Add auto_label tool, dataset config, and initial 152 auto-generated YOLO labels

.gitignore

@@ -20,3 +20,4 @@ logs/
 temp_*.png
 debug_*.png
 dataset/raw/*.png
+dataset/annotated/*.jpg

auto_label.py

@@ -0,0 +1,259 @@
+"""
+Auto-Labeling Assistant for DirtyLeague Dataset.
+Uses calibrated ROIs and templates to automatically generate
+YOLO-format annotations (.txt) for the collected raw screenshots in seconds.
+
+Outputs:
+  - dataset/labels/<image_name>.txt (normalized YOLO bbox: class_id cx cy w h)
+  - dataset/annotated/<image_name>.jpg (visual verification with labeled boxes)
+  - dataset/data.yaml (dataset configuration for YOLO training)
+"""
+
+import os
+import sys
+import cv2
+import numpy as np
+
+# Class definitions for DirtyLeague UI detection
+CLASSES = [
+    "btn_fight",          # 0: Yellow/Gold fight button in lobby
+    "btn_exit",           # 1: White battle exit button (bottom bar)
+    "btn_leave",          # 2: Red confirm surrender button
+    "btn_ok",             # 3: Defeat screen OK button
+    "btn_collect",        # 4: Victory / Chest collect button
+    "btn_turn_all",       # 5: Chest turn cards over button
+    "btn_close",          # 6: Offer / modal close 'X' button
+    "btn_remove_chest",   # 7: Discard extra chest button
+    "btn_open_chest",     # 8: Open chest button on no-slots screen
+    "crown_chest",        # 9: 5/5 Crown chest banner in lobby
+    "card",               # 10: Generic creature card / hero slot
+]
+
+CLASS_MAP = {name: i for i, name in enumerate(CLASSES)}
+
+# Template mappings to class names
+TEMPLATE_TO_CLASS = {
+    "btn_fight": "btn_fight",
+    "btn_exit": "btn_exit",
+    "btn_leave": "btn_leave",
+    "btn_ok": "btn_ok",
+    "btn_collect": "btn_collect",
+    "btn_collect_victory": "btn_collect",
+    "btn_turn_all_cards": "btn_turn_all",
+    "btn_close": "btn_close",
+    "btn_close_offer": "btn_close",
+    "btn_remove_chest": "btn_remove_chest",
+    "btn_open_chest": "btn_open_chest",
+}
+
+# Calibrated Relative ROIs (rx1, ry1, rx2, ry2) for lightning-fast localized search (<1ms)
+TEMPLATE_ROIS = {
+    "btn_fight": [(0.75, 0.35, 0.95, 0.50)],
+    "btn_exit": [(0.40, 0.90, 0.52, 1.00)],
+    "btn_leave": [(0.48, 0.54, 0.64, 0.70)],
+    "btn_ok": [(0.20, 0.70, 0.40, 0.85)],
+    "btn_collect": [(0.38, 0.63, 0.60, 0.78)],
+    "btn_collect_victory": [(0.18, 0.73, 0.33, 0.87)],
+    "btn_turn_all_cards": [(0.38, 0.63, 0.60, 0.78)],
+    "btn_close": [(0.85, 0.00, 1.00, 0.16)],
+    "btn_close_offer": [(0.85, 0.00, 1.00, 0.16)],
+    "btn_remove_chest": [(0.33, 0.80, 0.52, 0.92)],
+    "btn_open_chest": [(0.46, 0.80, 0.63, 0.92)],
+}
+
+# Color palette for visual annotations
+COLORS = [
+    (0, 215, 255),    # btn_fight (gold)
+    (255, 255, 255),  # btn_exit (white)
+    (0, 0, 255),      # btn_leave (red)
+    (255, 100, 0),    # btn_ok (blue)
+    (0, 200, 0),      # btn_collect (green)
+    (255, 0, 255),    # btn_turn_all (magenta)
+    (0, 165, 255),    # btn_close (orange)
+    (128, 0, 128),    # btn_remove_chest (purple)
+    (200, 200, 0),    # btn_open_chest (cyan)
+    (50, 205, 50),    # crown_chest (lime)
+    (255, 191, 0),    # card (deep sky blue)
+]
+
+
+def load_templates(assets_dir: str):
+    templates = {}
+    for tpl_name in TEMPLATE_TO_CLASS.keys():
+        path = os.path.join(assets_dir, f"{tpl_name}.png")
+        if os.path.exists(path):
+            img = cv2.imread(path)
+            if img is not None:
+                templates[tpl_name] = img
+    return templates
+
+
+def detect_boxes(image: np.ndarray, templates: dict, base_height: int = 2050):
+    fh, fw = image.shape[:2]
+    scale = fh / float(base_height)
+    boxes = []  # list of (class_id, x1, y1, x2, y2, conf)
+
+    # 1. Fast localized Template Matching via ROIs
+    for tpl_name, base_tpl in templates.items():
+        class_name = TEMPLATE_TO_CLASS[tpl_name]
+        class_id = CLASS_MAP[class_name]
+
+        interp = cv2.INTER_AREA if scale < 1.0 else cv2.INTER_CUBIC
+        tpl = cv2.resize(base_tpl, (0, 0), fx=scale, fy=scale, interpolation=interp)
+        th, tw = tpl.shape[:2]
+
+        rois = TEMPLATE_ROIS.get(tpl_name, [(0.0, 0.0, 1.0, 1.0)])
+        threshold = 0.55 if "close" in tpl_name else 0.70
+
+        for rx1, ry1, rx2, ry2 in rois:
+            crop_x1 = max(0, min(fw, int(rx1 * fw)))
+            crop_y1 = max(0, min(fh, int(ry1 * fh)))
+            crop_x2 = max(0, min(fw, int(rx2 * fw)))
+            crop_y2 = max(0, min(fh, int(ry2 * fh)))
+
+            if (crop_x2 - crop_x1) < tw or (crop_y2 - crop_y1) < th:
+                continue
+
+            roi_crop = image[crop_y1:crop_y2, crop_x1:crop_x2]
+            res = cv2.matchTemplate(roi_crop, tpl, cv2.TM_CCOEFF_NORMED)
+            min_v, max_v, min_l, max_l = cv2.minMaxLoc(res)
+
+            if max_v >= threshold:
+                x1 = crop_x1 + max_l[0]
+                y1 = crop_y1 + max_l[1]
+                x2 = x1 + tw
+                y2 = y1 + th
+                boxes.append((class_id, x1, y1, x2, y2, float(max_v)))
+
+    # 2. Geometric Anchor for Crown Chest (in lobby screen if btn_fight is detected)
+    has_fight = any(b[0] == CLASS_MAP["btn_fight"] for b in boxes)
+    if has_fight:
+        cx = int(0.2279 * fw)
+        cy = int(0.6146 * fh)
+        cw = int(0.12 * fw)
+        ch = int(0.14 * fh)
+        x1, y1 = cx - cw // 2, cy - ch // 2
+        x2, y2 = cx + cw // 2, cy + ch // 2
+        boxes.append((CLASS_MAP["crown_chest"], x1, y1, x2, y2, 0.95))
+
+    # 3. Detect cards in Battle Screen (if btn_exit is detected)
+    has_exit = any(b[0] == CLASS_MAP["btn_exit"] for b in boxes)
+    if has_exit:
+        # Detect card slots along the bottom player hand / deck bar
+        # In battle, player deck cards are anchored around y = 0.78..0.92, spaced across bottom
+        card_w = int(140 * scale)
+        card_h = int(190 * scale)
+        card_y = int(0.85 * fh)
+        card_xs = [int(x_ratio * fw) for x_ratio in [0.22, 0.32, 0.68, 0.78]]
+        for cx in card_xs:
+            x1 = cx - card_w // 2
+            y1 = card_y - card_h // 2
+            x2 = cx + card_w // 2
+            y2 = card_y + card_h // 2
+            boxes.append((CLASS_MAP["card"], x1, y1, x2, y2, 0.85))
+
+    return boxes
+
+
+def write_yolo_labels(boxes, image_shape, out_txt_path):
+    fh, fw = image_shape[:2]
+    lines = []
+    for class_id, x1, y1, x2, y2, _ in boxes:
+        cx = ((x1 + x2) / 2.0) / fw
+        cy = ((y1 + y2) / 2.0) / fh
+        w = (x2 - x1) / float(fw)
+        h = (y2 - y1) / float(fh)
+        lines.append(f"{class_id} {cx:.6f} {cy:.6f} {w:.6f} {h:.6f}\n")
+
+    with open(out_txt_path, "w", encoding="utf-8") as f:
+        f.writelines(lines)
+
+
+def draw_annotations(image, boxes):
+    annotated = image.copy()
+    for class_id, x1, y1, x2, y2, conf in boxes:
+        color = COLORS[class_id % len(COLORS)]
+        cv2.rectangle(annotated, (x1, y1), (x2, y2), color, 3)
+
+        label = f"{CLASSES[class_id]} {conf:.2f}"
+        (tw, th), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.7, 2)
+        cv2.rectangle(annotated, (x1, y1 - th - 10), (x1 + tw + 6, y1), color, -1)
+        cv2.putText(annotated, label, (x1 + 3, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 0), 2)
+    return annotated
+
+
+def main():
+    root_dir = os.path.dirname(__file__)
+    raw_dir = os.path.join(root_dir, "dataset", "raw")
+    labels_dir = os.path.join(root_dir, "dataset", "labels")
+    annotated_dir = os.path.join(root_dir, "dataset", "annotated")
+    assets_dir = os.path.join(root_dir, "assets")
+
+    os.makedirs(labels_dir, exist_ok=True)
+    os.makedirs(annotated_dir, exist_ok=True)
+
+    templates = load_templates(assets_dir)
+    print(f"Loaded {len(templates)} templates for ROI-accelerated pre-labeling.")
+
+    image_files = sorted([f for f in os.listdir(raw_dir) if f.endswith(".png")])
+    print(f"Found {len(image_files)} raw images in '{raw_dir}' to process.")
+
+    total_boxes = 0
+    annotated_images_count = 0
+
+    for i, fname in enumerate(image_files, 1):
+        fpath = os.path.join(raw_dir, fname)
+        img = cv2.imread(fpath)
+        if img is None:
+            continue
+
+        boxes = detect_boxes(img, templates)
+        base_name = os.path.splitext(fname)[0]
+
+        # 1. Save YOLO label .txt
+        txt_path = os.path.join(labels_dir, f"{base_name}.txt")
+        write_yolo_labels(boxes, img.shape, txt_path)
+
+        # 2. Save annotated verification image
+        annotated_img = draw_annotations(img, boxes)
+        preview_h = 1080
+        preview_w = int(img.shape[1] * (preview_h / img.shape[0]))
+        preview_img = cv2.resize(annotated_img, (preview_w, preview_h), interpolation=cv2.INTER_AREA)
+        jpg_path = os.path.join(annotated_dir, f"{base_name}.jpg")
+        cv2.imwrite(jpg_path, preview_img, [int(cv2.IMWRITE_JPEG_QUALITY), 90])
+
+        total_boxes += len(boxes)
+        if len(boxes) > 0:
+            annotated_images_count += 1
+
+        found_classes = ", ".join(sorted(set(CLASSES[b[0]] for b in boxes))) if boxes else "None"
+        print(f"[{i:02d}/{len(image_files)}] {fname} -> {len(boxes)} boxes ({found_classes})")
+
+    # 3. Generate data.yaml
+    yaml_path = os.path.join(root_dir, "dataset", "data.yaml")
+    yaml_content = f"""# DirtyLeague YOLOv8 Dataset Configuration
+path: {os.path.abspath(os.path.join(root_dir, 'dataset'))}
+train: images/train
+val: images/val
+
+names:
+"""
+    for idx, cname in enumerate(CLASSES):
+        yaml_content += f"  {idx}: {cname}\n"
+
+    with open(yaml_path, "w", encoding="utf-8") as f:
+        f.write(yaml_content)
+
+    print("\n" + "=" * 65)
+    print(f" AUTO-LABELING COMPLETE!")
+    print(f" Images processed   : {len(image_files)}")
+    print(f" Images with boxes  : {annotated_images_count} / {len(image_files)}")
+    print(f" Total boxes placed : {total_boxes}")
+    print(f" Labels directory   : {labels_dir}")
+    print(f" Visual previews    : {annotated_dir}")
+    print(f" Config file        : {yaml_path}")
+    print("=" * 65)
+
+
+if __name__ == "__main__":
+    main()

dataset/data.yaml

@@ -0,0 +1,17 @@
+# DirtyLeague YOLOv8 Dataset Configuration
+path: F:\Gitea\DirtyLeague_Bot\dataset
+train: images/train
+val: images/val
+
+names:
+  0: btn_fight
+  1: btn_exit
+  2: btn_leave
+  3: btn_ok
+  4: btn_collect
+  5: btn_turn_all
+  6: btn_close
+  7: btn_remove_chest
+  8: btn_open_chest
+  9: crown_chest
+  10: card

dataset/labels/dl_screen_20260909_092820_419.txt

@@ -0,0 +1,2 @@
+0 0.852469 0.425676 0.116821 0.080370
+9 0.227619 0.614509 0.119631 0.139403

dataset/labels/dl_screen_20260910_233702_650.txt

@@ -0,0 +1,2 @@
+0 0.853516 0.425610 0.110677 0.080488
+9 0.227865 0.614146 0.119792 0.139512

dataset/labels/dl_screen_20260910_233708_424.txt

@@ -0,0 +1,2 @@
+0 0.853516 0.425610 0.110677 0.080488
+9 0.227865 0.614146 0.119792 0.139512

dataset/labels/dl_screen_20260910_233719_952.txt

@@ -0,0 +1,2 @@
+0 0.853516 0.425610 0.110677 0.080488
+9 0.227865 0.614146 0.119792 0.139512

dataset/labels/dl_screen_20260910_233723_738.txt

@@ -0,0 +1,2 @@
+0 0.853516 0.425610 0.110677 0.080488
+9 0.227865 0.614146 0.119792 0.139512

dataset/labels/dl_screen_20260910_233732_097.txt

@@ -0,0 +1,2 @@
+0 0.853516 0.425610 0.110677 0.080488
+9 0.227865 0.614146 0.119792 0.139512

dataset/labels/dl_screen_20260910_233738_553.txt

@@ -0,0 +1,2 @@
+0 0.853516 0.425610 0.110677 0.080488
+9 0.227865 0.614146 0.119792 0.139512

dataset/labels/dl_screen_20260910_233741_601.txt

@@ -0,0 +1,5 @@
+1 0.459635 0.956098 0.052083 0.024390
+10 0.219792 0.849756 0.036458 0.092683
+10 0.319792 0.849756 0.036458 0.092683
+10 0.679948 0.849756 0.036458 0.092683
+10 0.779948 0.849756 0.036458 0.092683

dataset/labels/dl_screen_20260910_233743_310.txt

@@ -0,0 +1,5 @@
+1 0.459635 0.955610 0.052083 0.024390
+10 0.219792 0.849756 0.036458 0.092683
+10 0.319792 0.849756 0.036458 0.092683
+10 0.679948 0.849756 0.036458 0.092683
+10 0.779948 0.849756 0.036458 0.092683

dataset/labels/dl_screen_20260910_233745_861.txt

@@ -0,0 +1,5 @@
+1 0.459635 0.955610 0.052083 0.024390
+10 0.219792 0.849756 0.036458 0.092683
+10 0.319792 0.849756 0.036458 0.092683
+10 0.679948 0.849756 0.036458 0.092683
+10 0.779948 0.849756 0.036458 0.092683

dataset/labels/dl_screen_20260910_233746_421.txt

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+10 0.779948 0.849756 0.036458 0.092683

dataset/labels/dl_screen_20260910_233748_121.txt

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+10 0.779948 0.849756 0.036458 0.092683

dataset/labels/dl_screen_20260910_233750_285.txt

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+1 0.459635 0.955610 0.052083 0.024390
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dataset/labels/dl_screen_20260910_233751_818.txt

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dataset/labels/dl_screen_20260910_233753_339.txt

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dataset/labels/dl_screen_20260910_233754_842.txt

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+10 0.679948 0.849756 0.036458 0.092683
+10 0.779948 0.849756 0.036458 0.092683

dataset/labels/dl_screen_20260910_233801_779.txt

@@ -0,0 +1,2 @@
+0 0.853516 0.425610 0.110677 0.080488
+9 0.227865 0.614146 0.119792 0.139512

dataset/labels/dl_screen_20260910_233805_494.txt

@@ -0,0 +1,2 @@
+0 0.853516 0.425610 0.110677 0.080488
+9 0.227865 0.614146 0.119792 0.139512

dataset/labels/dl_screen_20260910_233813_319.txt

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+0 0.853516 0.425610 0.110677 0.080488
+9 0.227865 0.614146 0.119792 0.139512

dataset/labels/dl_screen_20260910_233822_125.txt

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+0 0.853516 0.425610 0.110677 0.080488
+9 0.227865 0.614146 0.119792 0.139512

dataset/labels/dl_screen_20260910_233825_207.txt

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dataset/labels/dl_screen_20260910_233827_908.txt

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+1 0.459635 0.956098 0.052083 0.024390
+2 0.554427 0.628780 0.079167 0.030244
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dataset/labels/dl_screen_20260910_233828_534.txt

@@ -0,0 +1,5 @@
+1 0.459635 0.956098 0.052083 0.024390
+10 0.219792 0.849756 0.036458 0.092683
+10 0.319792 0.849756 0.036458 0.092683
+10 0.679948 0.849756 0.036458 0.092683
+10 0.779948 0.849756 0.036458 0.092683

dataset/labels/dl_screen_20260910_233830_047.txt

@@ -0,0 +1,5 @@
+1 0.459635 0.956098 0.052083 0.024390
+10 0.219792 0.849756 0.036458 0.092683
+10 0.319792 0.849756 0.036458 0.092683
+10 0.679948 0.849756 0.036458 0.092683
+10 0.779948 0.849756 0.036458 0.092683

dataset/labels/dl_screen_20260910_233831_574.txt

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+1 0.459635 0.956098 0.052083 0.024390
+10 0.219792 0.849756 0.036458 0.092683
+10 0.319792 0.849756 0.036458 0.092683
+10 0.679948 0.849756 0.036458 0.092683
+10 0.779948 0.849756 0.036458 0.092683

dataset/labels/dl_screen_20260910_233839_343.txt

@@ -0,0 +1,2 @@
+0 0.853516 0.425610 0.110677 0.080488
+9 0.227865 0.614146 0.119792 0.139512

dataset/labels/dl_screen_20260910_233842_857.txt

@@ -0,0 +1,2 @@
+0 0.853516 0.425610 0.110677 0.080488
+9 0.227865 0.614146 0.119792 0.139512

dataset/labels/dl_screen_20260910_233845_895.txt

@@ -0,0 +1,5 @@
+1 0.459635 0.956098 0.052083 0.024390
+10 0.219792 0.849756 0.036458 0.092683
+10 0.319792 0.849756 0.036458 0.092683
+10 0.679948 0.849756 0.036458 0.092683
+10 0.779948 0.849756 0.036458 0.092683

dataset/labels/dl_screen_20260910_233846_656.txt

@@ -0,0 +1,5 @@
+1 0.459635 0.956098 0.052083 0.024390
+10 0.219792 0.849756 0.036458 0.092683
+10 0.319792 0.849756 0.036458 0.092683
+10 0.679948 0.849756 0.036458 0.092683
+10 0.779948 0.849756 0.036458 0.092683

dataset/labels/dl_screen_20260910_233848_158.txt

@@ -0,0 +1,5 @@
+1 0.459635 0.956098 0.052083 0.024390
+10 0.219792 0.849756 0.036458 0.092683
+10 0.319792 0.849756 0.036458 0.092683
+10 0.679948 0.849756 0.036458 0.092683
+10 0.779948 0.849756 0.036458 0.092683

dataset/labels/dl_screen_20260910_233849_674.txt

@@ -0,0 +1,5 @@
+1 0.459635 0.956098 0.052083 0.024390
+10 0.219792 0.849756 0.036458 0.092683
+10 0.319792 0.849756 0.036458 0.092683
+10 0.679948 0.849756 0.036458 0.092683
+10 0.779948 0.849756 0.036458 0.092683

dataset/labels/dl_screen_20260910_233854_854.txt

@@ -0,0 +1,5 @@
+1 0.459635 0.956098 0.052083 0.024390
+10 0.219792 0.849756 0.036458 0.092683
+10 0.319792 0.849756 0.036458 0.092683
+10 0.679948 0.849756 0.036458 0.092683
+10 0.779948 0.849756 0.036458 0.092683

dataset/labels/dl_screen_20260910_233856_369.txt

@@ -0,0 +1,5 @@
+1 0.459635 0.956098 0.052083 0.024390
+10 0.219792 0.849756 0.036458 0.092683
+10 0.319792 0.849756 0.036458 0.092683
+10 0.679948 0.849756 0.036458 0.092683
+10 0.779948 0.849756 0.036458 0.092683

dataset/labels/dl_screen_20260910_233858_891.txt

@@ -0,0 +1,5 @@
+1 0.459635 0.956098 0.052083 0.024390
+10 0.219792 0.849756 0.036458 0.092683
+10 0.319792 0.849756 0.036458 0.092683
+10 0.679948 0.849756 0.036458 0.092683
+10 0.779948 0.849756 0.036458 0.092683

dataset/labels/dl_screen_20260910_233859_411.txt

@@ -0,0 +1,5 @@
+1 0.459635 0.956098 0.052083 0.024390
+10 0.219792 0.849756 0.036458 0.092683
+10 0.319792 0.849756 0.036458 0.092683
+10 0.679948 0.849756 0.036458 0.092683
+10 0.779948 0.849756 0.036458 0.092683

dataset/labels/dl_screen_chest_collect_4k.txt

@@ -0,0 +1 @@
+4 0.500000 0.715854 0.148438 0.056098

dataset/labels/dl_screen_chest_reward_4k.txt

@@ -0,0 +1 @@
+5 0.500000 0.715854 0.148438 0.056098

dataset/labels/dl_screen_crown_chest_open_4k.txt

@@ -0,0 +1 @@
+5 0.500000 0.715854 0.148438 0.056098

dataset/labels/dl_screen_defeat_screen_4k.txt

@@ -0,0 +1 @@
+3 0.304297 0.775854 0.095052 0.044390

dataset/labels/dl_screen_leave_dialog_4k.txt

@@ -0,0 +1,6 @@
+1 0.459635 0.956098 0.052083 0.024390
+2 0.554427 0.628780 0.079167 0.030244
+10 0.219792 0.849756 0.036458 0.092683
+10 0.319792 0.849756 0.036458 0.092683
+10 0.679948 0.849756 0.036458 0.092683
+10 0.779948 0.849756 0.036458 0.092683

dataset/labels/dl_screen_no_free_slots_4k.txt

@@ -0,0 +1,2 @@
+7 0.431901 0.865610 0.117448 0.044390
+8 0.568099 0.865610 0.117448 0.044390

dataset/labels/dl_screen_offer_popup_4k.txt

@@ -0,0 +1,2 @@
+6 0.965885 0.060976 0.017708 0.033171
+6 0.965885 0.060976 0.017708 0.033171

dataset/labels/dl_screen_victory_screen_4k.txt

@@ -0,0 +1 @@
+4 0.250260 0.804634 0.095312 0.044390