| import time |
| from dataclasses import dataclass, field |
| from typing import Optional, Dict, Tuple, List |
|
|
| import gradio as gr |
| import numpy as np |
| from PIL import Image |
|
|
| from deepface import DeepFace |
|
|
|
|
| |
| |
| |
| ANALYZE_EVERY_SEC = 0.8 |
| DOWNSAMPLE_W = 720 |
| EMA_ALPHA = 0.45 |
| CONF_TH = 40.0 |
| SWITCH_CONFIRM_SEC = 2.5 |
| IOU_TH = 0.25 |
|
|
| |
| DETECTOR_BACKEND = "opencv" |
| ALIGN_FACE = True |
|
|
|
|
| |
| |
| |
| EMO_ZH = { |
| "angry": "生氣", |
| "disgust": "厭惡", |
| "fear": "害怕", |
| "happy": "開心", |
| "sad": "難過", |
| "surprise": "驚訝", |
| "neutral": "平靜", |
| "unknown": "未知", |
| } |
|
|
| EMO_TO_SCORE = { |
| "angry": 1, |
| "disgust": 1, |
| "fear": 1, |
| "sad": 2, |
| "neutral": 3, |
| "surprise": 4, |
| "happy": 5, |
| "unknown": 3, |
| } |
|
|
| |
| def get_emotion_score(emo: str) -> int: |
| return EMO_TO_SCORE.get(emo, 3) |
| |
|
|
| SCORE_LABELS = { |
| 1: "1（心情差）", |
| 2: "2（不太好）", |
| 3: "3（普通）", |
| 4: "4（不錯）", |
| 5: "5（超棒）", |
| } |
| SCORE_CHOICES = [SCORE_LABELS[i] for i in [1, 2, 3, 4, 5]] |
|
|
|
|
| |
| |
| |
| @dataclass |
| class AppState: |
| mode: str = "攝影機辨識" |
|
|
| running: bool = True |
| finished: bool = False |
| last_analyze_t: float = 0.0 |
|
|
| |
| track_bbox: Optional[Tuple[int, int, int, int]] = None |
|
|
| |
| ema_scores: Optional[np.ndarray] = None |
| emo_keys: List[str] = field(default_factory=list) |
|
|
| |
| stable_emo: str = "unknown" |
| candidate_emo: Optional[str] = None |
| candidate_since: Optional[float] = None |
|
|
| |
| final_emo: Optional[str] = None |
| final_conf: Optional[float] = None |
|
|
| |
| final_score: int = 3 |
| |
|
|
|
|
| |
| |
| |
| def _hint_html(msg: str) -> str: |
| return f"""<div style="border-radius:14px;padding:14px;border:1px dashed #DADADA;background:#FAFAFA;color:#111;">{msg}</div>""" |
|
|
| def _camera_hint_before_start() -> str: |
| return _hint_html("請先按「Click to access webcam / 允許」開啟攝影機；接著，請按「錄製（Record）」開始。") |
|
|
| def _camera_hint_done() -> str: |
| return _hint_html("已完成辨識，可按「重新攝影機辨識」再次進行辨識。") |
|
|
| def _camera_hint_stopped() -> str: |
| return _hint_html("已停止辨識。可按「重新攝影機辨識」再試一次。") |
|
|
| def _upload_hint_before() -> str: |
| return _hint_html("請上傳一張清楚的臉部照片（臉越大越準）。") |
|
|
| def _upload_hint_done() -> str: |
| return _hint_html("已完成圖片辨識，可按「重新上傳圖片辨識」再次辨識。") |
|
|
| def _upload_hint_stopped() -> str: |
| return _hint_html("已停止圖片辨識。可按「重新上傳圖片辨識」再試一次。") |
|
|
| def _result_card_html(emo_key: str, conf: Optional[float]) -> str: |
| zh = EMO_ZH.get(emo_key, emo_key) |
| score = EMO_TO_SCORE.get(emo_key, 3) |
| conf_txt = "" if conf is None else f"{conf:.1f}%" |
| score_desc = SCORE_LABELS[score].split("（")[1].rstrip("）") |
|
|
| return f""" |
| <div style="border-radius:16px;padding:20px;border:1px solid #E6E6E6;background:#FFFFFF;color:#111;"> |
| <div style="font-size:14px;color:#444;margin-bottom:8px;">辨識結果</div> |
| <div style="font-size:42px;font-weight:800;line-height:1.1;margin-bottom:14px;color:#000;">{zh}</div> |
| <div style="font-size:16px;color:#111;margin-bottom:6px;">心情分數：<b>{score}</b>（{score_desc}）</div> |
| <div style="font-size:14px;color:#111;">信心值：{conf_txt}</div> |
| </div> |
| """ |
|
|
| def _score_radio_value(score: int) -> str: |
| score = int(score) |
| score = min(5, max(1, score)) |
| return SCORE_LABELS[score] |
|
|
| def _downsample_rgb(frame: np.ndarray, target_w: int) -> np.ndarray: |
| if frame is None: return None |
| h, w = frame.shape[:2] |
| if w <= target_w: return frame |
| scale = target_w / w |
| new_h = max(1, int(h * scale)) |
| img = Image.fromarray(frame) |
| img = img.resize((target_w, new_h)) |
| return np.array(img) |
|
|
| def _iou(a, b) -> float: |
| ax, ay, aw, ah = a |
| bx, by, bw, bh = b |
| ax2, ay2 = ax + aw, ay + ah |
| bx2, by2 = bx + bw, by + bh |
| ix1, iy1 = max(ax, bx), max(ay, by) |
| ix2, iy2 = min(ax2, bx2), min(ay2, by2) |
| iw, ih = max(0, ix2 - ix1), max(0, iy2 - iy1) |
| inter = iw * ih |
| if inter <= 0: return 0.0 |
| union = aw * ah + bw * bh - inter |
| return inter / max(1e-6, union) |
|
|
| def _pick_face_by_tracking(faces, prev_bbox): |
| if not faces: return None, None |
| cand = [] |
| for f in faces: |
| fa = f.get("facial_area", {}) or {} |
| x, y, w, h = int(fa.get("x",0)), int(fa.get("y",0)), int(fa.get("w",0)), int(fa.get("h",0)) |
| if w > 0 and h > 0 and f.get("face") is not None: |
| cand.append((f, (x, y, w, h), w * h)) |
| if not cand: return None, None |
| if prev_bbox is not None: |
| best, best_i = None, 0.0 |
| for f, bb, area in cand: |
| i = _iou(prev_bbox, bb) |
| if i > best_i: best_i = i; best = (f, bb) |
| if best is not None and best_i >= IOU_TH: return best[0], best[1] |
| cand.sort(key=lambda x: x[2], reverse=True) |
| return cand[0][0], cand[0][1] |
|
|
| def _reset_state_for_camera(st: AppState) -> AppState: |
| st.running = True; st.finished = False; st.last_analyze_t = 0.0 |
| st.track_bbox = None; st.ema_scores = None; st.emo_keys = [] |
| st.stable_emo = "unknown"; st.candidate_emo = None; st.candidate_since = None |
| st.final_emo = None; st.final_conf = None |
| |
| st.final_score = 3 |
| return st |
|
|
| def _reset_state_for_upload(st: AppState) -> AppState: |
| st.running = False; st.finished = False; st.last_analyze_t = 0.0 |
| st.track_bbox = None; st.ema_scores = None; st.emo_keys = [] |
| st.stable_emo = "unknown"; st.candidate_emo = None; st.candidate_since = None |
| st.final_emo = None; st.final_conf = None |
| |
| st.final_score = 3 |
| return st |
|
|
|
|
| |
| |
| |
| def on_stream(frame_rgb: np.ndarray, st: AppState): |
| if st.mode != "攝影機辨識": |
| return gr.update(), gr.update(), gr.update(), gr.update(value=_upload_hint_before()), st, gr.update(value="重新上傳圖片辨識") |
|
|
| if st.finished or (not st.running): |
| cam_visible = False |
| if st.final_emo: |
| result_html = _result_card_html(st.final_emo, st.final_conf) |
| mood_val = _score_radio_value(EMO_TO_SCORE.get(st.final_emo, 3)) |
| hint_html = _camera_hint_done() |
| else: |
| result_html = _hint_html("尚未完成辨識。") |
| mood_val = _score_radio_value(3) |
| hint_html = _camera_hint_stopped() |
| return gr.update(visible=cam_visible), gr.update(value=result_html), gr.update(value=mood_val), gr.update(value=hint_html), st, gr.update(value="重新攝影機辨識") |
|
|
| if frame_rgb is None: |
| return gr.update(visible=True), gr.update(value=_hint_html("等待影像…（尚未開始串流）")), gr.update(value=_score_radio_value(3)), gr.update(value=_camera_hint_before_start()), st, gr.update(value="重新攝影機辨識") |
|
|
| now = time.time() |
| if now - st.last_analyze_t < ANALYZE_EVERY_SEC: |
| show = st.stable_emo or "unknown" |
| return gr.update(visible=True), gr.update(value=_result_card_html(show, st.final_conf)), gr.update(value=_score_radio_value(EMO_TO_SCORE.get(show, 3))), gr.update(value=_camera_hint_before_start()), st, gr.update(value="重新攝影機辨識") |
|
|
| st.last_analyze_t = now |
| small = _downsample_rgb(frame_rgb, DOWNSAMPLE_W) |
| faces = DeepFace.extract_faces(img_path=small, detector_backend=DETECTOR_BACKEND, enforce_detection=False, align=ALIGN_FACE) |
| pick, bbox = _pick_face_by_tracking(faces, st.track_bbox) |
|
|
| if pick is None or bbox is None or pick.get("face") is None: |
| show = st.stable_emo or "unknown" |
| return gr.update(visible=True), gr.update(value=_result_card_html(show, st.final_conf)), gr.update(value=_score_radio_value(EMO_TO_SCORE.get(show, 3))), gr.update(value=_camera_hint_before_start()), st, gr.update(value="重新攝影機辨識") |
|
|
| st.track_bbox = bbox |
| face = pick["face"] |
| if face.dtype != np.uint8: face = np.clip(face * 255.0, 0, 255).astype(np.uint8) |
|
|
| result = DeepFace.analyze(img_path=face, actions=["emotion"], enforce_detection=False) |
| if isinstance(result, list): result = result[0] |
| emo_dict = result.get("emotion", None) |
|
|
| if not isinstance(emo_dict, dict) or len(emo_dict) == 0: |
| show = st.stable_emo or "unknown" |
| return gr.update(visible=True), gr.update(value=_result_card_html(show, st.final_conf)), gr.update(value=_score_radio_value(EMO_TO_SCORE.get(show, 3))), gr.update(value=_camera_hint_before_start()), st, gr.update(value="重新攝影機辨識") |
|
|
| top_key = max(emo_dict, key=lambda k: float(emo_dict[k])) |
| top_conf = float(emo_dict[top_key]) |
|
|
| if top_conf < CONF_TH: |
| show = st.stable_emo or "unknown" |
| return gr.update(visible=True), gr.update(value=_result_card_html(show, st.final_conf)), gr.update(value=_score_radio_value(EMO_TO_SCORE.get(show, 3))), gr.update(value=_camera_hint_before_start()), st, gr.update(value="重新攝影機辨識") |
|
|
| keys = list(emo_dict.keys()) |
| vec = np.array([float(emo_dict[k]) for k in keys], dtype=np.float32) |
|
|
| if st.ema_scores is None or st.emo_keys != keys: |
| st.ema_scores = vec; st.emo_keys = keys |
| else: |
| st.ema_scores = EMA_ALPHA * vec + (1.0 - EMA_ALPHA) * st.ema_scores |
|
|
| stable_idx = int(np.argmax(st.ema_scores)) |
| stable_key = st.emo_keys[stable_idx] |
| st.stable_emo = stable_key |
|
|
| if st.candidate_emo != stable_key: |
| st.candidate_emo = stable_key; st.candidate_since = now |
| else: |
| if st.candidate_since is None: st.candidate_since = now |
|
|
| confirmed = (st.candidate_since is not None and (now - st.candidate_since) >= SWITCH_CONFIRM_SEC) |
|
|
| if confirmed and st.final_emo is None: |
| st.final_emo = stable_key; st.final_conf = top_conf |
| |
| |
| st.final_score = EMO_TO_SCORE.get(stable_key, 3) |
| |
| |
| st.running = False; st.finished = True |
| return gr.update(visible=False), gr.update(value=_result_card_html(stable_key, top_conf)), gr.update(value=_score_radio_value(EMO_TO_SCORE.get(stable_key, 3))), gr.update(value=_camera_hint_done()), st, gr.update(value="重新攝影機辨識") |
|
|
| return gr.update(visible=True), gr.update(value=_result_card_html(stable_key, top_conf)), gr.update(value=_score_radio_value(EMO_TO_SCORE.get(stable_key, 3))), gr.update(value=_camera_hint_before_start()), st, gr.update(value="重新攝影機辨識") |
|
|
|
|
| |
| |
| |
| def on_upload(img: np.ndarray, st: AppState): |
| if st.mode != "上傳圖片辨識": |
| return gr.update(), gr.update(), st, gr.update(value=_camera_hint_before_start()), gr.update(value="重新攝影機辨識") |
|
|
| st = _reset_state_for_upload(st) |
| if img is None: |
| return gr.update(value=_upload_hint_before()), gr.update(value=_score_radio_value(3)), st, gr.update(value=_upload_hint_before()), gr.update(value="重新上傳圖片辨識") |
|
|
| |
| if img.ndim == 2: |
| img = np.stack([img, img, img], axis=-1) |
| if img.shape[-1] == 4: |
| img = img[:, :, :3] |
| |
|
|
| small = _downsample_rgb(img, DOWNSAMPLE_W) |
| faces = DeepFace.extract_faces(img_path=small, detector_backend=DETECTOR_BACKEND, enforce_detection=False, align=ALIGN_FACE) |
| pick, bbox = _pick_face_by_tracking(faces, None) |
| |
| if pick is None or pick.get("face") is None: |
| return gr.update(value=_hint_html("沒有偵測到人臉，請換一張更清楚、臉更大的照片。")), gr.update(value=_score_radio_value(3)), st, gr.update(value=_upload_hint_before()), gr.update(value="重新上傳圖片辨識") |
|
|
| face = pick["face"] |
| if face.dtype != np.uint8: face = np.clip(face * 255.0, 0, 255).astype(np.uint8) |
|
|
| result = DeepFace.analyze(img_path=face, actions=["emotion"], enforce_detection=False) |
| if isinstance(result, list): result = result[0] |
| emo_dict = result.get("emotion", None) |
| if not isinstance(emo_dict, dict) or len(emo_dict) == 0: |
| return gr.update(value=_hint_html("辨識失敗，請換一張照片再試一次。")), gr.update(value=_score_radio_value(3)), st, gr.update(value=_upload_hint_before()), gr.update(value="重新上傳圖片辨識") |
|
|
| top_key = max(emo_dict, key=lambda k: float(emo_dict[k])) |
| top_conf = float(emo_dict[top_key]) |
|
|
| st.final_emo = top_key; st.final_conf = top_conf |
| |
| |
| st.final_score = EMO_TO_SCORE.get(top_key, 3) |
| |
| |
| st.finished = True |
| return gr.update(value=_result_card_html(top_key, top_conf)), gr.update(value=_score_radio_value(EMO_TO_SCORE.get(top_key, 3))), st, gr.update(value=_upload_hint_done()), gr.update(value="重新上傳圖片辨識") |
|
|
|
|
| |
| |
| |
| def on_mode_change(mode: str, st: AppState): |
| st.mode = mode |
| if mode == "攝影機辨識": |
| st = _reset_state_for_camera(st) |
| return gr.update(visible=True), gr.update(visible=False, value=None), gr.update(value=_hint_html("等待影像…（尚未開始串流）")), gr.update(value=_score_radio_value(3)), gr.update(value=_camera_hint_before_start()), st, gr.update(value="重新攝影機辨識") |
| else: |
| st = _reset_state_for_upload(st) |
| return gr.update(visible=False), gr.update(visible=True, value=None), gr.update(value=_upload_hint_before()), gr.update(value=_score_radio_value(3)), gr.update(value=_upload_hint_before()), st, gr.update(value="重新上傳圖片辨識") |
|
|
| def on_restart(st: AppState): |
| if st.mode == "攝影機辨識": |
| st = _reset_state_for_camera(st) |
| return gr.update(visible=True), gr.update(value=_hint_html("等待影像…（尚未開始串流）")), gr.update(value=_score_radio_value(3)), gr.update(value=_camera_hint_before_start()), st, gr.update(value="重新攝影機辨識"), gr.update() |
| else: |
| st = _reset_state_for_upload(st) |
| return gr.update(), gr.update(value=_upload_hint_before()), gr.update(value=_score_radio_value(3)), gr.update(value=_upload_hint_before()), st, gr.update(value="重新上傳圖片辨識"), gr.update(value=None) |
|
|
| def on_stop(st: AppState): |
| if st.mode == "攝影機辨識": |
| st.running = False; st.finished = True |
| if st.final_emo is None: |
| return gr.update(visible=False), gr.update(value=_hint_html("已停止辨識。")), gr.update(value=_score_radio_value(3)), gr.update(value=_camera_hint_stopped()), st, gr.update(value="重新攝影機辨識"), gr.update() |
| return gr.update(visible=False), gr.update(value=_result_card_html(st.final_emo, st.final_conf)), gr.update(value=_score_radio_value(EMO_TO_SCORE.get(st.final_emo, 3))), gr.update(value=_camera_hint_done()), st, gr.update(value="重新攝影機辨識"), gr.update() |
| |
| st = _reset_state_for_upload(st) |
| return gr.update(), gr.update(value=_hint_html("已停止圖片辨識。")), gr.update(value=_score_radio_value(3)), gr.update(value=_upload_hint_stopped()), st, gr.update(value="重新上傳圖片辨識"), gr.update(value=None) |
|
|
| css = """#app_container { max-width: 960px; margin: 0 auto; } .gradio-container { background: #f1f5f9 !important; }""" |
|
|
| if __name__ == "__main__": |
| with gr.Blocks(title="Emotion Detector", css=css) as demo: |
| |
| pass |
| |
