1005 Turn the corner

1005 Turn the corner

Mr.west 买了一辆新车,他在城市中旅行。一天,他驾车来到了一个垂直的角落。他想通过此弯道,现在给出车的长和宽,弯道的宽度和他现在没转弯之前的街道宽度。求出他能不能通过该弯道。

一个单调三角函数,所以是一个的二分法.

#include <iostream>
#include <cstring>
#include <cstdio>
#include <cmath>
#define pi 3.1415
using namespace std;

const double eps = 1e-4;

double l,x,y,w;

double calu(double a){
    return l*cos(a)+(w-x*cos(a))/sin(a);
}

double ternary_search(double l,double r){
    double ll,rr;
    while(r-l>eps){
        ll=(2*l+r)/3;
        rr=(2*r+l)/3;
        if(calu(ll)>calu(rr))
            r=rr;
        else
            l=ll;
    }
    return r;
}

int main()
{
    while(cin>>x>>y>>l>>w){
        double l=0,r=pi/2;
        double tmp=ternary_search(l,r);
        if(calu(tmp)<=y)
            puts("yes");
        else
            puts("no");
    }
    return 0;
}

talk2car: 用python实现逻辑,要代码简洁清晰,有一个expr_train.json文件,每个键对应train_commands.json的"command_token"字段的值,其中一条数据是 “4175173f5f60d19ecfc3712e960a1103”: { “obj_box”: [ 529, 458, 24, 41 ], “class”: “human.pedestrian.adult”, “img”: “train_0.jpg”, “action”: “standing”, “color”: “black”, “location”: “left”, “description”: “first adult on left” }, 还有一个train_commands.json文件,格式是: { “commands”: [ { “scene_token”: “f92422ed4b4e427194a4958ccf15709a”, “sample_token”: “c32d636e44604d77a1734386b3fe4a0d”, “translation”: [ -13.49250542687401, 0.43033061594724364, 59.28095610405408 ], “size”: [ 0.81, 0.73, 1.959 ], “rotation”: [ “-0.38666213835670615”, “-0.38076281276237284”, “-0.5922192111910205”, “0.5956412318459762” ], “command”: “turn left to pick up the pedestrian at the corner”, “obj_name”: “human.pedestrian.adult”, “box_token”: “0183ed8a474f411f8a3394eb78df7838”, “command_token”: “4175173f5f60d19ecfc3712e960a1103”, “2d_box”: [ 528, 457, 26, 43 ], “t2c_img”: “img_train_0.jpg” }, … } 现在需要将expr_train.json文件每条数据形成一个QA对,如: { “id”: 0, “image”: “obs://yw-2030-gy/data/opensource/talk2car/imgs/img_train_0.jpg”, #“t2c_img"前视图路径 “width”: XXX, “height”: XXX, “conver sations”: [ { “from”: “human”, “value”: “\nTurn left to pick up the pedestrian at the corner,please output the bounding box coordinates of the object referred to in this command with the attributes: “the action is standing, the color is black, the location is left”.” #取"command”、“action”、“color”、"location"值 }, { “from”: “gpt”, “value”: “first adult on left[[529, 458, 24, 41]]” #待归一化,取"obj_box"值,obj_box本身格式为[x,y,w,h],ref取"description"的值 } ] } 其中image字段取obs://talk2car/imgs/img_"t2c_img"字段拼接,需要在obs中检测下是否存在,检测用moxing的方法, widthheight取/talk2car/imgs/img_"t2c_img"字段拼接路径图片的宽高 human的value值,取\n拼接"command"的值再拼接, please output the bounding box coordinates of the object referred to in this command with the attributes: “the action is {action}, the color is {color}, the location is {location}”. gpt的value值,取first adult on left[[xxx, xxx, xx, xx]],box取"obj_box"转换为x1, y1, x2, y2后经过归一化的值,归一化代码: def normalize_coordinates(box, image_width, image_height): x1, y1, x2, y2 = box normalized_box = [ round((x1 / image_width) * 1000), round((y1 / image_height) * 1000), round((x2 / image_width) * 1000), round((y2 / image_height) * 1000) ] return normalized_box,把每一条结果输出到jsonl里,加上这个逻辑后给我一版新的完整代码
11-01
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