esp32与python-opencv的联动(micropython固件)-Toy模板网

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开发环境:Thonny,python

硬件要求:esp32cam

实现思路：

将esp32cam拍摄的图片通过网络协议传到电脑进行处理

下位机代码：

import camera
import socket
import network
import camera
import time

SSID='XXX'#输入wifi名字
password='XXX'#输入WIFI密码
IP='192.168.1.34'#换成自己的IPv4地址
port=9090

#连接wifi
wlan = network.WLAN(network.STA_IF)
wlan.active(True)
if not wlan.isconnected():
    print('connecting to network...')
    wlan.connect(SSID,password)
    while not wlan.isconnected():
        pass
print('网络配置:', wlan.ifconfig())
camera.init(0,format=3)
s = socket.socket(socket.AF_INET,socket.SOCK_DGRAM,0)
i=0
while 1:
    i=i+1
    buf = camera.capture() # 获取图像数据
    s.sendto(buf,(IP,port)) # 向服务器发送图像数据
    time.sleep(0.15)
    print(i,buf)

原理很简单，传输的途径是UDP，一开始是有想过用串口或TCP，但是串口就剩UART0，用不了，TCP与UDP相比，UDP更快，因此采用UDP。

上位机代码：

import socket
import cv2
import io
from PIL import Image
import numpy as np
import mediapipe as mp
import math

IP="192.168.1.34" #换成自己的IPv4地址
port=9090


def vector_2d_angle(v1,v2):
    '''
        求解二维向量的角度
    '''
    v1_x=v1[0]
    v1_y=v1[1]
    v2_x=v2[0]
    v2_y=v2[1]
    try:
        angle_= math.degrees(math.acos((v1_x*v2_x+v1_y*v2_y)/(((v1_x**2+v1_y**2)**0.5)*((v2_x**2+v2_y**2)**0.5))))
    except:
        angle_ =65535.
    if angle_ > 180.:
        angle_ = 65535.
    return angle_
def hand_angle(hand_):
    '''
        获取对应手相关向量的二维角度,根据角度确定手势
    '''
    angle_list = []
    #---------------------------- thumb 大拇指角度
    angle_ = vector_2d_angle(
        ((int(hand_[0][0])- int(hand_[2][0])),(int(hand_[0][1])-int(hand_[2][1]))),
        ((int(hand_[3][0])- int(hand_[4][0])),(int(hand_[3][1])- int(hand_[4][1])))
        )
    angle_list.append(angle_)
    #---------------------------- index 食指角度
    angle_ = vector_2d_angle(
        ((int(hand_[0][0])-int(hand_[6][0])),(int(hand_[0][1])- int(hand_[6][1]))),
        ((int(hand_[7][0])- int(hand_[8][0])),(int(hand_[7][1])- int(hand_[8][1])))
        )
    angle_list.append(angle_)
    #---------------------------- middle 中指角度
    angle_ = vector_2d_angle(
        ((int(hand_[0][0])- int(hand_[10][0])),(int(hand_[0][1])- int(hand_[10][1]))),
        ((int(hand_[11][0])- int(hand_[12][0])),(int(hand_[11][1])- int(hand_[12][1])))
        )
    angle_list.append(angle_)
    #---------------------------- ring 无名指角度
    angle_ = vector_2d_angle(
        ((int(hand_[0][0])- int(hand_[14][0])),(int(hand_[0][1])- int(hand_[14][1]))),
        ((int(hand_[15][0])- int(hand_[16][0])),(int(hand_[15][1])- int(hand_[16][1])))
        )
    angle_list.append(angle_)
    #---------------------------- pink 小拇指角度
    angle_ = vector_2d_angle(
        ((int(hand_[0][0])- int(hand_[18][0])),(int(hand_[0][1])- int(hand_[18][1]))),
        ((int(hand_[19][0])- int(hand_[20][0])),(int(hand_[19][1])- int(hand_[20][1])))
        )
    angle_list.append(angle_)
    return angle_list

def h_gesture(angle_list):
    thr_angle = 65.
    thr_angle_thumb = 53.
    thr_angle_s = 49.
    gesture_str = None
    if 65535. not in angle_list:
         if (angle_list[0]>thr_angle_thumb)  and (angle_list[1]<thr_angle_s) and (angle_list[2]>thr_angle_s) and (angle_list[3]>thr_angle) and (angle_list[4]>thr_angle):
              gesture_str = "one"
         elif (angle_list[0]>thr_angle_thumb)  and (angle_list[1]<thr_angle_s) and (angle_list[2]<thr_angle_s) and (angle_list[3]>thr_angle) and (angle_list[4]>thr_angle):
             gesture_str = "two"
         elif (angle_list[0]>thr_angle_thumb)  and (angle_list[1]<thr_angle_s) and (angle_list[2]<thr_angle_s) and (angle_list[3]<thr_angle) and (angle_list[4]>thr_angle):
             gesture_str = "three"
         elif (angle_list[0]>thr_angle_thumb)  and (angle_list[1]<thr_angle_s) and (angle_list[2]<thr_angle_s) and (angle_list[3]<thr_angle) and (angle_list[4]<thr_angle):
             gesture_str = "four"
         elif (angle_list[0] < thr_angle_s) and (angle_list[1] < thr_angle_s) and (angle_list[2] < thr_angle_s) and (angle_list[3] < thr_angle_s) and (angle_list[4] < thr_angle_s):
             gesture_str = "five"
    return gesture_str
mp_drawing = mp.solutions.drawing_utils
mp_hands = mp.solutions.hands
hands = mp_hands.Hands(
        static_image_mode=False,
        max_num_hands=1,
        min_detection_confidence=0.5,
        min_tracking_confidence=0.5)
print("开始")
s = socket.socket(socket.AF_INET,socket.SOCK_DGRAM,0)
s.bind((IP,port))
a=0
while True:
    data,IP = s.recvfrom(100000)
    a=a+1
    print(a,IP)
    bytes_stream = io.BytesIO(data)
    image = Image.open(bytes_stream)
    img = np.asarray(image)
    img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
    # BiCubic_small = cv2.resize(img, (int(img.shape[1] * 0.5), int(img.shape[0] * 0.5)), interpolation=cv2.INTER_CUBIC)
    # img = cv2.resize(BiCubic_small, (600, 400), interpolation=cv2.INTER_CUBIC)
    results = hands.process(img)
    if results.multi_hand_landmarks:
        for hand_landmarks in results.multi_hand_landmarks:
            mp_drawing.draw_landmarks(img, hand_landmarks, mp_hands.HAND_CONNECTIONS)
            hand_local = []
            for i in range(21):
                x = hand_landmarks.landmark[i].x * img.shape[1]
                y = hand_landmarks.landmark[i].y * img.shape[0]
                hand_local.append((x, y))
            if hand_local:
                angle_list = hand_angle(hand_local)
                gesture_str = h_gesture(angle_list)
                cv2.putText(img, gesture_str, (0, 100), 0, 1.3, (0, 0, 255), 3)
    cv2.imshow('MediaPipe Hands', img)
    if cv2.waitKey(1)==ord("q"):
        break

成品效果如下，图片发送速率和丢失率都是能接受的，毕竟30几块的模块，目前做了一个手部关键点的检测和人体关节的检测，有了这个成功的先例，接下来就搞物品分类。

micropython opencv,python,opencv,开发语言