{"id":1066652,"date":"2024-12-31T16:28:23","date_gmt":"2024-12-31T08:28:23","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1066652.html"},"modified":"2024-12-31T16:28:38","modified_gmt":"2024-12-31T08:28:38","slug":"%e5%be%ae%e4%bf%a1%e8%b7%b3%e4%b8%80%e8%b7%b3%e5%a6%82%e4%bd%95python","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1066652.html","title":{"rendered":"\u5fae\u4fe1\u8df3\u4e00\u8df3\u5982\u4f55python"},"content":{"rendered":"

\"\u5fae\u4fe1\u8df3\u4e00\u8df3\u5982\u4f55python\"<\/p>\n

\u5fae\u4fe1\u8df3\u4e00\u8df3\u5982\u4f55\u7528Python\u5b9e\u73b0<\/h3>\n<\/p>\n

\u8981\u5b9e\u73b0\u5fae\u4fe1\u8df3\u4e00\u8df3\u7684\u81ea\u52a8\u5316\uff0c\u9700\u8981\u501f\u52a9\u56fe\u50cf\u8bc6\u522b\u3001\u8ba1\u7b97\u8ddd\u79bb\u3001\u6a21\u62df\u70b9\u51fb\u7b49\u6280\u672f\u3002<\/strong> \u5176\u4e2d\uff0c\u56fe\u50cf\u8bc6\u522b\u662f\u6700\u91cd\u8981\u7684\u4e00\u73af\uff0c\u5b83\u51b3\u5b9a\u4e86\u6211\u4eec\u8df3\u8dc3\u7684\u7cbe\u51c6\u5ea6\u3002\u672c\u6587\u5c06\u8be6\u7ec6\u8bb2\u8ff0\u5982\u4f55\u4f7f\u7528Python\u5b9e\u73b0\u5fae\u4fe1\u8df3\u4e00\u8df3\u7684\u81ea\u52a8\u5316\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u6240\u9700\u5de5\u5177\u4e0e\u73af\u5883\u51c6\u5907<\/h3>\n<\/p>\n
    \n
  1. Python\u73af\u5883\uff1a<\/strong> \u6211\u4eec\u9700\u8981\u5b89\u88c5Python 3.x\u7248\u672c\uff0c\u63a8\u8350\u4f7f\u7528Anaconda\u7ba1\u7406Python\u73af\u5883\u3002<\/li>\n
  2. ADB\uff08Android Debug Bridge\uff09\uff1a<\/strong> \u7528\u4e8e\u8fde\u63a5\u624b\u673a\u5e76\u6267\u884c\u76f8\u5e94\u7684\u64cd\u4f5c\u547d\u4ee4\u3002<\/li>\n
  3. Pillow\u5e93\uff1a<\/strong> \u7528\u4e8e\u56fe\u50cf\u5904\u7406\u3002<\/li>\n
  4. OpenCV\u5e93\uff1a<\/strong> \u7528\u4e8e\u56fe\u50cf\u8bc6\u522b\u3002<\/li>\n
  5. numpy\u5e93\uff1a<\/strong> \u7528\u4e8e\u6570\u5b66\u8ba1\u7b97\u3002<\/li>\n
  6. matplotlib\u5e93\uff1a<\/strong> \u7528\u4e8e\u6570\u636e\u53ef\u89c6\u5316\u3002<\/li>\n<\/ol>\n

    \u5b89\u88c5\u547d\u4ee4\u5982\u4e0b\uff1a<\/p>\n<\/p>\n

    pip install pillow opencv-python numpy matplotlib<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e8c\u3001\u8fde\u63a5\u624b\u673a\u5e76\u83b7\u53d6\u622a\u56fe<\/h3>\n<\/p>\n
    \u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u901a\u8fc7ADB\u547d\u4ee4\u8fde\u63a5\u624b\u673a\uff0c\u5e76\u83b7\u53d6\u5f53\u524d\u5c4f\u5e55\u622a\u56fe\u3002<\/p>\n<\/p>\n
    import os<\/p>\n
    import time<\/p>\n
    def get_screenshot():<\/p>\n
        os.system('adb shell screencap -p \/sdcard\/screenshot.png')<\/p>\n
        os.system('adb pull \/sdcard\/screenshot.png .')<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e09\u3001\u56fe\u50cf\u5904\u7406\u4e0e\u76ee\u6807\u8bc6\u522b<\/h3>\n<\/p>\n
    \u6211\u4eec\u9700\u8981\u8bc6\u522b\u5f53\u524d\u5c0f\u4eba\u7684\u4f4d\u7f6e\u548c\u4e0b\u4e00\u4e2a\u76ee\u6807\u4f4d\u7f6e\u3002\u8fd9\u91cc\u6211\u4eec\u901a\u8fc7\u989c\u8272\u8bc6\u522b\u7684\u65b9\u6cd5\u6765\u786e\u5b9a\u4f4d\u7f6e\u3002<\/p>\n<\/p>\n
    from PIL import Image<\/p>\n
    import numpy as np<\/p>\n
    def find_piece_and_board(im):<\/p>\n
        w, h = im.size<\/p>\n
        im_pixel = np.array(im)<\/p>\n
        piece_x_sum = piece_x_c = piece_y_max = board_x = board_y = 0<\/p>\n
        piece_found = board_found = False<\/p>\n
        for i in range(h\/\/3, h*2\/\/3):<\/p>\n
            if piece_found:<\/p>\n
                break<\/p>\n
            for j in range(w\/\/8, w*7\/\/8):<\/p>\n
                pixel = im_pixel[i][j]<\/p>\n
                if (50 < pixel[0] < 60) and (53 < pixel[1] < 63) and (95 < pixel[2] < 110):<\/p>\n
                    piece_x_sum += j<\/p>\n
                    piece_x_c += 1<\/p>\n
                    piece_y_max = max(i, piece_y_max)<\/p>\n
                    piece_found = True<\/p>\n
        if piece_x_c:<\/p>\n
            piece_x = piece_x_sum \/\/ piece_x_c<\/p>\n
        for i in range(h\/\/3, h*2\/\/3):<\/p>\n
            if board_found:<\/p>\n
                break<\/p>\n
            for j in range(w\/\/8, w*7\/\/8):<\/p>\n
                pixel = im_pixel[i][j]<\/p>\n
                if (pixel[0] > 200) and (pixel[1] > 200) and (pixel[2] > 200):<\/p>\n
                    board_x = j<\/p>\n
                    board_y = i<\/p>\n
                    board_found = True<\/p>\n
        return (piece_x, piece_y_max), (board_x, board_y)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u56db\u3001\u8ba1\u7b97\u8ddd\u79bb\u5e76\u6a21\u62df\u8df3\u8dc3<\/h3>\n<\/p>\n
    \u6839\u636e\u8bc6\u522b\u5230\u7684\u4f4d\u7f6e\uff0c\u8ba1\u7b97\u5c0f\u4eba\u9700\u8981\u8df3\u8dc3\u7684\u8ddd\u79bb\u5e76\u6a21\u62df\u70b9\u51fb\u5c4f\u5e55\u3002<\/p>\n<\/p>\n
    def jump(distance):<\/p>\n
        press_time = distance * 1.35  # 1.35\u4e3a\u7ecf\u9a8c\u503c<\/p>\n
        press_time = max(press_time, 200)<\/p>\n
        press_time = int(press_time)<\/p>\n
        cmd = 'adb shell input swipe 320 410 320 410 {}'.format(press_time)<\/p>\n
        os.system(cmd)<\/p>\n
    def mAI<\/a>n():<\/p>\n
        while True:<\/p>\n
            get_screenshot()<\/p>\n
            im = Image.open("screenshot.png")<\/p>\n
            piece, board = find_piece_and_board(im)<\/p>\n
            distance = ((piece[0] - board[0]) <strong> 2 + (piece[1] - board[1]) <\/strong> 2)  0.5<\/p>\n
            jump(distance)<\/p>\n
            time.sleep(1)<\/p>\n
    if __name__ == '__main__':<\/p>\n
        main()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e94\u3001\u4f18\u5316\u4e0e\u8c03\u8bd5<\/h3>\n<\/p>\n
      \n
    1. \u8c03\u8bd5\u56fe\u50cf\u5904\u7406\uff1a<\/strong> \u53ef\u4ee5\u4f7f\u7528matplotlib<\/code>\u53ef\u89c6\u5316\u5904\u7406\u540e\u7684\u56fe\u50cf\uff0c\u68c0\u67e5\u8bc6\u522b\u662f\u5426\u51c6\u786e\u3002<\/li>\n
    2. \u8c03\u6574\u8df3\u8dc3\u7cfb\u6570\uff1a<\/strong> \u8df3\u8dc3\u7cfb\u6570\u9700\u8981\u6839\u636e\u5b9e\u9645\u60c5\u51b5\u8fdb\u884c\u5fae\u8c03\uff0c\u786e\u4fdd\u8df3\u8dc3\u7cbe\u51c6\u3002<\/li>\n
    3. \u591a\u6837\u5316\u8bc6\u522b\uff1a<\/strong> \u6709\u65f6\u76ee\u6807\u4f4d\u7f6e\u5e76\u4e0d\u660e\u663e\uff0c\u53ef\u4ee5\u52a0\u5165\u66f4\u591a\u7684\u56fe\u50cf\u5904\u7406\u7b97\u6cd5\u63d0\u9ad8\u8bc6\u522b\u7387\u3002<\/li>\n<\/ol>\n
      import matplotlib.pyplot as plt<\/p>\n
      def visualize(im, piece, board):<\/p>\n
          plt.imshow(im)<\/p>\n
          plt.scatter([piece[0], board[0]], [piece[1], board[1]], c='r')<\/p>\n
          plt.show()<\/p>\n
      def main():<\/p>\n
          while True:<\/p>\n
              get_screenshot()<\/p>\n
              im = Image.open("screenshot.png")<\/p>\n
              piece, board = find_piece_and_board(im)<\/p>\n
              visualize(im, piece, board)<\/p>\n
              distance = ((piece[0] - board[0]) <strong> 2 + (piece[1] - board[1]) <\/strong> 2)  0.5<\/p>\n
              jump(distance)<\/p>\n
              time.sleep(1)<\/p>\n
      if __name__ == '__main__':<\/p>\n
          main()<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u516d\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
      \u4f7f\u7528Python\u5b9e\u73b0\u5fae\u4fe1\u8df3\u4e00\u8df3\u81ea\u52a8\u5316\uff0c\u4e3b\u8981\u6d89\u53ca\u5230\u56fe\u50cf\u8bc6\u522b\u3001\u8ddd\u79bb\u8ba1\u7b97\u548c\u6a21\u62df\u70b9\u51fb\u3002\u901a\u8fc7\u8c03\u8bd5\u548c\u4f18\u5316\uff0c\u53ef\u4ee5\u5b9e\u73b0\u8f83\u4e3a\u7cbe\u51c6\u7684\u81ea\u52a8\u8df3\u8dc3\u3002\u5e0c\u671b\u672c\u6587\u7684\u5185\u5bb9\u5bf9\u4f60\u6709\u6240\u5e2e\u52a9\uff0c\u795d\u4f60\u8df3\u8dc3\u6109\u5feb\uff01<\/p>\n<\/p>\n
      \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
       \u5982\u4f55\u4f7f\u7528Python\u6a21\u62df\u5fae\u4fe1\u8df3\u4e00\u8df3\u6e38\u620f\u7684\u73a9\u6cd5\uff1f<\/strong>
      \u53ef\u4ee5\u901a\u8fc7Python\u7684\u56fe\u50cf\u5904\u7406\u5e93\u548c\u6a21\u62df\u70b9\u51fb\u529f\u80fd\u6765\u5b9e\u73b0\u5bf9\u5fae\u4fe1\u8df3\u4e00\u8df3\u7684\u6a21\u62df\u3002\u9996\u5148\uff0c\u4f7f\u7528Pillow<\/code>\u5e93\u6765\u6355\u6349\u6e38\u620f\u754c\u9762\uff0c\u63a5\u7740\u4f7f\u7528OpenCV<\/code>\u8fdb\u884c\u56fe\u50cf\u8bc6\u522b\uff0c\u4ee5\u786e\u5b9a\u8df3\u8dc3\u7684\u6700\u4f73\u65f6\u673a\u548c\u8ddd\u79bb\u3002\u6700\u540e\uff0c\u7ed3\u5408pyautogui<\/code>\u5e93\u6765\u6a21\u62df\u9f20\u6807\u70b9\u51fb\uff0c\u5b9e\u73b0\u81ea\u52a8\u8df3\u8dc3\u3002<\/p>\n
      \u9700\u8981\u54ea\u4e9bPython\u5e93\u6765\u5b9e\u73b0\u5fae\u4fe1\u8df3\u4e00\u8df3\u7684\u81ea\u52a8\u5316\uff1f<\/strong>
      \u5b9e\u73b0\u8fd9\u4e00\u529f\u80fd\u901a\u5e38\u9700\u8981\u51e0\u4e2a\u4e3b\u8981\u7684\u5e93\uff0c\u5305\u62ecPillow<\/code>\u7528\u4e8e\u56fe\u50cf\u5904\u7406\u3001OpenCV<\/code>\u7528\u4e8e\u56fe\u50cf\u5206\u6790\u3001\u4ee5\u53capyautogui<\/code>\u7528\u4e8e\u6a21\u62df\u9f20\u6807\u548c\u952e\u76d8\u64cd\u4f5c\u3002\u786e\u4fdd\u5728\u4f60\u7684Python\u73af\u5883\u4e2d\u5b89\u88c5\u8fd9\u4e9b\u5e93\uff0c\u53ef\u4ee5\u901a\u8fc7pip install<\/code>\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\u3002<\/p>\n
      \u5982\u4f55\u4f18\u5316Python\u4ee3\u7801\u4ee5\u63d0\u9ad8\u8df3\u4e00\u8df3\u7684\u6e38\u620f\u8868\u73b0\uff1f<\/strong>
      \u4f18\u5316\u4ee3\u7801\u53ef\u4ee5\u901a\u8fc7\u63d0\u9ad8\u56fe\u50cf\u5904\u7406\u901f\u5ea6\u548c\u54cd\u5e94\u65f6\u95f4\u6765\u5b9e\u73b0\u3002\u4f7f\u7528\u591a\u7ebf\u7a0b\u6765\u5904\u7406\u56fe\u50cf\u6355\u83b7\u4e0e\u5206\u6790\uff0c\u53ef\u4ee5\u52a0\u5feb\u6570\u636e\u5904\u7406\u901f\u5ea6\u3002\u540c\u65f6\uff0c\u8c03\u6574\u56fe\u50cf\u8bc6\u522b\u7684\u53c2\u6570\uff0c\u4ee5\u63d0\u9ad8\u51c6\u786e\u7387\u3002\u6b64\u5916\uff0c\u5b9a\u671f\u66f4\u65b0\u8bc6\u522b\u7b97\u6cd5\uff0c\u5229\u7528      \u673a\u5668\u5b66\u4e60<\/a>\u6280\u672f\u6765\u4e0d\u65ad\u4f18\u5316\u8df3\u8dc3\u7684\u9884\u6d4b\u7b97\u6cd5\uff0c\u53ef\u4ee5\u5927\u5e45\u5ea6\u63d0\u5347\u6e38\u620f\u7684\u8868\u73b0\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5fae\u4fe1\u8df3\u4e00\u8df3\u5982\u4f55\u7528Python\u5b9e\u73b0 \u8981\u5b9e\u73b0\u5fae\u4fe1\u8df3\u4e00\u8df3\u7684\u81ea\u52a8\u5316\uff0c\u9700\u8981\u501f\u52a9\u56fe\u50cf\u8bc6\u522b\u3001\u8ba1\u7b97\u8ddd\u79bb\u3001\u6a21\u62df\u70b9\u51fb\u7b49\u6280\u672f\u3002 \u5176\u4e2d […]","protected":false},"author":3,"featured_media":1066695,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1066652"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=1066652"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1066652\/revisions"}],"predecessor-version":[{"id":1066702,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1066652\/revisions\/1066702"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1066695"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1066652"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1066652"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1066652"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}