基于python和百度EsayDL实现自动驾驶算法+基于ESP32开发板作为智能车主控芯片的自动驾驶智能车项目+源码（高分项目）

import numpy as np import matplotlib.pyplot as plt import time from skimage import morphology import cv2 import os import random from scipy import ndimage ''' A*算法 ''' class Node(): """A node class for A* Pathfinding""" def __init__(self, parent=None, position=None): self.parent = parent self.position = position self.g = 0 self.h = 0 self.f = 0 def __eq__(self, other): return self.position == other.position def astar(maze, start, end, time_limit=1): """ Returns a list of tuples as a path from the given start to the given end in the given maze. if end point is not drivable: return None, 0 if exceed time limit: return None, 1 if no valid path: return None, 2 """ astar_start_time = time.time() if maze[end[0]][end[1]] != 0: return (None, 0) # Create start and end node start_node = Node(None, start) start_node.g = start_node.h = start_node.f = 0 end_node = Node(None, end) end_node.g = end_node.h = end_node.f = 0 # Initialize both open and closed list open_list = [] closed_list = [] # Add the start node open_list.append(start_node) # Loop until you find the end while len(open_list) > 0: if time.time() - astar_start_time >= time_limit: return (None, 1) # Get the current node current_node = open_list[0] current_index = 0 for index, item in enumerate(open_list): if item.f < current_node.f: current_node = item current_index = index # Pop current off open list, add to closed list open_list.pop(current_index) closed_list.append(current_node) # Found the goal if current_node == end_node: path = [] current = current_node while current is not None: path.append(current.position) current = current.parent path = path[::-1] # Return reversed path if len(path) == 0: return (None, 2) else: return (path, -1) # Generate children children = [] for new_position in [(0, -1), (0, 1), (-1, 0), (1, 0), (-1, -1), (-1, 1), (1, -1), (1, 1)]: # Adjacent squares # Get node position # node_position = (current_node.position[0] + new_position[0], current_node.position[1] + new_position[1]) node_position = [current_node.position[0] + new_position[0], current_node.position[1] + new_position[1]] # Make sure within range if node_position[0] > (len(maze) - 1) or node_position[0] < 0 or node_position[1] > (len(maze[len(maze)-1]) -1) or node_position[1] < 0: continue # Make sure walkable terrain if maze[node_position[0]][node_position[1]] != 0: continue # Create new node new_node = Node(current_node, node_position) # Append children.append(new_node) # Loop through children for child in children: # Child is on the closed list for closed_child in closed_list: if child == closed_child: continue # Create the f, g, and h values child.g = current_node.g + 1 child.h = ((child.position[0] - end_node.position[0]) ** 2) + ((child.position[1] - end_node.position[1]) ** 2) child.f = child.g + child.h # Child is already in the open list for open_node in open_list: if child == open_node and child.g > open_node.g: continue # Add the child to the open list open_list.append(child) ''' 根据道路分割结果, 规划行驶路线 ''' def find_path(img, time_limit=1, SHOW_IMAGE=False): ''' 固定图像大小, 便于像素长度和实际长度换算 ''' DIM=(800, 600) ''' 开始计时 ''' start_time = time.time() if SHOW_IMAGE: plt.imshow(img) plt.show() ''' 从网络预测大小放缩到拍摄大小 ''' img = cv2.resize(img, DIM, interpolation=cv2.INTER_NEAREST) img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) ''' 矫正图片 ''' K = np.loadtxt('K_SVGA.csv', dtype=np.float32, delimiter=',') D = np.loadtxt('D_SVGA.csv', dtype=np.float32, delimiter=',') nK = K.copy() nK[0,0]=K[0,0] nK[1,1]=K[1,1] map1, map2 = cv2.fisheye.initUndistortRectifyMap(K, D, np.eye(3), nK, DIM, cv2.CV_16SC2) # print('map:', map1, map2) def undistort(img): img += 1 undistorted_img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_NEAREST, borderMode=cv2.BORDER_CONSTANT) return undistorted_img undistorted_img = undistort(img) undistorted_img = undistorted_img[:, :, 0] if SHOW_IMAGE: plt.imshow(undistorted_img) plt.show() ''' 鸟瞰图 ''' # 1.读取透视变换矩阵 H = np.loadtxt('H_SVGA.csv', dtype=np.float32, delimiter=',') # 2.执行透视变换 birdview_img = cv2.warpPerspective(undistorted_img, H, (DIM[1], DIM[0]), flags=cv2.INTER_NEAREST) birdview_img = birdview_img.swapaxes(1, 0) if SHOW_IMAGE: plt.imshow(birdview_img) plt.show() ''' 获取可行驶区域图 实际尺寸与鸟瞰图像素换算比例为 2mm = 1px 车身半宽为 7.5 cm, 即 37.5 px ''' # 设置形态学操作的核 kernel_1 = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3)) #定义结构元素的形状和大小 kernel_2 = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (39, 39)) kernel_3 = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (37, 37)) # 获得 grass 标签 # grass = np.zeros(birdview_img.shape, dtype=np.uint8) # grass[birdview_img == 2] = 2 grass = birdview_img == 2 # 去除 grass 中误识别出的小区域 if SHOW_IMAGE: plt.subplot(1, 2, 1) plt.imshow(grass) grass = morphology.remove_small_objects(grass, min_size=60, connectivity=1) grass = grass*np.uint8(2) # print('back datatype:', grass.dtype) if SHOW_IMAGE: plt.subplot(1, 2, 2) plt.imshow(grass) plt.show() # 获得背景标签 back = birdview_img == 3 # 去除背景中误识别出的小区域 if SHOW_IMAGE: plt.subplot(1, 2, 1) plt.imshow(back) back = morphology.remove_small_objects(back, min_size=4000, connectivity=1) back = back*np.uint8(3) # print('back datatype:', back.dtype) if SHOW_IMAGE: plt.subplot(1, 2, 2) plt.imshow(back) plt.show() # 可视化 if SHOW_IMAGE: plt.subplot(1, 3, 1) plt.imshow(birdview_img, 'gray') # 初始化 drivable_img drivable_img = np.zeros(birdview_img.shape, dtype=np.uint8) drivable_img[birdview_img == 1] = 1 # 先扩展 grass 部分 dst = cv2.erode(grass, kernel_1) dst = cv2.dilate(dst, kernel_2) drivable_img = np.maximum(drivable_img, dst) if SHOW_IMAGE: plt.subplot(1, 3, 2) plt.imshow(drivable_img, 'gray') # 再扩展 back 部分 dst = cv2.dilate(back, kernel_3) # dst = cv2.erode(back, kernel_1) # dst = cv2.dilate(dst, kernel_2) drivable_img = np.maximum(drivable_img, dst) if SHOW_IMAGE: plt.subplot(1, 3, 3) plt.imshow(drivable_img, 'gray') plt.show() (h, w) = drivable_img.shape maze = np.zeros((h, w), dtype=np.uint8) maze[drivable_img==1] = 1 # print(cv2.resize(maze, (20, 20), interpolation=cv2.INTER_NEAREST))