优化声骸拾取

优化声骸拾取

优化声骸拾取

优化声骸拾取

优化声骸拾取

assets/echo_model/metadata.yaml

@@ -0,0 +1,21 @@
+description: Ultralytics YOLO11s model trained on split_dataset\data.yaml
+author: Ultralytics
+date: '2025-04-16T09:53:39.504253'
+version: 8.3.108
+license: AGPL-3.0 License (https://ultralytics.com/license)
+docs: https://docs.ultralytics.com
+stride: 32
+task: detect
+batch: 1
+imgsz:
+- 640
+- 640
+names:
+  0: echo
+args:
+  batch: 1
+  fraction: 1.0
+  half: false
+  int8: false
+  dynamic: false
+  nms: false

src/OpenVinoYolo8Detect.py

@@ -0,0 +1,271 @@
+import os
+import random
+import time
+from typing import Tuple
+
+# import onnxruntime as ort # Removed onnxruntime
+from openvino import Core  # Added OpenVINO Core
+import cv2
+import numpy as np
+
+from ok import Logger, Box, sort_boxes
+
+logger = Logger.get_logger(__name__)
+
+
+class OpenVinoYolo8Detect:  # Renamed class
+
+    def __init__(self, weights='echo.onnx', model_h=640, model_w=640, iou_thres=0.45):
+        """
+        yolov OpenVINO inference
+        dic_labels: {0: 'person', 1: 'bicycle'}
+        """
+        self.dic_labels = {0: 'echo'}
+        self.weights = weights
+        self.model_size = (model_w, model_h)
+        self.iou_threshold = iou_thres
+        self.openfile_name_model = weights
+
+        # --- OpenVINO Initialization ---
+        self.core = Core()
+        # self.core.set_property("CPU", {"INFERENCE_NUM_THREADS": str(1)})
+        device = "CPU"  # Default device, tries GPU then CPU etc.
+
+        try:
+            logger.info(f"Compiling OpenVINO model for {device}...")
+            # Read and compile the ONNX model directly
+            model = self.core.read_model(model=self.openfile_name_model)
+            self.compiled_model = self.core.compile_model(model=model, device_name=device,
+            config={"PERFORMANCE_HINT": "LATENCY"},)
+            # Get input/output names (usually one input, one output for YOLOv5)
+            self.input_layer = self.compiled_model.input(0)
+            self.output_layer = self.compiled_model.output(0)
+            self.input_width = self.input_layer.shape[2]
+            self.input_height = self.input_layer.shape[3]
+            logger.info(f"OpenVINO model compiled successfully for {self.compiled_model} {self.input_width}x{self.input_height}.")
+        except Exception as e:
+            logger.error(f"Error initializing OpenVINO: {e}")
+            raise RuntimeError("Could not initialize OpenVINO model") from e
+        # --- End OpenVINO Initialization ---
+
+    def letterbox(self, img: np.ndarray, new_shape: Tuple[int, int] = (640, 640)) -> Tuple[np.ndarray, Tuple[int, int]]:
+        """
+        Resize and reshape images while maintaining aspect ratio by adding padding.
+
+        Args:
+            img (np.ndarray): Input image to be resized.
+            new_shape (Tuple[int, int]): Target shape (height, width) for the image.
+
+        Returns:
+            (np.ndarray): Resized and padded image.
+            (Tuple[int, int]): Padding values (top, left) applied to the image.
+        """
+        shape = img.shape[:2]  # current shape [height, width]
+
+        # Scale ratio (new / old)
+        r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
+
+        # Compute padding
+        new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
+        dw, dh = (new_shape[1] - new_unpad[0]) / 2, (new_shape[0] - new_unpad[1]) / 2  # wh padding
+
+        if shape[::-1] != new_unpad:  # resize
+            img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
+        top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
+        left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
+        img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=(114, 114, 114))
+
+        return img, (top, left)
+
+    def _preprocess(self, img):
+        """图像预处理（保持宽高比的缩放填充） - unchanged"""
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+
+        img, pad = self.letterbox(img, (self.input_width, self.input_height))
+
+        # Normalize the image data by dividing it by 255.0
+        image_data = np.array(img) / 255.0
+
+        # Transpose the image to have the channel dimension as the first dimension
+        image_data = np.transpose(image_data, (2, 0, 1))  # Channel first
+
+        # Expand the dimensions of the image data to match the expected input shape
+        image_data = np.expand_dims(image_data, axis=0).astype(np.float32)
+
+        # Return the preprocessed image data
+        return image_data, pad
+
+
+    def _postprocess(self,  outputs, padding, orig_shape, confidence_threshold, label):
+        """
+        Perform post-processing on the model's output to extract and visualize detections.
+
+        This method processes the raw model output to extract bounding boxes, scores, and class IDs.
+        It applies non-maximum suppression to filter overlapping detections and draws the results on the input image.
+
+        Args:
+            input_image (np.ndarray): The input image.
+            output (List[np.ndarray]): The output arrays from the model.
+            pad (Tuple[int, int]): Padding values (top, left) used during letterboxing.
+
+        Returns:
+            (np.ndarray): The input image with detections drawn on it.
+        """
+        # Transpose and squeeze the output to match the expected shape
+        outputs = np.transpose(np.squeeze(outputs[0]))
+
+        # Get the number of rows in the outputs array
+        rows = outputs.shape[0]
+
+        # Lists to store the bounding boxes, scores, and class IDs of the detections
+        boxes = []
+        scores = []
+        class_ids = []
+
+        # Calculate the scaling factors for the bounding box coordinates
+        gain = min(orig_shape[0] / orig_shape[0], self.input_width / orig_shape[1])
+
+        outputs[:, 0] -= padding[1]
+        outputs[:, 1] -= padding[0]
+
+        # Iterate over each row in the outputs array
+        for i in range(rows):
+            # Extract the class scores from the current row
+            classes_scores = outputs[i][4:]
+
+            # Find the maximum score among the class scores
+            max_score = np.amax(classes_scores)
+            class_id = np.argmax(classes_scores)
+            # If the maximum score is above the confidence threshold
+            if max_score >= confidence_threshold and (label==-1 or label==class_id):
+                # Get the class ID with the highest score
+
+
+                # Extract the bounding box coordinates from the current row
+                x, y, w, h = outputs[i][0], outputs[i][1], outputs[i][2], outputs[i][3]
+
+                # Calculate the scaled coordinates of the bounding box
+                left = int((x - w / 2) / gain)
+                top = int((y - h / 2) / gain)
+                width = int(w / gain)
+                height = int(h / gain)
+
+                # Add the class ID, score, and box coordinates to the respective lists
+                class_ids.append(class_id)
+                scores.append(max_score)
+                boxes.append([left, top, width, height])
+
+        # Apply non-maximum suppression to filter out overlapping bounding boxes
+        indices = cv2.dnn.NMSBoxes(boxes, scores, confidence_threshold, self.iou_threshold)
+
+        # Iterate over the selected indices after non-maximum suppression
+        results = []
+        for i in indices:
+            # Get the box, score, and class ID corresponding to the index
+            box = boxes[i]
+            box_obj = Box(box[0], box[1], box[2], box[3])  # Use Box class if available
+            box_obj.name = self.dic_labels.get(int(class_ids[i]), 'unknown')
+            box_obj.confidence = scores[i]
+            results.append(box_obj)
+            # Draw the detection on the input image
+            # self.draw_detections(input_image, box, score, class_id)
+        return results
+
+    # 推理
+    def detect(self, image, threshold=0.5, label=-1):
+        '''
+        预测
+        '''
+        try:
+            h, w = image.shape[:2]
+            img_data, pad = self._preprocess(image)
+            # input_tensor = np.expand_dims(img_data, axis=0)  # Add batch dimension
+
+            # --- OpenVINO Inference ---
+            # Input is a dictionary {input_layer_name: data}
+            # Output is a dictionary {output_layer_name: data}
+            results = self.compiled_model({self.input_layer: img_data})
+            # Extract the output tensor using the output layer obtained during init
+            outputs = results[self.output_layer]
+            # --- End OpenVINO Inference ---
+            boxes = self._postprocess(outputs, pad, (h, w), threshold, label)
+
+            return sort_boxes(boxes)
+        except Exception as e:
+            logger.error(f'OpenVINO yolo detect error:', e)  # Added exc_info
+            return []
+
+
+# --- Main execution part needs to be updated to use the new class ---
+if __name__ == '__main__':
+    # Ensure ok module and Box class are available, or provide stubs
+    class MockLogger:
+        def get_logger(self, name): return self
+
+        def debug(self, msg, *args): print(f"DEBUG: {msg}")
+
+        def info(self, msg, *args): print(f"INFO: {msg}")
+
+        def warning(self, msg, *args): print(f"WARN: {msg}")
+
+        def error(self, msg, *args, **kwargs): print(f"ERROR: {msg}")
+
+
+    class MockOk:
+        class og: use_dml = False  # Simulate ok.og.use_dml if needed
+
+        Logger = MockLogger()
+
+
+    if 'ok' not in globals():  # Define stubs if 'ok' module is not present
+        ok = MockOk()
+        Logger = ok.Logger
+
+
+        class Box:
+            def __init__(self, x, y, w, h):
+                self.x, self.y, self.w, self.h = x, y, w, h
+                self.name = "unknown"
+                self.confidence = 0.0
+
+            def __repr__(self):
+                return f"Box(x={self.x}, y={self.y}, w={self.w}, h={self.h}, name='{self.name}', conf={self.confidence:.2f})"
+
+    image_path = "tests/images/echo.png"
+    weights = "assets/echo_model/best.xml"  # OpenVINO reads ONNX directly
+    model_h = 640
+    model_w = 640
+
+
+    # NOTE: You need a real ONNX model at the 'weights' path for inference to work.
+    if not os.path.exists(weights):
+        print(f"ERROR: Model file not found at {weights}. Inference will fail.")
+        print("Please download or place yolov5s_320.onnx in assets/yolo/")
+        # Create a dummy file to avoid immediate crash during init, but it won't work
+        # with open(weights, 'w') as f: f.write('') # This won't be a valid model
+
+    # Use the new OpenVINO class
+    yolov = OpenVinoYolo8Detect(weights=weights, model_w=model_w, model_h=model_h)
+
+    # Test 1
+    big_img = cv2.imdecode(np.fromfile(file=image_path, dtype=np.uint8), cv2.IMREAD_COLOR)
+    if big_img is None:
+        print(f"Error loading image: {image_path}")
+    else:
+        start_time = time.time()
+        for i in range(100):
+            res_loc = yolov.detect(big_img, label=0)  # label=12 -> '声骸'
+        end_time = time.time()
+        print(f"Detection 1 time: {(end_time - start_time) * 1000:.2f} ms")
+
+    # Test 2
+    img2 = cv2.imread("tests/images/echo2.png")
+    if img2 is None:
+        print("Error loading image: tests/images/echo2.png")
+    else:
+        start_time = time.time()
+        for i in range(100):
+            res_loc = yolov.detect(img2, label=0)
+        end_time = time.time()
+        print(f"Detection 2 time: {(end_time - start_time) * 1000:.2f} ms")
+

src/combat/CombatCheck.py

@@ -196,7 +196,7 @@ class CombatCheck(BaseWWTask):
                 return self.wait_until(self.has_target, time_out=self.target_enemy_time_out,
                                        pre_action=lambda: self.middle_click(interval=0.2))
 
-    def check_health_bar(self):
+    def has_health_bar(self):
         if self._in_combat:
             min_height = self.height_of_screen(12 / 2160)
             max_height = min_height * 3
@@ -221,8 +221,13 @@ class CombatCheck(BaseWWTask):
                 self.boss_health = self.boss_health_box.crop_frame(self.frame)
                 self.draw_boxes('boss_health', boxes, color='blue')
                 return True
+        return False
 
-        return self.find_boss_lv_text()
+    def check_health_bar(self):
+        if self.has_health_bar():
+            return True
+        else:
+            return self.find_boss_lv_text()
 
     def find_boss_lv_text(self):
         texts = self.ocr(box=self.box_of_screen(1269 / 3840, 10 / 2160, 2533 / 3840, 140 / 2160, hcenter=True),

src/globals.py

@@ -5,7 +5,7 @@ import cv2
 from PySide6.QtCore import Signal, QObject
 
 from ok import Config, Logger, get_path_relative_to_exe
-from src.OpenVinoYoloDetect import OpenVinoYoloDetect
+from src.OpenVinoYolo8Detect import OpenVinoYolo8Detect
 
 logger = Logger.get_logger(__name__)
 
@@ -21,10 +21,10 @@ class Globals(QObject):
     @property
     def yolo_model(self):
         if self._yolo_model is None:
-            self._yolo_model =  OpenVinoYoloDetect(weights=get_path_relative_to_exe(os.path.join("assets","yolo", "yolov5s_320.onnx")))
+            self._yolo_model =  OpenVinoYolo8Detect(weights=get_path_relative_to_exe(os.path.join("assets", "echo_model", "best.xml")))
         return self._yolo_model
 
-    def yolo_detect(self, image, threshold=0.5, label=-1):
+    def yolo_detect(self, image, threshold=0.6, label=-1):
         return self.yolo_model.detect(image, threshold=threshold, label=label)
 
 

src/task/AutoCombatTask.py

@@ -20,7 +20,7 @@ class AutoCombatTask(BaseCombatTask, TriggerTask):
         self.scene: WWScene | None = None
         self.default_config.update({
             'Auto Target': True,
-            'Auto Pick Echo After Combat': True,
+            'Auto Pick Echo After Combat': False,
         })
         self.config_description = {
             'Auto Target': 'Turn off to enable auto combat only when manually target enemy using middle click'

src/task/BaseCombatTask.py

@@ -116,7 +116,7 @@ class BaseCombatTask(CombatCheck):
         self.wait_in_team_and_world(time_out=10)
         self.sleep(1)
         self.middle_click()
-        self.sleep(0.2)
+        self.sleep(1)
 
     def run_in_circle_to_find_echo(self, circle_count=3):
         directions = ['w', 'a', 's', 'd']

src/task/BaseWWTask.py

@@ -141,15 +141,59 @@ class BaseWWTask(BaseTask):
                 return None
         return f
 
-    def walk_to_box(self, find_function, time_out=30, end_condition=None, y_offset=0.05, v_move_fix_time=0):
+    def walk_to_yolo_echo(self, time_out=15):
+        last_direction = None
+        start = time.time()
+        no_echo_start = 0
+        while time.time() - start < time_out:
+            self.next_frame()
+            if self.pick_echo():
+                return True
+            echos = self.find_echos()
+            if not echos:
+                if no_echo_start == 0:
+                    no_echo_start = time.time()
+                elif time.time() - no_echo_start > 1.5:
+                    self.log_debug(f'walk front to_echo, no echos found, break')
+                    break
+                continue
+            else:
+                no_echo_start = 0
+            echo = echos[0]
+            center_distance = echo.center()[0] - self.width_of_screen(0.5)
+            threshold = 0.05 if not last_direction else 0.15
+            if abs(center_distance) < self.height_of_screen(threshold):
+                if echo.y + echo.height > self.height_of_screen(0.65):
+                    next_direction = 's'
+                else:
+                    next_direction = 'w'
+            elif center_distance > 0:
+                next_direction = 'd'
+            else:
+                next_direction = 'a'
+            last_direction = self._walk_direction(last_direction, next_direction)
+        self._stop_last_direction(last_direction)
+
+
+    def _walk_direction(self, last_direction, next_direction):
+        if next_direction != last_direction:
+            self._stop_last_direction(last_direction)
+            if next_direction:
+                self.send_key_down(next_direction)
+        return next_direction
+
+    def _stop_last_direction(self, last_direction):
+        if last_direction:
+            self.send_key_up(last_direction)
+            self.sleep(0.01)
+        return None
+
+    def walk_to_box(self, find_function, time_out=30, end_condition=None, y_offset=0.05):
         if not find_function:
             self.log_info('find_function not found, break')
             return False
         last_direction = None
-        v_fix_count = 0
-        original_y_offset = y_offset
         start = time.time()
-        last_v_move = start
         ended = False
         last_target = None
         while time.time() - start < time_out:
@@ -166,27 +210,14 @@ class BaseWWTask(BaseTask):
                     treasure_icon = None
             if treasure_icon:
                 last_target = treasure_icon
-            next_direction = None
             if last_target is None:
-                if not end_condition:
+                next_direction = self.opposite_direction(last_direction)
-                    self.log_info('find_function not found, break')
+                self.log_info('find_function not found, change to opposite direction')
-                break
+            else:
-            if 0 < v_move_fix_time < time.time() - last_v_move:
-                if v_fix_count < 5:
-                    v_fix_count += 1
-                    y_offset = original_y_offset - 0.05 * v_fix_count
-                else:
-                    v_fix_count += 1
-                    y_offset = original_y_offset + 0.05 * (v_fix_count - 4)
-
-            if next_direction is None:
                 x, y = last_target.center()
                 y = max(0, y - self.height_of_screen(y_offset))
                 next_direction = self.get_direction(x, y, self.width, self.height, current_direction=last_direction)
 
-            if next_direction == 'w' or next_direction == 's':
-                last_v_move = time.time()
-
             if next_direction != last_direction:
                 if last_direction:
                     self.send_key_up(last_direction)
@@ -405,7 +436,7 @@ class BaseWWTask(BaseTask):
         result = self.executor.ocr_lib(image, use_det=True, use_cls=False, use_rec=True)
         self.logger.info(f'ocr_result {result}')
 
-    def find_echo(self, threshold=0.5):
+    def find_echos(self, threshold=0.6):
         """
         Main function to load ONNX model, perform inference, draw bounding boxes, and display the output image.
 
@@ -417,12 +448,12 @@ class BaseWWTask(BaseTask):
             list: List of dictionaries containing detection information such as class_id, class_name, confidence, etc.
         """
         # Load the ONNX model
-        boxes = og.my_app.yolo_detect(self.frame, threshold=threshold, label=12)
+        ret = og.my_app.yolo_detect(self.frame, threshold=threshold, label=0)
 
-        ret = sorted(boxes, key=lambda detection: detection.x, reverse=True)
         for box in ret:
-            box.y -= box.height * 2/3
+            box.y += box.height * 1/3
             box.height = 1
+        self.draw_boxes("echo", ret)
         return ret
 
     def yolo_find_all(self, threshold=0.3):
@@ -454,7 +485,7 @@ class BaseWWTask(BaseTask):
             self.send_key('f')
             return True
 
-    def yolo_find_echo(self, use_color=True, walk=True, turn=True):
+    def yolo_find_echo(self, use_color=False, turn=True):
         if self.debug:
             # self.draw_boxes('echo', echos)
             self.screenshot('yolo_echo_start')
@@ -467,14 +498,13 @@ class BaseWWTask(BaseTask):
         for i in range(4):
             if turn:
                 self.center_camera()
-            echos = self.find_echo()
+            echos = self.find_echos()
-            if len(echos) > 0:
-                self.draw_boxes('yolo_echo', echos)
             max_echo_count = max(max_echo_count, len(echos))
             self.log_debug(f'max_echo_count {max_echo_count}')
             if echos:
                 self.log_info(f'yolo found echo {echos}')
-                return self.walk_to_box(self.find_echo, time_out=15, end_condition=self.pick_echo, v_move_fix_time=5), max_echo_count > 1
+                # return self.walk_to_box(self.find_echos, time_out=15, end_condition=self.pick_echo), max_echo_count > 1
+                return self.walk_to_yolo_echo(), max_echo_count > 1
             if use_color:
                 color_percent = self.calculate_color_percentage(echo_color, front_box)
                 self.log_debug(f'pick_echo color_percent:{color_percent}')
@@ -482,28 +512,26 @@ class BaseWWTask(BaseTask):
                     # if self.debug:
                     #     self.screenshot('echo_color_picked')
                     self.log_debug(f'found color_percent {color_percent} > {color_threshold}, walk now')
-                    return self.walk_find_echo(), max_echo_count > 1
+                    #return self.walk_to_box(self.find_echos, time_out=15, end_condition=self.pick_echo), max_echo_count > 1
-            if not turn and i==0:
+                    return self.walk_to_yolo_echo(), max_echo_count > 1
+            if not turn and i == 0:
                 return False, max_echo_count > 1
             self.send_key('a', down_time=0.05)
             self.sleep(0.5)
 
         self.center_camera()
-        if walk:
-            picked = self.walk_find_echo()
-            return picked, max_echo_count > 1
         return False, max_echo_count > 1
 
     def center_camera(self):
-        self.click(0.5, 0.5, down_time=0.2, after_sleep=0.5, key='middle')
+        self.click(0.5, 0.5, down_time=0.2, after_sleep=1, key='middle')
 
     def turn_direction(self, direction):
         if direction != 'w':
             self.send_key(direction, down_time=0.05, after_sleep=0.5)
         self.center_camera()
 
-    def walk_find_echo(self, backward_time=1):
+    def walk_find_echo(self, backward_time=1, time_out=4):
-        if self.walk_until_f(time_out=4, backward_time=backward_time, target_text=self.absorb_echo_text(),
+        if self.walk_until_f(time_out=time_out, backward_time=backward_time, target_text=self.absorb_echo_text(),
                              raise_if_not_found=False):  # find and pick echo
             logger.debug(f'farm echo found echo move forward walk_until_f to find echo')
             return True
@@ -659,13 +687,13 @@ class BaseWWTask(BaseTask):
         self.sleep(1)
 
     def openF2Book(self, feature="gray_book_all_monsters"):
-        self.log_info('click f2 to open the book')
-        # self.send_key_down('alt')
-        # self.sleep(0.05)
-        # self.click_relative(0.77, 0.05)
-        # self.send_key_up('alt')
         self.sleep(1)
-        self.send_key('f2')
+        self.log_info('click f2 to open the book')
+        self.send_key_down('alt')
+        self.sleep(0.05)
+        self.click_relative(0.77, 0.05)
+        self.send_key_up('alt')
+        # self.send_key('f2')
         gray_book_boss = self.wait_book(feature)
         if not gray_book_boss:
             self.log_error("can't find gray_book_boss, make sure f2 is the hotkey for book", notify=True)

src/task/FarmMapTask.py

@@ -157,7 +157,7 @@ class BigMap(WWOneTimeTask, BaseCombatTask):
             self.draw_boxes('me', in_big_map.scale(0.1), color='blue')
             # self.screenshot('box_minimap', frame=frame, show_box=True)
             # self.screenshot('template_minimap', frame=mat)
-        self.my_box = in_big_map.scale(1.25)
+        self.my_box = in_big_map.scale(1.3)
         return in_big_map
 
 def create_circle_mask_with_hole(image):
@@ -201,6 +201,7 @@ class FarmMapTask(BigMap):
         self.stuck_keys = [['space', 0.02], ['a',2], ['d',2], ['t', 0.02]]
         self.stuck_index = 0
         self.last_distance = 0
+        self._has_health_bar = False
 
     @property
     def star_move_distance_threshold(self):
@@ -215,6 +216,8 @@ class FarmMapTask(BigMap):
     def on_combat_check(self):
         self.incr_drop(self.pick_f())
         self.find_my_location()
+        if not self._has_health_bar:
+            self._has_health_bar = self.has_health_bar()
         return True
 
     def go_to_star(self):
@@ -225,6 +228,7 @@ class FarmMapTask(BigMap):
         while True:
             self.sleep(0.01)
             self.middle_click(interval=1, after_sleep=0.2)
+            self._has_health_bar = False
             if self.in_combat():
                 self.sleep(2)
                 if current_direction is not None:
@@ -234,14 +238,13 @@ class FarmMapTask(BigMap):
                 start = time.time()
                 self.combat_once()
                 duration = time.time() - start
-                if duration > 8:
+
-                    self.my_box = self.my_box.scale(1.1)
+                while True:
-                    while True:
+                    dropped, has_more = self.yolo_find_echo(use_color=False, turn=duration > 15 or self._has_health_bar)
-                        dropped, has_more = self.yolo_find_echo(use_color=False, walk=False)
+                    self.incr_drop(dropped)
-                        self.incr_drop(dropped)
+                    self.sleep(0.5)
-                        self.sleep(0.5)
+                    if not dropped or not has_more:
-                        if not dropped or not has_more:
+                        break
-                            break
             star, distance, angle = self.find_direction_angle()
             # self.draw_boxes('next_star', star, color='green')
             if not star:

tests/TestCon.py

@@ -0,0 +1,29 @@
+import unittest
+from config import config
+from ok.test.TaskTestCase import TaskTestCase
+from src.task.AutoCombatTask import AutoCombatTask
+
+config['debug'] = True
+
+
+class TestCombatCheck(TaskTestCase):
+    task_class = AutoCombatTask
+    config = config
+
+    def test_con_full(self):
+        self.task.do_reset_to_false()
+        self.set_image('tests/images/con_full.png')
+        # in_combat = self.task.in_combat()
+        # self.assertTrue(in_combat)
+        #
+        # self.task.do_reset_to_false()
+        # self.set_image('tests/images/con_full.png')
+        # in_combat = self.task.in_combat()
+        # self.assertTrue(in_combat)
+        self.task.load_chars()
+        con_full = self.task.get_current_char().is_con_full()
+        self.assertTrue(con_full)
+
+
+if __name__ == '__main__':
+    unittest.main()

tests/TestEcho.py

@@ -0,0 +1,30 @@
+import time
+import unittest
+from config import config
+from ok.test.TaskTestCase import TaskTestCase
+from src.task.AutoCombatTask import AutoCombatTask
+from src.task.DailyTask import DailyTask
+
+config['debug'] = True
+
+
+class TestEcho(TaskTestCase):
+    task_class = DailyTask
+    config = config
+
+    def test_find_echo(self):
+        self.set_image('tests/images/echo.png')
+        echos = self.task.find_echo()
+        self.assertEqual(1, len(echos))
+        time.sleep(1)
+        self.task.screenshot('echo1', show_box=True)
+        self.set_image('tests/images/echo2.png')
+        echos = self.task.find_echo()
+        time.sleep(1)
+        self.task.screenshot('echo2', show_box=True)
+        self.assertEqual(1, len(echos))
+        time.sleep(1)
+
+
+if __name__ == '__main__':
+    unittest.main()