--->qq 1309399183----------<代码交流
	def from_pic(self):
		self.thread_run = False
		self.pic_path = askopenfilename(title="选择识别图片", filetypes=[("jpg图片", "*.jpg")])
		if self.pic_path:
			img_bgr = predict.imreadex(self.pic_path)
			self.imgtk = self.get_imgtk(img_bgr)
			self.image_ctl.configure(image=self.imgtk)
			resize_rates = (1, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4)
			for resize_rate in resize_rates:
				print("resize_rate:", resize_rate)
				r, roi, color = self.predictor.predict(img_bgr, resize_rate)
				if r:
					break
			#r, roi, color = self.predictor.predict(img_bgr, 1)
			self.show_roi(r, roi, color)

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        for path, img, im0s, vid_cap in dataset:
        img = torch.from_numpy(img).to(device)
        img = img.half() if half else img.float()  # uint8 to fp16/32
        img /= 255.0  # 0 - 255 to 0.0 - 1.0
        if img.ndimension() == 3:
            img = img.unsqueeze(0)

        # Warmup
        if device.type != 'cpu' and (old_img_b != img.shape[0] or old_img_h != img.shape[2] or old_img_w != img.shape[3]):
            old_img_b = img.shape[0]
            old_img_h = img.shape[2]
            old_img_w = img.shape[3]
            for i in range(3):
                model(img, augment=opt.augment)[0]

        # Inference
        t1 = time_synchronized()
        with torch.no_grad():   # Calculating gradients would cause a GPU memory leak
            pred = model(img, augment=opt.augment)[0]
        t2 = time_synchronized()
         distance=object_point_world_position(u, v, h, w, out_mat, in_mat):

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路径规划

本节针对越野场景路径规划问题，采用栅格法建立障碍物、威胁物和越野道路模型，模拟真实的越野环境场景。

• 引入方向变化惩罚和局部区域复杂度惩罚来优化A算法，使算法规划出的路径更平滑，算法效率更高效。

• 采用改进 Floyd 算法对路径进行双向平滑，并且进行了防碰撞处理，来确保规划出路径的安全可靠性。

• 仿真结果表明，所改进的 A算法与传统算法相比较，效率提高了 30%，拐点数减少了4
  倍，所提算法能够在越野环境多重因素综合影响以及不同车辆性能和任务的要求下快速的规划出安全的路径。
  传送门链接---------------->:A star
  

代码

###############创建A-Star类############
class AStar:
    
    # 描述AStar算法中的节点数据
    class Node:  
        #初始化
        def __init__(self, point, startPoint,endPoint, g=0,w=1,p=1):
            self.point = point  # 自己的坐标
            self.father = None  # 父节点
            self.g = g       # g值，g值在用到的时候会重新算
            
            # 计算h值，采用曼哈顿距离
            #self.h = (abs(endPoint.x - point.x) + abs(endPoint.y - point.y)) * 10  
            
            #采用欧几里得距离
            #self.h = math.pow((math.pow((endPoint.x - point.x),2) + math.pow((endPoint.y - point.y),2)),0.5)*10
            
            #采用对角距离
            pp=(1-p)+0.2*math.exp((math.pow((math.pow((endPoint.x - point.x),2) + math.pow((endPoint.y - point.y),2)),0.5))/(math.pow((math.pow((endPoint.x - startPoint.x),2) + math.pow((endPoint.y - startPoint.y),2)),0.5)))
            Diagonal_step = min((endPoint.x - point.x),(endPoint.y - point.y))
            straight_step = (abs(endPoint.x - point.x) + abs(endPoint.y - point.y)) - 2*Diagonal_step
            self.h  =(straight_step + math.pow(2,0.5)*Diagonal_step)*10*pp
            #print(pp)


            
    #初始化A-start
    def __init__(self, map2d, startPoint, endPoint, passTag=1.0):#map2d地图信息，startPoint起点, endPoint终点, passTag=1.0为不可行驶区域

        # 开启表
        self.openList = []
        # 关闭表
        self.closeList = []
        # 寻路地图
        self.map2d = map2d
        # 起点终点
        if isinstance(startPoint, Point) and isinstance(endPoint, Point):
            self.startPoint = startPoint
            self.endPoint = endPoint
        else:
            self.startPoint = Point(*startPoint)
            self.endPoint = Point(*endPoint)
 
        # 不可行走标记
        self.passTag = passTag
 
    def getMinNode(self):
        """
        获得openlist中F值最小的节点
        :return: Node
        """
        currentNode = self.openList[0]
        for node in self.openList:
            if node.g + node.h < currentNode.g + currentNode.h:
                currentNode = node
        return currentNode#返回最小代价的点
 

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