not save img

identify_cards.py

@@ -31,6 +31,8 @@ SUIT_TEMPLATE_PATH = "templates/suits/"
 SUITS_BY_COLOR = {"black": "S", "green": "C", "red": "H", "orange": "D"}
 SCALE_STANDARD = 2032
 
+IS_DEBUG = False
+
 
 def load_suit_templates(template_path):
     templates = {}
@@ -60,7 +62,8 @@ def get_img_with_rect(img, rects, color, thickness):
 
 
 def save_img_with_rect(filename, img, rects, color=(0, 255, 0), thickness=2):
-    cv2.imwrite(filename, get_img_with_rect(img, rects, color, thickness))
+    if IS_DEBUG:
+        cv2.imwrite(filename, get_img_with_rect(img, rects, color, thickness))
 
 
 def get_masks(hsv):
@@ -86,7 +89,8 @@ def find_best_contour(image, is_best):
             mask, cv2.MORPH_CLOSE, kernel, iterations=2
         )
 
-        cv2.imwrite(f"debug_mask{i}.jpg", closed_mask)
+        if IS_DEBUG:
+            cv2.imwrite(f"debug_mask{i}.jpg", closed_mask)
         contours, _ = cv2.findContours(
             closed_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
         )
@@ -167,7 +171,8 @@ def find_center_seal(image, bw, bh):
     kernel = np.ones((7, 7), np.uint8)
     closed_mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel, iterations=2)
 
-    cv2.imwrite(f"debug_seal.jpg", closed_mask)
+    if IS_DEBUG:
+        cv2.imwrite(f"debug_seal.jpg", closed_mask)
     contours, _ = cv2.findContours(
         closed_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
     )
@@ -321,12 +326,15 @@ def get_suit_from_image_rules(rank_image_patch, thresholds):
         rank_image_patch, rank_image_patch, mask=text_mask
     )
 
-    # ユニークなファイル名を生成
-    timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S_%f")
-    debug_filename = os.path.join(debug_dir, f"masked_char_{timestamp}.png")
+    if IS_DEBUG:
+        # ユニークなファイル名を生成
+        timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S_%f")
+        debug_filename = os.path.join(
+            debug_dir, f"masked_char_{timestamp}.png"
+        )
 
-    # 画像を保存
-    cv2.imwrite(debug_filename, masked_char_image)
+        # 画像を保存
+        cv2.imwrite(debug_filename, masked_char_image)
 
     lab_patch = cv2.cvtColor(masked_char_image, cv2.COLOR_BGR2LAB)
     avg_lab = cv2.mean(lab_patch, mask=text_mask)
@@ -405,7 +413,6 @@ def get_suit_from_color_rules(avg_lab, thresholds, timestamp=0):
 
 def preprocess_img(img):
     gray_region = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-    debug_region = img.copy()
 
     # --- ステップ1: カードマスクの作成 (変更なし) ---
     _, card_mask = cv2.threshold(gray_region, 160, 255, cv2.THRESH_BINARY)
@@ -452,24 +459,6 @@ def filter_size(contours, scale, img):
                 max(0, y) : min(y + h, img.shape[0]),
                 max(0, x) : min(x + w, img.shape[1]),
             ]
-
-            # hsv_patch = cv2.cvtColor(no_padding_img, cv2.COLOR_BGR2HSV)
-            # s_channel = hsv_patch[:, :, 1]
-            # v_channel = hsv_patch[:, :, 2]
-            # mask_s = cv2.threshold(s_channel, 30, 255, cv2.THRESH_BINARY)[1]
-            # mask_v = cv2.threshold(v_channel, 210, 255, cv2.THRESH_BINARY_INV)[
-            #     1
-            # ]
-            text_mask = get_not_white_mask(no_padding_img)
-            # デバッグ用のフォルダがなければ作成
-            debug_dir = "debug_chars"
-            if not os.path.exists(debug_dir):
-                os.makedirs(debug_dir)
-
-            # マスクを使って元のカラー画像から文字部分のみを抽出
-            masked_char_image = cv2.bitwise_and(
-                no_padding_img, no_padding_img, mask=text_mask
-            )
             res.append(
                 {
                     "img": cropped_img,
@@ -491,10 +480,6 @@ def filter_thickness(
         cropped_img = candidate["img"]
 
         text_mask = get_not_white_mask(no_padding_img)
-        # デバッグ用のフォルダがなければ作成
-        debug_dir = "debug_chars"
-        if not os.path.exists(debug_dir):
-            os.makedirs(debug_dir)
 
         # マスクを使って元のカラー画像から文字部分のみを抽出
         masked_char_image = cv2.bitwise_and(
@@ -504,12 +489,18 @@ def filter_thickness(
         cropped_dist = cv2.distanceTransform(cropped_bin, cv2.DIST_L2, 3)
         _, max_val, _, _ = cv2.minMaxLoc(cropped_dist)
 
-        debug_dir = "debug_chars"
-        timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S_%f")
-        debug_filename = os.path.join(debug_dir, f"dist_char_{timestamp}.png")
+        if IS_DEBUG:
+            debug_dir = "debug_chars"
+            if not os.path.exists(debug_dir):
+                os.makedirs(debug_dir)
+            debug_dir = "debug_chars"
+            timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S_%f")
+            debug_filename = os.path.join(
+                debug_dir, f"dist_char_{timestamp}.png"
+            )
 
-        # 画像を保存
-        cv2.imwrite(debug_filename, cropped_dist)
+            # 画像を保存
+            cv2.imwrite(debug_filename, cropped_dist)
 
         print(
             f"    候補 at ({candidate['pos']}) - 厚みスコア: {max_val:.2f}    {timestamp}"
@@ -610,7 +601,8 @@ def find_rank_candidates(region_image, suit_templates, player_name, scale=1):
         return []
 
     preprocessed = preprocess_img(region_image)
-    cv2.imwrite(f"debug_ocr_preprocess_{player_name}.jpg", preprocessed)
+    if IS_DEBUG:
+        cv2.imwrite(f"debug_ocr_preprocess_{player_name}.jpg", preprocessed)
 
     contours, _ = cv2.findContours(
         preprocessed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
@@ -729,21 +721,22 @@ def recognize_cards(region_image, suit_templates, player_name, trocr_pipeline):
                             {"name": card_name, "pos": candidate["pos"]}
                         )
 
-    for card in recognized_ranks:
-        cv2.putText(
-            debug_region,
-            card["name"],
-            (card["pos"][0], card["pos"][1] - 10),
-            cv2.FONT_HERSHEY_SIMPLEX,
-            1.0,
-            (255, 255, 0),
-            2,
-            cv2.LINE_AA,
+    if IS_DEBUG:
+        for card in recognized_ranks:
+            cv2.putText(
+                debug_region,
+                card["name"],
+                (card["pos"][0], card["pos"][1] - 10),
+                cv2.FONT_HERSHEY_SIMPLEX,
+                1.0,
+                (255, 255, 0),
+                2,
+                cv2.LINE_AA,
+            )
+        cv2.imwrite(f"debug_detection_{player_name}.jpg", debug_region)
+        print(
+            f"{player_name} の検出結果を debug_detection_{player_name}.jpg に保存しました。"
         )
-    cv2.imwrite(f"debug_detection_{player_name}.jpg", debug_region)
-    print(
-        f"{player_name} の検出結果を debug_detection_{player_name}.jpg に保存しました。"
-    )
     return [c["name"] for c in recognized_ranks]
 
 

main.py

@@ -127,7 +127,7 @@ async def analyze_image(image_paths: list[UploadFile]):
                 #     )
                 # NumPy配列（メモリ上のデータ）から画像をデコード
                 file_bytes = await image_path.read()
-                print("file", file_bytes)
+                # print("file", file_bytes)
                 file_array = np.asarray(bytearray(file_bytes), dtype=np.uint8)
                 image = cv2.imdecode(file_array, cv2.IMREAD_COLOR)
                 # image = image_path.file