To show a custom area for toolbar.

本文介绍了一种在窗口中根据鼠标位置动态显示和隐藏自定义工具栏的方法。通过监听鼠标移动事件并判断鼠标位置来决定工具栏的显示与否,同时实现了工具栏的布局和按钮设置。

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To show a custom area for toolbar.

public void postWindowOpen() {
//...
}

//Add to postWindowOpen()
final IWorkbenchWindow window = getWindowConfigurer().getWindow();

Listener listener = new Listener() {
private Shell toolbar;

public void handleEvent(Event event) {
Control control = (Control) event.widget;
Point mouse = control.toDisplay(event.x, event.y);
final Shell shell = window.getShell();
Point location = shell.getLocation();
Point size = shell.getSize();
int xIndent = 2;
int yIndent = 24;
int height = 30;
if (mouse.x > location.x
&& mouse.y > location.y + yIndent
&& location.x + size.x > mouse.x
&& location.y + yIndent + height > mouse.y) {
if (toolbar == null) {
toolbar = new Shell(shell, SWT.NO_TRIM);
toolbar.setSize(size.x - 2 * xIndent, height);
toolbar.setLocation(location.x + xIndent, location.y + yIndent);
GridLayout layout = new GridLayout(24, false);
layout.marginLeft = 0;
layout.marginTop = 0;
toolbar.setLayout(layout);
new Button(toolbar, SWT.PUSH).setText("action 1");
new Button(toolbar, SWT.PUSH).setText("action 2");
new Button(toolbar, SWT.PUSH).setText("action 3");
toolbar.setVisible(true);
}
} else {
if (toolbar != null) {
toolbar.dispose();
toolbar = null;
}
}
}
};

window.getShell().getDisplay().addFilter(SWT.MouseMove, listener);
# -*- coding: utf-8 -*- import sys import os import cv2 import numpy as np import time from PyQt5.QtWidgets import ( QApplication, QMainWindow, QPushButton, QWidget, QVBoxLayout, QHBoxLayout, QMessageBox, QLabel, QFileDialog, QToolBar, QComboBox, QStatusBar, QGroupBox, QSlider, QDockWidget, QProgressDialog, QLineEdit, QRadioButton, QGridLayout, QSpinBox ) from PyQt5.QtCore import QRect, Qt, QSettings, QThread, pyqtSignal from CamOperation_class import CameraOperation sys.path.append("D:\\海康\\MVS\\Development\\Samples\\Python\\BasicDemo") import ctypes from ctypes import cast, POINTER from datetime import datetime import logging import socket import serial import skimage import platform from CameraConstants import * import threading import time import sys class ManagedThread(threading.Thread): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self._stop_event = threading.Event() # 设置为非守护线程 self.daemon = False def stop(self): """安全停止线程""" self._stop_event.set() def should_stop(self): """检查是否应该停止""" return self._stop_event.is_set() def worker(): """线程工作函数""" try: while not threading.current_thread().should_stop(): # 模拟工作 time.sleep(1) # 安全输出 sys.stdout.write("Working...\n") except Exception as e: # 避免在关闭时使用stderr pass def main(): # 创建并启动线程 threads = [] for _ in range(3): t = ManagedThread(target=worker) t.start() threads.append(t) try: # 主程序逻辑 time.sleep(5) finally: # 安全停止所有线程 for t in threads: t.stop() for t in threads: t.join(timeout=2.0) # 设置超时避免无限等待 # 确保所有输出完成 sys.stdout.flush() sys.stderr.flush() # 在导入部分添加 from CameraParams_header import ( MV_GIGE_DEVICE, MV_USB_DEVICE, MV_GENTL_CAMERALINK_DEVICE, MV_GENTL_CXP_DEVICE, MV_GENTL_XOF_DEVICE ) # ===== 路径修复 ===== def fix_sdk_path(): """修复海康SDK的加载路径""" if getattr(sys, 'frozen', False): # 打包模式 base_path = sys._MEIPASS mvimport_path = os.path.join(base_path, "MvImport") if mvimport_path not in sys.path: sys.path.insert(0, mvimport_path) if sys.platform == 'win32': os.environ['PATH'] = base_path + os.pathsep + os.environ['PATH'] try: ctypes.WinDLL(os.path.join(base_path, "MvCamCtrldll.dll")) except OSError as e: print(f"核心DLL加载失败: {e}") sys.exit(1) else: # 开发模式 sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) # 立即执行路径修复 fix_sdk_path() # ===== 正确导入SDK模块 ===== try: from MvImport.MvCameraControl_class import MvCamera print("成功导入MvCamera类") from CameraParams_header import * from MvErrorDefine_const import * except ImportError as e: print(f"SDK导入失败: {e}") sys.exit(1) # 配置日志系统 logging.basicConfig( level=logging.DEBUG, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', handlers=[ logging.FileHandler("cloth_inspection_debug.log"), logging.StreamHandler() ] ) logging.info("布料印花检测系统启动") # 全局变量 current_sample_path = "" detection_history = [] isGrabbing = False isOpen = False obj_cam_operation = None frame_monitor_thread = None sensor_monitor_thread = None sensor_controller = None MV_OK = 0 MV_E_CALLORDER = -2147483647 # ==================== 传感器通讯模块 ==================== class SensorController: def __init__(self): self.connected = False self.running = False self.connection = None def connect(self, config): try: if config['type'] == 'serial': self.connection = serial.Serial( port=config['port'], baudrate=config['baudrate'], timeout=config.get('timeout', 1.0) ) else: self.connection = socket.socket(socket.AF_INET, socket.SOCK_STREAM) self.connection.connect((config['ip'], config['port'])) self.connection.settimeout(config.get('timeout', 1.0)) self.connected = True self.running = True logging.info(f"传感器连接成功: {config}") return True except Exception as e: logging.error(f"传感器连接失败: {str(e)}") return False def disconnect(self): if self.connection: try: self.connection.close() except: pass self.connection = None self.connected = False self.running = False logging.info("传感器已断开") def read_data(self): if not self.connected: return None return { 'tension': np.random.uniform(10.0, 20.0), 'speed': np.random.uniform(1.0, 5.0), 'temperature': np.random.uniform(20.0, 30.0), 'humidity': np.random.uniform(40.0, 60.0) } def wait_for_material(self, delay_seconds=0): if not self.connected: logging.warning("未连接传感器,跳过等待") return False logging.info(f"等待布料到达,延迟 {delay_seconds} 秒") start_time = time.time() while time.time() - start_time < delay_seconds: QThread.msleep(100) if not self.running: return False logging.info("布料已到位,准备拍摄") return True class SensorMonitorThread(QThread): data_updated = pyqtSignal(dict) def __init__(self, sensor_controller): super().__init__() self.sensor_controller = sensor_controller self.running = True def run(self): while self.running: if self.sensor_controller and self.sensor_controller.connected: try: data = self.sensor_controller.read_data() if data: self.data_updated.emit(data) except Exception as e: logging.error(f"传感器数据读取错误: {str(e)}") QThread.msleep(500) def stop(self): self.running = False self.wait(2000) def wait_for_material(self, delay_seconds): return self.sensor_controller.wait_for_material(delay_seconds) # ==================== 相机帧监控线程 ==================== class FrameMonitorThread(QThread): frame_status = pyqtSignal(str) # 用于发送状态消息的信号 def __init__(self, cam_operation): super().__init__() self.cam_operation = cam_operation self.running = True self.frame_count = 0 self.last_time = time.time() def run(self): """监控相机帧状态的主循环""" while self.running: try: if self.cam_operation and self.cam_operation.is_grabbing: # 获取帧统计信息 frame_info = self.get_frame_info() if frame_info: fps = frame_info.get('fps', 0) dropped = frame_info.get('dropped', 0) status = f"FPS: {fps:.1f} | 丢帧: {dropped}" self.frame_status.emit(status) else: self.frame_status.emit("取流中...") else: self.frame_status.emit("相机未取流") except Exception as e: self.frame_status.emit(f"监控错误: {str(e)}") # 每500ms检查一次 QThread.msleep(500) def stop(self): """停止监控线程""" self.running = False self.wait(1000) # 等待线程结束 def calculate_fps(self): """计算当前帧率""" current_time = time.time() elapsed = current_time - self.last_time if elapsed > 0: fps = self.frame_count / elapsed self.frame_count = 0 self.last_time = current_time return fps return 0 def get_frame_info(self): """获取帧信息""" try: # 更新帧计数 self.frame_count += 1 # 返回帧信息 return { 'fps': self.calculate_fps(), 'dropped': 0 # 实际应用中需要从相机获取真实丢帧数 } except Exception as e: logging.error(f"获取帧信息失败: {str(e)}") return None # ==================== 优化后的检测算法 ==================== def enhanced_check_print_quality(sample_image_path, test_image, threshold=0.05, sensor_data=None): if sensor_data: speed_factor = min(1.0 + sensor_data['speed'] * 0.1, 1.5) env_factor = 1.0 + abs(sensor_data['temperature'] - 25) * 0.01 + abs(sensor_data['humidity'] - 50) * 0.005 adjusted_threshold = threshold * speed_factor * env_factor logging.info(f"根据传感器数据调整阈值: 原始={threshold:.4f}, 调整后={adjusted_threshold:.4f}") else: adjusted_threshold = threshold try: sample_img_data = np.fromfile(sample_image_path, dtype=np.uint8) sample_image = cv2.imdecode(sample_img_data, cv2.IMREAD_GRAYSCALE) if sample_image is None: logging.error(f"无法解码样本图像: {sample_image_path}") return None, None, None except Exception as e: logging.exception(f"样本图像读取异常: {str(e)}") return None, None, None if len(test_image.shape) == 3: test_image_gray = cv2.cvtColor(test_image, cv2.COLOR_BGR2GRAY) else: test_image_gray = test_image.copy() sample_image = cv2.GaussianBlur(sample_image, (5, 5), 0) test_image_gray = cv2.GaussianBlur(test_image_gray, (5, 5), 0) try: orb = cv2.ORB_create(nfeatures=200) keypoints1, descriptors1 = orb.detectAndCompute(sample_image, None) keypoints2, descriptors2 = orb.detectAndCompute(test_image_gray, None) if descriptors1 is None or descriptors2 is None: logging.warning("无法提取特征描述符,跳过配准") aligned_sample = sample_image else: bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True) matches = bf.match(descriptors1, descriptors2) matches = sorted(matches, key=lambda x: x.distance) if len(matches) > 10: src_pts = np.float32([keypoints1[m.queryIdx].pt for m in matches]).reshape(-1, 1, 2) dst_pts = np.float32([keypoints2[m.trainIdx].pt for m in matches]).reshape(-1, 1, 2) H, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0) if H is not None: aligned_sample = cv2.warpPerspective( sample_image, H, (test_image_gray.shape[1], test_image_gray.shape[0]) ) logging.info("图像配准成功,使用配准后样本") else: aligned_sample = sample_image logging.warning("无法计算单应性矩阵,使用原始样本") else: aligned_sample = sample_image logging.warning("特征点匹配不足,跳过图像配准") except Exception as e: logging.error(f"图像配准失败: {str(e)}") aligned_sample = sample_image try: if aligned_sample.shape != test_image_gray.shape: test_image_gray = cv2.resize(test_image_gray, (aligned_sample.shape[1], aligned_sample.shape[0])) except Exception as e: logging.error(f"图像调整大小失败: {str(e)}") return None, None, None try: from skimage.metrics import structural_similarity as compare_ssim ssim_score, ssim_diff = compare_ssim( aligned_sample, test_image_gray, full=True, gaussian_weights=True, data_range=255 ) except ImportError: from skimage.measure import compare_ssim ssim_score, ssim_diff = compare_ssim( aligned_sample, test_image_gray, full=True, gaussian_weights=True ) except Exception as e: logging.error(f"SSIM计算失败: {str(e)}") abs_diff = cv2.absdiff(aligned_sample, test_image_gray) ssim_diff = abs_diff.astype(np.float32) / 255.0 ssim_score = 1.0 - np.mean(ssim_diff) ssim_diff = (1 - ssim_diff) * 255 abs_diff = cv2.absdiff(aligned_sample, test_image_gray) combined_diff = cv2.addWeighted(ssim_diff.astype(np.uint8), 0.7, abs_diff, 0.3, 0) _, thresholded = cv2.threshold(combined_diff, 30, 255, cv2.THRESH_BINARY) kernel = np.ones((3, 3), np.uint8) thresholded = cv2.morphologyEx(thresholded, cv2.MORPH_OPEN, kernel) thresholded = cv2.morphologyEx(thresholded, cv2.MORPH_CLOSE, kernel) diff_pixels = np.count_nonzero(thresholded) total_pixels = aligned_sample.size diff_ratio = diff_pixels / total_pixels is_qualified = diff_ratio <= adjusted_threshold marked_image = cv2.cvtColor(test_image_gray, cv2.COLOR_GRAY2BGR) marked_image[thresholded == 255] = [0, 0, 255] labels = skimage.measure.label(thresholded) properties = skimage.measure.regionprops(labels) for prop in properties: if prop.area > 50: y, x = prop.centroid cv2.putText(marked_image, f"Defect", (int(x), int(y)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 255), 1) return is_qualified, diff_ratio, marked_image # ==================== 传感器控制的质量检测流程 ==================== def sensor_controlled_check(): global isGrabbing, obj_cam_operation, current_sample_path, detection_history, sensor_controller logging.info("质量检测启动") sensor_data = None if sensor_controller and sensor_controller.connected: sensor_data = sensor_controller.read_data() if not sensor_data: QMessageBox.warning(mainWindow, "传感器警告", "无法读取传感器数据,将使用默认参数", QMessageBox.Ok) else: logging.info("未连接传感器,使用默认参数检测") check_print_with_sensor(sensor_data) def check_print_with_sensor(sensor_data=None): global isGrabbing, obj_cam_operation, current_sample_path, detection_history logging.info("检测印花质量按钮按下") if not isGrabbing: QMessageBox.warning(mainWindow, "错误", "请先开始取流并捕获图像!", QMessageBox.Ok) return if not obj_cam_operation: QMessageBox.warning(mainWindow, "错误", "相机未正确初始化!", QMessageBox.Ok) return if not current_sample_path or not os.path.exists(current_sample_path): QMessageBox.warning(mainWindow, "错误", "请先设置有效的标准样本图像!", QMessageBox.Ok) return progress = QProgressDialog("正在检测...", "取消", 0, 100, mainWindow) progress.setWindowModality(Qt.WindowModal) progress.setValue(10) try: test_image = obj_cam_operation.get_current_frame() progress.setValue(30) if test_image is None: QMessageBox.warning(mainWindow, "错误", "无法获取当前帧图像!", QMessageBox.Ok) return diff_threshold = mainWindow.sliderDiffThreshold.value() / 100.0 logging.info(f"使用差异度阈值: {diff_threshold}") progress.setValue(50) is_qualified, diff_ratio, marked_image = enhanced_check_print_quality( current_sample_path, test_image, threshold=diff_threshold, sensor_data=sensor_data ) progress.setValue(70) if is_qualified is None: QMessageBox.critical(mainWindow, "检测错误", "检测失败,请检查日志", QMessageBox.Ok) return logging.info(f"检测结果: 合格={is_qualified}, 差异={diff_ratio}") progress.setValue(90) update_diff_display(diff_ratio, is_qualified) result_text = f"印花是否合格: {'合格' if is_qualified else '不合格'}\n差异占比: {diff_ratio*100:.2f}%\n阈值: {diff_threshold*100:.2f}%" QMessageBox.information(mainWindow, "检测结果", result_text, QMessageBox.Ok) if marked_image is not None: cv2.imshow("缺陷标记结果", marked_image) cv2.waitKey(0) cv2.destroyAllWindows() detection_result = { 'timestamp': datetime.now(), 'qualified': is_qualified, 'diff_ratio': diff_ratio, 'threshold': diff_threshold, 'sensor_data': sensor_data if sensor_data else {} } detection_history.append(detection_result) update_history_display() progress.setValue(100) except Exception as e: logging.exception("印花检测失败") QMessageBox.critical(mainWindow, "检测错误", f"检测过程中发生错误: {str(e)}", QMessageBox.Ok) finally: progress.close() def update_diff_display(diff_ratio, is_qualified): mainWindow.lblCurrentDiff.setText(f"当前差异度: {diff_ratio*100:.2f}%") if is_qualified: mainWindow.lblDiffStatus.setText("状态: 合格") mainWindow.lblDiffStatus.setStyleSheet("color: green; font-size: 12px;") else: mainWindow.lblDiffStatus.setText("状态: 不合格") mainWindow.lblDiffStatus.setStyleSheet("color: red; font-size: 12px;") def update_diff_threshold(value): mainWindow.lblDiffValue.setText(f"{value}%") def save_sample_image(): global isGrabbing, obj_cam_operation, current_sample_path if not isGrabbing: QMessageBox.warning(mainWindow, "错误", "请先开始取流并捕获图像!", QMessageBox.Ok) return # 检查是否有可用帧 if not obj_cam_operation.is_frame_available(): QMessageBox.warning(mainWindow, "无有效图像", "未捕获到有效图像,请检查相机状态!", QMessageBox.Ok) return settings = QSettings("ClothInspection", "CameraApp") last_dir = settings.value("last_save_dir", os.path.join(os.getcwd(), "captures")) timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") default_filename = f"sample_{timestamp}" file_path, selected_filter = QFileDialog.getSaveFileName( mainWindow, "保存标准样本图像", os.path.join(last_dir, default_filename), "BMP Files (*.bmp);;PNG Files (*.png);;JPEG Files (*.jpg);;所有文件 (*)", options=QFileDialog.DontUseNativeDialog ) if not file_path: return file_extension = os.path.splitext(file_path)[1].lower() if not file_extension: if "BMP" in selected_filter: file_path += ".bmp" elif "PNG" in selected_filter: file_path += ".png" elif "JPEG" in selected_filter or "JPG" in selected_filter: file_path += ".jpg" else: file_path += ".bmp" file_extension = os.path.splitext(file_path)[1].lower() format_mapping = {".bmp": "bmp", ".png": "png", ".jpg": "jpg", ".jpeg": "jpg"} save_format = format_mapping.get(file_extension) if not save_format: QMessageBox.warning(mainWindow, "错误", "不支持的文件格式!", QMessageBox.Ok) return directory = os.path.dirname(file_path) if directory and not os.path.exists(directory): try: os.makedirs(directory, exist_ok=True) except OSError as e: QMessageBox.critical(mainWindow, "目录创建错误", f"无法创建目录 {directory}: {str(e)}", QMessageBox.Ok) return try: ret = obj_cam_operation.save_image(file_path, save_format) if ret != MV_OK: strError = f"保存样本图像失败: {hex(ret)}" QMessageBox.warning(mainWindow, "错误", strError, QMessageBox.Ok) else: QMessageBox.information(mainWindow, "成功", f"标准样本已保存至:\n{file_path}", QMessageBox.Ok) current_sample_path = file_path update_sample_display() settings.setValue("last_save_dir", os.path.dirname(file_path)) except Exception as e: QMessageBox.critical(mainWindow, "异常错误", f"保存图像时发生错误: {str(e)}", QMessageBox.Ok) def preview_sample(): global current_sample_path if not current_sample_path or not os.path.exists(current_sample_path): QMessageBox.warning(mainWindow, "错误", "请先设置有效的标准样本图像!", QMessageBox.Ok) return try: img_data = np.fromfile(current_sample_path, dtype=np.uint8) sample_img = cv2.imdecode(img_data, cv2.IMREAD_COLOR) if sample_img is None: raise Exception("无法加载图像") cv2.imshow("标准样本预览", sample_img) cv2.waitKey(0) cv2.destroyAllWindows() except Exception as e: QMessageBox.warning(mainWindow, "错误", f"预览样本失败: {str(e)}", QMessageBox.Ok) def update_sample_display(): global current_sample_path if current_sample_path: mainWindow.lblSamplePath.setText(f"当前样本: {os.path.basename(current_sample_path)}") mainWindow.lblSamplePath.setToolTip(current_sample_path) mainWindow.bnPreviewSample.setEnabled(True) else: mainWindow.lblSamplePath.setText("当前样本: 未设置样本") mainWindow.bnPreviewSample.setEnabled(False) def update_history_display(): global detection_history mainWindow.cbHistory.clear() for i, result in enumerate(detection_history[-10:]): timestamp = result['timestamp'].strftime("%H:%M:%S") status = "合格" if result['qualified'] else "不合格" ratio = f"{result['diff_ratio']*100:.2f}%" mainWindow.cbHistory.addItem(f"[极客{timestamp}] {status} - 差异: {ratio}") def TxtWrapBy(start_str, end, all): start = all.find(start_str) if start >= 0: start += len(start_str) end = all.find(end, start) if end >= 0: return all[start:end].strip() def ToHexStr(num): if not isinstance(num, int): try: num = int(num) except: return f"<非整数:{type(num)}>" chaDic = {10: 'a', 11: 'b', 12: 'c', 13: 'd', 14: 'e', 15: 'f'} hexStr = "" if num < 0: num = num + 2 ** 32 while num >= 16: digit = num % 16 hexStr = chaDic.get(digit, str(digit)) + hexStr num //= 16 hexStr = chaDic.get(num, str(num)) + hexStr return "0x" + hexStr def decoding_char(c_ubyte_value): c_char_p_value = ctypes.cast(c_ubyte_value, ctypes.c_char_p) try: decode_str = c_char_p_value.value.decode('gbk') except UnicodeDecodeError: decode_str = str(c_char_p_value.value) return decode_str def enum_devices(): global deviceList, obj_cam_operation # 使用正确的常量方式 n_layer_type = ( MV_GIGE_DEVICE | MV_USB_DEVICE | MV_GENTL_CAMERALINK_DEVICE | MV_GENTL_CXP_DEVICE | MV_GENTL_XOF_DEVICE ) # 正确创建设备列表 deviceList = MV_CC_DEVICE_INFO_LIST() # 调用枚举函数 ret = MvCamera.MV_CC_EnumDevices(n_layer_type, deviceList) if ret != 0: strError = "Enum devices fail! ret = :" + ToHexStr(ret) QMessageBox.warning(mainWindow, "Error", strError, QMessageBox.Ok) return ret if deviceList.nDeviceNum == 0: QMessageBox.warning(mainWindow, "Info", "Find no device", QMessageBox.Ok) return ret print(f"Find {deviceList.nDeviceNum} devices!") # 处理设备信息 devList = [] for i in range(0, deviceList.nDeviceNum): # 获取设备信息指针 device_info = deviceList.pDeviceInfo[i] # 转换为正确的结构体类型 mvcc_dev_info = cast(device_info, POINTER(MV_CC_DEVICE_INFO)).contents # 根据设备类型提取信息 - 关键修复:使用正确的结构体名称 if mvcc_dev_info.nTLayerType == MV_GIGE_DEVICE: # 处理GigE设备信息 - 使用正确的stGigEInfo try: # 尝试访问新版SDK的结构体 st_gige_info = mvcc_dev_info.SpecialInfo.stGigEInfo except AttributeError: try: # 尝试访问旧版SDK的结构体 st_gige_info = mvcc_dev_info.SpecialInfo.stGEInfo except AttributeError: # 如果两种结构体都不存在,使用默认值 devList.append(f"[{i}]GigE: Unknown Device") continue user_defined_name = decoding_char(st_gige_info.chUserDefinedName) model_name = decoding_char(st_gige_info.chModelName) nip1 = ((st_gige_info.nCurrentIp & 0xff000000) >> 24) nip2 = ((st_gige_info.nCurrentIp & 0x00ff0000) >> 16) nip3 = ((st_gige_info.nCurrentIp & 0x0000ff00) >> 8) nip4 = (st_gige_info.nCurrentIp & 0x000000ff) devList.append(f"[{i}]GigE: {user_defined_name} {model_name}({nip1}.{nip2}.{nip3}.{nip4})") elif mvcc_dev_info.nTLayerType == MV_USB_DEVICE: # 处理USB设备信息 try: st_usb_info = mvcc_dev_info.SpecialInfo.stUsb3VInfo except AttributeError: try: st_usb_info = mvcc_dev_info.SpecialInfo.stUsbInfo except AttributeError: devList.append(f"[{i}]USB: Unknown Device") continue user_defined_name = decoding_char(st_usb_info.chUserDefinedName) model_name = decoding_char(st_usb_info.chModelName) strSerialNumber = "" for per in st_usb_info.chSerialNumber: if per == 0: break strSerialNumber = strSerialNumber + chr(per) devList.append(f"[{i}]USB: {user_defined_name} {model_name}({strSerialNumber})") else: # 处理其他类型设备 devList.append(f"[{i}]Unknown Device Type: {mvcc_dev_info.nTLayerType}") # 更新UI mainWindow.ComboDevices.clear() mainWindow.ComboDevices.addItems(devList) mainWindow.ComboDevices.setCurrentIndex(0) # 更新状态栏 mainWindow.statusBar().showMessage(f"找到 {deviceList.nDeviceNum} 个设备", 3000) return MV_OK # ===== 关键改进:相机操作函数 ===== def open_device(): global deviceList, nSelCamIndex, obj_cam_operation, isOpen, frame_monitor_thread, mainWindow if isOpen: QMessageBox.warning(mainWindow, "Error", '相机已打开!', QMessageBox.Ok) return MV_E_CALLORDER nSelCamIndex = mainWindow.ComboDevices.currentIndex() if nSelCamIndex < 0: QMessageBox.warning(mainWindow, "Error", '请选择相机!', QMessageBox.Ok) return MV_E_CALLORDER # 创建相机控制对象 cam = MvCamera() # 初始化相机操作对象 - 确保传入有效的相机对象 obj_cam_operation = CameraOperation(cam, deviceList, nSelCamIndex) ret = obj_cam_operation.open_device() if 0 != ret: strError = "打开设备失败 ret:" + ToHexStr(ret) QMessageBox.warning(mainWindow, "Error", strError, QMessageBox.Ok) isOpen = False else: set_continue_mode() get_param() isOpen = True enable_controls() # 创建并启动帧监控线程 frame_monitor_thread = FrameMonitorThread(obj_cam_operation) frame_monitor_thread.frame_status.connect(mainWindow.statusBar().showMessage) frame_monitor_thread.start() def start_grabbing(): global obj_cam_operation, isGrabbing # 关键改进:添加相机状态检查 if not obj_cam_operation or not hasattr(obj_cam_operation, 'cam') or not obj_cam_operation.cam: QMessageBox.warning(mainWindow, "Error", "相机对象未正确初始化", QMessageBox.Ok) return ret = obj_cam_operation.start_grabbing(mainWindow.widgetDisplay.winId()) if ret != 0: strError = "开始取流失败 ret:" + ToHexStr(ret) QMessageBox.warning(mainWindow, "Error", strError, QMessageBox.Ok) else: isGrabbing = True enable_controls() # 等待第一帧到达 QThread.msleep(500) if not obj_cam_operation.is_frame_available(): QMessageBox.warning(mainWindow, "警告", "开始取流后未接收到帧,请检查相机连接!", QMessageBox.Ok) def stop_grabbing(): global obj_cam_operation, isGrabbing # 关键改进:添加相机状态检查 if not obj_cam_operation or not hasattr(obj_cam_operation, 'cam') or not obj_cam_operation.cam: QMessageBox.warning(mainWindow, "Error", "相机对象未正确初始化", QMessageBox.Ok) return # 关键改进:添加连接状态检查 if not hasattr(obj_cam_operation, 'connected') or not obj_cam_operation.connected: QMessageBox.warning(mainWindow, "Error", "相机未连接", QMessageBox.Ok) return ret = obj_cam_operation.Stop_grabbing() if ret != 0: strError = "停止取流失败 ret:" + ToHexStr(ret) QMessageBox.warning(mainWindow, "Error", strError, QMessageBox.Ok) else: isGrabbing = False enable_controls() def close_device(): global isOpen, isGrabbing, obj_cam_operation, frame_monitor_thread if frame_monitor_thread and frame_monitor_thread.isRunning(): frame_monitor_thread.stop() frame_monitor_thread.wait(2000) if isOpen and obj_cam_operation: # 关键改进:确保相机对象存在 if hasattr(obj_cam_operation, 'cam') and obj_cam_operation.cam: obj_cam_operation.close_device() isOpen = False isGrabbing = False enable_controls() def set_continue_mode(): # 关键改进:添加相机状态检查 if not obj_cam_operation or not hasattr(obj_cam_operation, 'cam') or not obj_cam_operation.cam: return ret = obj_cam_operation.set_trigger_mode(False) if ret != 0: strError = "设置连续模式失败 ret:" + ToHexStr(ret) QMessageBox.warning(mainWindow, "Error", strError, QMessageBox.Ok) else: mainWindow.radioContinueMode.setChecked(True) mainWindow.radioTriggerMode.setChecked(False) mainWindow.bnSoftwareTrigger.setEnabled(False) def set_software_trigger_mode(): # 关键改进:添加相机状态检查 if not obj_cam_operation or not hasattr(obj_cam_operation, 'cam') or not obj_cam_operation.cam: return ret = obj_cam_operation.set_trigger_mode(True) if ret != 0: strError = "设置触发模式失败 ret:" + ToHexStr(ret) QMessageBox.warning(mainWindow, "Error", strError, QMessageBox.Ok) else: mainWindow.radioContinueMode.setChecked(False) mainWindow.radioTriggerMode.setChecked(True) mainWindow.bnSoftwareTrigger.setEnabled(isGrabbing) def trigger_once(): # 关键改进:添加相机状态检查 if not obj_cam_operation or not hasattr(obj_cam_operation, 'cam') or not obj_cam_operation.cam: return ret = obj_cam_operation.trigger_once() if ret != 0: strError = "软触发失败 ret:" + ToHexStr(ret) QMessageBox.warning(mainWindow, "Error", strError, QMessageBox.Ok) def save_sample_image(): global isGrabbing, obj_cam_operation, current_sample_path if not isGrabbing: QMessageBox.warning(mainWindow, "错误", "请先开始取流并捕获图像!", QMessageBox.Ok) return # 尝试捕获当前帧 frame = obj_cam_operation.capture_frame() if frame is None: QMessageBox.warning(mainWindow, "无有效图像", "未捕获到有效图像,请检查相机状态!", QMessageBox.Ok) return # 确保图像有效 if frame.size == 0 or frame.shape[0] == 0 or frame.shape[1] == 0: QMessageBox.warning(mainWindow, "无效图像", "捕获的图像无效,请检查相机设置!", QMessageBox.Ok) return settings = QSettings("ClothInspection", "CameraApp") last_dir = settings.value("last_save_dir", os.path.join(os.getcwd(), "captures")) timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") default_filename = f"sample_{timestamp}" file_path, selected_filter = QFileDialog.getSaveFileName( mainWindow, "保存标准样本图像", os.path.join(last_dir, default_filename), "BMP Files (*.bmp);;PNG Files (*.png);;JPEG Files (*.jpg);;所有文件 (*)", options=QFileDialog.DontUseNativeDialog ) if not file_path: return # 确保文件扩展名正确 file_extension = os.path.splitext(file_path)[1].lower() if not file_extension: if "BMP" in selected_filter: file_path += ".bmp" elif "PNG" in selected_filter: file_path += ".png" elif "JPEG" in selected_filter or "JPG" in selected_filter: file_path += ".jpg" else: file_path += ".bmp" file_extension = os.path.splitext(file_path)[1].lower() # 创建目录(如果不存在) directory = os.path.dirname(file_path) if directory and not os.path.exists(directory): try: os.makedirs(directory, exist_ok=True) except OSError as e: QMessageBox.critical(mainWindow, "目录创建错误", f"无法创建目录 {directory}: {str(e)}", QMessageBox.Ok) return # 保存图像 try: # 使用OpenCV保存图像 if not cv2.imwrite(file_path, frame): raise Exception("OpenCV保存失败") # 更新状态 current_sample_path = file_path update_sample_display() settings.setValue("last_save_dir", os.path.dirname(file_path)) # 显示成功消息 QMessageBox.information(mainWindow, "成功", f"标准样本已保存至:\n{file_path}", QMessageBox.Ok) # 可选:自动预览样本 preview_sample() except Exception as e: logging.error(f"保存图像失败: {str(e)}") QMessageBox.critical(mainWindow, "保存错误", f"保存图像时发生错误:\n{str(e)}", QMessageBox.Ok) def preview_sample(): global current_sample_path if not current_sample_path or not os.path.exists(current_sample_path): QMessageBox.warning(mainWindow, "错误", "请先设置有效的标准样本图像!", QMessageBox.Ok) return try: # 直接使用OpenCV加载图像 sample_img = cv2.imread(current_sample_path) if sample_img is None: raise Exception("无法加载图像") # 显示图像 cv2.imshow("标准样本预览", sample_img) cv2.waitKey(0) cv2.destroyAllWindows() except Exception as e: QMessageBox.warning(mainWindow, "错误", f"预览样本失败: {str(e)}", QMessageBox.Ok) def start_grabbing(): global obj_cam_operation, isGrabbing ret = obj_cam_operation.start_grabbing(mainWindow.widgetDisplay.winId()) if ret != 0: strError = "开始取流失败 ret:" + ToHexStr(ret) QMessageBox.warning(mainWindow, "Error", strError, QMessageBox.Ok) else: isGrabbing = True enable_controls() # 等待第一帧到达 QThread.msleep(500) if not obj_cam_operation.is_frame_available(): QMessageBox.warning(mainWindow, "警告", "开始取流后未接收到帧,请检查相机连接!", QMessageBox.Ok) def is_float(str): try: float(str) return True except ValueError: return False def get_param(): try: ret = obj_cam_operation.get_parameters() if ret != MV_OK: strError = "获取参数失败,错误码: " + ToHexStr(ret) QMessageBox.warning(mainWindow, "错误", strError, QMessageBox.Ok) else: mainWindow.edtExposureTime.setText("{0:.2f}".format(obj_cam_operation.exposure_time)) mainWindow.edtGain.setText("{0:.2f}".format(obj_cam_operation.gain)) mainWindow.edtFrameRate.setText("{0:.2f}".format(obj_cam_operation.frame_rate)) except Exception as e: error_msg = f"获取参数时发生错误: {str(e)}" QMessageBox.critical(mainWindow, "严重错误", error_msg, QMessageBox.Ok) def set_param(): frame_rate = mainWindow.edtFrameRate.text() exposure = mainWindow.edtExposureTime.text() gain = mainWindow.edtGain.text() if not (is_float(frame_rate) and is_float(exposure) and is_float(gain)): strError = "设置参数失败: 参数必须是有效的浮点数" QMessageBox.warning(mainWindow, "错误", strError, QMessageBox.Ok) return MV_E_PARAMETER try: ret = obj_cam_operation.set_param( frame_rate=float(frame_rate), exposure_time=float(exposure), gain=float(gain) ) if ret != MV_OK: strError = "设置参数失败,错误码: " + ToHexStr(ret) QMessageBox.warning(mainWindow, "错误", strError, QMessageBox.Ok) except Exception as e: error_msg = f"设置参数时发生错误: {str(e)}" QMessageBox.critical(mainWindow, "严重错误", error_msg, QMessageBox.Ok) def enable_controls(): global isGrabbing, isOpen mainWindow.groupGrab.setEnabled(isOpen) mainWindow.paramgroup.setEnabled(isOpen) mainWindow.bnOpen.setEnabled(not isOpen) mainWindow.bnClose.setEnabled(isOpen) mainWindow.bnStart.setEnabled(isOpen and (not isGrabbing)) mainWindow.bnStop.setEnabled(isOpen and isGrabbing) mainWindow.bnSoftwareTrigger.setEnabled(isGrabbing and mainWindow.radioTriggerMode.isChecked()) mainWindow.bnSaveImage.setEnabled(isOpen and isGrabbing) mainWindow.bnCheckPrint.setEnabled(isOpen and isGrabbing) mainWindow.bnSaveSample.setEnabled(isOpen and isGrabbing) mainWindow.bnPreviewSample.setEnabled(bool(current_sample_path)) def update_sensor_display(data): if not data: return text = (f"张力: {data['tension']:.2f}N | " f"速度: {data['speed']:.2f}m/s | " f"温度: {data['temperature']:.1f}°C | " f"湿度: {data['humidity']:.1f}%") mainWindow.lblSensorData.setText(text) def connect_sensor(): global sensor_monitor_thread, sensor_controller sensor_type = mainWindow.cbSensorType.currentText() if sensor_controller is None: sensor_controller = SensorController() if sensor_type == "串口": config = { 'type': 'serial', 'port': mainWindow.cbComPort.currentText(), 'baudrate': int(mainWindow.cbBaudrate.currentText()), 'timeout': 1.0 } else: config = { 'type': 'ethernet', 'ip': mainWindow.edtIP.text(), 'port': int(mainWindow.edtPort.text()), 'timeout': 1.0 } if sensor_controller.connect(config): mainWindow.bnConnectSensor.setEnabled(False) mainWindow.bnDisconnectSensor.setEnabled(True) sensor_monitor_thread = SensorMonitorThread(sensor_controller) sensor_monitor_thread.data_updated.connect(update_sensor_display) sensor_monitor_thread.start() def disconnect_sensor(): global sensor_monitor_thread if sensor_controller: sensor_controller.disconnect() mainWindow.bnConnectSensor.setEnabled(True) mainWindow.bnDisconnectSensor.setEnabled(False) if sensor_monitor_thread and sensor_monitor_thread.isRunning(): sensor_monitor_thread.stop() sensor_monitor_thread.wait(2000) sensor_monitor_thread = None mainWindow.lblSensorData.setText("传感器数据: 未连接") def update_sensor_ui(index): mainWindow.serialGroup.setVisible(index == 0) mainWindow.ethernetGroup.setVisible(index == 1) class MainWindow(QMainWindow): def __init__(self): super().__init__() self.setWindowTitle("布料印花检测系统") self.resize(1200, 800) central_widget = QWidget() self.setCentralWidget(central_widget) main_layout = QVBoxLayout(central_widget) # 设备枚举区域 device_layout = QHBoxLayout() self.ComboDevices = QComboBox() self.bnEnum = QPushButton("枚举设备") self.bnOpen = QPushButton("打开设备") self.bnClose = QPushButton("关闭设备") device_layout.addWidget(self.ComboDevices) device_layout.addWidget(self.bnEnum) device_layout.addWidget(self.bnOpen) device_layout.addWidget(self.bnClose) main_layout.addLayout(device_layout) # 取流控制组 self.groupGrab = QGroupBox("取流控制") grab_layout = QHBoxLayout(self.groupGrab) self.bnStart = QPushButton("开始取流") self.bnStop = QPushButton("停止取流") self.radioContinueMode = QRadioButton("连续模式") self.radioTriggerMode = QRadioButton("触发模式") self.bnSoftwareTrigger = QPushButton("软触发") grab_layout.addWidget(self.bnStart) grab_layout.addWidget(self.bnStop) grab_layout.addWidget(self.radioContinueMode) grab_layout.addWidget(self.radioTriggerMode) grab_layout.addWidget(self.bnSoftwareTrigger) main_layout.addWidget(self.groupGrab) # 参数设置组 self.paramgroup = QGroupBox("相机参数") param_layout = QGridLayout(self.paramgroup) self.edtExposureTime = QLineEdit() self.edtGain = QLineEdit() self.edtFrameRate = QLineEdit() self.bnGetParam = QPushButton("获取参数") self.bnSetParam = QPushButton("设置参数") self.bnSaveImage = QPushButton("保存图像") param_layout.addWidget(QLabel("曝光时间:"), 0, 0) param_layout.addWidget(self.edtExposureTime, 0, 1) param_layout.addWidget(self.bnGetParam, 0, 2) param_layout.addWidget(QLabel("增益:"), 1, 0) param_layout.addWidget(self.edtGain, 1, 1) param_layout.addWidget(self.bnSetParam, 1, 2) param_layout.addWidget(QLabel("帧率:"), 2, 0) param_layout.addWidget(self.edtFrameRate, 2, 1) param_layout.addWidget(self.bnSaveImage, 2, 2) main_layout.addWidget(self.paramgroup) # 图像显示区域 self.widgetDisplay = QLabel() self.widgetDisplay.setMinimumSize(640, 480) self.widgetDisplay.setStyleSheet("background-color: black;") self.widgetDisplay.setAlignment(Qt.AlignCenter) self.widgetDisplay.setText("相机预览区域") main_layout.addWidget(self.widgetDisplay, 1) # 状态栏 #self.statusBar = QStatusBar() #self.setStatusBar(self.statusBar) # 创建自定义UI组件 self.setup_custom_ui() def setup_custom_ui(self): # 工具栏 toolbar = self.addToolBar("检测工具") self.bnCheckPrint = QPushButton("检测印花质量") self.bnSaveSample = QPushButton("保存标准样本") self.bnPreviewSample = QPushButton("预览样本") self.cbHistory = QComboBox() self.cbHistory.setMinimumWidth(300) toolbar.addWidget(self.bnCheckPrint) toolbar.addWidget(self.bnSaveSample) toolbar.addWidget(self.bnPreviewSample) toolbar.addWidget(QLabel("历史记录:")) toolbar.addWidget(self.cbHistory) # 状态栏样本路径 self.lblSamplePath = QLabel("当前样本: 未设置样本") self.statusBar().addPermanentWidget(self.lblSamplePath) # 右侧面板 right_panel = QWidget() right_layout = QVBoxLayout(right_panel) right_layout.setContentsMargins(10, 10, 10, 10) # 差异度调整组 diff_group = QGroupBox("差异度调整") diff_layout = QVBoxLayout(diff_group) self.lblDiffThreshold = QLabel("差异度阈值 (0-100%):") self.sliderDiffThreshold = QSlider(Qt.Horizontal) self.sliderDiffThreshold.setRange(0, 100) self.sliderDiffThreshold.setValue(5) self.lblDiffValue = QLabel("5%") self.lblCurrentDiff = QLabel("当前差异度: -") self.lblCurrentDiff.setStyleSheet("font-size: 14px; font-weight: bold;") self.lblDiffStatus = QLabel("状态: 未检测") self.lblDiffStatus.setStyleSheet("font-size: 12px;") diff_layout.addWidget(self.lblDiffThreshold) diff_layout.addWidget(self.sliderDiffThreshold) diff_layout.addWidget(self.lblDiffValue) diff_layout.addWidget(self.lblCurrentDiff) diff_layout.addWidget(self.lblDiffStatus) right_layout.addWidget(diff_group) # 传感器控制面板 sensor_panel = QGroupBox("传感器控制") sensor_layout = QVBoxLayout(sensor_panel) sensor_type_layout = QHBoxLayout() self.lblSensorType = QLabel("传感器类型:") self.cbSensorType = QComboBox() self.cbSensorType.addItems(["串口", "以太网"]) sensor_type_layout.addWidget(self.lblSensorType) sensor_type_layout.addWidget(self.cbSensorType) sensor_layout.addLayout(sensor_type_layout) # 串口参数 self.serialGroup = QGroupBox("串口参数") serial_layout = QVBoxLayout(self.serialGroup) self.lblComPort = QLabel("端口:") self.cbComPort = QComboBox() if platform.system() == 'Windows': ports = [f"COM{i}" for i in range(1, 21)] else: ports = [f"/dev/ttyS{i}" for i in range(0, 4)] + [f"/dev/ttyUSB{i}" for i in range(0, 4)] self.cbComPort.addItems(ports) self.lblBaudrate = QLabel("波特率:") self.cbBaudrate = QComboBox() self.cbBaudrate.addItems(["96000", "19200", "38400", "57600", "115200"]) self.cbBaudrate.setCurrentText("115200") serial_layout.addWidget(self.lblComPort) serial_layout.addWidget(self.cbComPort) serial_layout.addWidget(self.lblBaudrate) serial_layout.addWidget(self.cbBaudrate) sensor_layout.addWidget(self.serialGroup) # 以太网参数 self.ethernetGroup = QGroupBox("以太网参数") ethernet_layout = QVBoxLayout(self.ethernetGroup) self.lblIP = QLabel("IP地址:") self.edtIP = QLineEdit("192.168.1.100") self.lblPort = QLabel("端口:") self.edtPort = QLineEdit("502") ethernet_layout.addWidget(self.lblIP) ethernet_layout.addWidget(self.edtIP) ethernet_layout.addWidget(self.lblPort) ethernet_layout.addWidget(self.edtPort) sensor_layout.addWidget(self.ethernetGroup) # 连接按钮 self.bnConnectSensor = QPushButton("连接传感器") self.bnDisconnectSensor = QPushButton("断开传感器") self.bnDisconnectSensor.setEnabled(False) sensor_layout.addWidget(self.bnConnectSensor) sensor_layout.addWidget(self.bnDisconnectSensor) # 延迟设置 delay_layout = QHBoxLayout() self.lblDelay = QLabel("触发延迟(秒):") self.spinDelay = QSpinBox() self.spinDelay.setRange(0, 60) self.spinDelay.setValue(0) self.spinDelay.setToolTip("传感器检测到布料后延迟拍摄的时间") delay_layout.addWidget(self.lblDelay) delay_layout.addWidget(self.spinDelay) sensor_layout.addLayout(delay_layout) # 传感器数据 self.lblSensorData = QLabel("传感器数据: 未连接") self.lblSensorData.setStyleSheet("font-size: 10pt;") sensor_layout.addWidget(self.lblSensorData) right_layout.addWidget(sensor_panel) right_layout.addStretch(1) # 停靠窗口 dock = QDockWidget("检测控制面板", self) dock.setWidget(right_panel) dock.setFeatures(QDockWidget.DockWidgetMovable | QDockWidget.DockWidgetFloatable) self.addDockWidget(Qt.RightDockWidgetArea, dock) def closeEvent(self, event): logging.info("主窗口关闭,执行清理...") close_device() disconnect_sensor() event.accept() if __name__ == "__main__": app = QApplication(sys.argv) mainWindow = MainWindow() # 信号连接 mainWindow.cbSensorType.currentIndexChanged.connect(update_sensor_ui) update_sensor_ui(0) mainWindow.bnConnectSensor.clicked.connect(connect_sensor) mainWindow.bnDisconnectSensor.clicked.connect(disconnect_sensor) mainWindow.sliderDiffThreshold.valueChanged.connect(update_diff_threshold) mainWindow.bnCheckPrint.clicked.connect(sensor_controlled_check) mainWindow.bnSaveSample.clicked.connect(save_sample_image) mainWindow.bnPreviewSample.clicked.connect(preview_sample) mainWindow.bnEnum.clicked.connect(enum_devices) mainWindow.bnOpen.clicked.connect(open_device) mainWindow.bnClose.clicked.connect(close_device) mainWindow.bnStart.clicked.connect(start_grabbing) mainWindow.bnStop.clicked.connect(stop_grabbing) mainWindow.bnSoftwareTrigger.clicked.connect(trigger_once) mainWindow.radioTriggerMode.clicked.connect(set_software_trigger_mode) mainWindow.radioContinueMode.clicked.connect(set_continue_mode) mainWindow.bnGetParam.clicked.connect(get_param) mainWindow.bnSetParam.clicked.connect(set_param) mainWindow.bnSaveImage.clicked.connect(save_sample_image) main() mainWindow.show() app.exec_() close_device() disconnect_sensor() sys.exit() 我想把这个程序打包成exe文件我该怎么做
07-12
能否把下面代码里面在运行时展现出缺陷的显示变小一点,同时为什么我刚刚测试好几次我和标准样本类似的过来它都没有主动截取当前帧并检测 # -*- coding: utf-8 -*- import sys import os import cv2 import numpy as np import math import time import logging from collections import deque from PyQt5.QtWidgets import ( QApplication, QMainWindow, QPushButton, QWidget, QVBoxLayout, QHBoxLayout, QMessageBox, QLabel, QFileDialog, QToolBox, QComboBox, QStatusBar, QGroupBox, QSlider, QDockWidget, QProgressDialog, QLineEdit, QRadioButton, QGridLayout, QSpinBox, QCheckBox, QDialog, QDialogButtonBox, QDoubleSpinBox, QProgressBar ) from PyQt5.QtCore import QRect, Qt, QSettings, QThread, pyqtSignal, QTimer from PyQt5.QtGui import QImage, QPixmap from CamOperation_class import CameraOperation from MvCameraControl_class import * import ctypes from ctypes import cast, POINTER from datetime import datetime import skimage import platform from CameraParams_header import ( MV_GIGE_DEVICE, MV_USB_DEVICE, MV_GENTL_CAMERALINK_DEVICE, MV_GENTL_CXP_DEVICE, MV_GENTL_XOF_DEVICE ) # ===== 全局配置 ===== # 模板匹配参数 MATCH_THRESHOLD = 0.85 # 默认匹配置信度阈值 MIN_MATCH_COUNT = 10 # 最小匹配特征点数量 # ===== 全局变量 ===== current_sample_path = "" detection_history = [] isGrabbing = False isOpen = False obj_cam_operation = None frame_monitor_thread = None template_matcher_thread = None MV_OK = 0 MV_E_CALLORDER = -2147483647 # ==================== 优化后的质量检测算法 ==================== def enhanced_check_print_quality(sample_image_path, test_image, threshold=0.05): # 不再使用传感器数据调整阈值 adjusted_threshold = threshold try: sample_img_data = np.fromfile(sample_image_path, dtype=np.uint8) sample_image = cv2.imdecode(sample_img_data, cv2.IMREAD_GRAYSCALE) if sample_image is None: logging.error(f"无法解码样本图像: {sample_image_path}") return None, None, None except Exception as e: logging.exception(f"样本图像读取异常: {str(e)}") return None, None, None if len(test_image.shape) == 3: test_image_gray = cv2.cvtColor(test_image, cv2.COLOR_BGR2GRAY) else: test_image_gray = test_image.copy() sample_image = cv2.GaussianBlur(sample_image, (5, 5), 0) test_image_gray = cv2.GaussianBlur(test_image_gray, (5, 5), 0) try: orb = cv2.ORB_create(nfeatures=200) keypoints1, descriptors1 = orb.detectAndCompute(sample_image, None) keypoints2, descriptors2 = orb.detectAndCompute(test_image_gray, None) if descriptors1 is None or descriptors2 is None: logging.warning("无法提取特征描述符,跳过配准") aligned_sample = sample_image else: bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True) matches = bf.match(descriptors1, descriptors2) matches = sorted(matches, key=lambda x: x.distance) if len(matches) > MIN_MATCH_COUNT: src_pts = np.float32([keypoints1[m.queryIdx].pt for m in matches]).reshape(-1, 1, 2) dst_pts = np.float32([keypoints2[m.trainIdx].pt for m in matches]).reshape(-1, 1, 2) H, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0) if H is not None: aligned_sample = cv2.warpPerspective( sample_image, H, (test_image_gray.shape[1], test_image_gray.shape[0]) ) logging.info("图像配准成功,使用配准后样本") else: aligned_sample = sample_image logging.warning("无法计算单应性矩阵,使用原始样本") else: aligned_sample = sample_image logging.warning(f"特征点匹配不足({len(matches)}/{MIN_MATCH_COUNT}),跳过图像配准") except Exception as e: logging.error(f"图像配准失败: {str(e)}") aligned_sample = sample_image try: if aligned_sample.shape != test_image_gray.shape: test_image_g极ray = cv2.resize(test_image_gray, (aligned_sample.shape[1], aligned_sample.shape[0])) except Exception as e: logging.error(f"图像调整大小失败: {str(e)}") return None, None, None try: from skimage.metrics import structural_similarity as compare_ssim ssim_score, ssim_diff = compare_ssim( aligned_sample, test_image_gray, full=True, gaussian_weights=True, data_range=255 ) except ImportError: from skimage.measure import compare_ssim ssim_score, ssim_diff = compare_ssim( aligned_sample, test_image_gray, full=True, gaussian_weights=True ) except Exception as e: logging.error(f"SSIM计算失败: {str(e)}") abs_diff = cv2.absdiff(aligned_sample, test_image_gray) ssim_diff = abs_diff.astype(np.float32) / 255.0 ssim_score = 1.0 - np.mean(ssim_diff) ssim_diff = (1 - ssim_diff) * 255 abs_diff = cv2.absdiff(aligned_sample, test_image_gray) combined_diff = cv2.addWeighted(ssim_diff.astype(np.uint8), 0.7, abs_diff, 0.3, 0) _, thresholded = cv2.threshold(combined_diff, 30, 255, cv2.THRESH_BINARY) kernel = np.ones((3, 3), np.uint8) thresholded = cv2.morphologyEx(thresholded, cv2.MORPH_OPEN, kernel) thresholded = cv2.morphologyEx(thresholded, cv2.MORPH_CLOSE, kernel) diff_pixels = np.count_nonzero(thresholded) total_pixels = aligned_sample.size diff_ratio = diff_pixels / total_pixels is_qualified = diff_ratio <= adjusted_threshold marked_image = cv2.cvtColor(test_image_gray, cv2.COLOR_GRAY2BGR) marked_image[thresholded == 255] = [0, 0, 255] labels = skimage.measure.label(thresholded) properties = skimage.measure.regionprops(labels) for prop in properties: if prop.area > 50: y, x = prop.centroid cv2.putText(marked_image, f"Defect", (int(x), int(y)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 255), 1) return is_qualified, diff_ratio, marked_image # ==================== 视觉触发的质量检测流程 ==================== def vision_controlled_check(capture_path=None): global current_sample_path, detection_history logging.info("视觉触发质量检测启动") # 如果没有提供抓拍路径,使用当前帧 if capture_path is None: frame = obj_cam_operation.get_current_frame() else: # 从文件加载抓拍的图像 frame = cv2.imread(capture_path) if frame is None: logging.error(f"无法加载抓拍图像: {capture_path}") frame = obj_cam_operation.get_current_frame() if frame is None: QMessageBox.warning(mainWindow, "错误", "无法获取当前帧图像!", QMessageBox.Ok) return progress = QProgressDialog("正在检测...", "取消", 0, 100, mainWindow) progress.setWindowModality(Qt.WindowModal) progress.setValue(10) try: diff_threshold = mainWindow.sliderDiffThreshold.value() / 100.0 logging.info(f"使用差异度阈值: {diff_threshold}") progress.setValue(30) is_qualified, diff_ratio, marked_image = enhanced_check_print_quality( current_sample_path, frame, threshold=diff_threshold ) progress.setValue(70) if is_qualified is None: QMessageBox.critical(mainWindow, "检测错误", "检测失败,请检查日志", QMessageBox.Ok) return logging.info(f"检测结果: 合格={is_qualified}, 差异={diff_ratio}") progress.setValue(90) update_diff_display(diff_ratio, is_qualified) result_text = f"印花是否合格: {'合格' if is_qualified else '不合格'}\n差异占比: {diff_ratio*100:.2f}%\n阈值: {diff_threshold*100:.2f}%" QMessageBox.information(mainWindow, "检测结果", result_text, QMessageBox.Ok) if marked_image is not None: cv2.imshow("缺陷标记结果", marked_image) cv2.waitKey(0) cv2.destroyAllWindows() detection_result = { 'timestamp': datetime.now(), 'qualified': is_qualified, 'diff_ratio': diff_ratio, 'threshold': diff_threshold, 'trigger_type': 'vision' if capture_path else 'manual' } detection_history.append(detection_result) update_history_display() progress.setValue(100) except Exception as e: logging.exception("印花检测失败") QMessageBox.critical(mainWindow, "检测错误", f"检测过程中发生错误: {str(e)}", QMessageBox.Ok) finally: progress.close() # ==================== 相机操作函数 ==================== def open_device(): global deviceList, nSelCamIndex, obj_cam_operation, isOpen, frame_monitor_thread, mainWindow if isOpen: QMessageBox.warning(mainWindow, "Error", '相机已打开!', QMessageBox.Ok) return MV_E_CALLORDER nSelCamIndex = mainWindow.ComboDevices.currentIndex() if nSelCamIndex < 0: QMessageBox.warning(mainWindow, "Error", '请选择相机!', QMessageBox.Ok) return MV_E_CALLORDER # 创建相机控制对象 cam = MvCamera() # 初始化相机操作对象 obj_cam_operation = CameraOperation(cam, deviceList, nSelCamIndex) ret = obj_cam_operation.open_device() if 0 != ret: strError = "打开设备失败 ret:" + ToHexStr(ret) QMessageBox.warning(mainWindow, "Error", strError, QMessageBox.Ok) isOpen = False else: set_continue_mode() get_param() isOpen = True enable_controls() # 创建并启动帧监控线程 frame_monitor_thread = FrameMonitorThread(obj_cam_operation) frame_monitor_thread.frame_status.connect(mainWindow.statusBar().showMessage) frame_monitor_thread.start() def start_grabbing(): global obj_cam_operation, isGrabbing, template_matcher_thread ret = obj_cam_operation.start_grabbing(mainWindow.widgetDisplay.winId()) if ret != 0: strError = "开始取流失败 ret:" + ToHexStr(ret) QMessageBox.warning(mainWindow, "Error", strError, QMessageBox.Ok) else: isGrabbing = True enable_controls() # 等待第一帧到达 QThread.msleep(500) if not obj_cam_operation.is_frame_available(): QMessageBox.warning(mainWindow, "警告", "开始取流后未接收到帧,请检查相机连接!", QMessageBox.Ok) # 如果启用了自动检测,启动检测线程 if mainWindow.chkAutoDetect.isChecked(): toggle_template_matching(True) def stop_grabbing(): global obj_cam_operation, isGrabbing, template_matcher_thread ret = obj_cam_operation.Stop_grabbing() if ret != 0: strError = "停止取流失败 ret:" + ToHexStr(ret) QMessageBox.warning(mainWindow, "Error", strError, QMessageBox.Ok) else: isGrabbing = False enable_controls() # 停止模板匹配线程 if template_matcher_thread and template_matcher_thread.isRunning(): template_matcher_thread.stop() def close_device(): global isOpen, isGrabbing, obj_cam_operation, frame_monitor_thread, template_matcher_thread if frame_monitor_thread and frame_monitor_thread.isRunning(): frame_monitor_thread.stop() frame_monitor_thread.wait(2000) # 停止模板匹配线程 if template_matcher_thread and template_matcher_thread.isRunning(): template_matcher_thread.stop() template_matcher_thread.wait(2000) template_matcher_thread = None if isOpen and obj_cam_operation: obj_cam_operation.close_device() isOpen = False isGrabbing = False enable_controls() # ==================== 模板匹配检测器 ==================== class TemplateMatcherThread(QThread): template_detected = pyqtSignal(str) # 检测到匹配时发出的信号,传递图像路径 def __init__(self, cam_operation, parent=None): super().__init__(parent) self.cam_operation = cam_operation self.running = True self.last_detection_time = 0 self.sample_template = None self.min_match_count = MIN_MATCH_COUNT self.match_threshold = MATCH_THRESHOLD self.sample_kp = None self.sample_des = None # 特征检测器 self.orb = cv2.ORB_create(nfeatures=1000) # 特征匹配器 self.bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True) # 性能监控 self.processing_times = deque(maxlen=100) # 匹配历史记录 self.match_history = deque(maxlen=10) def set_sample(self, sample_img): """设置标准样本""" if sample_img is None or sample_img.size == 0: return False # 转换为灰度图 if len(sample_img.shape) == 3: # 彩色图像 (BGR) gray_sample = cv2.cvtColor(sample_img, cv2.COLOR_BGR2GRAY) elif len(sample_img.shape) == 2: # 已经是灰度图 gray_sample = sample_img else: logging.warning("不支持的图像格式") return False # 提取特征 self.sample_kp, self.sample_des = self.orb.detectAndCompute(gray_sample, None) if self.sample_des is None or len(self.sample_kp) < self.min_match_count: logging.warning("样本图像特征点不足") return False self.sample_template = sample_img logging.info(f"标准样本设置成功,特征点数: {len(self.sample_kp)}") return True def match_template(self, frame): """在帧中匹配标准样本""" start_time = time.time() if self.sample_kp is None or self.sample_des is None: return False, None # 转换为灰度图 if len(frame.shape) == 3: gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) else: gray_frame = frame.copy() # 检测当前帧特征点 frame_kp, frame_des = self.orb.detectAndCompute(gray_frame, None) if frame_des is None or len(frame_kp) < self.min_match_count: return False, None # 匹配特征点 matches = self.bf.match(self.sample_des, frame_des) matches = sorted(matches, key=lambda x: x.distance) # 检查匹配质量 if len(matches) < self.min_match_count: return False, None # 计算平均匹配距离 avg_distance = sum(m.distance for m in matches) / len(matches) max_distance = matches[0].distance * 2.0 # 筛选优质匹配 good_matches = [m for m in matches if m.distance < max(0.7 * avg_distance, 30.0)] if len(good_matches) < self.min_match_count: return False, None # 计算匹配分数 (0-1, 1表示完美匹配) match_score = 1.0 - (avg_distance / 100.0) match_score = max(0.0, min(1.0, match_score)) # 记录匹配历史 self.match_history.append(match_score) # 性能监控 proc_time = time.time() - start_time self.processing_times.append(proc_time) # 检查是否超过阈值 if match_score >= self.match_threshold: # 获取匹配位置 src_pts = np.float32([self.sample_kp[m.queryIdx].pt for m in good_matches]).reshape(-1,1,2) dst_pts = np.float32([frame_kp[m.trainIdx].pt for m in good_matches]).reshape(-1,1,2) # 计算变换矩阵 M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0) if M is not None: # 获取样本图像的尺寸 h, w = self.sample_template.shape[:2] if len(self.sample_template.shape) == 2 else self.sample_template.shape[:2][:2] # 计算样本在帧中的位置 pts = np.float32([[0,0], [0,h-1], [w-1,h-1], [w-1,0]]).reshape(-1,1,2) dst = cv2.perspectiveTransform(pts, M) return True, (match_score, dst) return False, None def run(self): """主检测循环""" last_process_time = time.time() min_frame_interval = 0.05 # 最小处理间隔20fps while self.running: current_time = time.time() # 控制处理频率 if current_time - last_process_time < min_frame_interval: time.sleep(0.001) continue last_process_time = current_time if not self.cam_operation or not self.cam_operation.is_frame_available(): time.sleep(0.01) continue frame = self.cam_operation.get_current_frame() if frame is None: continue # 尝试匹配模板 detected, match_data = self.match_template(frame) if detected: self.last_detection_time = current_time match_score, box_points = match_data # 在图像上绘制匹配框 marked_frame = frame.copy() cv2.polylines(marked_frame, [np.int32(box_points)], True, (0,255,0), 3, cv2.LINE_AA) cv2.putText(marked_frame, f"Match: {match_score:.2f}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,255,0), 2) # 保存用于质量检测的图像 timestamp = datetime.now().strftime("%Y%m%d_%H%M%S_%f") capture_dir = os.path.join("captures", "matches") os.makedirs(capture_dir, exist_ok=True) capture_path = os.path.join(capture_dir, f"match_{timestamp}.jpg") cv2.imwrite(capture_path, marked_frame) # 进行质量检测 self.template_detected.emit(capture_path) logging.info( f"检测到匹配目标 | 匹配度: {match_score:.2f} | " f"阈值: {self.match_threshold}" ) # ==================== 模板匹配控制函数 ==================== def toggle_template_matching(state): global template_matcher_thread, current_sample_path if state == Qt.Checked and isGrabbing: # 确保已设置样本 if not current_sample_path: QMessageBox.warning(mainWindow, "错误", "请先设置标准样本", QMessageBox.Ok) mainWindow.chkAutoDetect.setChecked(False) return if template_matcher_thread is None: template_matcher_thread = TemplateMatcherThread(obj_cam_operation) template_matcher_thread.template_detected.connect(vision_controlled_check) # 加载样本图像 sample_img = cv2.imread(current_sample_path) if sample_img is None: QMessageBox.warning(mainWindow, "错误", "无法加载标准样本图像", QMessageBox.Ok) mainWindow.chkAutoDetect.setChecked(False) return if not template_matcher_thread.set_sample(sample_img): QMessageBox.warning(mainWindow, "错误", "标准样本特征不足", QMessageBox.Ok) mainWindow.chkAutoDetect.setChecked(False) return template_matcher_thread.start() logging.info("模板匹配自动检测已启用") elif template_matcher_thread: template_matcher_thread.stop() logging.info("模板匹配自动检测已禁用") def update_match_threshold(value): global template_matcher_thread if template_matcher_thread: template_matcher_thread.match_threshold = value / 100.0 mainWindow.lblThresholdValue.setText(f"{value}%") # ==================== UI更新函数 ==================== def update_diff_display(diff_ratio, is_qualified): mainWindow.lblCurrentDiff.setText(f"当前差异度: {diff_ratio*100:.2f}%") if is_qualified: mainWindow.lblDiffStatus.setText("状态: 合格") mainWindow.lblDiffStatus.setStyleSheet("color: green; font-size: 12px;") else: mainWindow.lblDiffStatus.setText("状态: 不合格") mainWindow.lblDiffStatus.setStyleSheet("color: red; font-size: 12px;") def update_diff_threshold(value): mainWindow.lblDiffValue.setText(f"{value}%") def update_sample_display(): global current_sample_path if current_sample_path: mainWindow.lblSamplePath.setText(f"当前样本: {os.path.basename(current_sample_path)}") mainWindow.lblSamplePath.setToolTip(current_sample_path) mainWindow.bnPreviewSample.setEnabled(True) else: mainWindow.lblSamplePath.setText("当前样本: 未设置样本") mainWindow.bnPreviewSample.setEnabled(False) def update_history_display(): global detection_history mainWindow.cbHistory.clear() for i, result in enumerate(detection_history[-10:]): timestamp = result['timestamp'].strftime("%H:%M:%S") status = "合格" if result['qualified'] else "不合格" ratio = f"{result['diff_ratio']*100:.2f}%" trigger = "视觉" if result['trigger_type'] == 'vision' else "手动" mainWindow.cbHistory.addItem(f"[{trigger} {timestamp}] {status} - 差异: {ratio}") # ==================== 主窗口类 ==================== class MainWindow(QMainWindow): def __init__(self): super().__init__() self.setWindowTitle("布料印花检测系统 - 模板匹配版") self.resize(1200, 800) central_widget = QWidget() self.setCentralWidget(central_widget) main_layout = QVBoxLayout(central_widget) # 设备枚举区域 device_layout = QHBoxLayout() self.ComboDevices = QComboBox() self.bnEnum = QPushButton("枚举设备") self.bnOpen = QPushButton("打开设备") self.bnClose = QPushButton("关闭设备") device_layout.addWidget(self.ComboDevices) device_layout.addWidget(self.bnEnum) device_layout.addWidget(self.bnOpen) device_layout.addWidget(self.bnClose) main_layout.addLayout(device_layout) # 取流控制组 self.groupGrab = QGroupBox("取流控制") grab_layout = QHBoxLayout(self.groupGrab) self.bnStart = QPushButton("开始取流") self.bnStop = QPushButton("停止取流") self.radioContinueMode = QRadioButton("连续模式") self.radioTriggerMode = QRadioButton("触发模式") self.bnSoftwareTrigger = QPushButton("软触发") grab_layout.addWidget(self.bnStart) grab_layout.addWidget(self.bnStop) grab_layout.addWidget(self.radioContinueMode) grab_layout.addWidget(self.radioTriggerMode) grab_layout.addWidget(self.bnSoftwareTrigger) main_layout.addWidget(self.groupGrab) # 参数设置组 self.paramgroup = QGroupBox("相机参数") param_layout = QGridLayout(self.paramgroup) self.edtExposureTime = QLineEdit() self.edtGain = QLineEdit() self.edtFrameRate = QLineEdit() self.bnGetParam = QPushButton("获取参数") self.bnSetParam = QPushButton("设置参数") self.bnSaveImage = QPushButton("保存图像") param_layout.addWidget(QLabel("曝光时间:"), 0, 0) param_layout.addWidget(self.edtExposureTime, 0, 1) param_layout.addWidget(self.bnGetParam, 0, 2) param_layout.addWidget(QLabel("增益:"), 1, 0) param_layout.addWidget(self.edtGain, 1, 1) param_layout.addWidget(self.bnSetParam, 1, 2) param_layout.addWidget(QLabel("帧率:"), 2, 0) param_layout.addWidget(self.edtFrameRate, 2, 1) param_layout.addWidget(self.bnSaveImage, 2, 2) main_layout.addWidget(self.paramgroup) # 图像显示区域 self.widgetDisplay = QLabel() self.widgetDisplay.setMinimumSize(640, 480) self.widgetDisplay.setStyleSheet("background-color: black;") self.widgetDisplay.setAlignment(Qt.AlignCenter) self.widgetDisplay.setText("相机预览区域") main_layout.addWidget(self.widgetDisplay, 1) # 创建自定义UI组件 self.setup_custom_ui() def setup_custom_ui(self): # 工具栏 toolbar = self.addToolBar("检测工具") self.bnCheckPrint = QPushButton("手动检测") self.bnSaveSample = QPushButton("保存标准样本") self.bnPreviewSample = QPushButton("预览样本") self.cbHistory = QComboBox() self.cbHistory.setMinimumWidth(300) toolbar.addWidget(self.bnCheckPrint) toolbar.addWidget(self.bnSaveSample) toolbar.addWidget(self.bnPreviewSample) toolbar.addWidget(QLabel("历史记录:")) toolbar.addWidget(self.cbHistory) # 状态栏样本路径 self.lblSamplePath = QLabel("当前样本: 未设置样本") self.statusBar().addPermanentWidget(self.lblSamplePath) # 右侧面板 right_panel = QWidget() right_layout = QVBoxLayout(right_panel) right_layout.setContentsMargins(10, 10, 10, 10) # 差异度调整组 diff_group = QGroupBox("差异度调整") diff_layout = QVBoxLayout(diff_group) self.lblDiffThreshold = QLabel("差异度阈值 (0-100%):") self.sliderDiffThreshold = QSlider(Qt.Horizontal) self.sliderDiffThreshold.setRange(0, 100) self.sliderDiffThreshold.setValue(5) self.lblDiffValue = QLabel("5%") self.lblCurrentDiff = QLabel("当前差异度: -") self.lblCurrentDiff.setStyleSheet("font-size: 14px; font-weight: bold;") self.lblDiffStatus = QLabel("状态: 未检测") self.lblDiffStatus.setStyleSheet("font-size: 12px;") diff_layout.addWidget(self.lblDiffThreshold) diff_layout.addWidget(self.sliderDiffThreshold) diff_layout.addWidget(self.lblDiffValue) diff_layout.addWidget(self.lblCurrentDiff) diff_layout.addWidget(self.lblDiffStatus) right_layout.addWidget(diff_group) # ===== 模板匹配面板 ===== match_group = QGroupBox("模板匹配") match_layout = QVBoxLayout(match_group) # 样本设置 sample_layout = QHBoxLayout() self.bnSetSample = QPushButton("设置标准样本") self.bnPreviewSample = QPushButton("预览样本") self.lblSampleStatus = QLabel("状态: 未设置样本") sample_layout.addWidget(self.bnSetSample) sample_layout.addWidget(self.bnPreviewSample) sample_layout.addWidget(self.lblSampleStatus) match_layout.addLayout(sample_layout) # 匹配参数 param_layout = QHBoxLayout() self.lblMatchThreshold = QLabel("匹配阈值:") self.sliderThreshold = QSlider(Qt.Horizontal) self.sliderThreshold.setRange(50, 100) self.sliderThreshold.setValue(85) self.lblThresholdValue = QLabel("85%") param_layout.addWidget(self.lblMatchThreshold) param_layout.addWidget(self.sliderThreshold) param_layout.addWidget(self.lblThresholdValue) match_layout.addLayout(param_layout) # 自动检测开关 self.chkAutoDetect = QCheckBox("启用模板匹配自动检测") self.chkAutoDetect.setChecked(False) match_layout.addWidget(self.chkAutoDetect) right_layout.addWidget(match_group) right_layout.addStretch(1) # 停靠窗口 dock = QDockWidget("检测控制面板", self) dock.setWidget(right_panel) dock.setFeatures(QDockWidget.DockWidgetMovable | QDockWidget.DockWidgetFloatable) self.addDockWidget(Qt.RightDockWidgetArea, dock) def closeEvent(self, event): logging.info("主窗口关闭,执行清理...") close_device() event.accept() # ===== 辅助函数 ===== def ToHexStr(num): if not isinstance(num, int): try: num = int(num) except: return f"<非整数:{type(num)}>" chaDic = {10: 'a', 11: 'b', 12: 'c', 13: 'd', 14: 'e', 15: 'f'} hexStr = "" if num < 0: num = num + 2 ** 32 while num >= 16: digit = num % 16 hexStr = chaDic.get(digit, str(digit)) + hexStr num //= 16 hexStr = chaDic.get(num, str(num)) + hexStr return "0x" + hexStr def enum_devices(): global deviceList, obj_cam_operation n_layer_type = ( MV_GIGE_DEVICE | MV_USB_DEVICE | MV_GENTL_CAMERALINK_DEVICE | MV_GENTL_CXP_DEVICE | MV_GENTL_XOF_DEVICE ) # 创建设备列表 deviceList = MV_CC_DEVICE_INFO_LIST() # 枚举设备 ret = MvCamera.MV_CC_EnumDevices(n_layer_type, deviceList) if ret != MV_OK: error_msg = f"枚举设备失败! 错误码: 0x{ret:x}" logging.error(error_msg) QMessageBox.warning(mainWindow, "错误", error_msg, QMessageBox.Ok) return ret if deviceList.nDeviceNum == 0: QMessageBox.warning(mainWindow, "提示", "未找到任何设备", QMessageBox.Ok) return MV_OK logging.info(f"找到 {deviceList.nDeviceNum} 个设备") # 处理设备信息 devList = [] for i in range(deviceList.nDeviceNum): # 获取设备信息 mvcc_dev_info = ctypes.cast( deviceList.pDeviceInfo[i], ctypes.POINTER(MV_CC_DEVICE_INFO) ).contents # 根据设备类型提取信息 if mvcc_dev_info.nTLayerType == MV_GIGE_DEVICE: st_gige_info = mvcc_dev_info.SpecialInfo.stGigEInfo ip_addr = ( f"{(st_gige_info.nCurrentIp >> 24) & 0xFF}." f"{(st_gige_info.nCurrentIp >> 16) & 0xFF}." f"{(st_gige_info.nCurrentIp >> 8) & 0xFF}." f"{st_gige_info.nCurrentIp & 0xFF}" ) # 修复:将c_ubyte_Array_16转换为字节串再解码 user_defined_bytes = bytes(st_gige_info.chUserDefinedName) dev_name = f"GigE: {user_defined_bytes.decode('gbk', 'ignore')}" devList.append(f"[{i}] {dev_name} ({ip_addr})") elif mvcc_dev_info.nTLayerType == MV_USB_DEVICE: st_usb_info = mvcc_dev_info.SpecialInfo.stUsb3VInfo serial = bytes(st_usb_info.chSerialNumber).decode('ascii', 'ignore').rstrip('\x00') # 修复:同样处理用户自定义名称 user_defined_bytes = bytes(st_usb_info.chUserDefinedName) dev_name = f"USB: {user_defined_bytes.decode('gbk', 'ignore')}" devList.append(f"[{i}] {dev_name} (SN: {serial})") else: devList.append(f"[{i}] 未知设备类型: {mvcc_dev_info.nTLayerType}") # 更新UI mainWindow.ComboDevices.clear() mainWindow.ComboDevices.addItems(devList) if devList: mainWindow.ComboDevices.setCurrentIndex(0) mainWindow.statusBar().showMessage(f"找到 {deviceList.nDeviceNum} 个设备", 3000) return MV_OK def set_continue_mode(): ret = obj_cam_operation.set_trigger_mode(False) if ret != 0: strError = "设置连续模式失败 ret:" + ToHexStr(ret) QMessageBox.warning(mainWindow, "Error", strError, QMessageBox.Ok) else: mainWindow.radioContinueMode.setChecked(True) mainWindow.radioTriggerMode.setChecked(False) mainWindow.bnSoftwareTrigger.setEnabled(False) def set_software_trigger_mode(): ret = obj_cam_operation.set_trigger_mode(True) if ret != 0: strError = "设置触发模式失败 ret:" + ToHexStr(ret) QMessageBox.warning(mainWindow, "Error", strError, QMessageBox.Ok) else: mainWindow.radioContinueMode.setChecked(False) mainWindow.radioTriggerMode.setChecked(True) mainWindow.bnSoftwareTrigger.setEnabled(isGrabbing) def trigger_once(): ret = obj_cam_operation.trigger_once() if ret != 0: strError = "软触发失败 ret:" + ToHexStr(ret) QMessageBox.warning(mainWindow, "Error", strError, QMessageBox.Ok) def save_sample_image(): global isGrabbing, obj_cam_operation, current_sample_path if not isGrabbing: QMessageBox.warning(mainWindow, "错误", "请先开始取流并捕获图像!", QMessageBox.Ok) return # 尝试捕获当前帧 frame = obj_cam_operation.capture_frame() if frame is None: QMessageBox.warning(mainWindow, "无有效图像", "未捕获到有效图像,请检查相机状态!", QMessageBox.Ok) return # 确保图像有效 if frame.size == 0 or frame.shape[0] == 0 or frame.shape[1] == 0: QMessageBox.warning(mainWindow, "无效图像", "捕获的图像无效,请检查相机设置!", QMessageBox.Ok) return settings = QSettings("ClothInspection", "CameraApp") last_dir = settings.value("last_save_dir", os.path.join(os.getcwd(), "captures")) timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") default_filename = f"sample_{timestamp}" file_path, selected_filter = QFileDialog.getSaveFileName( mainWindow, "保存标准样本图像", os.path.join(last_dir, default_filename), "BMP Files (*.bmp);;PNG Files (*.png);;JPEG Files (*.jpg);;所有文件 (*)", options=QFileDialog.DontUseNativeDialog ) if not file_path: return # 确保文件扩展名正确 file_extension = os.path.splitext(file_path)[1].lower() if not file_extension: if "BMP" in selected_filter: file_path += ".bmp" elif "PNG" in selected_filter: file_path += ".png" elif "JPEG" in selected_filter or "JPG" in selected_filter: file_path += ".jpg" else: file_path += ".bmp" file_extension = os.path.splitext(file_path)[1].lower() # 创建目录(如果不存在) directory = os.path.dirname(file_path) if directory and not os.path.exists(directory): try: os.makedirs(directory, exist_ok=True) except OSError as e: QMessageBox.critical(mainWindow, "目录创建错误", f"无法创建目录 {directory}: {str(e)}", QMessageBox.Ok) return # 保存图像 try: # 使用OpenCV保存图像 if not cv2.imwrite(file_path, frame): raise Exception("OpenCV保存失败") # 更新状态 current_sample_path = file_path update_sample_display() settings.setValue("last_save_dir", os.path.dirname(file_path)) # 显示成功消息 QMessageBox.information(mainWindow, "成功", f"标准样本已保存至:\n{file_path}", QMessageBox.Ok) # 更新样本状态 mainWindow.lblSampleStatus.setText("状态: 样本已设置") mainWindow.lblSampleStatus.setStyleSheet("color: green;") except Exception as e: logging.error(f"保存图像失败: {str(e)}") QMessageBox.critical(mainWindow, "保存错误", f"保存图像时发生错误:\n{str(e)}", QMessageBox.Ok) def preview_sample(): global current_sample_path if not current_sample_path or not os.path.exists(current_sample_path): QMessageBox.warning(mainWindow, "错误", "请先设置有效的标准样本图像!", QMessageBox.Ok) return try: # 直接使用OpenCV加载图像 sample_img = cv2.imread(current_sample_path) if sample_img is None: raise Exception("无法加载图像") # 显示图像 cv2.imshow("标准样本预览", sample_img) cv2.waitKey(0) cv2.destroyAllWindows() except Exception as e: QMessageBox.warning(mainWindow, "错误", f"预览样本失败: {str(e)}", QMessageBox.Ok) def is_float(str): try: float(str) return True except ValueError: return False def get_param(): try: ret = obj_cam_operation.get_parameters() if ret != MV_OK: strError = "获取参数失败,错误码: " + ToHexStr(ret) QMessageBox.warning(mainWindow, "错误", strError, QMessageBox.Ok) else: mainWindow.edtExposureTime.setText("{0:.2f}".format(obj_cam_operation.exposure_time)) mainWindow.edtGain.setText("{0:.2f}".format(obj_cam_operation.gain)) mainWindow.edtFrameRate.setText("{0:.2f}".format(obj_cam_operation.frame_rate)) except Exception as e: error_msg = f"获取参数时发生错误: {str(e)}" QMessageBox.critical(mainWindow, "严重错误", error_msg, QMessageBox.Ok) def set_param(): frame_rate = mainWindow.edtFrameRate.text() exposure = mainWindow.edtExposureTime.text() gain = mainWindow.edtGain.text() if not (is_float(frame_rate) and is_float(exposure) and is_float(gain)): strError = "设置参数失败: 参数必须是有效的浮点数" QMessageBox.warning(mainWindow, "错误", strError, QMessageBox.Ok) return MV_E_PARAMETER try: ret = obj_cam_operation.set_param( frame_rate=float(frame_rate), exposure_time=float(exposure), gain=float(gain) ) if ret != MV_OK: strError = "设置参数失败,错误码: " + ToHexStr(ret) QMessageBox.warning(mainWindow, "错误", strError, QMessageBox.Ok) except Exception as e: error_msg = f"设置参数时发生错误: {str(e)}" QMessageBox.critical(mainWindow, "严重错误", error_msg, QMessageBox.Ok) def enable_controls(): global isGrabbing, isOpen mainWindow.groupGrab.setEnabled(isOpen) mainWindow.paramgroup.setEnabled(isOpen) mainWindow.bnOpen.setEnabled(not isOpen) mainWindow.bnClose.setEnabled(isOpen) mainWindow.bnStart.setEnabled(isOpen and (not isGrabbing)) mainWindow.bnStop.setEnabled(isOpen and isGrabbing) mainWindow.bnSoftwareTrigger.setEnabled(isGrabbing and mainWindow.radioTriggerMode.isChecked()) mainWindow.bnSaveImage.setEnabled(isOpen and isGrabbing) mainWindow.bnCheckPrint.setEnabled(isOpen and isGrabbing) mainWindow.bnSaveSample.setEnabled(isOpen and isGrabbing) mainWindow.bnPreviewSample.setEnabled(bool(current_sample_path)) # 模板匹配控制 mainWindow.chkAutoDetect.setEnabled(bool(current_sample_path) and isGrabbing) # ===== 相机帧监控线程 ===== class FrameMonitorThread(QThread): frame_status = pyqtSignal(str) # 用于发送状态消息的信号 def __init__(self, cam_operation): super().__init__() self.cam_operation = cam_operation self.running = True self.frame_count = 0 self.last_time = time.time() def run(self): """监控相机帧状态的主循环""" while self.running: try: if self.cam_operation and self.cam_operation.is_grabbing: # 获取帧统计信息 frame_info = self.get_frame_info() if frame_info: fps = frame_info.get('fps', 0) dropped = frame_info.get('dropped', 0) status = f"FPS: {fps:.1f} | 丢帧: {dropped}" self.frame_status.emit(status) else: self.frame_status.emit("取流中...") else: self.frame_status.emit("相机未取流") except Exception as e: self.frame_status.emit(f"监控错误: {str(e)}") # 每500ms检查一次 QThread.msleep(500) def stop(self): """停止监控线程""" self.running = False self.wait(1000) # 等待线程结束 def calculate_fps(self): """计算当前帧率""" current_time = time.time() elapsed = current_time - self.last_time if elapsed > 0: fps = self.frame_count / elapsed self.frame_count = 0 self.last_time = current_time return fps return 0 def get_frame_info(self): """获取帧信息""" try: # 更新帧计数 self.frame_count += 1 # 返回帧信息 return { 'fps': self.calculate_fps(), 'dropped': 0 # 实际应用中需要从相机获取真实丢帧数 } except Exception as e: logging.error(f"获取帧信息失败: {str(e)}") return None # ===== 主程序入口 ===== if __name__ == "__main__": # 配置日志系统 logging.basicConfig( level=logging.DEBUG, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', handlers=[ logging.FileHandler("cloth_inspection_template.log"), logging.StreamHandler() ] ) logging.info("布料印花检测系统(模板匹配版)启动") app = QApplication(sys.argv) mainWindow = MainWindow() # 信号连接 mainWindow.sliderDiffThreshold.valueChanged.connect(update_diff_threshold) mainWindow.bnCheckPrint.clicked.connect(lambda: vision_controlled_check(None)) mainWindow.bnSaveSample.clicked.connect(save_sample_image) mainWindow.bnPreviewSample.clicked.connect(preview_sample) mainWindow.bnEnum.clicked.connect(enum_devices) mainWindow.bnOpen.clicked.connect(open_device) mainWindow.bnClose.clicked.connect(close_device) mainWindow.bnStart.clicked.connect(start_grabbing) mainWindow.bnStop.clicked.connect(stop_grabbing) mainWindow.bnSoftwareTrigger.clicked.connect(trigger_once) mainWindow.radioTriggerMode.clicked.connect(set_software_trigger_mode) mainWindow.radioContinueMode.clicked.connect(set_continue_mode) mainWindow.bnGetParam.clicked.connect(get_param) mainWindow.bnSetParam.clicked.connect(set_param) mainWindow.bnSaveImage.clicked.connect(save_sample_image) # 模板匹配信号连接 mainWindow.sliderThreshold.valueChanged.connect(update_match_threshold) mainWindow.chkAutoDetect.stateChanged.connect(toggle_template_matching) mainWindow.show() app.exec_() close_device() sys.exit()
07-16
我运行这个程序放了标准样本后我不管往这个镜头面前换了啥东西,界面里的实时匹配数据还是纹丝不动而且还是5000%我根本无法知道有没有与标准样本进行比对 # -*- coding: utf-8 -*- import sys import os import cv2 import numpy as np import math import time import logging import threading from collections import deque from PyQt5.QtWidgets import ( QApplication, QMainWindow, QPushButton, QWidget, QVBoxLayout, QHBoxLayout, QMessageBox, QLabel, QFileDialog, QToolBox, QComboBox, QStatusBar, QGroupBox, QSlider, QDockWidget, QProgressDialog, QLineEdit, QRadioButton, QGridLayout, QSpinBox, QCheckBox, QDialog, QDialogButtonBox, QDoubleSpinBox, QProgressBar, ) from PyQt5.QtCore import QRect, Qt, QSettings, QThread, pyqtSignal, QTimer, QMetaObject, pyqtSlot,Q_ARG from PyQt5.QtGui import QImage, QPixmap from CamOperation_class import CameraOperation from MvCameraControl_class import * import ctypes from ctypes import cast, POINTER from datetime import datetime import skimage import platform from CameraParams_header import ( MV_GIGE_DEVICE, MV_USB_DEVICE, MV_GENTL_CAMERALINK_DEVICE, MV_GENTL_CXP_DEVICE, MV_GENTL_XOF_DEVICE ) # ===== 全局配置 ===== # 模板匹配参数 MATCH_THRESHOLD = 0.75 # 降低匹配置信度阈值以提高灵敏度 MIN_MATCH_COUNT = 10 # 最小匹配特征点数量 MIN_FRAME_INTERVAL = 0.1 # 最小检测间隔(秒) # ===== 全局变量 ===== current_sample_path = "" detection_history = [] isGrabbing = False isOpen = False obj_cam_operation = None frame_monitor_thread = None template_matcher_thread = None MV_OK = 0 MV_E_CALLORDER = -2147483647 # ==================== 优化后的质量检测算法 ==================== def enhanced_check_print_quality(sample_image_path, test_image, threshold=0.05): # 不再使用传感器数据调整阈值 adjusted_threshold = threshold try: sample_img_data = np.fromfile(sample_image_path, dtype=np.uint8) sample_image = cv2.imdecode(sample_img_data, cv2.IMREAD_GRAYSCALE) if sample_image is None: logging.error(f"无法解码样本图像: {sample_image_path}") return None, None, None except Exception as e: logging.exception(f"样本图像读取异常: {str(e)}") return None, None, None if len(test_image.shape) == 3: test_image_gray = cv2.cvtColor(test_image, cv2.COLOR_BGR2GRAY) else: test_image_gray = test_image.copy() sample_image = cv2.GaussianBlur(sample_image, (5, 5), 0) test_image_gray = cv2.GaussianBlur(test_image_gray, (5, 5), 0) try: # 使用更鲁棒的SIFT特征检测器 sift = cv2.SIFT_create() keypoints1, descriptors1 = sift.detectAndCompute(sample_image, None) keypoints2, descriptors2 = sift.detectAndCompute(test_image_gray, None) if descriptors1 is None or descriptors2 is None: logging.warning("无法提取特征描述符,跳过配准") aligned_sample = sample_image else: # 使用FLANN匹配器提高匹配精度 FLANN_INDEX_KDTREE = 0 index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5) search_params = dict(checks=50) flann = cv2.FlannBasedMatcher(index_params, search_params) matches = flann.knnMatch(descriptors1, descriptors2, k=2) # 应用Lowe's比率测试筛选优质匹配 good_matches = [] for m, n in matches: if m.distance < 0.7 * n.distance: good_matches.append(m) if len(good_matches) > MIN_MATCH_COUNT: src_pts = np.float32([keypoints1[m.queryIdx].pt for m in good_matches]).reshape(-1, 1, 2) dst_pts = np.float32([keypoints2[m.trainIdx].pt for m in good_matches]).reshape(-1, 1, 2) H, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0) if H is not None: aligned_sample = cv2.warpPerspective( sample_image, H, (test_image_gray.shape[1], test_image_gray.shape[0]) ) logging.info("图像配准成功,使用配准后样本") else: aligned_sample = sample_image logging.warning("无法计算单应性矩阵,使用原始样本") else: aligned_sample = sample_image logging.warning(f"特征点匹配不足({len(good_matches)}/{MIN_MATCH_COUNT}),跳过图像配准") except Exception as e: logging.error(f"图像配准失败: {str(e)}") aligned_sample = sample_image try: if aligned_sample.shape != test_image_gray.shape: test_image_gray = cv2.resize(test_image_gray, (aligned_sample.shape[1], aligned_sample.shape[0])) except Exception as e: logging.error(f"图像调整大小失败: {str(e)}") return None, None, None try: from skimage.metrics import structural_similarity as compare_ssim ssim_score, ssim_diff = compare_ssim( aligned_sample, test_image_gray, full=True, gaussian_weights=True, data_range=255 ) except ImportError: from skimage.measure import compare_ssim ssim_score, ssim_diff = compare_ssim( aligned_sample, test_image_gray, full=True, gaussian_weights=True ) except Exception as e: logging.error(f"SSIM计算失败: {str(e)}") abs_diff = cv2.absdiff(aligned_sample, test_image_gray) ssim_diff = abs_diff.astype(np.float32) / 255.0 ssim_score = 1.0 - np.mean(ssim_diff) ssim_diff = (1 - ssim_diff) * 255 abs_diff = cv2.absdiff(aligned_sample, test_image_gray) combined_diff = cv2.addWeighted(ssim_diff.astype(np.uint8), 0.7, abs_diff, 0.3, 0) _, thresholded = cv2.threshold(combined_diff, 30, 255, cv2.THRESH_BINARY) kernel = np.ones((3, 3), np.uint8) thresholded = cv2.morphologyEx(thresholded, cv2.MORPH_OPEN, kernel) threshold极光 = cv2.morphologyEx(thresholded, cv2.MORPH_CLOSE, kernel) diff_pixels = np.count_nonzero(thresholded) total_pixels = aligned_sample.size diff_ratio = diff_pixels / total_pixels is_qualified = diff_ratio <= adjusted_threshold marked_image = cv2.cvtColor(test_image_gray, cv2.COLOR_GRAY2BGR) marked_image[thresholded == 255] = [0, 0, 255] # 放大缺陷标记 scale_factor = 2.0 # 放大2倍 marked_image = cv2.resize(marked_image, None, fx=scale_factor, fy=scale_factor, interpolation=cv2.INTER_LINEAR) labels = skimage.measure.label(thresholded) properties = skimage.measure.regionprops(labels) for prop in properties: if prop.area > 50: y, x = prop.centroid # 根据放大比例调整坐标 x_scaled = int(x * scale_factor) y_scaled = int(y * scale_factor) cv2.putText(marked_image, f"Defect", (x_scaled, y_scaled), cv2.FONT_HERSHEY_SIMPLEX, 0.5 * scale_factor, (0, 255, 255), int(scale_factor)) return is_qualified, diff_ratio, marked_image # ==================== 视觉触发的质量检测流程 ==================== def vision_controlled_check(capture_image=None, match_score=0.0): """修改为接受图像帧和匹配分数""" global current_sample_path, detection_history logging.info("视觉触发质量检测启动") # 如果没有提供图像,使用当前帧 if capture_image is None: frame = obj_cam_operation.get_current_frame() else: frame = capture_image if frame is None: QMessageBox.warning(mainWindow, "错误", "无法获取当前帧图像!", QMessageBox.Ok) return progress = QProgressDialog("正在检测...", "取消", 0, 100, mainWindow) progress.setWindowModality(Qt.WindowModal) progress.setValue(10) try: diff_threshold = mainWindow.sliderDiffThreshold.value() / 100.0 logging.info(f"使用差异度阈值: {diff_threshold}") progress.setValue(30) is_qualified, diff_ratio, marked_image = enhanced_check_print_quality( current_sample_path, frame, threshold=diff_threshold ) progress.setValue(70) if is_qualified is None: QMessageBox.critical(mainWindow, "检测错误", "检测失败,请极光日志", QMessageBox.Ok) return logging.info(f"检测结果: 合格={is_qualified}, 差异={diff_ratio}") progress.setValue(90) update_diff_display(diff_ratio, is_qualified) result_text = f"印花是否合格: {'合格' if is_qualified else '不合格'}\n差异占比: {diff_ratio*100:.2f}%\n阈值: {diff_threshold*100:.2f}%" QMessageBox.information(mainWindow, "检测结果", result_text, QMessageBox.Ok) if marked_image is not None: # 创建可调整大小的窗口 cv2.namedWindow("缺陷标记结果", cv2.WINDOW_NORMAL) cv2.resizeWindow("缺陷标记结果", 800, 600) # 初始大小 cv2.imshow("缺陷标记结果", marked_image) cv2.waitKey(0) cv2.destroyAllWindows() detection_result = { 'timestamp': datetime.now(), 'qualified': is_qualified, 'diff_ratio': diff_ratio, 'threshold': diff_threshold, 'trigger_type': 'vision' if capture_image else 'manual' } detection_history.append(detection_result) update_history_display() progress.setValue(100) except Exception as e: logging.exception("印花检测失败") QMessageBox.critical(mainWindow, "检测错误", f"检测过程中发生错误: {str(e)}", QMessageBox.Ok) finally: progress.close() # ==================== 相机操作函数 ==================== def open_device(): global deviceList, nSelCamIndex, obj_cam_operation, isOpen, frame_monitor_thread, mainWindow if isOpen: QMessageBox.warning(mainWindow, "Error", '相机已打开!', QMessageBox.Ok) return MV_E_CALLORDER nSelCamIndex = mainWindow.ComboDevices.currentIndex() if nSelCamIndex < 0: QMessageBox.warning(mainWindow, "Error", '请选择相机!', QMessageBox.Ok) return MV_E_CALLORDER # 创建相机控制对象 cam = MvCamera() # 初始化相机操作对象 obj_cam_operation = CameraOperation(cam, deviceList, nSelCamIndex) ret = obj_cam_operation.open_device() if 0 != ret: strError = "打开设备失败 ret:" + ToHexStr(ret) QMessageBox.warning(mainWindow, "Error", strError, QMessageBox.Ok) isOpen = False else: set_continue_mode() get_param() isOpen = True enable_controls() # 创建并启动帧监控线程 frame_monitor_thread = FrameMonitorThread(obj_cam_operation) frame_monitor_thread.frame_status.connect(mainWindow.statusBar().showMessage) frame_monitor_thread.start() def start_grabbing(): global obj_cam_operation, isGrabbing, template_matcher_thread ret = obj_cam_operation.start_grabbing(mainWindow.widgetDisplay.winId()) if ret != 0: strError = "开始取流失败 ret:" + ToHexStr(ret) QMessageBox.warning(mainWindow, "Error", strError, QMessageBox.Ok) else: isGrabbing = True enable_controls() # 等待第一帧到达 QThread.msleep(500) if not obj_cam_operation.is_frame_available(): QMessageBox.warning(mainWindow, "警告", "开始取流后未接收到帧,请检查相机连接!", QMessageBox.Ok) # 如果启用了自动检测,启动检测线程 if mainWindow.chkContinuousMatch.isChecked(): toggle_template_matching(True) def stop_grabbing(): global obj_cam_operation, isGrabbing, template_matcher_thread ret = obj_cam_operation.Stop_grabbing() if ret != 0: strError = "停止取流失败 ret:" + ToHexStr(ret) QMessageBox.warning(mainWindow, "Error", strError, QMessageBox.Ok) else: isGrabbing = False enable_controls() # 停止模板匹配线程 if template_matcher_thread and template_matcher_thread.isRunning(): template_matcher_thread.stop() def close_device(): global isOpen, isGrabbing, obj_cam_operation, frame_monitor_thread, template_matcher_thread if frame_monitor_thread and frame_monitor_thread.isRunning(): frame_monitor_thread.stop() frame_monitor_thread.wait(2000) # 停止模板匹配线程 if template_matcher_thread and template_matcher_thread.isRunning(): template_matcher_thread.stop() template_matcher_thread.wait(2000) template_matcher_thread = None if isOpen and obj_cam_operation: obj_cam_operation.close_device() isOpen = False isGrabbing = False enable_controls() # ==================== 连续帧匹配检测器 ==================== class ContinuousFrameMatcher(QThread): frame_processed = pyqtSignal(np.ndarray, float, bool) # 处理后的帧, 匹配分数, 是否匹配 match_score_updated = pyqtSignal(float) # 匹配分数更新信号 match_success = pyqtSignal(np.ndarray, float) # 匹配成功信号 (帧, 匹配分数) trigger_activated = pyqtSignal(bool) # 新增信号:触发状态变化 def __init__(self, cam_operation, parent=None): super().__init__(parent) self.cam_operation = cam_operation self.running = True self.sample_template = None self.min_match_count = MIN_MATCH_COUNT self.match_threshold = MATCH_THRESHOLD self.sample_kp = None self.sample_des = None self.current_match_score = 0.0 self.last_match_time = 0 self.frame_counter = 0 self.consecutive_fail_count = 0 self.last_trigger_time = 0 # 上次触发时间 self.cool_down = 0.2 # 冷却时间(秒) self.trigger_threshold = 0.5 # 默认触发阈值 # 特征检测器 - 使用SIFT self.sift = cv2.SIFT_create() # 特征匹配器 - 使用FLANN提高匹配精度 FLANN_INDEX_KDTREE = 1 index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5) search_params = dict(checks=50) self.flann = cv2.FlannBasedMatcher(index_params, search_params) # 性能监控 self.processing_times = deque(maxlen=100) self.frame_rates = deque(maxlen=100) # 黑白相机优化 self.clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8, 8)) def calculate_match_score(self, good_matches): """更合理的匹配分数计算方法""" if not good_matches: return 0.0 # 计算平均匹配质量(距离的倒数) total_quality = 0.0 for match in good_matches: # 距离越小匹配越好,但避免除以0 if match.distance < 1e-5: total_quality += 1.0 else: total_quality += 1.0 / match.distance # 使用特征点数量和平均质量计算分数 avg_quality = total_quality / len(good_matches) match_score = min(1.0, max(0.0, avg_quality * 0.5)) # 调整比例因子 return match_score def preprocess_image(self, image): """增强黑白图像特征提取""" # 如果是单通道图像,转换为三通道 if len(image.shape) == 2: image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR) # 对比度增强 (LAB空间) lab = cv2.cvtColor(image, cv2.COLOR_BGR2LAB) l, a, b = cv2.split(lab) cl = self.clahe.apply(l) limg = cv2.merge((cl, a, b)) enhanced = cv2.cvtColor(limg, cv2.COLOR_LAB2BGR) # 边缘增强 gray = cv2.cvtColor(enhanced, cv2.COLOR_BGR2GRAY) edges = cv2.Canny(gray, 50, 150) # 组合特征 return cv2.addWeighted(enhanced, 0.7, cv2.cvtColor(edges, cv2.COLOR_GRAY2BGR), 0.3, 0) def set_sample(self, sample_img): """设置标准样本并提取特征""" # 保存样本图像 self.sample_img = sample_img # 预处理增强特征 processed_sample = self.preprocess_image(sample_img) # 提取样本特征点 self.sample_kp, self.sample_des = self.sift.detectAndCompute(processed_sample, None) if self.sample_des is None or len(self.sample_kp) < 100: # 增加最小特征点要求 logging.warning(f"样本特征点不足({len(self.sample_kp)}个),需要至少100个") return False logging.info(f"样本特征提取成功: {len(self.sample_kp)}个关键点") return True def set_threshold(self, threshold): """更新匹配阈值""" self.match_threshold = max(0.0, min(1.0, threshold)) logging.info(f"更新匹配阈值: {self.match_threshold:.2f}") def set_trigger_threshold(self, threshold): """更新触发阈值""" self.trigger_threshold = max(0.0, min(1.0, threshold)) logging.info(f"更新触发阈值: {self.trigger_threshold:.2f}") def process_frame(self, frame): """处理帧:特征提取、匹配和可视化""" is_matched = False match_score = 0.0 processed_frame = frame.copy() # 检查是否已设置样本 if self.sample_kp is None or self.sample_des is None: return processed_frame, match_score, is_matched # 预处理当前帧 processed_frame = self.preprocess_image(frame) # 转换为灰度图像用于特征提取 gray_frame = cv2.cvtColor(processed_frame, cv2.COLOR_BGR2GRAY) try: # 提取当前帧的特征点 kp, des = self.sift.detectAndCompute(gray_frame, None) if des is None or len(kp) < 5: # 特征点不足 return processed_frame, match_score, is_matched # 匹配特征点 matches = self.flann.knnMatch(self.sample_des, des, k=2) # 应用Lowe's比率测试 good_matches = [] for m, n in matches: if m.distance < 0.7 * n.distance: good_matches.append(m) # 使用更科学的匹配分数计算方法 match_score = self.calculate_match_score(good_matches) # 判断是否匹配成功(用于UI显示) if len(good_matches) >= self.min_match_count and match_score >= self.match_threshold: is_matched = True # 在图像上绘制匹配结果 if len(gray_frame.shape) == 2: processed_frame = cv2.cvtColor(gray_frame, cv2.COLOR_GRAY2BGR) # 绘制匹配点 processed_frame = cv2.drawMatches( self.sample_img, self.s极光, processed_frame, kp, good_matches, None, flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS ) # 在图像上显示匹配分数 cv2.putText(processed_frame, f"Match Score: {match_score:.2f}", (20, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2) # 更新当前匹配分数 self.current_match_score = match_score self.match_score_updated.emit(match_score) # 检查是否达到触发条件(50%以上)并且超过冷却时间 trigger_active = False current_time = time.time() if match_score >= self.trigger_threshold and (current_time - self.last_trigger_time) > self.cool_down: self.last_trigger_time = current_time trigger_active = True logging.info(f"匹配分数达到触发阈值! 分数: {match_score:.2f}, 触发质量检测") # 发出匹配成功信号 (传递当前帧) self.match_success.emit(frame.copy(), match_score) # 发送触发状态信号 self.trigger_activated.emit(trigger_active) except Exception as e: logging.error(f"帧处理错误: {str(e)}") return processed_frame, match_score, is_matched def run(self): """主处理循环 - 连续处理每一帧""" logging.info("连续帧匹配线程启动") self.last_match_time = time.time() self.consecutive_fail_count = 0 while self.running: start_time = time.time() # 检查相机状态 if not self.cam_operation or not self.cam_operation.is_grabbing: if self.consecutive_fail_count % 10 == 0: logging.debug("相机未取流,等待...") time.sleep(0.1) self.consecutive_fail_count += 1 continue # 获取当前帧 frame = self.cam_operation.get_current_frame() if frame is None: self.consecutive_fail_count += 1 if self.consecutive_fail_count % 10 == 0: logging.warning(f"连续{self.consecutive_fail_count}次获取帧失败") time.sleep(0.05) continue self.consecutive_fail_count = 0 try: # 处理帧 processed_frame, match_score, is_matched = self.process_frame(frame) # 发送处理结果 self.frame_processed.emit(processed_frame, match_score, is_matched) except Exception as e: logging.error(f"帧处理错误: {str(e)}") # 控制处理频率 processing_time = time.time() - start_time sleep_time = max(0.01, MIN_FRAME_INTERVAL - processing_time) time.sleep(sleep_time) logging.info("连续帧匹配线程退出") # ==================== 模板匹配控制函数 ==================== def toggle_template_matching(state): global template_matcher_thread, current_sample_path logging.debug(f"切换连续匹配状态: {state}") if state == Qt.Checked and isGrabbing: # 确保已设置样本 if not current_sample_path: logging.warning("尝试启动连续匹配但未设置样本") QMessageBox.warning(mainWindow, "错误", "请先设置标准样本", QMessageBox.Ok) mainWindow.chkContinuousMatch.setChecked(False) return if template_matcher_thread is None: logging.info("创建新的连续帧匹配线程") template_matcher_thread = ContinuousFrameMatcher(obj_cam_operation) template_matcher_thread.frame_processed.connect(update_frame_display) template_matcher_thread.match_score_updated.connect(update_match_score_display) template_matcher_thread.trigger_activated.connect( lambda active: mainWindow.update_trigger_indicator(active) ) # 正确连接匹配成功信号到质量检测函数 template_matcher_thread.match_success.connect( lambda frame, score: vision_controlled_check(frame, score) ) # 加载样本图像 sample_img = cv2.imread(current_sample_path) if sample_img is None: logging.error("无法加载标准样本图像") QMessageBox.warning(mainWindow, "错误", "无法加载标准样本图像", QMessageBox.Ok) mainWindow.chkContinuousMatch.setChecked(False) return if not template_matcher_thread.set_sample(sample_img): logging.warning("标准样本特征不足") QMessageBox.warning(mainWindow, "错误", "标准样本特征不足", QMessageBox.Ok) mainWindow.chkContinuousMatch.setChecked(False) return if not template_matcher_thread.isRunning(): logging.info("启动连续帧匹配线程") template_matcher_thread.start() elif template_matcher_thread and template_matcher_thread.isRunning(): logging.info("停止连续帧匹配线程") template_matcher_thread.stop() # 重置匹配分数显示 update_match_score_display(0.0) # 重置帧显示 if obj_cam_operation and obj_cam_operation.is_frame_available(): frame = obj_cam_operation.get_current_frame() if frame is not None: display_frame = frame.copy() # 添加状态信息 cv2.putText(display_frame, "Continuous Matching Disabled", (20, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2) update_frame_display(display_frame, 0.0, False) # 添加类型转换函数 def numpy_to_qimage(np_array): """将 numpy 数组转换为 QImage""" if np_array is None: return QImage() height, width, channel = np_array.shape bytes_per_line = 3 * width # 确保数据是连续的 if not np_array.flags['C_CONTIGUOUS']: np_array = np.ascontiguousarray(np_array) # 转换 BGR 到 RGB rgb_image = cv2.cvtColor(np_array, cv2.COLOR_BGR2RGB) # 创建 QImage qimg = QImage( rgb_image.data, width, height, bytes_per_line, QImage.Format_RGB888 ) # 复制数据以避免内存问题 return qimg.copy() # 修改 update_frame_display 函数 def update_frame_display(frame, match_score, is_matched): """更新主显示窗口(线程安全)""" # 确保在GUI线程中执行 if QThread.currentThread() != QApplication.instance().thread(): # 转换为 QImage 再传递 qimg = numpy_to_qimage(frame) QMetaObject.invokeMethod( mainWindow, "updateDisplay", Qt.QueuedConnection, Q_ARG(QImage, qimg), Q_ARG(float, match_score), Q_ARG(bool, is_matched) ) return # 如果已经在主线程,直接调用主窗口的更新方法 mainWindow.updateDisplay(frame, match_score, is_matched) def update_match_score_display(score): """更新匹配分数显示""" # 将分数转换为百分比显示 score_percent = score * 100 mainWindow.lblMatchScoreValue.setText(f"{score_percent:.1f}%") # 根据分数设置颜色 if score > 0.8: # 高于80%显示绿色 color = "green" elif score > 0.6: # 60%-80%显示黄色 color = "orange" else: # 低于60%显示红色 color = "red" mainWindow.lblMatchScoreValue.setStyleSheet(f"color: {color}; font-weight: bold;") def update_diff_display(diff_ratio, is_qualified): mainWindow.lblCurrentDiff.setText(f"当前差异度: {diff_ratio*100:.2f}%") if is_qualified: mainWindow.lblDiffStatus.setText("状态: 合格") mainWindow.lblDiffStatus.setStyleSheet("color: green; font-size: 12px;") else: mainWindow.lblDiffStatus.setText("状态: 不合格") mainWindow.lblDiffStatus.setStyleSheet("color: red; font-size: 12px;") def update_diff_threshold(value): mainWindow.lblDiffValue.setText(f"{value}%") def update_sample_display(): global current_sample_path if current_sample_path: mainWindow.lblSamplePath.setText(f"当前样本: {os.path.basename(current_sample_path)}") mainWindow.lblSamplePath.setToolTip(current_sample_path) mainWindow.bnPreviewSample.setEnabled(True) else: mainWindow.lblSamplePath.setText("当前样本: 未设置样本") mainWindow.bnPreviewSample.setEnabled(False) def update_history_display(): global detection_history mainWindow.cbHistory.clear() for i, result in enumerate(detection_history[-10:]): timestamp = result['timestamp'].strftime("%H:%M:%S") status = "合格" if result['qualified'] else "不合格" ratio = f"{result['diff_ratio']*100:.2f}%" trigger = "视觉" if result['trigger_type'] == 'vision' else "手动" mainWindow.cbHistory.addItem(f"[{trigger} {timestamp}] {status} - 差异: {ratio}") def update_match_threshold(value): """更新匹配阈值显示并应用到匹配器""" global template_matcher_thread # 更新UI显示 if mainWindow: mainWindow.lblThresholdValue.setText(f"{value}%") # 如果匹配线程存在,更新其匹配阈值 if template_matcher_thread: # 转换为0-1范围的浮点数 threshold = value / 100.0 template_matcher_thread.set_threshold(threshold) logging.debug(f"更新匹配阈值: {threshold:.2f}") # 新增函数:保存当前帧 def save_current_frame(): if not isGrabbing: QMessageBox.warning(mainWindow, "错误", "请先开始取流并捕获图像!", QMessageBox.Ok) return frame = obj_cam_operation.get_current_frame() if frame is None: QMessageBox.warning(mainWindow, "无有效图像", "未捕获到有效图像,请检查相机状态!", QMessageBox.Ok) return settings = QSettings("ClothInspection", "CameraApp") last_dir = settings.value("last_save_dir", os.path.join(os.getcwd(), "captures")) timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") default_filename = f"capture_{timestamp}" file_path, selected_filter = QFileDialog.getSaveFileName( mainWindow, "保存当前帧", os.path.join(last_dir, default_filename), "PNG Files (*.png);;BMP Files (*.bmp);;JPEG Files (*.jpg);;所有文件 (*)", options=QFileDialog.DontUseNativeDialog ) if not file_path: return # 确保文件扩展名正确 if not os.path.splitext(file_path)[1]: if "PNG" in selected_filter: file_path += ".png" elif "BMP" in selected_filter: file_path += ".bmp" elif "JPEG" in selected_filter or "JPG" in selected_filter: file_path += ".jpg" # 保存图像 try: if cv2.imwrite(file_path, frame): QMessageBox.information(mainWindow, "成功", f"当前帧已保存至:\n{file_path}", QMessageBox.Ok) else: raise Exception("OpenCV保存失败") except Exception as e: QMessageBox.critical(mainWindow, "保存错误", f"保存图像时发生错误:\n{str(e)}", QMessageBox.Ok) # ==================== 主窗口类 ==================== class MainWindow(QMainWindow): def __init__(self): super().__init__() self.setWindowTitle("布料印花检测系统 - 连续匹配版") self.resize(1200, 800) central_widget = QWidget() self.setCentralWidget(central_widget) main_layout = QVBoxLayout(central_widget) # 设备枚举区域 device_layout = QHBoxLayout() self.ComboDevices = QComboBox() self.bnEnum = QPushButton("枚举设备") self.bnOpen = QPushButton("打开设备") self.bnClose = QPushButton("关闭设备") device_layout.addWidget(self.ComboDevices) device_layout.addWidget(self.bnEnum) device_layout.addWidget(self.bnOpen) device_layout.addWidget(self.bnClose) main_layout.addLayout(device_layout) # 取流控制组 self.groupGrab = QGroupBox("取流控制") grab_layout = QHBoxLayout(self.groupGrab) self.bnStart = QPushButton("开始取流") self.bnStop = QPushButton("停止取流") self.radioContinueMode = QRadioButton("连续模式") self.radioTriggerMode = QRadioButton("触发模式") self.bnSoftwareTrigger = QPushButton("软触发") grab_layout.addWidget(self.bnStart) grab_layout.addWidget(self.bnStop) grab_layout.addWidget(self.radioContinueMode) grab_layout.addWidget(self.radioTriggerMode) grab_layout.addWidget(self.bnSoftwareTrigger) main_layout.addWidget(self.groupGrab) # 参数设置组 self.paramgroup = QGroupBox("相机参数") param_layout = QGridLayout(self.paramgroup) self.edtExposureTime = QLineEdit() self.edtGain = QLineEdit() self.edtFrameRate = QLineEdit() self.bnGetParam = QPushButton("获取参数") self.bnSetParam = QPushButton("设置参数") self.bnSaveImage = QPushButton("保存图像") param_layout.addWidget(QLabel("曝光时间:"), 0, 0) param_layout.addWidget(self.edtExposureTime, 0, 1) param_layout.addWidget(self.bnGetParam, 0, 2) param_layout.addWidget(QLabel("增益:"), 1, 0) param_layout.addWidget(self.edtGain, 1, 1) param_layout.addWidget(self.bnSetParam, 1, 2) param_layout.addWidget(QLabel("帧率:"), 2, 0) param_layout.addWidget(self.edtFrameRate, 2, 1) param_layout.addWidget(self.bnSaveImage, 2, 2) main_layout.addWidget(self.paramgroup) # 图像显示区域 self.widgetDisplay = QLabel() self.widgetDisplay.setMinimumSize(640, 480) self.widgetDisplay.setStyleSheet("background-color: black;") self.widgetDisplay.setAlignment(Qt.AlignCenter) self.widgetDisplay.setText("相机预览区域") main_layout.addWidget(self.widgetDisplay, 1) # 创建自定义UI组件 self.setup_custom_ui() # 添加阈值自适应定时器 self.threshold_timer = QTimer() self.threshold_timer.timeout.connect(self.auto_adjust_threshold) self.threshold_timer.start(2000) # 每2秒调整一次 def auto_adjust_threshold(self): """根据环境亮度自动调整匹配阈值""" if not obj_cam_operation or not isGrabbing: return # 获取当前帧 frame = obj_cam_operation.get_current_frame() if frame is None: return # 处理不同通道数的图像 if len(frame.shape) == 2 or frame.shape[2] == 1: # 已经是灰度图 gray = frame elif frame.shape[2] == 3: # 三通道彩色图 gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) elif frame.shape[2] == 4: # 四通道图(带alpha) gray = cv2.cvtColor(frame, cv2.COLOR_BGRA2GRAY) else: # 其他通道数,无法处理 logging.warning(f"无法处理的图像格式: {frame.shape}") return # 计算平均亮度 try: brightness = np.mean(gray) except Exception as e: logging.error(f"计算亮度失败: {str(e)}") return # 根据亮度动态调整阈值 (亮度低时降低阈值要求) if brightness < 50: # 暗环境 new_threshold = 40 # 40% elif brightness > 200: # 亮环境 new_threshold = 65 # 65% else: # 正常环境 new_threshold = 55 # 55% # 更新UI self.sliderThreshold.setValue(new_threshold) self.lblThresholdValue.setText(f"{new_threshold}%") # 更新匹配器阈值 update_match_threshold(new_threshold) # 状态栏显示调整信息 self.statusBar().showMessage(f"亮度: {brightness:.1f}, 自动调整阈值至: {new_threshold}%", 3000) def update_trigger_indicator(self, active): """更新触发指示灯状态""" if active: self.triggerIndicator.setStyleSheet("background-color: green; border-radius: 10px;") else: self.triggerIndicator.setStyleSheet("background-color: gray; border-radius: 10px;") def update_trigger_threshold(self, value): """更新触发阈值显示并应用到匹配器""" self.lblTriggerValue.setText(f"{value}%") threshold = value / 100.0 if template_matcher_thread: template_matcher_thread.set_trigger_threshold(threshold) def setup_custom_ui(self): # 工具栏 toolbar = self.addToolBar("检测工具") self.bnCheckPrint = QPushButton("手动检测") toolbar.addWidget(self.bnCheckPrint) # 添加分隔标签 toolbar.addWidget(QLabel("图像保存:")) self.bnSaveCurrentFrame = QPushButton("保存当前帧") toolbar.addWidget(self.bnSaveCurrentFrame) self.bnSaveSample = QPushButton("保存标准样本") toolbar.addWidget(self.bnSaveSample) self.bnPreviewSample = QPushButton("预览样本") toolbar.addWidget(self.bnPreviewSample) # 添加触发指示灯 self.triggerIndicator = QLabel() self.triggerIndicator.setFixedSize(20, 20) self.triggerIndicator.setStyleSheet("background-color: gray; border-radius: 10px;") toolbar.addWidget(QLabel("触发状态:")) toolbar.addWidget(self.triggerIndicator) # 历史记录 toolbar.addWidget(QLabel("历史记录:")) self.cbHistory = QComboBox() self.cbHistory.setMinimumWidth(300) toolbar.addWidget(self.cbHistory) # 状态栏样本路径 self.lblSamplePath = QLabel("当前样本: 未设置样本") self.statusBar().addPermanentWidget(self.lblSamplePath) # 右侧面板 right_panel = QWidget() right_layout = QVBoxLayout(right_panel) right_layout.setContentsMargins(10, 10, 10, 10) # 差异度调整组 diff_group = QGroupBox("差异度调整") diff_layout = QVBoxLayout(diff_group) self.lblDiffThreshold = QLabel("差异度阈值 (0-100%):") self.sliderDiffThreshold = QSlider(Qt.Horizontal) self.sliderDiffThreshold.setRange(0, 100) self.sliderDiffThreshold.setValue(5) self.lblDiffValue = QLabel("5%") self.lblCurrentDiff = QLabel("当前差异度: -") self.lblCurrentDiff.setStyleSheet("font-size: 14px; font-weight: bold;") self.lblDiffStatus = QLabel("状态: 未检测") self.lblDiffStatus.setStyleSheet("font-size: 12px;") diff_layout.addWidget(self.lblDiffThreshold) diff_layout.addWidget(self.sliderDiffThreshold) diff_layout.addWidget(self.lblDiffValue) diff_layout.addWidget(self.lblCurrentDiff) diff_layout.addWidget(self.lblDiffStatus) right_layout.addWidget(diff_group) # ===== 连续匹配面板 ===== match_group = QGroupBox("连续帧匹配") match_layout = QVBoxLayout(match_group) # 样本设置 sample_layout = QHBoxLayout() self.bnSetSample = QPushButton("设置标准样本") self.bnPreviewSample = QPushButton("预览样本") self.lblSampleStatus = QLabel("状态: 未设置样本") sample_layout.addWidget(self.bnSetSample) sample_layout.addWidget(self.bnPreviewSample) sample_layout.addWidget(self.lblSampleStatus) match_layout.addLayout(sample_layout) # 匹配参数 param_layout = QHBoxLayout() self.lblMatchThreshold = QLabel("匹配阈值:") self.sliderThreshold = QSlider(Qt.Horizontal) self.sliderThreshold.setRange(50, 100) self.sliderThreshold.setValue(75) # 降低默认阈值 self.lblThresholdValue = QLabel("75%") param_layout.addWidget(self.lblMatchThreshold) param_layout.addWidget(self.sliderThreshold) param_layout.addWidget(self.lblThresholdValue) match_layout.addLayout(param_layout) # 触发阈值调整 trigger_threshold_layout = QHBoxLayout() self.lblTriggerThreshold = QLabel("触发阈值(%):") self.sliderTriggerThreshold = QSlider(Qt.Horizontal) self.sliderTriggerThreshold.setRange(0, 100) self.sliderTriggerThreshold.setValue(50) # 默认50% self.lblTriggerValue = QLabel("50%") trigger_threshold_layout.addWidget(self.lblTriggerThreshold) trigger_threshold_layout.addWidget(self.sliderTriggerThreshold) trigger_threshold_layout.addWidget(self.lblTriggerValue) match_layout.addLayout(trigger_threshold_layout) # 匹配分数显示 match_score_layout = QHBoxLayout() self.lblMatchScore = QLabel("实时匹配分数:") self.lblMatchScoreValue = QLabel("0.0%") self.lblMatchScoreValue.setStyleSheet("font-weight: bold;") match_score_layout.addWidget(self.lblMatchScore) match_score_layout.addWidget(self.lblMatchScoreValue) match_layout.addLayout(match_score_layout) # 连续匹配开关 self.chkContinuousMatch = QCheckBox("启用连续帧匹配") self.chkContinuousMatch.setChecked(False) match_layout.addWidget(self.chkContinuousMatch) right_layout.addWidget(match_group) right_layout.addStretch(1) # 停靠窗口 dock = QDockWidget("检测控制面板", self) dock.setWidget(right_panel) dock.setFeatures(QDockWidget.DockWidgetMovable | QDockWidget.DockWidgetFloatable) self.addDockWidget(Qt.RightDockWidgetArea, dock) @pyqtSlot(QImage, float, bool) def updateDisplay(self, qimg, match_score, is_matched): """线程安全的显示更新方法(只接收 QImage)""" if qimg.isNull(): return # 创建QPixmap并缩放 pixmap = QPixmap.fromImage(qimg) scaled_pixmap = pixmap.scaled( self.widgetDisplay.size(), Qt.KeepAspectRatio, Qt.SmoothTransformation ) # 更新显示 self.widgetDisplay.setPixmap(scaled_pixmap) self.widgetDisplay.setAlignment(Qt.AlignCenter) def closeEvent(self, event): logging.info("主窗口关闭,执行清理...") close_device() event.accept() # ===== 辅助函数 ===== def ToHexStr(num): if not isinstance(num, int): try: num = int(num) except: return f"<非整数:{type(num)}>" chaDic = {10: 'a', 11: 'b', 12: 'c', 13: 'd', 14: 'e', 15: 'f'} hexStr = "" if num < 0: num = num + 2 ** 32 while num >= 16: digit = num % 16 hexStr = chaDic.get(digit, str(digit)) + hexStr num //= 16 hexStr = chaDic.get(num, str(num)) + hexStr return "0x" + hexStr def enum_devices(): global deviceList, obj_cam_operation n_layer_type = ( MV_GIGE_DEVICE | MV_USB_DEVICE | MV_GENTL_CAMERALINK_DEVICE | MV_GENTL_CXP_DEVICE | MV_GENTL_XOF_DEVICE ) # 创建设备列表 deviceList = MV_CC_DEVICE_INFO_LIST() # 枚举设备 ret = MvCamera.MV_CC_EnumDevices(n_layer_type, deviceList) if ret != MV_OK: error_msg = f"枚举设备失败! 错误码: 0x{ret:x}" logging.error(error_msg) QMessageBox.warning(mainWindow, "错误", error_msg, QMessageBox.Ok) return ret if deviceList.nDeviceNum == 0: QMessageBox.warning(mainWindow, "提示", "未找到任何设备", QMessageBox.Ok) return MV_OK logging.info(f"找到 {deviceList.nDeviceNum} 个设备") # 处理设备信息 devList = [] for i in range(deviceList.nDeviceNum): # 获取设备信息 mvcc_dev_info = ctypes.cast( deviceList.pDeviceInfo[i], ctypes.POINTER(MV_CC_DEVICE_INFO) ).contents # 根据设备类型提取信息 if mvcc_dev_info.nTLayerType == MV_GIGE_DEVICE: st_gige_info = mvcc_dev_info.SpecialInfo.stGigEInfo ip_addr = ( f"{(st_gige_info.nCurrentIp >> 24) & 0xFF}." f"{(st_gige_info.nCurrentIp >> 16) & 0xFF}." f"{(st_gige_info.nCurrentIp >> 8) & 0xFF}." f"{st_gige_info.nCurrentIp & 0xFF}" ) # 修复:将c_ubyte_Array_16转换为字节串再解码 user_defined_bytes = bytes(st_gige_info.chUserDefinedName) dev_name = f"GigE: {user_defined_bytes.decode('gbk', 'ignore')}" devList.append(f"[{i}] {dev_name} ({ip_addr})") elif mvcc_dev_info.nTLayerType == MV_USB_DEVICE: st_usb_info = mvcc_dev_info.SpecialInfo.stUsb3VInfo serial = bytes(st_usb_info.chSerialNumber).decode('ascii', 'ignore').rstrip('\x00') # 修复:同样处理用户自定义名称 user_defined_bytes = bytes(st_usb_info.chUserDefinedName) dev_name = f"USB: {user_defined_bytes.decode('gbk', 'ignore')}" devList.append(f"[{i}] {dev_name} (SN: {serial})") else: devList.append(f"[{i}] 未知设备类型: {mvcc_dev_info.nTLayerType}") # 更新UI mainWindow.ComboDevices.clear() mainWindow.ComboDevices.addItems(devList) if devList: mainWindow.ComboDevices.setCurrentIndex(0) mainWindow.statusBar().showMessage(f"找到 {deviceList.nDeviceNum} 个设备", 300) return MV_OK def set_continue_mode(): ret = obj_cam_operation.set_trigger_mode(False) if ret != 0: strError = "设置连续模式失败 ret:" + ToHexStr(ret) QMessageBox.warning(mainWindow, "Error", strError, QMessageBox.Ok) else: mainWindow.radioContinueMode.setChecked(True) mainWindow.radioTriggerMode.setChecked(False) mainWindow.bnSoftwareTrigger.setEnabled(False) def set_software_trigger_mode(): ret = obj_cam_operation.set_trigger_mode(True) if ret != 0: strError = "设置触发模式失败 ret:" + ToHexStr(ret) QMessageBox.warning(mainWindow, "Error", strError, QMessageBox.Ok) else: mainWindow.radioContinueMode.setChecked(False) mainWindow.radioTriggerMode.setChecked(True) mainWindow.bnSoftwareTrigger.setEnabled(isGrabbing) def trigger_once(): ret = obj_cam_operation.trigger_once() if ret != 0: strError = "软触发失败 ret:" + ToHexStr(ret) QMessageBox.warning(mainWindow, "Error", strError, QMessageBox.Ok) def save_sample_image(): global isGrabbing, obj_cam_operation, current_sample_path if not isGrabbing: QMessageBox.warning(mainWindow, "错误", "请先开始取流并捕获图像!", QMessageBox.Ok) return # 尝试捕获当前帧 frame = obj_cam_operation.capture_frame() if frame is None: QMessageBox.warning(mainWindow, "无有效图像", "未捕获到有效图像,请检查相机状态!", QMessageBox.Ok) return # 确保图像有效 if frame.size == 0 or frame.shape[0] == 0 or frame.shape[1] == 0: QMessageBox.warning(mainWindow, "无效图像", "捕获的图像无效,请检查相机设置!", QMessageBox.Ok) return settings = QSettings("ClothInspection", "CameraApp") last_dir = settings.value("last_save_dir", os.path.join(os.getcwd(), "captures")) timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") default_filename = f"sample_{timestamp}" file_path, selected_filter = QFileDialog.getSaveFileName( mainWindow, "保存标准样本图像", os.path.join(last_dir, default_filename), "BMP Files (*.bmp);;PNG Files (*.png);;JPEG Files (*.jpg);;所有文件 (*)", options=QFileDialog.DontUseNativeDialog ) if not file_path: return # 确保文件扩展名正确 file_extension = os.path.splitext(file_path)[1].lower() if not file_extension: if "BMP" in selected_filter: file_path += ".bmp" elif "PNG" in selected_filter: file_path += ".png" elif "JPEG" in selected_filter or "JPG" in selected_filter: file_path += ".jpg" else: file_path += ".bmp" file_extension = os.path.splitext(file_path)[1].lower() # 创建目录(如果不存在) directory = os.path.dirname(file_path) if directory and not os.path.exists(directory): try: os.makedirs(directory, exist_ok=True) except OSError as e: QMessageBox.critical(mainWindow, "目录创建错误", f"无法创建目录 {directory}: {str(e)}", QMessageBox.Ok) return # 保存图像 try: # 使用OpenCV保存图像 if not cv2.imwrite(file_path, frame): raise Exception("OpenCV保存失败") # 更新状态 current_sample_path = file_path update_sample_display() settings.setValue("last_save_dir", os.path.dirname(file_path)) # 显示成功消息 QMessageBox.information(mainWindow, "成功", f"标准样本已保存至:\n{file_path}", QMessageBox.Ok) # 更新样本状态 mainWindow.lblSampleStatus.setText("状态: 样本已设置") mainWindow.lblSampleStatus.setStyleSheet("color: green;") except Exception as e: logging.error(f"保存图像失败: {str(e)}") QMessageBox.critical(mainWindow, "保存错误", f"保存图像时发生错误:\n{str(e)}", QMessageBox.Ok) def preview_sample(): global current_sample_path if not current_sample_path or not os.path.exists(current_sample_path): QMessageBox.warning(mainWindow, "错误", "请先设置有效的标准样本图像!", QMessageBox.Ok) return try: # 直接使用OpenCV加载图像 sample_img = cv2.imread(current_sample_path) if sample_img is None: raise Exception("无法加载图像") # 显示图像 cv2.namedWindow("标准样本预览", cv2.WINDOW_NORMAL) cv2.resizeWindow("标准样本预览", 800, 600) cv2.imshow("标准样本预览", sample_img) cv2.waitKey(0) cv2.destroyAllWindows() except Exception as e: QMessageBox.warning(mainWindow, "错误", f"预览样本失败: {str(e)}", QMessageBox.Ok) def is_float(str): try: float(str) return True except ValueError: return False def get_param(): try: ret = obj_cam_operation.get_parameters() if ret != MV_OK: strError = "获取参数失败,错误码: " + ToHexStr(ret) QMessageBox.warning(mainWindow, "错误", strError, QMessageBox.Ok) else: mainWindow.edtExposureTime.setText("{0:.2f}".format(obj_cam_operation.exposure_time)) mainWindow.edtGain.setText("{0:.2f}".format(obj_cam_operation.gain)) mainWindow.edtFrameRate.setText("{0:.2f}".format(obj_cam_operation.frame_rate)) except Exception as e: error_msg = f"获取参数时发生错误: {str(e)}" QMessageBox.critical(mainWindow, "严重错误", error_msg, QMessageBox.Ok) def set_param(): frame_rate = mainWindow.edtFrameRate.text() exposure = mainWindow.edtExposureTime.text() gain = mainWindow.edtGain.text() if not (is_float(frame_rate) and is_float(exposure) and is_float(gain)): strError = "设置参数失败: 参数必须是有效的浮点数" QMessageBox.warning(mainWindow, "错误", strError, QMessageBox.Ok) return MV_E_PARAMETER try: ret = obj_cam_operation.set_param( frame_rate=float(frame_rate), exposure_time=float(exposure), gain=float(gain) ) if ret != MV_OK: strError = "设置参数失败,错误码: " + ToHexStr(ret) QMessageBox.warning(mainWindow, "错误", strError, QMessageBox.Ok) except Exception as e: error_msg = f"设置参数时发生错误: {str(e)}" QMessageBox.critical(mainWindow, "严重错误", error_msg, QMessageBox.Ok) def enable_controls(): global isGrabbing, isOpen mainWindow.groupGrab.setEnabled(isOpen) mainWindow.paramgroup.setEnabled(isOpen) mainWindow.bnOpen.setEnabled(not isOpen) mainWindow.bnClose.setEnabled(isOpen) mainWindow.bnStart.setEnabled(isOpen and (not isGrabbing)) mainWindow.bnStop.setEnabled(isOpen and isGrabbing) mainWindow.bnSoftwareTrigger.setEnabled(isGrabbing and mainWindow.radioTriggerMode.isChecked()) mainWindow.bnSaveImage.setEnabled(isOpen and isGrabbing) mainWindow.bnCheckPrint.setEnabled(isOpen and isGrabbing) mainWindow.bnSaveSample.setEnabled(isOpen and isGrabbing) mainWindow.bnPreviewSample.setEnabled(bool(current_sample_path)) mainWindow.bnSaveCurrentFrame.setEnabled(isOpen and isGrabbing) # 连续匹配控制 mainWindow.chkContinuousMatch.setEnabled(bool(current_sample_path) and isGrabbing) # ===== 相机帧监控线程 ===== class FrameMonitorThread(QThread): frame_status = pyqtSignal(str) # 用于发送状态消息的信号 def __init__(self, cam_operation): super().__init__() self.cam_operation = cam_operation self.running = True self.frame_count = 0 self.last_time = time.time() def run(self): """监控相机帧状态的主循环""" while self.running: try: if self.cam_operation and self.cam_operation.is_grabbing: # 获取帧统计信息 frame_info = self.get_frame_info() if frame_info: fps = frame_info.get('fps', 0) dropped = frame_info.get('dropped', 0) status = f"FPS: {fps:.1f} | 丢帧: {dropped}" self.frame_status.emit(status) else: self.frame_status.emit("取流中...") else: self.frame_status.emit("相机未取流") except Exception as e: self.frame_status.emit(f"监控错误: {str(e)}") # 每500ms检查一次 QThread.msleep(500) def stop(self): """停止监控线程""" self.running = False self.wait(1000) # 等待线程结束 def calculate_fps(self): """计算当前帧率""" current_time = time.time() elapsed = current_time - self.last_time if elapsed > 0: fps = self.frame_count / elapsed self.frame_count = 0 self.last_time = current_time return fps return 0 def get_frame_info(self): """获取帧信息""" try: # 更新帧计数 self.frame_count += 1 # 返回帧信息 return { 'fps': self.calculate_fps(), 'dropped': 0 # 实际应用中需要从相机获取真实丢帧数 } except Exception as e: logging.error(f"获取帧信息失败: {str(e)}") return None # ===== 主程序入口 ===== if __name__ == "__main__": # 配置日志系统 logging.basicConfig( level=logging.DEBUG, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', handlers=[ logging.FileHandler("cloth_inspection_continuous.log"), logging.StreamHandler() ] ) logging.info("布料印花检测系统(连续匹配版)启动") app = QApplication(sys.argv) mainWindow = MainWindow() # 信号连接 mainWindow.sliderThreshold.valueChanged.connect(update_match_threshold) mainWindow.sliderTriggerThreshold.valueChanged.connect( mainWindow.update_trigger_threshold ) # 其他信号连接 mainWindow.sliderDiffThreshold.valueChanged.connect(update_diff_threshold) mainWindow.bnCheckPrint.clicked.connect(lambda: vision_controlled_check(None)) mainWindow.bnSaveSample.clicked.connect(save_sample_image) mainWindow.bnPreviewSample.clicked.connect(preview_sample) mainWindow.bnEnum.clicked.connect(enum_devices) mainWindow.bnOpen.clicked.connect(open_device) mainWindow.bnClose.clicked.connect(close_device) mainWindow.bnStart.clicked.connect(start_grabbing) mainWindow.bnStop.clicked.connect(stop_grabbing) mainWindow.bnSoftwareTrigger.clicked.connect(trigger_once) mainWindow.radioTriggerMode.clicked.connect(set_software_trigger_mode) mainWindow.radioContinueMode.clicked.connect(set_continue_mode) mainWindow.bnGetParam.clicked.connect(get_param) mainWindow.bnSetParam.clicked.connect(set_param) mainWindow.bnSaveImage.clicked.connect(save_current_frame) mainWindow.bnSaveCurrentFrame.clicked.connect(save_current_frame) # 连续匹配信号连接 mainWindow.sliderThreshold.valueChanged.connect(update_match_score_display) mainWindow.chkContinuousMatch.stateChanged.connect(toggle_template_matching) mainWindow.show() app.exec_() close_device() sys.exit()
07-17
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