def inference(path):
net = JumpModel()
img = tf.placeholder(tf.float32, [None, 640, 720, 3], name='img')
label = tf.placeholder(tf.float32, [None, 2], name='label')
is_training = tf.placeholder(np.bool, name='is_training')
keep_prob = tf.placeholder(np.float32, name='keep_prob')
lr = tf.placeholder(np.float32, name='lr')
pred = net.forward(img, is_training, keep_prob)
saver = tf.train.Saver()
sess = tf.Session()
sess.run(tf.global_variables_initializer())
ckpt = tf.train.get_checkpoint_state('./train_logs')
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
print('==== successfully restored ====')
val_img = get_a_img(path)
feed_dict = {
img: val_img,
is_training: False,
keep_prob: 1.0,
}
pred_out = sess.run(pred, feed_dict=feed_dict)
return pred_out
def inference(path):
net = JumpModel()
img = tf.placeholder(tf.float32, [None, 640, 720, 3], name='img')
label = tf.placeholder(tf.float32, [None, 2], name='label')
is_training = tf.placeholder(np.bool, name='is_training')
keep_prob = tf.placeholder(np.float32, name='keep_prob')
lr = tf.placeholder(np.float32, name='lr')
pred = net.forward(img, is_training, keep_prob, 'coarse')
saver = tf.train.Saver()
sess = tf.Session()
sess.run(tf.global_variables_initializer())
ckpt = tf.train.get_checkpoint_state('./train_logs')
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
print('==== successfully restored ====')
val_img = get_a_img(path)
feed_dict = {
img: val_img,
is_training: False,
keep_prob: 1.0,
}
pred_out = sess.run(pred, feed_dict=feed_dict)
return pred_out
parser = argparse.ArgumentParser()
parser.add_argument('-g', '--gpu', default=None, type=int)
args = parser.parse_args()
if args is not None:
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)
net = JumpModel()
dataset = JumpData()
img = tf.placeholder(tf.float32, [None, 640, 720, 3], name='img')
label = tf.placeholder(tf.float32, [None, 2], name='label')
is_training = tf.placeholder(np.bool, name='is_training')
keep_prob = tf.placeholder(np.float32, name='keep_prob')
lr = tf.placeholder(np.float32, name='lr')
pred = net.forward(img, is_training, keep_prob, 'coarse')
loss = tf.reduce_mean(tf.sqrt(tf.pow(pred - label, 2) + 1e-12))
tf.summary.scalar('loss', loss)
optimizer = tf.train.AdamOptimizer(lr)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(loss)
saver = tf.train.Saver()
sess = tf.Session()
merged = tf.summary.merge_all()
if not os.path.isdir('./logs'):
os.mkdir('./logs')
train_writer = tf.summary.FileWriter(os.path.join('./logs'), sess.graph)
sess.run(tf.global_variables_initializer())
if not os.path.isdir('./train_logs'):
parser = argparse.ArgumentParser()
parser.add_argument('-g', '--gpu', default=None, type=int)
args = parser.parse_args()
if args is not None:
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)
net = JumpModel()
dataset = JumpData()
img = tf.placeholder(tf.float32, [None, 640, 720, 3], name='img')
label = tf.placeholder(tf.float32, [None, 2], name='label')
is_training = tf.placeholder(np.bool, name='is_training')
keep_prob = tf.placeholder(np.float32, name='keep_prob')
lr = tf.placeholder(np.float32, name='lr')
pred = net.forward(img, is_training, keep_prob, 'coarse')
loss = tf.reduce_mean(tf.sqrt(tf.pow(pred - label, 2) + 1e-12))
tf.summary.scalar('loss', loss)
optimizer = tf.train.AdamOptimizer(lr)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(loss)
saver = tf.train.Saver()
sess = tf.Session()
merged = tf.summary.merge_all()
if not os.path.isdir('./logs'):
os.mkdir('./logs')
train_writer = tf.summary.FileWriter(os.path.join('./logs'), sess.graph)
sess.run(tf.global_variables_initializer())
if not os.path.isdir('./train_logs'):
parser = argparse.ArgumentParser()
parser.add_argument('-g', '--gpu', default=None, type=int)
args = parser.parse_args()
if args is not None:
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)
net = JumpModel()
dataset = JumpData()
img = tf.placeholder(tf.float32, [None, 320, 320, 3], name='img')
label = tf.placeholder(tf.float32, [None, 2], name='label')
is_training = tf.placeholder(np.bool, name='is_training')
keep_prob = tf.placeholder(np.float32, name='keep_prob')
lr = tf.placeholder(np.float32, name='lr')
pred = net.forward(img, is_training, keep_prob)
loss = tf.reduce_mean(tf.sqrt(tf.pow(pred - label, 2) + 1e-12))
tf.summary.scalar('loss', loss)
optimizer = tf.train.AdamOptimizer(lr)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(loss)
saver = tf.train.Saver()
sess = tf.Session()
merged = tf.summary.merge_all()
if not os.path.isdir('./logs'):
os.mkdir('./logs')
train_writer = tf.summary.FileWriter(os.path.join('./logs'), sess.graph)
sess.run(tf.global_variables_initializer())
if not os.path.isdir('./train_logs'):
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 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
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
# 初始化已跳跃步数
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
# 加载：player.png，初始化tf.Session()
self.load_resource()
if self.phone == 'IOS':
import wda
# 连接到手机
self.client = wda.Client(self.serverURL)
# 启动应用
self.s = self.client.session()
if self.debug:
if not os.path.exists(self.debug):
os.mkdir(self.debug)
def load_resource(self):
# 加载 小人图片 player.png
self.player = cv2.imread(os.path.join(self.resource_dir, 'player.png'),
0)
# network initization
self.net = JumpModel()
self.net_fine = JumpModelFine()
# 定义占位符:
# 将采集到的大小为1280*720的图像沿x方向上下各截去320*720大小，只保留中心640*720的图像作为训练数据
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')
# 定义标签:
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)
# 初始化并运行 self.sess
self.sess = tf.Session()
# 对所有的图变量进行集体初始化并开始运行
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 ====')
# 获取手机屏幕当前截图, 将截屏缩放成尺寸为：1280*720的图片返回
def get_current_state(self):
# 获取当前手机屏截屏，并把图片拉取到程序运行的当前目录
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)))
# 读取这张截图
state = cv2.imread('state.png')
# iphone上得到的state的值是：(1334，750,3), 切片取前2个值
# resolution[0]=y, resolution[1]=x
# 另外一种赋值方式： rows, columns=state.shape[:2]
self.resolution = state.shape[:2]
# 下面要将采集到的图片等比例缩放成尺寸(x,y)：720*1280
scale = state.shape[1] / 720. # 计算x轴像素的缩放系数,然后应用到y轴进行缩放
# 这里 state.shape[0]/scale = 1280.639999，取整后刚好是1280
state = cv2.resize(state, (720, int(state.shape[0] / scale)),
interpolation=cv2.INTER_NEAREST)
# 如果缩放后，state.shape[0]的值还不是1280，要再进一步处理：
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)
# 后续操作：每张图有判断意义的区域只有屏幕中央位置，截图的上下两部分是没有意义的
# 后面会从上下各截去320*720大小，只保留中心640*720的图像作为训练数据
return state
def get_player_position(self, state):
# 转换为灰度图片
state = cv2.cvtColor(state, cv2.COLOR_BGR2GRAY)
# 搜索player的坐标
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)
# Added by yichen
def personification(self):
if self.step % 70 == 0:
next_rest = 18
rest = True
elif self.step % 40 == 0:
next_rest = 13
rest = True
elif self.step % 20 == 0:
next_rest = 11
rest = True
elif self.step % 10 == 0:
next_rest = 8
rest = True
else:
rest = False
if rest:
for rest_time in range(next_rest):
sys.stdout.write('\r程序将在 {}s 后继续'.format(next_rest -
rest_time))
sys.stdout.flush()
time.sleep(1)
print('\n继续')
time.sleep(random.uniform(1.5, 3.0))
if self.step % 5 == 0:
self.sensitivity = 2.145
elif self.step % 7 == 0:
self.sensitivity = 2.000
elif self.step % 9 == 0:
self.sensitivity = 1.985
elif self.step % 3 == 0:
self.sensitivity = 1.970
def play(self):
# 获取 1280*720大小的屏幕截图
self.state = self.get_current_state()
# 计算 player的坐标
self.player_pos = self.get_player_position(self.state)
# 计算player要跳到哪个坐标
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()
# 触发跳跃动作
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
class WechatAutoJump(object):
def __init__(self, phone, sensitivity, debug, resource_dir):
self.phone = phone
self.sensitivity = sensitivity
self.debug = debug
self.resource_dir = resource_dir
self.step = 0
self.load_resource()
if self.phone == 'IOS':
self.client = wda.Client()
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.img = tf.placeholder(tf.float32, [None, 640, 720, 3], name='img')
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.saver = tf.train.Saver()
self.sess = tf.Session()
self.sess.run(tf.global_variables_initializer())
ckpt = tf.train.get_checkpoint_state(
os.path.join(self.resource_dir, 'train_logs2'))
if ckpt and ckpt.model_checkpoint_path:
self.saver.restore(self.sess, ckpt.model_checkpoint_path)
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 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
state = state[s:, :, :]
elif state.shape[0] < 1280:
s = 1280 - state.shape[0]
state = np.concatenate((255 * np.ones(
(s, 720, 3), dtype=np.uint8), state), 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)
return pred_out[0].astype(int)
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)
# print('fast', len(x))
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(press_time)
if self.phone == 'Android':
press_h, press_w = int(0.82 *
self.resolution[0]), self.resolution[1] // 2
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(200, 200, 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)
try:
self.target_pos = self.get_target_position_fast(
self.state, self.player_pos)
except:
self.target_pos = self.get_target_position(self.state,
self.player_pos)
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
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 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(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
class JumpAI(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/best_model.ckpt-14499')
# 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.wdaSession = self.client.session()
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()
# (128, 144, 3)
self.img = tf.placeholder(tf.float32, [None, 128, 144, 3], name='img')
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):
self.client.screenshot('state.png')
state = cv2.imread('state.png')
print state.shape #(2208, 1242, 3), (height, width, channel)
self.resolution = state.shape[:2]
scale = state.shape[1] / 720. # 1242 / 720 = 1.725
# 2208 / 1.725 = 1280
state = cv2.resize(state, (720, int(state.shape[0] / scale)),
interpolation=cv2.INTER_NEAREST)
# state = (1280,720,3)
return state
def get_player_position(self, state):
# state = (1280,720,3)
state = cv2.cvtColor(state, cv2.COLOR_BGR2GRAY)
pos = multi_scale_search(self.player, state, 0.3, 10)
# start_h, start_w, end_h, end_w
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):
# (1280,720,3)
state = state[320:-320]
# (640,720,3)
scale = 5
state = cv2.resize(
state, (int(state.shape[1] / scale), int(state.shape[0] / scale)),
interpolation=cv2.INTER_NEAREST)
# (128,144,3)
feed_dict = {
self.img: np.expand_dims(state, 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])
# x, h
pred_out[0] = pred_out[0] * scale + 320
pred_out[1] = pred_out[1] * scale
print pred_out[0]
print pred_out[1]
return pred_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):
# dist = numpy.linalg.norm(a-b)
distance = np.linalg.norm(player_pos - target_pos)
print 'distance = %2d' % distance
# sensitivity = 2.045
press_time = distance * self.sensitivity
press_time = int(np.rint(press_time))
# press_h, press_w doesn't matter
press_h, press_w = int(0.82 *
self.resolution[0]), self.resolution[1] // 2
self.wdaSession.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):
# get current screen shot (2208, 1242, 3) , resize it to (1280,720,3)
# (2208, 1242, 3) --> (1280,720,3)
self.state = self.get_current_state()
# get player's position
self.player_pos = self.get_player_position(self.state)
if self.phone == 'IOS':
# state (1280,720,3)
self.target_pos = self.get_target_position(self.state,
self.player_pos)
#self.player_pos[0] h
#self.player_pos[1] w
print self.player_pos[0], self.player_pos[1]
print('CNN to search time: %04d ' % self.step)
print(
'------------------------------------------------------------------------'
)
self.jump(self.player_pos, self.target_pos)
self.step += 1
time.sleep(1.3)
def run(self):
try:
while True:
self.play()
except KeyboardInterrupt:
pass
class WechatAutoJump(object):
def __init__(self, phone, sensitivity, debug, resource_dir):
self.phone = phone
self.sensitivity = sensitivity
self.debug = debug
self.resource_dir = resource_dir
self.step = 0 # 小人第几跳
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_log_fine/best_model.ckpt-53999')
self.player = None
self.net = None
self.net_fine = None
self.img = None
self.img_fine = None
self.label = None
self.is_training = None
self.keep_prob = None
self.pred = None
self.pred_fine = None
self.sess = None
self.saver_coarse = None
self.saver_fine = None
self.resolution = None
self.state = None
self.player_pos = None
self.target_pos = None
self.load_resource()
if self.phone == 'IOS':
import wda
self.client = wda.Client('http://localhost:8100')
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 initialization
self.net = JumpModel()
self.net_fine = JumpModelFine()
self.img = tf.placeholder(tf.float32,
shape=(None, 640, 720, 3),
name='img')
self.img_fine = tf.placeholder(tf.float32,
shape=(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') # get_target_position()用到
# 这里的第三个参数self.keep_prob必须是string类型的
# 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)
# 第三个参数直接写成string类型,两个模型-分别为 coarse 与 fine
self.pred = self.net.forward(self.img, self.is_training, "coarse")
self.pred_fine = self.net_fine.forward(self.img_fine, self.is_training,
"fine")
self.sess = tf.Session()
self.sess.run(tf.global_variables_initializer())
all_vars = tf.global_variables(
) # tf.all_variables() # Please use tf.global_variables instead.
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 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
state = state[s:, :, :]
elif state.shape[0] < 1280:
s = 1280 - state.shape[0]
state = np.concatenate((255 * np.ones(
(s, 720, 3), dtype=np.uint8), state), 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):
feed_dict = {
self.img: np.expand_dims(state[320:-320], 0),
self.is_training: False,
self.keep_prob: 1.0, #
}
# self.pred -> "coarse" 模型
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,
}
# self.pred_fine -> "fine" 模型
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
@staticmethod
def get_target_position_fast(state, player_pos):
r_x, r_y = None, None
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)
# print('fast', len(x))
if 280 < len(x) < 310:
r_x, r_y = x, y
if r_x is not None and r_y is not None:
# if r_x.any() and r_y.any():
h, w = int(r_x.mean()), int(r_y.mean())
return np.array([h, w])
else:
return None
def jump(self, player_pos, target_pos):
distance = np.linalg.norm(player_pos - target_pos)
press_time = distance * self.sensitivity
press_time = int(press_time)
if self.phone == 'Android':
# press_h, press_w = int(0.82 * self.resolution[0]), self.resolution[1] // 2
# 按压点在一定范围内随机
press_h, press_w = random.randint(300,
800), random.randint(200, 800)
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(200, 200, press_time / 1000.)
def debugging(self):
current_state = self.state.copy()
# 标出小人位置 绿点
cv2.circle(current_state, (self.player_pos[1], self.player_pos[0]), 5,
(0, 255, 0), -1)
# 标出目标位置 红点
cv2.circle(current_state, (self.target_pos[1], self.target_pos[0]), 5,
(0, 0, 255), -1)
# 保存在路径下
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)
print('CNN-search: %04d' % self.step)
else:
self.target_pos = self.get_target_position_fast(
self.state, self.player_pos)
if self.target_pos is not None:
# if self.target_pos.any():
print('fast-search: %04d' % self.step)
else:
self.target_pos = self.get_target_position(self.state)
print('CNN-search: %04d' % self.step)
if self.debug:
self.debugging()
print(self.player_pos, self.target_pos)
self.jump(self.player_pos, self.target_pos)
# 等待时间 1~2秒随机
ts = random.uniform(1, 2)
time.sleep(ts)
def run(self):
try:
while True:
self.play()
except KeyboardInterrupt:
pass
