1633 lines
94 KiB
Plaintext
1633 lines
94 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd"
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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": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"from augmentations.augment import __preprocess_row_sign"
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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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"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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"0 path\n",
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"1 participant_id\n",
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"2 sequence_id\n",
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"3 sign\n",
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"4 labels\n",
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"5 nose_X\n",
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"6 nose_Y\n",
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"7 leftEye_X\n",
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"8 leftEye_Y\n",
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"9 rightEye_X\n",
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"10 rightEye_Y\n",
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"11 leftEar_X\n",
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"12 leftEar_Y\n",
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"13 rightEar_X\n",
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"14 rightEar_Y\n",
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"15 leftShoulder_X\n",
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"16 leftShoulder_Y\n",
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"17 rightShoulder_X\n",
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"18 rightShoulder_Y\n",
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"19 leftElbow_X\n",
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"20 leftElbow_Y\n",
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"21 rightElbow_X\n",
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"22 rightElbow_Y\n",
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"23 leftWrist_X\n",
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"24 leftWrist_Y\n",
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"25 rightWrist_X\n",
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"26 rightWrist_Y\n",
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"27 wrist_left_X\n",
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"28 wrist_left_Y\n",
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"29 thumbCMC_left_X\n",
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"30 thumbCMC_left_Y\n",
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"31 thumbMP_left_X\n",
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"32 thumbMP_left_Y\n",
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"33 thumbIP_left_X\n",
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"34 thumbIP_left_Y\n",
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"35 thumbTip_left_X\n",
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"36 thumbTip_left_Y\n",
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"37 indexMCP_left_X\n",
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"38 indexMCP_left_Y\n",
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"39 indexPIP_left_X\n",
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"40 indexPIP_left_Y\n",
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"41 indexDIP_left_X\n",
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"42 indexDIP_left_Y\n",
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"43 indexTip_left_X\n",
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"44 indexTip_left_Y\n",
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"45 middleMCP_left_X\n",
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"46 middleMCP_left_Y\n",
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"47 middlePIP_left_X\n",
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"48 middlePIP_left_Y\n",
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"49 middleDIP_left_X\n",
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"50 middleDIP_left_Y\n",
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"51 middleTip_left_X\n",
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"52 middleTip_left_Y\n",
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"53 ringMCP_left_X\n",
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"54 ringMCP_left_Y\n",
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"55 ringPIP_left_X\n",
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"56 ringPIP_left_Y\n",
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"57 ringDIP_left_X\n",
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"58 ringDIP_left_Y\n",
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"59 ringTip_left_X\n",
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"60 ringTip_left_Y\n",
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"61 littleMCP_left_X\n",
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"62 littleMCP_left_Y\n",
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"63 littlePIP_left_X\n",
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"64 littlePIP_left_Y\n",
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"65 littleDIP_left_X\n",
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"66 littleDIP_left_Y\n",
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"67 littleTip_left_X\n",
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"68 littleTip_left_Y\n",
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"69 wrist_right_X\n",
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"70 wrist_right_Y\n",
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"71 thumbCMC_right_X\n",
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"72 thumbCMC_right_Y\n",
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"73 thumbMP_right_X\n",
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"74 thumbMP_right_Y\n",
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"75 thumbIP_right_X\n",
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"76 thumbIP_right_Y\n",
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"77 thumbTip_right_X\n",
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"78 thumbTip_right_Y\n",
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"79 indexMCP_right_X\n",
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"80 indexMCP_right_Y\n",
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"81 indexPIP_right_X\n",
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"82 indexPIP_right_Y\n",
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"83 indexDIP_right_X\n",
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"84 indexDIP_right_Y\n",
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"85 indexTip_right_X\n",
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"86 indexTip_right_Y\n",
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"87 middleMCP_right_X\n",
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"88 middleMCP_right_Y\n",
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"89 middlePIP_right_X\n",
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"90 middlePIP_right_Y\n",
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"91 middleDIP_right_X\n",
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"92 middleDIP_right_Y\n",
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"93 middleTip_right_X\n",
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"94 middleTip_right_Y\n",
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"95 ringMCP_right_X\n",
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"96 ringMCP_right_Y\n",
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"97 ringPIP_right_X\n",
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"98 ringPIP_right_Y\n",
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"99 ringDIP_right_X\n",
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"100 ringDIP_right_Y\n",
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"101 ringTip_right_X\n",
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"102 ringTip_right_Y\n",
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"103 littleMCP_right_X\n",
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"104 littleMCP_right_Y\n",
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"105 littlePIP_right_X\n",
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"106 littlePIP_right_Y\n",
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"107 littleDIP_right_X\n",
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"108 littleDIP_right_Y\n",
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"109 littleTip_right_X\n",
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"110 littleTip_right_Y\n",
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"111 neck_X\n",
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"112 neck_Y\n"
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]
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}
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],
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"source": [
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"df = pd.read_csv('data/processed/spoter_train.csv')\n",
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"for i, e in enumerate(df.columns):\n",
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" print(i, e)"
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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": 21,
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"metadata": {},
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"outputs": [],
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"source": [
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"# dataset = 'data/processed/spoter_train.csv'\n",
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"\n",
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"def plot(dataset):\n",
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" df = pd.read_csv(dataset)\n",
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" # get columns 0 - 26\n",
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" # df = df.iloc[:, 5:27]\n",
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"\n",
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"\n",
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" # df = df.iloc[:, 27:69]\n",
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" # df = df.iloc[:, 69:]\n",
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"\n",
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" if 'wlasl' in dataset:\n",
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" df = df.iloc[:, :108]\n",
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"\n",
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" else:\n",
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" df = df.iloc[:, 5:]\n",
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"\n",
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"\n",
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" # get first row\n",
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" row = df.iloc[0]\n",
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"\n",
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" # use matplotlib to create a scatter plot (the columns are X, Y, X, Y, X, Y, X, Y)\n",
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" import matplotlib.pyplot as plt\n",
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" import ast\n",
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"\n",
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" def __process_row(coords):\n",
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" coords = coords.tolist()\n",
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"\n",
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" new_coords = [] # string to list\n",
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"\n",
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" for x in coords:\n",
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" new_coords.append(ast.literal_eval(x))\n",
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"\n",
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" coords = [x[2] for x in new_coords]\n",
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"\n",
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" return [float(x) for x in coords]\n",
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"\n",
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"\n",
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" # extract X and Y coordinates from the row\n",
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" x_coords = row.iloc[::2] # columns 0, 2, 4, 6, ...\n",
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" y_coords = row.iloc[1::2] # columns 1, 3, 5, 7, ...\n",
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"\n",
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" x_coords = __process_row(x_coords)\n",
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" y_coords = __process_row(y_coords)\n",
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"\n",
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" # create a scatter plot\n",
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" plt.scatter(x_coords, y_coords)\n",
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"\n",
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" # add axis labels and a title\n",
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" plt.xlabel('X')\n",
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" plt.ylabel('Y')\n",
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" plt.title('Scatter plot of X and Y coordinates')\n",
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"\n",
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" # show the plot\n",
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" plt.show()\n"
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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": 22,
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"metadata": {},
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"outputs": [
|
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{
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"data": {
|
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"plot('data/wlasl/WLASL100_train.csv')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 23,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
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"image/png": 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"text/plain": [
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"<Figure size 640x480 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"plot('data/processed/spoter_train.csv')"
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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": 11,
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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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"[18 19 20 21 22 23 24 25 26 27 28 29 30]\n",
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" frame row_id type landmark_index x y z\n",
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"3801 25 25-face-0 face 0 0.498506 0.494856 -0.018860\n",
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"3802 25 25-face-1 face 1 0.506121 0.470006 -0.056474\n",
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"3803 25 25-face-2 face 2 0.505620 0.475341 -0.025482\n",
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"3804 25 25-face-3 face 3 0.497120 0.439673 -0.049148\n",
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"3805 25 25-face-4 face 4 0.506552 0.461902 -0.062083\n"
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]
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},
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{
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"data": {
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"application/vnd.plotly.v1+json": {
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"config": {
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"plotlyServerURL": "https://plot.ly"
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"data": [
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[
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{\"responsive\": true} ).then(function(){\n",
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" \n",
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"var gd = document.getElementById('8f1cccf1-52ac-4cf0-8409-1c18e66d17c3');\n",
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"var x = new MutationObserver(function (mutations, observer) {{\n",
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" var display = window.getComputedStyle(gd).display;\n",
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" if (!display || display === 'none') {{\n",
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" console.log([gd, 'removed!']);\n",
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" Plotly.purge(gd);\n",
|
|
" observer.disconnect();\n",
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" }}\n",
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"}});\n",
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"\n",
|
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"// Listen for the removal of the full notebook cells\n",
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"var notebookContainer = gd.closest('#notebook-container');\n",
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"if (notebookContainer) {{\n",
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" x.observe(notebookContainer, {childList: true});\n",
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"}}\n",
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"\n",
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"// Listen for the clearing of the current output cell\n",
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"var outputEl = gd.closest('.output');\n",
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"if (outputEl) {{\n",
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" x.observe(outputEl, {childList: true});\n",
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"}}\n",
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"\n",
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" }) }; }); </script> </div>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
|
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"source": [
|
|
"# read parquet file at data/train_landmark_files/2044/635217.parquet\n",
|
|
"import pandas as pd\n",
|
|
"import matplotlib.pyplot as plt\n",
|
|
"\n",
|
|
"df = pd.read_parquet('data/train_landmark_files/37779/254554888.parquet')\n",
|
|
"\n",
|
|
"# print all unique frame numbers\n",
|
|
"print(df['frame'].unique())\n",
|
|
"\n",
|
|
"# filter where type is pose\n",
|
|
"df = df[df['frame'] == 25]\n",
|
|
"\n",
|
|
"print(df.head())\n",
|
|
"\n",
|
|
"mapping = {\n",
|
|
" 'pose_0': 'nose',\n",
|
|
" 'pose_1': 'leftEye',\n",
|
|
" 'pose_4': 'rightEye',\n",
|
|
" 'pose_7': 'leftEar',\n",
|
|
" 'pose_8': 'rightEar',\n",
|
|
" 'pose_11': 'leftShoulder',\n",
|
|
" 'pose_12': 'rightShoulder',\n",
|
|
" 'pose_13': 'leftElbow',\n",
|
|
" 'pose_14': 'rightElbow',\n",
|
|
" 'pose_15': 'leftWrist',\n",
|
|
" 'pose_16': 'rightWrist',\n",
|
|
"\n",
|
|
" 'left_hand_0': 'wrist_left',\n",
|
|
" 'left_hand_1': 'thumbCMC_left',\n",
|
|
" 'left_hand_2': 'thumbMP_left',\n",
|
|
" 'left_hand_3': 'thumbIP_left',\n",
|
|
" 'left_hand_4': 'thumbTip_left',\n",
|
|
" 'left_hand_5': 'indexMCP_left',\n",
|
|
" 'left_hand_6': 'indexPIP_left',\n",
|
|
" 'left_hand_7': 'indexDIP_left',\n",
|
|
" 'left_hand_8': 'indexTip_left',\n",
|
|
" 'left_hand_9': 'middleMCP_left',\n",
|
|
" 'left_hand_10': 'middlePIP_left',\n",
|
|
" 'left_hand_11': 'middleDIP_left',\n",
|
|
" 'left_hand_12': 'middleTip_left',\n",
|
|
" 'left_hand_13': 'ringMCP_left',\n",
|
|
" 'left_hand_14': 'ringPIP_left',\n",
|
|
" 'left_hand_15': 'ringDIP_left',\n",
|
|
" 'left_hand_16': 'ringTip_left',\n",
|
|
" 'left_hand_17': 'littleMCP_left',\n",
|
|
" 'left_hand_18': 'littlePIP_left',\n",
|
|
" 'left_hand_19': 'littleDIP_left',\n",
|
|
" 'left_hand_20': 'littleTip_left',\n",
|
|
"\n",
|
|
" 'right_hand_0': 'wrist_right',\n",
|
|
" 'right_hand_1': 'thumbCMC_right',\n",
|
|
" 'right_hand_2': 'thumbMP_right',\n",
|
|
" 'right_hand_3': 'thumbIP_right',\n",
|
|
" 'right_hand_4': 'thumbTip_right',\n",
|
|
" 'right_hand_5': 'indexMCP_right',\n",
|
|
" 'right_hand_6': 'indexPIP_right',\n",
|
|
" 'right_hand_7': 'indexDIP_right',\n",
|
|
" 'right_hand_8': 'indexTip_right',\n",
|
|
" 'right_hand_9': 'middleMCP_right',\n",
|
|
" 'right_hand_10': 'middlePIP_right',\n",
|
|
" 'right_hand_11': 'middleDIP_right',\n",
|
|
" 'right_hand_12': 'middleTip_right',\n",
|
|
" 'right_hand_13': 'ringMCP_right',\n",
|
|
" 'right_hand_14': 'ringPIP_right',\n",
|
|
" 'right_hand_15': 'ringDIP_right',\n",
|
|
" 'right_hand_16': 'ringTip_right',\n",
|
|
" 'right_hand_17': 'littleMCP_right',\n",
|
|
" 'right_hand_18': 'littlePIP_right',\n",
|
|
" 'right_hand_19': 'littleDIP_right',\n",
|
|
" 'right_hand_20': 'littleTip_right',\n",
|
|
" }\n",
|
|
"\n",
|
|
"# scatter plot and when hovering over the point, show the frame number\n",
|
|
"import plotly.express as px\n",
|
|
"\n",
|
|
"# combine type and landmark index\n",
|
|
"df['landmark_id'] = df['type'] + '_' + df['landmark_index'].astype(str)\n",
|
|
"# only keep rows where landmark_id is in the mapping\n",
|
|
"df = df[df['landmark_id'].isin(mapping.keys())]\n",
|
|
"df['name'] = df['landmark_id'].apply(lambda x: mapping[x])\n",
|
|
"\n",
|
|
"# flip vertically\n",
|
|
"df['y'] = 1 - df['y']\n",
|
|
"\n",
|
|
"# keep aspect ratio\n",
|
|
"\n",
|
|
"# scatter with px and set color of points based on the type\n",
|
|
"fig = px.scatter(df, x='x', y='y', color='type', hover_data=['name'])\n",
|
|
"\n",
|
|
"fig.update_xaxes(range=[0, 1], constrain='domain')\n",
|
|
"fig.update_yaxes(scaleanchor='x', scaleratio=1, range=[0, 1])\n",
|
|
"\n",
|
|
"# show the plot\n",
|
|
"fig.show()\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# from training dataset\n",
|
|
"import pandas as pd\n",
|
|
"import matplotlib.pyplot as plt\n",
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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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": "3.8.10"
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},
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"orig_nbformat": 4
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"nbformat": 4,
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"nbformat_minor": 2
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}
|