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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 = False # 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 = 0.5
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required = ['left_shoulder', 'right_shoulder', 'left_elbow', 'right_elbow',
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'left_wrist', 'right_wrist', 'nose']
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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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nose = np.array(landmarks[idx['nose']][:2])
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shoulder_center_y = (l_sh[1] + r_sh[1]) / 2
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hip_center_y = (l_hip[1] + r_hip[1]) / 2
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torso_height = hip_center_y - shoulder_center_y
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shoulder_width = np.linalg.norm(r_sh - l_sh)
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if shoulder_width < 30 or torso_height < 10:
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return 'none'
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# ---- Вспомогательные функции ----
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def angle_between_vectors(v1, v2):
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"""Угол между двумя векторами в градусах (0..180)"""
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cos_a = np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2) + 1e-6)
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return np.arccos(np.clip(cos_a, -1.0, 1.0)) * 180 / np.pi
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def elbow_angle(shoulder, elbow, wrist):
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"""Угол в локте (плечо-локоть-запястье)"""
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v1 = shoulder - elbow
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v2 = wrist - elbow
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return angle_between_vectors(v1, v2)
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# ---- Вычисляем углы ----
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l_angle = elbow_angle(l_sh, l_el, l_wr) # угол в левом локте
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r_angle = elbow_angle(r_sh, r_el, r_wr) # угол в правом локте
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# ---- КРЕСТ ----
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# 1. Оба локтя сильно согнуты (< 100°)
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elbows_bent = (l_angle < 100 and r_angle < 100)
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# 2. Левое запястье правее правого (перекрест)
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wrists_crossed = l_wr[0] > r_wr[0] + 5 # небольшой запас в пикселях (можно и 0)
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# 3. Запястья находятся между плечами и бёдрами по Y (уровень груди)
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wrists_at_chest = (
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shoulder_center_y - 0.3 * torso_height < l_wr[1] < hip_center_y + 0.3 * torso_height and
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shoulder_center_y - 0.3 * torso_height < r_wr[1] < hip_center_y + 0.3 * torso_height
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)
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cross = elbows_bent and wrists_crossed and wrists_at_chest
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if cross:
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return 'cross'
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# ---- ДОМИК ----
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# 1. Запястья выше носа
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wrists_above_nose = (l_wr[1] < nose[1] and r_wr[1] < nose[1])
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# 2. Локти выше плеч (верхняя граница плеч – min по Y среди плеч)
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shoulders_top_y = min(l_sh[1], r_sh[1])
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elbows_above_shoulders = (l_el[1] < shoulders_top_y and r_el[1] < shoulders_top_y)
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# 3. Расстояние между локтями > расстояние между плечами
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elbow_distance = np.linalg.norm(l_el - r_el)
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elbows_far_apart = elbow_distance > shoulder_width
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# 4. Расстояние между запястьями < половины ширины плеч
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wrist_distance = np.linalg.norm(l_wr - r_wr)
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wrists_near = wrist_distance < 0.5 * shoulder_width
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dome = wrists_above_nose and elbows_above_shoulders and elbows_far_apart and wrists_near
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if dome:
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return 'dome'
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return 'none'
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