Upd: threshold for predict_valid in campaign_bonus

module/statistics/campaign_bonus.py

@@ -9,7 +9,13 @@ from module.statistics.utils import *
 
 class BonusItem(Item):
     def predict_valid(self):
-        return np.mean(rgb2gray(self.image) > 160) > 0.3
+        image = rgb2gray(self.image)
+        width, height = image_size(image)
+        wm, hm = width // 2, height // 2
+        row_center = image[hm: hm + 5, :]
+        info_bar = image[64: 69, :]
+        column_center = image[:, wm: wm + 5]
+        return all(np.mean(img > 160) > 0.1 for img in [image, row_center, info_bar, column_center])
 
 
 class CampaignBonusStatistics(GetItemsStatistics):

module/statistics/drop_statistics.py

@@ -120,7 +120,7 @@ class DropStatistics:
             if self.campaign_bonus.appear_on(image, similarity=similarity):
                 for button in [CAMPAIGN_BONUS_SINGLE, CAMPAIGN_BONUS]:
                     self.campaign_bonus.bonus_button = button
-                    for item in self.campaign_bonus.stats_get_items(image):
+                    for item in self.campaign_bonus.stats_get_items(image, mode='known'):
                         yield [ts, campaign, enemy_name, button.name, item.name, item.amount]
             else:
                 raise ImageError('No campaign bonus on image.')
@@ -135,7 +135,7 @@ class DropStatistics:
         print('')
         logger.hr(f'Extract templates from {campaign}', level=1)
         self.check_server(campaign)
-        for ts, file in tqdm(load_folder(self.drop_folder(campaign)).items()):
+        for ts, file in tqdm(load_folder(self.drop_folder(campaign), ext=['.png', '.jpg']).items()):
             try:
                 self.parse_template(file)
             except ImageError as e:
@@ -160,7 +160,7 @@ class DropStatistics:
 
         with open(self.csv_file, 'a', newline='', encoding=DropStatistics.CSV_ENCODING) as csv_file:
             writer = csv.writer(csv_file)
-            for ts, file in tqdm(load_folder(self.drop_folder(campaign)).items()):
+            for ts, file in tqdm(load_folder(self.drop_folder(campaign), ext=['.png', '.jpg']).items()):
                 try:
                     rows = list(self.parse_drop(file))
                     writer.writerows(rows)

module/statistics/item.py

@@ -217,13 +217,15 @@ class ItemGrid:
             self.next_cost_template_index += 1
         self.next_cost_template_index = max(self.next_cost_template_index, max_digit + 1)
 
-    def match_template(self, image, similarity=None):
+    def match_template(self, image, similarity=None, mode='all'):
         """
         Match templates, try most frequent hit templates first.
 
         Args:
             image (np.ndarray):
             similarity (float):
+            mode (str): 'all' for all templates, may create new templates
+                        'known' for known templates only, which will skip images named with digit
 
         Returns:
             str: Template name.
@@ -234,7 +236,10 @@ class ItemGrid:
         # Match frequently hit templates first
         names = np.array(list(self.templates.keys()))[np.argsort(list(self.templates_hit.values()))][::-1]
         # Match known templates first
-        names = [name for name in names if not name.isdigit()] + [name for name in names if name.isdigit()]
+        tmp = [name for name in names if not name.isdigit()]
+        if mode == 'all':
+            tmp.extend([name for name in names if name.isdigit()])
+        names = tmp
         for name in names:
             if color_similar(color1=color, color2=self.colors[name], threshold=30):
                 res = cv2.matchTemplate(image, self.templates[name], cv2.TM_CCOEFF_NORMED)
@@ -243,6 +248,8 @@ class ItemGrid:
                     self.templates_hit[name] += 1
                     return name
 
+        if mode == 'known':
+            return 'unknown'
         self.next_template_index += 1
         name = str(self.next_template_index)
         logger.info(f'New template: {name}')
@@ -334,7 +341,7 @@ class ItemGrid:
         else:
             return None
 
-    def predict(self, image, name=True, amount=True, cost=False, price=False, tag=False):
+    def predict(self, image, name=True, amount=True, cost=False, price=False, tag=False, mode='all'):
         """
         Args:
             image (np.ndarray):
@@ -343,6 +350,7 @@ class ItemGrid:
             cost (bool): If predict the cost to buy item.
             price (bool): If predict item price.
             tag (bool): If predict item tag. Tags are like `catchup`, `bonus`.
+            mode (str): 'all' or 'known'
 
         Returns:
             list[Item]:
@@ -354,7 +362,7 @@ class ItemGrid:
             for item, a in zip(self.items, amount_list):
                 item.amount = a
         if name:
-            name_list = [self.match_template(item.image) for item in self.items]
+            name_list = [self.match_template(item.image, mode=mode) for item in self.items]
             for item, n in zip(self.items, name_list):
                 item.name = n
         if cost: