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nn_play.py
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nn_play.py
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# -*- coding:utf-8 -*-
# Created Time: 六 12/30 13:49:21 2017
# Author: Taihong Xiao <[email protected]>
import numpy as np
import time
import os, glob, shutil
import cv2
import argparse
import tensorflow as tf
from model import JumpModel
from model_fine import JumpModelFine
def multi_scale_search(pivot, screen, range=0.3, num=10):
H, W = screen.shape[:2]
h, w = pivot.shape[:2]
found = None
for scale in np.linspace(1-range, 1+range, num)[::-1]:
resized = cv2.resize(screen, (int(W * scale), int(H * scale)))
r = W / float(resized.shape[1])
if resized.shape[0] < h or resized.shape[1] < w:
break
res = cv2.matchTemplate(resized, pivot, cv2.TM_CCOEFF_NORMED)
loc = np.where(res >= res.max())
pos_h, pos_w = list(zip(*loc))[0]
if found is None or res.max() > found[-1]:
found = (pos_h, pos_w, r, res.max())
if found is None: return (0,0,0,0,0)
pos_h, pos_w, r, score = found
start_h, start_w = int(pos_h * r), int(pos_w * r)
end_h, end_w = int((pos_h + h) * r), int((pos_w + w) * r)
return [start_h, start_w, end_h, end_w, score]
class WechatAutoJump(object):
def __init__(self, phone, sensitivity, serverURL, debug, resource_dir):
self.phone = phone
self.sensitivity = sensitivity
self.debug = debug
self.resource_dir = resource_dir
self.step = 0
self.ckpt = os.path.join(self.resource_dir, 'train_logs_coarse/best_model.ckpt-13999')
self.ckpt_fine = os.path.join(self.resource_dir, 'train_logs_fine/best_model.ckpt-53999')
self.serverURL = serverURL
self.load_resource()
if self.phone == 'IOS':
import wda
self.client = wda.Client(self.serverURL)
self.s = self.client.session()
if self.debug:
if not os.path.exists(self.debug):
os.mkdir(self.debug)
def load_resource(self):
self.player = cv2.imread(os.path.join(self.resource_dir, 'player.png'), 0)
# network initization
self.net = JumpModel()
self.net_fine = JumpModelFine()
self.img = tf.placeholder(tf.float32, [None, 640, 720, 3], name='img')
self.img_fine = tf.placeholder(tf.float32, [None, 320, 320, 3], name='img_fine')
self.label = tf.placeholder(tf.float32, [None, 2], name='label')
self.is_training = tf.placeholder(np.bool, name='is_training')
self.keep_prob = tf.placeholder(np.float32, name='keep_prob')
self.pred = self.net.forward(self.img, self.is_training, self.keep_prob)
self.pred_fine = self.net_fine.forward(self.img_fine, self.is_training, self.keep_prob)
self.sess = tf.Session()
self.sess.run(tf.global_variables_initializer())
all_vars = tf.all_variables()
var_coarse = [k for k in all_vars if k.name.startswith('coarse')]
var_fine = [k for k in all_vars if k.name.startswith('fine')]
self.saver_coarse = tf.train.Saver(var_coarse)
self.saver_fine = tf.train.Saver(var_fine)
self.saver_coarse.restore(self.sess, self.ckpt)
self.saver_fine.restore(self.sess, self.ckpt_fine)
print('==== successfully restored ====')
def get_current_state(self):
if self.phone == 'Android':
os.system('adb shell screencap -p /sdcard/1.png')
os.system('adb pull /sdcard/1.png state.png')
elif self.phone == 'IOS':
self.client.screenshot('state.png')
if not os.path.exists('state.png'):
raise NameError('Cannot obtain screenshot from your phone! Please follow the instructions in readme!')
if self.debug:
shutil.copyfile('state.png', os.path.join(self.debug, 'state_{:03d}.png'.format(self.step)))
state = cv2.imread('state.png')
self.resolution = state.shape[:2]
scale = state.shape[1] / 720.
state = cv2.resize(state, (720, int(state.shape[0] / scale)), interpolation=cv2.INTER_NEAREST)
if state.shape[0] > 1280:
s = (state.shape[0] - 1280) // 2
state = state[s:(s+1280),:,:]
elif state.shape[0] < 1280:
s1 = (1280 - state.shape[0]) // 2
s2 = (1280 - state.shape[0]) - s1
pad1 = 255 * np.ones((s1, 720, 3), dtype=np.uint8)
pad2 = 255 * np.ones((s2, 720, 3), dtype=np.uint8)
state = np.concatenate((pad1, state, pad2), 0)
return state
def get_player_position(self, state):
state = cv2.cvtColor(state, cv2.COLOR_BGR2GRAY)
pos = multi_scale_search(self.player, state, 0.3, 10)
h, w = int((pos[0] + 13 * pos[2])/14.), (pos[1] + pos[3])//2
return np.array([h, w])
def get_target_position(self, state, player_pos):
feed_dict = {
self.img: np.expand_dims(state[320:-320], 0),
self.is_training: False,
self.keep_prob: 1.0,
}
pred_out = self.sess.run(self.pred, feed_dict=feed_dict)
pred_out = pred_out[0].astype(int)
x1 = pred_out[0] - 160
x2 = pred_out[0] + 160
y1 = pred_out[1] - 160
y2 = pred_out[1] + 160
if y1 < 0:
y1 = 0
y2 = 320
if y2 > state.shape[1]:
y2 = state.shape[1]
y1 = y2 - 320
img_fine_in = state[x1: x2, y1: y2, :]
feed_dict_fine = {
self.img_fine: np.expand_dims(img_fine_in, 0),
self.is_training: False,
self.keep_prob: 1.0,
}
pred_out_fine = self.sess.run(self.pred_fine, feed_dict=feed_dict_fine)
pred_out_fine = pred_out_fine[0].astype(int)
out = pred_out_fine + np.array([x1, y1])
return out
def get_target_position_fast(self, state, player_pos):
state_cut = state[:player_pos[0],:,:]
m1 = (state_cut[:, :, 0] == 245)
m2 = (state_cut[:, :, 1] == 245)
m3 = (state_cut[:, :, 2] == 245)
m = np.uint8(np.float32(m1 * m2 * m3) * 255)
b1, b2 = cv2.connectedComponents(m)
for i in range(1, np.max(b2) + 1):
x, y = np.where(b2 == i)
if len(x) > 280 and len(x) < 310:
r_x, r_y = x, y
h, w = int(r_x.mean()), int(r_y.mean())
return np.array([h, w])
def jump(self, player_pos, target_pos):
distance = np.linalg.norm(player_pos - target_pos)
press_time = distance * self.sensitivity
press_time = int(np.rint(press_time))
press_h, press_w = int(0.82*self.resolution[0]), self.resolution[1]//2
if self.phone == 'Android':
cmd = 'adb shell input swipe {} {} {} {} {}'.format(press_w, press_h, press_w, press_h, press_time)
print(cmd)
os.system(cmd)
elif self.phone == 'IOS':
self.s.tap_hold(press_w, press_h, press_time / 1000.)
def debugging(self):
current_state = self.state.copy()
cv2.circle(current_state, (self.player_pos[1], self.player_pos[0]), 5, (0,255,0), -1)
cv2.circle(current_state, (self.target_pos[1], self.target_pos[0]), 5, (0,0,255), -1)
cv2.imwrite(os.path.join(self.debug, 'state_{:03d}_res_h_{}_w_{}.png'.format(self.step, self.target_pos[0], self.target_pos[1])), current_state)
def play(self):
self.state = self.get_current_state()
self.player_pos = self.get_player_position(self.state)
if self.phone == 'IOS':
self.target_pos = self.get_target_position(self.state, self.player_pos)
print('CNN-search: %04d' % self.step)
else:
try:
self.target_pos = self.get_target_position_fast(self.state, self.player_pos)
print('fast-search: %04d' % self.step)
except UnboundLocalError:
self.target_pos = self.get_target_position(self.state, self.player_pos)
print('CNN-search: %04d' % self.step)
if self.debug:
self.debugging()
self.jump(self.player_pos, self.target_pos)
self.step += 1
time.sleep(1.5)
def run(self):
try:
while True:
self.play()
except KeyboardInterrupt:
pass
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--phone', default='Android', choices=['Android', 'IOS'], type=str, help='mobile phone OS')
parser.add_argument('--sensitivity', default=2.045, type=float, help='constant for press time')
parser.add_argument('--serverURL', default='http://localhost:8100', type=str, help='ServerURL for wda Client')
parser.add_argument('--resource', default='resource', type=str, help='resource dir')
parser.add_argument('--debug', default=None, type=str, help='debug mode, specify a directory for storing log files.')
args = parser.parse_args()
# print(args)
AI = WechatAutoJump(args.phone, args.sensitivity, args.serverURL, args.debug, args.resource)
AI.run()