mount versions of pose-detectors combined

README.md

@@ -52,24 +52,28 @@ cd gesture_rec
 git submodule update --init --recursive
 ```
 ### 2. Создание виртуального окружения
-Рекомендуется использовать виртуальное окружение и Python 3.11:
+Рекомендуется использовать виртуальное окружение и Python 3.10:
 ```
-python3.11 -m venv venv
+python3.10 -m venv venv
 source venv/bin/activate
 ```
 
 Если у вас не скачен питон этой версии, сначала выполните:
 ```
-sudo apt install python3.11 python3.11-venv
+sudo apt install python3.10 python3.10-venv
 ```
 
-Если у нас не устанавливается питон 3.11, то это потому что он отсутствует в официальных репозиториях по умолчанию, надо добавить репозиторий перед скачиванием:
+Если у нас не устанавливается питон 3.10, то это потому что он отсутствует в официальных репозиториях по умолчанию, надо добавить репозиторий перед скачиванием:
 ```
 sudo apt update && sudo apt install -y software-properties-common
 sudo add-apt-repository ppa:deadsnakes/ppa
 sudo apt update
 ```
 
+To install pip:
+
+    sudo apt install -y python3-pip
+
 ### 3. Установка зависимостей
 
 ### 3.1. Способ 1
@@ -86,6 +90,7 @@ sudo apt update
     pip install --upgrade pip
     pip install numpy==1.24.3
     pip install pandas==2.0.3
+    pip install pyyaml
     pip install opencv-python==4.12.0.88
     pip install opencv-python-headless==4.12.0.88
     pip install matplotlib==3.7.5
@@ -128,7 +133,7 @@ pip install protobuf==3.20.3
 
 2. Если будет проблема с функцией cv2.imshow() то, переустановите cv2 без заголовков:
 ```
-pip uninstall opencv-python pip install opencv-python-headless
+pip uninstall opencv-python opencv-python-headless
 pip install opencv-python==4.12.0.88
 ```
 3. Если будет ошибка с PIL, попробуйте обновить библиотеку:

gesture_control/arm_control.py

@@ -1,126 +1,324 @@
 import numpy as np
+import cv2
 
-class ArmController:
+
+def _clip_unit(v):
+    return float(np.clip(v, -1.0, 1.0))
+
+
+def _apply_dead_zone(v, dz):
+    return 0.0 if abs(v) < dz else v
+
+
+def _robust_metrics(landmarks, min_conf=0.5):
+    """
+    Compute shoulder_center, shoulder_width, torso_height robustly.
+    Uses hips if available; otherwise falls back to nose/shoulder geometry.
+
+    Returns:
+        shoulder_center (np.array shape (2,))
+        shoulder_width (float)
+        torso_height (float)
+        ok (bool)
+    """
+    def pt(i):
+        return np.array(landmarks[i][:2], dtype=float), float(landmarks[i][3])
+
+    l_sh, c_lsh = pt(11)
+    r_sh, c_rsh = pt(12)
+    if c_lsh < min_conf or c_rsh < min_conf:
+        return None, 0.0, 0.0, False
+
+    shoulder_center = (l_sh + r_sh) / 2.0
+    shoulder_width = float(np.linalg.norm(r_sh - l_sh))
+    if shoulder_width < 1e-3:
+        return shoulder_center, 0.0, 0.0, False
+
+    # Try hips
+    l_hip, c_lhip = pt(23)
+    r_hip, c_rhip = pt(24)
+
+    if c_lhip >= min_conf and c_rhip >= min_conf:
+        hip_center = (l_hip + r_hip) / 2.0
+        torso_height = float(np.linalg.norm(hip_center - shoulder_center))
+        if torso_height >= 1e-3:
+            return shoulder_center, shoulder_width, torso_height, True
+
+    # Fallbacks (upper-body only)
+    nose, c_nose = pt(0)
+    if c_nose >= min_conf:
+        nose_to_shoulder = abs(nose[1] - shoulder_center[1])
+        torso_height = max(1.6 * nose_to_shoulder, 0.9 * shoulder_width)
+    else:
+        torso_height = max(1.2 * shoulder_width, 1.0)
+
+    return shoulder_center, shoulder_width, float(torso_height), True
+
+
+class ArmControllerMethod1:
+    """
+    Method 1: Single-hand driving with right wrist.
+    - Linear: vertical offset of right wrist from shoulder center (normalized by torso height)
+    - Angular: horizontal offset of right wrist from shoulder center (normalized by shoulder width)
+    """
     def __init__(self, config, mirror=False):
         self.config = config
         self.mirror = mirror
-        self.shoulder_idx = {'left': 11, 'right': 12}
+        self.dead_zone = config.get('dead_zone', 0.1)
-        self.wrist_idx = {'left': 15, 'right': 16}
-        self.hip_idx = {'left': 23, 'right': 24}
-        self.dead_zone = config.get('dead_zone', 0.2)
         self.debug = config.get('debug', False)
-        
+        self.min_conf = config.get('min_conf', 0.5)
-    def _get_side_indices(self, side):
-        """
-        Возвращает (shoulder_idx, wrist_idx) для заданной стороны (left/right).
-        При mirror=True интерпретируем сторону как в реальности: левая/правая рука.
-        """
-        if self.mirror:
-            if side == 'left':
-                s_idx = 12
-                w_idx = 16
-            else:
-                s_idx = 11
-                w_idx = 15
-        else:
-            if side == 'left':
-                s_idx = 11
-                w_idx = 15
-            else:
-                s_idx = 12
-                w_idx = 16
-        return s_idx, w_idx
 
-    def _get_shoulder_width(self, landmarks):
+    def compute_speeds(self, landmarks, frame_shape=None):
-        """Ширина плеч для нормировки горизонтальных смещений."""
+        if landmarks is None:
-        left = landmarks[11][:2]
-        right = landmarks[12][:2]
-        width = np.linalg.norm(right - left)
-        if width < 50 or width > 300:
-            return None
-        return width
-
-    def _get_torso_height(self, landmarks):
-        """Высота торса для нормировки вертикальных смещений."""
-        left_shoulder = landmarks[11][:2]
-        right_shoulder = landmarks[12][:2]
-        left_hip = landmarks[23][:2]
-        right_hip = landmarks[24][:2]
-        shoulder_center = (left_shoulder + right_shoulder) / 2
-        hip_center = (left_hip + right_hip) / 2
-        height = np.linalg.norm(shoulder_center - hip_center)
-        if height < 50:
-            return None
-        return height
-
-    def _horizontal_displacement_rel(self, landmarks, side):
-        """
-        Нормированное горизонтальное смещение запястья относительно плеча.
-        Сторона `side` — это реальная сторона руки
-        """
-        s_idx, w_idx = self._get_side_indices(side)
-
-        if landmarks[s_idx][3] < 0.5 or landmarks[w_idx][3] < 0.5:
-            return 0.0
-
-        shoulder = landmarks[s_idx][:2]
-        wrist = landmarks[w_idx][:2]
-
-        shoulder_width = self._get_shoulder_width(landmarks)
-        if shoulder_width is None:
-            return 0.0
-
-        disp = wrist[0] - shoulder[0]
-        return disp / shoulder_width
-
-    def _vertical_displacement_rel(self, landmarks, side):
-        """
-        Вертикальное смещение: верх/низ запястья относительно плеча.
-        Сторона `side` — реальная сторона руки.
-        """
-        s_idx, w_idx = self._get_side_indices(side)
-
-        if landmarks[s_idx][3] < 0.5 or landmarks[w_idx][3] < 0.5:
-            return 0.0
-
-        shoulder = landmarks[s_idx][:2]
-        wrist = landmarks[w_idx][:2]
-
-        torso_height = self._get_torso_height(landmarks)
-        if torso_height is None:
-            return 0.0
-
-        disp = shoulder[1] - wrist[1]
-        return disp / torso_height
-
-    def compute_speeds(self, landmarks):
-        if (landmarks[11][3] < 0.5 or landmarks[12][3] < 0.5 or
-            landmarks[15][3] < 0.5 or landmarks[16][3] < 0.5):
-            if self.debug:
-                print("Руки не видны")
             return 0.0, 0.0
 
-        linear_side = self.config['linear_arm']
+        # Require: shoulders + right wrist
-        angular_side = self.config['angular_arm']
+        need = [11, 12, 16]
+        if any(landmarks[i][3] < self.min_conf for i in need):
+            return 0.0, 0.0
 
-        lin_rel = self._horizontal_displacement_rel(landmarks, linear_side)
+        shoulder_center, shoulder_width, torso_height, ok = _robust_metrics(landmarks, self.min_conf)
-        ang_rel = self._vertical_displacement_rel(landmarks, angular_side)
+        if not ok or shoulder_width < 1e-3 or torso_height < 1e-3:
+            return 0.0, 0.0
+
+        r_wr = landmarks[16][:2]
+
+        # Positive linear when wrist above shoulder center (forward)
+        linear = (shoulder_center[1] - r_wr[1]) / torso_height
+        # Positive angular when wrist to the right of shoulder center
+        angular = (r_wr[0] - shoulder_center[0]) / shoulder_width
+
+        if self.mirror:
+            angular = -angular
+
+        linear = _clip_unit(_apply_dead_zone(linear, self.dead_zone))
+        angular = _clip_unit(_apply_dead_zone(angular, self.dead_zone))
 
         if self.debug:
-            print(f"lin_rel={lin_rel:.3f}, ang_rel={ang_rel:.3f}")
+            print(f"[M1] L:{linear:.2f} A:{angular:.2f}")
-
-        # Линейная скорость (только вперёд)
-        if lin_rel < self.dead_zone:
-            linear = 0.0
-        else:
-            linear = min(lin_rel, 1.0) * self.config['max_speed_linear']
-
-        # Угловая скорость
-        if abs(ang_rel) < self.dead_zone:
-            angular = 0.0
-        else:
-            ang_rel_clipped = np.clip(ang_rel, -1.0, 1.0)
-            angular = ang_rel_clipped * self.config['max_speed_angular']
-
         return linear, angular
 
+    def draw_overlay(self, frame, landmarks=None):
+        if frame is None:
+            return frame
+        h, w = frame.shape[:2]
+        # Draw center cross (screen center approximation)
+        cv2.line(frame, (w // 2, 0), (w // 2, h), (0, 0, 0), 1)
+        cv2.line(frame, (0, h // 2), (w, h // 2), (0, 0, 0), 1)
+        # Draw right wrist
+        if landmarks is not None and landmarks[16][3] > 0.5:
+            x, y = int(landmarks[16][0]), int(landmarks[16][1])
+            cv2.circle(frame, (x, y), 8, (0, 255, 255), -1)
+        return frame
+
+
+class ArmControllerMethod2:
+    """
+    Method 2: Two-hand blended control.
+    - Linear: average vertical offset of both wrists from shoulder center (normalized by torso height)
+    - Angular: horizontal balance of wrists around shoulder center (normalized by shoulder width)
+    """
+    def __init__(self, config, mirror=False):
+        self.config = config
+        self.mirror = mirror
+        self.dead_zone = config.get('dead_zone', 0.1)
+        self.debug = config.get('debug', False)
+        self.min_conf = config.get('min_conf', 0.5)
+
+    def compute_speeds(self, landmarks, frame_shape=None):
+        if landmarks is None:
+            return 0.0, 0.0
+
+        # Require: shoulders + both wrists
+        need = [11, 12, 15, 16]
+        if any(landmarks[i][3] < self.min_conf for i in need):
+            return 0.0, 0.0
+
+        shoulder_center, shoulder_width, torso_height, ok = _robust_metrics(landmarks, self.min_conf)
+        if not ok or shoulder_width < 1e-3 or torso_height < 1e-3:
+            return 0.0, 0.0
+
+        l_wr = landmarks[15][:2]
+        r_wr = landmarks[16][:2]
+
+        # Linear: average elevation of both wrists
+        lin_l = (shoulder_center[1] - l_wr[1]) / torso_height
+        lin_r = (shoulder_center[1] - r_wr[1]) / torso_height
+        linear = 0.5 * (lin_l + lin_r)
+
+        # Angular: horizontal balance
+        ang = ((r_wr[0] - shoulder_center[0]) - (shoulder_center[0] - l_wr[0])) / shoulder_width
+        angular = ang
+
+        if self.mirror:
+            angular = -angular
+
+        linear = _clip_unit(_apply_dead_zone(linear, self.dead_zone))
+        angular = _clip_unit(_apply_dead_zone(angular, self.dead_zone))
+
+        if self.debug:
+            print(f"[M2] L:{linear:.2f} A:{angular:.2f}")
+        return linear, angular
+
+    def draw_overlay(self, frame, landmarks=None):
+        if frame is None:
+            return frame
+        if landmarks is not None:
+            for idx, color in [(15, (255, 0, 255)), (16, (0, 255, 255))]:
+                if landmarks[idx][3] > 0.5:
+                    x, y = int(landmarks[idx][0]), int(landmarks[idx][1])
+                    cv2.circle(frame, (x, y), 8, color, -1)
+        return frame
+
+
+class ArmControllerMethod3:
+    """
+    Method 3: Elbow-augmented control.
+    - Linear: average vertical offset of elbows (normalized by torso height)
+    - Angular: wrist horizontal balance (normalized by shoulder width)
+    """
+    def __init__(self, config, mirror=False):
+        self.config = config
+        self.mirror = mirror
+        self.dead_zone = config.get('dead_zone', 0.1)
+        self.debug = config.get('debug', False)
+        self.min_conf = config.get('min_conf', 0.5)
+
+    def compute_speeds(self, landmarks, frame_shape=None):
+        if landmarks is None:
+            return 0.0, 0.0
+
+        # Require shoulders; prefer elbows for linear; wrists for angular.
+        need_base = [11, 12]
+        if any(landmarks[i][3] < self.min_conf for i in need_base):
+            return 0.0, 0.0
+
+        elbows_ok = (landmarks[13][3] >= self.min_conf and landmarks[14][3] >= self.min_conf)
+        wrists_ok = (landmarks[15][3] >= self.min_conf and landmarks[16][3] >= self.min_conf)
+
+        if not elbows_ok and not wrists_ok:
+            return 0.0, 0.0
+
+        shoulder_center, shoulder_width, torso_height, ok = _robust_metrics(landmarks, self.min_conf)
+        if not ok or shoulder_width < 1e-3 or torso_height < 1e-3:
+            return 0.0, 0.0
+
+        # Linear: prefer elbows, fallback to wrists average if elbows missing
+        if elbows_ok:
+            l_el = landmarks[13][:2]
+            r_el = landmarks[14][:2]
+            lin_l = (shoulder_center[1] - l_el[1]) / torso_height
+            lin_r = (shoulder_center[1] - r_el[1]) / torso_height
+            linear = 0.5 * (lin_l + lin_r)
+        else:
+            l_wr = landmarks[15][:2]
+            r_wr = landmarks[16][:2]
+            lin_l = (shoulder_center[1] - l_wr[1]) / torso_height
+            lin_r = (shoulder_center[1] - r_wr[1]) / torso_height
+            linear = 0.5 * (lin_l + lin_r)
+
+        # Angular: use wrists if available, else 0
+        if wrists_ok:
+            l_wr = landmarks[15][:2]
+            r_wr = landmarks[16][:2]
+            angular = ((r_wr[0] + l_wr[0]) - 2 * shoulder_center[0]) / shoulder_width
+        else:
+            angular = 0.0
+
+        if self.mirror:
+            angular = -angular
+
+        linear = _clip_unit(_apply_dead_zone(linear, self.dead_zone))
+        angular = _clip_unit(_apply_dead_zone(angular, self.dead_zone))
+
+        if self.debug:
+            print(f"[M3] L:{linear:.2f} A:{angular:.2f}")
+        return linear, angular
+
+    def draw_overlay(self, frame, landmarks=None):
+        if frame is None:
+            return frame
+        if landmarks is not None:
+            for idx, color in [(13, (0, 200, 0)), (14, (0, 200, 0)), (15, (0, 255, 255)), (16, (255, 0, 255))]:
+                if landmarks[idx][3] > 0.5:
+                    x, y = int(landmarks[idx][0]), int(landmarks[idx][1])
+                    cv2.circle(frame, (x, y), 6, color, -1)
+        return frame
+
+
+class ArmControllerMethod4:
+    """
+    Method 4: 3x3 grid based on landmark 19 (right index finger tip).
+    Screen split at 2/5 and 3/5 (both axes). Center band = 0.
+    Proportional speed away from the center bands.
+    """
+    def __init__(self, config, mirror=False):
+        self.config = config
+        self.mirror = mirror
+        self.finger_idx = 19  # right index finger tip
+        self.debug = config.get('debug', False)
+
+    def compute_speeds(self, landmarks, frame_shape=None):
+        linear = 0.0
+        angular = 0.0
+
+        if frame_shape is None or landmarks is None:
+            return 0.0, 0.0
+        if landmarks[self.finger_idx][3] < 0.5:
+            return 0.0, 0.0
+
+        h, w = int(frame_shape[0]), int(frame_shape[1])
+        x = landmarks[self.finger_idx][0]
+        y = landmarks[self.finger_idx][1]
+
+        # Angular (horizontal): center band 2/5..3/5 = 0
+        if 2 * w / 5 <= x <= 3 * w / 5:
+            angular = 0.0
+        elif x > 3 * w / 5:
+            angular = (x * 5) / (2 * w) - 1
+        else:
+            angular = (x - 3 * w / 5) / (2 * w / 5)
+
+        # Linear (vertical): center band 2/5..3/5 = 0
+        if 2 * h / 5 <= y <= 3 * h / 5:
+            linear = 0.0
+        elif y > 3 * h / 5:
+            linear = -((y * 5) / (2 * h) - 1)
+        else:
+            linear = -(y - 3 * h / 5) / (2 * h / 5)
+
+        if self.mirror:
+            angular = -angular
+
+        if self.debug:
+            print(f"[M4] L:{linear:.2f} A:{angular:.2f}")
+        return _clip_unit(linear), _clip_unit(angular)
+
+    def draw_overlay(self, frame, landmarks=None):
+        if frame is None:
+            return frame
+
+        h, w = frame.shape[:2]
+        x1, x2 = int(w * 2 / 5), int(w * 3 / 5)
+        y1, y2 = int(h * 2 / 5), int(h * 3 / 5)
+
+        # Grid lines
+        cv2.line(frame, (x1, 0), (x1, h), (0, 0, 0), 2)
+        cv2.line(frame, (x2, 0), (x2, h), (0, 0, 0), 2)
+        cv2.line(frame, (0, y1), (w, y1), (0, 0, 0), 2)
+        cv2.line(frame, (0, y2), (w, y2), (0, 0, 0), 2)
+
+        # Highlight active cell + finger
+        if landmarks is not None and landmarks[self.finger_idx][3] > 0.5:
+            fx, fy = int(landmarks[self.finger_idx][0]), int(landmarks[self.finger_idx][1])
+            cx0, cx1 = (0, x1) if fx < x1 else ((x2, w) if fx > x2 else (x1, x2))
+            cy0, cy1 = (0, y1) if fy < y1 else ((y2, h) if fy > y2 else (y1, y2))
+
+            overlay = frame.copy()
+            cv2.rectangle(overlay, (cx0, cy0), (cx1, cy1), (0, 255, 255), -1)
+            frame = cv2.addWeighted(overlay, 0.2, frame, 0.8, 0)
+
+            cv2.circle(frame, (fx, fy), 8, (0, 255, 255), -1)
+            cv2.circle(frame, (fx, fy), 12, (0, 120, 120), 2)
+
+        return frame

gesture_control/special_gestures.py

@@ -1,116 +1,184 @@
 import numpy as np
-from ml_gestures.predict import MLGesturePredictor
+
 
 class SpecialGestureDetector:
-    def __init__(self, mode='geometric', model_path=None, class_names=None):
+    """
+    Detects special static gestures using either simple geometric rules or an ML classifier.
+
+    Supported gesture labels:
+      - 'cross' : forearms crossed near the chest
+      - 'light' : right arm pose approximating a 'light' toggle
+      - 'dome'  : arms forming a dome above the head
+      - 'none'  : no special gesture detected
+    """
+
+    def __init__(self, mode='geometric', model_path=None, class_names=None, debug=False, thresholds=None):
         self.mode = mode
+        self.debug = debug
+
+        # Defaults for geometric detection
+        self.th = {
+            'min_conf': 0.5,
+            'shoulder_width_min': 30.0,
+            'torso_height_min': 10.0,
+            'chest_band': 0.25,         # widened to be more forgiving
+            'wrists_near_factor': 0.6,  # relaxed for dome
+            'elbow_far_factor': 0.9,    # relaxed for dome
+            'light_elbow_min': 45.0,
+            'light_elbow_max': 120.0,
+            'light_shoulder_min': -5.0,
+            'light_shoulder_max': 20.0
+        }
+        if thresholds:
+            self.th.update(thresholds)
+
         if mode == 'ml':
+            from ml_gestures.predict import MLGesturePredictor
             if model_path is None or class_names is None:
-                raise ValueError("Для ML нужны model_path и class_names")
+                raise ValueError("For ML mode, provide model_path and class_names")
             self.ml_predictor = MLGesturePredictor(model_path, class_names)
-            print("Использую статический ML классификатор")
+            if self.debug:
+                print("SpecialGestureDetector: Using ML classifier")
         else:
             self.ml_predictor = None
-            print("Использую геометрические отношения для детекции специальных жестов")
+            if self.debug:
-        self.debug = False # Включите для отладки
+                print("SpecialGestureDetector: Using geometric rules")
 
     def predict(self, landmarks):
+        if landmarks is None:
+            return 'none'
         if self.mode == 'geometric':
             return self._geometric_predict(landmarks)
         else:
             return self.ml_predictor.predict(landmarks)
 
     def _geometric_predict(self, landmarks):
-        # Индексы MediaPipe
         idx = {
             'nose': 0,
-            'left_shoulder': 11,
+            'left_shoulder': 11, 'right_shoulder': 12,
-            'right_shoulder': 12,
+            'left_elbow': 13, 'right_elbow': 14,
-            'left_elbow': 13,
+            'left_wrist': 15, 'right_wrist': 16,
-            'right_elbow': 14,
+            'left_hip': 23, 'right_hip': 24,
-            'left_wrist': 15,
-            'right_wrist': 16,
-            'left_hip': 23,
-            'right_hip': 24,
         }
 
-        # Повышенный порог уверенности для специальных жестов
+        min_conf = self.th['min_conf']
-        min_conf = 0.5
+        # Only upper-body required (hips optional)
-        required = ['left_shoulder', 'right_shoulder', 'left_elbow', 'right_elbow',
+        required = [
-                    'left_wrist', 'right_wrist', 'nose']
+            'left_shoulder', 'right_shoulder',
+            'left_elbow', 'right_elbow',
+            'left_wrist', 'right_wrist',
+            'nose'
+        ]
         for p in required:
             if landmarks[idx[p]][3] < min_conf:
                 if self.debug:
-                    print(f"{p} low confidence")
+                    print(f"[SG] Low confidence for {p}: {landmarks[idx[p]][3]:.2f}")
                 return 'none'
 
-        # Координаты (x, y)
+        l_sh = np.array(landmarks[idx['left_shoulder']][:2], dtype=float)
-        l_sh = landmarks[idx['left_shoulder']][:2]
+        r_sh = np.array(landmarks[idx['right_shoulder']][:2], dtype=float)
-        r_sh = landmarks[idx['right_shoulder']][:2]
+        l_el = np.array(landmarks[idx['left_elbow']][:2], dtype=float)
-        l_el = landmarks[idx['left_elbow']][:2]
+        r_el = np.array(landmarks[idx['right_elbow']][:2], dtype=float)
-        r_el = landmarks[idx['right_elbow']][:2]
+        l_wr = np.array(landmarks[idx['left_wrist']][:2], dtype=float)
-        l_wr = landmarks[idx['left_wrist']][:2]
+        r_wr = np.array(landmarks[idx['right_wrist']][:2], dtype=float)
-        r_wr = landmarks[idx['right_wrist']][:2]
+        nose = np.array(landmarks[idx['nose']][:2], dtype=float)
-        l_hip = landmarks[idx['left_hip']][:2]
+
-        r_hip = landmarks[idx['right_hip']][:2]
+        l_hip = np.array(landmarks[idx['left_hip']][:2], dtype=float)
-        nose = np.array(landmarks[idx['nose']][:2])
+        r_hip = np.array(landmarks[idx['right_hip']][:2], dtype=float)
-        
+        c_lhip = landmarks[idx['left_hip']][3]
-        shoulder_center_y = (l_sh[1] + r_sh[1]) / 2
+        c_rhip = landmarks[idx['right_hip']][3]
-        hip_center_y = (l_hip[1] + r_hip[1]) / 2
+
-        torso_height = hip_center_y - shoulder_center_y
+        shoulder_center_y = (l_sh[1] + r_sh[1]) / 2.0
         shoulder_width = np.linalg.norm(r_sh - l_sh)
-        if shoulder_width < 30 or torso_height < 10:
+        if shoulder_width < self.th['shoulder_width_min']:
+            if self.debug:
+                print(f"[SG] Shoulder width too small: {shoulder_width:.1f}")
             return 'none'
 
-        # ---- Вспомогательные функции ----
+        # Torso height: prefer hips if visible, otherwise fallback using nose/shoulders
-        def angle_between_vectors(v1, v2):
+        if c_lhip >= min_conf and c_rhip >= min_conf:
-            """Угол между двумя векторами в градусах (0..180)"""
+            hip_center_y = (l_hip[1] + r_hip[1]) / 2.0
-            cos_a = np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2) + 1e-6)
+            torso_height = hip_center_y - shoulder_center_y
-            return np.arccos(np.clip(cos_a, -1.0, 1.0)) * 180 / np.pi
+        else:
+            nose_to_shoulder = abs(nose[1] - shoulder_center_y)
+            torso_height = max(1.6 * nose_to_shoulder, 0.9 * shoulder_width)
+            hip_center_y = shoulder_center_y + torso_height
 
-        def elbow_angle(shoulder, elbow, wrist):
+        if torso_height < self.th['torso_height_min']:
-            """Угол в локте (плечо-локоть-запястье)"""
+            if self.debug:
-            v1 = shoulder - elbow
+                print(f"[SG] Torso height too small: {torso_height:.1f}")
-            v2 = wrist - elbow
+            return 'none'
+
+        def angle_between_vectors(v1, v2):
+            n1 = np.linalg.norm(v1)
+            n2 = np.linalg.norm(v2)
+            if n1 < 1e-6 or n2 < 1e-6:
+                return 0.0
+            cos_a = np.dot(v1, v2) / (n1 * n2)
+            cos_a = float(np.clip(cos_a, -1.0, 1.0))
+            return np.degrees(np.arccos(cos_a))
+
+        def joint_angle(p_prev, p_joint, p_next):
+            v1 = p_prev - p_joint
+            v2 = p_next - p_joint
             return angle_between_vectors(v1, v2)
 
-        # ---- Вычисляем углы ----
+        def segments_intersect(p1, p2, p3, p4):
-        l_angle = elbow_angle(l_sh, l_el, l_wr)   # угол в левом локте
+            def cross(o, a, b):
-        r_angle = elbow_angle(r_sh, r_el, r_wr)   # угол в правом локте
+                return (a[0] - o[0]) * (b[1] - o[1]) - (a[1] - o[1]) * (b[0] - o[0])
+            d1 = cross(p3, p4, p1)
+            d2 = cross(p3, p4, p2)
+            d3 = cross(p1, p2, p3)
+            d4 = cross(p1, p2, p4)
+            return (d1 * d2 < 0) and (d3 * d4 < 0)
 
-        # ---- КРЕСТ ----
+        def line_intersection(p1, p2, p3, p4):
-        # 1. Оба локтя сильно согнуты (< 100°)
+            d1 = p2 - p1
-        elbows_bent = (l_angle < 100 and r_angle < 100)
+            d2 = p4 - p3
-        # 2. Левое запястье правее правого (перекрест)
+            denom = d1[0] * d2[1] - d1[1] * d2[0]
-        wrists_crossed = l_wr[0] > r_wr[0] + 5  # небольшой запас в пикселях (можно и 0)
+            if abs(denom) < 1e-6:
-        # 3. Запястья находятся между плечами и бёдрами по Y (уровень груди)
+                return None
-        wrists_at_chest = (
+            t = ((p3[0] - p1[0]) * d2[1] - (p3[1] - p1[1]) * d2[0]) / denom
-            shoulder_center_y - 0.3 * torso_height < l_wr[1] < hip_center_y + 0.3 * torso_height and
+            return p1 + t * d1
-            shoulder_center_y - 0.3 * torso_height < r_wr[1] < hip_center_y + 0.3 * torso_height
-        )
 
-        cross = elbows_bent and wrists_crossed and wrists_at_chest
+        # Angles (geometric cues)
-        if cross:
+        l_elbow_angle = joint_angle(l_sh, l_el, l_wr)
+        r_elbow_angle = joint_angle(r_sh, r_el, r_wr)
+        r_shoulder_like_angle = joint_angle(r_hip, r_sh, r_el)
+
+        # 1) CROSS
+        forearms_cross = segments_intersect(l_el, l_wr, r_el, r_wr)
+        intersection = line_intersection(l_el, l_wr, r_el, r_wr)
+        intersection_on_chest = False
+        if intersection is not None:
+            band = self.th['chest_band'] * torso_height
+            intersection_on_chest = (shoulder_center_y - band) < intersection[1] < (hip_center_y + band)
+
+        if self.debug:
+            print(f"[SG] cross_check: intersect={forearms_cross}, chest={intersection_on_chest}")
+
+        if forearms_cross and intersection_on_chest:
             return 'cross'
 
-        # ---- ДОМИК ----
+        # 2) LIGHT
-        # 1. Запястья выше носа
+        if (self.th['light_elbow_min'] < r_elbow_angle < self.th['light_elbow_max'] and
-        wrists_above_nose = (l_wr[1] < nose[1] and r_wr[1] < nose[1])
+                self.th['light_shoulder_min'] < r_shoulder_like_angle < self.th['light_shoulder_max']):
+            return 'light'
 
-        # 2. Локти выше плеч (верхняя граница плеч – min по Y среди плеч)
+        # 3) DOME
+        wrists_above_nose = (l_wr[1] < nose[1] and r_wr[1] < nose[1])
         shoulders_top_y = min(l_sh[1], r_sh[1])
         elbows_above_shoulders = (l_el[1] < shoulders_top_y and r_el[1] < shoulders_top_y)
-
-        # 3. Расстояние между локтями > расстояние между плечами
         elbow_distance = np.linalg.norm(l_el - r_el)
-        elbows_far_apart = elbow_distance > shoulder_width
+        elbows_far_apart = elbow_distance > (self.th['elbow_far_factor'] * shoulder_width)
-
-        # 4. Расстояние между запястьями < половины ширины плеч
         wrist_distance = np.linalg.norm(l_wr - r_wr)
-        wrists_near = wrist_distance < 0.5 * shoulder_width
+        wrists_near = wrist_distance < (self.th['wrists_near_factor'] * shoulder_width)
 
-        dome = wrists_above_nose and elbows_above_shoulders and elbows_far_apart and wrists_near
+        if self.debug:
-        if dome:
+            print(f"[SG] dome_check: wrists_above={wrists_above_nose}, elbows_above={elbows_above_shoulders}, "
+                  f"elbow_d={elbow_distance:.1f}, wrist_d={wrist_distance:.1f}")
+
+        if wrists_above_nose and elbows_above_shoulders and elbows_far_apart and wrists_near:
             return 'dome'
 
         return 'none'

install_deps.sh

@@ -6,6 +6,7 @@ pip install --upgrade pip
 
 pip install numpy==1.24.3
 pip install pandas==2.0.3
+pip install pyyaml==6.0.3
 pip install opencv-python==4.12.0.88
 pip install opencv-python-headless==4.12.0.88
 pip install matplotlib==3.7.5