diff --git a/app.py b/app.py new file mode 100644 index 0000000000000000000000000000000000000000..ab1ff360838756d905f5fcf0d979f3a4f06a9686 --- /dev/null +++ b/app.py @@ -0,0 +1,729 @@ +import os +import sys +import glob +import json +import random +import re +from functools import partial +from datetime import datetime +from collections import defaultdict, Counter + +import gradio as gr +from loguru import logger + +# --- Global State (unchanged) --- +# --- Global State (unchanged) --- +GLOBAL_STATE = { + "participant_id": None, + "data_loaded": False, + "all_eval_data": [], + "shuffled_indices": [], + "current_prompt_index": 0, + "current_criterion_index": 0, + "image_mapping": {}, + "image_dir": "", + "evaluation_results": {}, + "image_orders": {}, + "start_time": None, + "end_time": None, + "current_ranks": {}, + "current_absolute_score": None, + # ▼▼▼ 追加 ▼▼▼ + "current_absolute_score_worst": None, +} + +# --- Configuration (unchanged) --- +BASE_RESULTS_DIR = "./results" +LOG_DIR = "./logs" +COMBINED_DATA_DIR = "./combined_data" +IMAGE_SUBDIR = os.path.join("lapwing", "images") +MAPPING_FILENAME = "combination_to_filename.json" +CONDITIONS = ["Ours", "w_o_Proto_Loss", "w_o_HitL", "w_o_Tuning", "LLM-based"] +CRITERIA = ["Alignment", "Naturalness", "Attractiveness"] + +CRITERIA_GUIDANCE_JP = [ + "テキストと表情がどれだけ一致しているか", + "テキストの感情に沿ったセリフを言っていると想像したとき、表情がどれだけ自然か", + "テキストの感情に沿ったセリフを言っていると想像したとき、表情がどれだけ魅力的か" +] +CRITERIA_GUIDANCE_EN = [ + "how well the expression aligns with the text", + "imagining the character is speaking a line that matches the emotion of the text, how natural the facial expression is", + "imagining the character is speaking a line that matches the emotion of the text, how attractive the facial expression is" +] +IMAGE_LABELS = ['A', 'B', 'C', 'D', 'E'] + + +# --- Helper Functions --- +def get_image_path_from_prediction(prediction: dict) -> str: + if not GLOBAL_STATE["image_mapping"]: + logger.error("Image mapping is not loaded.") + return "" + indices = prediction.get("blendshape_index", {}) + if not isinstance(indices, dict): + logger.error(f"blendshape_index is not a dictionary: {indices}") + return "" + sorted_indices = sorted(indices.items(), key=lambda item: int(item[0])) + key = ",".join(str(idx) for _, idx in sorted_indices) + filename = GLOBAL_STATE["image_mapping"].get(key) + if not filename: + logger.warning(f"No image found for blendshape key: {key}") + return "" + return os.path.join(GLOBAL_STATE["image_dir"], filename) + + +# ▼▼▼ 2. prompt_categoryを読み込むように修正 ▼▼▼ +def load_evaluation_data(participant_id: str): + mapping_path = os.path.join(COMBINED_DATA_DIR, MAPPING_FILENAME) + if not os.path.exists(mapping_path): + return f"

Error: Mapping file not found at {mapping_path}

", gr.update( + interactive=True), gr.update(interactive=False) + + with open(mapping_path, 'r', encoding='utf-8') as f: + GLOBAL_STATE["image_mapping"] = json.load(f)["mapping"] + GLOBAL_STATE["image_dir"] = os.path.join(COMBINED_DATA_DIR, IMAGE_SUBDIR) + logger.info(f"Successfully loaded image mapping. Image directory: {GLOBAL_STATE['image_dir']}") + + participant_dir = os.path.join(BASE_RESULTS_DIR, participant_id) + if not os.path.isdir(participant_dir): + return f"

Error: Participant directory not found: {participant_dir}

", gr.update( + interactive=True), gr.update(interactive=False) + + merged_data = defaultdict(lambda: {"predictions": {}, "category": None}) + found_files = 0 + for cond in CONDITIONS: + cond_dir = os.path.join(participant_dir, cond) + pattern = os.path.join(cond_dir, f"{participant_id}_{cond}_*.jsonl") + files = glob.glob(pattern) + if not files: + logger.warning(f"No prediction file found for condition '{cond}' with pattern: {pattern}") + continue + found_files += 1 + with open(files[0], 'r', encoding='utf-8') as f: + for line in f: + data = json.loads(line) + prompt = data["text_prompt"] + merged_data[prompt]["predictions"][cond] = data["prediction"] + if not merged_data[prompt]["category"]: + merged_data[prompt]["category"] = data.get("prompt_category") + + if found_files != len(CONDITIONS): + return f"

Error: Found prediction files for only {found_files}/{len(CONDITIONS)} conditions.

", gr.update( + interactive=True), gr.update(interactive=False) + + GLOBAL_STATE["all_eval_data"] = [ + {"prompt": p, "predictions": d["predictions"], "category": d["category"]} + for p, d in merged_data.items() if len(d["predictions"]) == len(CONDITIONS) + ] + # ▲▲▲ END OF UPDATE ▲▲▲ + + if not GLOBAL_STATE["all_eval_data"]: + return "

Error: No valid evaluation data could be loaded.

", gr.update( + interactive=True), gr.update(interactive=False) + + GLOBAL_STATE["shuffled_indices"] = list(range(len(GLOBAL_STATE["all_eval_data"]))) + random.shuffle(GLOBAL_STATE["shuffled_indices"]) + GLOBAL_STATE["current_prompt_index"] = 0 + GLOBAL_STATE["current_criterion_index"] = 0 + GLOBAL_STATE["data_loaded"] = True + GLOBAL_STATE["start_time"] = datetime.now() + for i in range(len(GLOBAL_STATE["all_eval_data"])): + prompt_text = GLOBAL_STATE["all_eval_data"][i]["prompt"] + GLOBAL_STATE["evaluation_results"][prompt_text] = {} + + logger.info(f"Loaded and merged data for {len(GLOBAL_STATE['all_eval_data'])} prompts.") + done_msg = "

Data loaded successfully. Please proceed to the 'Evaluation' tab. / データの読み込みに成功しました。「評価」タブに進んでください。

" + return done_msg, gr.update(interactive=False, visible=False), gr.update(interactive=True) + + +# --- Core Logic --- + +def _create_button_updates(): + updates = [] + for img_label in IMAGE_LABELS: + selected_rank = GLOBAL_STATE["current_ranks"].get(img_label) + for rank_val in range(1, 6): + if rank_val == selected_rank: + updates.append(gr.update(variant='primary')) + else: + updates.append(gr.update(variant='secondary')) + return updates + + +def handle_rank_button_click(image_label, rank): + if GLOBAL_STATE["current_ranks"].get(image_label) == rank: + GLOBAL_STATE["current_ranks"][image_label] = None + else: + GLOBAL_STATE["current_ranks"][image_label] = rank + return _create_button_updates() + + +def handle_absolute_score_click(score): + if GLOBAL_STATE["current_absolute_score"] == score: + GLOBAL_STATE["current_absolute_score"] = None + else: + GLOBAL_STATE["current_absolute_score"] = score + + updates = [] + for i in range(1, 8): + if i == GLOBAL_STATE["current_absolute_score"]: + updates.append(gr.update(variant='primary')) + else: + updates.append(gr.update(variant='secondary')) + return updates + + +# ▼▼▼ 追加 ▼▼▼ +def handle_absolute_score_worst_click(score): + if GLOBAL_STATE["current_absolute_score_worst"] == score: + GLOBAL_STATE["current_absolute_score_worst"] = None + else: + GLOBAL_STATE["current_absolute_score_worst"] = score + + updates = [] + for i in range(1, 8): + if i == GLOBAL_STATE["current_absolute_score_worst"]: + updates.append(gr.update(variant='primary')) + else: + updates.append(gr.update(variant='secondary')) + return updates + +# ▼▼▼ 1. UIフリーズ問題を修正 ▼▼▼ + + +# ▼▼▼ 修正後の display_current_prompt_and_criterion 関数 ▼▼▼ +def display_current_prompt_and_criterion(): + if not GLOBAL_STATE["data_loaded"] or GLOBAL_STATE["current_prompt_index"] >= len(GLOBAL_STATE["all_eval_data"]): + done_msg = "

All prompts have been evaluated! Please proceed to the 'Export' tab.
すべてのプロンプトの評価が完了しました！「エクスポート」タブに進んでください。

" + empty_button_updates = [gr.update(variant='secondary')] * 25 + empty_abs_updates = [gr.update(variant='secondary')] * 7 + return [ + gr.update(value="Finished! / 完了!"), + gr.update(value=""), + gr.update(value=done_msg), + gr.update(value="", visible=False), + *[gr.update(value=None)] * 5, + *empty_button_updates, + gr.update(visible=False), # abs_group_best + *empty_abs_updates, + gr.update(visible=False), # abs_group_worst + *empty_abs_updates, + gr.update(interactive=False), + gr.update(interactive=False) + ] + + prompt_idx = GLOBAL_STATE["shuffled_indices"][GLOBAL_STATE["current_prompt_index"]] + criterion_idx = GLOBAL_STATE["current_criterion_index"] + + current_data = GLOBAL_STATE["all_eval_data"][prompt_idx] + prompt_text = current_data["prompt"] + criterion_name = CRITERIA[criterion_idx] + + progress_text = f"Prompt {GLOBAL_STATE['current_prompt_index'] + 1} / {len(GLOBAL_STATE['all_eval_data'])} - **{criterion_name}**" + prompt_display_text = f"## \"{prompt_text}\"" + guidance_text = f"### Please rank the 5 images based on **{CRITERIA_GUIDANCE_EN[criterion_idx]}**.
5つの画像を、**「{CRITERIA_GUIDANCE_JP[criterion_idx]}」**を基準にランキング付けしてください。" + + if criterion_idx == 0: + GLOBAL_STATE["image_orders"] = {} + + if criterion_name not in GLOBAL_STATE["image_orders"]: + conditions_shuffled = random.sample(CONDITIONS, len(CONDITIONS)) + GLOBAL_STATE["image_orders"][criterion_name] = conditions_shuffled + + current_image_order = GLOBAL_STATE["image_orders"][criterion_name] + image_updates = [] + for cond_name in current_image_order: + prediction = current_data["predictions"][cond_name] + img_path = get_image_path_from_prediction(prediction) + image_updates.append(gr.update(value=img_path if img_path and os.path.exists(img_path) else None)) + + saved_ranks_dict = GLOBAL_STATE["evaluation_results"].get(prompt_text, {}).get("ranks", {}).get(criterion_name) + if saved_ranks_dict: + label_to_condition = {label: cond for label, cond in zip(IMAGE_LABELS, current_image_order)} + condition_to_label = {v: k for k, v in label_to_condition.items()} + GLOBAL_STATE["current_ranks"] = { + condition_to_label[cond]: rank for cond, rank in saved_ranks_dict.items() if cond in condition_to_label + } + else: + GLOBAL_STATE["current_ranks"] = {label: None for label in IMAGE_LABELS} + + button_updates = _create_button_updates() + + # --- Absolute Score (Best) --- + is_alignment_criterion = (criterion_name == "Alignment") + abs_group_update = gr.update(visible=is_alignment_criterion) + saved_abs_score = GLOBAL_STATE["evaluation_results"].get(prompt_text, {}).get("absolute_score") + GLOBAL_STATE["current_absolute_score"] = saved_abs_score if is_alignment_criterion else None + + abs_button_updates = [] + for i in range(1, 8): + variant = 'primary' if i == GLOBAL_STATE["current_absolute_score"] else 'secondary' + abs_button_updates.append(gr.update(variant=variant)) + + # --- Absolute Score (Worst) --- + abs_group_worst_update = gr.update(visible=is_alignment_criterion) + saved_abs_score_worst = GLOBAL_STATE["evaluation_results"].get(prompt_text, {}).get("absolute_score_worst") + GLOBAL_STATE["current_absolute_score_worst"] = saved_abs_score_worst if is_alignment_criterion else None + + abs_button_worst_updates = [] + for i in range(1, 8): + variant = 'primary' if i == GLOBAL_STATE["current_absolute_score_worst"] else 'secondary' + abs_button_worst_updates.append(gr.update(variant=variant)) + + return [ + gr.update(value=progress_text), + gr.update(value=prompt_display_text), + gr.update(value=guidance_text), + gr.update(value="", visible=False), + *image_updates, + *button_updates, + abs_group_update, + *abs_button_updates, + abs_group_worst_update, + *abs_button_worst_updates, + gr.update( + interactive=(GLOBAL_STATE["current_prompt_index"] > 0 or GLOBAL_STATE["current_criterion_index"] > 0)), + gr.update(interactive=True) + ] + + +# ▼▼▼ 修正後の validate_and_navigate 関数 ▼▼▼ +def validate_and_navigate(): + ranks = GLOBAL_STATE["current_ranks"] + error_msg = None + criterion_name = CRITERIA[GLOBAL_STATE["current_criterion_index"]] + is_alignment_criterion = (criterion_name == "Alignment") + + # --- Validation --- + if any(r is None for r in ranks.values()): + error_msg = "Please rank all 5 images. / 5つすべての画像を評価してください。" + elif 1 not in ranks.values(): + error_msg = "You must assign a rank of '1' to at least one image. / 最低1つは「1位」を付けてください。" + elif is_alignment_criterion and GLOBAL_STATE["current_absolute_score"] is None: + error_msg = "Please provide an absolute score for the BEST matching image (1-7). / 最も一致している画像について、絶対評価（1～7）を選択してください。" + elif is_alignment_criterion and GLOBAL_STATE["current_absolute_score_worst"] is None: + error_msg = "Please provide an absolute score for the WORST matching image (1-7). / 最も一致していない画像について、絶対評価（1～7）を選択してください。" + + if error_msg: + # The number of components to update is now 53 (1 tab + 52 eval components) + no_change_updates = [gr.update()] * 53 + no_change_updates[4] = gr.update( # error_display is the 5th component (index 4) + value=f"

{error_msg}

", + visible=True) + return no_change_updates + + # ... (Rank tie-breaking validation logic is unchanged) ... + sorted_ranks = sorted(list(ranks.values())) + rank_counts = Counter(sorted_ranks) + i = 0 + while i < len(sorted_ranks): + current_rank = sorted_ranks[i] + count = rank_counts[current_rank] + if i + count < len(sorted_ranks): + next_rank = sorted_ranks[i + count] + expected_next_rank = current_rank + count + if next_rank < expected_next_rank: + error_msg = f"Ranking rule violation (tie-breaking). After {count} instance(s) of rank '{current_rank}', the next rank must be >= {expected_next_rank}, but it is '{next_rank}'. / 順位付けのルール違反です。'{current_rank}'位が{count}つあるため、次の順位は{expected_next_rank}位以上である必要がありますが、'{next_rank}'位が入力されています。" + break + i += count + if error_msg: + no_change_updates = [gr.update()] * 53 + no_change_updates[4] = gr.update( + value=f"

{error_msg}

", + visible=True) + return no_change_updates + # --- End of Validation --- + + prompt_idx = GLOBAL_STATE["shuffled_indices"][GLOBAL_STATE["current_prompt_index"]] + current_data = GLOBAL_STATE["all_eval_data"][prompt_idx] + prompt_text = current_data["prompt"] + current_image_order = GLOBAL_STATE["image_orders"][criterion_name] + + label_to_condition = {label: cond for label, cond in zip(IMAGE_LABELS, current_image_order)} + ranks_by_condition = {label_to_condition[label]: rank for label, rank in ranks.items()} + + if "ranks" not in GLOBAL_STATE["evaluation_results"][prompt_text]: + GLOBAL_STATE["evaluation_results"][prompt_text]["ranks"] = {} + if "orders" not in GLOBAL_STATE["evaluation_results"][prompt_text]: + GLOBAL_STATE["evaluation_results"][prompt_text]["orders"] = {} + + GLOBAL_STATE["evaluation_results"][prompt_text]["ranks"][criterion_name] = ranks_by_condition + GLOBAL_STATE["evaluation_results"][prompt_text]["orders"][criterion_name] = current_image_order + + if is_alignment_criterion: + GLOBAL_STATE["evaluation_results"][prompt_text]["absolute_score"] = GLOBAL_STATE["current_absolute_score"] + GLOBAL_STATE["evaluation_results"][prompt_text]["absolute_score_worst"] = GLOBAL_STATE[ + "current_absolute_score_worst"] + + logger.info( + f"Saved rank for P:{GLOBAL_STATE['participant_id']}, Prompt:'{prompt_text}', Criterion:{criterion_name}, Ranks:{ranks_by_condition}") + + GLOBAL_STATE["current_criterion_index"] += 1 + if GLOBAL_STATE["current_criterion_index"] >= len(CRITERIA): + GLOBAL_STATE["current_criterion_index"] = 0 + GLOBAL_STATE["current_prompt_index"] += 1 + + if GLOBAL_STATE["current_prompt_index"] >= len(GLOBAL_STATE["all_eval_data"]): + GLOBAL_STATE["end_time"] = datetime.now() + eval_panel_updates = display_current_prompt_and_criterion() + # Activate export tab on completion + return [gr.update(interactive=True)] + eval_panel_updates + else: + # Keep export tab state as is + return [gr.update()] + display_current_prompt_and_criterion() + + + +def navigate_previous(): + GLOBAL_STATE["current_criterion_index"] -= 1 + if GLOBAL_STATE["current_criterion_index"] < 0: + GLOBAL_STATE["current_criterion_index"] = len(CRITERIA) - 1 + GLOBAL_STATE["current_prompt_index"] -= 1 + GLOBAL_STATE["current_prompt_index"] = max(0, GLOBAL_STATE["current_prompt_index"]) + return display_current_prompt_and_criterion() + +# ▼▼▼ 修正後の export_results 関数 ▼▼▼ +def export_results(participant_id, alignment_reason, naturalness_reason, attractiveness_reason, optional_comment): + if not alignment_reason.strip() or not naturalness_reason.strip() or not attractiveness_reason.strip(): + error_msg = "

Please fill in the reasoning for all three criteria (Alignment, Naturalness, Attractiveness). / 3つの評価基準（一致度, 自然さ, 魅力度）すべての判断理由を記入してください。

" + return None, error_msg + + if not participant_id: + return None, "

Participant ID is missing. / 参加者IDがありません。

" + + output_dir = os.path.join(BASE_RESULTS_DIR, participant_id) + os.makedirs(output_dir, exist_ok=True) + filename = f"evaluation_results_{participant_id}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" + filepath = os.path.join(output_dir, filename) + + duration = (GLOBAL_STATE["end_time"] - GLOBAL_STATE["start_time"]).total_seconds() if GLOBAL_STATE.get( + "start_time") and GLOBAL_STATE.get("end_time") else None + + prompt_to_category = {item["prompt"]: item["category"] for item in GLOBAL_STATE["all_eval_data"]} + + final_results_list = [] + for prompt, data in GLOBAL_STATE["evaluation_results"].items(): + if not data: continue + + ranks_data = data.get("ranks", {}) + orders_data = data.get("orders", {}) + + final_results_list.append({ + "prompt": prompt, + "prompt_category": prompt_to_category.get(prompt), + "image_order_alignment": orders_data.get("Alignment", []), + "image_order_naturalness": orders_data.get("Naturalness", []), + "image_order_attractiveness": orders_data.get("Attractiveness", []), + "alignment_ranks": ranks_data.get("Alignment", {}), + "naturalness_ranks": ranks_data.get("Naturalness", {}), + "attractiveness_ranks": ranks_data.get("Attractiveness", {}), + "alignment_absolute_score": data.get("absolute_score"), + # ▼▼▼ 追加 ▼▼▼ + "alignment_absolute_score_worst": data.get("absolute_score_worst") + }) + + export_data = { + "metadata": { + "participant_id": participant_id, + "export_timestamp": datetime.now().isoformat(), + "total_prompts_evaluated": len(final_results_list), + "evaluation_duration_seconds": duration, + "reasoning": { + "alignment": alignment_reason, + "naturalness": naturalness_reason, + "attractiveness": attractiveness_reason, + }, + "optional_comment": optional_comment, + }, + "results": final_results_list + } + + try: + with open(filepath, 'w', encoding='utf-8') as f: + json.dump(export_data, f, ensure_ascii=False, indent=2) + logger.info(f"Successfully exported results to: {filepath}") + except Exception as e: + logger.error(f"Failed to write export file: {e}") + return None, f"

An error occurred during file export: {e}

" + + upload_link = "https://drive.google.com/drive/folders/1ujIPF-67Y6OG8qBm1TYG3FsmuYxqSAcR?usp=drive_link" + status_message = f""" +
+

エクスポートが完了しました。/ Export complete.

+

上のボタンからJSONファイルをダウンロードし、指定された場所にアップロードして実験を終了してください。ご協力ありがとうございました。

+

Please download the JSON file and upload it to the designated location. Thank you for your cooperation.

+

アップロード先 / Upload to: {upload_link}

+
""" + return gr.update(value=filepath, visible=True), status_message + + +## ▼▼▼ 修正後の create_gradio_interface 関数 ▼▼▼ +def create_gradio_interface(): + css = """ + .gradio-container { font-family: 'Arial', sans-serif; } + .feedback { padding: 10px; border-radius: 5px; font-weight: bold; text-align: center; margin-top: 10px; } + .feedback.green { background-color: #e6ffed; color: #2f6f4a; } + .feedback.red { background-color: #ffe6e6; color: #b30000; } + .image-label { font-size: 2.5em; font-weight: bold; margin-bottom: 10px; color: #333; } + .prompt-display { text-align: center; margin-bottom: 5px; padding: 15px; background-color: #f0f8ff; border-radius: 8px; } + .prompt-sub-guidance { text-align: center; font-size: 0.9em; color: #555; margin-top: 5px; margin-bottom: 15px; } + .rank-instruction { + color: #D32F2F; + font-size: 1.1em; + text-align: left; + margin-bottom: 20px; + padding: 15px; + border: 1px solid #f5c6cb; + border-radius: 8px; + background-color: #f8d7da; + line-height: 1.6; + } + .rank-instruction ul { padding-left: 20px; margin: 0; } + .rank-guidance { text-align: center; margin-bottom: 10px; font-size: 1.2em; } + .rank-btn-row { justify-content: center; gap: 5px !important; } + .rank-btn { + min-width: 45px !important; + max-width: 45px !important; + height: 45px !important; + font-size: 1.2em !important; + font-weight: bold !important; + border-radius: 8px !important; + border: 1px solid #ccc !important; + } + .rank-btn.secondary { + background: #f0f0f0 !important; + color: #333 !important; + } + .rank-btn.secondary:hover { + background: #e0e0e0 !important; + border-color: #bbb !important; + } + .absolute-eval-group { + border: 1px solid #ddd; + border-radius: 8px; + padding: 15px; + margin-top: 20px; + } + """ + + with gr.Blocks(title="Expression Evaluation Experiment", css=css) as app: + gr.Markdown("# Text-to-Expression Evaluation Experiment / テキストからの表情生成 評価実験") + + with gr.Tabs() as tabs: + with gr.TabItem("1. Setup / セットアップ") as tab_setup: + gr.Markdown("## (A) Participant Information / 参加者情報") + gr.Markdown("Please enter your participant ID and click 'Confirm'. / 参加者IDを入力して「確定」を押してください。") + with gr.Row(): + participant_id_input = gr.Textbox(label="Participant ID", placeholder="e.g., P01") + confirm_id_btn = gr.Button("Confirm / 確定", variant="primary") + setup_warning = gr.Markdown(visible=False) + with gr.Group(visible=False) as setup_main_group: + gr.Markdown("---") + gr.Markdown("## (B) Instructions & Data Loading / 注意事項とデータ読み込み") + gr.Markdown( + """
+

注意事項 / Instructions

+
""") + gr.Markdown( + "Click the button below to load your evaluation data. / 下のボタンを押して、評価データを読み込んでください。") + load_data_btn = gr.Button("Load Data / データ読み込み", variant="primary") + setup_status = gr.Markdown("Waiting to start...") + + with gr.TabItem("2. Evaluation / 評価", interactive=False) as tab_evaluation: + progress_text = gr.Markdown("Prompt 0 / 0") + + image_components = [] + rank_buttons = [] + with gr.Row(equal_height=False): + for label in IMAGE_LABELS: + with gr.Column(scale=1): + with gr.Group(): + gr.Markdown(f"
{label}
") + img = gr.Image(type="filepath", show_label=False, height=300) + image_components.append(img) + with gr.Row(elem_classes="rank-btn-row"): + for rank_val in range(1, 6): + btn = gr.Button(str(rank_val), variant='secondary', elem_classes="rank-btn") + rank_buttons.append(btn) + + prompt_display = gr.Markdown("## \"Prompt Text Here\"", elem_classes="prompt-display") + gr.Markdown( + "

You may use AI or web search for the meaning of the text. However, please do not ask an AI about the emotion of the image itself.
意味についてはAIに聞いたりネット検索しても構いません。ただし、画像そのものの感情をAIに尋ねるのを止めてください。

") + guidance_display = gr.Markdown("### Guidance", elem_classes="rank-guidance") + error_display = gr.Markdown(visible=False) + + gr.Markdown( + """ + ランキングの付け方 / How to Rank: + + """, + elem_classes="rank-instruction" + ) + + # ▼▼▼ 修正: 絶対評価（Best）のUI ▼▼▼ + with gr.Group(visible=False, elem_classes="absolute-eval-group") as absolute_eval_group_best: + gr.Markdown("---") + gr.Markdown( + "#### 絶対評価 (Best) / Absolute Score (Best)\n最もテキストと一致している画像について、どのていど一致しているかを評価してください。\n(Please evaluate the degree of alignment for the image that **best** matches the text.)") + absolute_score_buttons = [] + with gr.Row(): + with gr.Column(scale=1): + gr.Markdown( + "

1 (全く一致してない / Not at all)

") + with gr.Column(scale=3): + with gr.Row(elem_classes="rank-btn-row"): + for i in range(1, 8): + btn = gr.Button(str(i), variant='secondary', elem_classes="rank-btn") + absolute_score_buttons.append(btn) + with gr.Column(scale=1): + gr.Markdown("

7 (完全に一致 / Absolutely)

") + + # ▼▼▼ 追加: 絶対評価（Worst）のUI ▼▼▼ + with gr.Group(visible=False, elem_classes="absolute-eval-group") as absolute_eval_group_worst: + gr.Markdown( + "#### 絶対評価 (Worst) / Absolute Score (Worst)\n最もテキストと一致していない画像について、どのていど一致していないかを評価してください。\n(Please evaluate the degree of alignment for the image that **least** matches the text.)") + absolute_score_worst_buttons = [] + with gr.Row(): + with gr.Column(scale=1): + gr.Markdown( + "

1 (全く一致してない / Not at all)

") + with gr.Column(scale=3): + with gr.Row(elem_classes="rank-btn-row"): + for i in range(1, 8): + btn = gr.Button(str(i), variant='secondary', elem_classes="rank-btn") + absolute_score_worst_buttons.append(btn) + with gr.Column(scale=1): + gr.Markdown("

7 (完全に一致 / Absolutely)

") + + with gr.Row(): + prev_btn = gr.Button("← Previous / 前へ", interactive=False) + next_btn = gr.Button("Save & Next / 保存して次へ →", variant="primary") + + with gr.TabItem("3. Export / エクスポート", interactive=False) as tab_export: + gr.Markdown("## (C) Final Comments & Export / 最終コメントとエクスポート") + gr.Markdown( + "Thank you for completing the evaluation. Please provide the reasoning for your judgments for each criterion below. / 評価お疲れ様でした。以下の各評価基準について、判断の理由をご記入ください。") + + with gr.Group(): + gr.Markdown("#### Reasoning for Judgments (Required) / 判断理由（必須）") + alignment_reason_box = gr.Textbox(label="Alignment / 一致度", lines=3, + placeholder="Why did you rank them this way for alignment? / なぜ一致度について、このような順位付けをしましたか？") + naturalness_reason_box = gr.Textbox(label="Naturalness / 自然さ", lines=3, + placeholder="Why did you rank them this way for naturalness? / なぜ自然さについて、このような順位付けをしましたか？") + attractiveness_reason_box = gr.Textbox(label="Attractiveness / 魅力度", lines=3, + placeholder="Why did you rank them this way for attractiveness? / なぜ魅力度について、このような順位付けをしましたか？") + + with gr.Group(): + gr.Markdown("#### Overall Comments (Optional) / 全体的な感想（任意）") + optional_comment_box = gr.Textbox(label="Any other comments? / その他、実験全体に関するご意見・ご感想", + lines=4, + placeholder="e.g., 'Image B often looked the most natural.' / 例：「Bの画像が最も自然に見えることが多かったです。」") + + gr.Markdown("---") + gr.Markdown( + "Finally, click the button below to export your results. / 最後に、下のボタンを押して結果をエクスポートしてください。") + export_btn = gr.Button("Export Results / 結果をエクスポート", variant="primary") + download_file = gr.File(label="Download JSON", visible=False) + export_status = gr.Markdown() + + # --- Event Handlers --- + def check_and_confirm_id(pid): + pid = pid.strip() + if re.fullmatch(r"P\d{2}", pid): + GLOBAL_STATE["participant_id"] = pid + return gr.update(visible=False), gr.update(visible=True) + else: + error_msg = "

Invalid ID. Must be 'P' followed by two digits (e.g., P01). / 無効なIDです。「P」と数字2桁の形式（例: P01）で入力してください。

" + return gr.update(value=error_msg, visible=True), gr.update(visible=False) + + confirm_id_btn.click(check_and_confirm_id, [participant_id_input], [setup_warning, setup_main_group]) + load_data_btn.click(load_evaluation_data, [participant_id_input], [setup_status, load_data_btn, tab_evaluation]) + + # ▼▼▼ 修正: all_eval_outputs に新しいUIコンポーネントを追加 ▼▼▼ + all_eval_outputs = [ + progress_text, prompt_display, guidance_display, error_display, *image_components, + *rank_buttons, + absolute_eval_group_best, *absolute_score_buttons, + absolute_eval_group_worst, *absolute_score_worst_buttons, + prev_btn, next_btn + ] + + btn_idx = 0 + for label in IMAGE_LABELS: + for rank_val in range(1, 6): + btn = rank_buttons[btn_idx] + btn.click( + partial(handle_rank_button_click, label, rank_val), + [], + rank_buttons + ) + btn_idx += 1 + + for i, btn in enumerate(absolute_score_buttons): + btn.click( + partial(handle_absolute_score_click, i + 1), + [], + absolute_score_buttons + ) + + # ▼▼▼ 追加: 新しいボタンのイベントハンドラを接続 ▼▼▼ + for i, btn in enumerate(absolute_score_worst_buttons): + btn.click( + partial(handle_absolute_score_worst_click, i + 1), + [], + absolute_score_worst_buttons + ) + + tab_evaluation.select(display_current_prompt_and_criterion, [], all_eval_outputs) + + # ▼▼▼ 修正: next_btn の出力に tab_export を追加 ▼▼▼ + next_btn.click(validate_and_navigate, [], [tab_export, *all_eval_outputs]) + + prev_btn.click(navigate_previous, [], all_eval_outputs) + + export_tab_interactive_components = [alignment_reason_box, naturalness_reason_box, attractiveness_reason_box, + optional_comment_box, export_btn] + + def on_select_export_tab(): + # end_time is set only when all evaluations are complete + if GLOBAL_STATE.get("end_time"): + return [gr.update(interactive=True)] * 5 + # This logic is now handled by next_btn click, but kept as a fallback. + return [gr.update(interactive=False)] * 5 + + tab_export.select(on_select_export_tab, [], export_tab_interactive_components) + + export_btn.click( + export_results, + [participant_id_input, alignment_reason_box, naturalness_reason_box, attractiveness_reason_box, + optional_comment_box], + [download_file, export_status] + ) + + return app + +if __name__ == "__main__": + os.makedirs(LOG_DIR, exist_ok=True) + log_file_path = os.path.join(LOG_DIR, "evaluation_ui_log_{time}.log") + + random.seed(datetime.now().timestamp()) + logger.remove() + logger.add(sys.stderr, level="INFO") + logger.add(log_file_path, rotation="10 MB") + + app = create_gradio_interface() + app.launch(share=True, debug=True) \ No newline at end of file diff --git a/combined_data/blendshape_annotation_preprocess.py b/combined_data/blendshape_annotation_preprocess.py new file mode 100644 index 0000000000000000000000000000000000000000..573ba8b1278c46c1ff43fb44117c749f4fdb4d9a --- /dev/null +++ b/combined_data/blendshape_annotation_preprocess.py @@ -0,0 +1,114 @@ +import os +import json +import torch +import numpy as np +from PIL import Image +from tqdm import tqdm +from loguru import logger +from typing import Dict, List, Tuple + +# ---- あなたの定義済み Dataset, transforms があるなら import ---- +# from your_dataset import BlendShapeDataset, image_transform + +def unify_index(idx: int, group_size: int) -> int: + """ + BlendShape の選択 index が group_size と同じ場合、-1 (none) に変換して返す。 + """ + if idx == group_size: + return -1 + return idx + +def build_combination_to_filename( + teacher_data_file: str, + meta_file: str, +) -> Tuple[Dict[Tuple[int, ...], str], List[int]]: + """ + BlendShapeData.json を読み込み、 + 例: + { (g0_idx, g1_idx, g2_idx): "000001.png", ... } + のような辞書を作成して返す。 + + さらに各グループのサイズ (blendShapeNames数 + 1) のリスト group_sizes も返す。 + """ + with open(meta_file, "r", encoding="utf-8") as f: + meta = json.load(f) + blend_shape_groups = meta["blendShapeGroupsMeta"] + # group_sizes[i] = len(そのグループの blendShapeNames) + 1(none枠) + group_sizes = [len(g["blendShapeNames"]) + 1 for g in blend_shape_groups] + + # teacher_data_file 読み込み + with open(teacher_data_file, "r", encoding="utf-8") as f: + teacher_data = json.load(f) + data_list = teacher_data["dataList"] + + combination_to_filename = {} + for data in data_list: + photo_filename = data["photoFileName"] + blendShapeSelections = data["blendShapeSelectionsPerGroup"] + + # グループ順に selectedBlendShapeIndex を取得しつつ、-1の場合は-1のまま、 + # group_sizeと一致していたら-1へ変換(理想的には -1 しか登場しない想定だが一応対応) + combo = [] + for group_idx, selection in enumerate(blendShapeSelections): + sel_idx = selection["selectedBlendShapeIndex"] + # group_sizes[group_idx] と同じなら none として -1 + sel_idx = unify_index(sel_idx, group_sizes[group_idx]) + combo.append(sel_idx) + + combo = tuple(combo) # dictのキーにするのでtuple化 + combination_to_filename[combo] = photo_filename + + return combination_to_filename, group_sizes + + + +def main_offline_precompute(): + """ + 1. Dataset(JSON)から (組み合わせ -> filename) を作る + 2. CLIP モデルロード + 3. filename -> embedding + 4. ペアワイズ類似度 + 5. 保存 + """ + import argparse + parser = argparse.ArgumentParser() + parser.add_argument("--image_dir", type=str, default="lapwing/images") + parser.add_argument("--meta_file", type=str, default="lapwing/texts/BlendShapeGroupsMeta.json") + parser.add_argument("--teacher_data_file", type=str, default="lapwing/texts/BlendShapeData.json") + parser.add_argument("--clip_model_name", type=str, default="ViT-L-14") + parser.add_argument("--clip_pretrained", type=str, default="openai") + parser.add_argument("--batch_size", type=int, default=8) + parser.add_argument("--device", type=str, default="cuda") + parser.add_argument("--out_comb2fn", type=str, default="combination_to_filename.json") + parser.add_argument("--out_sims", type=str, default="pairwise_clip_sims.json") + + args = parser.parse_args() + + # 1. 組み合わせ->filename辞書の作成 + combination_to_filename, group_sizes = build_combination_to_filename( + teacher_data_file=args.teacher_data_file, + meta_file=args.meta_file, + ) + + + # 5. 保存 (JSON形式) + # 5.1 (combination -> filename) + # group_sizes も保存しておくと後段のオンライン時に参照しやすい + # tupleは文字列化する必要あり + comb2fn_dict = { + "group_sizes": group_sizes, + "mapping": { + ",".join(map(str, comb)): fn + for comb, fn in combination_to_filename.items() + } + } + with open(args.out_comb2fn, "w", encoding="utf-8") as f: + json.dump(comb2fn_dict, f, ensure_ascii=False, indent=2) + + # データ出力 + for combo, filename in combination_to_filename.items(): + logger.info(f"Combination: {combo}, Filename: {filename}") + + +if __name__ == "__main__": + main_offline_precompute() diff --git a/combined_data/combination_to_filename.json b/combined_data/combination_to_filename.json new file mode 100644 index 0000000000000000000000000000000000000000..ce29c8241683b9b5129e9985b257f8aedaf165b0 --- /dev/null +++ b/combined_data/combination_to_filename.json @@ -0,0 +1,80 @@ +{ + "group_sizes": [ + 8, + 9 + ], + "mapping": { + "-1,-1": "000001.png", + "-1,0": "000002.png", + "-1,1": "000003.png", + "-1,2": "000004.png", + "-1,3": "000005.png", + "-1,4": "000006.png", + "-1,5": "000007.png", + "-1,6": "000008.png", + "-1,7": "000009.png", + "0,-1": "000010.png", + "0,0": "000011.png", + "0,1": "000012.png", + "0,2": "000013.png", + "0,3": "000014.png", + "0,4": "000015.png", + "0,5": "000016.png", + "0,6": "000017.png", + "0,7": "000018.png", + "1,-1": "000019.png", + "1,0": "000020.png", + "1,1": "000021.png", + "1,2": "000022.png", + "1,3": "000023.png", + "1,4": "000024.png", + "1,5": "000025.png", + "1,6": "000026.png", + "1,7": "000027.png", + "2,-1": "000028.png", + "2,0": "000029.png", + "2,1": "000030.png", + "2,2": "000031.png", + "2,3": "000032.png", + "2,4": "000033.png", + "2,5": "000034.png", + "2,6": "000035.png", + "2,7": "000036.png", + "3,-1": "000037.png", + "3,0": "000038.png", + "3,1": "000039.png", + "3,2": "000040.png", + "3,3": "000041.png", + "3,4": "000042.png", + "3,5": "000043.png", + "3,6": "000044.png", + "3,7": "000045.png", + "4,-1": "000046.png", + "4,0": "000047.png", + "4,1": "000048.png", + "4,2": "000049.png", + "4,3": "000050.png", + "4,4": "000051.png", + "4,5": "000052.png", + "4,6": "000053.png", + "4,7": "000054.png", + "5,-1": "000055.png", + "5,0": "000056.png", + "5,1": "000057.png", + "5,2": "000058.png", + "5,3": "000059.png", + "5,4": "000060.png", + "5,5": "000061.png", + "5,6": "000062.png", + "5,7": "000063.png", + "6,-1": "000064.png", + "6,0": "000065.png", + "6,1": "000066.png", + "6,2": "000067.png", + "6,3": "000068.png", + "6,4": "000069.png", + "6,5": "000070.png", + "6,6": "000071.png", + "6,7": "000072.png" + } +} \ No newline at end of file diff --git a/combined_data/lapwing/images/000001.png b/combined_data/lapwing/images/000001.png new file mode 100644 index 0000000000000000000000000000000000000000..8e104298bec687d00baf9965217f8cd9d8e38f5f Binary files /dev/null and b/combined_data/lapwing/images/000001.png differ diff --git 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