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check_size.py

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+import cv2
+
+# 你的文件路径（注意：WSL 用 /mnt/d/...）
+jpg_path = "/mnt/d/mysite/SamVG/Dataset/rico/2/unique_uis/combined/54.jpg"
+png_path = "/mnt/d/mysite/SamVG/Dataset/rico/2/rico_dataset_v0.1_semantic_annotations/semantic_annotations/54.png"
+
+def print_size(path):
+    img = cv2.imread(path)
+    if img is None:
+        print(f"❌ Cannot read image: {path}")
+        return
+    h, w = img.shape[:2]
+    print(f"{path} ---> {w} × {h}")
+
+print("\n--- Image Sizes ---")
+print_size(jpg_path)
+print_size(png_path)

get_yolo_dataset.py

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+import os
+import json
+from typing import Dict, Any, List
+
+import cv2
+from tqdm import tqdm
+
+
+# ---------- 路径配置：按你当前工程来的 ----------
+# 原始 UI screenshot（combined）
+SCREENSHOT_DIR = "/mnt/d/mysite/SamVG/Dataset/rico/2/unique_uis/combined"
+
+# semantic annotation 路径（含 *.json 和 *.png）
+SEM_DIR = "/mnt/d/mysite/SamVG/Dataset/rico/2/rico_dataset_v0.1_semantic_annotations/semantic_annotations"
+
+# YOLO 输出根目录（最终训练数据集就在这里）
+OUT_ROOT = "/mnt/d/mysite/SamVG/Dataset/rico/yolo_icon_full"
+
+OUT_IMAGES = os.path.join(OUT_ROOT, "images")
+OUT_LABELS = os.path.join(OUT_ROOT, "labels")
+OUT_VIS = os.path.join(OUT_ROOT, "vis")
+
+
+def ensure_dirs():
+    os.makedirs(OUT_IMAGES, exist_ok=True)
+    os.makedirs(OUT_LABELS, exist_ok=True)
+    os.makedirs(OUT_VIS, exist_ok=True)
+
+
+def collect_icon_nodes(node: Dict[str, Any]) -> List[Dict[str, Any]]:
+    """
+    递归收集所有 componentLabel == 'Icon' 的节点
+    这里直接使用 semantic json 的结构
+    """
+    icons = []
+    if isinstance(node, dict):
+        if node.get("componentLabel") == "Icon":
+            icons.append(node)
+        for ch in node.get("children", []):
+            icons.extend(collect_icon_nodes(ch))
+    return icons
+
+
+def visualize_icons(image, icons, save_path):
+    """
+    可视化函数：仅用于人工抽查，
+    在 image 上画出 icon 的框和简单文字
+    """
+    vis = image.copy()
+    font = cv2.FONT_HERSHEY_SIMPLEX
+    color = (0, 0, 255)
+
+    h, w = vis.shape[:2]
+
+    for node in icons:
+        bounds = node.get("bounds")
+        if not bounds or len(bounds) != 4:
+            continue
+        x1, y1, x2, y2 = bounds
+
+        # 转 int + 简单裁剪，防止越界
+        x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
+        x1 = max(0, min(x1, w - 1))
+        x2 = max(0, min(x2, w - 1))
+        y1 = max(0, min(y1, h - 1))
+        y2 = max(0, min(y2, h - 1))
+
+        icon_cls = node.get("iconClass") or "icon"
+        label = f"Icon({icon_cls})"
+
+        cv2.rectangle(vis, (x1, y1), (x2, y2), color, 2)
+        (tw, th), _ = cv2.getTextSize(label, font, 0.6, 2)
+        top_left = (x1, max(0, y1 - th - 4))
+        bottom_right = (x1 + tw + 4, y1)
+        cv2.rectangle(vis, top_left, bottom_right, color, -1)
+        cv2.putText(vis, label, (x1 + 2, y1 - 4), font, 0.6, (255, 255, 255), 2)
+
+    cv2.imwrite(save_path, vis)
+    print(f"[vis] {save_path}")
+
+
+def main():
+    ensure_dirs()
+
+    # 列出所有 semantic json，按数字排序
+    json_files = [f for f in os.listdir(SEM_DIR) if f.lower().endswith(".json")]
+
+    def get_id(name: str) -> int:
+        base = os.path.splitext(name)[0]
+        try:
+            return int(base)
+        except ValueError:
+            # 如果有不是纯数字的文件名，就排在后面
+            return 10 ** 9
+
+    json_files.sort(key=get_id)
+
+    total_sem = len(json_files)
+    selected = 0
+    no_icon = 0
+    no_screenshot = 0
+    errors = 0
+
+    # tqdm 显示进度
+    for fname in tqdm(json_files, desc="Building YOLO icon dataset"):
+        ui_id = os.path.splitext(fname)[0]
+        sem_json_path = os.path.join(SEM_DIR, fname)
+
+        try:
+            # 读取 semantic json
+            with open(sem_json_path, "r", encoding="utf-8") as f:
+                data = json.load(f)
+
+            # 收集所有 icon 节点
+            icons = collect_icon_nodes(data)
+            if not icons:
+                no_icon += 1
+                continue
+
+            # 读取 semantic png（用来获取原始坐标所在的分辨率）
+            sem_png_path = os.path.join(SEM_DIR, f"{ui_id}.png")
+            sem_img = cv2.imread(sem_png_path)
+            if sem_img is None:
+                print(f"[!] semantic png not found or unreadable: {sem_png_path}")
+                errors += 1
+                continue
+
+            sem_h, sem_w = sem_img.shape[:2]
+
+            # 读取 screenshot，并 resize 到 semantic 的尺寸
+            screenshot_path = os.path.join(SCREENSHOT_DIR, f"{ui_id}.jpg")
+            if not os.path.isfile(screenshot_path):
+                no_screenshot += 1
+                continue
+
+            scr = cv2.imread(screenshot_path)
+            if scr is None:
+                print(f"[!] cannot read screenshot: {screenshot_path}")
+                errors += 1
+                continue
+
+            # ★ 核心：把 screenshot 拉伸到 semantic 的大小（例如 540x960 -> 1440x2560）
+            img_resized = cv2.resize(scr, (sem_w, sem_h), interpolation=cv2.INTER_LINEAR)
+
+            # 生成 YOLO label（单类 icon -> class_id = 0）
+            label_lines = []
+            for node in icons:
+                bounds = node.get("bounds")
+                if not bounds or len(bounds) != 4:
+                    continue
+
+                x1, y1, x2, y2 = bounds
+                x1, y1, x2, y2 = float(x1), float(y1), float(x2), float(y2)
+
+                # 简单裁剪，确保在图像内
+                x1 = max(0.0, min(x1, sem_w - 1.0))
+                x2 = max(0.0, min(x2, sem_w - 1.0))
+                y1 = max(0.0, min(y1, sem_h - 1.0))
+                y2 = max(0.0, min(y2, sem_h - 1.0))
+
+                box_w = x2 - x1
+                box_h = y2 - y1
+                if box_w <= 1 or box_h <= 1:
+                    continue
+
+                x_center = x1 + box_w / 2.0
+                y_center = y1 + box_h / 2.0
+
+                x_center_n = x_center / sem_w
+                y_center_n = y_center / sem_h
+                w_n = box_w / sem_w
+                h_n = box_h / sem_h
+
+                class_id = 0  # 只有一个类：icon
+
+                label_lines.append(
+                    f"{class_id} {x_center_n:.6f} {y_center_n:.6f} {w_n:.6f} {h_n:.6f}"
+                )
+
+            if not label_lines:
+                # 防止所有 bbox 都被过滤掉
+                no_icon += 1
+                continue
+
+            # 保存 label
+            label_path = os.path.join(OUT_LABELS, f"{ui_id}.txt")
+            with open(label_path, "w", encoding="utf-8") as f_lab:
+                f_lab.write("\n".join(label_lines))
+
+            # 保存训练图片（jpg）
+            out_img_path = os.path.join(OUT_IMAGES, f"{ui_id}.jpg")
+            cv2.imwrite(out_img_path, img_resized)
+
+            selected += 1
+
+            # 每 100 条保存一张 vis 图方便你检查（第 100, 200, 300, ...）
+            if selected % 100 == 0:
+                vis_path = os.path.join(OUT_VIS, f"{ui_id}_icons.jpg")
+                visualize_icons(img_resized, icons, vis_path)
+
+        except Exception as e:
+            errors += 1
+            print(f"[ERROR] {fname} -> {repr(e)}")
+
+    # 写 classes.txt（单类：icon）
+    classes_path = os.path.join(OUT_ROOT, "classes.txt")
+    with open(classes_path, "w", encoding="utf-8") as f_cls:
+        f_cls.write("icon\n")
+
+    print("\n=== DONE ===")
+    print(f"Total semantic json : {total_sem}")
+    print(f"Selected (with icon & screenshot): {selected}")
+    print(f"No icon                       : {no_icon}")
+    print(f"No screenshot                 : {no_screenshot}")
+    print(f"Errors                        : {errors}")
+    print(f"Output root                   : {OUT_ROOT}")
+    print(f"classes.txt                   : {classes_path}")
+
+
+if __name__ == "__main__":
+    main()

get_yolo_small.py

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+import os
+import shutil
+import random
+from tqdm import tqdm
+
+# ============================
+# ★★ 在这里填你的 YOLO 数据集路径 ★★
+# ============================
+DATASET_ROOT = "/mnt/d/mysite/SamVG/Dataset/rico/yolo_icon_full"
+
+# 输出目标路径
+OUT_ROOT = "/mnt/d/mysite/SamVG/Dataset/rico/yolo_icon_10k_2plus"
+
+# 创建目录结构
+for split in ["train", "test", "val"]:
+    os.makedirs(os.path.join(OUT_ROOT, "images", split), exist_ok=True)
+    os.makedirs(os.path.join(OUT_ROOT, "labels", split), exist_ok=True)
+
+def collect_candidates(split_name):
+    """收集每个 split 中具有 >=2 icons 的样本"""
+    split_labels = os.path.join(DATASET_ROOT, "labels", split_name)
+    split_images = os.path.join(DATASET_ROOT, "images", split_name)
+
+    if not os.path.isdir(split_labels):
+        print(f"[WARN] No label folder: {split_labels}")
+        return []
+
+    label_files = [f for f in os.listdir(split_labels) if f.endswith(".txt")]
+
+    candidates = []
+    print(f"\nScanning {split_name} ({len(label_files)} files)...")
+
+    for lf in tqdm(label_files, desc=f"Reading {split_name}", ncols=100):
+        label_path = os.path.join(split_labels, lf)
+
+        try:
+            with open(label_path, "r") as f:
+                lines = f.readlines()
+        except Exception as e:
+            print(f"[ERR] Cannot read {label_path}: {e}")
+            continue
+
+        if len(lines) < 2:  # 至少两个 icon
+            continue
+
+        img_id = lf.replace(".txt", "")
+        img_path = os.path.join(split_images, img_id + ".jpg")
+
+        if not os.path.isfile(img_path):
+            continue
+
+        candidates.append((split_name, img_id))
+
+    print(f"[OK] Found {len(candidates)} samples (>=2 icons) in {split_name}")
+    return candidates
+
+
+# === Step1：收集全部候选 ===
+all_candidates = []
+for sp in ["train", "test", "val"]:
+    all_candidates.extend(collect_candidates(sp))
+
+print(f"\nTotal qualified samples across all splits: {len(all_candidates)}")
+
+# === Step2：随机选取 10k ===
+total_needed = 10000
+train_n = 8500
+test_n = 1000
+val_n = 500
+
+random.shuffle(all_candidates)
+subset = all_candidates[:total_needed]
+
+train_set = subset[:train_n]
+test_set = subset[train_n : train_n + test_n]
+val_set = subset[train_n + test_n : train_n + test_n + val_n]
+
+
+# === Step3：拷贝对应 sample ===
+def copy_split(samples, split_name):
+    print(f"\nCopying {split_name} ({len(samples)})...")
+
+    for orig_split, img_id in tqdm(samples, desc=f"Copying {split_name}", ncols=100):
+        src_img = os.path.join(DATASET_ROOT, "images", orig_split, f"{img_id}.jpg")
+        src_lbl = os.path.join(DATASET_ROOT, "labels", orig_split, f"{img_id}.txt")
+
+        dst_img = os.path.join(OUT_ROOT, "images", split_name, f"{img_id}.jpg")
+        dst_lbl = os.path.join(OUT_ROOT, "labels", split_name, f"{img_id}.txt")
+
+        try:
+            shutil.copy2(src_img, dst_img)
+            shutil.copy2(src_lbl, dst_lbl)
+        except Exception as e:
+            print(f"[ERR] copying {img_id}: {e}")
+
+
+copy_split(train_set, "train")
+copy_split(test_set, "test")
+copy_split(val_set, "val")
+
+print("\n=== DONE ===")
+print(f"Train: {len(train_set)}")
+print(f"Test : {len(test_set)}")
+print(f"Val  : {len(val_set)}")
+print(f"Output root: {OUT_ROOT}")

initial_kaggle_dataset.py

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+import kagglehub
+
+# Download latest version
+path = kagglehub.dataset_download("onurgunes1993/rico-dataset")
+
+print("Path to dataset files:", path)

make_yolo_train_icon_first100.py

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+import os
+import json
+import shutil
+from typing import Dict, Any, List
+
+import cv2
+
+
+# ---------- 根据你当前路径配置 ----------
+SCREENSHOT_DIR = "/mnt/d/mysite/SamVG/Dataset/rico/2/unique_uis/combined"
+SEM_DIR = "/mnt/d/mysite/SamVG/Dataset/rico/2/rico_dataset_v0.1_semantic_annotations/semantic_annotations"
+
+# YOLO 子集输出目录
+OUT_ROOT = "/mnt/d/mysite/SamVG/Dataset/rico/yolo_icon_first100"
+OUT_IMAGES = os.path.join(OUT_ROOT, "images")
+OUT_LABELS = os.path.join(OUT_ROOT, "labels")
+OUT_VIS = os.path.join(OUT_ROOT, "vis")  # 可选：画框检查用
+
+
+def ensure_dirs():
+    os.makedirs(OUT_IMAGES, exist_ok=True)
+    os.makedirs(OUT_LABELS, exist_ok=True)
+    os.makedirs(OUT_VIS, exist_ok=True)
+
+
+def collect_icon_nodes(node: Dict[str, Any]) -> List[Dict[str, Any]]:
+    """递归收集所有 componentLabel == 'Icon' 的节点"""
+    icons = []
+    if node.get("componentLabel") == "Icon":
+        icons.append(node)
+    for child in node.get("children", []):
+        icons.extend(collect_icon_nodes(child))
+    return icons
+
+
+def visualize_icons(image, icons, save_path):
+    """仅用于人工检查：在 image 上画出 icons"""
+    vis = image.copy()
+    font = cv2.FONT_HERSHEY_SIMPLEX
+    color = (0, 0, 255)
+
+    h, w = vis.shape[:2]
+
+    for node in icons:
+        bounds = node.get("bounds")
+        if not bounds or len(bounds) != 4:
+            continue
+        x1, y1, x2, y2 = bounds
+        x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
+        # 简单裁剪一下，防止越界
+        x1 = max(0, min(x1, w - 1))
+        x2 = max(0, min(x2, w - 1))
+        y1 = max(0, min(y1, h - 1))
+        y2 = max(0, min(y2, h - 1))
+
+        icon_class = node.get("iconClass") or "icon_generic"
+        label = f"Icon({icon_class})"
+
+        cv2.rectangle(vis, (x1, y1), (x2, y2), color, 3)
+        (tw, th), _ = cv2.getTextSize(label, font, 0.6, 2)
+        top_left = (x1, max(0, y1 - th - 4))
+        bottom_right = (x1 + tw + 4, y1)
+        cv2.rectangle(vis, top_left, bottom_right, color, -1)
+        cv2.putText(vis, label, (x1 + 2, y1 - 4), font, 0.6, (255, 255, 255), 2)
+
+    cv2.imwrite(save_path, vis)
+    print(f"[vis] {save_path}")
+
+
+def main(max_num: int = 100):
+    ensure_dirs()
+
+    # 列出所有 semantic json，按数字排序
+    json_files = [
+        f for f in os.listdir(SEM_DIR) if f.lower().endswith(".json")
+    ]
+
+    def get_id(name: str) -> int:
+        base = os.path.splitext(name)[0]
+        try:
+            return int(base)
+        except ValueError:
+            return 10 ** 9
+
+    json_files.sort(key=get_id)
+
+    class2id: Dict[str, int] = {}
+    selected = 0
+
+    for fname in json_files:
+        if selected >= max_num:
+            break
+
+        ui_id = os.path.splitext(fname)[0]
+        json_path = os.path.join(SEM_DIR, fname)
+
+        with open(json_path, "r", encoding="utf-8") as f:
+            data = json.load(f)
+
+        # 这个 data 就是 semantic 的根
+        icons = collect_icon_nodes(data)
+        if not icons:
+            continue  # 没有 icon，跳过
+
+        # 读 semantic png 以得到宽高
+        sem_png_path = os.path.join(SEM_DIR, f"{ui_id}.png")
+        sem_img = cv2.imread(sem_png_path)
+        if sem_img is None:
+            print(f"[!] semantic png not found or unreadable: {sem_png_path}")
+            continue
+
+        sem_h, sem_w = sem_img.shape[:2]
+
+        # 读 screenshot，并 resize 到 semantic 尺寸
+        screenshot_path = os.path.join(SCREENSHOT_DIR, f"{ui_id}.jpg")
+        if os.path.isfile(screenshot_path):
+            scr = cv2.imread(screenshot_path)
+            if scr is None:
+                print(f"[!] cannot read screenshot: {screenshot_path}")
+                continue
+            img_resized = cv2.resize(scr, (sem_w, sem_h), interpolation=cv2.INTER_LINEAR)
+        else:
+            # 没有 screenshot 时，就直接用 semantic png 作为训练图
+            img_resized = sem_img
+
+        selected += 1
+        print(f"[{selected}/{max_num}] UI {ui_id} with {len(icons)} icons")
+
+        # -------- 生成 YOLO label --------
+        label_lines = []
+        for node in icons:
+            bounds = node.get("bounds")
+            if not bounds or len(bounds) != 4:
+                continue
+            x1, y1, x2, y2 = bounds
+            x1, y1, x2, y2 = float(x1), float(y1), float(x2), float(y2)
+
+            # 坐标转 YOLO 格式（归一化）
+            box_w = x2 - x1
+            box_h = y2 - y1
+            x_center = x1 + box_w / 2.0
+            y_center = y1 + box_h / 2.0
+
+            x_center_n = x_center / sem_w
+            y_center_n = y_center / sem_h
+            w_n = box_w / sem_w
+            h_n = box_h / sem_h
+
+            icon_class = (node.get("iconClass") or "icon_generic").strip()
+            if icon_class not in class2id:
+                class2id[icon_class] = len(class2id)
+            cid = class2id[icon_class]
+
+            label_lines.append(
+                f"{cid} {x_center_n:.6f} {y_center_n:.6f} {w_n:.6f} {h_n:.6f}"
+            )
+
+        # 保存 label
+        label_path = os.path.join(OUT_LABELS, f"{ui_id}.txt")
+        with open(label_path, "w", encoding="utf-8") as f_lab:
+            f_lab.write("\n".join(label_lines))
+
+        # 保存 image（统一存 jpg）
+        out_img_path = os.path.join(OUT_IMAGES, f"{ui_id}.jpg")
+        cv2.imwrite(out_img_path, img_resized)
+
+        # 画一个只含 icon 的可视化图（方便你肉眼检查，可以删）
+        vis_path = os.path.join(OUT_VIS, f"{ui_id}_icons.jpg")
+        visualize_icons(img_resized, icons, vis_path)
+
+    # 保存 classes.txt
+    classes_path = os.path.join(OUT_ROOT, "classes.txt")
+    # 按 id 顺序写出类名
+    id2class = [""] * len(class2id)
+    for name, idx in class2id.items():
+        id2class[idx] = name
+    with open(classes_path, "w", encoding="utf-8") as f_cls:
+        for name in id2class:
+            f_cls.write(name + "\n")
+
+    print("\nDone.")
+    print(f"Total selected UIs: {selected}")
+    print(f"Num of icon classes: {len(class2id)}")
+    print(f"classes.txt saved to: {classes_path}")
+
+
+if __name__ == "__main__":
+    main(max_num=100)

show_semantic_meaning.py

@@ -0,0 +1,89 @@
+import json
+import os
+from typing import Dict, Any
+
+import cv2
+
+
+def draw_node_boxes(img, node: Dict[str, Any]):
+    """
+    在 img 上根据 node 的 bounds 画框（不做任何缩放变换）
+    """
+    bounds = node.get("bounds")
+    if bounds and len(bounds) == 4:
+        x1, y1, x2, y2 = bounds
+        # 这里不做缩放，只画原始坐标的框
+        cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)
+
+    for child in node.get("children", []):
+        draw_node_boxes(img, child)
+
+
+def show_semantic_meaning_stretched(
+    screenshot_path: str,
+    semantic_json_path: str,
+    semantic_png_path: str,
+    save_dir: str,
+) -> str:
+    """
+    用 semantic PNG 的尺寸把 screenshot 拉伸，然后把 semantic JSON 的框画上去。
+
+    screenshot_path: 例如 /mnt/d/mysite/SamVG/Dataset/rico/2/unique_uis/combined/54.jpg
+    semantic_json_path: 例如 /mnt/d/mysite/SamVG/Dataset/rico/2/rico_dataset_v0.1_semantic_annotations/semantic_annotations/54.json
+    semantic_png_path: 例如 /mnt/d/mysite/SamVG/Dataset/rico/2/rico_dataset_v0.1_semantic_annotations/semantic_annotations/54.png
+    save_dir: 保存目录，例如 /mnt/d/mysite/SamVG/Dataset/rico/test
+    """
+
+    # 1. 读 screenshot（540×960）
+    src_img = cv2.imread(screenshot_path)
+    if src_img is None:
+        raise FileNotFoundError(f"Cannot read screenshot: {screenshot_path}")
+    h_src, w_src = src_img.shape[:2]
+    print(f"[info] screenshot size: {w_src} x {h_src}")
+
+    # 2. 读 semantic PNG（1440×2560，用来获取目标 size）
+    sem_img = cv2.imread(semantic_png_path)
+    if sem_img is None:
+        raise FileNotFoundError(f"Cannot read semantic png: {semantic_png_path}")
+    h_tgt, w_tgt = sem_img.shape[:2]
+    print(f"[info] semantic png size: {w_tgt} x {h_tgt}")
+
+    # 3. 把 screenshot 拉伸到 semantic png 同样尺寸
+    stretched = cv2.resize(src_img, (w_tgt, h_tgt), interpolation=cv2.INTER_LINEAR)
+
+    # 4. 读 semantic JSON
+    with open(semantic_json_path, "r", encoding="utf-8") as f:
+        data = json.load(f)
+
+    # 语义 json 根节点结构：你发的例子是直接就有 "ancestors" / "class" / "bounds" / "children"
+    # 也就是说 data 本身就是 root
+    root = data
+    print("[info] start drawing boxes with original bounds ...")
+
+    draw_node_boxes(stretched, root)
+
+    # 5. 保存结果
+    os.makedirs(save_dir, exist_ok=True)
+    base_name = os.path.splitext(os.path.basename(screenshot_path))[0]
+    out_path = os.path.join(save_dir, f"{base_name}_stretched_semantic.png")
+
+    cv2.imwrite(out_path, stretched)
+    print(f"[+] saved to: {out_path}")
+
+    return out_path
+
+
+if __name__ == "__main__":
+    # 路径按你现在的环境改成 /mnt/d/ 版本
+    screenshot_path = "/mnt/d/mysite/SamVG/Dataset/rico/2/unique_uis/combined/54.jpg"
+    semantic_json_path = "/mnt/d/mysite/SamVG/Dataset/rico/2/rico_dataset_v0.1_semantic_annotations/semantic_annotations/54.json"
+    semantic_png_path = "/mnt/d/mysite/SamVG/Dataset/rico/2/rico_dataset_v0.1_semantic_annotations/semantic_annotations/54.png"
+
+    save_dir = "/mnt/d/mysite/SamVG/Dataset/rico/test"
+
+    show_semantic_meaning_stretched(
+        screenshot_path,
+        semantic_json_path,
+        semantic_png_path,
+        save_dir,
+    )

show_semantic_meaning_icon.py

@@ -0,0 +1,126 @@
+import json
+import os
+from typing import Dict, Any
+
+import cv2
+
+
+def draw_icon_boxes(img, node: Dict[str, Any], show_icon_only: bool):
+    """
+    在 img 上根据 semantic json 节点画框：
+    - 如果 show_icon_only=True，只画 componentLabel == "Icon" 的节点
+    - 坐标完全使用原始 bounds，不做缩放
+    """
+    bounds = node.get("bounds")
+    comp_label = node.get("componentLabel", "")
+
+    # 判断是否需要画当前节点
+    should_draw = True
+    if show_icon_only:
+        # 只画 Icon
+        should_draw = (comp_label == "Icon")
+
+    if bounds and len(bounds) == 4 and should_draw:
+        x1, y1, x2, y2 = bounds
+        # 原始坐标，直接画
+        color = (0, 0, 255)  # 红色框表示 Icon
+        cv2.rectangle(img, (x1, y1), (x2, y2), color, 3)
+
+        # 标签：Icon 或 Icon(iconClass)
+        label = "Icon"
+        icon_cls = node.get("iconClass")
+        if icon_cls:
+            label = f"Icon({icon_cls})"
+
+        font = cv2.FONT_HERSHEY_SIMPLEX
+        (tw, th), baseline = cv2.getTextSize(label, font, 0.6, 2)
+        top_left = (x1, max(0, y1 - th - 4))
+        bottom_right = (x1 + tw + 4, y1)
+
+        cv2.rectangle(img, top_left, bottom_right, color, thickness=-1)
+        cv2.putText(
+            img,
+            label,
+            (x1 + 2, y1 - 4),
+            font,
+            0.6,
+            (255, 255, 255),
+            2,
+            lineType=cv2.LINE_AA,
+        )
+
+    # 继续递归 children（即使自己不画，也要看子节点里有没有 Icon）
+    for child in node.get("children", []):
+        draw_icon_boxes(img, child, show_icon_only)
+
+
+def show_semantic_meaning_stretched(
+    screenshot_path: str,
+    semantic_json_path: str,
+    semantic_png_path: str,
+    save_dir: str,
+    show_icon_only: bool = False,
+) -> str:
+    """
+    用 semantic PNG 的尺寸把 screenshot 拉伸，然后把 semantic JSON 的框画上去。
+
+    screenshot_path:    /mnt/d/.../unique_uis/combined/54.jpg
+    semantic_json_path: /mnt/d/.../semantic_annotations/54.json
+    semantic_png_path:  /mnt/d/.../semantic_annotations/54.png
+    save_dir:           /mnt/d/.../test
+    show_icon_only:     True 时只画 Icon
+    """
+
+    # 1. 读 screenshot（例如 540×960）
+    src_img = cv2.imread(screenshot_path)
+    if src_img is None:
+        raise FileNotFoundError(f"Cannot read screenshot: {screenshot_path}")
+    h_src, w_src = src_img.shape[:2]
+    print(f"[info] screenshot size: {w_src} x {h_src}")
+
+    # 2. 读 semantic PNG（例如 1440×2560）
+    sem_img = cv2.imread(semantic_png_path)
+    if sem_img is None:
+        raise FileNotFoundError(f"Cannot read semantic png: {semantic_png_path}")
+    h_tgt, w_tgt = sem_img.shape[:2]
+    print(f"[info] semantic png size: {w_tgt} x {h_tgt}")
+
+    # 3. 把 screenshot 拉伸到 semantic png 同样尺寸
+    stretched = cv2.resize(src_img, (w_tgt, h_tgt), interpolation=cv2.INTER_LINEAR)
+
+    # 4. 读 semantic JSON
+    with open(semantic_json_path, "r", encoding="utf-8") as f:
+        data = json.load(f)
+
+    # 你给的 semantic json 根结构就是包含 bounds / children 的 root
+    root = data
+    print(f"[info] drawing boxes, show_icon_only={show_icon_only} ...")
+
+    draw_icon_boxes(stretched, root, show_icon_only=show_icon_only)
+
+    # 5. 保存结果
+    os.makedirs(save_dir, exist_ok=True)
+    base_name = os.path.splitext(os.path.basename(screenshot_path))[0]
+    suffix = "_icons" if show_icon_only else "_all"
+    out_path = os.path.join(save_dir, f"{base_name}_stretched{suffix}.png")
+
+    cv2.imwrite(out_path, stretched)
+    print(f"[+] saved to: {out_path}")
+
+    return out_path
+
+
+if __name__ == "__main__":
+    screenshot_path = "/mnt/d/mysite/SamVG/Dataset/rico/2/unique_uis/combined/100.jpg"
+    semantic_json_path = "/mnt/d/mysite/SamVG/Dataset/rico/2/rico_dataset_v0.1_semantic_annotations/semantic_annotations/100.json"
+    semantic_png_path = "/mnt/d/mysite/SamVG/Dataset/rico/2/rico_dataset_v0.1_semantic_annotations/semantic_annotations/100.png"
+    save_dir = "/mnt/d/mysite/SamVG/Dataset/rico/test"
+
+    # ✅ 只画 Icon
+    show_semantic_meaning_stretched(
+        screenshot_path,
+        semantic_json_path,
+        semantic_png_path,
+        save_dir,
+        show_icon_only=True,
+    )

split_train_test.py

@@ -0,0 +1,145 @@
+import os
+import random
+import shutil
+from tqdm import tqdm
+
+# ==== 路径配置 ====
+ROOT = "/mnt/d/mysite/SamVG/Dataset/rico/yolo_icon_full"
+
+IMAGES_DIR = os.path.join(ROOT, "images")
+LABELS_DIR = os.path.join(ROOT, "labels")
+
+TRAIN_IMG_DIR = os.path.join(IMAGES_DIR, "train")
+VAL_IMG_DIR   = os.path.join(IMAGES_DIR, "val")
+TEST_IMG_DIR  = os.path.join(IMAGES_DIR, "test")
+
+TRAIN_LBL_DIR = os.path.join(LABELS_DIR, "train")
+VAL_LBL_DIR   = os.path.join(LABELS_DIR, "val")
+TEST_LBL_DIR  = os.path.join(LABELS_DIR, "test")
+
+TRAIN_RATIO = 0.90
+VAL_RATIO   = 0.05
+RANDOM_SEED = 42
+
+
+def ensure_dirs():
+    print(f"[LOG] 确保 train/val/test 子目录存在...")
+    os.makedirs(TRAIN_IMG_DIR, exist_ok=True)
+    os.makedirs(VAL_IMG_DIR,   exist_ok=True)
+    os.makedirs(TEST_IMG_DIR,  exist_ok=True)
+
+    os.makedirs(TRAIN_LBL_DIR, exist_ok=True)
+    os.makedirs(VAL_LBL_DIR,   exist_ok=True)
+    os.makedirs(TEST_LBL_DIR,  exist_ok=True)
+    print(f"[LOG] 子目录检查完成。")
+
+
+def main():
+    print("===== YOLO train/val/test 拆分开始 =====")
+    print(f"[LOG] ROOT       = {ROOT}")
+    print(f"[LOG] IMAGES_DIR = {IMAGES_DIR}")
+    print(f"[LOG] LABELS_DIR = {LABELS_DIR}")
+
+    if not os.path.isdir(IMAGES_DIR):
+        print(f"[ERROR] 图像目录不存在: {IMAGES_DIR}")
+        return
+    if not os.path.isdir(LABELS_DIR):
+        print(f"[ERROR] 标签目录不存在: {LABELS_DIR}")
+        return
+
+    ensure_dirs()
+
+    print("[LOG] 扫描 images 顶层（不含 train/val/test 子目录）...")
+
+    # 🔴 不再使用 os.path.isfile，只按后缀筛选
+    all_imgs = [
+        f for f in os.listdir(IMAGES_DIR)
+        if f.lower().endswith((".jpg", ".jpeg", ".png"))
+    ]
+
+    n_total = len(all_imgs)
+    print(f"[LOG] 找到图片数量: {n_total}")
+
+    if n_total == 0:
+        print("[ERROR] images/ 里没有任何顶层图片（可能已经全部被移动到 train/val/test 了？）")
+        return
+
+    print("[LOG] 示例前 5 张图片: ", all_imgs[:5])
+
+    random.seed(RANDOM_SEED)
+    random.shuffle(all_imgs)
+    print("[LOG] 打乱顺序完成。")
+
+    n_train = int(n_total * TRAIN_RATIO)
+    n_val   = int(n_total * VAL_RATIO)
+    n_test  = n_total - n_train - n_val
+
+    train_files = all_imgs[:n_train]
+    val_files   = all_imgs[n_train:n_train + n_val]
+    test_files  = all_imgs[n_train + n_val:]
+
+    print(f"[LOG] 划分结果：train={len(train_files)}, val={len(val_files)}, test={len(test_files)}")
+
+    # ---------- 移动 train ----------
+    print("[LOG] 开始移动 train 文件...")
+    for fname in tqdm(train_files, desc="Moving train set"):
+        base, _ = os.path.splitext(fname)
+        src_img = os.path.join(IMAGES_DIR, fname)
+        src_lbl = os.path.join(LABELS_DIR, base + ".txt")
+
+        if not os.path.exists(src_lbl):
+            # 理论上不该发生，如果出现就提醒一下
+            print(f"[WARN] 找不到标签文件: {src_lbl}，跳过这张图。")
+            continue
+
+        dst_img = os.path.join(TRAIN_IMG_DIR, fname)
+        dst_lbl = os.path.join(TRAIN_LBL_DIR, base + ".txt")
+
+        shutil.move(src_img, dst_img)
+        shutil.move(src_lbl, dst_lbl)
+
+    # ---------- 移动 val ----------
+    print("[LOG] 开始移动 val 文件...")
+    for fname in tqdm(val_files, desc="Moving val set"):
+        base, _ = os.path.splitext(fname)
+        src_img = os.path.join(IMAGES_DIR, fname)
+        src_lbl = os.path.join(LABELS_DIR, base + ".txt")
+
+        if not os.path.exists(src_lbl):
+            print(f"[WARN] 找不到标签文件: {src_lbl}，跳过这张图。")
+            continue
+
+        dst_img = os.path.join(VAL_IMG_DIR, fname)
+        dst_lbl = os.path.join(VAL_LBL_DIR, base + ".txt")
+
+        shutil.move(src_img, dst_img)
+        shutil.move(src_lbl, dst_lbl)
+
+    # ---------- 移动 test ----------
+    print("[LOG] 开始移动 test 文件...")
+    for fname in tqdm(test_files, desc="Moving test set"):
+        base, _ = os.path.splitext(fname)
+        src_img = os.path.join(IMAGES_DIR, fname)
+        src_lbl = os.path.join(LABELS_DIR, base + ".txt")
+
+        if not os.path.exists(src_lbl):
+            print(f"[WARN] 找不到标签文件: {src_lbl}，跳过这张图。")
+            continue
+
+        dst_img = os.path.join(TEST_IMG_DIR, fname)
+        dst_lbl = os.path.join(TEST_LBL_DIR, base + ".txt")
+
+        shutil.move(src_img, dst_img)
+        shutil.move(src_lbl, dst_lbl)
+
+    print("===== 拆分完成 =====")
+    print(f"Images train dir : {TRAIN_IMG_DIR}")
+    print(f"Images val dir   : {VAL_IMG_DIR}")
+    print(f"Images test dir  : {TEST_IMG_DIR}")
+    print(f"Labels train dir : {TRAIN_LBL_DIR}")
+    print(f"Labels val dir   : {VAL_LBL_DIR}")
+    print(f"Labels test dir  : {TEST_LBL_DIR}")
+
+
+if __name__ == "__main__":
+    main()

test.ipynb

@@ -0,0 +1,409 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d67c350f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sat Dec  6 12:09:27 2025       \n",
+      "+-----------------------------------------------------------------------------------------+\n",
+      "| NVIDIA-SMI 550.54.15              Driver Version: 550.54.15      CUDA Version: 12.4     |\n",
+      "|-----------------------------------------+------------------------+----------------------+\n",
+      "| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |\n",
+      "| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |\n",
+      "|                                         |                        |               MIG M. |\n",
+      "|=========================================+========================+======================|\n",
+      "|   0  Tesla T4                       Off |   00000000:00:04.0 Off |                    0 |\n",
+      "| N/A   36C    P8              9W /   70W |       0MiB /  15360MiB |      0%      Default |\n",
+      "|                                         |                        |                  N/A |\n",
+      "+-----------------------------------------+------------------------+----------------------+\n",
+      "                                                                                         \n",
+      "+-----------------------------------------------------------------------------------------+\n",
+      "| Processes:                                                                              |\n",
+      "|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |\n",
+      "|        ID   ID                                                               Usage      |\n",
+      "|=========================================================================================|\n",
+      "|  No running processes found                                                             |\n",
+      "+-----------------------------------------------------------------------------------------+\n"
+     ]
+    }
+   ],
+   "source": [
+    "!nvidia-smi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "5197eeca",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting ultralytics\n",
+      "  Downloading ultralytics-8.3.235-py3-none-any.whl.metadata (37 kB)\n",
+      "Requirement already satisfied: numpy>=1.23.0 in /usr/local/lib/python3.12/dist-packages (from ultralytics) (2.0.2)\n",
+      "Requirement already satisfied: matplotlib>=3.3.0 in /usr/local/lib/python3.12/dist-packages (from ultralytics) (3.10.0)\n",
+      "Requirement already satisfied: opencv-python>=4.6.0 in /usr/local/lib/python3.12/dist-packages (from ultralytics) (4.12.0.88)\n",
+      "Requirement already satisfied: pillow>=7.1.2 in /usr/local/lib/python3.12/dist-packages (from ultralytics) (11.3.0)\n",
+      "Requirement already satisfied: pyyaml>=5.3.1 in /usr/local/lib/python3.12/dist-packages (from ultralytics) (6.0.3)\n",
+      "Requirement already satisfied: requests>=2.23.0 in /usr/local/lib/python3.12/dist-packages (from ultralytics) (2.32.4)\n",
+      "Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.12/dist-packages (from ultralytics) (1.16.3)\n",
+      "Requirement already satisfied: torch>=1.8.0 in /usr/local/lib/python3.12/dist-packages (from ultralytics) (2.9.0+cu126)\n",
+      "Requirement already satisfied: torchvision>=0.9.0 in /usr/local/lib/python3.12/dist-packages (from ultralytics) (0.24.0+cu126)\n",
+      "Requirement already satisfied: psutil>=5.8.0 in /usr/local/lib/python3.12/dist-packages (from ultralytics) (5.9.5)\n",
+      "Requirement already satisfied: polars>=0.20.0 in /usr/local/lib/python3.12/dist-packages (from ultralytics) (1.31.0)\n",
+      "Collecting ultralytics-thop>=2.0.18 (from ultralytics)\n",
+      "  Downloading ultralytics_thop-2.0.18-py3-none-any.whl.metadata (14 kB)\n",
+      "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib>=3.3.0->ultralytics) (1.3.3)\n",
+      "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.12/dist-packages (from matplotlib>=3.3.0->ultralytics) (0.12.1)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib>=3.3.0->ultralytics) (4.60.1)\n",
+      "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib>=3.3.0->ultralytics) (1.4.9)\n",
+      "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib>=3.3.0->ultralytics) (25.0)\n",
+      "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib>=3.3.0->ultralytics) (3.2.5)\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.12/dist-packages (from matplotlib>=3.3.0->ultralytics) (2.9.0.post0)\n",
+      "Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests>=2.23.0->ultralytics) (3.4.4)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.12/dist-packages (from requests>=2.23.0->ultralytics) (3.11)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests>=2.23.0->ultralytics) (2.5.0)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.12/dist-packages (from requests>=2.23.0->ultralytics) (2025.11.12)\n",
+      "Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (3.20.0)\n",
+      "Requirement already satisfied: typing-extensions>=4.10.0 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (4.15.0)\n",
+      "Requirement already satisfied: setuptools in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (75.2.0)\n",
+      "Requirement already satisfied: sympy>=1.13.3 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (1.14.0)\n",
+      "Requirement already satisfied: networkx>=2.5.1 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (3.6)\n",
+      "Requirement already satisfied: jinja2 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (3.1.6)\n",
+      "Requirement already satisfied: fsspec>=0.8.5 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (2025.3.0)\n",
+      "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.6.77 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (12.6.77)\n",
+      "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.6.77 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (12.6.77)\n",
+      "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.6.80 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (12.6.80)\n",
+      "Requirement already satisfied: nvidia-cudnn-cu12==9.10.2.21 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (9.10.2.21)\n",
+      "Requirement already satisfied: nvidia-cublas-cu12==12.6.4.1 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (12.6.4.1)\n",
+      "Requirement already satisfied: nvidia-cufft-cu12==11.3.0.4 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (11.3.0.4)\n",
+      "Requirement already satisfied: nvidia-curand-cu12==10.3.7.77 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (10.3.7.77)\n",
+      "Requirement already satisfied: nvidia-cusolver-cu12==11.7.1.2 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (11.7.1.2)\n",
+      "Requirement already satisfied: nvidia-cusparse-cu12==12.5.4.2 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (12.5.4.2)\n",
+      "Requirement already satisfied: nvidia-cusparselt-cu12==0.7.1 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (0.7.1)\n",
+      "Requirement already satisfied: nvidia-nccl-cu12==2.27.5 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (2.27.5)\n",
+      "Requirement already satisfied: nvidia-nvshmem-cu12==3.3.20 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (3.3.20)\n",
+      "Requirement already satisfied: nvidia-nvtx-cu12==12.6.77 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (12.6.77)\n",
+      "Requirement already satisfied: nvidia-nvjitlink-cu12==12.6.85 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (12.6.85)\n",
+      "Requirement already satisfied: nvidia-cufile-cu12==1.11.1.6 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (1.11.1.6)\n",
+      "Requirement already satisfied: triton==3.5.0 in /usr/local/lib/python3.12/dist-packages (from torch>=1.8.0->ultralytics) (3.5.0)\n",
+      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.7->matplotlib>=3.3.0->ultralytics) (1.17.0)\n",
+      "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.12/dist-packages (from sympy>=1.13.3->torch>=1.8.0->ultralytics) (1.3.0)\n",
+      "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.12/dist-packages (from jinja2->torch>=1.8.0->ultralytics) (3.0.3)\n",
+      "Downloading ultralytics-8.3.235-py3-none-any.whl (1.1 MB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m25.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m\n",
+      "\u001b[?25hDownloading ultralytics_thop-2.0.18-py3-none-any.whl (28 kB)\n",
+      "Installing collected packages: ultralytics-thop, ultralytics\n",
+      "Successfully installed ultralytics-8.3.235 ultralytics-thop-2.0.18\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install ultralytics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7ed3adf8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Creating new Ultralytics Settings v0.0.6 file ✅ \n",
+      "View Ultralytics Settings with 'yolo settings' or at '/root/.config/Ultralytics/settings.json'\n",
+      "Update Settings with 'yolo settings key=value', i.e. 'yolo settings runs_dir=path/to/dir'. For help see https://docs.ultralytics.com/quickstart/#ultralytics-settings.\n",
+      "\u001b[KDownloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8n.pt to 'yolov8n.pt': 100% ━━━━━━━━━━━━ 6.2MB 130.3MB/s 0.0s\n"
+     ]
+    }
+   ],
+   "source": [
+    "from ultralytics import YOLO\n",
+    "\n",
+    "model = YOLO(\"yolov8n.pt\")  # 载入预训练模型\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a6fe0e55",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CUDA available: True\n",
+      "GPU: Tesla T4\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "print(\"CUDA available:\", torch.cuda.is_available())\n",
+    "print(\"GPU:\", torch.cuda.get_device_name(0))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c9e6b658",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "     <input type=\"file\" id=\"files-c36b4f65-2de8-41fb-b639-9ec5da9486a9\" name=\"files[]\" multiple disabled\n",
+       "        style=\"border:none\" />\n",
+       "     <output id=\"result-c36b4f65-2de8-41fb-b639-9ec5da9486a9\">\n",
+       "      Upload widget is only available when the cell has been executed in the\n",
+       "      current browser session. Please rerun this cell to enable.\n",
+       "      </output>\n",
+       "      <script>// Copyright 2017 Google LLC\n",
+       "//\n",
+       "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+       "// you may not use this file except in compliance with the License.\n",
+       "// You may obtain a copy of the License at\n",
+       "//\n",
+       "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+       "//\n",
+       "// Unless required by applicable law or agreed to in writing, software\n",
+       "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+       "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+       "// See the License for the specific language governing permissions and\n",
+       "// limitations under the License.\n",
+       "\n",
+       "/**\n",
+       " * @fileoverview Helpers for google.colab Python module.\n",
+       " */\n",
+       "(function(scope) {\n",
+       "function span(text, styleAttributes = {}) {\n",
+       "  const element = document.createElement('span');\n",
+       "  element.textContent = text;\n",
+       "  for (const key of Object.keys(styleAttributes)) {\n",
+       "    element.style[key] = styleAttributes[key];\n",
+       "  }\n",
+       "  return element;\n",
+       "}\n",
+       "\n",
+       "// Max number of bytes which will be uploaded at a time.\n",
+       "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+       "\n",
+       "function _uploadFiles(inputId, outputId) {\n",
+       "  const steps = uploadFilesStep(inputId, outputId);\n",
+       "  const outputElement = document.getElementById(outputId);\n",
+       "  // Cache steps on the outputElement to make it available for the next call\n",
+       "  // to uploadFilesContinue from Python.\n",
+       "  outputElement.steps = steps;\n",
+       "\n",
+       "  return _uploadFilesContinue(outputId);\n",
+       "}\n",
+       "\n",
+       "// This is roughly an async generator (not supported in the browser yet),\n",
+       "// where there are multiple asynchronous steps and the Python side is going\n",
+       "// to poll for completion of each step.\n",
+       "// This uses a Promise to block the python side on completion of each step,\n",
+       "// then passes the result of the previous step as the input to the next step.\n",
+       "function _uploadFilesContinue(outputId) {\n",
+       "  const outputElement = document.getElementById(outputId);\n",
+       "  const steps = outputElement.steps;\n",
+       "\n",
+       "  const next = steps.next(outputElement.lastPromiseValue);\n",
+       "  return Promise.resolve(next.value.promise).then((value) => {\n",
+       "    // Cache the last promise value to make it available to the next\n",
+       "    // step of the generator.\n",
+       "    outputElement.lastPromiseValue = value;\n",
+       "    return next.value.response;\n",
+       "  });\n",
+       "}\n",
+       "\n",
+       "/**\n",
+       " * Generator function which is called between each async step of the upload\n",
+       " * process.\n",
+       " * @param {string} inputId Element ID of the input file picker element.\n",
+       " * @param {string} outputId Element ID of the output display.\n",
+       " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+       " */\n",
+       "function* uploadFilesStep(inputId, outputId) {\n",
+       "  const inputElement = document.getElementById(inputId);\n",
+       "  inputElement.disabled = false;\n",
+       "\n",
+       "  const outputElement = document.getElementById(outputId);\n",
+       "  outputElement.innerHTML = '';\n",
+       "\n",
+       "  const pickedPromise = new Promise((resolve) => {\n",
+       "    inputElement.addEventListener('change', (e) => {\n",
+       "      resolve(e.target.files);\n",
+       "    });\n",
+       "  });\n",
+       "\n",
+       "  const cancel = document.createElement('button');\n",
+       "  inputElement.parentElement.appendChild(cancel);\n",
+       "  cancel.textContent = 'Cancel upload';\n",
+       "  const cancelPromise = new Promise((resolve) => {\n",
+       "    cancel.onclick = () => {\n",
+       "      resolve(null);\n",
+       "    };\n",
+       "  });\n",
+       "\n",
+       "  // Wait for the user to pick the files.\n",
+       "  const files = yield {\n",
+       "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+       "    response: {\n",
+       "      action: 'starting',\n",
+       "    }\n",
+       "  };\n",
+       "\n",
+       "  cancel.remove();\n",
+       "\n",
+       "  // Disable the input element since further picks are not allowed.\n",
+       "  inputElement.disabled = true;\n",
+       "\n",
+       "  if (!files) {\n",
+       "    return {\n",
+       "      response: {\n",
+       "        action: 'complete',\n",
+       "      }\n",
+       "    };\n",
+       "  }\n",
+       "\n",
+       "  for (const file of files) {\n",
+       "    const li = document.createElement('li');\n",
+       "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+       "    li.append(span(\n",
+       "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+       "        `last modified: ${\n",
+       "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+       "                                    'n/a'} - `));\n",
+       "    const percent = span('0% done');\n",
+       "    li.appendChild(percent);\n",
+       "\n",
+       "    outputElement.appendChild(li);\n",
+       "\n",
+       "    const fileDataPromise = new Promise((resolve) => {\n",
+       "      const reader = new FileReader();\n",
+       "      reader.onload = (e) => {\n",
+       "        resolve(e.target.result);\n",
+       "      };\n",
+       "      reader.readAsArrayBuffer(file);\n",
+       "    });\n",
+       "    // Wait for the data to be ready.\n",
+       "    let fileData = yield {\n",
+       "      promise: fileDataPromise,\n",
+       "      response: {\n",
+       "        action: 'continue',\n",
+       "      }\n",
+       "    };\n",
+       "\n",
+       "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+       "    let position = 0;\n",
+       "    do {\n",
+       "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+       "      const chunk = new Uint8Array(fileData, position, length);\n",
+       "      position += length;\n",
+       "\n",
+       "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+       "      yield {\n",
+       "        response: {\n",
+       "          action: 'append',\n",
+       "          file: file.name,\n",
+       "          data: base64,\n",
+       "        },\n",
+       "      };\n",
+       "\n",
+       "      let percentDone = fileData.byteLength === 0 ?\n",
+       "          100 :\n",
+       "          Math.round((position / fileData.byteLength) * 100);\n",
+       "      percent.textContent = `${percentDone}% done`;\n",
+       "\n",
+       "    } while (position < fileData.byteLength);\n",
+       "  }\n",
+       "\n",
+       "  // All done.\n",
+       "  yield {\n",
+       "    response: {\n",
+       "      action: 'complete',\n",
+       "    }\n",
+       "  };\n",
+       "}\n",
+       "\n",
+       "scope.google = scope.google || {};\n",
+       "scope.google.colab = scope.google.colab || {};\n",
+       "scope.google.colab._files = {\n",
+       "  _uploadFiles,\n",
+       "  _uploadFilesContinue,\n",
+       "};\n",
+       "})(self);\n",
+       "</script> "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m/tmp/ipython-input-264872163.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mfiles\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0muploaded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfiles\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/google/colab/files.py\u001b[0m in \u001b[0;36mupload\u001b[0;34m(target_dir)\u001b[0m\n\u001b[1;32m     70\u001b[0m   \"\"\"\n\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m   \u001b[0muploaded_files\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_upload_files\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmultiple\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m   \u001b[0;31m# Mapping from original filename to filename as saved locally.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m   \u001b[0mlocal_filenames\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/google/colab/files.py\u001b[0m in \u001b[0;36m_upload_files\u001b[0;34m(multiple)\u001b[0m\n\u001b[1;32m    162\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    163\u001b[0m   \u001b[0;31m# First result is always an indication that the file picker has completed.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 164\u001b[0;31m   result = _output.eval_js(\n\u001b[0m\u001b[1;32m    165\u001b[0m       'google.colab._files._uploadFiles(\"{input_id}\", \"{output_id}\")'.format(\n\u001b[1;32m    166\u001b[0m           \u001b[0minput_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_id\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/google/colab/output/_js.py\u001b[0m in \u001b[0;36meval_js\u001b[0;34m(script, ignore_result, timeout_sec)\u001b[0m\n\u001b[1;32m     38\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mignore_result\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m     \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0m_message\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_reply_from_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_sec\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/google/colab/_message.py\u001b[0m in \u001b[0;36mread_reply_from_input\u001b[0;34m(message_id, timeout_sec)\u001b[0m\n\u001b[1;32m     94\u001b[0m     \u001b[0mreply\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_read_next_input_message\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mreply\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_NOT_READY\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreply\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 96\u001b[0;31m       \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.025\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     97\u001b[0m       \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m     if (\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "from google.colab import files\n",
+    "uploaded = files.upload()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dee3f32f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

upload_dataset.py

@@ -0,0 +1,7 @@
+from huggingface_hub import login, upload_folder
+
+
+login()
+
+
+upload_folder(folder_path=".", repo_id="lili24/yolo_rico_icon_48k", repo_type="dataset")