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import numpy as np
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from ml_gestures.predict import MLGesturePredictor
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class SpecialGestureDetector:
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def __init__(self, mode='geometric', model_path=None, class_names=None):
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self.mode = mode
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if mode == 'ml':
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if model_path is None or class_names is None:
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raise ValueError("Для ML нужны model_path и class_names")
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self.ml_predictor = MLGesturePredictor(model_path, class_names)
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print("Использую статический ML классификатор")
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else:
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self.ml_predictor = None
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print("Использую геометрические отношения для детекции специальных жестов")
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self.debug = True # Включите для отладки
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def predict(self, landmarks):
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if self.mode == 'geometric':
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return self._geometric_predict(landmarks)
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else:
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return self.ml_predictor.predict(landmarks)
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def _geometric_predict(self, landmarks):
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# Индексы MediaPipe
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idx = {
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'nose': 0,
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'left_shoulder': 11,
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'right_shoulder': 12,
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'left_elbow': 13,
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'right_elbow': 14,
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'left_wrist': 15,
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'right_wrist': 16,
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'left_hip': 23,
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'right_hip': 24,
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}
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# Повышенный порог уверенности для специальных жестов
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min_conf = 0.7
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required = ['left_shoulder', 'right_shoulder', 'left_elbow', 'right_elbow',
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'left_wrist', 'right_wrist', 'left_hip', 'right_hip']
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for p in required:
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if landmarks[idx[p]][3] < min_conf:
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if self.debug:
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print(f"{p} low confidence")
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return 'none'
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# Координаты (x, y)
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l_sh = landmarks[idx['left_shoulder']][:2]
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r_sh = landmarks[idx['right_shoulder']][:2]
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l_el = landmarks[idx['left_elbow']][:2]
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r_el = landmarks[idx['right_elbow']][:2]
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l_wr = landmarks[idx['left_wrist']][:2]
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r_wr = landmarks[idx['right_wrist']][:2]
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l_hip = landmarks[idx['left_hip']][:2]
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r_hip = landmarks[idx['right_hip']][:2]
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hip_center = (l_hip + r_hip) / 2
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shoulder_width = np.linalg.norm(r_sh - l_sh)
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if shoulder_width < 50 or shoulder_width > 300:
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if self.debug:
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print("shoulder_width out of range")
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return 'none'
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# ----- КРЕСТ (предплечья скрещены на груди) -----
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# 1. Запястья перекрещены (левое правее правого) И на уровне груди (ниже плеч, выше бедер)
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wrists_crossed = l_wr[0] > r_wr[0]
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wrists_chest_level = (max(l_wr[1], r_wr[1]) > max(l_sh[1], r_sh[1]) and
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min(l_wr[1], r_wr[1]) < hip_center[1])
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# 2. Локти тоже на уровне груди (примерно)
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elbows_chest_level = (max(l_el[1], r_el[1]) > max(l_sh[1], r_sh[1]) * 0.9 and
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min(l_el[1], r_el[1]) < hip_center[1] * 1.1)
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# 3. Руки согнуты (предплечья короче верхней части руки)
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left_bent = np.linalg.norm(l_wr - l_el) < np.linalg.norm(l_el - l_sh) * 0.9
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right_bent = np.linalg.norm(r_wr - r_el) < np.linalg.norm(r_el - r_sh) * 0.9
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arms_bent = left_bent and right_bent
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# 4. Запястья близко к центру тела (не сильно отведены, типично для креста на груди)
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body_center_x = (l_sh[0] + r_sh[0]) / 2
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wrists_near_center = (abs(l_wr[0] - body_center_x) < shoulder_width * 0.6 and
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abs(r_wr[0] - body_center_x) < shoulder_width * 0.6)
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cross = (wrists_crossed and wrists_chest_level and elbows_chest_level and
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arms_bent and wrists_near_center)
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if self.debug and cross:
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print(f"CROSS: crossed={wrists_crossed}, chest_w={wrists_chest_level}, "
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f"chest_e={elbows_chest_level}, bent={arms_bent}, near_center={wrists_near_center}")
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if cross:
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return 'cross'
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# ----- ДОМИК (руки над головой) -----
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head_y = landmarks[idx['nose']][1] - 50
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arms_up = (l_wr[1] < head_y and r_wr[1] < head_y)
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if arms_up:
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dist = np.linalg.norm(l_wr - r_wr)
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rel_dist = dist / shoulder_width
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if rel_dist < 1.5:
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return 'dome'
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return 'none'
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