カメラ画像から、BLEマウスを使ってiPadを自動制御しよう

import sys
import time
import cv2
import numpy as np
import requests

# camera
WIDTH = 1280
HEIGHT = 960
FPS = 30

# esp32 ip address
TARGET_IP = "192.168.xxx.yyy"

# img_path
PATH_IMG_ICON = "icon_podcast.png"
PATH_IMG_POINTER = "pointer.png"
PATH_IMG_OUT = "out.jpg"


def find_img(img_base, img_search, meth="cv2.TM_CCOEFF_NORMED"):
    """テンプレートマッチング

    Args:
        img_base (np.arrasy): カメラ画像
        img_search (np.array): 探す画像
        meth (str, optional): テンプレートマッチングメソッド. Defaults to "cv2.TM_CCOEFF_NORMED".

    Returns:
        tapple: 探す画像の座標値
    """
    img = frame
    method = eval(meth)

    # Apply template Matching
    res = cv2.matchTemplate(img_base, img_search, method)
    # print("res", res)
    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
    print(min_val, max_val, min_loc, max_loc)

    # If the method is TM_SQDIFF or TM_SQDIFF_NORMED, take minimum
    if method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]:
        top_left = min_loc
    else:
        top_left = max_loc
    w, h = img_search.shape[::-1]
    bottom_right = (top_left[0] + w, top_left[1] + h)

    cv2.rectangle(img, top_left, bottom_right, 255, 2)
    print(top_left, bottom_right)

    return ((top_left[0] + bottom_right[0]) // 2, (top_left[1] + bottom_right[1]) // 2)


def calc_shiftcrd(dst_crd, cur_crd):
    """座標値の算出

    Args:
        dst_crd (taple): 目標画像の座標値
        cur_crd (taple): マウスポインタ画像の座標位置

    Returns:
        taple: マウスポインタの移動量
    """
    x = (dst_crd[0] - cur_crd[0]) // 2
    y = (dst_crd[1] - cur_crd[1]) // 2
    x = 100 if x > 100 else x
    x = -100 if x < -100 else x
    y = 100 if y > 100 else y
    y = -100 if y < -100 else y
    return x, y


if __name__ == "__main__":
    # キャプチャデバイスの準備
    capture = cv2.VideoCapture(0)

    # キャプチャデバイス設定
    capture.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc("Y", "U", "Y", "V"))
    capture.set(cv2.CAP_PROP_FRAME_WIDTH, WIDTH)
    capture.set(cv2.CAP_PROP_FRAME_HEIGHT, HEIGHT)
    capture.set(cv2.CAP_PROP_FPS, FPS)

    # template - icon画像の準備
    icon_podcast = cv2.imread(PATH_IMG_ICON, 0)
    icon_podcast = cv2.rotate(icon_podcast, cv2.ROTATE_90_CLOCKWISE)

    # template - pointer画像の準備
    pointer = cv2.imread(PATH_IMG_POINTER, 0)
    pointer = cv2.rotate(pointer, cv2.ROTATE_90_CLOCKWISE)

    # ->unlock->home
    requests.get(f"http://{TARGET_IP}/mouse?code=click&type=middle")
    time.sleep(0.3)
    requests.get(f"http://{TARGET_IP}/mouse?code=click&type=middle")
    time.sleep(1.0)

    dst_crd_flg = False
    shift_x, shift_y = 1, 1

    for _ in range(10):
        # カーソルの移動
        requests.get(f"http://{TARGET_IP}/mouse?code=move&x={shift_x}&y={shift_y}")
        time.sleep(0.3)

        # 画像のキャプチャ
        ret, frame = capture.read()
        cv2.imwrite(PATH_IMG_OUT, frame)
        frame = cv2.imread(PATH_IMG_OUT, 0)
        frame = cv2.rotate(frame, cv2.ROTATE_90_CLOCKWISE)

        # アイコン画像(目標画像)の座標取得
        if not dst_crd_flg:
            crd_icon = find_img(frame, icon_podcast)
            print("crd_icon:", crd_icon)
            dst_crd_flg = True

        # マウスポインタ画像の座標取得
        crd_point = find_img(frame, pointer)
        print("crd_point:", crd_point)

        # マウスポインタの移動量の算出
        shift_x, shift_y = calc_shiftcrd(crd_icon, crd_point)
        print("shift_x, shift_y:", shift_x, shift_y)

        # アイコンとマウスポインタが重なった場合の処理
        if abs(shift_x) < 10 and abs(shift_y) < 10:
            # タップしてアプリケーションの起動
            requests.get(f"http://{TARGET_IP}/mouse?code=click&type=left")
            break

[ 81120][E][esp32-hal-adc.c:186] __analogRead(): GPIO25: ESP_ERR_TIMEOUT: ADC2 is in use by Wi-Fi. Please see https://docs.espressif.com/projects/esp-idf/en/latest/esp32/api-reference/peripherals/adc.html#adc-limitations for more info
[ 81151][E][esp32-hal-adc.c:186] __analogRead(): GPIO27: ESP_ERR_TIMEOUT: ADC2 is in use by Wi-Fi. Please see https://docs.espressif.com/projects/esp-idf/en/latest/esp32/api-reference/peripherals/adc.html#adc-limitations for more info