Upd: resize image in campaign bonus

module/statistics/campaign_bonus.py

@@ -25,8 +25,8 @@ class CampaignBonusStatistics(GetItemsStatistics):
         if CAMPAIGN_BONUS_STRATEGY_CHECK.match(image, offset=(200, 500), similarity=similarity):
             return False
         if AUTO_SEARCH_MENU_EXIT.match(image, offset=(200, 20), similarity=similarity) \
-                and (CAMPAIGN_BONUS.match(image, offset=(200, 500), similarity=similarity) \
+                and CAMPAIGN_BONUS_SINGLE.match(image, offset=(200, 100), similarity=similarity) \
-                and CAMPAIGN_BONUS_SINGLE.match(image, offset=(200, 500), similarity=similarity)):
+                and CAMPAIGN_BONUS.match(image, offset=(200, 500), similarity=similarity):
             return True
 
         return False

module/statistics/drop_statistics.py

@@ -24,6 +24,7 @@ class DropStatistics:
     TEMPLATE_FOLDER = 'item_templates'
     TEMPLATE_BASIC = './assets/stats_basic'
     SKIP_IMAGE_FOLDER = 'skip_images'
+    IMAGE_EXTENSION = '.png'
     CNOCR_CONTEXT = 'cpu'
     CSV_FILE = 'drop_result.csv'
     CSV_OVERWRITE = True
@@ -94,6 +95,8 @@ class DropStatistics:
         """
         Move a image file to {SKIP_IMAGE_FOLDER}/{CAMPAIGN}.
         """
+        if not self.SKIP_IMAGE_FOLDER:
+            return False
         campaign = os.path.basename(os.path.abspath(os.path.join(file, '../')))
         folder = self.skip_file_folder(campaign)
         os.makedirs(folder, exist_ok=True)
@@ -104,10 +107,10 @@ class DropStatistics:
         Extract template from a single file.
         New templates will be given an auto-increased ID.
         """
-        image = load_image(file)
+        images = unpack(load_image(file))[-1::]
-        similarity = get_similarity(image)
+        similarities = [get_similarity(image) for image in images]
-        images = unpack(image)[-1::]
+        images = [resize_image(image) for image in images]
-        for image in images:
+        for image, similarity in zip(images, similarities):
             # if self.get_items.appear_on(image):
             #     self.get_items.extract_template(image, folder=self.template_folder)
             if self.campaign_bonus.appear_on(image, similarity=similarity):
@@ -129,11 +132,11 @@ class DropStatistics:
         """
         ts = os.path.splitext(os.path.basename(file))[0]
         campaign = os.path.basename(os.path.abspath(os.path.join(file, '../')))
-        image = load_image(file)
+        images = unpack(load_image(file))[-1::]
-        similarity = get_similarity(image)
+        similarities = [get_similarity(image) for image in images]
-        images = unpack(image)[-1::]
+        images = [resize_image(image) for image in images]
         enemy_name = 'unknown'
-        for image in images:
+        for image, similarity in zip(images, similarities):
             # if self.battle_status.appear_on(image):
             #     enemy_name = self.battle_status.stats_battle_status(image)
             # if self.get_items.appear_on(image):
@@ -157,7 +160,8 @@ 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), ext=['.png', '.jpg']).items()):
+        drop_folder = load_folder(self.drop_folder(campaign), ext=DropStatistics.IMAGE_EXTENSION)
+        for ts, file in tqdm(drop_folder.items()):
             try:
                 self.parse_template(file)
             except ImageError as e:
@@ -184,7 +188,8 @@ 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), ext=['.png', '.jpg']).items()):
+            drop_folder = load_folder(self.drop_folder(campaign), ext=DropStatistics.IMAGE_EXTENSION)
+            for ts, file in tqdm(drop_folder.items()):
                 try:
                     rows = list(self.parse_drop(file))
                     writer.writerows(rows)
@@ -210,6 +215,8 @@ if __name__ == '__main__':
     # This will save images {DROP_FOLDER}/{SKIP_IMAGE_FOLDER}/{CAMPAIGN}.
     # If folder doesn't exist, auto create
     DropStatistics.SKIP_IMAGE_FOLDER = 'skip_images'
+    # image file extension suck as '.png', '.jpg'
+    DropStatistics.IMAGE_EXTENSION = ['.png', '.jpg', '.PNG']
     # 'cpu' or 'gpu', default to 'cpu'.
     # Use 'gpu' for faster prediction, but you must have the gpu version of mxnet installed.
     DropStatistics.CNOCR_CONTEXT = 'cpu'

module/statistics/utils.py

@@ -66,18 +66,48 @@ def unpack(image):
         list: List of np.ndarray.
     """
     size = image_size(image)
-    if size == (1280, 720):
+    if size == (1280, 720) or size[0] == round(size[1] * 16 / 9) \
+            or size[0] != 1280 or size[1] % 720 != 0:
         return [image]
-    elif size[0] / 1280 == size[1] / 720:
-        return [cv2.resize(image, (1280, 720), interpolation=cv2.INTER_LANCZOS4)]
     else:
-        if size[0] != 1280 or size[1] % 720 != 0:
-            raise ImageInvalidResolution(f'Unexpected image size: {size}')
         return [crop(image, (0, n * 720, 1280, (n + 1) * 720)) for n in range(size[1] // 720)]
 
+
+def resize_image(image):
+    """
+    Crop and resize to 1280x720.
+
+    Args:
+        image:
+
+    Returns:
+        np.ndarray:
+    """
+    size = image_size(image)
+    width, height = size
+    if size == (1280, 720):
+        return image
+    elif width == round(height * 16 / 9):
+        return cv2.resize(image, (1280, 720), interpolation=cv2.INTER_LANCZOS4)
+    elif width != 1280 or height % 720 != 0:
+        if width / height < 16 / 9:
+            crop_height = width * 9 / 16
+            y1 = round(height / 2 - crop_height / 2)
+            y2 = round(height / 2 + crop_height / 2)
+            crop_img = crop(image, (0, y1, width, y2))
+        else:
+            crop_width = height * 16 / 9
+            x1 = round(width / 2 - crop_width / 2)
+            x2 = round(width / 2 + crop_width / 2)
+            crop_img = crop(image, (x1, 0, x2, height))
+        return cv2.resize(crop_img, (1280, 720), interpolation=cv2.INTER_LANCZOS4)
+    else:
+        raise ImageInvalidResolution(f'Unexpected image size: {size}')
+
+
 def get_similarity(image):
     """
-    Get similarity to.
+    Get similarity from a image.
 
     Args:
         image:
@@ -88,9 +118,7 @@ def get_similarity(image):
     size = image_size(image)
     if size == (1280, 720):
         return 0.85
-    elif size[0] / 1280 == size[1] / 720:
+    elif size[0] == round(size[1] * 16 / 9) or size[0] != 1280 or size[1] % 720 != 0:
-        return 0.7
+        return 0.69
     else:
-        if size[0] != 1280 or size[1] % 720 != 0:
-            raise ImageInvalidResolution(f'Unexpected image size: {size}')
         return 0.85