event handlers in assemblies

When an event is handled by a pre-compiled assembly, you can use the short form of specifying just the event handler's method name if the method name is unique. MyXaml searches all object targets that have been registered with the parser using Add Target.

 

Passing An Event Target When Loading A Form

 

A typical usage is as follows:

 

public class Startup
{
  public void Start()
  {
    Parser p=new Parser();
    Form form=(Form)p.LoadForm("app.myxaml", "AppMainForm", this, null);
    Application.Run(form);
  }

  public void OnClick(object sender, EventArgs e)
  {
    // ... event handler
  }
}

 

In the above example, an event target is automatically added to the parser's event target collection because the Startup class is specified in the third parameter ("this") when loading the form. In the markup, a typical usage would be:

 

<Button Click="OnClick"/>

 

Pre-registering Event Targets

 

A slightly more complicated example illustrates pre-registering an event target:

 

public class Startup
{
  public void Start()
  {
    MyOtherClass c=new MyOtherClass();
    Parser p=new Parser();
    p.AddTarget(c);
    Form form=(Form)p.LoadForm("app.myxaml", "AppMainForm", this, null);
    Application.Run(form);
  }

  public void OnClick(object sender, EventArgs e)
  {
    // ... event handler
  }
}

public class MyOtherClass
{
  public void OnDoubleClick(object sender, EventArs e)
  { 
    // ... event handler
  }
}

 

In the markup, a typical usage would be:

 

<Button Click="OnClick" DoubleClick="OnDoubleClick"/>

 

In the above example, the parser inspects both targets to resolve the event handler.

 

Specifying A Handler In A Specific Instance

 

For complex programs, you will usually find yourself wanting to specify the instance that is to handle the event. The format for this markup is:

 

<Button Click="{MyInstance.OnClick}"/>

 

IMPORTANT: The instance "MyInstance" is resolved by inspecting the definition list not the event target list.

 

You can either use Add Reference to specify the definition in the imperative code:

 

public class Startup
{
  public void Start()
  {
    MyOtherClass c=new MyOtherClass();
    Parser p=new Parser();
    p.AddReference("MyInstance", c);
    Form form=(Form)p.LoadForm("app.myxaml", "AppMainForm", this, null);
    Application.Run(form);
  }
}
...

 

or isntantiate the class declaratively using the def:Name construct to add the instance to the definition lis:

 

<MyOtherClass def:Name="MyInstance"/>
<Button Click="{MyInstance.OnClick}"/>

 

Late binding is supported using this form of event wire-ups. For example, the following markup works equally well:

 

<Button Click="{MyInstance.OnClick}"/>
<MyOtherClass def:Name="MyInstance"/>
 
# -*- mode: python ; coding: utf-8 -*- a = Analysis( ['wanzheng.py'], pathex=[], binaries=[], datas=[], hiddenimports=[], hookspath=[], hooksconfig={}, runtime_hooks=[], excludes=[], noarchive=False, optimize=0, ) pyz = PYZ(a.pure) exe = EXE( pyz, a.scripts, [], exclude_binaries=True, name='wanzheng', debug=False, bootloader_ignore_signals=False, strip=False, upx=True, console=True, disable_windowed_traceback=False, argv_emulation=False, target_arch=None, codesign_identity=None, entitlements_file=None, ) coll = COLLECT( exe, a.binaries, a.datas, strip=False, upx=True, upx_exclude=[], name='wanzheng', ) 这是我wanzheng.py的spec的打包代码下面是我wanzheng.py的完整代码,要求根据我的wanzheng.py的完整代码,填充spec的必要依赖 # -*- 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\\wanzheng.py") 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 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 ) # 获取当前文件所在目录 current_dir = os.path.dirname(os.path.abspath(__file__)) # ===== 路径修复 ===== sdk_path = os.path.join(current_dir, "MvImport") if sdk_path not in sys.path: sys.path.append(sdk_path) 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 # 添加DLL目录到系统路径 dll_path = os.path.join(base_path, "dlls") os.environ['PATH'] = dll_path + os.pathsep + os.environ['PATH'] try: # 直接加载DLL ctypes.WinDLL(os.path.join(dll_path, "MvCamCtrldll.dll")) ctypes.WinDLL(os.path.join(dll_path, "MvCameraControl.dll")) except OSError as e: logging.error(f"核心DLL加载失败: {e}") sys.exit(1) else: # 开发模式 # 确保SDK路径存在 if sdk_path not in sys.path: sys.path.append(sdk_path) # 添加DLL到系统路径 dll_dir = r"D:\海康\MVS\Runtime\Win64" if dll_dir not in os.environ['PATH']: os.environ['PATH'] = dll_dir + os.pathsep + os.environ['PATH'] # 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 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 # ===== 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()
07-12
内容概要:该论文聚焦于T2WI核磁共振图像超分辨率问题,提出了一种利用T1WI模态作为辅助信息的跨模态解决方案。其主要贡献包括:提出基于高频信息约束的网络框架,通过主干特征提取分支和高频结构先验建模分支结合Transformer模块和注意力机制有效重建高频细节;设计渐进式特征匹配融合框架,采用多阶段相似特征匹配算法提高匹配鲁棒性;引入模型量化技术降低推理资源需求。实验结果表明,该方法不仅提高了超分辨率性能,还保持了图像质量。 适合人群:从事医学图像处理、计算机视觉领域的研究人员和工程师,尤其是对核磁共振图像超分辨率感兴趣的学者和技术开发者。 使用场景及目标:①适用于需要提升T2WI核磁共振图像分辨率的应用场景;②目标是通过跨模态信息融合提高图像质量,解决传统单模态方法难以克服的高频细节丢失问题;③为临床诊断提供更高质量的影像资料,帮助医生更准确地识别病灶。 其他说明:论文不仅提供了详细的网络架构设计与实现代码,还深入探讨了跨模态噪声的本质、高频信息约束的实现方式以及渐进式特征匹配的具体过程。此外,作者还对模型进行了量化处理,使得该方法可以在资源受限环境下高效运行。阅读时应重点关注论文中提到的技术创新点及其背后的原理,理解如何通过跨模态信息融合提升图像重建效果。
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