测试帖子2
2025-08-07 18:01:52
发布于：浙江
6阅读
0回复
0点赞
这个文件夹在人脸识别中！
photo文件
import cv2
import numpy as np
from PIL import Image, ImageDraw
import tkinter as tk
from threading import Thread, Event
import queue
import time
class FaceDetectorApp:
def __init__(self):
# 初始化人脸检测器
self.face_cascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
# 线程控制和数据共享
self.camera_running = Event()
self.camera_running.set()
self.frame_queue = queue.Queue(maxsize=5) # 原始帧队列
# 检测参数优化
self.detect_interval = 3 # 每3帧检测一次人脸
self.face_tracker = {} # 人脸跟踪器
self.track_id = 0 # 跟踪ID计数器
def run_camera(self):
"""运行摄像头捕获和人脸检测线程"""
cap = cv2.VideoCapture("rtsp://192.168.1.120:8554/test")
# 降低分辨率以提高性能
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)
frame_count = 0
while self.camera_running.is_set():
ret, frame = cap.read()
if not ret:
break
# 保存原始帧用于截图
try:
self.frame_queue.put(frame, block=False)
except queue.Full:
self.frame_queue.get()
self.frame_queue.put(frame, block=False)
# 人脸检测优化：隔帧检测
frame_count += 1
if frame_count % self.detect_interval == 0:
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# 优化检测参数
faces = self.face_cascade.detectMultiScale(
gray,
scaleFactor=1.2, # 稍大的缩放因子，提高速度
minNeighbors=5, # 平衡误检和漏检
#minSize=(40, 40), # 忽略过小的人脸
#flags=cv2.CASCADE_SCALE_IMAGE
)
# 更新人脸跟踪信息
self.update_face_tracking(faces)
# 绘制人脸框（使用跟踪结果，保持框的连续性）
display_frame = frame.copy()
for face_data in self.face_tracker.values():
x, y, w, h = face_data['box']
cv2.rectangle(display_frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
cv2.imshow('RTSP Stream', display_frame)
if cv2.waitKey(1) == 27: # ESC键退出
break
cap.release()
cv2.destroyAllWindows()
def update_face_tracking(self, faces):
"""更新人脸跟踪信息，保持检测框的连续性"""
current_faces = [(x, y, w, h) for (x, y, w, h) in faces]
matched_ids = []
# 匹配现有跟踪ID
for face in current_faces:
x, y, w, h = face
center_x, center_y = x + w//2, y + h//2
best_match = None
min_distance = float('inf')
# 寻找最接近的历史人脸
for track_id, data in self.face_tracker.items():
if track_id in matched_ids:
continue
tx, ty, tw, th = data['box']
t_center_x, t_center_y = tx + tw//2, ty + th//2
# 计算中心距离
distance = np.sqrt((center_x - t_center_x)** 2 + (center_y - t_center_y)**2)
# 如果距离小于阈值且尺寸相近，认为是同一人脸
if distance < max(w, h) * 0.5 and distance < min_distance:
min_distance = distance
best_match = track_id
# 更新匹配的人脸信息
if best_match is not None:
self.face_tracker[best_match]['box'] = face
self.face_tracker[best_match]['age'] = 0 # 重置消失计时器
matched_ids.append(best_match)
else:
# 创建新跟踪ID
self.track_id += 1
self.face_tracker[self.track_id] = {
'box': face,
'age': 0 # 记录未被检测到的帧数
}
# 增加未匹配人脸的消失计时器
for track_id in list(self.face_tracker.keys()):
if track_id not in matched_ids:
self.face_tracker[track_id]['age'] += 1
# 如果连续多帧未检测到，移除跟踪
if self.face_tracker[track_id]['age'] > 5:
del self.face_tracker[track_id]
def take_screenshot(self):
"""截取当前画面并保存"""
try:
frame = self.frame_queue.get(block=False)
if frame is not None:
# 转换为PIL图像
rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
pil_img = Image.fromarray(rgb_frame)
draw = ImageDraw.Draw(pil_img)
# 保存截图
pil_img.save("screenshot.png", "PNG")
print("截图已保存为 screenshot.png")
return True
except queue.Empty:
print("无可用画面")
return False
def start_gui(self):
"""启动Tkinter图形界面"""
root = tk.Tk()
root.title("机械狗人脸检测系统")
root.geometry("400x200")
# 拍照按钮
btn_capture = tk.Button(
root, text="拍照", font=("Arial", 14),
command=self.take_screenshot
)
btn_capture.pack(padx=20, pady=20)
# 退出按钮
btn_quit = tk.Button(
root, text="退出", font=("Arial", 14),
command=lambda: [self.camera_running.clear(), root.destroy()]
)
btn_quit.pack(padx=20, pady=10)
# 窗口关闭时的处理
root.protocol("WM_DELETE_WINDOW", lambda: [self.camera_running.clear(), root.destroy()])
root.mainloop()
if __name__ == "__main__":
app = FaceDetectorApp()
# 启动摄像头线程
camera_thread = Thread(target=app.run_camera, daemon=True)
camera_thread.start()
# 启动GUI线程
app.start_gui()
haarcascade_frontalface_default文件
[haarcascade_frontalface_default文件](file:///C:/Users/Administrator/Desktop/0804%E7%B4%A0%E6%9D%90/AI%E4%BA%BA%E8%84%B8%E5%AF%B9%E6%AF%94%E5%AD%A6%E7%94%9F%E7%B4%A0%E6%9D%90/%E6%9C%BA%E5%99%A8%E7%8B%97%E6%8B%8D%E6%91%84%E7%85%A7%E7%89%87%E5%92%8C%E4%BA%BA%E8%84%B8%E8%AF%86%E5%88%AB/haarcascade_frontalface_default.xml)
人脸识别操作说明：
首先按F5运行main程序，在弹出的界面中输入第一个要对比的图片的名称及后缀名称（如man1.jpg），按下空格，再输入第二个要对比的图片的名称及后缀名称，最后按下空格，完成图片相似度输出。
这里空空如也
有帮助，赞一个