Local image preview sample

本文提供了一段用于图片预览的JavaScript脚本代码,该脚本可在用户选择文件后立即显示图片预览效果,但目前仅支持部分浏览器。

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<SCRIPT LANGUAGE="JavaScript">
<!--
function  DrawImage(ImgD,ImgWidth,ImgHeight){
    
var  image=new  Image();
    image.src
=ImgD.src;    
    
if(ImgWidth<=0)ImgWidth=image.width;
    
if(ImgHeight<=0)ImgHeight=image.height;
    
if(image.width>ImgWidth  || image.height>ImgHeight){
          
if(image.width/image.height<ImgWidth/ImgHeight){
            ImgD.width
=(image.width*ImgHeight)/image.height;
             ImgD.height
=ImgHeight;
        }
else{
            ImgD.height
=(image.height*ImgWidth)/image.width;
            ImgD.width
=ImgWidth;
        }

    }

}


function LoadPreviewImage(path){
    document.all.ImagePreview.innerHTML 
= "<img border=0 onload=DrawImage(this,300,300)  src='"+path+"' >";
}

//-->
</SCRIPT>
<input type="file" onchange="LoadPreviewImage(this.value);">
<DIV id="ImagePreview">

</DIV>

貌似不支持FireFox,IE7;有解决方案请补充.......

# -*- coding: utf-8 -*- import sys import os import cv2 import numpy as np import json import time import logging import platform from datetime import datetime from scipy import ndimage from scipy.spatial import distance # PyQt5 导入 from PyQt5.QtWidgets import ( QApplication, QMainWindow, QPushButton, QWidget, QVBoxLayout, QHBoxLayout, QMessageBox, QLabel, QFileDialog, QToolBar, QComboBox, QStatusBar, QGroupBox, QSlider, QDockWidget, QProgressDialog, QCheckBox # 添加 QCheckBox ) from PyQt5.QtCore import QRect, Qt, QSettings, QThread, pyqtSignal from PyQt5.QtGui import QImage, QPixmap # 可能需要这些用于图像显示 # 海康 SDK 导入 sys.path.append("D:\\海康\\MVS\\Development\\Samples\\Python\\BasicDemo") from MvCameraControl_class import * from MvErrorDefine_const import * from CameraParams_header import * # 自定义模块导入 from CamOperation_class import CameraOperation from PyUICBasicDemo import Ui_MainWindow # 如果使用 UI 文件生成 # 图像处理库导入 try: from skimage.feature import local_binary_pattern from skimage import exposure except ImportError: # 提供替代方案或错误处理 pass # 配置日志系统 logging.basicConfig( level=logging.DEBUG, # 设置为DEBUG级别获取更多信息 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 - 增强版") # 全局变量 current_sample_path = "" # 当前使用的样本路径 detection_history = [] # 检测历史记录 sample_features = {} # 存储样本特征数据 config_file = "detection_config.json" # 配置文件路径 # 加载配置文件 def load_config(): global config_file if os.path.exists(config_file): try: with open(config_file, 'r') as f: return json.load(f) except Exception as e: logging.error(f"加载配置文件失败: {str(e)}") return {} return {} # 保存配置文件 def save_config(config): global config_file try: with open(config_file, 'w') as f: json.dump(config, f, indent=4) logging.info("配置文件已保存") except Exception as e: logging.error(f"保存配置文件失败: {str(e)}") # 初始化配置 default_config = { "threshold": 0.05, "min_defect_area": 50, "max_defect_area": 5000, "texture_analysis": True, "color_sensitivity": 0.8, "edge_detection": True, "adaptive_threshold": True, "multi_scale_levels": 3, "feature_extraction": "histogram", # histogram, lbp, sift, orb "defect_classification": True } app_config = {**default_config, **load_config()} # 帧监控线程 class FrameMonitorThread(QThread): frame_status = pyqtSignal(str) def __init__(self, cam_operation): super().__init__() self.cam_operation = cam_operation self.running = True def run(self): while self.running: if self.cam_operation: status = self.cam_operation.get_frame_status() frame_text = "有帧" if status.get('current_frame', False) else "无帧" self.frame_status.emit(f"帧状态: {frame_text}") QThread.msleep(500) def stop(self): self.running = False # ======================== 增强版布料印花检测算法 ======================== def extract_features(image): """ 提取图像的多尺度特征 :param image: 输入图像 (灰度或彩色) :return: 特征字典 """ features = {} # 确保图像是灰度图 if len(image.shape) == 3: gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) else: gray = image # 1. 直方图特征 hist = cv2.calcHist([gray], [0], None, [256], [0, 256]) hist = cv2.normalize(hist, hist).flatten() features['histogram'] = hist.tolist() # 2. LBP纹理特征 (局部二值模式) radius = 3 n_points = 8 * radius lbp = local_binary_pattern(gray, n_points, radius, method='uniform') lbp_hist, _ = np.histogram(lbp.ravel(), bins=np.arange(0, n_points + 3), range=(0, n_points + 2)) lbp_hist = lbp_hist.astype("float") lbp_hist /= (lbp_hist.sum() + 1e-7) # 归一化 features['lbp'] = lbp_hist.tolist() # 3. 颜色特征 (如果是彩色图像) if len(image.shape) == 3: # 在HSV空间中计算颜色直方图 hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) h_hist = cv2.calcHist([hsv], [0], None, [180], [0, 180]) s_hist = cv2.calcHist([hsv], [1], None, [256], [0, 256]) v_hist = cv2.calcHist([hsv], [2], None, [256], [0, 256]) h_hist = cv2.normalize(h_hist, h_hist).flatten() s_hist = cv2.normalize(s_hist, s_hist).flatten() v_hist = cv2.normalize(v_hist, v_hist).flatten() features['h_hist'] = h_hist.tolist() features['s_hist'] = s_hist.tolist() features['v_hist'] = v_hist.tolist() # 4. 边缘特征 edges = cv2.Canny(gray, 100, 200) edge_density = np.sum(edges > 0) / (edges.shape[0] * edges.shape[1]) features['edge_density'] = edge_density # 5. 多尺度分析 scales = [1.0, 0.5, 0.25] scale_features = [] for scale in scales: scaled = cv2.resize(gray, None, fx=scale, fy=scale) # 计算小波变换 (近似) coeffs = cv2.dct(np.float32(scaled)/255.0) coeffs_flat = coeffs[:10, :10].flatten() scale_features.extend(coeffs_flat.tolist()) features['multi_scale'] = scale_features return features def compute_feature_distance(features1, features2): """ 计算两个特征集之间的距离 :param features1: 特征集1 :param features2: 特征集2 :return: 距离分数 (0-1, 0表示完全相同) """ # 直方图距离 (卡方距离) hist_dist = distance.chisquare(features1['histogram'], features2['histogram'])[0] / 1000 # LBP距离 (巴氏距离) lbp_dist = distance.bhattacharyya(features1['lbp'], features2['lbp']) # 颜色距离 (如果存在) color_dist = 0 if 'h_hist' in features1 and 'h_hist' in features2: h_dist = distance.chisquare(features1['h_hist'], features2['h_hist'])[0] / 1000 s_dist = distance.chisquare(features1['s_hist'], features2['s_hist'])[0] / 1000 v_dist = distance.chisquare(features1['v_hist'], features2['v_hist'])[0] / 1000 color_dist = (h_dist + s_dist + v_dist) / 3 # 边缘密度差异 edge_dist = abs(features1['edge_density'] - features2['edge_density']) # 多尺度特征差异 (欧氏距离) scale_dist = distance.euclidean(features1['multi_scale'], features2['multi_scale']) / 100 # 加权组合距离 weights = { 'hist': 0.3, 'lbp': 0.3, 'color': 0.2, 'edge': 0.1, 'scale': 0.1 } total_dist = ( weights['hist'] * hist_dist + weights['lbp'] * lbp_dist + weights['color'] * color_dist + weights['edge'] * edge_dist + weights['scale'] * scale_dist ) # 归一化到0-1范围 total_dist = min(1.0, max(0.0, total_dist)) return total_dist def adaptive_threshold_diff(sample_gray, test_gray): """ 自适应阈值差异检测 :param sample_gray: 样本灰度图像 :param test_gray: 测试灰度图像 :return: 二值差异图像 """ # 计算绝对差异 diff = cv2.absdiff(sample_gray, test_gray) # 自适应阈值 block_size = max(11, min(sample_gray.shape[0] // 20, 51)) adaptive_thresh = cv2.adaptiveThreshold( diff, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, block_size, 5 ) # 形态学操作增强缺陷区域 kernel = np.ones((3, 3), np.uint8) enhanced = cv2.morphologyEx(adaptive_thresh, cv2.MORPH_CLOSE, kernel) enhanced = cv2.morphologyEx(enhanced, cv2.MORPH_OPEN, kernel) return enhanced def detect_defects(sample_gray, test_gray, diff_map): """ 检测并分类缺陷 :param sample_gray: 样本灰度图像 :param test_gray: 测试灰度图像 :param diff_map: 差异图 :return: 缺陷列表, 标记图像 """ # 查找连通区域 num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(diff_map, connectivity=8) defects = [] marked_image = cv2.cvtColor(test_gray, cv2.COLOR_GRAY2BGR) # 最小和最大缺陷面积 min_area = app_config.get("min_defect_area", 50) max_area = app_config.get("max_defect_area", 5000) for i in range(1, num_labels): # 跳过背景 area = stats[i, cv2.CC_STAT_AREA] # 过滤太小或太大的区域 if area < min_area or area > max_area: continue # 获取缺陷区域 x, y, w, h = stats[i, cv2.CC_STAT_LEFT], stats[i, cv2.CC_STAT_TOP], stats[i, cv2.CC_STAT_WIDTH], stats[i, cv2.CC_STAT_HEIGHT] defect_roi = diff_map[y:y+h, x:x+w] # 计算缺陷特征 sample_roi = sample_gray[y:y+h, x:x+w] test_roi = test_gray[y:y+h, x:x+w] # 缺陷类型分类 defect_type = classify_defect(sample_roi, test_roi, defect_roi) # 保存缺陷信息 defects.append({ "type": defect_type, "area": area, "location": (x, y, w, h), "centroid": (int(centroids[i][0]), int(centroids[i][1])) }) # 在标记图像上绘制 color = (0, 0, 255) # 默认红色 if defect_type == "color": color = (0, 165, 255) # 橙色 elif defect_type == "texture": color = (0, 255, 255) # 黄色 elif defect_type == "missing": color = (255, 0, 0) # 蓝色 elif defect_type == "stain": color = (0, 255, 0) # 绿色 cv2.rectangle(marked_image, (x, y), (x+w, y+h), color, 2) cv2.putText(marked_image, f"{defect_type}", (x, y-5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 1) return defects, marked_image def classify_defect(sample_roi, test_roi, defect_mask): """ 分类缺陷类型 :param sample_roi: 样本区域 :param test_roi: 测试区域 :param defect_mask: 缺陷掩码 :return: 缺陷类型 (color, texture, missing, stain) """ # 计算颜色差异 color_diff = np.mean(np.abs(sample_roi.astype(np.float32) - test_roi.astype(np.float32))) # 计算纹理差异 (使用LBP) radius = 2 n_points = 8 * radius lbp_sample = local_binary_pattern(sample_roi, n_points, radius, method='uniform') lbp_test = local_binary_pattern(test_roi, n_points, radius, method='uniform') hist_sample, _ = np.histogram(lbp_sample.ravel(), bins=np.arange(0, n_points + 3), range=(0, n_points + 2)) hist_test, _ = np.histogram(lbp_test.ravel(), bins=np.arange(0, n_points + 3), range=(0, n_points + 2)) hist_sample = hist_sample.astype("float") hist_test = hist_test.astype("float") hist_sample /= (hist_sample.sum() + 1e-7) hist_test /= (hist_test.sum() + 1e-7) texture_diff = distance.chisquare(hist_sample, hist_test)[0] # 计算边缘密度差异 edges_sample = cv2.Canny(sample_roi, 50, 150) edges_test = cv2.Canny(test_roi, 50, 150) edge_density_sample = np.sum(edges_sample > 0) / edges_sample.size edge_density_test = np.sum(edges_test > 0) / edges_test.size edge_diff = abs(edge_density_sample - edge_density_test) # 根据特征分类 if color_diff > 30 and texture_diff < 10: return "color" # 颜色缺陷 elif texture_diff > 15 and color_diff < 20: return "texture" # 纹理缺陷 elif edge_diff > 0.1 and np.mean(test_roi[defect_mask > 0]) < np.mean(sample_roi[defect_mask > 0]) - 20: return "missing" # 缺失图案 else: return "stain" # 污渍 def check_print_quality(sample_image_path, test_image, threshold=None): """ 增强版布料印花检测算法 :param sample_image_path: 合格样本图像路径 :param test_image: 内存中的测试图像 (numpy数组) :param threshold: 差异阈值 :return: 是否合格, 差异值, 标记图像, 缺陷列表 """ global sample_features, app_config # 使用配置中的阈值 if threshold is None: threshold = app_config.get("threshold", 0.05) # 读取样本图像 try: sample_img_data = np.fromfile(sample_image_path, dtype=np.uint8) sample_image = cv2.imdecode(sample_img_data, cv2.IMREAD_COLOR) if sample_image is None: logging.error(f"无法解码样本图像: {sample_image_path}") return None, None, None, None except Exception as e: logging.exception(f"样本图像读取异常: {str(e)}") return None, None, None, None # 确保测试图像是彩色 if len(test_image.shape) == 2: # 如果是灰度图像 test_image = cv2.cvtColor(test_image, cv2.COLOR_GRAY2BGR) # 确保两个图像大小一致 try: test_image = cv2.resize(test_image, (sample_image.shape[1], sample_image.shape[0])) except Exception as e: logging.error(f"图像调整大小失败: {str(e)}") return None, None, None, None # 特征提取和比较 if sample_image_path not in sample_features: logging.info(f"提取样本特征: {sample_image_path}") sample_features[sample_image_path] = extract_features(sample_image) test_features = extract_features(test_image) feature_diff = compute_feature_distance(sample_features[sample_image_path], test_features) # 转换为灰度图像进行像素级比较 sample_gray = cv2.cvtColor(sample_image, cv2.COLOR_BGR2GRAY) test_gray = cv2.cvtColor(test_image, cv2.COLOR_BGR2GRAY) # 自适应阈值差异检测 diff_map = adaptive_threshold_diff(sample_gray, test_gray) # 计算差异比例 diff_pixels = np.count_nonzero(diff_map) total_pixels = sample_gray.size pixel_diff_ratio = diff_pixels / total_pixels # 综合差异分数 combined_diff = 0.7 * feature_diff + 0.3 * pixel_diff_ratio # 检测和分类缺陷 defects, marked_image = detect_defects(sample_gray, test_gray, diff_map) # 判断是否合格 is_qualified = combined_diff <= threshold and len(defects) == 0 return is_qualified, combined_diff, marked_image, defects # ======================== UI更新函数 ======================== def update_diff_display(diff_ratio, is_qualified, defects): """ 更新差异度显示控件 """ # 更新当前差异度显示 ui.lblCurrentDiff.setText(f"当前差异度: {diff_ratio*100:.2f}%") # 显示缺陷数量 defect_count = len(defects) ui.lblDefectCount.setText(f"缺陷数量: {defect_count}") # 根据合格状态设置颜色 if is_qualified: ui.lblDiffStatus.setText("状态: 合格") ui.lblDiffStatus.setStyleSheet("color: green; font-size: 12px;") ui.lblDefectCount.setStyleSheet("color: green;") else: ui.lblDiffStatus.setText("状态: 不合格") ui.lblDiffStatus.setStyleSheet("color: red; font-size: 12px;") ui.lblDefectCount.setStyleSheet("color: red;") def update_diff_threshold(value): """ 当滑块值改变时更新阈值显示 """ global app_config app_config["threshold"] = value / 100.0 ui.lblDiffValue.setText(f"{value}%") save_config(app_config) def update_min_defect_area(value): """ 更新最小缺陷面积阈值 """ global app_config app_config["min_defect_area"] = value ui.lblMinAreaValue.setText(f"{value} px²") save_config(app_config) def update_max_defect_area(value): """ 更新最大缺陷面积阈值 """ global app_config app_config["max_defect_area"] = value ui.lblMaxAreaValue.setText(f"{value} px²") save_config(app_config) def toggle_texture_analysis(state): """ 切换纹理分析功能 """ global app_config app_config["texture_analysis"] = (state == Qt.Checked) save_config(app_config) def toggle_edge_detection(state): """ 切换边缘检测功能 """ global app_config app_config["edge_detection"] = (state == Qt.Checked) save_config(app_config) def toggle_adaptive_threshold(state): """ 切换自适应阈值功能 """ global app_config app_config["adaptive_threshold"] = (state == Qt.Checked) save_config(app_config) def update_color_sensitivity(value): """ 更新颜色敏感度 """ global app_config app_config["color_sensitivity"] = value / 100.0 ui.lblColorSensitivityValue.setText(f"{value}%") save_config(app_config) # 布料印花检测功能(增强版) def check_print(): global isGrabbing, obj_cam_operation, current_sample_path, detection_history, app_config logging.info("检测印花质量按钮按下") # 1. 检查相机状态 if not isGrabbing: logging.warning("相机未取流") QMessageBox.warning(mainWindow, "错误", "请先开始取流并捕获图像!", QMessageBox.Ok) return # 2. 检查相机操作对象 if not obj_cam_operation: logging.error("相机操作对象未初始化") QMessageBox.warning(mainWindow, "错误", "相机未正确初始化!", QMessageBox.Ok) return # 3. 检查样本路径 if not current_sample_path or not os.path.exists(current_sample_path): logging.warning(f"无效样本路径: {current_sample_path}") QMessageBox.warning(mainWindow, "错误", "请先设置有效的标准样本图像!", QMessageBox.Ok) return # 使用进度对话框防止UI阻塞 progress = QProgressDialog("正在检测...", "取消", 0, 100, mainWindow) progress.setWindowModality(Qt.WindowModal) progress.setValue(10) try: # 4. 获取当前帧 logging.info("尝试获取当前帧") test_image = obj_cam_operation.get_current_frame() progress.setValue(30) if test_image is None: logging.warning("获取当前帧失败") QMessageBox.warning(mainWindow, "错误", "无法获取当前帧图像!", QMessageBox.Ok) return # 5. 获取差异度阈值 diff_threshold = app_config.get("threshold", 0.05) logging.info(f"使用差异度阈值: {diff_threshold}") progress.setValue(50) # 6. 执行检测 is_qualified, diff_ratio, marked_image, defects = check_print_quality( current_sample_path, test_image, threshold=diff_threshold ) progress.setValue(70) # 检查返回结果是否有效 if is_qualified is None: logging.error("检测函数返回无效结果") 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}, 缺陷数={len(defects)}") progress.setValue(90) # 7. 更新UI update_diff_display(diff_ratio, is_qualified, defects) # 显示详细结果 result_text = f"印花是否合格: {'合格' if is_qualified else '不合格'}\n" result_text += f"综合差异度: {diff_ratio*100:.2f}%\n" result_text += f"缺陷数量: {len(defects)}\n" result_text += f"阈值: {diff_threshold*100:.2f}%" if not is_qualified and defects: result_text += "\n\n缺陷详情:" for i, defect in enumerate(defects[:3]): # 最多显示3个主要缺陷 result_text += f"\n{i+1}. {defect['type']} (大小: {defect['area']}像素)" 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() else: logging.warning("标记图像为空") # 8. 记录检测结果 detection_result = { 'timestamp': datetime.now(), 'qualified': is_qualified, 'diff_ratio': diff_ratio, 'defects': defects, 'threshold': diff_threshold } 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() # 保存标准样本函数(增强版) def save_sample_image(): global isGrabbing, obj_cam_operation, current_sample_path, sample_features if not isGrabbing: QMessageBox.warning(mainWindow, "错误", "请先开始取流并捕获图像!", QMessageBox.Ok) return # 检查是否有有效图像 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 # 读取上次使用的路径 settings = QSettings("ClothInspection", "CameraApp") last_dir = settings.value("last_save_dir", os.path.join(os.getcwd(), "captures")) # 创建默认文件名 timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") default_filename = f"sample_{timestamp}" # 弹出文件保存对话框 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: logging.info("用户取消了图像保存操作") return # 用户取消保存 # 处理文件扩展名 file_extension = os.path.splitext(file_path)[1].lower() if not file_extension: # 根据选择的过滤器添加扩展名 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: # 默认使用BMP格式 file_path += ".bmp" file_extension = os.path.splitext(file_path)[1].lower() # 根据扩展名设置保存格式 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 # 确保目录存在 directory = os.path.dirname(file_path) if directory and not os.path.exists(directory): try: os.makedirs(directory, exist_ok=True) logging.info(f"创建目录: {directory}") except OSError as e: error_msg = f"无法创建目录 {directory}: {str(e)}" QMessageBox.critical(mainWindow, "目录创建错误", error_msg, QMessageBox.Ok) return # 保存当前帧作为标准样本 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: success_msg = f"标准样本已保存至:\n{file_path}" QMessageBox.information(mainWindow, "成功", success_msg, QMessageBox.Ok) # 更新当前样本路径 current_sample_path = file_path update_sample_display() # 提取并保存样本特征 sample_img = cv2.imread(file_path) if sample_img is not None: sample_features[file_path] = extract_features(sample_img) logging.info(f"样本特征已提取并保存: {file_path}") # 保存当前目录 settings.setValue("last_save_dir", os.path.dirname(file_path)) except Exception as e: error_msg = f"保存图像时发生错误: {str(e)}" QMessageBox.critical(mainWindow, "异常错误", error_msg, QMessageBox.Ok) logging.exception("保存样本图像时发生异常") # 预览当前样本 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: # 使用安全方法读取图像 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") # 显示特征信息 if current_sample_path in sample_features: features = sample_features[current_sample_path] edge_density = features.get('edge_density', 0) feature_info = f"边缘密度: {edge_density:.4f}\n特征点: {len(features.get('multi_scale', []))}" cv2.putText(sample_img, feature_info, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2) 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) # 更新样本路径显示 def update_sample_display(): global current_sample_path if current_sample_path: ui.lblSamplePath.setText(f"当前样本: {os.path.basename(current_sample_path)}") ui.lblSamplePath.setToolTip(current_sample_path) ui.bnPreviewSample.setEnabled(True) else: ui.lblSamplePath.setText("当前样本: 未设置样本") ui.bnPreviewSample.setEnabled(False) # 更新历史记录显示 def update_history_display(): global detection_history ui.cbHistory.clear() for i, result in enumerate(detection_history[-10:]): # 显示最近10条记录 timestamp = result['timestamp'].strftime("%H:%M:%S") status = "合格" if result['qualified'] else "不合格" ratio = f"{result['diff_ratio']*100:.2f}%" defects = len(result.get('defects', [])) ui.cbHistory.addItem(f"[{timestamp}] {status} - 差异: {ratio} - 缺陷: {defects}") # ... [其余原有函数保持不变,如设备枚举、相机操作等] ... if __name__ == "__main__": # ... [原有初始化代码] ... # 初始化UI app = QApplication(sys.argv) mainWindow = QMainWindow() ui = Ui_MainWindow() ui.setupUi(mainWindow) # 扩大主窗口尺寸 mainWindow.resize(1400, 900) # 更宽的界面 # 创建工具栏 toolbar = mainWindow.addToolBar("检测工具") # 添加检测按钮 ui.bnCheckPrint = QPushButton("检测印花质量") toolbar.addWidget(ui.bnCheckPrint) # 添加保存样本按钮 ui.bnSaveSample = QPushButton("保存标准样本") toolbar.addWidget(ui.bnSaveSample) # 添加预览样本按钮 ui.bnPreviewSample = QPushButton("预览样本") toolbar.addWidget(ui.bnPreviewSample) # 添加历史记录下拉框 ui.cbHistory = QComboBox() ui.cbHistory.setMinimumWidth(350) toolbar.addWidget(QLabel("历史记录:")) toolbar.addWidget(ui.cbHistory) # 添加当前样本显示标签 ui.lblSamplePath = QLabel("当前样本: 未设置样本") status_bar = mainWindow.statusBar() status_bar.addPermanentWidget(ui.lblSamplePath) # === 增强版差异度调整面板 === # 创建右侧面板容器 right_panel = QWidget() right_layout = QVBoxLayout(right_panel) right_layout.setContentsMargins(10, 10, 10, 10) # 创建差异度调整组 diff_group = QGroupBox("检测参数设置") diff_layout = QVBoxLayout(diff_group) # 差异度阈值控制 ui.lblDiffThreshold = QLabel("差异度阈值 (0-100%):") ui.sliderDiffThreshold = QSlider(Qt.Horizontal) ui.sliderDiffThreshold.setRange(0, 100) # 0-100% ui.sliderDiffThreshold.setValue(int(app_config.get("threshold", 0.05) * 100)) ui.lblDiffValue = QLabel(f"{ui.sliderDiffThreshold.value()}%") # 当前差异度显示 ui.lblCurrentDiff = QLabel("当前差异度: -") ui.lblCurrentDiff.setStyleSheet("font-size: 14px; font-weight: bold;") # 缺陷数量显示 ui.lblDefectCount = QLabel("缺陷数量: -") ui.lblDefectCount.setStyleSheet("font-size: 14px;") # 差异度状态指示器 ui.lblDiffStatus = QLabel("状态: 未检测") ui.lblDiffStatus.setStyleSheet("font-size: 12px;") # 添加分隔线 diff_layout.addWidget(QLabel(" ")) diff_layout.addWidget(QLabel("缺陷检测参数")) # 最小缺陷面积 ui.lblMinArea = QLabel("最小缺陷面积:") ui.sliderMinArea = QSlider(Qt.Horizontal) ui.sliderMinArea.setRange(10, 500) ui.sliderMinArea.setValue(app_config.get("min_defect_area", 50)) ui.lblMinAreaValue = QLabel(f"{ui.sliderMinArea.value()} px²") # 最大缺陷面积 ui.lblMaxArea = QLabel("最大缺陷面积:") ui.sliderMaxArea = QSlider(Qt.Horizontal) ui.sliderMaxArea.setRange(1000, 10000) ui.sliderMaxArea.setValue(app_config.get("max_defect_area", 5000)) ui.lblMaxAreaValue = QLabel(f"{ui.sliderMaxArea.value()} px²") # 颜色敏感度 ui.lblColorSensitivity = QLabel("颜色敏感度:") ui.sliderColorSensitivity = QSlider(Qt.Horizontal) ui.sliderColorSensitivity.setRange(0, 100) ui.sliderColorSensitivity.setValue(int(app_config.get("color_sensitivity", 0.8) * 100)) ui.lblColorSensitivityValue = QLabel(f"{ui.sliderColorSensitivity.value()}%") # 添加分隔线 diff_layout.addWidget(QLabel(" ")) diff_layout.addWidget(QLabel("高级分析选项")) # 纹理分析开关 ui.chkTextureAnalysis = QCheckBox("启用纹理分析") ui.chkTextureAnalysis.setChecked(app_config.get("texture_analysis", True)) # 边缘检测开关 ui.chkEdgeDetection = QCheckBox("启用边缘检测") ui.chkEdgeDetection.setChecked(app_config.get("edge_detection", True)) # 自适应阈值开关 ui.chkAdaptiveThreshold = QCheckBox("使用自适应阈值") ui.chkAdaptiveThreshold.setChecked(app_config.get("adaptive_threshold", True)) # 布局控件 diff_layout.addWidget(ui.lblDiffThreshold) diff_layout.addWidget(ui.sliderDiffThreshold) diff_layout.addWidget(ui.lblDiffValue) diff_layout.addWidget(ui.lblCurrentDiff) diff_layout.addWidget(ui.lblDefectCount) diff_layout.addWidget(ui.lblDiffStatus) diff_layout.addWidget(ui.lblMinArea) diff_layout.addWidget(ui.sliderMinArea) diff_layout.addWidget(ui.lblMinAreaValue) diff_layout.addWidget(ui.lblMaxArea) diff_layout.addWidget(ui.sliderMaxArea) diff_layout.addWidget(ui.lblMaxAreaValue) diff_layout.addWidget(ui.lblColorSensitivity) diff_layout.addWidget(ui.sliderColorSensitivity) diff_layout.addWidget(ui.lblColorSensitivityValue) diff_layout.addWidget(ui.chkTextureAnalysis) diff_layout.addWidget(ui.chkEdgeDetection) diff_layout.addWidget(ui.chkAdaptiveThreshold) # 添加拉伸项使控件靠上 diff_layout.addStretch(1) # 创建停靠窗口 dock = QDockWidget("检测参数面板", mainWindow) dock.setWidget(right_panel) dock.setFeatures(QDockWidget.DockWidgetMovable | QDockWidget.DockWidgetFloatable) mainWindow.addDockWidget(Qt.RightDockWidgetArea, dock) # === 连接信号 === # 差异度阈值滑块 ui.sliderDiffThreshold.valueChanged.connect(update_diff_threshold) # 最小缺陷面积滑块 ui.sliderMinArea.valueChanged.connect(update_min_defect_area) # 最大缺陷面积滑块 ui.sliderMaxArea.valueChanged.connect(update_max_defect_area) # 颜色敏感度滑块 ui.sliderColorSensitivity.valueChanged.connect(update_color_sensitivity) # 分析选项复选框 ui.chkTextureAnalysis.stateChanged.connect(toggle_texture_analysis) ui.chkEdgeDetection.stateChanged.connect(toggle_edge_detection) ui.chkAdaptiveThreshold.stateChanged.connect(toggle_adaptive_threshold) # 绑定按钮事件 ui.bnCheckPrint.clicked.connect(check_print) ui.bnSaveSample.clicked.connect(save_sample_image) ui.bnPreviewSample.clicked.connect(preview_sample) # ... [其余原有绑定代码] ... # 显示主窗口 mainWindow.show() # 执行应用 app.exec_() # 关闭设备 close_device() # ch:反初始化SDK | en: finalize SDK MvCamera.MV_CC_Finalize() sys.exit() 这个程序运行后出现了查找不到打不开相机同时原有的差异度检查还有新增的功能都看不到了
07-09
# -*- coding: utf-8 -*- import sys import os import cv2 import numpy as np from PyQt5.QtWidgets import (QApplication, QMainWindow, QPushButton, QWidget, QVBoxLayout, QHBoxLayout, QMessageBox, QLabel, QFileDialog, QToolBar, QComboBox, QStatusBar, QGroupBox, QSlider, QDockWidget, QProgressDialog) 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") from MvCameraControl_class import * from MvErrorDefine_const import * from CameraParams_header import * from PyUICBasicDemo import Ui_MainWindow import ctypes from datetime import datetime import logging import platform # 配置日志系统 logging.basicConfig( level=logging.DEBUG, # 设置为DEBUG级别获取更多信息 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 = "" # 当前使用的样本路径 detection_history = [] # 检测历史记录 # 帧监控线程 class FrameMonitorThread(QThread): frame_status = pyqtSignal(str) def __init__(self, cam_operation): super().__init__() self.cam_operation = cam_operation self.running = True def run(self): while self.running: if self.cam_operation: status = self.cam_operation.get_frame_status() frame_text = "有帧" if status.get('current_frame', False) else "无帧" self.frame_status.emit(f"帧状态: {frame_text}") QThread.msleep(500) def stop(self): self.running = False # 布料印花检测函数(修复版) def check_print_quality(sample_image_path, test_image, threshold=0.05): """ 检测布料印花是否合格,直接使用内存中的测试图像 :param sample_image_path: 合格样本图像路径 :param test_image: 内存中的测试图像 (numpy数组) :param threshold: 差异阈值 :return: 是否合格,差异值,标记图像 """ # 读取样本图像 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 # 确保测试图像是灰度图 if len(test_image.shape) == 3: # 如果是彩色图像 test_image = cv2.cvtColor(test_image, cv2.COLOR_BGR2GRAY) # 确保两个图像大小一致 try: test_image = cv2.resize(test_image, (sample_image.shape[1], sample_image.shape[0])) except Exception as e: logging.error(f"图像调整大小失败: {str(e)}") return None, None, None # 计算差异 diff = cv2.absdiff(sample_image, test_image) # 二值化差异 _, thresholded = cv2.threshold(diff, 30, 255, cv2.THRESH_BINARY) # 计算差异比例 diff_pixels = np.count_nonzero(thresholded) total_pixels = sample_image.size diff_ratio = diff_pixels / total_pixels # 判断是否合格 is_qualified = diff_ratio <= threshold # 创建标记图像(红色标记差异区域) marked_image = cv2.cvtColor(test_image, cv2.COLOR_GRAY2BGR) marked_image[thresholded == 255] = [0, 0, 255] # 红色标记 return is_qualified, diff_ratio, marked_image # 更新检测结果显示 def update_diff_display(diff_ratio, is_qualified): """ 更新差异度显示控件 """ # 更新当前差异度显示 ui.lblCurrentDiff.setText(f"当前差异度: {diff_ratio*100:.2f}%") # 根据合格状态设置颜色 if is_qualified: ui.lblDiffStatus.setText("状态: 合格") ui.lblDiffStatus.setStyleSheet("color: green; font-size: 12px;") else: ui.lblDiffStatus.setText("状态: 不合格") ui.lblDiffStatus.setStyleSheet("color: red; font-size: 12px;") # 更新差异度阈值显示 def update_diff_threshold(value): """ 当滑块值改变时更新阈值显示 """ ui.lblDiffValue.setText(f"{value}%") # 布料印花检测功能(修复版) def check_print(): global isGrabbing, obj_cam_operation, current_sample_path, detection_history logging.info("检测印花质量按钮按下") # 1. 检查相机状态 if not isGrabbing: logging.warning("相机未取流") QMessageBox.warning(mainWindow, "错误", "请先开始取流并捕获图像!", QMessageBox.Ok) return # 2. 检查相机操作对象 if not obj_cam_operation: logging.error("相机操作对象未初始化") QMessageBox.warning(mainWindow, "错误", "相机未正确初始化!", QMessageBox.Ok) return # 3. 检查样本路径 if not current_sample_path or not os.path.exists(current_sample_path): logging.warning(f"无效样本路径: {current_sample_path}") QMessageBox.warning(mainWindow, "错误", "请先设置有效的标准样本图像!", QMessageBox.Ok) return # 使用进度对话框防止UI阻塞 progress = QProgressDialog("正在检测...", "取消", 0, 100, mainWindow) progress.setWindowModality(Qt.WindowModal) progress.setValue(10) try: # 4. 获取当前帧 logging.info("尝试获取当前帧") test_image = obj_cam_operation.get_current_frame() progress.setValue(30) if test_image is None: logging.warning("获取当前帧失败") QMessageBox.warning(mainWindow, "错误", "无法获取当前帧图像!", QMessageBox.Ok) return # 5. 获取差异度阈值 diff_threshold = ui.sliderDiffThreshold.value() / 100.0 logging.info(f"使用差异度阈值: {diff_threshold}") progress.setValue(50) # 6. 执行检测 is_qualified, diff_ratio, marked_image = check_print_quality( current_sample_path, test_image, threshold=diff_threshold ) progress.setValue(70) # 检查返回结果是否有效 if is_qualified is None: logging.error("检测函数返回无效结果") 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) # 7. 更新UI 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() else: logging.warning("标记图像为空") # 8. 记录检测结果 detection_result = { 'timestamp': datetime.now(), 'qualified': is_qualified, 'diff_ratio': diff_ratio, 'threshold': diff_threshold } 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() # 保存标准样本函数 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 # 检查是否有有效图像 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 # 读取上次使用的路径 settings = QSettings("ClothInspection", "CameraApp") last_dir = settings.value("last_save_dir", os.path.join(os.getcwd(), "captures")) # 创建默认文件名 timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") default_filename = f"sample_{timestamp}" # 弹出文件保存对话框 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: logging.info("用户取消了图像保存操作") return # 用户取消保存 # 处理文件扩展名 file_extension = os.path.splitext(file_path)[1].lower() if not file_extension: # 根据选择的过滤器添加扩展名 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: # 默认使用BMP格式 file_path += ".bmp" file_extension = os.path.splitext(file_path)[1].lower() # 根据扩展名设置保存格式 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 # 确保目录存在 directory = os.path.dirname(file_path) if directory and not os.path.exists(directory): try: os.makedirs(directory, exist_ok=True) logging.info(f"创建目录: {directory}") except OSError as e: error_msg = f"无法创建目录 {directory}: {str(e)}" QMessageBox.critical(mainWindow, "目录创建错误", error_msg, QMessageBox.Ok) return # 保存当前帧作为标准样本 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: success_msg = f"标准样本已保存至:\n{file_path}" QMessageBox.information(mainWindow, "成功", success_msg, QMessageBox.Ok) # 更新当前样本路径 current_sample_path = file_path update_sample_display() # 保存当前目录 settings.setValue("last_save_dir", os.path.dirname(file_path)) except Exception as e: error_msg = f"保存图像时发生错误: {str(e)}" QMessageBox.critical(mainWindow, "异常错误", error_msg, QMessageBox.Ok) logging.exception("保存样本图像时发生异常") # 预览当前样本 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: # 使用安全方法读取图像 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) # 更新样本路径显示 def update_sample_display(): global current_sample_path if current_sample_path: ui.lblSamplePath.setText(f"当前样本: {os.path.basename(current_sample_path)}") ui.lblSamplePath.setToolTip(current_sample_path) ui.bnPreviewSample.setEnabled(True) else: ui.lblSamplePath.setText("当前样本: 未设置样本") ui.bnPreviewSample.setEnabled(False) # 更新历史记录显示 def update_history_display(): global detection_history ui.cbHistory.clear() for i, result in enumerate(detection_history[-10:]): # 显示最近10条记录 timestamp = result['timestamp'].strftime("%H:%M:%S") status = "合格" if result['qualified'] else "不合格" ratio = f"{result['diff_ratio']*100:.2f}%" ui.cbHistory.addItem(f"[{timestamp}] {status} - 差异: {ratio}") # 获取选取设备信息的索引,通过[]之间的字符去解析 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() # 将返回的错误码转换为十六进制显示 def ToHexStr(num): """将错误码转换为十六进制字符串""" # 处理非整数输入 if not isinstance(num, int): try: # 尝试转换为整数 num = int(num) except: # 无法转换时返回类型信息 return f"<非整数:{type(num)}>" chaDic = {10: 'a', 11: 'b', 12: 'c', 13: 'd', 14: 'e', 15: 'f'} hexStr = "" # 处理负数 if num < 0: num = num + 2 ** 32 # 转换为十六进制 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 # ch:初始化SDK | en: initialize SDK MvCamera.MV_CC_Initialize() global deviceList deviceList = MV_CC_DEVICE_INFO_LIST() global cam cam = MvCamera() global nSelCamIndex nSelCamIndex = 0 global obj_cam_operation obj_cam_operation = 0 global isOpen isOpen = False global isGrabbing isGrabbing = False global isCalibMode # 是否是标定模式(获取原始图像) isCalibMode = True global frame_monitor_thread # 绑定下拉列表至设备信息索引 def xFunc(event): global nSelCamIndex nSelCamIndex = TxtWrapBy("[", "]", ui.ComboDevices.get()) # Decoding Characters 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') # Chinese characters except UnicodeDecodeError: decode_str = str(c_char_p_value.value) return decode_str # ch:枚举相机 | en:enum devices def enum_devices(): global deviceList global obj_cam_operation deviceList = MV_CC_DEVICE_INFO_LIST() n_layer_type = (MV_GIGE_DEVICE | MV_USB_DEVICE | MV_GENTL_CAMERALINK_DEVICE | MV_GENTL_CXP_DEVICE | MV_GENTL_XOF_DEVICE) 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("Find %d devices!" % deviceList.nDeviceNum) devList = [] for i in range(0, deviceList.nDeviceNum): mvcc_dev_info = cast(deviceList.pDeviceInfo[i], POINTER(MV_CC_DEVICE_INFO)).contents if mvcc_dev_info.nTLayerType == MV_GIGE_DEVICE or mvcc_dev_info.nTLayerType == MV_GENTL_GIGE_DEVICE: print("\ngige device: [%d]" % i) user_defined_name = decoding_char(mvcc_dev_info.SpecialInfo.stGigEInfo.chUserDefinedName) model_name = decoding_char(mvcc_dev_info.SpecialInfo.stGigEInfo.chModelName) print("device user define name: " + user_defined_name) print("device model name: " + model_name) nip1 = ((mvcc_dev_info.SpecialInfo.stGigEInfo.nCurrentIp & 0xff000000) >> 24) nip2 = ((mvcc_dev_info.SpecialInfo.stGigEInfo.nCurrentIp & 0x00ff0000) >> 16) nip3 = ((mvcc_dev_info.SpecialInfo.stGigEInfo.nCurrentIp & 0x0000ff00) >> 8) nip4 = (mvcc_dev_info.SpecialInfo.stGigEInfo.nCurrentIp & 0x000000ff) print("current ip: %d.%d.%d.%d " % (nip1, nip2, nip3, nip4)) devList.append( "[" + str(i) + "]GigE: " + user_defined_name + " " + model_name + "(" + str(nip1) + "." + str( nip2) + "." + str(nip3) + "." + str(nip4) + ")") elif mvcc_dev_info.nTLayerType == MV_USB_DEVICE: print("\nu3v device: [%d]" % i) user_defined_name = decoding_char(mvcc_dev_info.SpecialInfo.stUsb3VInfo.chUser极DefinedName) model_name = decoding_char(mvcc_dev_info.SpecialInfo.stUsb3VInfo.chModelName) print("device user define name: " + user_defined_name) print("device model name: " + model_name) strSerialNumber = "" for per in mvcc_dev_info.SpecialInfo.stUsb3VInfo.chSerialNumber: if per == 0: break strSerialNumber = strSerialNumber + chr(per) print("user serial number: " + strSerialNumber) devList.append("[" + str(i) + "]USB: " + user_defined_name + " " + model_name + "(" + str(strSerialNumber) + ")") elif mvcc_dev_info.nTLayerType == MV_GENTL_CAMERALINK_DEVICE: print("\nCML device: [%d]" % i) user_defined_name = decoding_char(mvcc_dev_info.SpecialInfo.stCMLInfo.chUserDefinedName) model_name = decoding_char(mvcc_dev_info.SpecialInfo.stCMLInfo.chModelName) print("device user define name: " + user_defined_name) print("device model name: " + model_name) strSerialNumber = "" for per in mvcc_dev_info.SpecialInfo.stCMLInfo.chSerialNumber: if per == 0: break strSerialNumber = strSerialNumber + chr(per) print("user serial number: " + strSerialNumber) devList.append("[" + str(i) + "]CML: " + user_defined_name + " " + model_name + "(" + str(strSerialNumber) + ")") elif mvcc_dev_info.nTLayerType == MV_GENTL_CXP_DEVICE: print("\nCXP device: [%d]" % i) user_defined_name = decoding_char(mvcc_dev_info.SpecialInfo.stCXPInfo.chUserDefinedName) model_name = decoding_char(mvcc_dev_info.SpecialInfo.stCXPInfo.chModelName) print("device user define name: " + user_defined_name) print("device model name: " + model_name) strSerialNumber = "" for per in mvcc_dev_info.SpecialInfo.stCXPInfo.chSerialNumber: if per == 0: break strSerialNumber = strSerialNumber + chr(per) print("user serial number: "+strSerialNumber) devList.append("[" + str(i) + "]CXP: " + user_defined_name + " " + model_name + "(" + str(strSerialNumber) + ")") elif mvcc_dev_info.nTLayerType == MV_GENTL_XOF_DEVICE: print("\nXoF device: [%d]" % i) user_defined_name = decoding_char(mvcc_dev_info.SpecialInfo.stXoFInfo.chUserDefinedName) model_name = decoding_char(mvcc_dev_info.SpecialInfo.stXoFInfo.chModelName) print("device user define name: " + user_defined_name) print("device model name: " + model_name) strSerialNumber = "" for per in mvcc_dev_info.SpecialInfo.stXoFInfo.chSerialNumber: if per == 0: break strSerialNumber = strSerialNumber + chr(per) print("user serial number: " + strSerialNumber) devList.append("[" + str(i) + "]XoF: " + user_defined_name + " " + model_name + "(" + str(strSerialNumber) + ")") ui.ComboDevices.clear() ui.ComboDevices.addItems(devList) ui.ComboDevices.setCurrentIndex(0) # ch:打开相机 | en:open device def open_device(): global deviceList global nSelCamIndex global obj_cam_operation global isOpen global frame_monitor_thread if isOpen: QMessageBox.warning(mainWindow, "Error", 'Camera is Running!', QMessageBox.Ok) return MV_E_CALLORDER nSelCamIndex = ui.ComboDevices.currentIndex() if nSelCamIndex < 0: QMessageBox.warning(mainWindow, "Error", 'Please select a camera!', QMessageBox.Ok) return MV_E_CALLORDER obj_cam_operation = CameraOperation(cam, deviceList, nSelCamIndex) ret = obj_cam_operation.open_device() if 0 != ret: strError = "Open device failed ret:" + ToHexStr(ret) QMessageBox.warning(mainWindow, "Error", strError, QMessageBox.Ok) isOpen = False else: set_continue_mode() get_param() isOpen = True enable_controls() # 启动帧监控线程 frame_monitor_thread = FrameMonitorThread(obj_cam_operation) frame_monitor_thread.frame_status.connect(ui.statusBar.showMessage) frame_monitor_thread.start() # ch:开始取流 | en:Start grab image def start_grabbing(): global obj_cam_operation global isGrabbing ret = obj_cam_operation.start_grabbing(ui.widgetDisplay.winId()) if ret != 0: strError = "Start grabbing failed ret:" + ToHexStr(ret) QMessageBox.warning(mainWindow, "Error", strError, QMessageBox.Ok) else: isGrabbing = True enable_controls() # ch:停止取流 | en:Stop grab image def stop_grabbing(): global obj_cam_operation global isGrabbing ret = obj_cam_operation.Stop_grabbing() if ret != 0: strError = "Stop grabbing failed ret:" + ToHexStr(ret) QMessageBox.warning(mainWindow, "Error", strError, QMessageBox.Ok) else: isGrabbing = False enable_controls() # ch:关闭设备 | Close device def close_device(): global isOpen global isGrabbing global obj_cam_operation global frame_monitor_thread # 停止帧监控线程 if frame_monitor_thread and frame_monitor_thread.isRunning(): frame_monitor_thread.stop() frame_monitor_thread.wait(2000) if isOpen: obj_cam_operation.close_device() isOpen = False isGrabbing = False enable_controls() # ch:设置触发模式 | en:set trigger mode def set_continue_mode(): ret = obj_cam_operation.set_trigger_mode(False) if ret != 0: strError = "Set continue mode failed ret:" + ToHexStr(ret) QMessageBox.warning(mainWindow, "Error", strError, QMessageBox.Ok) else: ui.radioContinueMode.setChecked(True) ui.radioTriggerMode.setChecked(False) ui.bnSoftwareTrigger.setEnabled(False) # ch:设置软触发模式 | en:set software trigger mode def set_software_trigger_mode(): ret = obj_cam_operation.set_trigger_mode(True) if ret != 0: strError = "Set trigger mode failed ret:" + ToHexStr(ret) QMessageBox.warning(mainWindow, "Error", strError, QMessageBox.Ok) else: ui.radioContinueMode.setChecked(False) ui.radioTriggerMode.setChecked(True) ui.bnSoftwareTrigger.setEnabled(isGrabbing) # ch:设置触发命令 | en:set trigger software def trigger_once(): ret = obj_cam_operation.trigger_once() if ret != 0: strError = "TriggerSoftware failed ret:" + ToHexStr(ret) QMessageBox.warning(mainWindow, "Error", strError, QMessageBox.Ok) # 保存图像对话框 def save_image_dialog(): """ 打开保存图像对话框并保存当前帧 """ global isGrabbing, obj_cam_operation # 检查相机状态 if not isGrabbing: QMessageBox.warning(mainWindow, "相机未就绪", "请先开始取流并捕获图像!", QMessageBox.Ok) return # 检查是否有有效图像 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 # 读取上次使用的路径 settings = QSettings("ClothInspection", "CameraApp") last_dir = settings.value("last_save_dir", os.path.join(os.getcwd(), "captures")) # 创建默认文件名 timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") default_filename = f"capture_{timestamp}" # 弹出文件保存对话框 file_path, selected_filter = QFileDialog.getSaveFileName( mainWindow, "保存图像", os.path.join(last_dir, default_filename), # 初始路径 "BMP 图像 (*.bmp);;JPEG 图像 (*.jpg);;PNG 图像 (*.png);;TIFF 图像 (*.tiff);;所有文件 (*)", options=QFileDialog.DontUseNativeDialog ) # 用户取消操作 if not file_path: logging.info("用户取消了图像保存操作") return # 处理文件扩展名 file_extension = os.path.splitext(file_path)[1].lower() if not file_extension: # 根据选择的过滤器添加扩展名 if "BMP" in selected_filter: file_path += ".bmp" elif "JPEG" in selected_filter or "JPG" in selected_filter: file_path += ".jpg" elif "PNG" in selected_filter: file_path += ".png" elif "TIFF" in selected_filter: file_path += ".tiff" else: # 默认使用BMP格式 file_path += ".bmp" # 确定保存格式 format_mapping = { ".bmp": "bmp", ".jpg": "jpg", ".jpeg": "jpg", ".png": "png", ".tiff": "tiff", ".tif": "tiff" } file_extension = os.path.splitext(file_path)[1].lower() save_format = format_mapping.get(file_extension, "bmp") # 确保目录存在 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, "目录错误", f"无法创建目录:\n{str(e)}", QMessageBox.Ok) return # 保存图像 try: ret = obj_cam_operation.save_image(file_path, save_format) if ret == MV_OK: QMessageBox.information(mainWindow, "保存成功", f"图像已保存至:\n{file_path}", QMessageBox.Ok) logging.info(f"图像保存成功: {file_path}") # 保存当前目录 settings.setValue("last_save_dir", os.path.dirname(file_path)) else: error_msg = f"保存失败! 错误代码: {hex(ret)}" QMessageBox.warning(mainWindow, "保存失败", error_msg, QMessageBox.Ok) logging.error(f"图像保存失败: {file_path}, 错误代码: {hex(ret)}") 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) logging.exception(f"保存图像时发生异常: {file_path}") def is_float(str): try: float(str) return True except ValueError: return False # ch: 获取参数 | en:get param def get_param(): try: # 调用方法获取参数 ret = obj_cam_operation.get_parameters() # 记录调用结果(调试用) logging.debug(f"get_param() 返回: {ret} (类型: {type(ret)})") # 处理错误码 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: # 成功获取参数后更新UI ui.edtExposureTime.setText("{0:.2f}".format(obj_cam_operation.exposure_time)) ui.edtGain.setText("{0:.2f}".format(obj_cam_operation.gain)) ui.edtFrameRate.setText("{0:.2f}".format(obj_cam_operation.frame_rate)) # 记录成功信息 logging.info("成功获取相机参数") except Exception as e: # 处理所有异常 error_msg = f"获取参数时发生错误: {str(e)}" logging.error(error_msg) QMessageBox.critical(mainWindow, "严重错误", error_msg, QMessageBox.Ok) # ch: 设置参数 | en:set param def set_param(): frame_rate = ui.edtFrameRate.text() exposure = ui.edtExposureTime.text() gain = ui.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: # 使用正确的参数顺序和关键字 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) else: logging.info("参数设置成功") return MV_OK except Exception as e: error_msg = f"设置参数时发生错误: {str(e)}" logging.error(error_msg) QMessageBox.critical(mainWindow, "严重错误", error_msg, QMessageBox.Ok) return MV_E_STATE # ch: 设置控件状态 | en:set enable status def enable_controls(): global isGrabbing global isOpen # 先设置group的状态,再单独设置各控件状态 ui.groupGrab.setEnabled(isOpen) ui.groupParam.setEnabled(isOpen) ui.bnOpen.setEnabled(not isOpen) ui.bnClose.setEnabled(isOpen) ui.bnStart.setEnabled(isOpen and (not isGrabbing)) ui.bnStop.setEnabled(isOpen and isGrabbing) ui.bnSoftwareTrigger.setEnabled(isGrabbing and ui.radioTriggerMode.isChecked()) ui.bnSaveImage.setEnabled(isOpen and isGrabbing) # 添加检测按钮控制 ui.bnCheckPrint.setEnabled(isOpen and isGrabbing) ui.bnSaveSample.setEnabled(isOpen and isGrabbing) ui.bnPreviewSample.setEnabled(bool(current_sample_path)) if __name__ == "__main__": # ch:初始化SDK | en: initialize SDK MvCamera.MV_CC_Initialize() deviceList = MV_CC_DEVICE_INFO_LIST() cam = MvCamera() nSelCamIndex = 0 obj_cam_operation = 0 isOpen = False isGrabbing = False isCalibMode = True # 是否是标定模式(获取原始图像) frame_monitor_thread = None # 初始化UI app = QApplication(sys.argv) mainWindow = QMainWindow() ui = Ui_MainWindow() ui.setupUi(mainWindow) # 扩大主窗口尺寸 mainWindow.resize(1200, 800) # 宽度1200,高度800 # 创建工具栏 toolbar = mainWindow.addToolBar("检测工具") # 添加检测按钮 ui.bnCheckPrint = QPushButton("检测印花质量") toolbar.addWidget(ui.bnCheckPrint) # 添加保存样本按钮 ui.bnSaveSample = QPushButton("保存标准样本") toolbar.addWidget(ui.bnSaveSample) # 添加预览样本按钮 ui.bnPreviewSample = QPushButton("预览样本") toolbar.addWidget(ui.bnPreviewSample) # 添加历史记录下拉框 ui.cbHistory = QComboBox() ui.cbHistory.setMinimumWidth(300) toolbar.addWidget(QLabel("历史记录:")) toolbar.addWidget(ui.cbHistory) # 添加当前样本显示标签 ui.lblSamplePath = QLabel("当前样本: 未设置样本") status_bar = mainWindow.statusBar() status_bar.addPermanentWidget(ui.lblSamplePath) # === 新增差异度调整控件 === # 创建右侧面板容器 right_panel = QWidget() right_layout = QVBoxLayout(right_panel) right_layout.setContentsMargins(10, 10, 10, 10) # 创建差异度调整组 diff_group = QGroupBox("差异度调整") diff_layout = QVBoxLayout(diff_group) # 差异度阈值控制 ui.lblDiffThreshold = QLabel("差异度阈值 (0-100%):") ui.sliderDiffThreshold = QSlider(Qt.Horizontal) ui.sliderDiffThreshold.setRange(0, 100) # 0-100% ui.sliderDiffThreshold.setValue(5) # 默认5% ui.lblDiffValue = QLabel("5%") # 当前差异度显示 ui.lblCurrentDiff = QLabel("当前差异度: -") ui.lblCurrentDiff.setStyleSheet("font-size: 14px; font-weight: bold;") # 差异度状态指示器 ui.lblDiffStatus = QLabel("状态: 未检测") ui.lblDiffStatus.setStyleSheet("font-size: 12px;") # 布局控件 diff_layout.addWidget(ui.lblDiffThreshold) diff_layout.addWidget(ui.sliderDiffThreshold) diff_layout.addWidget(ui.lblDiffValue) diff_layout.addWidget(ui.lblCurrentDiff) diff_layout.addWidget(ui.lblDiffStatus) # 添加差异度组到右侧布局 right_layout.addWidget(diff_group) # 添加拉伸项使控件靠上 right_layout.addStretch(1) # 创建停靠窗口 dock = QDockWidget("检测控制面板", mainWindow) dock.setWidget(right_panel) dock.setFeatures(QDockWidget.DockWidgetMovable | QDockWidget.DockWidgetFloatable) mainWindow.addDockWidget(Qt.RightDockWidgetArea, dock) # === 差异度调整功能实现 === # 更新差异度阈值显示 def update_diff_threshold(value): ui.lblDiffValue.setText(f"{value}%") # 连接滑块信号 ui.sliderDiffThreshold.valueChanged.connect(update_diff_threshold) # 更新检测结果显示 def update_diff_display(diff_ratio, is_qualified): # 更新当前差异度显示 ui.lblCurrentDiff.setText(f"当前差异度: {diff_ratio*100:.2f}%") # 根据合格状态设置颜色 if is_qualified: ui.lblDiffStatus.setText("状态: 合格") ui.lblDiffStatus.setStyleSheet("color: green; font-size: 12px;") else: ui.lblDiffStatus.setText("状态: 不合格") ui.lblDiffStatus.setStyleSheet("color: red; font-size: 12px;") # 绑定按钮事件 ui.bnCheckPrint.clicked.connect(check_print) ui.bnSaveSample.clicked.connect(save_sample_image) ui.bnPreviewSample.clicked.connect(preview_sample) # 绑定其他按钮事件 ui.bnEnum.clicked.connect(enum_devices) ui.bnOpen.clicked.connect(open_device) ui.bnClose.clicked.connect(close_device) ui.bnStart.clicked.connect(start_grabbing) ui.bnStop.clicked.connect(stop_grabbing) ui.bnSoftwareTrigger.clicked.connect(trigger_once) ui.radioTriggerMode.clicked.connect(set_software_trigger_mode) ui.radioContinueMode.clicked.connect(set_continue_mode) ui.bnGetParam.clicked.connect(get_param) ui.bnSetParam.clicked.connect(set_param) # 修改保存图像按钮连接 ui.bnSaveImage.clicked.connect(save_image_dialog) # 显示主窗口 mainWindow.show() # 执行应用 app.exec_() # 关闭设备 close_device() # ch:反初始化SDK | en: finalize SDK MvCamera.MV_CC_Finalize() sys.exit() 我感觉这个代码对于印花图案缺陷检测能力还是不够精准能否更新算法让它缺陷检测更加敏锐
07-09
如何将下面的程序打包成exe文件同时尽量不要动下面程序的代码 # -*- 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}" ) dev_name = f"GigE: {st_gige_info.chUserDefinedName.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') dev_name = f"USB: {st_usb_info.chUserDefinedName.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()
最新发布
07-12
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