嘿开发社区! ?我很高兴与大家分享构建 ErgoVision 的历程,这是一个人工智能驱动的系统,通过实时姿势分析使工作场所更加安全。让我们深入探讨技术挑战和解决方案!
当德克萨斯 A&M 大学的 SIIR-Lab 向我寻求构建实时姿势分析系统时,我们面临几个关键挑战:
# Core dependencies import mediapipe as mp import cv2 import numpy as np
最大的挑战是实现实时分析。我们是这样解决的:
def process_frame(self, frame): # Convert to RGB for MediaPipe rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) results = self.pose.process(rgb_frame) if results.pose_landmarks: # Process landmarks self.analyze_pose(results.pose_landmarks) return results
def calculate_angle(self, a, b, c): vector1 = np.array([a[0] - b[0], a[1] - b[1], a[2] - b[2]]) vector2 = np.array([c[0] - b[0], c[1] - b[1], c[2] - b[2]]) # Handle edge cases if np.linalg.norm(vector1) == 0 or np.linalg.norm(vector2) == 0: return 0.0 cosine_angle = np.dot(vector1, vector2) / ( np.linalg.norm(vector1) * np.linalg.norm(vector2) ) return np.degrees(np.arccos(np.clip(cosine_angle, -1.0, 1.0)))
def calculate_reba_score(self, angles): # Initialize scores neck_score = self._get_neck_score(angles['neck']) trunk_score = self._get_trunk_score(angles['trunk']) legs_score = self._get_legs_score(angles['legs']) # Calculate final score return neck_score + trunk_score + legs_score
优化帧处理
错误处理
def safe_angle_calculation(self, landmarks): try: angles = self.calculate_angles(landmarks) return angles except Exception as e: self.log_error(e) return self.default_angles
我们的实施实现了:
ergovision/ ├── src/ │ ├── analyzer.py │ ├── pose_detector.py │ └── reba_calculator.py ├── tests/ │ └── test_analyzer.py └── README.md
# Planned optimization @numba.jit(nopython=True) def optimized_angle_calculation(self, vectors): # Optimized computation pass
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