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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Preprocessing for LSTM: Blobdetection and Cutting"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"from scipy.odr import *\n",
"from scipy.stats import *\n",
"import numpy as np\n",
"import pandas as pd\n",
"import os\n",
"import time\n",
"import matplotlib.pyplot as plt\n",
"import ast\n",
"from multiprocessing import Pool, cpu_count\n",
"\n",
"import scipy\n",
"\n",
"from IPython import display\n",
"from matplotlib.patches import Rectangle\n",
"\n",
"from sklearn.metrics import mean_squared_error\n",
"import json\n",
"\n",
"import scipy.stats as st\n",
"from sklearn.metrics import r2_score\n",
"\n",
"\n",
"from matplotlib import cm\n",
"from mpl_toolkits.mplot3d import axes3d\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import copy\n",
"\n",
"from sklearn.model_selection import LeaveOneOut, LeavePOut\n",
"\n",
"from multiprocessing import Pool\n",
"import cv2"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"df_filtered = pd.read_pickle(\"DataStudyCollection/df_lstm.pkl\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
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" .dataframe tbody tr th:only-of-type {\n",
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>userID</th>\n",
" <th>Timestamp</th>\n",
" <th>Current_Task</th>\n",
" <th>Task_amount</th>\n",
" <th>TaskID</th>\n",
" <th>VersionID</th>\n",
" <th>RepetitionID</th>\n",
" <th>Actual_Data</th>\n",
" <th>Is_Pause</th>\n",
" <th>Image</th>\n",
" <th>IsMax</th>\n",
" <th>MaxRepetition</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>291980</th>\n",
" <td>1</td>\n",
" <td>1,54515E+12</td>\n",
" <td>33</td>\n",
" <td>680</td>\n",
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" <th>291981</th>\n",
" <td>1</td>\n",
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" <td>True</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>291982</th>\n",
" <td>1</td>\n",
" <td>1,54515E+12</td>\n",
" <td>33</td>\n",
" <td>680</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>True</td>\n",
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" <tr>\n",
" <th>291983</th>\n",
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" <td>33</td>\n",
" <td>680</td>\n",
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" <td>True</td>\n",
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" <tr>\n",
" <th>291984</th>\n",
" <td>1</td>\n",
" <td>1,54515E+12</td>\n",
" <td>33</td>\n",
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" <td>False</td>\n",
" <td>[0, 2, 0, 0, 0, 0, 1, 2, 2, 3, 2, 1, 1, 1, 0, ...</td>\n",
" <td>True</td>\n",
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"</div>"
],
"text/plain": [
" userID Timestamp Current_Task Task_amount TaskID VersionID \\\n",
"291980 1 1,54515E+12 33 680 0 2 \n",
"291981 1 1,54515E+12 33 680 0 2 \n",
"291982 1 1,54515E+12 33 680 0 2 \n",
"291983 1 1,54515E+12 33 680 0 2 \n",
"291984 1 1,54515E+12 33 680 0 2 \n",
"\n",
" RepetitionID Actual_Data Is_Pause \\\n",
"291980 0 True False \n",
"291981 0 True False \n",
"291982 0 True False \n",
"291983 0 True False \n",
"291984 0 True False \n",
"\n",
" Image IsMax \\\n",
"291980 [0, 2, 0, 0, 0, 0, 1, 2, 2, 3, 2, 1, 1, 1, 0, ... True \n",
"291981 [0, 2, 0, 0, 0, 0, 1, 2, 2, 3, 2, 1, 1, 1, 0, ... True \n",
"291982 [0, 2, 0, 0, 0, 0, 1, 2, 2, 3, 2, 1, 1, 1, 0, ... True \n",
"291983 [0, 2, 0, 0, 0, 0, 1, 2, 2, 3, 2, 1, 1, 1, 0, ... True \n",
"291984 [0, 2, 0, 0, 0, 0, 1, 2, 2, 3, 2, 1, 1, 1, 0, ... True \n",
"\n",
" MaxRepetition \n",
"291980 0 \n",
"291981 0 \n",
"291982 0 \n",
"291983 0 \n",
"291984 0 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_filtered.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"df_filtered.Image = df_filtered.Image.apply(lambda x: x.reshape(27, 15))\n",
"df_filtered.Image = df_filtered.Image.apply(lambda x: x.clip(min=0, max=255))\n",
"df_filtered.Image = df_filtered.Image.apply(lambda x: x.astype(np.uint8))\n",
"df_filtered[\"ImageSum\"] = df_filtered.Image.apply(lambda x: np.sum(x))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"#LSTMs new Blob detection (only detect, if there are blobs)\n",
"def detect_blobs(image):\n",
" #image = image.reshape(27, 15)\n",
" large = np.ones((29,17), dtype=np.uint8)\n",
" large[1:28,1:16] = image\n",
" temp, thresh = cv2.threshold(cv2.bitwise_not(large), 200, 255, cv2.THRESH_BINARY)\n",
" contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n",
" contours = [a for a in contours if cv2.contourArea(a) > 8 and cv2.contourArea(a) < 255]\n",
" lstBlob = []\n",
" lstMin = []\n",
" lstMax = []\n",
" count = 0\n",
" return len(contours) > 0"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 3.42 s, sys: 1.14 s, total: 4.57 s\n",
"Wall time: 4.94 s\n"
]
}
],
"source": [
"%%time\n",
"pool = Pool(cpu_count() - 1)\n",
"temp_blobs = pool.map(detect_blobs, df_filtered.Image)\n",
"pool.close()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"df_filtered[\"ContainsBlobs\"] = temp_blobs"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"#Label if knuckle or finger\n",
"def f(row):\n",
" if row['TaskID'] < 17:\n",
" #val = \"Knuckle\"\n",
" val = 0\n",
" elif row['TaskID'] >= 17:\n",
" #val = \"Finger\"\n",
" val = 1\n",
" return val\n",
"df_filtered['InputMethod'] = df_filtered.apply(f, axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"df_filtered.index = range(len(df_filtered))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1\n",
"2\n",
"3\n",
"4\n",
"5\n",
"6\n",
"7\n",
"8\n",
"9\n",
"10\n",
"11\n",
"12\n",
"13\n",
"14\n",
"15\n",
"16\n",
"17\n",
"18\n",
"CPU times: user 4min 7s, sys: 424 ms, total: 4min 8s\n",
"Wall time: 4min 8s\n"
]
}
],
"source": [
"%%time\n",
"# trim image sequences down to only between first and last detected blob\n",
"UserIDs = []\n",
"TaskIDs = []\n",
"VersionIDs = []\n",
"Blobs = []\n",
"for userID in df_filtered.userID.unique():\n",
" print(userID)\n",
" for TaskID in df_filtered[df_filtered.userID == userID].TaskID.unique():\n",
" for VersionID in df_filtered[(df_filtered.userID == userID) & (df_filtered.TaskID == TaskID)].VersionID.unique():\n",
" first_blob = -1\n",
" last_blob = -1\n",
" for index, row in df_filtered[(df_filtered.userID == userID) & (df_filtered.TaskID == TaskID) & (df_filtered.VersionID == VersionID)].iterrows():\n",
" if row.ContainsBlobs:\n",
" last_blob = index\n",
" if first_blob == -1:\n",
" first_blob = index\n",
" if first_blob >= 0 and last_blob >= 0:\n",
" UserIDs.append(userID)\n",
" TaskIDs.append(TaskID)\n",
" VersionIDs.append(VersionID)\n",
" Blobs.append(df_filtered[(df_filtered.userID == userID) & (df_filtered.TaskID == TaskID) & (df_filtered.VersionID == VersionID) & (df_filtered.index >= first_blob) & (df_filtered.index <= last_blob)].Image.tolist())"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"UserIDs = np.array(UserIDs, dtype=np.int64)\n",
"TaskIDs = np.array(TaskIDs, dtype=np.int64)\n",
"VersionIDs = np.array(VersionIDs, dtype=np.int64)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
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"text/plain": [
" userID TaskID VersionID \\\n",
"0 1 0 3 \n",
"1 1 0 5 \n",
"2 1 0 6 \n",
"3 1 0 7 \n",
"4 1 0 8 \n",
"\n",
" Blobs \n",
"0 [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0... \n",
"1 [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0... \n",
"2 [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 239,... \n",
"3 [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0... \n",
"4 [[[0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0... "
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_lstm_all = pd.DataFrame()\n",
"df_lstm_all[\"userID\"] = UserIDs\n",
"df_lstm_all[\"TaskID\"] = TaskIDs\n",
"df_lstm_all[\"VersionID\"] = VersionIDs\n",
"df_lstm_all[\"Blobs\"] = Blobs\n",
"df_lstm_all.Blobs = df_lstm_all.Blobs.map(np.array)\n",
"df_lstm_all.head()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
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"text/plain": [
" userID TaskID VersionID \\\n",
"0 1 0 3 \n",
"1 1 0 5 \n",
"2 1 0 6 \n",
"3 1 0 7 \n",
"4 1 0 8 \n",
"\n",
" Blobs BlobCount GestureOnly \n",
"0 [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0... 38 0 \n",
"1 [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0... 57 0 \n",
"2 [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 239,... 41 0 \n",
"3 [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0... 20 0 \n",
"4 [[[0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0... 41 0 "
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_lstm_all.head()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"df_lstm_all[\"Length\"] = df_lstm_all.Blobs.apply(lambda x: x.shape[0])"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
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"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df_lstm_all.Length.hist(range=(0,300), bins=30)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.02870949403069926"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(df_lstm_all[df_lstm_all.Length > 50]) / len(df_lstm_all)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 10554.0\n",
"mean 15.9\n",
"std 13.6\n",
"min 1.0\n",
"25% 8.0\n",
"50% 13.0\n",
"75% 19.0\n",
"max 301.0\n",
"Name: Length, dtype: float64"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_lstm_all.Length.describe().round(1)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"lengths = []\n",
"for index, row in df_lstm_all.iterrows():\n",
" lengths.append(row.Blobs.shape[0])\n",
"df_lstm_all[\"BlobCount\"] = lengths\n",
"# add a column for pure gesture recognition without finger/knuckle\n",
"df_lstm_all[\"GestureOnly\"] = df_lstm_all.TaskID % 17"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 10554.000000\n",
"mean 15.906576\n",
"std 13.605214\n",
"min 1.000000\n",
"25% 8.000000\n",
"50% 13.000000\n",
"75% 19.000000\n",
"max 301.000000\n",
"Name: BlobCount, dtype: float64"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_lstm_all.BlobCount.describe()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" </tr>\n",
" <tr>\n",
" <th>GestureOnly</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>11.421429</td>\n",
" <td>8.940925</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>13.618683</td>\n",
" <td>13.864708</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>8.852596</td>\n",
" <td>6.315931</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>8.672913</td>\n",
" <td>5.580500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>9.828767</td>\n",
" <td>6.793559</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>9.211221</td>\n",
" <td>6.861675</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>14.622496</td>\n",
" <td>8.338379</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>13.684524</td>\n",
" <td>13.263753</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>20.397129</td>\n",
" <td>12.916920</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>14.468599</td>\n",
" <td>10.042060</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>14.921440</td>\n",
" <td>8.909217</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>13.695578</td>\n",
" <td>7.661549</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>17.070853</td>\n",
" <td>11.755087</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>15.712219</td>\n",
" <td>10.545010</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>16.468354</td>\n",
" <td>9.826818</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>19.840836</td>\n",
" <td>11.239255</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>42.931624</td>\n",
" <td>21.024635</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" mean std\n",
"GestureOnly \n",
"0 11.421429 8.940925\n",
"1 13.618683 13.864708\n",
"2 8.852596 6.315931\n",
"3 8.672913 5.580500\n",
"4 9.828767 6.793559\n",
"5 9.211221 6.861675\n",
"6 14.622496 8.338379\n",
"7 13.684524 13.263753\n",
"8 20.397129 12.916920\n",
"9 14.468599 10.042060\n",
"10 14.921440 8.909217\n",
"11 13.695578 7.661549\n",
"12 17.070853 11.755087\n",
"13 15.712219 10.545010\n",
"14 16.468354 9.826818\n",
"15 19.840836 11.239255\n",
"16 42.931624 21.024635"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_lstm_all.groupby(df_lstm_all.GestureOnly)[\"BlobCount\"].agg([\"mean\", \"std\"])"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"before: 10554\n",
"after: 9193\n",
"ratio: 12.895584612469206\n"
]
}
],
"source": [
"# filter on gesture lengths\n",
"print(\"before: %s\" % len(df_lstm_all))\n",
"df_lstm = df_lstm_all[(df_lstm_all.BlobCount <= 100) & (df_lstm_all.BlobCount >= 5)]\n",
"print(\"after: %s\" % len(df_lstm))\n",
"print(\"ratio: %s\" % ((len(df_lstm_all) - len(df_lstm)) / len(df_lstm_all) * 100))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 9193.000000\n",
"mean 17.678995\n",
"std 12.059369\n",
"min 5.000000\n",
"25% 10.000000\n",
"50% 15.000000\n",
"75% 20.000000\n",
"max 97.000000\n",
"Name: BlobCount, dtype: float64"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_lstm.BlobCount.describe()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"lengths = []\n",
"for index, row in df_lstm.iterrows():\n",
" lengths.append(row.Blobs.shape[0])"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" \"\"\"Entry point for launching an IPython kernel.\n"
]
}
],
"source": [
"df_lstm[\"BlobCount\"] = lengths"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
""
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 9193.000000\n",
"mean 17.678995\n",
"std 12.059369\n",
"min 5.000000\n",
"25% 10.000000\n",
"50% 15.000000\n",
"75% 20.000000\n",
"max 97.000000\n",
"Name: BlobCount, dtype: float64"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_lstm.BlobCount.describe()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"def lerp(a, b, c=0.5):\n",
" return c * b + (1.0 - c) * a\n",
"\n",
"#Svens new Blob detection\n",
"def detect_blobs_return_old(image, task):\n",
" #image = e.Image\n",
" large = np.ones((29,17), dtype=np.uint8)\n",
" large[1:28,1:16] = np.copy(image)\n",
" temp, thresh = cv2.threshold(cv2.bitwise_not(large), 205, 255, cv2.THRESH_BINARY)\n",
" contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n",
" contours = [a for a in contours if cv2.contourArea(a) > 8 and cv2.contourArea(a) < 255]\n",
" lstBlob = []\n",
" lstCenter = []\n",
" lstMin = []\n",
" lstMax = []\n",
" count = 0\n",
" contours.sort(key=lambda a: cv2.contourArea(a))\n",
" if len(contours) > 0:\n",
" # if two finger or knuckle\n",
" cont_count = 2 if task in [1, 6, 7, 18, 23, 24] and len(contours) > 1 else 1\n",
" for i in range(1, cont_count + 1):\n",
" max_contour = contours[-1 * i]\n",
" xmax, ymax = np.max(max_contour.reshape(len(max_contour),2), axis=0)\n",
" xmin, ymin = np.min(max_contour.reshape(len(max_contour),2), axis=0)\n",
" M = cv2.moments(max_contour)\n",
" cX = int(M[\"m10\"] / M[\"m00\"]) - 1\n",
" cY = int(M[\"m01\"] / M[\"m00\"]) - 1\n",
" #croped_im = np.zeros((27,15))\n",
" blob = large[max(ymin - 1, 0):min(ymax + 1, large.shape[0]),max(xmin - 1, 0):min(xmax + 1, large.shape[1])]\n",
" #croped_im[0:blob.shape[0],0:blob.shape[1]] = blob\n",
" #return (1, [croped_im])\n",
" lstBlob.append(blob)\n",
" lstCenter.append((cY, cX))\n",
" lstMin.append(xmax-xmin)\n",
" lstMax.append(ymax-ymin)\n",
" count = count + 1\n",
" return (count, lstBlob, lstCenter)\n",
" else:\n",
" return (0, [np.zeros((29, 19))], 0, 0)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"# descides whether or not a normalization is neccessary\n",
"# and cuts or adds zeros\n",
"def normalize_blobs(blobs, new_len=50):\n",
" new_count = new_len - blobs.shape[0]\n",
" if new_count == 0:\n",
" return blobs\n",
" elif new_count > 0:\n",
" temp = np.array([np.zeros((27, 15))] * new_count)\n",
" return np.append(blobs, temp, axis=0)\n",
" else:\n",
" return blobs[0:new_len]"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 3.24 s, sys: 556 ms, total: 3.8 s\n",
"Wall time: 3.8 s\n"
]
}
],
"source": [
"%%time\n",
"# normalizes all image sequences\n",
"df_lstm_norm = df_lstm.copy(deep=True)\n",
"new_blobs = []\n",
"for index, row in df_lstm.iterrows():\n",
" new_blobs.append(normalize_blobs(row.Blobs, 50))\n",
"\n",
"df_lstm_norm.Blobs = new_blobs\n",
"\n",
"lengths = []\n",
"for index, row in df_lstm_norm.iterrows():\n",
" lengths.append(row.Blobs.shape[0])\n",
"df_lstm_norm[\"BlobCount\"] = lengths"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 9193.0\n",
"mean 50.0\n",
"std 0.0\n",
"min 50.0\n",
"25% 50.0\n",
"50% 50.0\n",
"75% 50.0\n",
"max 50.0\n",
"Name: BlobCount, dtype: float64"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_lstm_norm.BlobCount.describe()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"df_lstm_norm.to_pickle(\"DataStudyCollection/df_lstm_norm50.pkl\")"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>userID</th>\n",
" <th>TaskID</th>\n",
" <th>VersionID</th>\n",
" <th>Blobs</th>\n",
" <th>BlobCount</th>\n",
" <th>GestureOnly</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>[[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0...</td>\n",
" <td>50</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>[[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...</td>\n",
" <td>50</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>[[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0...</td>\n",
" <td>50</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>[[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0...</td>\n",
" <td>50</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>[[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0...</td>\n",
" <td>50</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" userID TaskID VersionID \\\n",
"0 1 0 3 \n",
"1 1 0 5 \n",
"2 1 0 6 \n",
"3 1 0 7 \n",
"4 1 0 8 \n",
"\n",
" Blobs BlobCount GestureOnly \n",
"0 [[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0... 50 0 \n",
"1 [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0... 50 0 \n",
"2 [[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0... 50 0 \n",
"3 [[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0... 50 0 \n",
"4 [[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0... 50 0 "
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_lstm_norm.head()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"finished\n"
]
}
],
"source": [
"print(\"finished\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.6.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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