplotlib\n",
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"import gc\n",
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"import torchsig.utils as u\n",
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"import torchsig.transforms.transforms as T\n",
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"from torchsig.transforms import functional as F\n",
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"matplotlib.use('Agg')\n",
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"import matplotlib as mpl\n",
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"mpl.rcParams['agg.path.chunksize'] = 256*256\n",
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"plt.ioff()"
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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": 4,
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"id": "4848b066-2e09-4c1c-b8fa-8e3fa84d907a",
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"metadata": {},
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"outputs": [],
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"source": [
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"s = T.Spectrogram(nperseg=1024)"
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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": 5,
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"id": "9267fbe1",
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"metadata": {},
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"outputs": [],
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"source": [
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"def sig2pic_with_spec(path_to_data, filename, specT=None,figsize=(16,16), dpi=16, resize = None):\n",
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" try:\n",
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" if specT is None:\n",
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" specT = T.Spectrogram(nperseg=1024)\n",
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" with open(path_to_data + filename, 'rb') as file:\n",
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" tmp = np.frombuffer(file.read(), dtype=np.complex64)\n",
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" signal = tmp\n",
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"\n",
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" #rint(\"vSE ok\")\n",
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"\n",
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" spectr = np.array(F.spectrogram(signal, fft_size=specT.fft_size, fft_stride=specT.fft_stride)[:, :figsize[0] * dpi])\n",
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" #print(\"VSE OK\")\n",
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" mag = np.abs(signal)\n",
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" real = signal.real\n",
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"\n",
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" fig2 = plt.figure(figsize = figsize)\n",
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" plt.axes(ylim=(-1, 1))\n",
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"\n",
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" plt.plot(real, color='black')\n",
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" plt.gca().set_axis_off()\n",
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" plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, hspace = 0, wspace = 0)\n",
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" plt.margins(0,0)\n",
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" buf2 = io.BytesIO()\n",
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" fig2.savefig(buf2, format=\"png\", dpi=dpi)\n",
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" buf2.seek(0)\n",
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" img_arr2 = np.frombuffer(buf2.getvalue(), dtype=np.uint8)\n",
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" buf2.close()\n",
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" img2 = cv2.imdecode(img_arr2, 1)\n",
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" img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)\n",
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" plt.clf()\n",
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" plt.cla()\n",
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" plt.close()\n",
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" plt.close(fig2)\n",
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"\n",
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" fig3 = plt.figure(figsize = figsize)\n",
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" plt.axes(ylim=(-1, 1))\n",
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"\n",
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" plt.plot(mag, color='black')\n",
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" plt.gca().set_axis_off()\n",
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" plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, hspace = 0, wspace = 0)\n",
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" plt.margins(0,0)\n",
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" buf3 = io.BytesIO()\n",
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" fig3.savefig(buf3, format=\"png\", dpi=dpi)\n",
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" buf3.seek(0)\n",
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" img_arr3 = np.frombuffer(buf3.getvalue(), dtype=np.uint8)\n",
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" buf3.close()\n",
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" img3 = cv2.imdecode(img_arr3, 1)\n",
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" img3 = cv2.cvtColor(img3, cv2.COLOR_BGR2GRAY)\n",
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" plt.clf()\n",
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" plt.cla()\n",
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" plt.close()\n",
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" plt.close(fig3)\n",
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"\n",
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" if resize != None:\n",
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" resized_real = cv2.resize(img2, resize)\n",
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" resized_mag = cv2.resize(img3, resize)\n",
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" resized_spectr = cv2.resize(spectr, resize)\n",
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" img = np.asarray([resized_real, resized_mag, resized_spectr], dtype=np.float32)\n",
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" return img\n",
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" img = np.asarray([img2, img3, spectr], dtype=np.float32)\n",
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" return img\n",
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" except Exception as e:\n",
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" print(str(e))\n",
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" return None"
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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": "448da74a-e0ae-44d8-9877-8dd1f257a24f",
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"metadata": {},
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"outputs": [],
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"source": [
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"selected_freq=915\n",
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"\n",
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"path_to_binaries = f'/home/sibsci/dataset/drone/{selected_freq}'\n",
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"path_to_pictures = f'/home/sibsci/dataset_img/drone/{selected_freq}_jpg'"
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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": 7,
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"id": "ac4945a8-29c4-4da4-945f-08658953e3e5",
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"metadata": {},
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"outputs": [],
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"source": [
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"from tqdm import tqdm"
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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": 8,
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"id": "6f226f86-5d72-4573-8af6-750128b70263",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2026-04-06_11-37-43: 100%|██████████| 42/42 [00:11<00:00, 3.55it/s]"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Dir: 2026-04-06_11-37-43 finished!\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\n"
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]
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}
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],
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"source": [
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"size = (256,256)\n",
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"\n",
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"if not os.path.exists(path_to_pictures):\n",
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" os.makedirs(path_to_pictures,exist_ok=True)\n",
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"\n",
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"for subdir in os.listdir(path_to_binaries):\n",
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" filepath = path_to_binaries + '/' + subdir + '/'\n",
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"\n",
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" if not os.path.isdir(filepath):\n",
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" continue\n",
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"\n",
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" files = os.listdir(filepath)\n",
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" k = max(1, int(len(files) * 0.04))\n",
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" files = random.sample(files, k)\n",
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" for file in tqdm(files, desc=subdir):\n",
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" full_input_path = filepath + file\n",
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"\n",
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" if not os.path.isfile(full_input_path):\n",
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" continue\n",
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"\n",
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" if file in ('run.log', 'reading_in_progress'):\n",
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" continue\n",
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"\n",
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" save_base = subdir + '__' + file\n",
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"\n",
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" savepath = path_to_pictures + '/' + save_base + '.npy'\n",
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" savepath_real_png = path_to_pictures + '/' + save_base + '_real' + '.png'\n",
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" savepath_imag_png = path_to_pictures + '/' + save_base + '_imag' + '.png'\n",
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" savepath_spec_png = path_to_pictures + '/' + save_base + '_spec' + '.png'\n",
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"\n",
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" if not os.path.exists(savepath):\n",
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" img = sig2pic_with_spec(path_to_data=filepath, filename=file, specT=s, resize=size)\n",
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" gc.collect()\n",
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"\n",
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" try:\n",
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" plt.imshow(img[0])\n",
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" plt.savefig(savepath_real_png)\n",
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" plt.clf()\n",
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" plt.cla()\n",
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" plt.close()\n",
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"\n",
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" plt.imshow(img[1])\n",
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" plt.savefig(savepath_imag_png)\n",
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" plt.clf()\n",
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" plt.cla()\n",
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" plt.close()\n",
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"\n",
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" plt.imshow(img[2])\n",
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" plt.savefig(savepath_spec_png)\n",
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" plt.clf()\n",
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" plt.cla()\n",
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" plt.close()\n",
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"\n",
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" np.save(savepath, img)\n",
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"\n",
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" except Exception:\n",
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" continue\n",
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"\n",
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" print('Dir: ', subdir , ' finished!')"
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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": "58ff5fbd",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv-train",
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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": "3.12.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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