335. Self Crossing

博客围绕判断路径是否自交叉的问题展开,给定正数数组表示移动距离,方向按逆时针变化。提出用Java实现的单遍算法,分析了自交叉的三种场景,如第四行与第一行交叉等,并给出参考链接。

You are given an array x of n positive numbers. You start at point (0,0) and moves x[0] metres to the north, then x[1] metres to the west, x[2] metres to the south, x[3] metres to the east and so on. In other words, after each move your direction changes counter-clockwise.

Write a one-pass algorithm with O(1) extra space to determine, if your path crosses itself, or not.

 

Example 1:

┌───┐
│   │
└───┼──>
    │

Input: [2,1,1,2]
Output: true

Example 2:

┌──────┐
│      │
│
│
└────────────>

Input: [1,2,3,4]
Output: false 

Example 3:

┌───┐
│   │
└───┼>

Input: [1,1,1,1]
Output: true 

 

Approach #1: Math. [Java]

class Solution {
    public boolean isSelfCrossing(int[] x) {
        if (x.length < 4) return false;
        for (int i = 3; i < x.length; ++i) {
            if (x[i] >= x[i-2] && x[i-3] >= x[i-1]) return true;
            if (i >= 4) {
                if (x[i-3] == x[i-1] && x[i] + x[i-4] >= x[i-2]) return true;
            }
            if (i >= 5) {
                if (x[i-2] >= x[i-4] && x[i-1] + x[i-5] >= x[i-3] && x[i] >= x[i-2] - x[i-4] && x[i-1] <= x[i-3]) 
                    return true;
            }
        }
        return false;
    }
}

  

Analysis:

Categarize the self-crossing scenarios, there are 3 of them:

1. Fourth line crosses first line and works for fifth line crosses second 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.

 

Reference:

https://leetcode.com/problems/self-crossing/discuss/79131/Java-Oms-with-explanation

 

转载于:https://www.cnblogs.com/ruruozhenhao/p/10809104.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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