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transform_data.ipynb
39984 lines (39983 with data), 1.1 MB
{
"cells": [
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import time\n",
"import pywt\n",
"import cv2\n",
"import json\n",
"import pickle\n",
"import itertools\n",
"import operator\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from scipy.io import loadmat\n",
"from tqdm import tqdm\n",
"from scipy import signal\n",
"from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas\n",
"from matplotlib.figure import Figure\n",
"# from stockwell import st\n",
"from scipy.signal import chirp"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"def most_common(L):\n",
" # get an iterable of (item, iterable) pairs\n",
" SL = sorted((x, i) for i, x in enumerate(L))\n",
" # print 'SL:', SL\n",
" groups = itertools.groupby(SL, key=operator.itemgetter(0))\n",
" # auxiliary function to get \"quality\" for an item\n",
" def _auxfun(g):\n",
" item, iterable = g\n",
" count = 0\n",
" min_index = len(L)\n",
" for _, where in iterable:\n",
" count += 1\n",
" min_index = min(min_index, where)\n",
" # print 'item %r, count %r, minind %r' % (item, count, min_index)\n",
" return count, -min_index\n",
" # pick the highest-count/earliest item\n",
" return max(groups, key=_auxfun)[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"PHYSIONET"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"STFT"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"save_path = '/home/ishaan/Desktop/stft/'\n",
"json_path = '/home/ishaan/github/ecg/examples/cinc17/'\n",
"json_path_train = os.path.join(json_path,'train.json')\n",
"json_path_test = os.path.join(json_path,'dev.json')\n",
"\n",
"if not os.path.exists(save_path):\n",
" os.mkdir(save_path)\n",
"\n",
"if not os.path.exists(os.path.join(save_path,'train')):\n",
" os.mkdir(os.path.join(save_path,'train'))\n",
"os.mkdir(os.path.join(save_path,'train','ecg'))\n",
"if not os.path.exists(os.path.join(save_path,'val')):\n",
" os.mkdir(os.path.join(save_path,'val'))\n",
"os.mkdir(os.path.join(save_path,'val','ecg'))\n",
"\n",
"with open(json_path_train) as f:\n",
" data_train = [json.loads(l) for l in f]\n",
" \n",
"with open(json_path_test) as f:\n",
" data_test = [json.loads(l) for l in f]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# s = loadmat(data_train[2]['ecg'])['val'][0,:]\n",
"# l = data_train[2]['labels']"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(9000,)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# s.shape"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"30"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# len(l)"
]
},
{
"cell_type": "code",
"execution_count": 10,
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],
"source": [
"fs = 300\n",
"STEP = 300\n",
"win_sz = 100;\n",
"overlap = 50;\n",
"win = signal.windows.hann(win_sz)\n",
"name = 0\n",
"js = {}\n",
"subject = 0\n",
"imgs = []\n",
"for line in tqdm(data_test):\n",
" s = loadmat(line['ecg'])['val'][0,:]\n",
" l = line['labels']\n",
"# break\n",
" length = s.shape[0]\n",
" start = 0\n",
" end = STEP\n",
"# # for i in range(0,int(np.floor(length/STEP))):\n",
" while end < length:\n",
" \n",
" w = s[start: end]\n",
" st = time.time()\n",
" f,t,zxx = signal.stft(s[start : end],fs, window=win, nperseg=win_sz, noverlap=overlap, nfft=win_sz, return_onesided=True, boundary='zeros', padded=True, axis=- 1)\n",
" \n",
" # plt.figure(figsize=(33,21))\n",
" fig = Figure()\n",
" fig.subplots_adjust(0,0,1,1)\n",
" # fig.add_axes([0,0,1,1])\n",
" canvas = FigureCanvas(fig)\n",
" ax = fig.gca()\n",
" ax.pcolormesh(t, f, 20*np.log10(np.abs(zxx)), shading='gouraud')\n",
" ax.axis('off')\n",
" canvas.draw()\n",
" width, height = fig.get_size_inches() * fig.get_dpi()\n",
" image = np.frombuffer(canvas.tostring_rgb(), dtype='uint8').reshape(int(height), int(width), 3)\n",
"# # if name%10 == 0:\n",
" c = cv2.resize(image,(224,224))\n",
" plt.imsave(os.path.join(save_path,'val','ecg',str(subject)+'_'+str(name)+'.jpg'),c)\n",
" np.save(os.path.join(save_path,'val','ecg',str(subject)+'_'+str(name)+'.npy'),w)\n",
" imgs.append(str(subject)+'_'+str(name))\n",
" print(time.time()-st)\n",
" name +=1\n",
" if name > 9:\n",
" js[str(subject)] = {'x': imgs, 'y': l[0]}\n",
" imgs = []\n",
" subject +=1\n",
" name = 0\n",
"# # else:\n",
"# c = cv2.resize(image,(224,224))\n",
"# plt.imsave(os.path.join(save_path,'train',l,str(name)+'.jpg'),c)\n",
" \n",
"# name += 1\n",
" start += STEP\n",
" end += STEP\n",
"\n",
"with open(os.path.join(save_path,'val','ecg_labels.json'),'w') as f:\n",
" json.dump(js,f,indent=2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"save_path = '/home/vidhiwar/Desktop/stft/'\n",
"json_path = '/home/vidhiwar/github/ecg/examples/cinc17/'\n",
"json_path_train = os.path.join(json_path,'train.json')\n",
"json_path_test = os.path.join(json_path,'dev.json')\n",
"\n",
"if not os.path.exists(os.path.join(save_path,'train')):\n",
" os.mkdir(os.path.join(save_path,'train'))\n",
"if not os.path.exists(os.path.join(save_path,'val')):\n",
" os.mkdir(os.path.join(save_path,'val'))\n",
"\n",
"\n",
"\n",
"if not os.path.exists(os.path.join(save_path,'train','~')):\n",
" os.mkdir(os.path.join(save_path,'train','~'))\n",
"if not os.path.exists(os.path.join(save_path,'train','A')):\n",
" os.mkdir(os.path.join(save_path,'train','A'))\n",
"if not os.path.exists(os.path.join(save_path,'train','N')):\n",
" os.mkdir(os.path.join(save_path,'train','N'))\n",
"if not os.path.exists(os.path.join(save_path,'train','O')):\n",
" os.mkdir(os.path.join(save_path,'train','O'))\n",
" \n",
"if not os.path.exists(os.path.join(save_path,'val','~')):\n",
" os.mkdir(os.path.join(save_path,'val','~'))\n",
"if not os.path.exists(os.path.join(save_path,'val','A')):\n",
" os.mkdir(os.path.join(save_path,'val','A'))\n",
"if not os.path.exists(os.path.join(save_path,'val','N')):\n",
" os.mkdir(os.path.join(save_path,'val','N'))\n",
"if not os.path.exists(os.path.join(save_path,'val','O')):\n",
" os.mkdir(os.path.join(save_path,'val','O'))\n",
" \n",
"with open(json_path_train) as f:\n",
" data_train = [json.loads(l) for l in f]\n",
" \n",
"with open(json_path_test) as f:\n",
" data_test = [json.loads(l) for l in f]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 7676/7676 [19:27<00:00, 6.57it/s]\n"
]
}
],
"source": [
"fs = 300\n",
"STEP = 3000\n",
"win_sz = 512;\n",
"overlap = 256;\n",
"win = signal.windows.hann(win_sz)\n",
"name = 0\n",
"for line in tqdm(data_train):\n",
" s = loadmat(line['ecg'])['val'][0,:]\n",
" l = line['labels'][0]\n",
"# break\n",
" length = s.shape[0]\n",
" start = 0\n",
" end = STEP\n",
"# for i in range(0,int(np.floor(length/STEP))):\n",
" while end < length:\n",
" f,t,zxx = signal.stft(s[start : end],fs, window=win, nperseg=win_sz, noverlap=overlap, nfft=win_sz, return_onesided=True, boundary='zeros', padded=True, axis=- 1)\n",
" # plt.figure(figsize=(33,21))\n",
" fig = Figure()\n",
" fig.subplots_adjust(0,0,1,1)\n",
" # fig.add_axes([0,0,1,1])\n",
" canvas = FigureCanvas(fig)\n",
" ax = fig.gca()\n",
" ax.pcolormesh(t, f, 20*np.log10(np.abs(zxx)), shading='gouraud')\n",
" ax.axis('off')\n",
" canvas.draw()\n",
" width, height = fig.get_size_inches() * fig.get_dpi()\n",
" image = np.frombuffer(canvas.tostring_rgb(), dtype='uint8').reshape(int(height), int(width), 3)\n",
"# if name%10 == 0:\n",
"# c = cv2.resize(image,(224,224))\n",
"# plt.imsave(os.path.join(save_path,'val',l,str(name)+'.jpg'),c)\n",
"# else:\n",
" c = cv2.resize(image,(224,224))\n",
" plt.imsave(os.path.join(save_path,'train',l,str(name)+'.jpg'),c)\n",
" \n",
" name += 1\n",
" start += 1500\n",
" end += 1500\n",
"conda install cv2"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 852/852 [02:10<00:00, 6.55it/s]\n"
]
}
],
"source": [
"fs = 300\n",
"STEP = 3000\n",
"win_sz = 512\n",
"overlap = 256\n",
"win = signal.windows.hann(win_sz)\n",
"name = 0\n",
"for line in tqdm(data_test):\n",
" s = loadmat(line['ecg'])['val'][0,:]\n",
" l = line['labels'][0]\n",
"# break\n",
" length = s.shape[0]\n",
" start = 0\n",
" end = STEP\n",
"# for i in range(0,int(np.floor(length/STEP))):\n",
" while end < length:\n",
" f,t,zxx = signal.stft(s[start : end],fs, window=win, nperseg=win_sz, noverlap=overlap, nfft=win_sz, return_onesided=True, boundary='zeros', padded=True, axis=- 1)\n",
" # plt.figure(figsize=(33,21))\n",
" fig = Figure()\n",
" fig.subplots_adjust(0,0,1,1)\n",
" # fig.add_axes([0,0,1,1])\n",
" canvas = FigureCanvas(fig)\n",
" ax = fig.gca()\n",
" ax.pcolormesh(t, f, 20*np.log10(np.abs(zxx)), shading='gouraud')\n",
" ax.axis('off')\n",
" canvas.draw()\n",
" width, height = fig.get_size_inches() * fig.get_dpi()\n",
" image = np.frombuffer(canvas.tostring_rgb(), dtype='uint8').reshape(int(height), int(width), 3)\n",
"# if name%10 == 0:\n",
"# c = cv2.resize(image,(224,224))\n",
"# plt.imsave(os.path.join(save_path,'val',l,str(name)+'.jpg'),c)\n",
"# else:\n",
" c = cv2.resize(image,(224,224))\n",
" plt.imsave(os.path.join(save_path,'val',l,str(name)+'.jpg'),c)\n",
" \n",
" name += 1\n",
" start += 1500\n",
" end += 1500"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"CWT"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"save_path = '/home/vidhiwar/Desktop/cwt/'\n",
"json_path = '/home/vidhiwar/github/ecg/examples/cinc17/'\n",
"json_path_train = os.path.join(json_path,'train.json')\n",
"json_path_test = os.path.join(json_path,'dev.json')\n",
"\n",
"if not os.path.exists(os.path.join(save_path,'train')):\n",
" os.mkdir(os.path.join(save_path,'train'))\n",
"if not os.path.exists(os.path.join(save_path,'val')):\n",
" os.mkdir(os.path.join(save_path,'val'))\n",
"\n",
"\n",
"if not os.path.exists(os.path.join(save_path,'train','~')):\n",
" os.mkdir(os.path.join(save_path,'train','~'))\n",
"if not os.path.exists(os.path.join(save_path,'train','A')):\n",
" os.mkdir(os.path.join(save_path,'train','A'))\n",
"if not os.path.exists(os.path.join(save_path,'train','N')):\n",
" os.mkdir(os.path.join(save_path,'train','N'))\n",
"if not os.path.exists(os.path.join(save_path,'train','O')):\n",
" os.mkdir(os.path.join(save_path,'train','O'))\n",
" \n",
"if not os.path.exists(os.path.join(save_path,'val','~')):\n",
" os.mkdir(os.path.join(save_path,'val','~'))\n",
"if not os.path.exists(os.path.join(save_path,'val','A')):\n",
" os.mkdir(os.path.join(save_path,'val','A'))\n",
"if not os.path.exists(os.path.join(save_path,'val','N')):\n",
" os.mkdir(os.path.join(save_path,'val','N'))\n",
"if not os.path.exists(os.path.join(save_path,'val','O')):\n",
" os.mkdir(os.path.join(save_path,'val','O'))\n",
" \n",
"with open(json_path_train) as f:\n",
" data_train = [json.loads(l) for l in f]\n",
" \n",
"with open(json_path_test) as f:\n",
" data_test = [json.loads(l) for l in f]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 7676/7676 [2:08:28<00:00, 1.00s/it] \n"
]
}
],
"source": [
"n = 0\n",
"widths = np.arange(1, 512)\n",
"\n",
"for line in tqdm(data_train):\n",
" s = loadmat(line['ecg'])['val']\n",
" l = line['labels']\n",
" start = 0\n",
" end = 3000\n",
" while end < s.shape[1]:\n",
" c = signal.cwt(s[0,start:end],signal.ricker,widths)\n",
"\n",
"\n",
" plt.imsave(os.path.join(save_path,'train',l[0],str(n)+'.jpg'),c)\n",
" c = plt.imread(os.path.join(save_path,'train',l[0],str(n)+'.jpg'))\n",
" c = cv2.resize(c,(224,224))\n",
" plt.imsave(os.path.join(save_path,'train',l[0],str(n)+'.jpg'),c)\n",
" \n",
" start += 1500\n",
" end += 1500\n",
" n += 1\n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 852/852 [14:17<00:00, 1.01s/it]\n"
]
}
],
"source": [
"n = 0\n",
"widths = np.arange(1, 512)\n",
"\n",
"for line in tqdm(data_test):\n",
" s = loadmat(line['ecg'])['val']\n",
" l = line['labels']\n",
" start = 0\n",
" end = 3000\n",
" while end < s.shape[1]:\n",
" c = signal.cwt(s[0,start:end],signal.ricker,widths)\n",
"\n",
" plt.imsave(os.path.join(save_path,'val',l[0],str(n)+'.jpg'),c)\n",
" c = plt.imread(os.path.join(save_path,'val',l[0],str(n)+'.jpg'))\n",
" c = cv2.resize(c,(224,224))\n",
" plt.imsave(os.path.join(save_path,'val',l[0],str(n)+'.jpg'),c)\n",
"\n",
" start += 1500\n",
" end += 1500\n",
" n += 1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"MIT-BIH"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"STFT"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_excel('/home/vidhiwar/Desktop/ECG_Data_Collect.xlsx')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fs = 360\n",
"STEP = 3600\n",
"win_sz = 512;\n",
"overlap = 256;\n",
"zero = 1024\n",
"g_dict = {'M':[0,1],'F':[1,0]}\n",
"save_path = '/home/vidhiwar/Desktop/stft/'\n",
"\n",
"l2n = {'N':0, 'L':1, 'R':2, 'V':3, 'A':4,'~':5}\n",
"\n",
"train = []\n",
"val = []\n",
"\n",
"win = signal.windows.hann(win_sz)\n",
"name = 0\n",
"for j in tqdm(range(0,48,1)):\n",
" i = df['#record'][j]\n",
" if os.path.exists('/home/vidhiwar/github/ecg/examples/mitdb/mit/' + str(i) + '.pkl'):\n",
" with open('/home/vidhiwar/github/ecg/examples/mitdb/mit/' + str(i) + '.pkl', 'rb') as f:\n",
" data = pickle.load(f)\n",
" w = np.load('/home/vidhiwar/github/ecg/examples/mitdb/mit/' + str(i) + '.npy')\n",
" l_dict = {}\n",
" for a in data:\n",
" if a[2] in ['N', 'L', 'R', 'V', 'A']:\n",
" l_dict[a[1]] = a[2]\n",
" else:\n",
" l_dict[a[1]] = '~'\n",
"\n",
" s = w[:,2]\n",
" g = g_dict[df['Sex'][j]]\n",
" age = 0\n",
" if df['Age'][j] > 0 and df['Age'][j] < 100:\n",
" age = df['Age'][j]/100\n",
" \n",
" \n",
" # break\n",
" length = s.shape[0]\n",
" start = 0\n",
" end = STEP\n",
" # for i in range(0,int(np.floor(length/STEP))):\n",
" while end < length:\n",
" sample = {}\n",
" f,t,zxx = signal.stft(s[start : end]-zero,fs, window=win, nperseg=win_sz, noverlap=overlap, nfft=win_sz, return_onesided=True, boundary='zeros', padded=True, axis=- 1)\n",
" # plt.figure(figsize=(33,21))\n",
" fig = Figure()\n",
" fig.subplots_adjust(0,0,1,1)\n",
" # fig.add_axes([0,0,1,1])\n",
" canvas = FigureCanvas(fig)\n",
" ax = fig.gca()\n",
" ax.pcolormesh(t, f, 20*np.log10(np.abs(zxx)), shading='gouraud')\n",
" ax.axis('off')\n",
" canvas.draw()\n",
" width, height = fig.get_size_inches() * fig.get_dpi()\n",
" image = np.frombuffer(canvas.tostring_rgb(), dtype='uint8').reshape(int(height), int(width), 3)\n",
"# key = list(l_dict.keys())[0]\n",
" label_list = []\n",
" for k in range(start,end):\n",
" if k in list(l_dict.keys()):conda install cv2\n",
" label_list.append(l_dict[k])\n",
" \n",
" l = most_common(label_list)\n",
" \n",
" sample['image'] = str(name)+'.jpg'\n",
" sample['id'] = name\n",
" sample['label'] = l2n[l]\n",
" sample['age'] = age\n",
" sample['gender'] = g\n",
" if name%10 == 0: conda install -c conda-forge opencv \n",
" c = cv2.resize(image,(224,224))\n",
" plt.imsave(os.path.join(save_path,'val','images',str(name)+'.jpg'),c)\n",
" val.append(sample)\n",
" else:\n",
" c = cv2.resize(image,(224,224))\n",
" plt.imsave(os.path.join(save_path,'train','images',str(name)+'.jpg'),c)\n",
" train.append(sample)\n",
"\n",
" name += 1\n",
" start += 1800\n",
" end += 1800\n",
"ecg_train = {}\n",
"ecg_val = {}\n",
"\n",
"\n",
"ecg_train['data'] = train\n",
"ecg_val['data'] = val\n",
"\n",
"with open('/home/vidhiwar/Desktop/stft/train/ecg_labels.json','w') as f:\n",
" json.dump(ecg_train,f,indent=2)\n",
"\n",
"with open('/home/vidhiwar/Desktop/stft/val/ecg_labels.json','w') as f:\n",
" json.dump(ecg_val,f,indent=2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"CWT"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_excel('/home/vidhiwar/Desktop/ecg/ECG_Data_Collect.xlsx')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"STEP = 3600\n",
"\n",
"zero = 1024\n",
"g_dict = {'M':[0,1],'F':[1,0]}\n",
"save_path = '/home/vidhiwar/Desktop/cwt/'\n",
"df = pd.read_excel('/home/vidhiwar/Desktop/ECG_Data_Collect.xlsx')\n",
"l2n = {'N':0, 'L':1, 'R':2, 'V':3, 'A':4,'~':5}\n",
"widths = np.arange(1, 512)\n",
"train = []\n",
"val = []\n",
"name = 0\n",
"for j in tqdm(range(0,48,1)):\n",
" i = df['#record'][j]\n",
" if os.path.exists('/home/vidhiwar/github/ecg/examples/mitdb/mit/' + str(i) + '.pkl'):\n",
" with open('/home/vidhiwar/github/ecg/examples/mitdb/mit/' + str(i) + '.pkl', 'rb') as f:\n",
" data = pickle.load(f)\n",
" w = np.load('/home/vidhiwar/github/ecg/examples/mitdb/mit/' + str(i) + '.npy')\n",
" l_dict = {}\n",
" for a in data:\n",
" if a[2] in ['N', 'L', 'R', 'V', 'A']:\n",
" l_dict[a[1]] = a[2]\n",
" else:\n",
" l_dict[a[1]] = '~'\n",
"\n",
" s = w[:,2]\n",
" g = g_dict[df['Sex'][j]]\n",
" age = 0conda install cv2\n",
" if df['Age'][j] > 0 and df['Age'][j] < 100:\n",
" age = df['Age'][j]/100\n",
" \n",
" \n",
" # break\n",
" length = s.shape[0]\n",
" start = 0\n",
" end = STEP\n",
" # for i in range(0,int(np.floor(length/STEP))):\n",
" while end < length:\n",
" sample = {}\n",
" c = signal.cwt(s[start:end]-zero,signal.ricker,widths)\n",
"# key = list(l_dict.keys())[0]\n",
" label_list = []\n",
" for k in range(start,end):\n",
" if k in list(l_dict.keys()):\n",
" label_list.append(l_dict[k])\n",
" conda install cv2\n",
" l = most_common(label_list)\n",
" \n",
" sample['image'] = str(name)+'.jpg'/home/ishaan/data/ecg/res/mitdb/mit\n",
" sample['id'] = name\n",
" sample['label'] = l2n[l]\n",
" sample['age'] = age\n",
" sample['gender'] = g\n",
" if name%10 == 0:\n",
" plt.imsave(os.path.join(save_path,'val','images',str(name)+'.jpg'),c)\n",
" c = plt.imread(os.path.join(save_path,'val','images',str(name)+'.jpg'))\n",
" c = cv2.resize(c,(224,224))\n",
" plt.imsave(os.path.join(save_path,'val','images',str(name)+'.jpg'),c)\n",
" val.append(sample)\n",
" else:\n",
" plt.imsave(os.path.join(save_path,'train','images',str(name)+'.jpg'),c)\n",
" c = plt.imread(os.path.join(save_path,'train','images',str(name)+'.jpg'))\n",
" c = cv2.resize(c,(224,224))\n",
" plt.imsave(os.path.join(save_path,'train','images',str(name)+'.jpg'),c)\n",
" train.append(sample)\n",
"\n",
" name += 1\n",
" start += 1800\n",
" end += 1800\n",
"ecg_train = {}\n",
"ecg_val = {}\n",
"\n",
"\n",
"ecg_train['data'] = train\n",
"ecg_val['data'] = val\n",
"conda install cv2\n",
"with open('/home/vidhiwar/Desktop/cwt/train/ecg_labels.json','w') as f:\n",
" json.dump(ecg_train,f,indent=2)\n",
"\n",
"with open('/home/vidhiwar/Desktop/cwt/val/ecg_labels.json','w') as f:\n",
" json.dump(ecg_val,f,indent=2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"MITBIH\n",
"\n",
"STFT, AGE, GENDER, RAW"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"save_path = '/home/vidhiwar/Desktop/ecg/'\n",
"\n",
"mitbih_path = '/home/vidhiwar/github/ecg/examples/mitdb/mit/'\n",
"\n",
"df = pd.read_excel('/home/vidhiwar/work_dir/ecg_spectogram/phase1/ecg_res/ECG_Data_Collect.xlsx')\n",
"\n",
"if not os.path.exists(os.path.join(save_path,'mitbih_rl')):\n",
" os.mkdir(os.path.join(save_path,'mitbih_rl'))\n",
"\n",
"save_path = os.path.join(save_path, 'mitbih_rl')\n",
"\n",
"# for i in ['stft','cwt']:\n",
"\n",
"\n",
"# if not os.path.exists(os.path.join(save_path,'img_folder')):\n",
"# os.mkdir(os.path.join(save_path,'img_folder'))\n",
"\n",
"# save_path_img = os.path.join(save_path,'img_folder',i)\n",
"\n",
"# if not os.path.exists(os.path.join(save_path_img,'train')):\n",
"# os.mkdir(os.path.join(save_path_img,'train'))\n",
"# if not os.path.exists(os.path.join(save_path_img,'val')):\n",
"# os.mkdir(os.path.join(save_path_img,'val'))\n",
"\n",
"# if not os.path.exists(os.path.join(save_path_img,'train','~')):\n",
"# os.mkdir(os.path.join(save_path_img,'train','~'))\n",
"# if not os.path.exists(os.path.join(save_path_img,'train','A')):\n",
"# os.mkdir(os.path.join(save_path_img,'train','A'))\n",
"# if not os.path.exists(os.path.join(save_path_img,'train','N')):\n",
"# os.mkdir(os.path.join(save_path_img,'train','N'))\n",
"# if not os.path.exists(os.path.join(save_path_img,'train','L')):\n",
"# os.mkdir(os.path.join(save_path_img,'train','L'))\n",
"# if not os.path.exists(os.path.join(save_path_img,'train','R')):\n",
"# os.mkdir(os.path.join(save_path_img,'train','R'))\n",
"# if not os.path.exists(os.path.join(save_path_img,'train','V')):\n",
"# os.mkdir(os.path.join(save_path_img,'train','V'))\n",
"\n",
"# if not os.path.exists(os.path.join(save_path_img,'val','~')):\n",
"# os.mkdir(os.path.join(save_path_img,'val','~'))\n",
"# if not os.path.exists(os.path.join(save_path_img,'val','A')):\n",
"# os.mkdir(os.path.join(save_path_img,'val','A'))\n",
"# if not os.path.exists(os.path.join(save_path_img,'val','N')):\n",
"# os.mkdir(os.path.join(save_path_img,'val','N'))\n",
"# if not os.path.exists(os.path.join(save_path_img,'val','L')):\n",
"# os.mkdir(os.path.join(save_path_img,'val','L'))\n",
"# if not os.path.exists(os.path.join(save_path_img,'val','R')):\n",
"# os.mkdir(os.path.join(save_path_img,'val','R'))\n",
"# if not os.path.exists(os.path.join(save_path_img,'val','V')):\n",
"# os.mkdir(os.path.join(save_path_img,'val','V'))\n",
"\n",
"if not os.path.exists(os.path.join(save_path,'train')):\n",
" os.mkdir(os.path.join(save_path,'train'))\n",
"if not os.path.exists(os.path.join(save_path,'val')):\n",
" os.mkdir(os.path.join(save_path,'val'))\n",
" \n",
"if not os.path.exists(os.path.join(save_path,'train','images')):\n",
" os.mkdir(os.path.join(save_path,'train','images'))\n",
"if not os.path.exists(os.path.join(save_path,'val','images')):\n",
" os.mkdir(os.path.join(save_path,'val','images'))\n",
" \n",
"if not os.path.exists(os.path.join(save_path,'train','images_full')):\n",
" os.mkdir(os.path.join(save_path,'train','images_full'))\n",
"if not os.path.exists(os.path.join(save_path,'val','images_full')):\n",
" os.mkdir(os.path.join(save_path,'val','images_full'))\n",
"\n",
"for i in ['stft','cwt']:\n",
" if not os.path.exists(os.path.join(save_path,'train','images',i)):\n",
" os.mkdir(os.path.join(save_path,'train','images',i))\n",
" if not os.path.exists(os.path.join(save_path,'val','images',i)):\n",
" os.mkdir(os.path.join(save_path,'val','images',i))\n",
" \n",
" if not os.path.exists(os.path.join(save_path,'train','images_full',i)):\n",
" os.mkdir(os.path.join(save_path,'train','images_full',i))\n",
" if not os.path.exists(os.path.join(save_path,'val','images_full',i)):\n",
" os.mkdir(os.path.join(save_path,'val','images_full',i))\n",
" \n",
"if not os.path.exists(os.path.join(save_path,'train','ecg')):\n",
" os.mkdir(os.path.join(save_path,'train','ecg'))\n",
"if not os.path.exists(os.path.join(save_path,'val','ecg')):\n",
" os.mkdir(os.path.join(save_path,'val','ecg'))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 2/2 [13:19<00:00, 399.85s/it]\n"
]
}
],
"source": [
"fs = 360\n",
"STEP = 3600\n",
"win_sz = 72\n",
"overlap = 36\n",
"\n",
"outer_win_sz = 180\n",
"outer_overlap = 90\n",
"\n",
"zero = 1024\n",
"inner_step = 360\n",
"widths = np.concatenate([np.flip(np.arange(1, 181)),np.arange(1, 181)])\n",
"outer_widths = np.concatenate([np.flip(np.arange(1, 721)),np.arange(1, 721)])\n",
"g_dict = {'M':[0,1],'F':[1,0]}\n",
"\n",
"l2n = {'N':0, 'L':1, 'R':2, 'V':3, 'A':4,'~':5}\n",
"\n",
"n2l = {0:'N', 1:'L', 2:'R', 3:'V', 4:'A',5:'~'}\n",
"\n",
"train = []\n",
"val = []\n",
"\n",
"win = signal.windows.hann(win_sz)\n",
"outer_win = signal.windows.hann(outer_win_sz)\n",
"name = 0\n",
"l_defaullt = '~'\n",
"for j in tqdm(range(0,2,1)):\n",
" i = df['#record'][j]\n",
" if os.path.exists(os.path.join(mitbih_path,str(i) + '.pkl')):\n",
" with open(os.path.join(mitbih_path,str(i) + '.pkl'), 'rb') as f:\n",
" data = pickle.load(f)\n",
" w = np.load(os.path.join(mitbih_path,str(i) + '.npy'))\n",
" l_dict = {}\n",
" for a in data:\n",
" if a[2] in ['N', 'L', 'R', 'V', 'A']:\n",
" l_dict[a[1]] = a[2]\n",
" else:\n",
" l_dict[a[1]] = '~'\n",
"\n",
" s = w[:,2]\n",
" g = g_dict[df['Sex'][j]]\n",
" age = 0\n",
" if df['Age'][j] > 0 and df['Age'][j] < 100:\n",
" age = df['Age'][j]/100\n",
" \n",
" \n",
" # break\n",
" length = s.shape[0]\n",
" start = 0\n",
" end = STEP\n",
" # for i in range(0,int(np.floor(length/STEP))):\n",
" while end < length:\n",
"\n",
" sample = {}\n",
" \n",
" inner_start = start\n",
" inner_end = start+inner_step\n",
" \n",
" segment_images = []\n",
" segment_ecg = []\n",
" segment_id = 0\n",
" f,t,zxx = signal.stft(s[start : end]-zero,fs, window=outer_win, nperseg=outer_win_sz, noverlap=outer_overlap, nfft=outer_win_sz, return_onesided=True, boundary='zeros', padded=True, axis=- 1)\n",
" # plt.figure(figsize=(33,21))\n",
" fig = Figure()\n",
" fig.subplots_adjust(0,0,1,1)\n",
" # fig.add_axes([0,0,1,1])\n",
" canvas = FigureCanvas(fig)\n",
" ax = fig.gca()\n",
" ax.pcolormesh(t, f, 20*np.log10(np.abs(zxx)), shading='gouraud')\n",
" ax.axis('off')\n",
" canvas.draw()\n",
" width, height = fig.get_size_inches() * fig.get_dpi()\n",
" image = np.frombuffer(canvas.tostring_rgb(), dtype='uint8').reshape(int(height), int(width), 3)\n",
" \n",
" cwt = signal.cwt(s[start:end]-zero,signal.ricker,outer_widths)\n",
" labels = []\n",
" \n",
" if name%10 == 0:\n",
" plt.imsave(os.path.join(save_path,'val','images_full','cwt',str(name)+'.jpg'),cwt)\n",
" cwt = plt.imread(os.path.join(save_path,'val','images_full','cwt',str(name)+'.jpg'))\n",
" cwt = cv2.resize(cwt,(224,224))\n",
" plt.imsave(os.path.join(save_path,'val','images_full','cwt',str(name)+'.jpg'),cwt)\n",
" c = cv2.resize(image,(224,224))\n",
" plt.imsave(os.path.join(save_path,'val','images_full','stft',str(name)+'.jpg'),c)\n",
" \n",
" else:\n",
" plt.imsave(os.path.join(save_path,'train','images_full','cwt',str(name)+'.jpg'),cwt)\n",
" cwt = plt.imread(os.path.join(save_path,'train','images_full','cwt',str(name)+'.jpg'))\n",
" cwt = cv2.resize(cwt,(224,224))\n",
" plt.imsave(os.path.join(save_path,'train','images_full','cwt',str(name)+'.jpg'),cwt)\n",
" c = cv2.resize(image,(224,224))\n",
" plt.imsave(os.path.join(save_path,'train','images_full','stft',str(name)+'.jpg'),c)\n",
" while inner_end <= end:\n",
" \n",
" f,t,zxx = signal.stft(s[inner_start : inner_end]-zero,fs, window=win, nperseg=win_sz, noverlap=overlap, nfft=win_sz, return_onesided=True, boundary='zeros', padded=True, axis=- 1)\n",
" # plt.figure(figsize=(33,21))\n",
" fig = Figure()\n",
" fig.subplots_adjust(0,0,1,1)\n",
" # fig.add_axes([0,0,1,1])\n",
" canvas = FigureCanvas(fig)\n",
" ax = fig.gca()\n",
" ax.pcolormesh(t, f, 20*np.log10(np.abs(zxx)), shading='gouraud')\n",
" ax.axis('off')\n",
" canvas.draw()\n",
" width, height = fig.get_size_inches() * fig.get_dpi()\n",
" image = np.frombuffer(canvas.tostring_rgb(), dtype='uint8').reshape(int(height), int(width), 3)\n",
" \n",
" cwt = signal.cwt(s[inner_start:inner_end]-zero,signal.ricker,outer_widths)\n",
" segment_ecg.append(str(name)+'_'+str(segment_id)+'.npy')\n",
" segment_images.append(str(name)+'_'+str(segment_id)+'.jpg')\n",
" if name%10 == 0:\n",
" plt.imsave(os.path.join(save_path,'val','images','cwt',str(name)+'_'+str(segment_id)+'.jpg'),cwt)\n",
" cwt = plt.imread(os.path.join(save_path,'val','images','cwt',str(name)+'_'+str(segment_id)+'.jpg'))\n",
" cwt = cv2.resize(cwt,(224,224))\n",
" plt.imsave(os.path.join(save_path,'val','images','cwt',str(name)+'_'+str(segment_id)+'.jpg'),cwt)\n",
" c = cv2.resize(image,(224,224))\n",
" plt.imsave(os.path.join(save_path,'val','images','stft',str(name)+'_'+str(segment_id)+'.jpg'),c)\n",
" np.save(os.path.join(save_path,'val','ecg',str(name)+'_'+str(segment_id)+'.npy'),s[inner_start : inner_end]-zero)\n",
" else:\n",
" plt.imsave(os.path.join(save_path,'train','images','cwt',str(name)+'_'+str(segment_id)+'.jpg'),cwt)\n",
" cwt = plt.imread(os.path.join(save_path,'train','images','cwt',str(name)+'_'+str(segment_id)+'.jpg'))\n",
" cwt = cv2.resize(cwt,(224,224))\n",
" plt.imsave(os.path.join(save_path,'train','images','cwt',str(name)+'_'+str(segment_id)+'.jpg'),cwt)\n",
" c = cv2.resize(image,(224,224))\n",
" plt.imsave(os.path.join(save_path,'train','images','stft',str(name)+'_'+str(segment_id)+'.jpg'),c)\n",
" np.save(os.path.join(save_path,'train','ecg',str(name)+'_'+str(segment_id)+'.npy'),s[inner_start : inner_end]-zero)\n",
" label_list = []\n",
" for k in range(inner_start,inner_end):\n",
" if k in list(l_dict.keys()):\n",
" label_list.append(l_dict[k])\n",
" \n",
" if len(label_list) == 0:\n",
" l = default_label\n",
" else:\n",
" l = most_common(label_list)\n",
" default_label = l\n",
" labels.append(l2n[l])\n",
" segment_id += 1\n",
" inner_start += inner_step\n",
" inner_end += inner_step\n",
"# key = list(l_dict.keys())[0]\n",
"# label_list = []\n",
"# for k in range(start,end):\n",
"# if k in list(l_dict.keys()):\n",
"# label_list.append(l_dict[k])\n",
" \n",
"# l = most_common(label_list)\n",
" \n",
" sample['images'] = segment_images\n",
" sample['images_full'] = str(name)+'.jpg'\n",
" sample['ecg'] = segment_ecg\n",
" sample['id'] = name\n",
" sample['label'] = labels\n",
" sample['age'] = age\n",
" sample['gender'] = g\n",
" if name%10 == 0:\n",
" val.append(sample)\n",
" else:\n",
" train.append(sample)\n",
"\n",
" name += 1\n",
" start += STEP\n",
" end += STEP\n",
"ecg_train = {}\n",
"ecg_val = {}\n",
"\n",
"\n",
"ecg_train['data'] = train\n",
"ecg_val['data'] = val\n",
"ecg_train['labels'] = n2l\n",
"ecg_val['labels'] = n2l\n",
"\n",
"\n",
"with open(os.path.join(save_path,'train','ecg_labels.json'),'w') as f:\n",
" json.dump(ecg_train,f,indent=2)\n",
"\n",
"with open(os.path.join(save_path,'val','ecg_labels.json'),'w') as f:\n",
" json.dump(ecg_val,f,indent=2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
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