444 lines
17 KiB
Python
444 lines
17 KiB
Python
import sys
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import cv2
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import os
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import time
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from PyQt5.QtCore import QThread, pyqtSignal, QObject
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import face_recognition
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current_dir = os.path.dirname(__file__)
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share_code_dir = os.path.abspath(os.path.join(current_dir, '..', '..'))
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sys.path.append(share_code_dir)
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from print_color import *
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DEFAULT_VIDEO_SLEEP_MS = 20
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# 图像处理线程
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class ImageProcessingThread(QThread):
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processed_image_signal = pyqtSignal(object)
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def __init__(self):
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super().__init__()
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def run(self):
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while True:
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# 在这里添加图像处理代码
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# 这里暂时只是将图像传递给下一个线程
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time.sleep(0.01)
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self.processed_image_signal.emit(None)
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# 摄像头采集线程
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class CameraThread(QThread):
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image_signal = pyqtSignal(object)
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def __init__(self, camera_url, circuit_id: int = 0):
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super().__init__()
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self.camera_url = camera_url # 摄像头url地址, 整数 或者 字符串
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self.face_detection: bool = False
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if isinstance(camera_url, int):
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self.camera_url_str = str(camera_url)
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else:
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self.camera_url_str = camera_url
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self.cap: cv2.VideoCapture = None
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self.running: bool = True
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self.fps = 0
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self.cycle_limit = DEFAULT_VIDEO_SLEEP_MS
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self.cycle_ms = 0
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self.circuit_id = circuit_id # 摄像头对应的回路, 组合开关每一个回路都有对应的摄像头
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self.is_signal_connect = False
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self.is_emit = False
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self.set_video_cycle_ms(2000)
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def signal_connect(self, slot_func):
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self.image_signal.connect(slot_func)
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self.is_signal_connect = True
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def signal_disconnect(self):
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# 判断信号是否已连接
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if self.is_signal_connect:
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self.image_signal.disconnect()
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self.is_signal_connect = False
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def set_video_cycle_ms(self, cycle_ms: int = 10):
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if self.cycle_limit != cycle_ms:
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if cycle_ms <= DEFAULT_VIDEO_SLEEP_MS:
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self.cycle_limit = DEFAULT_VIDEO_SLEEP_MS
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else:
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self.cycle_limit = cycle_ms
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# 新建函数，改变函数延时方法
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def change_camera_url(self, camera_url: str):
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if self.circuit_id == camera_url:
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self.set_video_cycle_ms(DEFAULT_VIDEO_SLEEP_MS)
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else:
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self.set_video_cycle_ms(1000)
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def close(self):
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if self.cap == None: # 初始化一直未完成
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self.terminate()
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self.running = False
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def run(self):
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process_count = 0
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fps_time = 0
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while self.running == True:
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inform_msg = "cameral init start, url = " + self.camera_url_str
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print_inform_msg(inform_msg)
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try:
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if isinstance(self.camera_url, int):
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# 在 Windows 平台下，使用默认的 cv2.VideoCapture 接口
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self.cap = cv2.VideoCapture(self.camera_url, cv2.CAP_DSHOW)
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else:
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# 在 Linux 平台下，使用默认的 cv2.VideoCapture 接口
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self.cap = cv2.VideoCapture(self.camera_url)
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if self.cap != None:
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inform_msg = "cameral Init Success, url = " + self.camera_url_str
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self.is_emit = True
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print_inform_msg(inform_msg)
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else:
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inform_msg = "cameral connection timeout, url = " + self.camera_url_str
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print_inform_msg(inform_msg)
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except Exception as e:
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self.cap = None
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inform_msg = "cameral camera Init Fail, url = " + self.camera_url_str
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print_error_msg(inform_msg)
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base_time = time.time()
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while self.running and self.cap != None:
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# 延时20ms
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time.sleep(DEFAULT_VIDEO_SLEEP_MS / 1000)
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if self.cycle_ms + DEFAULT_VIDEO_SLEEP_MS < self.cycle_limit:
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self.cycle_ms += DEFAULT_VIDEO_SLEEP_MS
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self.cap.grab() # 抛弃多余的帧，保持最新帧
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continue
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else:
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self.cycle_ms = 0
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try:
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ret, frame = self.cap.read()
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cur_time = time.time()
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execution_time = cur_time - base_time
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base_time = cur_time
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process_count += 1
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fps_time += execution_time
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if fps_time >= 1:
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self.fps = process_count
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process_count = 0
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fps_time = 0
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if execution_time < 5:
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image_object: QObject = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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self.image_signal.emit(image_object)
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else: # 时间差大于5秒, 表示网络可能中断过, 退出并重新连接
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err_message = "cameral read timeout, url = " + self.camera_url_str
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print_error_msg(err_message)
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break
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except Exception as e:
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err_message = "cameral read timeout, url = " + self.camera_url_str
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self.is_emit = False
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print_error_msg(err_message)
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if self.running == True:
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time.sleep(2)
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break
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print_inform_msg("cameral connection End")
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time.sleep(0.01)
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if self.cap != None:
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self.cap.release()
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self.cap = None
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# - * - coding:utf - 8 - * -
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# import sys
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# import cv2
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# import os
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# import time
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# import subprocess
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# import face_recognition
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# import numpy as np
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# from PIL import Image, ImageDraw, ImageFont
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# from PyQt5.QtCore import QThread, pyqtSignal, QObject
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# from print_color import *
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# current_dir = os.path.dirname(__file__)
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# share_code_dir = os.path.abspath(os.path.join(current_dir, '..', '..'))
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# sys.path.append(share_code_dir)
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# DEFAULT_VIDEO_SLEEP_MS = 20
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# # 定义路径常量
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# npy_file_path = os.path.abspath(os.path.join(os.path.dirname(__file__), 'face_data/face_data.npy'))
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# image_folder_path = os.path.abspath(os.path.join(os.path.dirname(__file__), 'face_data'))
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# font_path = os.path.abspath(os.path.join(os.path.dirname(__file__), 'font/hanzi.ttc'))
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# def load_face_data():
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# face_data = {}
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# try:
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# face_data = np.load(npy_file_path, allow_pickle=True).item()
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# print_debug_msg(f"Loaded {len(face_data)} face encodings from {npy_file_path}.")
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# except Exception as e:
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# print_error_msg(f"Error loading face encodings: {e}")
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# current_images = [f for f in os.listdir(image_folder_path) if f.endswith('.jpg')]
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# current_image_set = set(os.path.splitext(f)[0] for f in current_images)
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# known_image_set = set(face_data.keys())
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# added_images = current_image_set - known_image_set
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# removed_images = known_image_set - current_image_set
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# for image_name in added_images:
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# image_path = os.path.join(image_folder_path, f"{image_name}.jpg")
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# try:
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# image = face_recognition.load_image_file(image_path)
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# face_encodings = face_recognition.face_encodings(image)
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# if face_encodings:
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# face_data[image_name] = face_encodings[0].tolist()
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# print_debug_msg(f"Added encoding for {image_name}.")
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# else:
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# print_warning_msg(f"[WARNING] No face detected in {image_name}.jpg.")
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# except Exception as e:
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# print_error_msg(f"[ERROR] Error processing {image_name}.jpg: {e}")
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# for removed_image in removed_images:
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# del face_data[removed_image]
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# print_debug_msg(f"Removed encoding for {removed_image}.")
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# np.save(npy_file_path, face_data)
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# print_debug_msg(f"Updated face data saved with {len(face_data)} entries.")
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# return face_data
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# class ImageProcessingThread(QThread):
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# processed_image_signal = pyqtSignal(object)
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# def __init__(self, frame=None):
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# super().__init__()
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# self.frame = frame # 添加 frame 参数
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# self.face_encodings = []
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# self.face_names = []
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# self.face_locations = []
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# self.face_data = load_face_data()
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# self.total_image_name = list(self.face_data.keys())
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# self.total_face_encoding = [np.array(self.face_data[name]) for name in self.total_image_name]
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# def process_frame(self, frame):
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# """实时处理帧，匹配已知人脸"""
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# self.face_locations = face_recognition.face_locations(frame)
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# face_encodings = face_recognition.face_encodings(frame, self.face_locations)
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# names = []
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# for face_encoding in face_encodings:
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# name = "Unknown"
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# face_distances = face_recognition.face_distance(self.total_face_encoding, face_encoding)
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# if face_distances.size > 0:
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# min_distance = np.min(face_distances)
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# best_match_index = np.argmin(face_distances)
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# if min_distance < self.dynamic_tolerance(): # 使用动态容忍度
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# name = self.total_image_name[best_match_index]
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# names.append(name)
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# self.face_names = names
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# return frame
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# def dynamic_tolerance(self):
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# # 动态调整容忍度，可以基于一些规则来调整，如帧内错误率、识别准确率等
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# error_rate = self.calculate_error_rate()
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# if error_rate > 0.1:
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# return 0.5 # 高容忍度以减少错误
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# else:
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# return 0.4 # 适中容忍度
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# def calculate_error_rate(self):
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# # 计算识别错误率，作为动态调整容忍度的依据
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# total_faces = len(self.total_face_encoding)
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# unmatched_faces = sum(1 for name in self.face_names if name == "Unknown")
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# return unmatched_faces / total_faces if total_faces > 0 else 0
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# def set_frame(self, frame):
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# self.frame = frame
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# def run(self):
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# while True:
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# if self.frame is not None:
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# self.process_frame(self.frame)
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# self.processed_image_signal.emit(self.frame)
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# time.sleep(0.01) # 控制帧率
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# # 摄像头采集线程
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# class CameraThread(QThread):
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# image_signal = pyqtSignal(object)
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# def __init__(self, camera_url, circuit_id: int = 0):
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# super().__init__()
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# self.camera_url = camera_url
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# self.face_detection: bool = True
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# if isinstance(camera_url, int):
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# self.camera_url_str = str(camera_url)
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# else:
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# self.camera_url_str = camera_url
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# self.cap: cv2.VideoCapture = None
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# self.running: bool = True
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# self.fps = 0
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# self.frame_count = 0
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# self.fps_time = 0
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# self.cycle_limit = DEFAULT_VIDEO_SLEEP_MS
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# self.cycle_ms = 0
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# self.circuit_id = circuit_id
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# self.is_signal_connect = False
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# self.is_emit = False
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# self.set_video_cycle_ms(2000)
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# def signal_connect(self, slot_func):
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# self.image_signal.connect(slot_func)
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# self.is_signal_connect = True
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# def signal_disconnect(self):
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# if self.is_signal_connect:
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# self.image_signal.disconnect()
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# self.is_signal_connect = False
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# def set_video_cycle_ms(self, cycle_ms: int = 10):
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# if self.cycle_limit != cycle_ms:
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# if cycle_ms <= DEFAULT_VIDEO_SLEEP_MS:
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# self.cycle_limit = DEFAULT_VIDEO_SLEEP_MS
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# else:
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# self.cycle_limit = cycle_ms
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# def change_camera_url(self, camera_url: str):
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# if self.circuit_id == camera_url:
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# self.set_video_cycle_ms(DEFAULT_VIDEO_SLEEP_MS)
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# else:
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# self.set_video_cycle_ms(1000)
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# def close(self):
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# if self.cap is None:
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# self.terminate()
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# self.running = False
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# def run(self):
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# process_count = 0
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# fps_time = 0
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# face_thread = ImageProcessingThread()
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# face_thread.start()
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# self.match_count = 0 # 初始化匹配帧数计数器
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# while self.running:
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# inform_msg = "Camera init start, URL = " + self.camera_url_str
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# print_inform_msg(inform_msg)
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# try:
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# self.cap = cv2.VideoCapture(self.camera_url) # 强制使用 BGR 格式
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# if self.cap.isOpened():
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# inform_msg = "Camera Init Success, URL = " + self.camera_url_str
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# self.is_emit = True
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# print_inform_msg(inform_msg)
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# else:
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# inform_msg = "Camera connection timeout, URL = " + self.camera_url_str
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# print_error_msg(inform_msg)
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# except Exception as e:
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# self.cap = None
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# inform_msg = "Camera Init Fail, URL = " + self.camera_url_str
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# print_error_msg(inform_msg)
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# base_time = time.time()
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# while self.running and self.cap:
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# time.sleep(DEFAULT_VIDEO_SLEEP_MS / 1000)
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# if self.cycle_ms + DEFAULT_VIDEO_SLEEP_MS < self.cycle_limit:
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# self.cycle_ms += DEFAULT_VIDEO_SLEEP_MS
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# self.cap.grab()
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# continue
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# else:
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# self.cycle_ms = 0
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# try:
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# ret, frame = self.cap.read()
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# if not ret:
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# break
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# face_thread.set_frame(frame)
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# cur_time = time.time()
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# execution_time = cur_time - base_time
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# base_time = cur_time
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# process_count += 1
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# fps_time += execution_time
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# if fps_time >= 1: # 每隔1秒重新计算 FPS
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# self.fps = process_count
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# process_count = 0
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# fps_time = 0
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# if execution_time < 5:
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# frame: QObject = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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# for (top, right, bottom, left), name in zip(face_thread.face_locations, face_thread.face_names):
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# if name != "Unknown": # 只绘制已匹配的结果
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# self.match_count += 1 # 计数成功匹配的帧
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# # cv2.rectangle(frame, (left, top), (right, bottom), (0, 255, 0), 2)
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# # cv2.putText(frame, name, (left + 6, bottom - 6), cv2.FONT_HERSHEY_DUPLEX, 1.0, (255, 255, 255), 1)
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# # 将OpenCV图像转换为PIL图像格式（注意要转换色彩空间，OpenCV是BGR，PIL是RGB）
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# pil_image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
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# draw = ImageDraw.Draw(pil_image)
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# # 这里选择合适的中文字体文件路径，比如系统自带的宋体（不同系统路径可能有差异），并设置字体大小
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# font = ImageFont.truetype(font_path, 15)
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# # 在指定位置绘制中文
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# draw.text((left + 12, bottom + 12), name, font=font, fill=(255, 255, 255))
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# # 再将PIL图像转换回OpenCV图像格式
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# frame = cv2.cvtColor(np.array(pil_image), cv2.COLOR_RGB2BGR)
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# cv2.rectangle(frame, (left, top), (right, bottom), (0, 255, 0), 2)
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# else:
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# name = "未在人脸库中，无操作权限"
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# pil_image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
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# draw = ImageDraw.Draw(pil_image)
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# #设置字体大小
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# font = ImageFont.truetype(font_path, 15)
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# # 在指定位置绘制中文
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# draw.text((left + 12, bottom + 12), name, font=font, fill=(255, 255, 255))
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# # 再将PIL图像转换回OpenCV图像格式
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# frame = cv2.cvtColor(np.array(pil_image), cv2.COLOR_RGB2BGR)
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# cv2.rectangle(frame, (left, top), (right, bottom), (255, 0, 0), 2)
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# self.image_signal.emit(frame)
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# else:
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# err_message = "Camera read timeout, URL = " + self.camera_url_str
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# print_error_msg(err_message)
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# break
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# except Exception as e:
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# err_message = "Camera read exception, URL = " + self.camera_url_str
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# print_error_msg(err_message)
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# if self.running:
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# time.sleep(2)
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# break
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# if self.cap:
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# self.cap.release()
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# self.cap = None
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# print_inform_msg("Camera connection ended")
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# time.sleep(0.01)
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