Fix: Accurate search_half and optimize rotation parameters

tasks/map/minimap/minimap.py

@@ -67,7 +67,7 @@ class Minimap(MapResource):
             search_position = np.array(self.position, dtype=np.int64)
             search_position += self.POSITION_FEATURE_PAD
             search_size = np.array(image_size(local)) * self.POSITION_SEARCH_RADIUS
-            search_half = (search_size // 2 * 2).astype(np.int64)
+            search_half = (search_size // 2).astype(np.int64)
             search_area = area_offset((0, 0, *(search_half * 2)), offset=-search_half)
             search_area = area_offset(search_area, offset=np.multiply(search_position, self.POSITION_SEARCH_SCALE))
             search_area = np.array(search_area).astype(np.int64)
@@ -269,11 +269,12 @@ class Minimap(MapResource):
         # Extract
         minimap = self.get_minimap(image, radius=self.MINIMAP_RADIUS)
         _, _, v = cv2.split(rgb2yuv(minimap))
-        image = cv2.subtract(255, v)
 
-        # image = cv2.GaussianBlur(image, (3, 3), 0)
+        image = cv2.subtract(128, v)
+
+        image = cv2.GaussianBlur(image, (3, 3), 0)
         # Expand circle into rectangle
-        remap = cv2.remap(image, *self.RotationRemapData, cv2.INTER_LINEAR)[d * 2 // 10:d * 6 // 10].astype(np.float32)
+        remap = cv2.remap(image, *self.RotationRemapData, cv2.INTER_LINEAR)[d * 1 // 10:d * 6 // 10].astype(np.float32)
         remap = cv2.resize(remap, None, fx=scale, fy=scale, interpolation=cv2.INTER_LINEAR)
         # Find derivative
         gradx = cv2.Scharr(remap, cv2.CV_32F, 1, 0)
@@ -284,7 +285,7 @@ class Minimap(MapResource):
         # Magic parameters for scipy.find_peaks
         para = {
             # 'height': (50, 800),
-            'height': 50,
+            'height': 35,
             # 'prominence': (0, 400),
             # 'width': (0, d * scale / 20),
             # 'distance': d * scale / 18,
@@ -356,13 +357,14 @@ class Minimap(MapResource):
         self.rotation_confidence = rotation_confidence
         self.rotation = rotation
 
-    def update(self, image):
+    def update(self, image, show_log=True):
         """
         Update minimap, costs about 7.88ms.
         """
         self.update_position(image)
         self.update_direction(image)
         self.update_rotation(image)
+        if show_log:
             self.log_minimap()
 
     def log_minimap(self):

tasks/map/resource/const.py

@@ -1,5 +1,6 @@
 import os
 
+import numpy as np
 from PIL import Image
 
 from module.base.utils import load_image
@@ -16,13 +17,14 @@ class ResourceConst:
     # Downscale GIMAP and minimap for faster run
     POSITION_SEARCH_SCALE = 0.5
     # Search the area that is 1.666x minimap, about 100px in wild on GIMAP
-    POSITION_SEARCH_RADIUS = 1.333
+    POSITION_SEARCH_RADIUS = 1.666
     # Can't figure out why but the result_of_0.5_lookup_scale + 0.5 ~= result_of_1.0_lookup_scale
     POSITION_MOVE_PATCH = (0.5, 0.5)
     # Position starting from the upper-left corner of the template image
     # but search an area larger than map
     # MINIMAP_RADIUS * POSITION_SEARCH_RADIUS * <max_scale>
-    POSITION_FEATURE_PAD = int(MINIMAP_RADIUS * POSITION_SEARCH_RADIUS * 1.5)
+    # POSITION_FEATURE_PAD = int(MINIMAP_RADIUS * POSITION_SEARCH_RADIUS * 1.5)
+    POSITION_FEATURE_PAD = 155
     # Must be odd, equals int(9 * POSITION_SEARCH_SCALE) + 1
     POSITION_AREA_DILATE = 5
 
@@ -99,14 +101,25 @@ class ResourceConst:
         Returns:
             float: Distance to current position
         """
-        x1, y1 = self.position
-        x2, y2 = target
-        diff = ((x2 - x1) ** 2 + (y2 - y1) ** 2) ** 0.5
-        return diff
+        return np.linalg.norm(np.subtract(target, self.position))
 
     def is_position_near(self, target, threshold=5):
         return self.position_diff(target) <= threshold
 
+    def position2direction(self, target):
+        """
+        Args:
+            target: Target position (x, y)
+
+        Returns:
+            float: Direction from current position to target position (0~360)
+        """
+        diff = np.subtract(target, self.position)
+        theta = np.rad2deg(np.arccos(-diff[1] / np.linalg.norm(diff)))
+        if diff[0] < 0:
+            theta = 360 - theta
+        return theta
+
     def direction_diff(self, target):
         """
         Args:
@@ -136,5 +149,5 @@ class ResourceConst:
             diff -= 360
         return diff
 
-    def is_rotation_near(self, target, threshold=15):
+    def is_rotation_near(self, target, threshold=10):
         return abs(self.rotation_diff(target)) <= threshold