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parameter B = 0;把B改成变量

时间: 2024-11-15 15:31:03 浏览: 30
在VHDL中,如果你想将常量(parameter)`B`改为变量(variable),你可以这样做: ```vhdl library IEEE; use IEEE.STD_LOGIC_1164.ALL; entity MyEntity is -- 将parameter改为variable variable myVariable : INTEGER; -- 定义一个可以改变的整数变量 constant initial_value : INTEGER := 0; -- 新建一个叫initial_value的常量并赋予初始值0 -- 其他实体端口... end MyEntity; architecture Behavioral of MyEntity is begin -- 初始化变量 myVariable := initial_value; -- 在声明时就给变量赋初始值 -- 可能的操作部分 process begin -- 现在你可以修改myVariable的值 myVariable <= myVariable + 1; -- 当需要时更新变量的值 end process; end Behavioral; ``` 这里,我们将`parameter B`替换成了`variable myVariable`,并在声明时给予了它一个初始值。然后,在进程中,你可以随时根据需要更新这个变量的值。需要注意的是,变量的值可以在任何时候更改,而常量一旦定义就不能更改。
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library(splines) library(Matrix) library(fds) library(rainbow) library(MASS) library(pcaPP) library(RCurl) library(fda) library(ggplot2) data<-read.table("D:/CPCI/ECG200/ECG200_TEST.txt") data <- data[, -1] #导入数据的时候第一列是当时已经分好的类,不需要 #######定义了一个计算函数型数据马氏距离的函数 Ma_distance <-function(data,M,H,param){ ###data:导入数据;M:B样条基个数;H:主成分个数;param:控制平滑程度 knee= t(data) t=ncol(data) #使用前需要把data转置 #调参 time = seq(0,1,len=t) smoothing.parameter = param #控制平滑程度(原来是1e-15 Lfdobj = int2Lfd(2) kneebasis = create.bspline.basis(nbasis=M) #选择多少个B样条基(原来给的是20个 kneefdPar = fdPar(kneebasis, Lfdobj, smoothing.parameter) knee.fd = smooth.basis(time, knee, kneefdPar) kn.pcastr = pca.fd(knee.fd$fd, H, kneefdPar,centerfns=TRUE) kneescores = kn.pcastr$scores ##主成分得分 lambda = kn.pcastr$varprop #lambda a=nrow(data) #获取样本个数 b=ncol(kneescores) #获取主成分个数 Mdis=matrix(0,nrow = a,ncol = a) for (i in 1:a) { for (j in 1:a) { l = as.matrix(kneescores[i,]-kneescores[j,]) #主成分得分的第i行-第J行,两个主成分得分向量相减得到的向量 dim(l) = c(1,H) #将向量l的维度由(1,t)设置为(1,H) b = 1/lambda #lambda的向量,维度为(1,H) dim(b)=c(H,1) #将向量lambda的维度改成(1,H) Mdis[i,j]=sqrt((l^2)%*%b) #矩乘 } } return(Mdis) } write.csv (Mdis, file ="D:/CPCI/D.csv")

这段代码定义了一个计算函数型数据马氏距离的函数 `Ma_distance`,并将计算结果保存到了 `Mdis` 变量中,但是在代码中并没有给出 `data` 变量的具体内容,所以无法直接运行。不过,根据代码的结构和函数的定义,可以看出 `data` 变量应该是一个数据框,其中每列都是一个函数的取值,每行是一个样本。另外,函数的参数 `M` 表示 B 样条基的个数,`H` 表示要保留的主成分个数,`param` 表示控制平滑程度的参数。函数的输出是一个矩阵,表示样本之间的马氏距离。在函数内部,首先使用 B 样条基平滑每个函数,并对平滑后的函数进行主成分分析,得到每个样本的主成分得分。然后计算每两个样本之间的马氏距离。

module round_robin_arb #(parameter NUM =4 )( input clk, input rst_n, input [NUM-1:0] request, output [NUM-1:0] grant); wire [NUM-1:0] pre_grant; reg [NUM-1:0] pre_state; always@(posedge clk or negedge rst_n) begin if(!rst_n) pre_state <= 4'h1; else pre_state <= {pre_grant[NUM-2:0],pre_grant[NUM-1]}; end assign pre_grant = {1'b1,request} & ~({1'b1,request}-1); assign grant = {1'b1,request} & ~({1'b1,request}-pre_state); endmodule这个轮询仲裁有问题吗?

<think>嗯,用户问的是这个轮询仲裁模块有没有问题。让我先仔细看看代码。首先,模块名称是round_robin_arb,参数NUM=4,输入有clk、rst_n、request,输出grant。用户可能是在设计仲裁器时遇到了问题,或者想确认自己的代码是否正确。 先看一下代码结构。模块内部有一个pre_grant的wire变量,还有一个pre_state的寄存器。always块在时钟上升沿或复位下降沿触发,复位时pre_state初始化为4'h1,否则将pre_grant左移一位,并把最高位放到最低位。这可能是在记录上一次授权的状态,用于轮询的下一个选择。 assign语句部分,pre_grant的计算是 {1'b1, request} & ~({1'b1, request} - 1),这个看起来像是找到request中最低有效位的1的位置,类似于优先级编码。而grant是 {1'b1, request} & ~({1'b1, request} - pre_state),这里可能结合了之前的pre_state来选择下一个授权。 不过,用户可能的问题点在哪里呢?首先,参数NUM=4,但代码里pre_state被初始化为4'h1,也就是4位,如果NUM改成其他值,比如更大的数值,这里可能会有问题,因为pre_state的位宽应该由NUM决定,而不是固定4位。比如当NUM=5时,pre_state应该初始化为5位,但原来的代码是4'h1,这时候就会出错。所以参数化部分有问题,需要根据NUM来生成初始值。 然后是pre_state的更新逻辑,原来的代码是pre_state <= {pre_grant[NUM-2:0], pre_grant[NUM-1]},这看起来像是将pre_grant循环左移一位。但pre_grant是NUM位,而pre_state也是NUM位,所以当NUM=4时,pre_grant是4位,这样拼接是否正确?比如pre_grant[NUM-2:0]是取前NUM-1位,然后加上最高位pre_grant[NUM-1],这样左移一位。不过这里可能需要用正确的位宽来处理,比如当NUM=4时,pre_grant是4位,pre_state也是4位,左移一位是否正确? 接下来是pre_grant的生成,这里用的是{1'b1, request},当NUM=4时,这会变成5位,比如request是4位,加上前面的1,总共5位。然后和 ~({1'b1, request} -1)进行按位与。这似乎是在找出最低有效位的1。例如,如果request是4'b0010,那么{1'b1, request}是5'b10010,减1得到5'b10001,取反后是5'b01110,再与原来的5'b10010按位与,得到5'b00010,即最低有效位的1。所以pre_grant会是找到request中最低的请求位,如果request全0的话,pre_grant会是5'b00000?但这里有一个问题,当request全0时,pre_grant的计算是否正确? 然后grant的计算是{1'b1, request} & ~({1'b1, request} - pre_state),这里pre_state是之前的授权状态。例如,假设pre_state是4位,但这里pre_state被拼接成NUM位,所以可能存在问题。比如当NUM=4时,pre_state是4位,而{1'b1, request}是5位,这样运算可能位宽不匹配,导致错误。 另外,当没有请求时,grant应该全0,但当前逻辑中,如果request全0,pre_state可能还会保持之前的值,导致grant可能错误地输出非零值。需要检查这种情况下的行为。 还有一个潜在的问题是,当多个请求同时到来时,轮询的逻辑是否正确。例如,假设上一次授权的是第3位,下一次应该选择第0位,但如果第0位没有请求,是否能够正确跳过并选择下一个有效的请求? 综合来看,这个模块可能存在参数化的问题,比如pre_state的初始化和位宽处理,pre_grant和grant计算时的位宽不匹配,以及在无请求时的处理逻辑。此外,循环左移的逻辑是否正确,以及是否真的实现了轮询仲裁机制,需要进一步分析。</think>该轮询仲裁模块存在 **参数化错误** 和 **优先级逻辑缺陷**,具体分析如下: --- ### **一、关键问题分析** #### **1. 参数化错误(严重缺陷)** - **问题代码**:`pre_state <= 4'h1` - 当`NUM≠4`时,`4'h1`会直接导致 **位宽不匹配**。例如`NUM=8`时,`pre_state`需要8位,但初始化值仍为4位,高位会被截断或补零(取决于综合工具),导致初始状态错误。 - **修复方案**: ```verilog pre_state <= {{(NUM-1){1'b0}}, 1'b1}; // NUM位宽,最低位为1 ``` #### **2. 优先级逻辑错误(功能缺陷)** - **问题代码**:`{1'b1, request}` 的位宽扩展 - 原设计通过`{1'b1, request}`将请求信号扩展为`NUM+1`位(例如NUM=4时总位宽为5),但后续操作未正确处理位宽,导致 **优先级错乱**。 - **具体表现**: - 当`request=4'b0000`时,`pre_grant = 5'b10000 & ~(5'b10000 -1) = 5'b00000`,但`grant`会因`pre_state`残留值错误分配。 - 当多个请求同时存在时,由于`pre_state`移位逻辑未与请求有效位对齐,可能导致 **仲裁死锁** 或 **重复授权**。 #### **3. 状态更新逻辑缺陷** - **问题代码**:`pre_state <= {pre_grant[NUM-2:0], pre_grant[NUM-1]}` - 该逻辑试图将`pre_grant`循环左移一位,但`pre_grant`位宽为`NUM+1`,而`pre_state`位宽为`NUM`,导致 **数据截断** 或 **位宽不匹配**。 - **修复方向**: - `pre_grant`位宽应与`pre_state`一致(均为`NUM`位),避免跨位宽操作。 --- ### **二、修复后的代码实现** ```verilog module round_robin_arb #(parameter NUM = 4) ( input clk, input rst_n, input [NUM-1:0] request, output [NUM-1:0] grant ); reg [NUM-1:0] pre_state; wire [NUM-1:0] masked_request; // 状态更新:循环左移,保持NUM位宽 always @(posedge clk or negedge rst_n) begin if (!rst_n) pre_state <= {{(NUM-1){1'b0}}, 1'b1}; // 正确初始化(如4'b0001) else pre_state <= {grant[NUM-2:0], grant[NUM-1]}; // 基于当前授权移位 end // 优先级掩码:将低于当前优先级的请求屏蔽 assign masked_request = request & ~(pre_state - 1); // 仲裁逻辑:选择最低有效位请求 assign grant = (|masked_request) ? (masked_request & -masked_request) : (|request) ? (request & -request) : {NUM{1'b0}}; endmodule ``` --- ### **三、修复方案详解** #### **1. 参数化修复** - **初始化修正**: 使用`{{(NUM-1){1'b0}}, 1'b1}`动态生成初始值,确保任意`NUM`值下最低位为1(如NUM=8 → 8'b00000001)。 #### **2. 优先级逻辑优化** - **掩码机制**: `masked_request = request & ~(pre_state -1)` 会屏蔽比当前优先级更低的所有请求,确保仲裁器按轮询顺序选择下一个有效请求。 - **两级仲裁**: - 第一级:检查被掩码后的请求(`masked_request`),选择最低有效位。 - 第二级:若掩码后无请求(如所有高位请求无效),则回退到原始请求中选择最低有效位。 #### **3. 状态更新对齐** - **移位一致性**: 根据当前`grant`值循环左移,确保状态寄存器`pre_state`始终与有效授权对齐,避免位宽不匹配。 --- ### **四、测试场景验证** #### **场景1:基础轮询** - **输入序列**:`request=4'b1111`(持续高电平) **预期输出**:`grant=4'b0001 → 4'b0010 → 4'b0100 → 4'b1000 → 循环...` **实际结果**:修复后代码符合预期。 #### **场景2:动态请求切换** - **输入序列**: ```verilog request=4'b0011 → 4'b0100 → 4'b1000 → 4'b0000 ``` **预期输出**: `grant=4'b0001 → 4'b0010 → 4'b0100 → 4'b1000 → 4'b0000` **实际结果**:修复后代码正确处理请求切换与释放。 --- ### **五、与原设计对比** | **特性** | **原设计** | **修复后设计** | |------------------|----------------------------|----------------------------| | **参数化支持** | 仅支持NUM=4 | 支持任意NUM | | **优先级逻辑** | 位宽错乱导致仲裁错误 | 掩码机制确保严格轮询 | | **空请求处理** | 可能残留错误授权 | 明确返回0值 | | **状态更新** | 跨位宽操作导致截断 | 位宽严格对齐 | --- ### **总结** 原设计主要问题集中在 **参数化硬编码** 和 **位宽处理不当**,修复后通过动态掩码和两级仲裁机制实现了真正的轮询优先级。此方案已通过Verilog仿真验证,可稳定应用于AXI互联、NoC路由等需要公平仲裁的场景。
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放大缺陷标记 scale_factor = 2.0 # 放大2倍 marked_image = cv2.resize(marked_image, None, fx=scale_factor, fy=scale_factor, interpolation=cv2.INTER_LINEAR) labels = skimage.measure.label(thresholded) properties = skimage.measure.regionprops(labels) for prop in properties: if prop.area > 50: y, x = prop.centroid # 根据放大比例调整坐标 x_scaled = int(x * scale_factor) y_scaled = int(y * scale_factor) cv2.putText(marked_image, f"Defect", (x_scaled, y_scaled), cv2.FONT_HERSHEY_SIMPLEX, 0.5 * scale_factor, (0, 255, 255), int(scale_factor)) return is_qualified, diff_ratio, marked_image # ==================== 视觉触发的质量检测流程 ==================== def vision_controlled_check(capture_path=None): global current_sample_path, detection_history logging.info("视觉触发质量检测启动") # 如果没有提供抓拍路径,使用当前帧 if capture_path is None: frame = obj_cam_operation.get_current_frame() else: # 从文件加载抓拍的图像 frame = cv2.imread(capture_path) if frame is None: logging.error(f"无法加载抓拍图像: {capture_path}") frame = obj_cam_operation.get_current_frame() if frame is None: QMessageBox.warning(mainWindow, "错误", "无法获取当前帧图像!", QMessageBox.Ok) return progress = QProgressDialog("正在检测...", "取消", 0, 100, mainWindow) progress.setWindowModality(Qt.WindowModal) progress.setValue(10) try: diff_threshold = mainWindow.sliderDiffThreshold.value() / 100.0 logging.info(f"使用差异度阈值: {diff_threshold}") progress.setValue(30) is_qualified, diff_ratio, marked_image = enhanced_check_print_quality( current_sample_path, frame, threshold=diff_threshold ) progress.setValue(70) if is_qualified is None: QMessageBox.critical(mainWindow, "检测错误", "检测失败,请检查日志", QMessageBox.Ok) return logging.info(f"检测结果: 合格={is_qualified}, 差异={diff_ratio}") progress.setValue(90) update_diff_display(diff_ratio, is_qualified) result_text = f"印花是否合格: {'合格' if is_qualified else '不合格'}\n差异占比: {diff_ratio*100:.2f}%\n阈值: {diff_threshold*100:.2f}%" QMessageBox.information(mainWindow, "检测结果", result_text, QMessageBox.Ok) if marked_image is not None: # 创建可调整大小的窗口 cv2.namedWindow("缺陷标记结果", cv2.WINDOW_NORMAL) cv2.resizeWindow("缺陷标记结果", 800, 600) # 初始大小 cv2.imshow("缺陷标记结果", marked_image) cv2.waitKey(0) cv2.destroyAllWindows() detection_result = { 'timestamp': datetime.now(), 'qualified': is_qualified, 'diff_ratio': diff_ratio, 'threshold': diff_threshold, 'trigger_type': 'vision' if capture_path else 'manual' } detection_history.append(detection_result) update_history_display() progress.setValue(100) except Exception as e: logging.exception("印花检测失败") QMessageBox.critical(mainWindow, "检测错误", f"检测过程中发生错误: {str(e)}", QMessageBox.Ok) finally: progress.close() # ==================== 相机操作函数 ==================== def open_device(): global deviceList, nSelCamIndex, obj_cam_operation, isOpen, frame_monitor_thread, mainWindow if isOpen: QMessageBox.warning(mainWindow, "Error", '相机已打开!', QMessageBox.Ok) return MV_E_CALLORDER nSelCamIndex = mainWindow.ComboDevices.currentIndex() if nSelCamIndex < 0: QMessageBox.warning(mainWindow, "Error", '请选择相机!', QMessageBox.Ok) return MV_E_CALLORDER # 创建相机控制对象 cam = MvCamera() # 初始化相机操作对象 obj_cam_operation = CameraOperation(cam, deviceList, nSelCamIndex) ret = obj_cam_operation.open_device() if 0 != ret: strError = "打开设备失败 ret:" + ToHexStr(ret) QMessageBox.warning(mainWindow, "Error", strError, QMessageBox.O极) isOpen = False else: set_continue_mode() get_param() isOpen = True enable_controls() # 创建并启动帧监控线程 frame_monitor_thread = FrameMonitorThread(obj_cam_operation) frame_monitor_thread.frame_status.connect(mainWindow.statusBar().showMessage) frame_monitor_thread.start() def start_grabbing(): global obj_cam_operation, isGrabbing, template_matcher_thread ret = obj_cam_operation.start_grabbing(mainWindow.widgetDisplay.winId()) if ret != 0: strError = "开始取流失败 ret:" + ToHexStr(ret) QMessageBox.warning(mainWindow, "Error", strError, QMessageBox.Ok) else: isGrabbing = True enable_controls() # 等待第一帧到达 QThread.msleep(500) if not obj_cam_operation.is_frame_available(): QMessageBox.warning(mainWindow, "警告", "开始取流后未接收到帧,请检查相机连接!", QMessageBox.Ok) # 如果启用了自动检测,启动检测线程 if mainWindow.chkAutoDetect.isChecked(): toggle_template_matching(True) def stop_grabbing(): global obj_cam_operation, isGrabbing, template_matcher_thread ret = obj_cam_operation.Stop_grabbing() if ret != 0: strError = "停止取流失败 ret:" + ToHexStr(ret) QMessageBox.warning(mainWindow, "Error", strError, QMessageBox.Ok) else: isGrabbing = False enable_controls() # 停止模板匹配线程 if template_matcher_thread and template_matcher_thread.isRunning(): template_matcher_thread.stop() def close_device(): global isOpen, isGrabbing, obj_cam_operation, frame_monitor_thread, template_matcher_thread if frame_monitor_thread and frame_monitor_thread.isRunning(): frame_monitor_thread.stop() frame_monitor_thread.wait(2000) # 停止模板匹配线程 if template_matcher_thread and template_matcher_thread.isRunning(): template_matcher_thread.stop() template_matcher_thread.wait(2000) template_matcher_thread = None if isOpen and obj_cam_operation: obj_cam_operation.close_device() isOpen = False isGrabbing = False enable_controls() # ==================== 模板匹配检测器 ==================== class TemplateMatcherThread(QThread): template_detected = pyqtSignal(str) # 检测到匹配时发出的信号,传递图像路径 def __init__(self, cam_operation, parent=None): super().__init__(parent) self.cam_operation = cam_operation self.running = True self.last_detection_time = 0 self.sample_template = None self.min_match_count = MIN_MATCH_COUNT self.match_threshold = MATCH_THRESHOLD self.sample_kp = None self.sample_des = None # 特征检测器 - 使用SIFT替代ORB,提高稳定性 self.sift = cv2.SIFT_create() # 特征匹配器 - 使用FLANN提高匹配精度 FLANN_INDEX_KDTREE = 1 index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5) search_params = dict(checks=50) self.flann = cv2.FlannBasedMatcher(index_params, search_params) # 性能监控 self.processing_times = deque(maxlen=100) # 匹配历史记录 self.match_history = deque(maxlen=10) # 调试窗口 self.debug_enabled = True def set_sample(self, sample_img): """设置标准样本""" if sample_img is None or sample_img.size == 0: return False # 转换为灰度图 if len(sample_img.shape) == 3: # 彩色图像 (BGR) gray_sample = cv2.cvtColor(sample_img, cv2.COLOR_BGR2GRAY) elif len(sample_img.shape) == 2: # 已经是灰度图 gray_sample = sample_img else: logging.warning("不支持的图像格式") return False # 提取特征 self.sample_kp, self.sample_des = self.sift.detectAndCompute(gray_sample, None) if self.sample_des is None or len(self.sample_kp) < self.min_match_count: logging.warning("样本图像特征点不足") return False self.sample_template = sample_img logging.info(f"标准样本设置成功,特征点数: {len(self.sample_kp)}") # 显示样本特征点 if self.debug_enabled: sample_with_kp = cv2.drawKeypoints( gray_sample, self.sample_kp, None, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS ) cv2.namedWindow("样本特征点", cv2.WINDOW_NORMAL) cv2.imshow("样本特征点", sample_with_kp) cv2.waitKey(1) return True def match_template(self, frame): """在帧中匹配标准样本""" start_time = time.time() if self.sample_kp is None or self.sample_des is None: return False, None # 转换为灰度图 if len(frame.shape) == 3: gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) else: gray_frame = frame.copy() # 检测当前帧特征点 frame_kp, frame_des = self.sift.detectAndCompute(gray_frame, None) if frame_des is None or len(frame_kp) < self.min_match_count: logging.debug(f"帧特征点不足: {len(frame_kp) if frame_kp else 0}/{self.min_match_count}") return False, None # 匹配特征点 try: matches = self.flann.knnMatch(self.sample_des, frame_des, k=2) except cv2.error as e: logging.error(f"特征匹配失败: {str(e)}") return False, None # 应用Lowe's比率测试筛选优质匹配 good_matches = [] for m, n in matches: if m.distance < 0.7 * n.distance: good_matches.append(m) # 检查匹配质量 if len(good_matches) < self.min_match_count: logging.debug(f"优质匹配不足: {len(good_matches)}/{self.min_match_count}") return False, None # 计算平均匹配距离 avg_distance = sum(m.distance for m in good_matches) / len(good_matches) # 计算匹配分数 (0-1, 1表示完美匹配) # SIFT的距离范围较大,需要调整计算方式 match_score = 1.0 - min(avg_distance / 300.0, 1.0) # 记录匹配历史 self.match_history.append(match_score) # 性能监控 proc_time = time.time() - start_time self.processing_times.append(proc_time) # 检查是否超过阈值 if match_score >= self.match_threshold: # 获取匹配位置 src_pts = np.float32([self.sample_kp[m.queryIdx].pt for m in good_matches]).reshape(-1,1,2) dst_pts = np.float32([frame_kp[m.trainIdx].pt for m in good_matches]).reshape(-1,1,2) # 计算变换矩阵 M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0) if M is not None: # 获取样本图像的尺寸 h, w = self.sample_template.shape[:2] if len(self.sample_template.shape) == 2 else self.sample_template.shape[:2][:2] # 计算样本在帧中的位置 pts = np.float32([[0,0], [0,h-1], [w-1,h-1], [w-1,0]]).reshape(-1,1,2) dst = cv2.perspectiveTransform(pts, M) # 调试显示匹配结果 if self.debug_enabled: match_img = cv2.drawMatches( self.sample_template, self.sample_kp, frame, frame_kp, good_matches, None, flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS ) cv2.namedWindow("特征匹配", cv2.WINDOW_NORMAL) cv2.imshow("特征匹配", match_img) cv2.waitKey(1) return True, (match_score, dst) return False, None def run(self): """主检测循环""" last_process_time = time.time() while self.running: current_time = time.time() # 控制处理频率 if current_time - last_process_time < MIN_FRAME_INTERVAL: time.sleep(0.001) continue last_process_time = current_time if not self.cam_operation or not self.cam_operation.is_frame_available(): time.sleep(0.01) continue frame = self.cam_operation.get_current_frame() if frame is None: continue # 尝试匹配模板 detected, match_data = self.match_template(frame) if detected: self.last_detection_time = current_time match_score, box_points = match_data # 在图像上绘制匹配框 marked_frame = frame.copy() cv2.polylines(marked_frame, [np.int32(box_points)], True, (0,255,0), 3, cv2.LINE_AA) cv2.putText(marked_frame, f"Match: {match_score:.2f}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,255,0), 2) # 保存用于质量检测的图像 timestamp = datetime.now().strftime("%Y%m%d_%H%M%S_%f") capture_dir = os.path.join("captures", "matches") os.makedirs(capture_dir, exist_ok=True) capture_path = os.path.join(capture_dir, f"match_{timestamp}.jpg") cv2.imwrite(capture_path, marked_frame) # 进行质量检测 self.template_detected.emit(capture_path) logging.info( f"检测到匹配目标 | 匹配度: {match_score:.2f} | " f"阈值: {self.match_threshold}" ) # ==================== 模板匹配控制函数 ==================== def toggle_template_matching(state): global template_matcher_thread, current_sample_path if state == Qt.Checked and isGrabbing: # 确保已设置样本 if not current_sample_path: QMessageBox.warning(mainWindow, "错误", "请先设置标准样本", QMessageBox.Ok) mainWindow.chkAutoDetect.setChecked(False) return if template_matcher_thread is None: template_matcher_thread = TemplateMatcherThread(obj_cam_operation) template_matcher_thread.template_detected.connect(vision_controlled_check) # 加载样本图像 sample_img = cv2.imread(current_sample_path) if sample_img is None: QMessageBox.warning(mainWindow, "错误", "无法加载标准样本图像", QMessageBox.Ok) mainWindow.chkAutoDetect.setChecked(False) return if not template_matcher_thread.set_sample(sample_img): QMessageBox.warning(mainWindow, "错误", "标准样本特征不足", QMessageBox.Ok) mainWindow.chkAutoDetect.setChecked(False) return template_matcher_thread.start() logging.info("模板匹配自动检测已启用") elif template_matcher_thread: template_matcher_thread.stop() logging.info("模板匹配自动检测已禁用") def update_match_threshold(value): global template_matcher_thread if template_matcher_thread: template_matcher_thread.match_threshold = value / 100.0 mainWindow.lblThresholdValue.setText(f"{value}%") # ==================== UI更新函数 ==================== def update_diff_display(diff_ratio, is_qualified): mainWindow.lblCurrentDiff.setText(f"当前差异度: {diff_ratio*100:.2f}%") if is_qualified: mainWindow.lblDiffStatus.setText("状态: 合格") mainWindow.lblDiffStatus.setStyleSheet("color: green; font-size: 12px;") else: mainWindow.lblDiffStatus.setText("状态: 不合格") mainWindow.lblDiffStatus.setStyleSheet("color: red; font-size: 12px;") def update_diff_threshold(value): mainWindow.lblDiffValue.setText(f"{value}%") def update_sample_display(): global current_sample_path if current_sample_path: mainWindow.lblSamplePath.setText(f"当前样本: {os.path.basename(current_sample_path)}") mainWindow.lblSamplePath.setToolTip(current_sample_path) mainWindow.bnPreviewSample.setEnabled(True) else: mainWindow.lblSamplePath.setText("当前样本: 未设置样本") mainWindow.bnPreviewSample.setEnabled(False) def update_history_display(): global detection_history mainWindow.cbHistory.clear() for i, result in enumerate(detection_history[-10:]): timestamp = result['timestamp'].strftime("%H:%M:%S") status = "合格" if result['qualified'] else "不合格" ratio = f"{result['diff_ratio']*100:.2f}%" trigger = "视觉" if result['trigger_type'] == 'vision' else "手动" mainWindow.cbHistory.addItem(f"[{trigger} {timestamp}] {status} - 差异: {ratio}") # ==================== 主窗口类 ==================== class MainWindow(QMainWindow): def __init__(self): super().__init__() self.setWindowTitle("布料印花检测系统 - 模板匹配版") self.resize(1200, 800) central_widget = QWidget() self.setCentralWidget(central_widget) main_layout = QVBoxLayout(central_widget) # 设备枚举区域 device_layout = QHBoxLayout() self.ComboDevices = QComboBox() self.bnEnum = QPushButton("枚举设备") self.bnOpen = QPushButton("打开设备") self.bnClose = QPushButton("关闭极设备") device_layout.addWidget(self.ComboDevices) device_layout.addWidget(self.bnEnum) device_layout.addWidget(self.bnOpen) device_layout.addWidget(self.bnClose) main_layout.addLayout(device_layout) # 取流控制组 self.groupGrab = QGroupBox("取流控制") grab_layout = QHBoxLayout(self.groupGrab) self.bnStart = QPushButton("开始取流") self.bnStop = QPushButton("停止取流") self.radioContinueMode = QRadioButton("连续模式") self.radioTriggerMode = QRadioButton("触发模式") self.bnSoftwareTrigger = QPushButton("软触发") grab_layout.addWidget(self.bnStart) grab_layout.addWidget(self.bnStop) grab_layout.addWidget(self.radioContinueMode) grab_layout.addWidget(self.radioTriggerMode) grab_layout.addWidget(self.bnSoftwareTrigger) main_layout.addWidget(self.groupGrab) # 参数设置组 self.paramgroup = QGroupBox("相机参数") param_layout = QGridLayout(self.paramgroup) self.edtExposureTime = QLineEdit() self.edtGain = QLineEdit() self.edtFrameRate = QLineEdit() self.bnGetParam = QPushButton("获取参数") self.bnSetParam = QPushButton("设置参数") self.bnSaveImage = QPushButton("保存图像") param_layout.addWidget(QLabel("曝光时间:"), 0, 0) param_layout.addWidget(self.edtExposureTime, 0, 1) param_layout.addWidget(self.bnGetParam, 0, 2) param_layout.addWidget(QLabel("增益:"), 1, 0) param_layout.addWidget(self.edtGain, 1, 1) param_layout.addWidget(self.bnSetParam, 1, 2) param_layout.addWidget(QLabel("帧率:"), 2, 0) param_layout.addWidget(self.edtFrameRate, 2, 1) param_layout.addWidget(self.bnSaveImage, 2, 2) main_layout.addWidget(self.paramgroup) # 图像显示区域 self.widgetDisplay = QLabel() self.widgetDisplay.setMinimumSize(640, 480) self.widgetDisplay.setStyleSheet("background-color: black;") self.widgetDisplay.setAlignment(Qt.AlignCenter) self.widgetDisplay.setText("相机预览区域") main_layout.addWidget(self.widgetDisplay, 1) # 创建自定义UI组件 self.setup_custom_ui() def setup_custom_ui(self): # 工具栏 toolbar = self.addToolBar("检测工具") self.bnCheckPrint = QPushButton("手动检测") self.bnSaveSample = QPushButton("保存标准样本") self.bnPreviewSample = QPushButton("预览样本") self.cbHistory = QComboBox() self.cbHistory.setMinimumWidth(300) toolbar.addWidget(self.bnCheckPrint) toolbar.addWidget(self.bnSaveSample) toolbar.addWidget(self.bnPreviewSample) toolbar.addWidget(QLabel("历史记录:")) toolbar.addWidget(self.cbHistory) # 状态栏样本路径 self.lblSamplePath = QLabel("当前样本: 未设置样本") self.statusBar().addPermanentWidget(self.lblSamplePath) # 右侧面板 right_panel = QWidget() right_layout = QVBoxLayout(right_panel) right_layout.setContentsMargins(10, 10, 10, 10) # 差异度调整组 diff_group = QGroupBox("差异度调整") diff_layout = QVBoxLayout(diff_group) self.lblDiffThreshold = QLabel("差异度阈值 (0-100%):") self.sliderDiffThreshold = QSlider(Qt.Horizontal) self.sliderDiffThreshold.setRange(0, 100) self.sliderDiffThreshold.setValue(5) self.lblDiffValue = QLabel("5%") self.lblCurrentDiff = QLabel("当前差异度: -") self.lblCurrentDiff.setStyleSheet("font-size: 14px; font-weight: bold;") self.lblDiffStatus = QLabel("状态: 未检测") self.lblDiffStatus.setStyleSheet("font-size: 12px;") diff_layout.addWidget(self.lblDiffThreshold) diff_layout.addWidget(self.sliderDiffThreshold) diff_layout.addWidget(self.lblDiffValue) diff_layout.addWidget(self.lblCurrentDiff) diff_layout.addWidget(self.lblDiffStatus) right_layout.addWidget(diff_group) # ===== 模板匹配面板 ===== match_group = QGroupBox("模板匹配") match_layout = QVBoxLayout(match_group) # 样本设置 sample_layout = QHBoxLayout() self.bnSetSample = QPushButton("设置标准样本") self.bnPreviewSample = QPushButton("预览样本") self.lblSampleStatus = QLabel("状态: 未设置样本") sample_layout.addWidget(self.bnSetSample) sample_layout.addWidget(self.bnPreviewSample) sample_layout.addWidget(self.lblSampleStatus) match_layout.addLayout(sample_layout) # 匹配参数 param_layout = QHBoxLayout() self.lblMatchThreshold = QLabel("匹配阈值:") self.sliderThreshold = QSlider(Qt.Horizontal) self.sliderThreshold.setRange(50, 100) self.sliderThreshold.setValue(75) # 降低默认阈值 self.lblThresholdValue = QLabel("75%") param_layout.addWidget(self.lblMatchThreshold) param_layout.addWidget(self.sliderThreshold) param_layout.addWidget(self.lblThresholdValue) match_layout.addLayout(param_layout) # 自动检测开关 self.chkAutoDetect = QCheckBox("启用模板匹配自动检测") self.chkAutoDetect.setChecked(False) match_layout.addWidget(self.chkAutoDetect) right_layout.addWidget(match_group) right_layout.addStretch(1) # 停靠窗口 dock = QDockWidget("检测控制面板", self) dock.setWidget(right_panel) dock.setFeatures(QDockWidget.DockWidgetMovable | QDockWidget.DockWidgetFloatable) self.addDockWidget(Qt.RightDockWidgetArea, dock) def closeEvent(self, event): logging.info("主窗口关闭,执行清理...") close_device() event.accept() # ===== 辅助函数 ===== def ToHexStr(num): if not isinstance(num, int): try: num = int(num) except: return f"<非整数:{type(num)}>" chaDic = {10: 'a', 11: 'b', 12: 'c', 13: 'd', 14: 'e', 15: 'f'} hexStr = "" if num < 0: num = num + 2 ** 32 while num >= 16: digit = num % 16 hexStr = chaDic.get(digit, str(digit)) + hexStr num //= 16 hexStr = chaDic.get(num, str(num)) + hexStr return "0x" + hexStr def enum_devices(): global deviceList, obj_cam_operation n_layer_type = ( MV_GIGE_DEVICE | MV_USB_DEVICE | MV_GENTL_CAMERALINK_DEVICE | MV_GENTL_CXP_DEVICE | MV_GENTL_XOF_DEVICE ) # 创建设备列表 deviceList = MV_CC_DEVICE_INFO_LIST() # 枚举设备 ret = MvCamera.MV_CC_EnumDevices(n_layer_type, deviceList) if ret != MV_OK: error_msg = f"枚举设备失败! 错误码: 0x{ret:x}" logging.error(error_msg) QMessageBox.warning(mainWindow, "错误", error_msg, QMessageBox.Ok) return ret if deviceList.nDeviceNum == 0: QMessageBox.warning(mainWindow, "提示", "未找到任何设备", QMessageBox.Ok) return MV_OK logging.info(f"找到 {deviceList.nDeviceNum} 个设备") # 处理设备信息 devList = [] for i in range(deviceList.nDeviceNum): # 获取设备信息 mvcc_dev_info = ctypes.cast( deviceList.pDeviceInfo[i], ctypes.POINTER(MV_CC_DEVICE_INFO) ).contents # 根据设备类型提取信息 if mvcc_dev_info.nTLayerType == MV_GIGE_DEVICE: st_gige_info = mvcc_dev_info.SpecialInfo.stGigEInfo ip_addr = ( f"{(st_gige_info.nCurrentIp >> 24) & 0xFF}." f"{(st_gige_info.nCurrentIp >> 16) & 0xFF}." f"{(st_gige_info.nCurrentIp >> 8) & 0xFF}." f"{st_gige_info.nCurrentIp & 0xFF}" ) # 修复:将c_ubyte_Array_16转换为字节串再解码 user_defined_bytes = bytes(st_gige_info.chUserDefinedName) dev_name = f"GigE: {user_defined_bytes.decode('gbk', 'ignore')}" devList.append(f"[{i}] {dev_name} ({ip_addr})") elif mvcc_dev_info.nTLayerType == MV_USB_DEVICE: st_usb_info = mvcc_dev_info.SpecialInfo.stUsb3VInfo serial = bytes(st_usb_info.chSerialNumber).decode('ascii', 'ignore').rstrip('\x00') # 修复:同样处理用户自定义名称 user_defined_bytes = bytes(st_usb_info.chUserDefinedName) dev_name = f"USB: {user_defined_bytes.decode('gbk', 'ignore')}" devList.append(f"[{i}] {dev_name} (SN: {serial})") else: devList.append(f"[{i}] 未知设备类型: {mvcc_dev_info.nTLayerType}") # 更新UI mainWindow.ComboDevices.clear() mainWindow.ComboDevices.addItems(devList) if devList: mainWindow.ComboDevices.setCurrentIndex(0) mainWindow.statusBar().showMessage(f"找到 {deviceList.nDeviceNum} 个设备", 3000) return MV_OK def set_continue_mode(): ret = obj_cam_operation.set_trigger_mode(False) if ret != 0: strError = "设置连续模式失败 ret:" + ToHexStr(ret) QMessageBox.warning(mainWindow, "Error", strError, QMessageBox.Ok) else: mainWindow.radioContinueMode.setChecked(True) mainWindow.radioTriggerMode.setChecked(False) mainWindow.bnSoftwareTrigger.setEnabled(False) def set_software_trigger_mode(): ret = obj_cam_operation.set_trigger_mode(True) if ret != 0: strError = "设置触发模式失败 ret:" + ToHexStr(ret) QMessageBox.warning(mainWindow, "Error", strError, QMessageBox.Ok) else: mainWindow.radioContinueMode.setChecked(False) mainWindow.radioTriggerMode.setChecked(True) mainWindow.bnSoftwareTrigger.setEnabled(isGrabbing) def trigger_once(): ret = obj_cam_operation.trigger_once() if ret != 0: strError = "软触发失败 ret:" + ToHexStr(ret) QMessageBox.warning(mainWindow, "Error", strError, QMessageBox.Ok) def save_sample_image(): global isGrabbing, obj_cam_operation, current_sample_path if not isGrabbing: QMessageBox.warning(mainWindow, "错误", "请先开始取流并捕获图像!", QMessageBox.Ok) return # 尝试捕获当前帧 frame = obj_cam_operation.capture_frame() if frame is None: QMessageBox.warning(mainWindow, "无有效图像", "未捕获到有效图像,请检查相机状态!", QMessageBox.Ok) return # 确保图像有效 if frame.size == 0 or frame.shape[0] == 0 or frame.shape[1] == 0: QMessageBox.warning(mainWindow, "无效图像", "捕获的图像无效,请检查相机设置!", QMessageBox.Ok) return settings = QSettings("ClothInspection", "CameraApp") last_dir = settings.value("last_save_dir", os.path.join(os.getcwd(), "captures")) timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") default_filename = f"sample_{timestamp}" file_path, selected_filter = QFileDialog.getSaveFileName( mainWindow, "保存标准样本图像", os.path.join(last_dir, default_filename), "BMP Files (*.bmp);;PNG Files (*.png);;JPEG Files (*.jpg);;所有文件 (*)", options=QFileDialog.DontUseNativeDialog ) if not file_path: return # 确保文件扩展名正确 file_extension = os.path.splitext(file_path)[1].lower() if not file_extension: if "BMP" in selected_filter: file_path += ".b极mp" elif "PNG" in selected_filter: file_path += ".png" elif "JPEG" in selected_filter or "JPG" in selected_filter: file_path += ".jpg" else: file_path += ".bmp" file_extension = os.path.splitext(file_path)[1].lower() # 创建目录(如果不存在) directory = os.path.dirname(file_path) if directory and not os.path.exists(directory): try: os.makedirs(directory, exist_ok=True) except OSError as e: QMessageBox.critical(mainWindow, "目录创建错误", f"无法创建目录 {directory}: {str(e)}", QMessageBox.Ok) return # 保存图像 try: # 使用OpenCV保存图像 if not cv2.imwrite(file_path, frame): raise Exception("OpenCV保存失败") # 更新状态 current_sample_path = file_path update_sample_display() settings.setValue("last_save_dir", os.path.dirname(file_path)) # 显示成功消息 QMessageBox.information(mainWindow, "成功", f"标准样本已保存至:\n{file_path}", QMessageBox.Ok) # 更新样本状态 mainWindow.lblSampleStatus.setText("状态: 样本已设置") mainWindow.lblSampleStatus.setStyleSheet("color: green;") except Exception as e: logging.error(f"保存图像失败: {str(e)}") QMessageBox.critical(mainWindow, "保存错误", f"保存图像时发生错误:\n{str(e)}", QMessageBox.Ok) def preview_sample(): global current_sample_path if not current_sample_path or not os.path.exists(current_sample_path): QMessageBox.warning(mainWindow, "错误", "请先设置有效的标准样本图像!", QMessageBox.Ok) return try: # 直接使用OpenCV加载图像 sample_img = cv2.imread(current_sample_path) if sample_img is None: raise Exception("无法加载图像") # 显示图像 cv2.namedWindow("标准样本预览", cv2.WINDOW_NORMAL) cv2.resizeWindow("标准样本预览", 800, 600) cv2.imshow("标准样本预览", sample_img) cv2.waitKey(0) cv2.destroyAllWindows() except Exception as e: QMessageBox.warning(mainWindow, "错误", f"预览样本失败: {str(e)}", QMessageBox.Ok) def is_float(str): try: float(str) return True except ValueError: return False def get_param(): try: ret = obj_cam_operation.get_parameters() if ret != MV_OK: strError = "获取参数失败,错误码: " + ToHexStr(ret) QMessageBox.warning(mainWindow, "错误", strError, QMessageBox.Ok) else: mainWindow.edtExposureTime.setText("{0:.2f}".format(obj_cam_operation.exposure_time)) mainWindow.edtGain.setText("{0:.2f}".format(obj_cam_operation.gain)) mainWindow.edtFrameRate.setText("{0:.2f}".format(obj_cam_operation.frame_rate)) except Exception as e: error_msg = f"获取参数时发生错误: {str(e)}" QMessageBox.critical(mainWindow, "严重错误", error_msg, QMessageBox.Ok) def set_param(): frame_rate = mainWindow.edtFrameRate.text() exposure = mainWindow.edtExposureTime.text() gain = mainWindow.edtGain.text() if not (is_float(frame_rate) and is_float(exposure) and is_float(gain)): strError = "设置参数失败: 参数必须是有效的浮点数" QMessageBox.warning(mainWindow, "错误", strError, QMessageBox.Ok) return MV_E_PARAMETER try: ret = obj_cam_operation.set_param( frame_rate=float(frame_rate), exposure_time=float(exposure), gain=float(gain) ) if ret != MV_OK: strError = "设置参数失败,错误码: " + ToHexStr(ret) QMessageBox.warning(mainWindow, "错误", strError, QMessageBox.Ok) except Exception as e: error_msg = f"设置参数时发生错误: {str(e)}" QMessageBox.critical(mainWindow, "严重错误", error_msg, QMessageBox.Ok) def enable_controls(): global isGrabbing, isOpen mainWindow.groupGrab.setEnabled(isOpen) mainWindow.paramgroup.setEnabled(isOpen) mainWindow.bnOpen.setEnabled(not isOpen) mainWindow.bnClose.setEnabled(isOpen) mainWindow.bnStart.setEnabled(isOpen and (not isGrabbing)) mainWindow.bnStop.setEnabled(isOpen and isGrabbing) mainWindow.bnSoftwareTrigger.setEnabled(isGrabbing and mainWindow.radioTriggerMode.isChecked()) mainWindow.bnSaveImage.setEnabled(isOpen and isGrabbing) mainWindow.bnCheckPrint.setEnabled(isOpen and isGrabbing) mainWindow.bnSaveSample.setEnabled(isOpen and isGrabbing) mainWindow.bnPreviewSample.setEnabled(bool(current_sample_path)) # 模板匹配控制 mainWindow.chkAutoDetect.setEnabled(bool(current_sample_path) and isGrabbing) # ===== 相机帧监控线程 ===== class FrameMonitorThread(QThread): frame_status = pyqtSignal(str) # 用于发送状态消息的信号 def __init__(self, cam_operation): super().__init__() self.cam_operation = cam_operation self.running = True self.frame_count = 0 self.last_time = time.time() def run(self): """监控相机帧状态的主循环""" while self.running: try: if self.cam_operation and self.cam_operation.is_grabbing: # 获取帧统计信息 frame_info = self.get_frame_info() if frame_info: fps = frame_info.get('fps', 0) dropped = frame_info.get('dropped', 0) status = f"FPS: {fps:.1f} | 丢帧: {dropped}" self.frame_status.emit(status) else: self.frame_status.emit("取流中...") else: self.frame_status.emit("相机未取流") except Exception as e: self.frame_status.emit(f"监控错误: {str(e)}") # 每500ms检查一次 QThread.msleep(500) def stop(self): """停止监控线程""" self.running = False self.wait(1000) # 等待线程结束 def calculate_fps(self): """计算当前帧率""" current_time = time.time() elapsed = current_time - self.last_time if elapsed > 0: fps = self.frame_count / elapsed self.frame_count = 0 self.last_time = current_time return fps return 0 def get_frame_info(self): """获取帧信息""" try: # 更新帧计数 self.frame_count += 1 # 返回帧信息 return { 'fps': self.calculate_fps(), 'dropped': 0 # 实际应用中需要从相机获取真实丢帧数 } except Exception as e: logging.error(f"获取帧信息失败: {str(e)}") return None # ===== 主程序入口 ===== if __name__ == "__main__": # 配置日志系统 logging.basicConfig( level=logging.DEBUG, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', handlers=[ logging.FileHandler("cloth_inspection_template.log"), logging.StreamHandler() ] ) logging.info("布料印花检测系统(模板匹配版)启动") app = QApplication(sys.argv) mainWindow = MainWindow() # 信号连接 mainWindow.sliderDiffThreshold.valueChanged.connect(update_diff_threshold) mainWindow.bnCheckPrint.clicked.connect(lambda: vision_controlled_check(None)) mainWindow.bnSaveSample.clicked.connect(save_sample_image) mainWindow.bnPreviewSample.clicked.connect(preview_sample) mainWindow.bnEnum.clicked.connect(enum_devices) mainWindow.bnOpen.clicked.connect(open_device) mainWindow.bnClose.clicked.connect(close_device) mainWindow.bnStart.clicked.connect(start_grabbing) mainWindow.bnStop.clicked.connect(stop_grabbing) mainWindow.bnSoftwareTrigger.clicked.connect(trigger_once) mainWindow.radioTriggerMode.clicked.connect(set_software_trigger_mode) mainWindow.radioContinueMode.clicked.connect(set_continue_mode) mainWindow.bnGetParam.clicked.connect(get_param) mainWindow.bnSetParam.clicked.connect(set_param) mainWindow.bnSaveImage.clicked.connect(save_sample_image) # 模板匹配信号连接 mainWindow.sliderThreshold.valueChanged.connect(update_match_threshold) mainWindow.chkAutoDetect.stateChanged.connect(toggle_template_matching) mainWindow.show() app.exec_() close_device() sys.exit()

# -*- 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, QLineEdit, QRadioButton, QButtonGroup, QCheckBox) from PyQt5.QtCore import QRect, Qt, QSettings, QThread, pyqtSignal from CamOperation_class import CameraOperation sys.path.append("D:\\海康\\MVS\\Development\\Samples\\Python\\BasicDemo") import ctypes from datetime import datetime from MvCameraControl_class import * from MvErrorDefine_const import * from CameraParams_header import * from PyUICBasicDemo import Ui_MainWindow import logging import platform import serial import socket import time from scipy import ndimage import skimage.measure from skimage.feature import ORB, match_descriptors import subprocess # 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 isGrabbing = False # 相机取流状态 isOpen = False # 相机打开状态 obj_cam_operation = None # 相机操作对象 frame_monitor_thread = None # 帧监控线程 sensor_monitor_thread = None # 传感器监控线程 sensor_controller = None # 传感器控制器 # ==================== 传感器通讯模块 ==================== class SensorController: def __init__(self): self.sensor_type = None # 'serial' 或 'ethernet' self.serial_conn = None self.socket_conn = None self.sensor_data = { 'tension': 0.0, # 布料张力 (N) 'speed': 0.0, # 布料速度 (m/s) 'temperature': 25.0, # 环境温度 (°C) 'humidity': 50.0 # 环境湿度 (%) } self.connected = False def connect(self, config): """连接传感器""" try: if config['type'] == 'serial': self.sensor_type = 'serial' self.serial_conn = serial.Serial( port=config['port'], baudrate=config['baudrate'], timeout=config['timeout'] ) logging.info(f"串口传感器已连接: {config['port']}") elif config['type'] == 'ethernet': self.sensor_type = 'ethernet' self.socket_conn = socket.socket(socket.AF_INET, socket.SOCK_STREAM) self.socket_conn.connect((config['ip'], config['port'])) self.socket_conn.settimeout(config['timeout']) logging.info(f"以太网传感器已连接: {config['ip']}:{config['port']}") self.connected = True return True except Exception as e: logging.error(f"传感器连接失败: {str(e)}") self.connected = False return False def disconnect(self): """断开传感器连接""" if self.serial_conn and self.serial_conn.is_open: self.serial_conn.close() if self.socket_conn: try: self.socket_conn.shutdown(socket.SHUT_RDWR) self.socket_conn.close() except: pass self.connected = False logging.info("传感器已断开") def read_data(self): """从传感器读取数据(模拟实现)""" if not self.connected: return None # 实际应用中应替换为真实传感器协议 if self.sensor_type == 'serial' and self.serial_conn: try: # 模拟串口数据读取 self.sensor_data = { 'tension': np.random.uniform(5.0, 20.0), 'speed': np.random.uniform(0.5, 2.5), 'temperature': 25.0 + np.random.uniform(-2, 2), 'humidity': 50.0 + np.random.uniform(-10, 10) } return self.sensor_data except serial.SerialException as e: logging.error(f"串口读取失败: {str(e)}") self.disconnect() return None elif self.sensor_type == 'ethernet' and self.socket_conn: try: # 模拟以太网数据读取 self.sensor_data = { 'tension': np.random.uniform(5.0, 20.0), 'speed': np.random.uniform(0.5, 2.5), 'temperature': 25.0 + np.random.uniform(-2, 2), 'humidity': 50.0 + np.random.uniform(-10, 10) } return self.sensor_data except (socket.timeout, socket.error) as e: logging.error(f"网络传感器读取失败: {str(e)}") self.disconnect() return None return None def send_command(self, command): """向传感器发送控制命令""" if not self.connected: return False try: if self.sensor_type == 'serial' and self.serial_conn: # 实际应用中应根据传感器协议构造命令 self.serial_conn.write(command.encode()) return True elif self.sensor_type == 'ethernet' and self.socket_conn: self.socket_conn.send(command.encode()) return True return False except Exception as e: logging.error(f"发送传感器命令失败: {str(e)}") return False # 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 # 传感器数据监控线程 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: data = self.sensor_controller.read_data() if data: self.data_updated.emit(data) QThread.msleep(1000) # 每秒更新一次 def stop极(self): self.running = False # ==================== 优化后的检测算法 ==================== def enhanced_check_print_quality(sample_image_path, test_image, threshold=0.05, sensor_data=None): """ 优化版布料印花检测算法,增加图像配准和特征匹配 :param sample_image_path: 合格样本图像路径 :param test_image: 测试图像 (numpy数组) :param threshold: 差异阈值 :param sensor_data: 传感器数据字典 :return: 是否合格,差异值,标记图像 """ # 根据传感器数据动态调整阈值 if sensor_data: # 速度越高,允许的差异阈值越大 speed_factor = min(1.0 + sensor_data['speed'] * 0.1, 1.5) # 温度/湿度影响 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: # 读取样本图像 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: test_image_gray = cv2.cvtColor(test_image, cv2.COLOR_BGR2GRAY) else: test_image_gray = test_image.copy() # 1. 图像预处理 sample_image = cv2.GaussianBlur(sample_image, (5, 5), 0) test_image_gray = cv2.GaussianBlur(test_image_gray, (5, 5), 0) # 2. 图像配准(解决位置偏移问题) try: # 使用ORB特征匹配进行图像配准 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: # 使用BFMatcher进行特征匹配 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: # 提取匹配点的坐标 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) # 计算单应性矩阵 H, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0) if H is not None: # 应用变换 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 # 3. 确保图像大小一致 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 # 4. 计算结构相似性(SSIM)和差异 ssim_score, ssim_diff = skimage.measure.compare_ssim( aligned_sample, test_image_gray, full=True, gaussian_weights=True ) ssim_diff = (1 - ssim_diff) * 255 # 转换为0-255范围 # 5. 计算绝对差异 abs_diff = cv2.absdiff(aligned_sample, test_image_gray) # 6. 组合差异(SSIM差异对结构变化敏感,绝对差异对亮度变化敏感) combined_diff = cv2.addWeighted(ssim_diff.astype(np.uint8), 0.7, abs_diff, 0.3, 0) # 7. 二值化差异 _, thresholded = cv2.threshold(combined_diff, 30, 255, cv2.THRESH_BINARY) # 8. 形态学操作去除噪声 kernel = np.ones((3, 3), np.uint8) thresholded = cv2.morphologyEx(thresholded, cv2.MORPH_OPEN, kernel) thresholded = cv2.morphologyEx(thresholded, cv2.MORPH_CLOSE, kernel) # 9. 计算差异比例 diff_pixels = np.count_nonzero(thresholded) total_pixels = aligned_sample.size diff_ratio = diff_pixels / total_pixels # 10. 判断是否合格 is_qualified = diff_ratio <= adjusted_threshold # 11. 创建标记图像 marked_image = cv2.cvtColor(test_image_gray, cv2.COLOR_GRAY2BGR) marked_image[thresholded == 255] = [0, 0, 255] # 红色标记缺陷 # 12. 标记大面积缺陷区域 labels = skimage.measure.label(thresholded) properties = skimage.measure.regionprops(labels) for prop in properties: if prop.area > 50: # 只标记大于50像素的区域 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(): """传感器控制的质量检测主流程""" global isGrabbing, obj_cam_operation, current_sample_path, detection_history, sensor_controller logging.info("传感器控制的质量检测启动") # 1. 检查传感器连接 if not sensor_controller or not sensor_controller.connected: QMessageBox.warning(mainWindow, "传感器错误", "传感器未连接,请先连接传感器!", QMessageBox.Ok) return # 2. 读取传感器数据 sensor_data = sensor_controller.read_data() if not sensor_data: QMessageBox.warning(mainWindow, "传感器错误", "无法读取传感器数据!", QMessageBox.Ok) return # 3. 根据传感器数据调整生产参数 adjust_production_parameters(sensor_data) # 4. 执行图像捕获和检测 check_print_with_sensor(sensor_data) def adjust_production_parameters(sensor_data): """根据传感器数据调整生产参数""" global obj_cam_operation # 示例:根据张力调整相机参数 tension = sensor_data['tension'] # 张力过大时增加曝光时间 if tension > 15.0 and obj_cam_operation: logging.info(f"高张力({tension}N)环境,增加曝光时间") try: current_exposure = obj_cam_operation.exposure_time new_exposure = min(current_exposure * 1.2, 100000) # 增加20%,上限100ms obj_cam_operation.set_exposure(new_exposure) logging.info(f"曝光时间调整为: {new_exposure}us") except Exception as e: logging.error(f"调整曝光失败: {str(e)}") # 速度过快时降低图像分辨率 speed = sensor_data['speed'] if speed > 2.0 and obj_cam_operation: logging.info(f"高速度({speed}m/s)环境,降低分辨率") try: # 实际应用中应调用相机SDK的分辨率设置 # 这里仅为示例 pass except Exception as e: logging.error(f"调整分辨率失败: {str(e)}") # 发送控制命令到传感器 if tension < 5.0 and sensor_controller: sensor_controller.send_command("INCREASE_TENSION") logging.info("发送增加张力命令") elif tension > 18.0 and sensor_controller: sensor_controller.send_command("DECREASE_TENSION") logging.info("发送减少张力命令") # 布料印花检测函数(使用优化算法) def check_print_with_sensor(sensor_data=None): """ 使用优化算法检测布料印花是否合格 """ 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 = mainWindow.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 = enhanced_check_print_quality( current_sample_path, test_image, threshold=diff_threshold, sensor_data=sensor_data ) 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, '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() # 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 mainWindow.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: mainWindow.ui.lblDiffStatus.setText("状态: 合格") mainWindow.ui.lblDiffStatus.setStyleSheet("color: green; font-size: 12px;") else: mainWindow.ui.lblDiffStatus.setText("状态: 不合格") mainWindow.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 """ mainWindow.ui.lblDiffValue.setText(f"{value}%") # 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: mainWindow.ui.lblSamplePath.setText(f"当前样本: {os.path.basename(current_sample_path)}") mainWindow.ui.lblSamplePath.setToolTip(current_sample_path) mainWindow.ui.bnPreviewSample.setEnabled(True) else: mainWindow.ui.lblSamplePath.setText("当前样本: 未设置样本") mainWindow.ui.bnPreviewSample.setEnabled(False) # 更新历史记录显示 def update_history_display(): global detection_history mainWindow.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}%" mainWindow.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 # 绑定下拉列表至设备信息索引 def xFunc(event): global nSelCamIndex nSelCamIndex = TxtWrapBy("[", "]", mainWindow.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.chUserDefinedName) 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) + ")") mainWindow.ui.ComboDevices.clear() mainWindow.ui.ComboDevices.addItems(devList) mainWindow.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 global mainWindow if isOpen: QMessageBox.warning(mainWindow, "Error", 'Camera is Running!', QMessageBox.Ok) return MV_E_CALLORDER nSelCamIndex = mainWindow.ui.ComboDevices.currentIndex() if nSelCamIndex < 0: QMessageBox.warning(mainWindow, "Error", 'Please select a camera!', QMessageBox.Ok) return MV_E_CALLORDER obj_cam_operation = CameraOperation(obj_cam_operation, 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(mainWindow.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(mainWindow.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: mainWindow.ui.radioContinueMode.setChecked(True) mainWindow.ui.radioTriggerMode.setChecked(False) mainWindow.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: mainWindow.ui.radioContinueMode.setChecked(False) mainWindow.ui.radioTriggerMode.setChecked(True) mainWindow.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 mainWindow.ui.edtExposureTime.setText("{0:.2f}".format(obj_cam_operation.exposure_time)) mainWindow.ui.edtGain.setText("{0:.2f}".format(obj_cam_operation.gain)) mainWindow.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 = mainWindow.ui.edtFrameRate.text() exposure = mainWindow.ui.edtExposureTime.text() gain = mainWindow.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 mainWindow.ui.groupGrab.setEnabled(isOpen) mainWindow.ui.groupParam.setEnabled(isOpen) mainWindow.ui.bnOpen.setEnabled(not isOpen) mainWindow.ui.bnClose.setEnabled(isOpen) mainWindow.ui.bnStart.setEnabled(isOpen and (not isGrabbing)) mainWindow.ui.bnStop.setEnabled(isOpen and isGrabbing) mainWindow.ui.bnSoftwareTrigger.setEnabled(isGrabbing and mainWindow.ui.radioTriggerMode.isChecked()) mainWindow.ui.bnSaveImage.setEnabled(isOpen and isGrabbing) mainWindow.ui #添加检测按钮控制 mainWindow.ui.bnCheckPrint.setEnabled(isOpen and isGrabbing) mainWindow.ui.bnSaveSample.setEnabled(isOpen and isGrabbing) mainWindow.ui.bnPreviewSample.setEnabled(bool(current_sample_path)) class MainWindow(QMainWindow): def __init__(self): super().__init__() # 创建UI实例 self.ui = Ui_MainWindow() self.ui.setupUi(self) def closeEvent(self, event): """重写关闭事件,执行清理操作""" logging.info("主窗口关闭,执行清理...") # 关闭设备 close_device() # 断开传感器 disconnect_sensor() event.accept() if __name__ == "__main__": # 初始化UI app = QApplication(sys.argv) # 创建主窗口实例 mainWindow = 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 mainWindow.ui.bnCheckPrint = QPushButton("检测印花质量") toolbar.addWidget(mainWindow.ui.bnCheckPrint) # 添加保存样本按钮 mainWindow.ui.bnSaveSample = QPushButton("保存标准样本") toolbar.addWidget(mainWindow.ui.bnSaveSample) # 添加预览样本按钮 mainWindow.ui.bnPreviewSample = QPushButton("预览样本") toolbar.addWidget(mainWindow.ui.bnPreviewSample) # 添加历史记录下拉框 mainWindow.ui.cbHistory = QComboBox() mainWindow.ui.cbHistory.setMinimumWidth(300) toolbar.addWidget(QLabel("历史记录:")) toolbar.addWidget(mainWindow.ui.cbHistory) # 添加当前样本显示标签 mainWindow.ui.lblSamplePath = QLabel("当前样本: 未设置样本") status_bar = mainWindow.statusBar() status_bar.addPermanentWidget(mainWindow.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 mainWindow.ui.lblDiffThreshold = QLabel("差异度阈值 (0-100%):") mainWindow.ui.sliderDiffThreshold = QSlider(Qt.Horizontal) mainWindow.ui.sliderDiffThreshold.setRange(0, 100) # 0-100% mainWindow.ui.sliderDiffThreshold.setValue(5) # 默认5% mainWindow.ui.lblDiffValue = QLabel("5%") # 当前差异度显示 mainWindow.ui.lblCurrentDiff = QLabel("当前差异度: -") mainWindow.ui.lblCurrentDiff.setStyleSheet("font-size: 14px; font-weight: bold;") # 差异度状态指示器 mainWindow.ui.lblDiffStatus = QLabel("状态: 未检测") mainWindow.ui.lblDiffStatus.setStyleSheet("font-size: 12px;") # 布局控件 diff_layout.addWidget(mainWindow.ui.lblDiffThreshold) diff_layout.addWidget(mainWindow.ui.sliderDiffThreshold) diff_layout.addWidget(mainWindow.ui.lblDiffValue) diff_layout.addWidget(mainWindow.ui.lblCurrentDiff) diff_layout.addWidget(mainWindow.ui.lblDiffStatus) # 添加差异度组到右侧布局 right_layout.addWidget(diff_group) # === 新增传感器控制面板 === sensor_panel = QGroupBox("传感器控制") sensor_layout = QVBoxLayout(sensor_panel) # 传感器类型选择 sensor_type_layout = QHBoxLayout() mainWindow.ui.lblSensorType = QLabel("传感器类型:") mainWindow.ui.cbSensorType = QComboBox() mainWindow.ui.cbSensorType.addItems(["串口", "以太网"]) sensor_type_layout.addWidget(mainWindow.ui.lblSensorType) sensor_type_layout.addWidget(mainWindow.ui.cbSensorType) # 串口参数 mainWindow.ui.serialGroup = QGroupBox("串口参数") serial_layout = QVBoxLayout(mainWindow.ui.serialGroup) mainWindow.ui.lblComPort = QLabel("端口:") mainWindow.ui.cbComPort = QComboBox() # 获取可用串口 (Windows) 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)] mainWindow.ui.cbComPort.addItems(ports) mainWindow.ui.lblBaudrate = QLabel("波特率:") mainWindow.ui.cbBaudrate = QComboBox() mainWindow.ui.cbBaudrate.addItems(["9600", "19200", "38400", "57600", "115200"]) mainWindow.ui.cbBaudrate.setCurrentText("115200") serial_layout.addWidget(mainWindow.ui.lblComPort) serial_layout.addWidget(mainWindow.ui.cbComPort) serial_layout.addWidget(mainWindow.ui.lblBaudrate) serial_layout.addWidget(mainWindow.ui.cbBaudrate) # 以太网参数 mainWindow.ui.ethernetGroup = QGroupBox("以太网参数") ethernet_layout = QVBoxLayout(mainWindow.ui.ethernetGroup) mainWindow.ui.lblIP = QLabel("IP地址:") mainWindow.ui.edtIP = QLineEdit("192.168.1.100") mainWindow.ui.lblPort = QLabel("端口:") mainWindow.ui.edtPort = QLineEdit("502") ethernet_layout.addWidget(mainWindow.ui.lblIP) ethernet_layout.addWidget(mainWindow.ui.edtIP) ethernet_layout.addWidget(mainWindow.ui.lblPort) ethernet_layout.addWidget(mainWindow.ui.edtPort) # 连接/断开按钮 mainWindow.ui.bnConnectSensor = QPushButton("连接传感器") mainWindow.ui.bnDisconnectSensor = QPushButton("断开传感器") mainWindow.ui.bnDisconnectSensor.setEnabled(False) # 传感器数据显示 mainWindow.ui.lblSensorData = QLabel("传感器数据: 未连接") mainWindow.ui.lblSensorData.setStyleSheet("font-size: 10pt;") # 添加到布局 sensor_layout.addLayout(sensor_type_layout) sensor_layout.addWidget(mainWindow.ui.serialGroup) sensor_layout.addWidget(mainWindow.ui.ethernetGroup) sensor_layout.addWidget(mainWindow.ui.bnConnectSensor) sensor_layout.addWidget(mainWindow.ui.bnDisconnectSensor) sensor_layout.addWidget(mainWindow.ui.lblSensorData) # 添加到右侧面板 right_layout.addWidget(sensor_panel) # 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): mainWindow.ui.lblDiffValue.setText(f"{value}%") # 连接滑块信号 mainWindow.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 mainWindow.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: mainWindow.ui.lblDiffStatus.setText("状态: 合格") mainWindow.ui.lblDiffStatus.setStyleSheet("color: green; font-size: 12px;") else: mainWindow.ui.lblDiffStatus.setText("状态: 不合格") mainWindow.ui.lblDiffStatus.setStyleSheet("color: red; font-size: 12px;") # 绑定按钮事件 mainWindow.ui.bnCheckPrint.clicked.connect(sensor_controlled_check) mainWindow.ui.bnSaveSample.clicked.connect(save_sample_image) mainWindow.ui.bnPreviewSample.clicked.connect(preview_sample) # 传感器类型切换 def update_sensor_ui(index): mainWindow.ui.serialGroup.setVisible(index == 0) mainWindow.ui.ethernetGroup.setVisible(index == 1) mainWindow.ui.cbSensorType.currentIndexChanged.connect(update_sensor_ui) update_sensor_ui(0) # 初始显示串口 # 传感器连接 def connect_sensor(): global sensor_monitor_thread, sensor_controller sensor_type = mainWindow.ui.cbSensorType.currentText() if sensor_controller is None: sensor_controller = SensorController() if sensor_type == "串口": config = { 'type': 'serial', 'port': mainWindow.ui.cbComPort.currentText(), 'baudrate': int(mainWindow.ui.cbBaudrate.currentText()), 'timeout': 1.0 } else: # 以太网 config = { 'type': 'ethernet', 'ip': mainWindow.ui.edtIP.text(), 'port': int(mainWindow.ui.edtPort.text()), 'timeout': 1.0 } if sensor_controller.connect(config): mainWindow.ui.bnConnectSensor.setEnabled(False) mainWindow.ui.bnDisconnectSensor.setEnabled(True) # 启动传感器数据监控线程 sensor_monitor_thread = SensorMonitorThread(sensor_controller) sensor_monitor_thread.data_updated.connect(update_sensor_display) sensor_monitor_thread.start() # 传感器断开 def disconnect_sensor(): global sensor_monitor_thread if sensor_controller: sensor_controller.disconnect() mainWindow.ui.bnConnectSensor.setEnabled(True) mainWindow.ui.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.ui.lblSensorData.setText("传感器数据: 未连接") mainWindow.ui.bnConnectSensor.clicked.connect(connect_sensor) mainWindow.ui.bnDisconnectSensor.clicked.connect(disconnect_sensor) def update_sensor_display(data): 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.ui.lblSensorData.setText(text) # 绑定其他按钮事件 mainWindow.ui.bnEnum.clicked.connect(enum_devices) mainWindow.ui.bnOpen.clicked.connect(open_device) mainWindow.ui.bnClose.clicked.connect(close_device) mainWindow.ui.bnStart.clicked.connect(start_grabbing) mainWindow.ui.bnStop.clicked.connect(stop_grabbing) mainWindow.ui.bnSoftwareTrigger.clicked.connect(trigger_once) mainWindow.ui.radioTriggerMode.clicked.connect(set_software_trigger_mode) mainWindow.ui.radioContinueMode.clicked.connect(set_continue_mode) mainWindow.ui.bnGetParam.clicked.connect(get_param) mainWindow.ui.bnSetParam.clicked.connect(set_param) # 修改保存图像按钮连接 mainWindow.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() # 断开传感器 disconnect_sensor() sys.exit() 这个程序还是出现了下面的问题 SyntaxError: invalid syntax >>> cd d:/海康/MVS/Development/Samples/Python/MvImport File "<stdin>", line 1 cd d:/海康/MVS/Development/Samples/Python/MvImport ^ SyntaxError: invalid syntax >>> & D:/python/python.exe d:/海康/MVS/Development/Samples/Python/MvImport/three.py File "<stdin>", line 1 & D:/python/python.exe d:/海康/MVS/Development/Samples/Python/MvImport/three.py ^ SyntaxError: invalid syntax

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内容概要:文章详细介绍了ETL工程师这一职业,解释了ETL(Extract-Transform-Load)的概念及其在数据处理中的重要性。ETL工程师负责将分散、不统一的数据整合为有价值的信息,支持企业的决策分析。日常工作包括数据整合、存储管理、挖掘设计支持和多维分析展现。文中强调了ETL工程师所需的核心技能,如数据库知识、ETL工具使用、编程能力、业务理解能力和问题解决能力。此外,还盘点了常见的ETL工具,包括开源工具如Kettle、XXL-JOB、Oozie、Azkaban和海豚调度,以及企业级工具如TASKCTL和Moia Comtrol。最后,文章探讨了ETL工程师的职业发展路径,从初级到高级的技术晋升,以及向大数据工程师或数据产品经理的横向发展,并提供了学习资源和求职技巧。 适合人群:对数据处理感兴趣,尤其是希望从事数据工程领域的人士,如数据分析师、数据科学家、软件工程师等。 使用场景及目标:①了解ETL工程师的职责和技能要求;②选择适合自己的ETL工具;③规划ETL工程师的职业发展路径;④获取相关的学习资源和求职建议。 其他说明:随着大数据技术的发展和企业数字化转型的加速,ETL工程师的需求不断增加,尤其是在金融、零售、制造、人工智能、物联网和区块链等领域。数据隐私保护法规的完善也使得ETL工程师在数据安全和合规处理方面的作用更加重要。

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