Hey there! Let's tackle your two main issues—fixing that frustrating lightblue error, and finding a straightforward way to scan Bluetooth devices, grab their RSSI values, and estimate distance for your social distance app. Since your team is newer to programming, I'll keep things as simple and actionable as possible.

一、Why lightblue isn't working (and how to fix it) #

First off, that ImportError and compile failure you're seeing? It's because lightblue is a very outdated library (last updated over a decade ago) that doesn't support modern macOS systems—especially Apple Silicon (arm64/M-series chips). The LightAquaBlue framework it relies on isn't maintained anymore, so trying to compile it will keep failing on new hardware/OS versions.

The fix here is to abandon lightblue entirely and use a modern, maintained alternative. The best option for cross-platform Bluetooth LE (which is what most modern devices use) is bleak.

二、Scanning Bluetooth Devices & Getting RSSI with Bleak #

Bleak is a lightweight, easy-to-use library that works on macOS, Windows, Linux, and even mobile (with some extra setup). It handles all the low-level Bluetooth stuff so you don't have to.

Step 1: Install Bleak #

Run this command in your terminal:

pip install bleak

Step 2: Basic Scan Code to Get RSSI #

Here's a simple script that scans for nearby Bluetooth devices and prints their name, address, and RSSI value:

from bleak import BleakScanner
import asyncio

async def scan_bluetooth_devices():
    # 扫描8秒，return_adv=True让我们能拿到广播数据（包括RSSI）
    devices = await BleakScanner.discover(timeout=8, return_adv=True)
    
    for device, adv_data in devices.items():
        print(f"设备名称: {device.name or '未知设备'}")
        print(f"MAC/蓝牙地址: {device.address}")
        print(f"RSSI信号强度: {adv_data.rssi} dBm")
        print("---")

# 运行异步函数
asyncio.run(scan_bluetooth_devices())

This code uses async/await because Bluetooth scanning is an asynchronous operation—Bleak is built around asyncio, which is standard in Python now.

三、Estimating Distance from RSSI #

RSSI (Received Signal Strength Indicator) can be used to estimate distance, but it's not 100% precise—signal strength is affected by obstacles, device hardware, and environment. That said, it's good enough for a prototype.

The Formula #

The most common way to calculate distance is using the free space propagation model:

distance = 10^((tx_power - rssi) / (10 * path_loss_exponent))

• tx_power: The signal strength the device emits at 1 meter (usually between -50 to -60 dBm; some devices broadcast this value, others don't)
• path_loss_exponent: A value that accounts for environment (2 for open outdoor spaces, 2.5-4 for indoor areas with walls/obstacles)

Example Calculation Code #

Here's a function that implements this, with fallback values if tx_power isn't available:

def estimate_distance(tx_power, rssi, path_loss_exponent=2.5):
    # 如果RSSI为0，说明无法检测到设备
    if rssi == 0:
        return -1.0
    
    ratio = rssi / tx_power
    if ratio < 1.0:
        return pow(ratio, 10 / path_loss_exponent)
    else:
        # 另一个经验公式，适用于信号较强的情况
        return (0.89976) * pow(ratio, 7.7095) + 0.111

How to Use It #

Update the scan code to include distance estimation:

async def scan_bluetooth_devices():
    devices = await BleakScanner.discover(timeout=8, return_adv=True)
    
    for device, adv_data in devices.items():
        # 如果设备没有广播tx_power，用常见的-59 dBm作为默认值
        tx_power = adv_data.tx_power if adv_data.tx_power is not None else -59
        distance = estimate_distance(tx_power, adv_data.rssi)
        
        print(f"设备名称: {device.name or '未知设备'}")
        print(f"RSSI: {adv_data.rssi} dBm")
        print(f"估算距离: {distance:.2f} 米" if distance != -1 else "无法估算距离")
        print("---")

Pro Tip: For better accuracy in your prototype, have users calibrate the app by standing at a known distance (like 1 meter) from a device, then adjust the tx_power or path_loss_exponent to match.

四、Integrating Bleak with Kivy #

Since your team is using Kivy for the UI, you can easily integrate the async scan code into your Kivy app. Here's a simple example:

from kivy.app import App
from kivy.uix.boxlayout import BoxLayout
from kivy.uix.label import Label
from kivy.clock import mainthread
from bleak import BleakScanner
import asyncio

def estimate_distance(tx_power, rssi, path_loss_exponent=2.5):
    if rssi == 0:
        return -1.0
    ratio = rssi / tx_power
    if ratio < 1.0:
        return pow(ratio, 10 / path_loss_exponent)
    else:
        return (0.89976) * pow(ratio, 7.7095) + 0.111

class SocialDistanceApp(App):
    def build(self):
        self.layout = BoxLayout(orientation="vertical", padding=20, spacing=10)
        self.status_label = Label(text="点击下方按钮开始扫描蓝牙设备...")
        self.results_label = Label(text="", halign="left", valign="top")
        self.layout.add_widget(self.status_label)
        self.layout.add_widget(self.results_label)
        
        # 添加扫描按钮（简化版，你可以用Kivy的Button组件）
        scan_button = Label(text="开始扫描", color=(0,1,0,1), font_size=18)
        scan_button.bind(on_touch_down=self.start_scan)
        self.layout.add_widget(scan_button)
        
        return self.layout
    
    def start_scan(self, instance, touch):
        if instance.collide_point(*touch.pos):
            self.status_label.text = "正在扫描中..."
            self.results_label.text = ""
            # 启动异步扫描任务
            asyncio.create_task(self.scan_and_update_ui())
    
    @mainthread
    def update_results(self, results_text):
        self.status_label.text = "扫描完成！"
        self.results_label.text = results_text
    
    async def scan_and_update_ui(self):
        devices = await BleakScanner.discover(timeout=8, return_adv=True)
        results = []
        for device, adv_data in devices.items():
            tx_power = adv_data.tx_power or -59
            distance = estimate_distance(tx_power, adv_data.rssi)
            distance_str = f"{distance:.2f} 米" if distance != -1 else "无法估算"
            results.append(f"设备: {device.name or '未知'}\nRSSI: {adv_data.rssi} dBm\n距离: {distance_str}\n---")
        
        self.update_results("\n".join(results) if results else "未发现任何蓝牙设备")

if __name__ == "__main__":
    SocialDistanceApp().run()

This app has a simple UI that starts scanning when you tap the "开始扫描" label, then displays the results with estimated distances. The @mainthread decorator ensures we update the UI from the main thread (required for Kivy).

Final Notes #

• Forget lightblue: It's obsolete and won't work on modern macOS. Bleak is the way to go.
• RSSI is approximate: Don't expect perfect distance accuracy—use it as a relative measure for your prototype.
• Test on real devices: Bluetooth scanning works best on physical devices; emulators might not support it.

内容的提问来源于stack exchange，提问作者thomasMI