335. Self Crossing

本文介绍了一种用于判断二维平面上由一系列线段组成的路径是否出现交叉的算法。该算法通过三种不同的交叉场景来判断:第四条线段与第一条线段交叉;第五条线段与第一条线段相交;第六条线段与第一条线段交叉。
    /*
     * 335. Self Crossing
     * 2016-7-10 by Mingyang
     */
    // Categorize the self-crossing scenarios, there are 3 of them: 
    // 1. Fourth line crosses first line and works for fifth line crosses second line and so on...
    // 2. Fifth line meets first line and works for the lines after
    // 3. Sixth line crosses first line and works for the lines after
    public boolean isSelfCrossing(int[] x) {
        int l = x.length;
        if(l <= 3) return false;
        
        for(int i = 3; i < l; i++){
            if(x[i] >= x[i-2] && x[i-1] <= x[i-3]) return true;  //Fourth line crosses first line and onward
            if(i >=4)
            {
                if(x[i-1] == x[i-3] && x[i] + x[i-4] >= x[i-2]) return true; // Fifth line meets first line and onward
            }
            if(i >=5)
            {
                if(x[i-2] - x[i-4] >= 0 && x[i] >= x[i-2] - x[i-4] && x[i-1] >= x[i-3] - x[i-5] && x[i-1] <= x[i-3]) return true;  // Sixth line crosses first line and onward
            }
        }
        return false;
    }

 

转载于:https://www.cnblogs.com/zmyvszk/p/5659041.html

我希望他能保留上个点点位并连线class FaceTracker: def __init__(self): self.known_encodings, self.known_names = load_face_data("face_data.json") self.current_faces = [] self.lock = threading.Lock() self.frame_queue = Queue(maxsize=1) self.running = True def recognition_thread(self): while self.running: frame = self.frame_queue.get() if frame is None: continue rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) face_locations = face_recognition.face_locations(rgb_frame) face_encodings = face_recognition.face_encodings(rgb_frame, face_locations) current = [] for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings): matches = face_recognition.compare_faces( np.array(self.known_encodings), face_encoding, tolerance=0.4 ) name = "未知" if True in matches: index = matches.index(True) name = self.known_names[index] center = ((left + right) // 2, (top + bottom) // 2) current.append((name, (left, top, right, bottom), center)) with self.lock: self.current_faces = current def run(self): cap = cv2.VideoCapture(0) cap.set(cv2.CAP_PROP_FPS, 30) # 启动识别线程 threading.Thread(target=self.recognition_thread, daemon=True).start() tracked_positions = {} while True: ret, frame = cap.read() if not ret: break frame = draw_edges(frame) # 更新识别线程 if self.frame_queue.empty(): self.frame_queue.put(frame.copy()) # 绘制人脸框和轨迹 with self.lock: for name, (left, top, right, bottom), center in self.current_faces: # 绘制中文姓名(需要PIL支持) cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2) cv2.putText(frame, name, (left + 6, bottom - 6), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1) # 运动轨迹跟踪 if name in tracked_positions: cv2.line(frame, tracked_positions[name], center, (255, 0, 0), 2) tracked_positions[name] = center # 边缘检测 direction = check_edge_crossing(center, frame.shape) if direction: save_record(name, direction) if name in tracked_positions: del tracked_positions[name] cv2.imshow('Face Tracking', frame) if cv2.waitKey(1) & 0xFF == ord('q'): self.running = False break cap.release() cv2.destroyAllWindows()
03-21
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