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:

Given x = [2, 1, 1, 2],
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?   ?
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    ?

Return true (self crossing)

Example 2:

Given x = [1, 2, 3, 4],
????????
?      ?
?
?
?????????????>

Return false (not self crossing)

Example 3:

Given x = [1, 1, 1, 1],
?????
?   ?
?????>

Return true (self crossing)
分为三种情况分析:

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

这3种情况包含了所有可能cross的情况。

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

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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