280 lines
1.9 MiB
Text
280 lines
1.9 MiB
Text
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Visualise tracking data\n",
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"\n",
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"This notebook is an adapted version of `process.py` as it is included in the Trajectron++ package. It can be used to \n",
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"\n",
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"* Visualise the recorded trajectories, which are normally parsed to a Trajectron++ Node"
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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": 64,
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"metadata": {},
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"outputs": [],
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"source": [
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"import sys\n",
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"import os\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import dill\n",
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"import tqdm\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"#sys.path.append(\"../../\")\n",
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"from trajectron.environment import Environment, Scene, Node\n",
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"from trajectron.environment import derivative_of\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": 65,
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"desired_max_time = 100\n",
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"pred_indices = [2, 3]\n",
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"state_dim = 6\n",
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"frame_diff = 10\n",
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"desired_frame_diff = 1\n",
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"dt = 0.1\n",
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"\n",
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"standardization = {\n",
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" 'PEDESTRIAN': {\n",
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" 'position': {\n",
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" 'x': {'mean': 0, 'std': 1},\n",
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" 'y': {'mean': 0, 'std': 1}\n",
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" },\n",
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" 'velocity': {\n",
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" 'x': {'mean': 0, 'std': 2},\n",
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" 'y': {'mean': 0, 'std': 2}\n",
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" },\n",
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" 'acceleration': {\n",
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" 'x': {'mean': 0, 'std': 1},\n",
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" 'y': {'mean': 0, 'std': 1}\n",
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" }\n",
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" }\n",
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"}\n",
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"\n",
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"data_columns = pd.MultiIndex.from_product([['position', 'velocity', 'acceleration'], ['x', 'y']])\n",
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"\n",
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"\n",
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"# desired_source = 'EXPERIMENTS/raw/hof-meter-maskrcnn2'\n",
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"# desired_source = 'EXPERIMENTS/20240424-hof-meter-maskrcnn2'\n",
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"# desired_source = 'EXPERIMENTS/20240426-hof-yolo'\n",
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"desired_source = 'EXPERIMENTS/raw/hof2'\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": 66,
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"metadata": {},
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"outputs": [],
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"source": [
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"# run some tests\n",
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"\n",
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"if not os.path.exists(desired_source):\n",
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" raise FileNotFoundError(f\"Path does not exist {desired_source=}\")"
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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": 67,
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"metadata": {},
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"outputs": [],
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"source": [
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"from matplotlib.axes import Axes\n",
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"from trap.frame_emitter import DetectionState\n",
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"\n",
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"def parse_txt(data_path, dt, axes: Axes, axes2: Axes) -> dict[str, pd.DataFrame]:\n",
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" skipped_for_error = 0\n",
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" created = 0\n",
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"\n",
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" data = pd.read_csv(data_path, sep='\\t', index_col=False, header=None)\n",
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" data.columns = ['frame_id', 'track_id', 'l','t', 'w','h', 'pos_x', 'pos_y', 'state']\n",
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" # data['frame_id'] = pd.to_numeric(data['frame_id'], downcast='integer')\n",
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" data['track_id'] = pd.to_numeric(data['track_id'], downcast='integer')\n",
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"\n",
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" data['frame_id'] = data['frame_id'] // 10\n",
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" data['frame_id'] -= data['frame_id'].min()\n",
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"\n",
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" data['node_type'] = 'PEDESTRIAN'\n",
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" data['node_id'] = data['track_id'].astype(str)\n",
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" data['state'] = data['state'].apply(lambda x: (eval(x) if type(x) is str else DetectionState(x)).value)\n",
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" data.sort_values('frame_id', inplace=True)\n",
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"\n",
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" # Mean Position\n",
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"\n",
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" print(\"Means: x:\", data['pos_x'].mean(), \"y:\", data['pos_y'].mean())\n",
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" data['pos_x'] = data['pos_x'] - data['pos_x'].mean()\n",
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" data['pos_y'] = data['pos_y'] - data['pos_y'].mean()\n",
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"\n",
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" max_timesteps = data['frame_id'].max()\n",
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"\n",
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" nodes = {}\n",
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"\n",
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" # only keep tentative and confirmed detections\n",
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" # print(data)\n",
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" data = data.loc[data['state'] != DetectionState.Lost.value]\n",
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"\n",
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" print(data[['track_id', 'node_id', 'state']])\n",
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"\n",
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" for node_id in tqdm.tqdm(pd.unique(data['node_id'])):\n",
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" node_df = data[data['node_id'] == node_id]\n",
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" if not np.all(np.diff(node_df['frame_id']) == 1):\n",
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" # print(f\"Interval in {node_id} not always 1\")\n",
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" # print(node_df['frame_id'])\n",
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" # print(np.diff(node_df['frame_id']) != 1)\n",
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" # mask=np.append(False, np.diff(node_df['frame_id']) != 1)\n",
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" # print(node_df[mask]['frame_id'])\n",
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" skipped_for_error += 1\n",
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" continue\n",
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"\n",
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"\n",
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" node_values = node_df[['pos_x', 'pos_y']].values\n",
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"\n",
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" if node_values.shape[0] <= 10:\n",
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" continue\n",
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"\n",
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" new_first_idx = node_df['frame_id'].iloc[0]\n",
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"\n",
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" x = node_values[:, 0]\n",
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" y = -node_values[:, 1]\n",
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" vx = derivative_of(x, dt)\n",
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" vy = derivative_of(y, dt)\n",
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" ax = derivative_of(vx, dt)\n",
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" ay = derivative_of(vy, dt)\n",
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"\n",
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" \n",
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" axes.plot(x, y, alpha=.3)\n",
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" axes.scatter(x, y, marker='x', alpha=.3)\n",
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"\n",
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" nv = node_df[['l','t', 'w', 'h']].values\n",
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" x = nv[:, 0] + .5*nv[:, 2]\n",
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" y = nv[:, 1] + nv[:, 3]\n",
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" axes2.plot(x, y, alpha=.3)\n",
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" axes2.scatter(x, y, marker='x', alpha=.3)\n",
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"\n",
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" nodes[node_id] = [x,y]\n",
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" # data_dict = {'node_id': node_id, ('position', 'x'): x,\n",
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" # ('position', 'y'): y,\n",
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" # ('velocity', 'x'): vx,\n",
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" # ('velocity', 'y'): vy,\n",
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" # ('acceleration', 'x'): ax,\n",
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" # ('acceleration', 'y'): ay}\n",
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" \n",
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" # node_data = pd.DataFrame(data_dict, columns=data_columns)\n",
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" # nodes[node_id] = node_data\n",
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" return nodes"
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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": 68,
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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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"Means: x: 1281.0115073996574 y: 801.2940430516817\n",
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" track_id node_id state\n",
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"63577 1300 1300 2\n",
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"87646 1853 1853 2\n",
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"87645 1852 1852 2\n",
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"87644 1851 1851 2\n",
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"129133 2360 2360 2\n",
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"... ... ... ...\n",
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"38247 742 742 2\n",
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"38248 742 742 2\n",
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"38249 742 742 2\n",
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"38250 742 742 2\n",
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"38251 742 742 2\n",
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"\n",
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"[157580 rows x 3 columns]\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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"100%|██████████| 1719/1719 [00:12<00:00, 138.23it/s]\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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"178"
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]
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},
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"execution_count": 68,
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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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"data": {
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 2000x1600 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"fig = plt.figure(figsize=(20,16))\n",
|
||
|
|
"ax1, ax2 = fig.subplots(2)\n",
|
||
|
|
"\n",
|
||
|
|
"import cv2\n",
|
||
|
|
"# ax = fig1.gca()\n",
|
||
|
|
"# ax2 = fig2.gca()\n",
|
||
|
|
"im = cv2.imread(\"../DATASETS/hof2/output.png\")\n",
|
||
|
|
"ax2.imshow(im)\n",
|
||
|
|
"ax1.set_aspect(1)\n",
|
||
|
|
"ax2.set_aspect(1)\n",
|
||
|
|
"nodes = []\n",
|
||
|
|
"for data_class in ['train', 'val', 'test']:\n",
|
||
|
|
" target_dir = os.path.join(desired_source, data_class)\n",
|
||
|
|
" for subdir, dirs, files in os.walk(target_dir):\n",
|
||
|
|
" for file in files:\n",
|
||
|
|
" if file.endswith('.txt'):\n",
|
||
|
|
" input_data_dict = dict()\n",
|
||
|
|
" full_data_path = os.path.join(subdir, file)\n",
|
||
|
|
" nodes.extend(parse_txt(full_data_path, dt, ax1, ax2))\n",
|
||
|
|
" break\n",
|
||
|
|
" break\n",
|
||
|
|
" break\n",
|
||
|
|
"fig.show()\n",
|
||
|
|
"len(nodes)"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": ".venv",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.10.4"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 2
|
||
|
|
}
|