def rule_action(frame):
"""
function: according to the relative location of closest enemy and player return the action based rule
:param frame: the input frame
:return: the action based rule
"""
objects = imageProcessing.extract_objects(frame)
player_coordinate = []
enemy_coordinate = []
for ob in objects:
if PLAYER_RADIUS - THRESH < ob[1] < PLAYER_RADIUS + THRESH:
player_coordinate.append(ob)
if ENEMY_RADIUS - THRESH < ob[1] < ENEMY_RADIUS + THRESH:
enemy_coordinate.append(ob)
# if there are more than one player, raise a error
# if len(player_coordinate) > 1:
# raise Exception("More than one player!")
# action options
options = ["left", "right"]
# if no enemy or more than one player, return random choice
if len(enemy_coordinate) == 0 or len(player_coordinate) > 1:
return choice(options)
else:
# find the enemy that closest to player, record it at a tuple
closest_enemy = min(enemy_coordinate, key=lambda x: abs(x[0][0] - player_coordinate[0][0][0]))
# compare the X coordinate of player and closest enemy, choose action
if closest_enemy[0][0] > player_coordinate[0][0][0]:
return "right"
else:
return "left"
def rule_action(frame): """ function: according to the relative location of enemy and player return the action based on rules :param frame: the input frame :return: the action based on rule """ objects = imageProcessing.extract_objects(frame) player_coordinate = [] enemy_coordinate = [] for ob in objects: if PLAYER_RADIUS - THRESH < ob[1] < PLAYER_RADIUS + THRESH: player_coordinate.append(ob) if ENEMY_RADIUS - THRESH < ob[1] < ENEMY_RADIUS + THRESH: enemy_coordinate.append(ob) # action options options = ["left", "right"] unsafe_zone = [] for enemy in enemy_coordinate: if SAFE_HIGH < enemy[1] < PLAYER_HIGH and abs(enemy[0] - player_coordinate[0][0]) < SAFE_WIDTH: unsafe_zone.append(enemy) # if no enemy or more than one player, return random choice if len(enemy_coordinate) == 0 or len(player_coordinate) > 1: ruleAction = choice(options) safeAction = "safe" elif len(unsafe_zone) == 0: # there are enemy in the screen, but no enemy in the unsafe zone. # find the enemy that closest to player, record it at a tuple closest_enemy = min(enemy_coordinate, key=lambda x: abs(x[0][0] - player_coordinate[0][0][0])) # compare the X coordinate of player and closest enemy, choose action if closest_enemy[0][0] > player_coordinate[0][0][0]: ruleAction = "right" safeAction = "safe" else: ruleAction = "left" safeAction = "safe" elif len(unsafe_zone) == 1: # there is one enemy in the unsafe zone ruleAction = choice(options) if unsafe_zone[0][0][0] > player_coordinate[0][0][0]: safeAction = "left" else: safeAction = "right" else: ruleAction = choice(options) most_dangerous_enemy = max(unsafe_zone, key=lambda x: x[0][1]) if most_dangerous_enemy[0][0] > player_coordinate[0][0][0]: safeAction = "left" else: safeAction = "right" return ruleAction, safeAction
def rule_action(frame): """ function: according to the relative location of closest enemy and player return the action based rule :param frame: the input frame :return: the action based rule """ player_coordinate = imageProcessing.extract_objects(frame, PLAYER_COLOR, THRESH) flag_coordinate = imageProcessing.extract_objects(frame, FLAG_COLOR, THRESH) # action options options = ["left", "right"] # if no enemy or more than one player, return random choice if len(flag_coordinate) == 0 or len(player_coordinate) > 1: return choice(options) else: # find the enemy that closest to player, record it at a tuple closest_flag = min(flag_coordinate, key=lambda x: x[1]) # compare the X coordinate of player and closest enemy, choose action if closest_flag[0] > player_coordinate[0][0]: return "right" else: return "left"
def rule_action(frame): """ function: according to the incline orientation of the pole return the action based rule :param frame: the input frame :return: the action based rule """ box = imageProcessing.extract_objects(frame, POLE_COLOR, THRESH) # numpy array to list box_list = box.tolist() # sorted according to Y coordinate box_list = sorted(box_list, key=lambda x: x[1]) if box_list[0][0] >= box_list[2][0]: return "right" else: return "left"
import cv2 from random import choice import imageProcessing import numpy as np PLAYER_COLOR = [1, 240, 255] FLAG_COLOR = [1, 1, 255] TREE_COLOR = [1, 255, 1] THRESH = 40 frame = cv2.imread(r"E:\Reserch\GameAI\skier\image_processing_lab\sample1.png") player_coordinate = imageProcessing.extract_objects(frame, PLAYER_COLOR, THRESH) tree_coordinate = imageProcessing.extract_objects(frame, TREE_COLOR, THRESH) distance = [((player_coordinate[0][0] - tree[0])**2 + (player_coordinate[0][1] - tree[1])**2)**0.5 for tree in tree_coordinate] print(tree_coordinate) print(player_coordinate) min_distance_index = np.argmin(distance) print(tree_coordinate[min_distance_index])