dominika version

gesture_control/arm_control.py

@@ -1,324 +1,73 @@
 import numpy as np
-import cv2
 
-
-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)
-    """
+class ArmController:
     def __init__(self, config, mirror=False):
-        self.config = config
-        self.mirror = mirror
-        self.dead_zone = config.get('dead_zone', 0.1)
+        #self.config = config
+        #self.mirror = mirror
+
+        #self.shoulder_idx = {'left': 11, 'right': 12}
+        #self.elbow_idx = {'left': 13, 'ri ght': 14}
+        #self.wrist_idx = {'left': 15, 'right': 16}
+        #self.hip_idx = {'left': 23, 'right': 24}
+
+        self.r_wirst_id = 15 #16
+        self.r_elbow_id = 13 #14
+        self.r_shoulder_id = 11 # 12
+
+        self.error = 23
+        self.bag = 27
+
         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 + right wrist
-        need = [11, 12, 16]
-        if any(landmarks[i][3] < self.min_conf for i in need):
-            return 0.0, 0.0
+    def _get_angle(self, x1, y1, x2, y2):
+        angle = np.arctan2(y2 - y1, x2 - x1)
+        return angle
 
-        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
 
-        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
+    def compute_speeds(self, landmarks):
 
-        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))
+        linear = 0.
+        angular = 0.
 
+        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(f"[M1] L:{linear:.2f} A:{angular:.2f}")
+                print("Руки не видны")
+            return 0.0, 0.0
+
+        right_shoulder_point = landmarks[self.r_shoulder_id]
+        right_shoulder_x = right_shoulder_point[0]
+        right_shoulder_y = right_shoulder_point[1]
+
+        right_elbow_point = landmarks[self.r_elbow_id] # [x, y, z, v]
+        right_elbow_x = right_elbow_point[0]
+        right_elbow_y = right_elbow_point[1]
+
+        angle_shoulder_elbow = self._get_angle(right_shoulder_x, right_shoulder_y, right_elbow_x, right_elbow_y)
+
+        right_wirst_point = landmarks[self.r_wirst_id]
+        right_wirst_x = right_wirst_point[0]
+        right_wirst_y = right_wirst_point[1]
+
+        angle_elbow_wirst = self._get_angle(right_elbow_x, right_elbow_y, right_wirst_x, right_wirst_y)
+
+        five_deg_in_rad = np.deg2rad(self.error)
+
+
+        if (angle_shoulder_elbow < np.pi/2.5 + five_deg_in_rad) and (angle_shoulder_elbow > np.pi/2.5 -five_deg_in_rad):
+            print(f"Angle between shoulder and elbow is {angle_shoulder_elbow} {np.rad2deg(angle_shoulder_elbow)}")
+            #print(f"Angle between elbow and wirst is {angle_elbow_wirst}")
+            if (angle_elbow_wirst > np.deg2rad(-90 - self.error)) and (angle_elbow_wirst < np.deg2rad(-90 + self.error)):
+                linear = 1.
+            if (angle_elbow_wirst > np.deg2rad(90 - self.error)) and (angle_elbow_wirst < np.deg2rad(90 + self.error)):
+                linear = -1.
+            if (angle_elbow_wirst > np.deg2rad(-45 - self.bag)) and (angle_elbow_wirst < np.deg2rad(-45 + self.bag)):
+                angular = -1.
+            if (angle_elbow_wirst > np.deg2rad(-135 - self.bag)) and (angle_elbow_wirst < np.deg2rad(-135 + self.bag)):
+                angular = 1.
+
         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

@@ -1,131 +1,82 @@
 import numpy as np
 
-
 class SpecialGestureDetector:
-    """
-    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):
+    def __init__(self, mode='geometric', model_path=None, class_names=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("For ML mode, provide model_path and class_names")
+                raise ValueError("Для ML нужны model_path и class_names")
             self.ml_predictor = MLGesturePredictor(model_path, class_names)
-            if self.debug:
-                print("SpecialGestureDetector: Using ML classifier")
+            print("Использую статический ML классификатор")
         else:
             self.ml_predictor = None
-            if self.debug:
-                print("SpecialGestureDetector: Using geometric rules")
+            print("Использую геометрические отношения для детекции специальных жестов")
+        self.debug = False # Включите для отладки
 
     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, 'right_shoulder': 12,
-            'left_elbow': 13, 'right_elbow': 14,
-            'left_wrist': 15, 'right_wrist': 16,
-            'left_hip': 23, 'right_hip': 24,
+            'left_shoulder': 11,
+            'right_shoulder': 12,
+            'left_elbow': 13,
+            'right_elbow': 14,
+            'left_wrist': 15,
+            'right_wrist': 16,
+            'left_hip': 23,
+            'right_hip': 24,
         }
 
-        min_conf = self.th['min_conf']
-        # Only upper-body required (hips optional)
-        required = [
-            'left_shoulder', 'right_shoulder',
-            'left_elbow', 'right_elbow',
-            'left_wrist', 'right_wrist',
-            'nose'
-        ]
+        # Повышенный порог уверенности для специальных жестов
+        min_conf = 0.5
+        required = ['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"[SG] Low confidence for {p}: {landmarks[idx[p]][3]:.2f}")
+                    print(f"{p} low confidence")
                 return 'none'
 
-        l_sh = np.array(landmarks[idx['left_shoulder']][:2], dtype=float)
-        r_sh = np.array(landmarks[idx['right_shoulder']][:2], dtype=float)
-        l_el = np.array(landmarks[idx['left_elbow']][:2], dtype=float)
-        r_el = np.array(landmarks[idx['right_elbow']][:2], dtype=float)
-        l_wr = np.array(landmarks[idx['left_wrist']][:2], dtype=float)
-        r_wr = np.array(landmarks[idx['right_wrist']][:2], dtype=float)
-        nose = np.array(landmarks[idx['nose']][:2], dtype=float)
+        # Координаты (x, y)
+        l_sh = landmarks[idx['left_shoulder']][:2]
+        r_sh = landmarks[idx['right_shoulder']][:2]
+        l_el = landmarks[idx['left_elbow']][:2]
+        r_el = landmarks[idx['right_elbow']][:2]
+        l_wr = landmarks[idx['left_wrist']][:2]
+        r_wr = landmarks[idx['right_wrist']][:2]
+        l_hip = landmarks[idx['left_hip']][:2]
+        r_hip = landmarks[idx['right_hip']][:2]
+        nose = np.array(landmarks[idx['nose']][:2])
 
-        l_hip = np.array(landmarks[idx['left_hip']][:2], dtype=float)
-        r_hip = np.array(landmarks[idx['right_hip']][:2], dtype=float)
-        c_lhip = landmarks[idx['left_hip']][3]
-        c_rhip = landmarks[idx['right_hip']][3]
-
-        shoulder_center_y = (l_sh[1] + r_sh[1]) / 2.0
-        shoulder_width = np.linalg.norm(r_sh - l_sh)
-        if shoulder_width < self.th['shoulder_width_min']:
-            if self.debug:
-                print(f"[SG] Shoulder width too small: {shoulder_width:.1f}")
-            return 'none'
-
-        # Torso height: prefer hips if visible, otherwise fallback using nose/shoulders
-        if c_lhip >= min_conf and c_rhip >= min_conf:
-            hip_center_y = (l_hip[1] + r_hip[1]) / 2.0
+        shoulder_center_y = (l_sh[1] + r_sh[1]) / 2
+        hip_center_y = (l_hip[1] + r_hip[1]) / 2
         torso_height = hip_center_y - shoulder_center_y
-        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
-
-        if torso_height < self.th['torso_height_min']:
-            if self.debug:
-                print(f"[SG] Torso height too small: {torso_height:.1f}")
+        shoulder_width = np.linalg.norm(r_sh - l_sh)
+        if shoulder_width < 30 or torso_height < 10:
             return 'none'
 
+        # ---- Вспомогательные функции ----
         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))
+            """Угол между двумя векторами в градусах (0..180)"""
+            cos_a = np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2) + 1e-6)
+            return np.arccos(np.clip(cos_a, -1.0, 1.0)) * 180 / np.pi
 
-        def joint_angle(p_prev, p_joint, p_next):
-            v1 = p_prev - p_joint
-            v2 = p_next - p_joint
+        def elbow_angle(shoulder, elbow, wrist):
+            """Угол в локте (плечо-локоть-запястье)"""
+            v1 = shoulder - elbow
+            v2 = wrist - elbow
             return angle_between_vectors(v1, v2)
 
         def segments_intersect(p1, p2, p3, p4):
             def cross(o, a, b):
-                return (a[0] - o[0]) * (b[1] - o[1]) - (a[1] - o[1]) * (b[0] - o[0])
+                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)
@@ -134,51 +85,60 @@ class SpecialGestureDetector:
 
         def line_intersection(p1, p2, p3, p4):
             d1 = p2 - p1
-            d2 = p4 - p3
+            d2 = p4 -p3
             denom = d1[0] * d2[1] - d1[1] * d2[0]
             if abs(denom) < 1e-6:
                 return None
             t = ((p3[0] - p1[0]) * d2[1] - (p3[1] - p1[1]) * d2[0]) / denom
             return p1 + t * d1
 
-        # Angles (geometric cues)
-        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)
+        # ---- Вычисляем углы ----
+        l_angle = elbow_angle(l_sh, l_el, l_wr)   # угол в левом локте
+        r_angle = elbow_angle(r_sh, r_el, r_wr)   # угол в правом локте
 
-        # 1) CROSS
+        # ---- КРЕСТ ----
+        '''
+        # 1. Оба локтя сильно согнуты (< 100°)
+        elbows_bent = (l_angle < 100 and r_angle < 100)
+        # 2. Левое запястье правее правого (перекрест)
+        wrists_crossed = l_wr[0] > r_wr[0] + 5  # небольшой запас в пикселях (можно и 0)
+        # 3. Запястья находятся между плечами и бёдрами по Y (уровень груди)
+        wrists_at_chest = (
+            shoulder_center_y - 0.3 * torso_height < l_wr[1] < hip_center_y + 0.3 * torso_height and
+            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
+        if cross:
+            return '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:
+            intersection_on_chest = (shoulder_center_y - 0.2 * torso_height < intersection[1] < hip_center_y + 0.2 * torso_height)
+        cross = forearms_cross and intersection_on_chest
+        if cross:
             return 'cross'
 
-        # 2) LIGHT
-        if (self.th['light_elbow_min'] < r_elbow_angle < self.th['light_elbow_max'] and
-                self.th['light_shoulder_min'] < r_shoulder_like_angle < self.th['light_shoulder_max']):
-            return 'light'
-
-        # 3) DOME
+        # ---- ДОМИК ----
+        # 1. Запястья выше носа
         wrists_above_nose = (l_wr[1] < nose[1] and r_wr[1] < nose[1])
+
+        # 2. Локти выше плеч (верхняя граница плеч – min по Y среди плеч)
         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 > (self.th['elbow_far_factor'] * shoulder_width)
+        elbows_far_apart = elbow_distance > shoulder_width
+
+        # 4. Расстояние между запястьями < половины ширины плеч
         wrist_distance = np.linalg.norm(l_wr - r_wr)
-        wrists_near = wrist_distance < (self.th['wrists_near_factor'] * shoulder_width)
+        wrists_near = wrist_distance < 0.5 * shoulder_width
 
-        if self.debug:
-            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:
+        dome = wrists_above_nose and elbows_above_shoulders and elbows_far_apart and wrists_near
+        if dome:
             return 'dome'
 
         return 'none'