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Merge pull request #129 from inverted-ai/develop
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‎README.md

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[pypi-link]: https://pypi.org/project/invertedai/
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[colab-badge]: https://colab.research.google.com/assets/colab-badge.svg
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[colab-link]: https://colab.research.google.com/github/inverted-ai/invertedai/blob/develop/examples/IAI_demo.ipynb
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[rest-link]: https://app.swaggerhub.com/apis/swaggerhub59/Inverted-AI
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[rest-link]: https://app.swaggerhub.com/apis-docs/InvertedAI/InvertedAI
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[examples-link]: https://github.com/inverted-ai/invertedai/tree/master/examples
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[![Documentation Status](https://readthedocs.org/projects/inverted-ai/badge/?version=latest)](https://inverted-ai.readthedocs.io/en/latest/?badge=latest)

‎examples/blame_example.ipynb

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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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"id": "uYrB4Yk3aizw"
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},
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"source": [
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"<img src=\"https://raw.githubusercontent.com/inverted-ai/invertedai/master/docs/images/banner-small.png\" alt=\"InvertedAI\" width=\"200\"/>\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": null,
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"metadata": {
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"id": "b7l_n8sULmAX"
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},
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"outputs": [],
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"source": [
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"import IPython\n",
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"from IPython.display import display, Image, clear_output\n",
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"from ipywidgets import interact\n",
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"from IPython.utils import io\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"import imageio\n",
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"import numpy as np\n",
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"import cv2\n",
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"import invertedai as iai\n",
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"\n",
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"from shapely.geometry import Polygon\n",
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"from shapely.errors import GEOSException\n",
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"\n",
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"from dataclasses import dataclass\n",
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"from typing import Tuple"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "SFu9oOcDIQGs"
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},
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"outputs": [],
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"source": [
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"# API key:\n",
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"iai.add_apikey(\"\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "PiySgisPG6mG"
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},
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"outputs": [],
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"source": [
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"# pick a location (4 way, signalized intgersection)\n",
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"location = \"iai:drake_street_and_pacific_blvd\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "kT3m0-zoNdvW"
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},
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"outputs": [],
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"source": [
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"location_info_response = iai.location_info(location=location)\n",
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"rendered_static_map = location_info_response.birdview_image.decode()\n",
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"scene_plotter = iai.utils.ScenePlotter(rendered_static_map,\n",
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" location_info_response.map_fov,\n",
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" (location_info_response.map_center.x, location_info_response.map_center.y),\n",
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" location_info_response.static_actors)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "L0ufxMO8NdvX",
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"@dataclass\n",
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"class LogCollision:\n",
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" collision_agents: Tuple[int, int]\n",
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" start_time: int \n",
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" end_time: int\n",
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"\n",
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"def transform_all_agent_vertices_into_world_frame(agent_states,agent_attributes):\n",
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" \"\"\"\n",
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" Transform the vertices of all agents into points within the world frame of the map environment\n",
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" Args:\n",
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" agent_states: List[AgentState] #List of all current agent states including x and y coordinates and angle.\n",
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" agent_attributes: List[AgentAttributes] #List of static attributes of all agents including agent length and width.\n",
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" Returns:\n",
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" List[Polygon] #List of Polygon data types containing a list of vertices for each agent\n",
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" \"\"\"\n",
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" polygons = [None]*len(agent_states)\n",
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" for i, (state, attributes) in enumerate(zip(agent_states,agent_attributes)):\n",
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" dx = attributes.length/2\n",
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" dy = attributes.width/2\n",
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"\n",
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" vehicle_origin = np.array([state.center.x, state.center.y])\n",
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"\n",
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" vehicle_orientation = state.orientation\n",
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" c, s = np.cos(vehicle_orientation), np.sin(vehicle_orientation)\n",
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"\n",
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" rotation_matrix = np.array([[c, s],\n",
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" [-s, c]])\n",
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" stacked_vertices = np.array([[dx,dy],[dx,-dy],[-dx,-dy],[-dx,dy]]) #Formatted in a continuous sequence\n",
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" rotated_vertices = np.matmul(stacked_vertices,rotation_matrix)\n",
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" \n",
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" polygons[i] = vehicle_origin + rotated_vertices\n",
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"\n",
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" return [Polygon(p) for p in polygons]\n",
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"\n",
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"def check_agent_pairwise_intersections(polygons):\n",
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" \"\"\"\n",
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" Check all pairs of polygons in a list for intersections in their area.\n",
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" Args:\n",
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" polygons: List[Polygon] #List of polygons representing agents in an environment\n",
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" Returns:\n",
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" List[Tuple[int,int]] #List of agent ID pair tuples indicating collisions\n",
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" \"\"\"\n",
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" \n",
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" detected_overlap_agent_pairs = []\n",
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" num_agents = len(polygons)\n",
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" for j in range(num_agents):\n",
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" for k in range(j+1,num_agents):\n",
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" try:\n",
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" if polygons[j].intersection(polygons[k]).area:\n",
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" detected_overlap_agent_pairs.append((j,k))\n",
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" except GEOSException as e:\n",
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" print(f\"Collision candidates {j} and {k} failed with error {e}.\")\n",
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" pass\n",
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" \n",
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" return detected_overlap_agent_pairs\n",
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" \n",
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"def compute_pairwise_collisions(agent_states_history,agent_attributes):\n",
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" \"\"\"\n",
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" Use polygon intersections to check each agent combination whether there is a collision.\n",
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" Args:\n",
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" agent_states: List[List[AgentState]] #At all time steps, list of all current agent states including x and y coordinates and angle.\n",
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" agent_attributes: List[AgentAttributes] #List of static attributes of all agents including agent length and width.\n",
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" Returns:\n",
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" List[LogCollision] #List of collisions logs containing information about the colliding agent pairs IDs and the time period of the collision\n",
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" \"\"\"\n",
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" \n",
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" collisions_ongoing = {}\n",
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" collisions_all = []\n",
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" \n",
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" for t, agent_states in enumerate(agent_states_history):\n",
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" if len(agent_states) != len(agent_attributes):\n",
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" raise Exception(\"Incorrect number of agents or agent attributes.\")\n",
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"\n",
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" polygons = transform_all_agent_vertices_into_world_frame(agent_states,agent_attributes)\n",
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" detected_agent_pairs = check_agent_pairwise_intersections(polygons) \n",
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" \n",
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" for agent_tuple in detected_agent_pairs:\n",
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" if agent_tuple not in collisions_ongoing:\n",
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" collisions_ongoing[agent_tuple] = LogCollision(\n",
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" collision_agents=agent_tuple,\n",
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" start_time=t,\n",
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" end_time=None\n",
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" )\n",
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" untracked_agent_pairs = []\n",
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" for agent_tuple, collision in collisions_ongoing.items():\n",
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" if agent_tuple not in detected_agent_pairs:\n",
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" #The previous time step is the last in which the collision was observed\n",
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" collisions_ongoing[agent_tuple].end_time = t-1\n",
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" elif t >= SIMULATION_LENGTH-1:\n",
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" #The collision has not necessarily ended at this time step but it is the last time step it was observed to occur\n",
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" collisions_ongoing[agent_tuple].end_time = t\n",
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" \n",
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" if collisions_ongoing[agent_tuple].end_time is not None:\n",
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" collisions_all.append(collisions_ongoing[agent_tuple])\n",
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" untracked_agent_pairs.append(agent_tuple)\n",
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" \n",
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" collisions_ongoing = {k:v for k, v in collisions_ongoing.items() if not k in untracked_agent_pairs}\n",
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" \n",
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" return collisions_all\n",
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"\n",
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"# Simulate with `initialize`, `drive` and `light` until there are collisions.\n",
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"for _ in range(20): #Attempt 20 simulations looking for a collision\n",
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" light_response = iai.light(location=location)\n",
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"\n",
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" response = iai.initialize(\n",
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" location=location,\n",
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" agent_count=15,\n",
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" get_birdview=True,\n",
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" traffic_light_state_history=[light_response.traffic_lights_states]\n",
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" )\n",
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" agent_attributes = response.agent_attributes\n",
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" scene_plotter.initialize_recording(\n",
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" response.agent_states,\n",
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" agent_attributes=agent_attributes,\n",
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" traffic_light_states=light_response.traffic_lights_states\n",
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" )\n",
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"\n",
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" agent_state_history = []\n",
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" traffic_light_state_history = []\n",
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"\n",
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" # 10-second scene\n",
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" SIMULATION_LENGTH = 100\n",
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" for t in range(SIMULATION_LENGTH):\n",
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" light_response = iai.light(\n",
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" location=location, \n",
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" recurrent_states=light_response.recurrent_states\n",
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" )\n",
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" response = iai.drive(\n",
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" location=location,\n",
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" agent_attributes=agent_attributes,\n",
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" agent_states=response.agent_states,\n",
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" recurrent_states=response.recurrent_states,\n",
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" get_birdview=False,\n",
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" traffic_lights_states=light_response.traffic_lights_states,\n",
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" get_infractions=True,\n",
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" random_seed=1\n",
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" )\n",
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" scene_plotter.record_step(\n",
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" response.agent_states, \n",
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" traffic_light_states=light_response.traffic_lights_states\n",
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" )\n",
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" agent_state_history.append(response.agent_states)\n",
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" traffic_light_state_history.append(light_response.traffic_lights_states)\n",
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" \n",
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" print(f\"Attempted collision simulation number {_} iteration number {t}.\")\n",
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" clear_output(wait=True)\n",
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" \n",
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" collisions = compute_pairwise_collisions(agent_state_history,agent_attributes)\n",
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" if collisions: \n",
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" #If a collision is detected, cease generating more simulations\n",
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" break\n",
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"\n",
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"print(collisions)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "YQ4dXaKQNdvZ"
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},
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"outputs": [],
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"source": [
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"blame_responses = []\n",
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"all_collision_agents = []\n",
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"for collision_data in collisions:\n",
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" all_collision_agents.extend(list(collision_data.collision_agents))\n",
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" blame_response = iai.blame(\n",
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" location=location,\n",
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" colliding_agents=collision_data.collision_agents,\n",
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" agent_state_history=agent_state_history[:collision_data.start_time],\n",
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" traffic_light_state_history=traffic_light_state_history[:collision_data.start_time],\n",
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" agent_attributes=agent_attributes,\n",
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" get_reasons=True,\n",
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" get_confidence_score=True,\n",
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" get_birdviews=False\n",
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" )\n",
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" print(blame_response.agents_at_fault)\n",
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" blame_responses.append(blame_response)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "jRRaiLInNdvZ"
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},
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"outputs": [],
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"source": [
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"for response in blame_responses:\n",
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" print(response.reasons)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "oZ79k112Ndva"
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},
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"outputs": [],
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"source": [
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"for response in blame_responses:\n",
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" print(response.confidence_score)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "QzbqXvS7Ndva"
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},
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"outputs": [],
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"source": [
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"%%capture\n",
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"fig, ax = plt.subplots(constrained_layout=True, figsize=(50, 50))\n",
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"gif_name = 'blame-example.gif'\n",
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"scene_plotter.animate_scene(\n",
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" output_name=gif_name,\n",
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" ax=ax,\n",
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" numbers=all_collision_agents,\n",
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" direction_vec=False,\n",
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" velocity_vec=False,\n",
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" plot_frame_number=True\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "aUYCAHfsNdva"
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},
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"outputs": [],
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"source": [
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"Image(gif_name, width=1000, height=800)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"colab": {
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.10"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}

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