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main.py
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#!/usr/bin/env python
import cv2
from matplotlib.widgets import Cursor
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import subprocess
import random
import matplotlib.patches as patches
coefficient = 1.5 # modify for your resolution, e.g. 1280 to 1920 is x1.5, 1920 to 1920 is x1.0
click_data = []
debug = False
rr = None
def get_height():
info = call_cmd("adb shell wm size")
height = info[0].split("x")[1]
print("Height {}".format(height))
return height
def call_cmd(cmd):
# ps = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
# output = ps.communicate()[0]
# return output.decode("utf-8").split('\n')
s = subprocess.check_output(cmd.split())
return s.decode("utf-8").split('\n')
def get_object_center(img, bottle_filter):
pin_point = []
for i in range(int(len(img)/5), int(len(img)*2/3)):
for j in range(100, len(img[i])-100):
if img[i][j] != 0 and (j < (bottle_filter[0]-5) or j > (bottle_filter[0]+int(90/coefficient))):
# print("%s,%s,%s" % (i, j, img[i][j]))
pin_point.append([j, i])
if len(pin_point) > 0:
break
center_top = [int((pin_point[0][0] + pin_point[-1][0])/2), pin_point[0][1]]
center = [center_top[0], center_top[1]+int(80/coefficient)]
# supposed border width is 5 pix
# for i in range(center_top[1]+5, int(len(img)*2/3)):
# if img[i, center_top[0]] == 255:
# center[1] = int((i+center_top[1])/2)
# break
return center
# def on_motion(event):
# global ax, rr, click_data
# if rr is not None:
# rr.set_visible(False)
# Create a Rectangle patch
# rect = patches.Rectangle((event.xdata, event.ydata), 40, 30, linewidth=1, edgecolor='r', facecolor='none')
# Add the patch to the Axes
# if event.xdata and event.ydata:
# rr = patches.Rectangle([event.xdata-40, event.ydata-15], 80, 30, linewidth=1, edgecolor='r', facecolor='none')
# if len(click_data) == 0:
# ax.add_patch(rr)
def jump(self_kill=False):
global fig
global click_data
distance_2 = (click_data[0][0] - click_data[1][0]) * (click_data[0][0] - click_data[1][0]) + \
(click_data[0][1] - click_data[1][1]) * (click_data[0][1] - click_data[1][1])
distance = pow(distance_2, 0.5)
print("Distance is {}".format(distance))
# delay = int(distance/540*806)
delay = int(distance / 540 * (745*coefficient)) # change to your value properly, 1080*1920=>755
x1 = round(random.randint(100, 500) + random.random(), 3)
y1 = round(random.randint(100, 500) + random.random(), 3)
x2 = round(x1 + random.random(), 3)
y2 = round(y1 + random.random(), 3)
if self_kill:
delay = int(delay*1.3)
print("delay is:%s" % delay)
call_cmd("adb shell input swipe {} {} {} {} {}".format(x1, y1, x2, y2, delay))
plt.pause(1.2)
plt.close()
return
# def onclick(event):
# global fig
# global click_data
# """Deal with click events"""
# button = ['left', 'middle', 'right']
# toolbar = plt.get_current_fig_manager().toolbar
# if toolbar.mode != '':
# print("You clicked on something, but toolbar is in mode {:s}.".format(toolbar.mode))
# else:
# click_data.append([event.xdata, event.ydata])
# print("You {0}-clicked coords ({1},{2}) (pix ({3},{4}))".format(button[event.button+1],\
# event.xdata,\
# event.ydata,\
# event.x,\
# event.y))
def main():
global coefficient
coefficient = 1920/int(get_height())
learning_seq = []
# 30-80: 240
# 50-110:730
for i in range(0, 5): # default jump 5 rounds
learning_seq.append(random.randint(50, 110))
cur_jump_count = 0
learning_idx = 0
global ax
global fig
global click_data
cmd = [
"bash get_screenshot.sh",
# "adb shell screencap -p | sed 's/\r$//' > screen.png", # for ubuntu
# 'adb shell screencap -p /sdcard/screenshot.png',
'adb pull /sdcard/screenshot.png',
]
while True:
if learning_idx >= len(learning_seq):
break
plt.close()
global click_data
click_data = []
if not debug:
call_cmd(cmd[0])
# call_cmd(cmd[1])
screenshot = mpimg.imread('screenshot.png')
print(screenshot.shape)
# imread()函数读取目标图片和模板
img_rgb = cv2.imread("screenshot.png", 0)
template = cv2.imread('bottle.png', 0)
template = cv2.resize(template, None, fx=1/coefficient, fy=1/coefficient, interpolation=cv2.INTER_CUBIC)
# matchTemplate 函数：在模板和输入图像之间寻找匹配,获得匹配结果图像
# minMaxLoc 函数：在给定的矩阵中寻找最大和最小值，并给出它们的位置
res = cv2.matchTemplate(img_rgb, template, cv2.TM_CCOEFF_NORMED)
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
print("%s,%s,%s,%s" % (min_val, max_val, min_loc, max_loc))
bottle_center = [max_loc[0]+int(40/coefficient), max_loc[1]+int(185/coefficient)]
click_data.append(bottle_center)
# plt.subplots(figsize=(6, 5))
plt.subplots(figsize=(0, 0))
ax = plt.gca()
fig = plt.gcf()
img = cv2.GaussianBlur(img_rgb, (5, 5), 0)
canny = cv2.Canny(img, 1, 10)
object_center = get_object_center(canny, max_loc)
click_data.append(object_center)
implot = ax.imshow(canny)
# cid = fig.canvas.mpl_connect('button_press_event', onclick)
# fig.canvas.mpl_connect('motion_notify_event', on_motion)
cursor = Cursor(ax, useblit=True, color='red', linewidth=1)
rb = patches.Rectangle(object_center, 1, 1, linewidth=1, edgecolor='r', facecolor='none')
ax.add_patch(rb)
rr = patches.Rectangle(bottle_center, 1, 1, linewidth=1, edgecolor='r', facecolor='none')
ax.add_patch(rr)
print("Round:%s-%s Leap: %s-%s" %
(len(learning_seq), learning_idx+1, learning_seq[learning_idx], cur_jump_count + 1))
if cur_jump_count < learning_seq[learning_idx]:
print("to jump")
cur_jump_count += 1
jump()
elif cur_jump_count == learning_seq[learning_idx]:
print("to self KILL")
cur_jump_count += 1
jump(self_kill=True)
else:
plt.pause(10)
print("to restart")
cur_jump_count = 0
learning_idx += 1
# restart game
call_cmd("adb shell input tap %s %s" % (random.randint(int(400/coefficient), int(700/coefficient)),
random.randint(int(1500/coefficient), int(1600/coefficient))))
plt.pause(4)
plt.close()
plt.show()
if __name__ == '__main__':
print("Welcome")
main()
print("Done")