#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jan  1 16:04:51 2018

@author: no1
"""

import os
import time

import cv2


# 使用的Python库及对应版本：
# python 3.6
# opencv-python 3.3.0
# numpy 1.13.3
# 用到了opencv库中的模板匹配和边缘检测功能


def get_screenshot(i):
    name = '{:05d}_{}'.format(i, int(time.time()))
    os.system('adb shell screencap -p /sdcard/{}.png'.format(name))
    os.system('adb pull /sdcard/{}.png images/{}.png'.format(name, name))
    return name


def jump(distance):
    # 这个参数还需要针对屏幕分辨率进行优化
    press_time = int(distance * 1.35)
    print('distance={} press_time={}'.format(distance, press_time))
    cmd = 'adb shell input swipe 320 410 320 410 ' + str(press_time)
    os.system(cmd)


def find_top(img1, x1):
    H, W, _ = img1.shape
    img1 = cv2.GaussianBlur(img1, (5, 5), 0)
    canny_img = cv2.Canny(img1, 1, 10)

    for row in range(300, H):
        for col in range(W // 8, W):
            # 当检测到切点，且切点与棋子的水平距离大于棋子的一半时（排除棋子高于下一个目标的情况）
            if canny_img[row, col] != 0 and abs(x1 - col) > 39:
                return row, col, canny_img


def find_bottom(canny_img, top_x, top_y):
    H, W = canny_img.shape
    for row in range(top_y + 5, H):
        if canny_img[row, top_x] != 0:
            return row


if __name__ == '__main__':
    # 第一次跳跃的距离是固定的
    # jump(530)
    time.sleep(1)

    # 匹配小跳棋的模板
    temp1 = cv2.imread('temp_player.jpg', 0)
    w1, h1 = temp1.shape[::-1]
    # 匹配游戏结束画面的模板
    temp_end = cv2.imread('temp_end.jpg', 0)
    # 匹配点错了的分享挑战的模板
    temp_search = cv2.imread('temp_search.png', 0)
    # 匹配中心小圆点的模板
    temp_white_circle = cv2.imread('temp_white_circle.jpg', 0)
    w2, h2 = temp_white_circle.shape[::-1]

    # 循环直到游戏失败结束
    for i in range(10000):
        name = get_screenshot(i)
        path = os.path.join('images', name + '.png')
        img_rgb = cv2.imread(path)
        img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY)

        # 如果在游戏截图中匹配到带"再玩一局"字样的模板，则循环中止
        res_end1 = cv2.matchTemplate(img_gray,
                                     temp_end,
                                     cv2.TM_CCOEFF_NORMED
                                     )
        res_end2 = cv2.matchTemplate(img_gray,
                                     temp_search,
                                     cv2.TM_CCOEFF_NORMED)

        res1_match_ratio = cv2.minMaxLoc(res_end1)[1]
        res2_match_ratio = cv2.minMaxLoc(res_end2)[1]
        print('res1_match_ratio={} res2_match_ratio={}'.format(res1_match_ratio, res2_match_ratio))
        if res1_match_ratio > 0.95 or res2_match_ratio > 0.95:
            print('Game over!')
            break

        # 模板匹配截图中小跳棋的位置
        res1 = cv2.matchTemplate(img_gray, temp1, cv2.TM_CCOEFF_NORMED)
        min_val1, max_val1, min_loc1, max_loc1 = cv2.minMaxLoc(res1)
        print('min_val1={} max_val1={} min_loc1={} max_loc1={}'.format(min_val1, max_val1, min_loc1, max_loc1))
        center1_loc = (max_loc1[0] + 39, max_loc1[1] + 189)
        x1 = max_loc1[0] + 39
        y1 = max_loc1[1] + 189
        # 先尝试匹配截图中的中心原点，
        # 如果匹配值没有达到0.95，则使用边缘检测匹配物块上沿
        res2 = cv2.matchTemplate(img_gray, temp_white_circle, cv2.TM_CCOEFF_NORMED)
        min_val2, max_val2, min_loc2, max_loc2 = cv2.minMaxLoc(res2)
        if max_val2 > 0.95:
            print('found white circle!')
            x, y = max_loc2[0] + w2 // 2, max_loc2[1] + h2 // 2
            processed = os.path.join('images', '{}_circle.png'.format(name))
        else:
            top_y, top_x, canny_img = find_top(img_rgb, x1)
            bottom_y = find_bottom(canny_img, top_x, top_y)
            x = top_x
            y = (top_y + bottom_y) / 2
            canny_path = os.path.join('images', '{}_canny.png'.format(name))
            cv2.imwrite(canny_path, canny_img)
            processed = os.path.join('images', '{}_processed.png'.format(name))
        center2_loc = (int(x), int(y))
        # 将图片输出以供调试
        img_gray = cv2.line(img_gray, center1_loc, center2_loc, 255, 3)
        img_rgb = cv2.cvtColor(img_gray, cv2.COLOR_GRAY2RGB)
        img_rgb = cv2.circle(img_rgb, center1_loc, 3, (255, 255, 0), -1)
        img_rgb = cv2.circle(img_rgb, min_loc1, 3, (65, 105, 225), -1)
        img_rgb = cv2.circle(img_rgb, max_loc1, 3, (65, 105, 225), -1)
        # img_gray = cv2.line(img_gray, center1_loc, center2_loc, 255, 3)
        # cv2.rectangle(canny_img, max_loc1, center1_loc, 255, 2)
        cv2.imwrite(processed, img_rgb)

        distance = (center1_loc[0] - x) ** 2 + (center1_loc[1] - y) ** 2
        distance = distance ** 0.5
        # jump(distance)
        time.sleep(1.5)