feat: add sweep and task reward automation

automation/engine.py

@@ -51,6 +51,9 @@ class Matcher:
         return np.asarray(crop.convert('RGB').resize((562, 1002), Image.Resampling.LANCZOS))
 
     def score(self, frame, spec):
+        return self.locate(frame, spec)[0]
+
+    def locate(self, frame, spec):
         name = spec['template']
         if name not in self.cache:
             p = self.assets / name
@@ -70,8 +73,10 @@ class Matcher:
         # for the active home page. Also require similar absolute colors.
         difference = float(np.abs(candidate.astype(float) - template.astype(float)).mean())
         if difference > spec.get('max_color_difference', 28):
-            return 0.0
-        return float(score) if math.isfinite(score) else 0.0
+            return 0.0, None
+        if not math.isfinite(score):
+            return 0.0, None
+        return float(score), (x1 + x, y1 + y, w, h)
 
     def matches(self, frame, specs):
         return all(self.score(frame, s) >= s.get('threshold', 0.86) for s in specs)

automation/sweep.py

@@ -0,0 +1,61 @@
+"""首页小金猪快速扫荡流程。"""
+import time
+
+from .engine import WorkflowError
+
+
+class SweepRunner:
+    def __init__(self, desktop, matcher, flow, region_provider, log=print, timeout=12):
+        self.desktop = desktop
+        self.matcher = matcher
+        self.flow = flow
+        self.region_provider = region_provider
+        self.log = log
+        self.timeout = timeout
+
+    def wait(self, *names):
+        deadline = time.monotonic() + self.timeout
+        previous, stable = None, 0
+        while time.monotonic() < deadline:
+            screenshot = self.desktop.screenshot()
+            region = self.region_provider(screenshot)
+            frame = self.matcher.frame(screenshot, region)
+            found = next((name for name in names
+                          if self.matcher.matches(frame, self.flow['states'][name])), None)
+            stable = stable + 1 if found and found == previous else 1 if found else 0
+            previous = found
+            if stable >= 2:
+                return found, screenshot, region
+            time.sleep(0.25)
+        raise WorkflowError('未识别到预期页面 ' + '/'.join(names) + '，已停止。')
+
+    def click(self, action, observation, hold=0.2):
+        _, screenshot, region = observation
+        if self.region_provider(screenshot) != region:
+            raise WorkflowError('识别后游戏窗口移动，已停止，请重新运行。')
+        self.desktop.click(self.flow['clicks'][action], region, screenshot.size, hold=hold)
+        self.log('已执行：' + action)
+
+    def run(self, times=1, dry_run=False):
+        if not 1 <= times <= 20:
+            raise WorkflowError('扫荡次数必须在 1 到 20 之间。')
+        home = self.wait('home')
+        if dry_run:
+            self.log('首页识别通过。计划：小金猪 → 快速扫荡 %s 次 → 每次关闭奖励 → 返回首页。未点击。' % times)
+            return 'preview'
+
+        self.click('open_pig', home)
+        for current in range(1, times + 1):
+            idle = self.wait('sweep_available')
+            self.click('sweep', idle, hold=0.8)
+            reward = self.wait('reward')
+            self.log('第 %s/%s 次扫荡已确认奖励弹框。' % (current, times))
+            self.click('dismiss_reward', reward)
+            if current < times:
+                self.log('等待 %s 秒后进行下一次扫荡。' % self.flow['interval_seconds'])
+                time.sleep(self.flow['interval_seconds'])
+
+        self.click('close', self.wait('sweep_available'))
+        self.wait('home')
+        self.log('快速扫荡完成，已确认返回首页。')
+        return 'completed'

automation/tasks.py

@@ -0,0 +1,102 @@
+"""首页任务奖励领取流程。"""
+import time
+
+from .engine import WorkflowError
+
+
+class TaskRunner:
+    def __init__(self, desktop, matcher, flow, region_provider, log=print, timeout=12):
+        self.desktop = desktop
+        self.matcher = matcher
+        self.flow = flow
+        self.region_provider = region_provider
+        self.log = log
+        self.timeout = timeout
+
+    def observe(self):
+        screenshot = self.desktop.screenshot()
+        region = self.region_provider(screenshot)
+        return screenshot, region, self.matcher.frame(screenshot, region)
+
+    def wait(self, *names):
+        deadline = time.monotonic() + self.timeout
+        previous, stable = None, 0
+        while time.monotonic() < deadline:
+            screenshot, region, frame = self.observe()
+            found = next((name for name in names
+                          if self.matcher.matches(frame, self.flow['states'][name])), None)
+            stable = stable + 1 if found and found == previous else 1 if found else 0
+            previous = found
+            if stable >= 2:
+                return found, screenshot, region, frame
+            time.sleep(0.25)
+        raise WorkflowError('未识别到预期页面 ' + '/'.join(names) + '，已停止。')
+
+    def click(self, point, screenshot, region, hold=0.2):
+        if self.region_provider(screenshot) != region:
+            raise WorkflowError('识别后游戏窗口移动，已停止，请重新运行。')
+        self.desktop.click(point, region, screenshot.size, hold=hold)
+
+    def click_action(self, action, observation):
+        _, screenshot, region, _ = observation
+        self.click(self.flow['clicks'][action], screenshot, region)
+        self.log('已执行：' + action)
+
+    def claim_location(self, frame):
+        spec = self.flow['claim_button']
+        score, location = self.matcher.locate(frame, spec)
+        if score < spec.get('threshold', 0.86):
+            return None
+        x, y, width, height = location
+        return ((x + width / 2) / 562, (y + height / 2) / 1002)
+
+    def panel_dot(self, frame, panel):
+        spec = self.flow['tab_dots'][panel]
+        return self.matcher.score(frame, spec) >= spec.get('threshold', 0.86)
+
+    def claim_panel(self, panel):
+        count = 0
+        while count < self.flow['max_claims_per_panel']:
+            name, screenshot, region, frame = self.wait(panel)
+            location = self.claim_location(frame)
+            if location is None:
+                return count
+            self.click(location, screenshot, region, hold=0.8)
+            count += 1
+            self.log('已执行：%s 第 %s 个领取按钮。' % (panel, count))
+            reward = self.wait('reward')
+            self.click_action('dismiss_reward', reward)
+        raise WorkflowError('单个面板领取次数超过安全上限，已停止。')
+
+    def close_and_home(self):
+        panel = self.wait('daily', 'weekly')
+        self.click_action('close', panel)
+        self.wait('home')
+        self.log('已确认返回首页。')
+
+    def run(self, dry_run=False):
+        home = self.wait('home')
+        if dry_run:
+            self.log('首页识别通过。计划：检测任务红点 → 日常/周常领取 → 关闭任务 → 返回首页。未点击。')
+            return 'preview'
+
+        _, screenshot, region, frame = home
+        if not self.matcher.matches(frame, self.flow['task_dot']):
+            self.log('任务按钮没有红点，本次不进入任务面板。')
+            return 'no_task_dot'
+        self.click_action('open_tasks', home)
+        panel = self.wait('daily', 'weekly')
+        current = panel[0]
+        claimed = self.claim_panel(current)
+        self.log('%s 面板领取 %s 个奖励。' % (current, claimed))
+
+        other = 'weekly' if current == 'daily' else 'daily'
+        _, screenshot, region, frame = self.wait(current)
+        if self.panel_dot(frame, other):
+            self.click_action('open_' + other, (current, screenshot, region, frame))
+            claimed += self.claim_panel(other)
+            self.log('%s 面板领取完成。' % other)
+        else:
+            self.log('%s 标签没有红点，不切换。' % other)
+        self.close_and_home()
+        return 'claimed' if claimed else 'no_claim_button'