335. Self Crossing

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]

---------
|       |
|       |
------------------>
        |
Input: true 
Explanation: self crossing

Example 2:

Input: [1,2,3,4]

---------
|       |
|
|
--------------->

Output: false 
Explanation: not self crossing

Example 3:

Input: [1,1,1,1]

---------
|       |
|       |
-------->

Output: true 
Explanation: self crossing


There will be a self crossing problem when the following situations happens.

All the situations’s directions to roate are counter-clockwise.

Situation 1:

这里写图片描述
Situation 2:

这里写图片描述
Situation 3:
这里写图片描述

Then we code arrcording to our above analysis.

bool isSelfCrossing(vector<int>& x)
{
    int size = x.size();
    if(size <= 3)return false;
    for(int i = 3; i < size; i++)
    {
        if(x[i] >= x[i -2] && x[i - 1] <= x[i - 3])return true;
        if(i >= 4 && x[i - 1] == x[i - 3] && x[i - 2] <= x[i] + x[i - 4])return true;
        if(i >= 5 && x[i - 3] >= x[i - 1] && x[i - 2] >= x[i - 4] && x[i - 2] <= x[i - 4] + x[i] && x[i - 3] <= x[i - 5] + x[i -1])return true;
    }   
    return false;
}
int main()
{
    return 0;
}

The third ”if” in above code includes x[i - 3] >= x[i - 1] and x[i - 2] >= x[i - 4], which is in case of the following situations.
这里写图片描述

这里写图片描述

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()
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03-21
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