pip install opencv-python<\/code><\/pre><\/div>\n\n\n\n\n- NumPy<\/strong>
\u6570\u5024\u8a08\u7b97\u30e9\u30a4\u30d6\u30e9\u30ea\u3002\u753b\u50cf\u30c7\u30fc\u30bf\u3092\u6271\u3046\u969b\u306b\u5fc5\u8981\u3067\u3059\u3002<\/li>\n<\/ul>\n\n\n\npip install numpy<\/code><\/pre><\/div>\n\n\n\n\n- pyautogui<\/strong>
\u753b\u9762\u30ad\u30e3\u30d7\u30c1\u30e3\u3001\u30de\u30a6\u30b9\u64cd\u4f5c\uff08\u30af\u30ea\u30c3\u30af\u3001\u30c9\u30e9\u30c3\u30b0\u306a\u3069\uff09\u3092\u81ea\u52d5\u5316\u3059\u308b\u305f\u3081\u306e\u30e9\u30a4\u30d6\u30e9\u30ea\u3067\u3059\u3002<\/li>\n<\/ul>\n\n\n\npip install pyautogui<\/code><\/pre><\/div>\n\n\n\n\n- time, os<\/strong>
Python \u6a19\u6e96\u30e9\u30a4\u30d6\u30e9\u30ea\u3002\u30bf\u30a4\u30df\u30f3\u30b0\u5236\u5fa1\u3084\u30b7\u30b9\u30c6\u30e0\u30b3\u30de\u30f3\u30c9\u5b9f\u884c\u306b\u4f7f\u7528\u3057\u307e\u3059\u3002<\/li>\n<\/ul>\n\n\n\n
\n\n\n\n2. \u30b5\u30f3\u30d7\u30eb\u30b3\u30fc\u30c9\u306e\u6982\u8981<\/h2>\n\n\n\n
\u3053\u306e\u30b5\u30f3\u30d7\u30eb\u30d7\u30ed\u30b0\u30e9\u30e0\u306f\u3001\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u6d41\u308c\u3067\u52d5\u4f5c\u3057\u307e\u3059\u3002<\/p>\n\n\n\n
\n- \u30a2\u30d7\u30ea\u306e\u30a2\u30af\u30c6\u30a3\u30d9\u30fc\u30c8<\/strong>
Mac \u4e0a\u3067 AppleScript \u3092\u5229\u7528\u3057\u3066\u3001\u6307\u5b9a\u3057\u305f\u30a2\u30d7\u30ea\uff08iPhone \u30df\u30e9\u30fc\u30ea\u30f3\u30b0\uff09\u3092\u30a2\u30af\u30c6\u30a3\u30d6\u306b\u3057\u307e\u3059\u3002<\/li>\n\n\n\n- \u753b\u9762\u30ad\u30e3\u30d7\u30c1\u30e3<\/strong>
pyautogui \u3092\u4f7f\u3044\u3001\u73fe\u5728\u306e\u753b\u9762\u3092\u30ad\u30e3\u30d7\u30c1\u30e3\u3057\u3001\u753b\u50cf\u30d5\u30a1\u30a4\u30eb\u3068\u3057\u3066\u4fdd\u5b58\u3057\u307e\u3059\u3002<\/li>\n\n\n\n- \u753b\u50cf\u691c\u7d22<\/strong>
OpenCV \u306e\u30c6\u30f3\u30d7\u30ec\u30fc\u30c8\u30de\u30c3\u30c1\u30f3\u30b0\u3092\u5229\u7528\u3057\u3066\u3001\u30ad\u30e3\u30d7\u30c1\u30e3\u753b\u50cf\u5185\u304b\u3089\u6307\u5b9a\u306e\u30c6\u30f3\u30d7\u30ec\u30fc\u30c8\u753b\u50cf\u3092\u63a2\u3057\u307e\u3059\u3002<\/li>\n\n\n\n- \u30a2\u30af\u30b7\u30e7\u30f3\u5b9f\u884c<\/strong>
\u691c\u51fa\u3057\u305f\u5ea7\u6a19\u306b\u5bfe\u3057\u3066\u3001pyautogui \u306b\u3088\u308b\u30bf\u30c3\u30d7\uff08\u30af\u30ea\u30c3\u30af\uff09\u3084\u30b9\u30ef\u30a4\u30d7\uff08\u30c9\u30e9\u30c3\u30b0\uff09\u64cd\u4f5c\u3092\u884c\u3044\u307e\u3059\u3002
\u307e\u305f\u3001Retina \u30c7\u30a3\u30b9\u30d7\u30ec\u30a4\u306a\u3069\u3067\u30ad\u30e3\u30d7\u30c1\u30e3\u753b\u50cf\u3068\u5b9f\u969b\u306e\u5ea7\u6a19\u7cfb\u306e\u30b9\u30b1\u30fc\u30eb\u304c\u7570\u306a\u308b\u5834\u5408\u306b\u5099\u3048\u3001\u30b9\u30b1\u30fc\u30ea\u30f3\u30b0\u8abf\u6574\u3092\u884c\u3063\u3066\u3044\u307e\u3059\u3002<\/li>\n\n\n\n- \u30bf\u30a4\u30e0\u30a2\u30a6\u30c8\u4ed8\u304d\u753b\u50cf\u691c\u7d22<\/strong>
\u6307\u5b9a\u753b\u50cf\u306e\u691c\u7d22\u306f\u3001\u4e00\u5b9a\u6642\u9593\u5185\uff08\u4f8b\uff1a10\u79d2\uff09\u306b\u898b\u3064\u304b\u3089\u306a\u3051\u308c\u3070 False \u3092\u8fd4\u3057\u3001\u30bf\u30a4\u30e0\u30a2\u30a6\u30c8\u6642\u306e\u5206\u5c90\u51e6\u7406\u304c\u53ef\u80fd\u3067\u3059\u3002<\/li>\n<\/ol>\n\n\n\n
\n\n\n\n3. \u30b5\u30f3\u30d7\u30eb\u30b3\u30fc\u30c9<\/h2>\n\n\n\n
\u4ee5\u4e0b\u306e\u30b3\u30fc\u30c9\u306f\u3001\u4e0a\u8a18\u306e\u5185\u5bb9\u3092\u5b9f\u88c5\u3057\u305f\u30b5\u30f3\u30d7\u30eb\u3067\u3059\u3002
\u203b IMG_DIR \u5185\u306b\u306f\u691c\u7d22\u3057\u305f\u3044\u753b\u50cf\u3092\u7528\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n\n\n\n
import numpy as np\nimport pyautogui\nimport time\nimport os\n\n# \u8a2d\u5b9a\u30d1\u30e9\u30e1\u30fc\u30bf\nIMG_DIR = ".\/img" # \u30c6\u30f3\u30d7\u30ec\u30fc\u30c8\u753b\u50cf\u306e\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\nMATCH_THRESHOLD = 0.8 # \u753b\u50cf\u4e00\u81f4\u306e\u95be\u5024\nCAPTURE_IMAGE = "current_screen.png" # \u30ad\u30e3\u30d7\u30c1\u30e3\u753b\u50cf\u4fdd\u5b58\u5148\nSCALING = 2 # \u30ad\u30e3\u30d7\u30c1\u30e3\u753b\u50cf\u3068\u5b9f\u969b\u306e\u5ea7\u6a19\u306e\u30b9\u30b1\u30fc\u30eb\u6bd4\uff08Retina\u30c7\u30a3\u30b9\u30d7\u30ec\u30a4\u306e\u5834\u5408\u306f2\uff09\n\ndef capture_screen(save_path):\n """\n \u73fe\u5728\u306e\u753b\u9762\u3092\u30ad\u30e3\u30d7\u30c1\u30e3\u3057\u3066\u3001\u6307\u5b9a\u30d1\u30b9\u306b\u4fdd\u5b58\u5f8c\u3001OpenCV\u5f62\u5f0f\u306e\u753b\u50cf\u3092\u8fd4\u3059\n """\n screenshot = pyautogui.screenshot()\n screenshot.save(save_path)\n img = cv2.imread(save_path)\n return img\n\ndef find_image(template_path, screen_img, threshold=MATCH_THRESHOLD):\n """\n screen_img\u5185\u304b\u3089 template_path \u306e\u753b\u50cf\u3092\u63a2\u3057\u3001\u4e00\u81f4\u3057\u305f\u90e8\u5206\u306e\u4e2d\u5fc3\u5ea7\u6a19\u3092\u8fd4\u3059\u3002\n \u4e00\u81f4\u304c\u898b\u3064\u304b\u3089\u306a\u3044\u5834\u5408\u306f None \u3092\u8fd4\u3059\u3002\n """\n template = cv2.imread(template_path)\n if template is None:\n print("\u30c6\u30f3\u30d7\u30ec\u30fc\u30c8\u753b\u50cf\u304c\u898b\u3064\u304b\u308a\u307e\u305b\u3093:", template_path)\n return None\n res = cv2.matchTemplate(screen_img, template, cv2.TM_CCOEFF_NORMED)\n loc = np.where(res >= threshold)\n try:\n y = loc[0][0]\n x = loc[1][0]\n h, w = template.shape[:2]\n center = (x + w \/\/ 2, y + h \/\/ 2)\n return center\n except IndexError:\n return None\n\ndef find_image_ignore_color(template_path, screen_img, threshold=MATCH_THRESHOLD, use_edges=False):\n """\n screen_img\u5185\u304b\u3089\u3001\u30c6\u30f3\u30d7\u30ec\u30fc\u30c8\u753b\u50cf(template_path)\u306e\u5f62\u72b6\u3092\u63a2\u3057\u3001\u4e2d\u5fc3\u5ea7\u6a19\u3092\u8fd4\u3059\u3002\n \u8272\u306e\u5f71\u97ff\u3092\u6392\u9664\u3059\u308b\u305f\u3081\u3001\u30b0\u30ec\u30fc\u30b9\u30b1\u30fc\u30eb\u306b\u5909\u63db\u3057\u3066\u30de\u30c3\u30c1\u30f3\u30b0\u3092\u884c\u3046\u3002\n use_edges \u304c True \u306e\u5834\u5408\u3001Canny \u30a8\u30c3\u30b8\u691c\u51fa\u3092\u9069\u7528\u3057\u3066\u5f62\u72b6\u60c5\u5831\u3092\u5f37\u8abf\u3059\u308b\u3002\n """\n template = cv2.imread(template_path)\n if template is None:\n print("\u30c6\u30f3\u30d7\u30ec\u30fc\u30c8\u753b\u50cf\u304c\u898b\u3064\u304b\u308a\u307e\u305b\u3093:", template_path)\n return None\n\n # \u30b0\u30ec\u30fc\u30b9\u30b1\u30fc\u30eb\u306b\u5909\u63db\n template_gray = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY)\n screen_gray = cv2.cvtColor(screen_img, cv2.COLOR_BGR2GRAY)\n \n # \u30a8\u30c3\u30b8\u691c\u51fa\u3092\u9069\u7528\u3059\u308b\u5834\u5408\n if use_edges:\n template_gray = cv2.Canny(template_gray, 50, 150)\n screen_gray = cv2.Canny(screen_gray, 50, 150)\n \n # \u30c6\u30f3\u30d7\u30ec\u30fc\u30c8\u30de\u30c3\u30c1\u30f3\u30b0\n res = cv2.matchTemplate(screen_gray, template_gray, cv2.TM_CCOEFF_NORMED)\n loc = np.where(res >= threshold)\n \n try:\n y = loc[0][0]\n x = loc[1][0]\n h, w = template_gray.shape[:2]\n center = (x + w \/\/ 2, y + h \/\/ 2)\n return center\n except IndexError:\n return None\n\ndef tap(position, scaling=SCALING):\n """\n \u6307\u5b9a\u4f4d\u7f6e\u3078\u30bf\u30c3\u30d7\uff08\u30af\u30ea\u30c3\u30af\uff09\u64cd\u4f5c\u3092\u5b9f\u884c\u3059\u308b\u95a2\u6570\n scaling: \u30ad\u30e3\u30d7\u30c1\u30e3\u753b\u50cf\u3068\u5b9f\u969b\u306e\u5ea7\u6a19\u7cfb\u306e\u6bd4\u7387\n """\n if position:\n adjusted_pos = (int(position[0] \/ scaling), int(position[1] \/ scaling))\n pyautogui.moveTo(adjusted_pos[0], adjusted_pos[1])\n pyautogui.click()\n print(f"\u30bf\u30c3\u30d7\uff1a {adjusted_pos} (\u5143: {position}, scaling: {scaling})")\n time.sleep(0.5)\n else:\n print("\u30bf\u30c3\u30d7\u5bfe\u8c61\u306e\u4f4d\u7f6e\u304c\u53d6\u5f97\u3067\u304d\u307e\u305b\u3093\u3067\u3057\u305f\u3002")\n\ndef swipe(start, end, duration=0.5, scaling=SCALING):\n """\n \u6307\u5b9a\u958b\u59cb\u4f4d\u7f6e\u304b\u3089\u7d42\u4e86\u4f4d\u7f6e\u3078\u30b9\u30ef\u30a4\u30d7\uff08\u30c9\u30e9\u30c3\u30b0\uff09\u64cd\u4f5c\u3092\u5b9f\u884c\u3059\u308b\u95a2\u6570\n scaling: \u30ad\u30e3\u30d7\u30c1\u30e3\u753b\u50cf\u3068\u5b9f\u969b\u306e\u5ea7\u6a19\u7cfb\u306e\u6bd4\u7387\n """\n adjusted_start = (int(start[0] \/ scaling), int(start[1] \/ scaling))\n adjusted_end = (int(end[0] \/ scaling), int(end[1] \/ scaling))\n pyautogui.moveTo(adjusted_start[0], adjusted_start[1])\n pyautogui.dragTo(adjusted_end[0], adjusted_end[1], duration=duration, button='left')\n print(f"\u30b9\u30ef\u30a4\u30d7\uff1a {adjusted_start} \u304b\u3089 {adjusted_end} \u3078 (scaling: {scaling})")\n time.sleep(0.5)\n\ndef activate_app(app_name):\n """\n \u6307\u5b9a\u3057\u305f\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u3092\u30a2\u30af\u30c6\u30a3\u30d6\u306b\u3059\u308b\uff08Mac\u306e\u5834\u5408\u3001AppleScript\u3092\u5229\u7528\uff09\n """\n os.system(f"osascript -e 'tell application \\"{app_name}\\" to activate'")\n time.sleep(0.5)\n\ndef search_for_image(template_path, threshold=MATCH_THRESHOLD, timeout=10, use_edges=False):\n """\n \u6307\u5b9a\u3057\u305f\u30c6\u30f3\u30d7\u30ec\u30fc\u30c8\u753b\u50cf\u3092\u753b\u9762\u4e0a\u3067\u63a2\u7d22\u3059\u308b\u95a2\u6570\n - timeout\u79d2\u4ee5\u5185\u306b\u753b\u50cf\u304c\u898b\u3064\u304b\u308c\u3070\u4e2d\u5fc3\u5ea7\u6a19\u3092\u8fd4\u3059\n - \u30bf\u30a4\u30e0\u30a2\u30a6\u30c8\u3057\u305f\u5834\u5408\u306f False \u3092\u8fd4\u3059\n """\n # IMG_DIR\u5185\u306e\u30c6\u30f3\u30d7\u30ec\u30fc\u30c8\u753b\u50cf\u306e\u30d5\u30eb\u30d1\u30b9\u3092\u7d44\u307f\u7acb\u3066\u308b\n template_path = os.path.join(IMG_DIR, template_path)\n start_time = time.time()\n while time.time() - start_time < timeout:\n screen_img = capture_screen(CAPTURE_IMAGE)\n pos = find_image_ignore_color(template_path, screen_img, threshold, use_edges)\n if pos:\n return pos\n time.sleep(0.5)\n return False\n\ndef main():\n # iPhone Mirroring\u30a2\u30d7\u30ea\u3092\u30a2\u30af\u30c6\u30a3\u30d6\u306b\u3059\u308b\n activate_app("iPhone Mirroring")\n print("BOT\u52d5\u4f5c\u958b\u59cb")\n \n while True:\n # \u753b\u50cf\u691c\u7d22\u4f8b \u4e0a\u65b9\u5411\u30b9\u30ef\u30a4\u30d7\n pos = search_for_image("SWIPE.png", threshold=MATCH_THRESHOLD, timeout=10, use_edges=False)\n if pos:\n print("\u30c6\u30f3\u30d7\u30ec\u30fc\u30c8\u753b\u50cf\u304c\u898b\u3064\u304b\u308a\u307e\u3057\u305f:", pos)\n swipe(pos, (pos[0], pos[1] - 100), scaling=SCALING)\n else:\n print("\u30bf\u30a4\u30e0\u30a2\u30a6\u30c8\uff1a\u30c6\u30f3\u30d7\u30ec\u30fc\u30c8\u753b\u50cf\u304c\u898b\u3064\u304b\u308a\u307e\u305b\u3093\u3067\u3057\u305f\u3002")\n break\n time.sleep(1)\n \n # \u753b\u50cf\u691c\u7d22\u4f8b \u30bf\u30c3\u30d7\n pos = search_for_image("TAP.png", threshold=MATCH_THRESHOLD, timeout=10, use_edges=False)\n if pos:\n print("\u30c6\u30f3\u30d7\u30ec\u30fc\u30c8\u753b\u50cf\u304c\u898b\u3064\u304b\u308a\u307e\u3057\u305f:", pos)\n tap(pos)\n else:\n print("\u30bf\u30a4\u30e0\u30a2\u30a6\u30c8\uff1a\u30c6\u30f3\u30d7\u30ec\u30fc\u30c8\u753b\u50cf\u304c\u898b\u3064\u304b\u308a\u307e\u305b\u3093\u3067\u3057\u305f\u3002")\n break\n time.sleep(1)\n \n # \u753b\u50cf\u691c\u7d22\u4f8b\uff1a\u30bf\u30c3\u30d7\u30fb\u80cc\u666f\u8272\u306e\u5909\u5316\u304c\u3042\u308b\u5834\u5408\u3001\u30a8\u30c3\u30b8\u691c\u51fa\u3092\u9069\u7528\n pos = search_for_image("TAP2.png", threshold=MATCH_THRESHOLD, timeout=10, use_edges=True)\n if pos:\n print("\u30c6\u30f3\u30d7\u30ec\u30fc\u30c8\u753b\u50cf\u304c\u898b\u3064\u304b\u308a\u307e\u3057\u305f:", pos)\n tap(pos)\n else:\n print("\u30bf\u30a4\u30e0\u30a2\u30a6\u30c8\uff1a\u30c6\u30f3\u30d7\u30ec\u30fc\u30c8\u753b\u50cf\u304c\u898b\u3064\u304b\u308a\u307e\u305b\u3093\u3067\u3057\u305f\u3002")\n break\n time.sleep(1)\n\nif __name__ == '__main__':\n main()<\/code><\/pre><\/div>\n\n\n\n
\n\n\n\n
![](\"https:\/\/m.media-amazon.com\/images\/I\/71wVar72dkL._AC_SX679_.jpg\")
<\/a>![](\"https:\/\/m.media-amazon.com\/images\/I\/61veAtnXzWL._AC_SX679_.jpg\")
<\/a>![](\"https:\/\/m.media-amazon.com\/images\/I\/71jt9BEXU-L._SY522_.jpg\")
<\/a>