plt.imshow(img_arr)

from PIL import Image import numpy as np import matplotlib.pyplot as plt img = Image.open("work/Lena_RGB.jpg") plt.imshow(img) plt.show() plt.close() img_arr = np.array(img) h,w = img_arr.shape gray_levels = np.arange(256) freq = np.zeros(256) for i in range(h): for j in range(w): freq[img_arr[i, j]] += 1 prob = freq / (h * w)

然后使用matplotlib库中的plt.imshow()和plt.show()函数将图片显示出来。接下来，代码计算了图片中每个像素值的出现频率，并将其存储在名为freq的数组中。最后，根据频率数组计算每个像素值的概率，并将其存储在名为...

from PIL import Image import numpy as np import matplotlib.pyplot as plt img = Image.open("work/Lena_RGB.jpg") plt.imshow(img) plt.show() plt.close() img_gray = img.convert('L') # 转换为灰度图像 img_arr = np.array(img_gray) h, w = img_arr.shape gray_levels = np.arange(256) freq = np.zeros(256) for i in range(h): for j in range(w): freq[img_arr[i, j]] += 1 prob = freq / (h * w) plt.bar(gray_levels, prob) fig, axs = plt.subplots(2, 2, figsize=(12, 8)) axs[0, 0].imshow(img, cmap='gray') axs[0, 0].set_title('Original Image') axs[0, 1].bar(gray_levels, freq) axs[0, 1].set_title('Gray Level Frequency') axs[1, 0].bar(gray_levels, self_info) axs[1, 0].set_title('Self Information') axs[1, 1].text(0.5, 0.5, f'Entropy: {entropy:.2f}', fontsize=20, ha='center') axs[1, 1].axis('off') plt.show()

这段代码使用了 Python 的PIL库来打开一张RGB图像，然后将其转换为灰度图像。接着，使用numpy库将灰度图像转换为数组...其中，plt是matplotlib库中的绘图函数。如果你对其中的某些函数不熟悉，我可以为你详细解释一下。

给以下的代码加详细的中文注释#导入相关库 from skimage import data,color import numpy as np import matplotlib.pyplot as plt #中文显示工具函数 def set_ch(): from pylab import mpl mpl.rcParams['font.sans-serif'] = ['FangSong'] mpl.rcParams['axes.unicode_minus'] = False set_ch() D= 10 #读入图片 new_img = data.coffee() new_img = color.rgb2gray(new_img) #numpy中的傅里叶变化 f1 = np.fft.fft2(new_img) f1_shift = np.fft.fftshift(f1) #np.fft.fftshift()函数来实现平移，让直流分量在输出图像的重心 #实现理想低通滤波器 rows,cols = new_img.shape crow,ccol=int(rows/2),int(cols/2)#计算频谱中心 mask= np.zeros((rows,cols),np.uint8)#生成rows行cols的矩阵，数据格式为uint8 for i in range(rows): for j in range(cols): if np.sqrt(ii+jj)<=D: #将距离频谱中心小于D的部分低通信息，设置为1，属于低通滤波 mask[crow - D:crow + D, ccol - D:ccol + D] = 1 f1_shift = f1_shift*mask #傅里叶逆变换 f_ishift = np.fft.ifftshift(f1_shift) img_back=np.fft.ifft2(f_ishift) img_back=np.abs(img_back) img_back=(img_back-np.amin(img_back))/(np.amax(img_back)-np.amin(img_back)) #plt.figure(figsize=(15,8)) plt.figure() plt.subplot(121),plt.imshow(new_img,cmap='gray'),plt.title('原始图像') plt.subplot(122),plt.imshow(img_back,cmap='gray'),plt.title('滤波后图像') plt.show()

def binary_search(arr, target): """ 二分查找函数参数： arr: 有序数组（从小到大排列） target: 目标元素返回值：如果找到目标元素，返回其下标；如果没找到，返回 -1。 """ # 定义左右边界 left...

import nibabel as nib import matplotlib.pyplot as plt import numpy as np img = nib.load('/root/autodl-tmp/MICCAI_BraTS_2019_Data_Training/MICCAI_BraTS_2019_Data_Training/HGG/BraTS19_2013_10_1/BraTS19_2013_10_1_flair.nii.gz') print(img.shape) # shape(240, 240, 155) print(img.header['db_name']) width, height, queue = img.dataobj.shape # 由文件本身维度确定，可能是3维，也可能是4维 # print("width",width) # 240 # print("height",height) # 240 # print("queue",queue) # 155 nib.viewers.OrthoSlicer3D(img.dataobj).show() num = 1 for i in range(0, queue, 10): img_arr = img.dataobj[:, :, i] plt.subplot(5, 4, num) plt.imshow(img_arr, cmap='gray') num += 1 plt.show()

plt.imshow(slice_data, cmap='gray', aspect='auto') plt.axis('off') plt.title(f"切片 {slice_index}") plt.show() ![示例图像](https://via.placeholder.com/200x200/CCCCCC/808080?text=NIfTI+Slice) --- ...

1.The following libraries can be imported to reconstruct images using python import numpy as np import pandas as pd from PIL import Image import matplotlib.pyplot as plt from numpy import * 2.The following code can be used to upload data from csv file data = np.array (pd.read_csv ( csv_file_name, delimiter=";", header=None)) 3.The code block below can be used to read the image and convert it into a matrix img = Image. open (file_name + str(i) + '.png') img_B = img. convert('L') img_arr = asarray (img2) 4.Use the functions below to display the resulting image plt. imshow(Image. fromarray (img)) plt.show() 5.The code for histograms is given below gist, x = np. histogram(img, bins=255) plt.hist(x[:-1], x, weights=gist) plt.show()

plt.hist(img_array.ravel(), bins=256, range=[0,256], color='blue', alpha=0.7) plt.title('Pixel Intensity Histogram of Example Image') plt.xlabel('Intensity Value') plt.ylabel('Frequency') plt.grid...

def get_img(field,targetImgSrc,resImgSrc): con = connect(host='localhost', user='root', password='root', database='boss', port=3306, charset='utf8mb4') cursor = con.cursor() sql = f"select {field} from jobinfo" cursor.execute(sql) data = cursor.fetchall() text = '' for i,item in enumerate(data): text += item[0] cursor.close() con.close() # 分词 cut = jieba.cut(text) string = ' '.join(cut) print(string) # 图片 img = Image.open(targetImgSrc) # 打开遮罩图片 img_arr = np.array(img) # 将图片转化为列表 wc = WordCloud( background_color='white', mask=img_arr, font_path='STHUPO.TTF' ) wc.generate_from_text(string) # 绘制图片 fig = plt.figure(1) plt.imshow(wc) plt.axis('off') # 不显示坐标轴 # 显示生成的词语图片 # plt.show() # 输入词语图片到文件 plt.savefig(resImgSrc, dpi=500)解释代码

4. 打开指定的遮罩图片（targetImgSrc 参数），将其转化为列表 img_arr。 5. 使用 WordCloud 库生成词云对象 wc，并将分词后的字符串 string 作为输入，生成词云。 6. 使用 Matplotlib 库绘制词云图片并保存...

def get_addressCompanyTags_img(targetImgSrc,resImgSrc,addrress): con = connect(host='localhost', user='root', password='root', database='boss', port=3306, charset='utf8mb4') cursor = con.cursor() sql = f"select companyTags from jobinfo where address = {addrress}" cursor.execute(sql) data = cursor.fetchall() text = '' for i, item in enumerate(data): tags = json.loads(item[0]) for j in tags: text += j cursor.close() con.close() # 分词 cut = jieba.cut(text) string = ' '.join(cut) print(string) # 图片 img = Image.open(targetImgSrc) # 打开遮罩图片 img_arr = np.array(img) # 将图片转化为列表 wc = WordCloud( background_color='white', mask=img_arr, font_path='STHUPO.TTF' ) wc.generate_from_text(string) # 绘制图片 fig = plt.figure(1) plt.imshow(wc) plt.axis('off') # 不显示坐标轴 # 显示生成的词语图片 # plt.show() # 输入词语图片到文件 plt.savefig(resImgSrc, dpi=500)有数据分析吗？具体是哪几行

这段代码是一个生成词云图的函数，它将从数据库中查询出符合地址要求的公司标签，将这些标签进行分词处理并生成词云图。具体的数据分析包括：第4行的SQL查询语句，是用来从数据库中获取符合地址要求的公司标签的...

介绍一下plt.imshow的用法

plt.imshow(arr, cmap='gray') plt.show() 2. 显示图像文件 python from matplotlib import pyplot as plt from PIL import Image # 打开图像文件 img = Image.open('image.jpg') # 显示图像文件 plt....

print('每一类数据及其样本点数量为：') d={} n=[] num=[] for i in range(len(t_train)): d[t_train[i]]=d.setdefault(t_train[i],0)+1 for j in sorted(d): n.append((j,d[j])) num.append(d[j]) print(n) arr=np.zeros((10,784)) for t in range(10): for k in range(len(t_train)): if t_train[k]==t: arr[t]=arr[t]+x_train[k] else: continue arr[t]=arr[t]/num[t] plt.figure() for m in range(1,11): plt.subplot(3,4,m) a=np.reshape(arr[m-1],(28,28)) gray_img=a plt.imshow(gray_img,cmap='gray') 每行代码的解释

这段代码的作用是分析和可视化MNIST数据集中每个类别的样本，并计算每个类别的平均图像。 1. d={}：定义一个空字典d用于...19. plt.imshow(gray_img,cmap='gray')：将该图像显示在当前子图中，使用灰度色彩映射。

import time import cv2 import tkinter as tk from tkinter import filedialog, ttk, messagebox from PIL import Image, ImageTk import numpy as np from sklearn.metrics import confusion_matrix, precision_recall_curve, average_precision_score import matplotlib.pyplot as plt from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg import pandas as pd import subprocess import os import random from threading import Thread # 用于训练时不阻塞界面 class ObjectDetectionApp: def init(self): self.window = tk.Tk() self.window.title("YOLOv4训练&验证系统") self.window.geometry("1600x1000") # 模式变量（训练/验证/原评估） self.mode = tk.StringVar(value="validation") self.train_running = False # 训练状态标志 # 模型配置（可自定义） self.base_cfg = "yolov4.cfg" # 基础配置文件 self.custom_cfg = "yolov4-custom.cfg" # 训练用自定义配置 self.weights_path = "yolov4.weights" # 默认权重/训练后权重 self.classes_file = "coco.names" self.classes = self.load_classes() self.COLORS = np.random.uniform(0, 255, size=(len(self.classes), 3)) self.voc_root = None # 新增：VOC数据集根路径 # 训练参数 self.train_params = { "epochs": 100, "batch": 8, "subdivisions": 4, "learning_rate": 0.001, "data_file": "custom.data", "names_file": self.classes_file } # 摄像头初始化 self.cap = None self.image_flipped = True # 评估相关变量（验证阶段使用） self.predicted_labels = [] self.ground_truths = [] self.performance_data = {} # 创建界面 self.create_widgets() self.window.mainloop() def create_widgets(self): # -------------------- 顶部模式栏 -------------------- mode_frame = tk.Frame(self.window, padx=10, pady=5) mode_frame.pack(fill=tk.X) tk.Radiobutton(mode_frame, text="模型训练", variable=self.mode, value="training", command=self.switch_mode).pack(side=tk.LEFT, padx=10) tk.Radiobutton(mode_frame, text="实时验证", variable=self.mode, value="validation", command=self.switch_mode).pack(side=tk.LEFT, padx=10) # -------------------- 训练模块控件 -------------------- self.train_frame = tk.Frame(self.window, padx=10, pady=5) # VOC数据集选择按钮 self.select_voc_btn = tk.Button(self.train_frame, text="选择VOC数据集", command=self.select_voc_dataset) self.select_voc_btn.pack(side=tk.LEFT, padx=10) # 训练参数输入 param_frame = tk.Frame(self.train_frame) tk.Label(param_frame, text="迭代次数:").grid(row=0, column=0, padx=5) self.epochs_entry = ttk.Entry(param_frame, width=8) self.epochs_entry.grid(row=0, column=1, padx=5) self.epochs_entry.insert(0, "100") tk.Label(param_frame, text="批次大小:").grid(row=1, column=0, padx=5) self.batch_entry = ttk.Entry(param_frame, width=8) self.batch_entry.grid(row=1, column=1, padx=5) self.batch_entry.insert(0, "8") param_frame.pack(side=tk.LEFT, padx=10) # 数据集加载按钮 self.load_train_btn = tk.Button(self.train_frame, text="加载训练集", command=self.load_train_dataset) self.load_train_btn.pack(side=tk.LEFT, padx=10) self.load_val_btn = tk.Button(self.train_frame, text="加载验证集", command=self.load_val_dataset) self.load_val_btn.pack(side=tk.LEFT, padx=10) # 开始训练按钮（正确顺序：先定义，再布局） self.start_train_btn = tk.Button(self.train_frame, text="开始训练", command=self.start_training_thread) self.start_train_btn.pack(side=tk.LEFT, padx=10) # -------------------- 主显示区域 -------------------- self.main_frame = tk.Frame(self.window) self.main_frame.pack(fill=tk.BOTH, expand=True) # 视频/图像显示区 self.photo_label = tk.Label(self.main_frame, width=1000, height=600) self.photo_label.pack(side=tk.LEFT, padx=10, pady=10) # 右侧信息区 self.right_frame = tk.Frame(self.main_frame, width=400) self.right_frame.pack(side=tk.RIGHT, padx=10, pady=10, fill=tk.Y) # 日志文本框 self.log_text = tk.Text(self.right_frame, width=40, height=15) self.log_text.pack(pady=5, fill=tk.X) self.log_text.insert(tk.END, "系统日志：\n") # 性能报告区 self.report_canvas = None self.report_frame = tk.Frame(self.right_frame) self.report_frame.pack(pady=5, fill=tk.BOTH, expand=True) # 初始显示验证模式 self.switch_mode() def select_voc_dataset(self): """选择VOC数据集根目录并执行划分""" self.voc_root = filedialog.askdirectory(title="选择VOC数据集根目录（如VOCdevkit/VOC2007）") if not self.voc_root: return # 检查所选目录下是否存在JPEGImages和Annotations目录 jpeg_dir = os.path.join(self.voc_root, "JPEGImages") ann_dir = os.path.join(self.voc_root, "Annotations") if not (os.path.isdir(jpeg_dir) and os.path.isdir(ann_dir)): messagebox.showerror("错误", "所选目录不是正确的VOC数据集根目录（缺少JPEGImages或Annotations目录）") return try: # 执行VOC数据集划分（75:25） self.split_voc_dataset(self.voc_root) self.log_text.insert(tk.END, "数据集划分完成！\n") # 自动设置训练/验证集路径到参数中 self.train_params["train_images"] = os.path.join(self.voc_root, "ImageSets/Main/train.txt") self.train_params["val_images"] = os.path.join(self.voc_root, "ImageSets/Main/val.txt") self.log_text.insert(tk.END, f"训练集路径：{self.train_params['train_images']}\n") self.log_text.insert(tk.END, f"验证集路径：{self.train_params['val_images']}\n") except Exception as e: messagebox.showerror("错误", f"数据集划分失败：{str(e)}") self.log_text.insert(tk.END, f"划分错误：{str(e)}\n") def split_voc_dataset(self, voc_root, train_ratio=0.75): """VOC数据集划分核心逻辑（集成到应用中）""" img_dir = os.path.join(voc_root, "JPEGImages") ann_dir = os.path.join(voc_root, "Annotations") sets_dir = os.path.join(voc_root, "ImageSets", "Main") os.makedirs(sets_dir, exist_ok=True) img_files = [f for f in os.listdir(img_dir) if f.lower().endswith((".jpg", ".jpeg", ".png"))] valid_ids = [] for img_file in img_files: img_id = os.path.splitext(img_file)[0] ann_path = os.path.join(ann_dir, f"{img_id}.xml") if os.path.isfile(ann_path): valid_ids.append(img_file) # 保存带扩展名的图像文件名 if not valid_ids: messagebox.showerror("错误", "未找到任何有效标注文件，确保JPEGImages和Annotations目录中的文件一一对应（除扩展名外文件名相同）") return random.shuffle(valid_ids) total = len(valid_ids) train_count = int(total * train_ratio) train_ids = valid_ids[:train_count] val_ids = valid_ids[train_count:] train_txt = os.path.join(sets_dir, "train.txt") val_txt = os.path.join(sets_dir, "val.txt") # 写入完整路径（如：D:/pycharm/pythonProject/VOCdevkit/VOC2007/JPEGImages/1.jpg） with open(train_txt, "w") as f: for img_file in train_ids: img_full_path = os.path.join(voc_root, "JPEGImages", img_file) f.write(img_full_path + '\n') with open(val_txt, "w") as f: for img_file in val_ids: img_full_path = os.path.join(voc_root, "JPEGImages", img_file) f.write(img_full_path + '\n') self.log_text.insert(tk.END, f"总样本数: {total}，训练集: {len(train_ids)}，验证集: {len(val_ids)}\n") def switch_mode(self): """模式切换逻辑""" current_mode = self.mode.get() # 关闭摄像头 if self.cap: self.cap.release() self.cap = None self.photo_label.config(image=None) # 清空界面 self.log_text.delete(1.0, tk.END) self.log_text.insert(tk.END, "系统日志：\n") if current_mode == "training": self.train_frame.pack(fill=tk.X, pady=5) self.report_frame.pack_forget() self.log_text.insert(tk.END, "切换到训练模式\n") else: # 验证模式 self.train_frame.pack_forget() self.report_frame.pack(fill=tk.BOTH, expand=True) self.cap = cv2.VideoCapture(0) # 打开摄像头 self.update_validation_frame() # 启动实时验证 self.log_text.insert(tk.END, "切换到实时验证模式\n") def load_train_dataset(self): """加载训练数据集（图像+标注）""" img_dir = filedialog.askdirectory(title="选择训练图像文件夹") label_dir = filedialog.askdirectory(title="选择训练标注文件夹") if img_dir and label_dir: self.train_params["train_images"] = img_dir self.train_params["train_labels"] = label_dir self.log_text.insert(tk.END, f"训练集加载完成：{img_dir}\n") def load_val_dataset(self): """加载验证数据集""" img_dir = filedialog.askdirectory(title="选择验证图像文件夹") label_dir = filedialog.askdirectory(title="选择验证标注文件夹") if img_dir and label_dir: self.train_params["val_images"] = img_dir self.train_params["val_labels"] = label_dir self.log_text.insert(tk.END, f"验证集加载完成：{img_dir}\n") def start_training_thread(self): """启动训练线程（防止界面阻塞）""" if self.train_running: messagebox.showwarning("提示", "训练已在进行中") return self.train_running = True Thread(target=self.start_training, daemon=True).start() def start_training(self): """执行训练流程（调用Darknet命令）""" try: # 生成训练配置文件 self.generate_training_config() # 训练命令（示例，根据实际Darknet路径调整） cmd = [ "darknet.exe", "detector", "train", self.train_params["data_file"], self.custom_cfg, "yolov4.conv.137", # 预训练权重 "-map", # 计算mAP "-gpus", "0", "-batch", self.batch_entry.get(), "-subdivisions", "4", "-epochs", self.epochs_entry.get() ] self.log_text.insert(tk.END, "开始训练...\n") process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True) # 实时输出训练日志 for line in process.stdout: self.log_text.insert(tk.END, line) self.log_text.see(tk.END) # 自动滚动 process.wait() self.train_running = False self.log_text.insert(tk.END, "训练完成！最佳权重已保存\n") # 训练完成后生成性能报告 self.generate_training_report() except Exception as e: self.log_text.insert(tk.END, f"训练错误：{str(e)}\n") self.train_running = False def generate_training_config(self): """生成自定义训练配置文件""" with open(self.base_cfg, 'r') as f: cfg_lines = f.readlines() # 修改批次和subdivisions for i, line in enumerate(cfg_lines): if line.startswith("batch="): cfg_lines[i] = f"batch={self.batch_entry.get()}\n" if line.startswith("subdivisions="): cfg_lines[i] = "subdivisions=4\n" with open(self.custom_cfg, 'w') as f: f.writelines(cfg_lines) # 生成data文件，指定训练和验证的图像列表路径 data_content = f""" train = {os.path.join(self.voc_root, "ImageSets/Main/train.txt")} # 训练集图像列表 valid = {os.path.join(self.voc_root, "ImageSets/Main/val.txt")} # 验证集图像列表 names = {self.classes_file} backup = backup/ eval = coco """ with open(self.train_params["data_file"], 'w') as f: f.write(data_content) def generate_training_report(self): """生成训练性能报告""" # 假设从训练日志或结果文件中读取数据 # 这里模拟加载验证集结果 y_true = ["car", "person", "car", "bike"] y_pred = ["car", "person", "bike", "bike"] confidences = [0.92, 0.85, 0.78, 0.91] # 计算指标 cm = confusion_matrix(y_true, y_pred, labels=self.classes) precision, recall, _ = precision_recall_curve(y_true, confidences, pos_label="car") ap = average_precision_score([1 if x == "car" else 0 for x in y_true], confidences) # 绘制报告 fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5)) # 混淆矩阵 ax1.imshow(cm, cmap=plt.cm.Blues) ax1.set_title("验证集混淆矩阵") ax1.set_xticks(range(len(self.classes))) ax1.set_xticklabels(self.classes, rotation=45) ax1.set_yticks(range(len(self.classes))) ax1.set_yticklabels(self.classes) # PR曲线 ax2.plot(recall, precision) ax2.set_title(f"PR曲线 (AP={ap:.2f})") ax2.set_xlabel("召回率") ax2.set_ylabel("精确率") # 在界面显示 if self.report_canvas: self.report_canvas.get_tk_widget().destroy() self.report_canvas = FigureCanvasTkAgg(fig, master=self.report_frame) self.report_canvas.draw() self.report_canvas.get_tk_widget().pack(fill=tk.BOTH, expand=True) def update_validation_frame(self): """实时验证摄像头画面""" if not self.cap or not self.mode.get() == "validation": return ret, frame = self.cap.read() if ret: if self.image_flipped: frame = cv2.flip(frame, 1) # 使用训练后的权重进行检测 frame, results = self.detect_objects(frame) # 显示结果 frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) pil_img = Image.fromarray(frame_rgb) tk_img = ImageTk.PhotoImage(image=pil_img) self.photo_label.config(image=tk_img) self.photo_label.image = tk_img # 记录验证结果（可选） self.record_validation_results(results) self.window.after(30, self.update_validation_frame) def detect_objects(self, frame): """使用当前权重进行目标检测""" # 改为使用实例变量 self.net 存储网络 self.net = cv2.dnn.readNetFromDarknet( self.custom_cfg if self.mode.get() == "training" else self.base_cfg, self.weights_path ) # 使用 self.net 获取层信息 layer_names = self.net.getLayerNames() output_layers = [layer_names[i - 1] for i in self.net.getUnconnectedOutLayers()] # 图像预处理 height, width = frame.shape[:2] blob = cv2.dnn.blobFromImage(frame, 0.00392, (416, 416), (0, 0, 0), True, crop=False) # 前向传播（使用 self.net） self.net.setInput(blob) outs = self.net.forward(output_layers) # 注意：output_layers 已提前计算 # 后续代码保持不变... class_ids = [] confidences = [] boxes = [] for out in outs: for detection in out: scores = detection[5:] class_id = np.argmax(scores) confidence = scores[class_id] if confidence > 0.5: # 置信度阈值 center_x = int(detection[0] * width) center_y = int(detection[1] * height) w = int(detection[2] * width) h = int(detection[3] * height) # 计算边界框坐标 x = int(center_x - w / 2) y = int(center_y - h / 2) boxes.append([x, y, w, h]) confidences.append(float(confidence)) class_ids.append(class_id) # 非极大值抑制 indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4) results = [] for i in range(len(boxes)): if i in indexes: x, y, w, h = boxes[i] label = str(self.classes[class_ids[i]]) confidence = confidences[i] results.append((label, x, y, x + w, y + h, confidence)) # 绘制边界框和标签 color = self.COLORS[class_ids[i]] cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2) cv2.putText(frame, f"{label}: {confidence:.2f}", (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2) return frame, results def record_validation_results(self, results): """记录验证结果（预测标签和置信度）""" # 从results中提取预测的标签（假设results格式为：(label, x1, y1, x2, y2, confidence)） for result in results: predicted_label = result[0] # 标签 confidence = result[5] # 置信度 self.predicted_labels.append(predicted_label) # 如果有真实标签（例如从验证集标注文件读取），可添加类似逻辑： # self.ground_truths.append(ground_truth_label) def load_classes(self): with open(self.classes_file, "r") as f: return [line.strip() for line in f.readlines()] def close_app(self): if self.cap: self.cap.release() self.window.destroy() if name == "main": app = ObjectDetectionApp() 代码检测

return sorted([arr[0], arr[mid], arr[-1]])[1] 2. **尾递归优化** 将最后一个递归调用改为循环，避免栈溢出风险： python def quick_sort(arr): while len(arr) > 1: pivot = choose_pivot(arr) left...

from time import time #记录时间 import logging #打印程序的运行日志 import matplotlib.pyplot as plt from sklearn.cross_validation import train_test_split #划分训练集和测试集 from sklearn.datasets import fetch_lfw_people #导入数据集（名人） from sklearn.grid_search import GridSearchCV #调试函数的参数 from sklearn.metrics import classification_report #显示分类报告，显示主要的分类指标，准确率，召回率以及F1得分 from sklearn.metrics import confusion_matrix #对真实类别和预测类别做出判断，用矩阵形式表示出来 from sklearn.decomposition import RandomizedPCA #pca降维 from sklearn.svm import SVC #svm的svc方程 from sklearn.cluster.tests.test_k_means import n_samples #训练集：sklearn自带的人脸图片数据集 lfw_people = fetch_lfw_people(min_faces_per_person=70,resize=0.4) n_samples,h,w = lfw_people.images.shape #实例数目、h、w x = lfw_people.data #所有的训练数据,1288张图片，每张图片1850个特征值 n_features = x.shape[1] #特征向量的维度1850 y =lfw_people.target #对应的人脸标记 target_names = lfw_people.target_names #需要识别的人名字 n_class = target_names.shape[0] #几个人需要识别 print("total dataset size:") print("n_samples: %d" % n_samples) print("n_features: %d" % n_features) print("n_class: %d" % n_class) x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.25) n_components = 150 t0 = time() #随机将高维的特征向量降低为低维的,先建立模型 pca = RandomizedPCA(n_components=n_components,whiten=True).fit(x_train) print("done in %0.3fs" %(time() - t0)) #提取人脸的特征值 eigenfaces = pca.components_.reshape((n_components,h,w)) #将人脸特征转化为特征向量集 print('projecting the input data on the eigenfaces orthonomal basis') t0 = time() #进行数据模型降维,降成了150 x_train_pca = pca.transform(x_train) x_test_pca = pca.transform(x_test) print("done in %0.3fs" % (time() - t0)) print("fitting the classfier to the training set") t0 = time() #C 是对错误部分的惩罚；gamma 合成点 para_grid = {'C':[1e3,5e3,1e4,5e4,1e5],'gamma':[0.0001,0.0005,0.0001,0.005,0.01,0.1],} #rbf处理图像较好，C和gamma组合，穷举出最好的一个组合使用GridSearchCV进行自由组合，最终确定合适的组合 clf = GridSearchCV(SVC(kernel='rbf'),para_grid) clf = clf.fit(x_train_pca,y_train) print("done in %0.3fs" % (time() - t0)) print("best estimator found by grid search:") print(clf.best_estimator_) #最好的模型的信息 print("predict the people's name on the test set") t0 = time() y_pred = clf.predict(x_test_pca) print("done in %0.3fs" % (time() - t0)) print(classification_report(y_test,y_pred,target_names=target_names)) print(confusion_matrix(y_test,y_pred,labels=range(n_class))) def plot_gallery(images,titles,h,w,n_row = 3,n_col = 4): plt.figure(figsize=(1.8n_col,2.4n_row)) plt.subplots_adjust(bottom = 0,left = .01,right = .99,top = .90,hspace = .35) for i in range(n_row * n_col): plt.subplot(n_row,n_col,i+1) plt.imshow(images[i].reshape((h,w)),cmap=plt.cm.gray) plt.title(titles[i],size = 12) plt.xticks() plt.yticks() def title(y_pred,y_test,target_names,i): pred_name = target_names[y_pred[i]].rsplit(' ',1)[-1] true_name = target_names[y_test[i]].rsplit(' ',1)[-1] return 'predicted : %s \nture: %s' % (pred_name,true_name) prediction_titles = [title(y_pred,y_test,target_names,i) for i in range(y_pred.shape[0])] plot_gallery(x_test,prediction_titles,h,w) eigenface_title = ["eigenface %d" % i for i in range(eigenfaces.shape[0])] plot_gallery(eigenfaces,eigenface_title,h,w) plt.show()其中在‘D:\cxdownload\ORL人脸识别数据集’中里面有40个子文件，每个子文件中有10张图，后缀为.pgm格式，这些图为存放的人脸测试数据

images_list.append(arr.flatten()) # 平坦化的像素值序列 labels_list.append(idx) X_custom = np.vstack(images_list) # 堆叠得到 X 特征矩阵 Y_custom = np.array(labels_list) # 对应 Y 标签向量然后...

random.shuffle(self.img_paths)

ax.imshow(img) ax.set_title(os.path.basename(img_path)) ax.axis('off') plt.show() ## 高级用法和注意事项 1. **固定随机种子确保可复现性**: python def set_seed(seed=42): """设置随机种子...

import pickle import numpy as np import matplotlib.pyplot as plt import seaborn as sns import pandas as pd import os from sklearn.metrics import confusion_matrix from sklearn.preprocessing import LabelEncoder from sklearn.model_selection import train_test_split import torch import torch.nn as nn from torch.utils.data import TensorDataset, DataLoader from tqdm import tqdm # 设置随机种子 torch.manual_seed(42) np.random.seed(42) def load_data(file_path): """安全加载并预处理数据""" with open(file_path, 'rb') as f: data = pickle.load(f, encoding='latin1') # 调试信息 print(f"成功加载数据，总样本数: {len(data)}") sample_shape = next(iter(data.values())).shape print(f"单个样本形状: {sample_shape} (格式: [时间步长, 通道数, 特征数])") # 统一处理所有样本为相同长度（取最小长度） min_length = min(arr.shape[0] for arr in data.values()) print(f"统一长度: {min_length} (取所有样本的最小长度)") # 提取I路信号并统一长度 X = np.array([arr[:min_length, 0, 0] for arr in data.values()], dtype=np.float32) X = X.reshape(-1, min_length, 1) # 形状: (样本数, 时间步长, 特征数) # 提取调制类型标签 modulations = [key[0] for key in data.keys()] label_encoder = LabelEncoder() y = label_encoder.fit_transform(modulations) print(f"调制类型及对应标签:\n{pd.Series(label_encoder.classes_).to_frame('标签')}") # 分割数据集 X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=42) # 归一化 mean, std = X_train.mean(), X_train.std() X_train = (X_train - mean) / (std + 1e-8) X_test = (X_test - mean) / (std + 1e-8) return X_train, y_train, X_test, y_test, label_encoder class SignalTransformer(nn.Module): """稳健的Transformer模型""" def init(self, input_dim=1, num_classes=11, d_model=128, nhead=8, num_layers=3, dropout=0.1): super().init() self.input_proj = nn.Linear(input_dim, d_model) self.pos_encoder = nn.Parameter(torch.zeros(1, 1000, d_model)) encoder_layer = nn.TransformerEncoderLayer( d_model=d_model, nhead=nhead, dim_feedforward=512, dropout=dropout, batch_first=True) self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=num_layers) self.classifier = nn.Sequential( nn.LayerNorm(d_model), nn.Dropout(dropout), nn.Linear(d_model, num_classes) ) nn.init.xavier_uniform_(self.pos_encoder) def forward(self, x): x = self.input_proj(x) seq_len = x.size(1) x = x + self.pos_encoder[:, :seq_len, :] x = self.transformer(x).mean(dim=1) return self.classifier(x) def train_model(): # 设备配置 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(f"\n训练设备: {device}") # 加载数据 data_path = r"D:\Anaconda\envs\DL\Lib\site-packages\caffe2\python\data\train\2016.04C.multisnr.pkl" try: X_train, y_train, X_test, y_test, label_encoder = load_data(data_path) except Exception as e: print(f"数据加载失败: {str(e)}") return # 转换为PyTorch张量 train_data = TensorDataset( torch.from_numpy(X_train).float(), torch.from_numpy(y_train).long() ) test_data = TensorDataset( torch.from_numpy(X_test).float(), torch.from_numpy(y_test).long() ) # 数据加载器 (batch size改回32) train_loader = DataLoader(train_data, batch_size=32, shuffle=True) test_loader = DataLoader(test_data, batch_size=32) # 模型初始化 model = SignalTransformer(num_classes=len(label_encoder.classes_)).to(device) criterion = nn.CrossEntropyLoss() optimizer = torch.optim.AdamW(model.parameters(), lr=3e-4, weight_decay=1e-4) # 训练记录 history = { 'epoch': [], 'train_loss': [], 'val_loss': [], 'val_acc': [], 'best_epoch': 0, 'best_acc': 0 } # 训练循环（保持5轮不变） for epoch in range(5): model.train() train_loss = 0 # 使用with语句管理进度条 with tqdm(train_loader, desc=f'Epoch {epoch+1}/5') as progress_bar: for inputs, labels in progress_bar: inputs, labels = inputs.to(device), labels.to(device) optimizer.zero_grad() outputs = model(inputs) loss = criterion(outputs, labels) loss.backward() optimizer.step() train_loss += loss.item() progress_bar.set_postfix({'loss': loss.item()}) # 验证 model.eval() val_loss, correct = 0, 0 all_preds, all_labels = [], [] # 验证阶段也添加进度条 with tqdm(test_loader, desc='验证中') as val_bar: with torch.no_grad(): for inputs, labels in val_bar: inputs, labels = inputs.to(device), labels.to(device) outputs = model(inputs) val_loss += criterion(outputs, labels).item() preds = outputs.argmax(dim=1) correct += (preds == labels).sum().item() all_preds.extend(preds.cpu().numpy()) all_labels.extend(labels.cpu().numpy()) val_bar.set_postfix({'val_loss': val_loss/len(val_bar)}) # 记录结果 val_acc = correct / len(test_data) history['epoch'].append(epoch+1) history['train_loss'].append(train_loss/len(train_loader)) history['val_loss'].append(val_loss/len(test_loader)) history['val_acc'].append(val_acc) if val_acc > history['best_acc']: history['best_acc'] = val_acc history['best_epoch'] = epoch+1 torch.save(model.state_dict(), 'best_model.pth') # 使用tqdm.write确保输出不干扰进度条 tqdm.write(f"Epoch {epoch+1}: 训练损失={history['train_loss'][-1]:.4f}, " f"验证损失={history['val_loss'][-1]:.4f}, " f"准确率={val_acc:.4f}") # 保存结果 save_results(history, all_labels, all_preds, label_encoder.classes_) print(f"\n最佳模型在Epoch {history['best_epoch']}, 准确率={history['best_acc']:.4f}") def save_results(history, true_labels, pred_labels, classes): """保存所有结果并显示可视化""" os.makedirs('results', exist_ok=True) # 创建两个独立图形 fig1, ax1 = plt.subplots(figsize=(12, 10)) fig2, (ax2, ax3) = plt.subplots(1, 2, figsize=(12, 5)) # 1. 混淆矩阵 cm = confusion_matrix(true_labels, pred_labels) sns.heatmap(cm, annot=True, fmt='d', xticklabels=classes, yticklabels=classes, ax=ax1) ax1.set_title("Confusion Matrix") fig1.savefig("results/confusion_matrix.png", bbox_inches='tight') # 2. 训练曲线 ax2.plot(history['epoch'], history['train_loss'], label='Train Loss') ax2.plot(history['epoch'], history['val_loss'], label='Validation Loss') ax2.set_xlabel('Epoch') ax2.set_ylabel('Loss') ax2.legend() ax3.plot(history['epoch'], history['val_acc'], label='Validation Accuracy', color='green') ax3.set_xlabel('Epoch') ax3.set_ylabel('Accuracy') ax3.legend() fig2.savefig("results/training_metrics.png", bbox_inches='tight') # 同时显示两个窗口 plt.show(block=False) # 不阻塞代码执行 plt.pause(0.1) # 短暂暂停确保窗口创建 # 3. 训练日志表格 df = pd.DataFrame({ 'Epoch': history['epoch'], 'Training Loss': history['train_loss'], 'Validation Loss': history['val_loss'], 'Accuracy': history['val_acc'] }) df.to_csv("results/training_log.csv", index=False) print("\n训练日志摘要:") print(df.round(4)) if name == "main": train_model() 以上代码输出的时候，混淆矩阵弹窗和损失精度曲线图弹窗一闪即逝，我需要这两个弹窗能正常显示，请你修改后发我完整代码

matrix_path)plt.figure()plt.imshow(img)plt.axis('off')plt.title('ConfusionMatrix')plt.show()#显示训练曲线图img2=mpimg.imread(results_plot_path)plt.figure()plt.imshow(img2)plt.axis('off')plt.title('...

from tkinter import * import tkinter as tk from tkinter import ttk, filedialog, Text from tkinter import Toplevel, Entry, messagebox from tkinter.filedialog import askdirectory,askopenfilename from PIL import Image, ImageTk import cv2 #import pytesseract import threading import pyautogui import os import numpy as np import pandas as pd #import random #import sched #（计划任务） #import schedule import winsound #电脑报警音 #import pyodbc import pymysql import mysql.connector from datetime import datetime import time import matplotlib.pyplot as plt from matplotlib.figure import Figure from matplotlib.backends.backend_tkagg import (FigureCanvasTkAgg,NavigationToolbar2Tk) from matplotlib.font_manager import FontProperties class MainWindow: def init(self, master): #self.master = master #self.master.title("登录数据库") self.master = master self.master.title("登录数据库") print('系统登录-总界面') self.flag_conn = 0 label_topic_0 = tk.Label(self.master, text="电力设备热致故障温度预警系统", font=('楷体', 22)) label_topic_0.place(x=40, y=50) #label_topic_0.attributes("-alpha", 0.5) # 设置透明度为50% label_topic_1 = ttk.Label(self.master, text="登录数据库", font=('楷体', 22)) label_topic_1.place(x=140, y=100) label_server=tk.Label(self.master,text = "Server",width=8, height=2,font=('宋体', 14)) label_server.place(x=50, y=200) self.entry_item_01 = tk.Entry(self.master,width=30, bg="white",font=('宋体', 14)) self.entry_item_01.place(x=150 , y=210) default_text1 = 'localhost' self.entry_item_01.insert(0, default_text1) label_database=tk.Label(self.master,text = "Database",width=8, height=2,font=('宋体', 14)) label_database.place(x=50, y=250) self.entry_item_02 = tk.Entry(self.master,width=30, bg="white",font=('宋体', 14)) self.entry_item_02.place(x=150 , y=260) default_text2= 'pic_temp' self.entry_item_02.insert(0, default_text2) label_Username=tk.Label(self.master,text = "Username",width=8, height=2,font=('宋体', 14)) label_Username.place(x=50, y=300) self.entry_item_03 = tk.Entry(self.master,width=30, bg="white",font=('宋体', 14)) self.entry_item_03.place(x=150 , y=310) default_text3= 'root' self.entry_item_03.insert(0, default_text3) label_Password=tk.Label(self.master,text = "Password",width=8, height=2,font=('宋体', 14)) label_Password.place(x=50, y=350) self.entry_item_04 = tk.Entry(self.master,width=30, bg="white",font=('宋体', 14)) self.entry_item_04.place(x=150 , y=360) default_text4= '******' self.entry_item_04.insert(0, default_text4) self.radio_win = tk.StringVar() # 创建一个StringVar变量，用于存储选中的值 self.radio_win.set('1') #radio_var.set(None) # 创建第一个单选框 #radio_button1 = tk.Radiobutton(self.master, text="Windows认证",variable=self.radio_win, value="1",font=('宋体', 14)) #radio_button1.place(x=150, y=400) # 创建第二个单选框 #radio_button2 = tk.Radiobutton(self.master, text="Sql认证",variable=self.radio_win, value="2",font=('宋体', 14)) #radio_button2.place(x=300, y=400) label_status=tk.Label(self.master,text = "连接状态",width=8, height=2,font=('宋体', 14)) label_status.place(x=50, y=450) self.label_conn=tk.Label(self.master,text = "------",width=15, height=2,font=('宋体', 14)) self.label_conn.place(x=150, y=450) self.button_login_sql = tk.Button(self.master, text="连接", command=self.login_sql,width=10, height=2,font=('宋体', 14)) self.button_login_sql.place(x=50, y=550) self.button_login = tk.Button(self.master, text="登录", command=self.open_window_main,width=10, height=2,font=('宋体', 14)) self.button_login.place(x=200, y=550) self.button_close = tk.Button(self.master, text="退出", command=self.close,width=10, height=2,font=('宋体', 14)) self.button_close.place(x=350, y=550) def login_sql(self): # 数据库连接字符串 server = self.entry_item_01.get() database = self.entry_item_02.get() username1 = self.entry_item_03.get() password1 = self.entry_item_04.get() # 尝试连接数据库 try: conn=pymysql.connect(host=server, user=username1, password=password1, port=3306, db=database) print("数据库连接成功！") self.label_conn.configure(bg='lightgreen',fg='blue',text='数据库连接成功') self.flag_conn = 1 except Exception as e: print("Excel连接成功：", e) self.flag_conn = 1 self.label_conn.configure(bg='lightgreen',fg='blue',text='Excel连接成功') finally: if conn: # 确保在操作完成后关闭连接: conn.close() # 关闭连接 def open_window_main(self): if self.flag_conn == 1: self.master.iconify() # 最小化窗口 # 创建新窗口的实例 self.new_window_main = tk.Toplevel(self.master) # 传递新窗口作为参数给另一个窗口类 self.app_main = NewWindow_main(self.new_window_main) self.flag_conn = 0 else: print('连接错误') messagebox.showinfo("提示", "连接错误，请重新连接") def close(self): print('关闭窗口') #self.btn_open_window_sql.config(state=tk.NORMAL) #self.btn_open_window_video.config(state=tk.NORMAL) self.master.destroy() #root.deiconify() # 显示主窗口 class NewWindow_main: def init(self, master): self.window_main = master # setting the title self.window_main.title('电力设备热致故障温度预警系统') self.window_main.geometry("1300x700+150+150") print('系统登录-主界面') label_topic_0 = ttk.Label(self.window_main, text="电力设备热致故障温度预警系统", font=('楷体', 36)).place(x=310, y=200) #label_topic_1 = ttk.Label(self.window_main, text="一、图片数据采集", font=('楷体', 26)).place(x=410, y=400) label_topic_2 = ttk.Label(self.window_main, text=" 图片数据采集", font=('楷体', 26)).place(x=410, y=500) #self.btn_open_window_sql = tk.Button(self.window_main, text="进入",font=('宋体', 16), command=self.open_window_sql,width=10, height=2) #self.btn_open_window_sql.place(x=820, y=390) #self.btn_open_window_sql.config(state=tk.DISABLED) self.btn_open_window_video = tk.Button(self.window_main, text="进入", font=('宋体', 16) ,command=self.open_window_video,width=10, height=2) self.btn_open_window_video.place(x=820, y=490) #self.btn_open_window_video.config(state=tk.DISABLED) self.window_main.mainloop() def open_window_sql(self): # 创建新窗口的实例 self.new_window_sql = tk.Toplevel(self.window_main) # 传递新窗口作为参数给另一个窗口类 self.app_sql = NewWindow_sql(self.new_window_sql) def open_window_video(self): # 创建新窗口的实例 self.new_window_video = tk.Toplevel(self.window_main) # 传递新窗口作为参数给另一个窗口类 self.app_video = NewWindow_video(self.new_window_video) #self.window_main.mainloop() class NewWindow_video: def init(self, master): #self.master_video = master #self.master_video.title("主窗口") window_video = master # 在新窗口中添加一些组件 #tk.Label(self.master_video, text="这是一个新窗口_video").pack() window_video.title('电力设备热致故障温度预警系统') self.window_video = window_video print('系统登录-视频图像') global frame1,point_count global output,fig output = None fig = None frame1 = None point_count = 0 # dimensions of the main window window_video.geometry("1300x700+150+150") label_topic_1 = Label(window_video, text="实时温度检测与预警", font=('楷体', 26)).place(x=450, y=30) def open_camera(): print('进入_摄像头开启') global cap,video_path global flag_picture,flag_video cap = cv2.VideoCapture(0,cv2.CAP_DSHOW) # 激活摄像头 flag_video = 1 flag_picture = 0 if not cap.isOpened(): print("无法打开摄像头") return play_display() def close_camera(): print('进入_摄像头关闭') global cap,video_path if cap: cap.release() # 释放摄像头 #cap = None canvas1.delete("all") # 清空Canvas上的所有内容 time.sleep(1) cv2.destroyAllWindows() # 销毁窗口 if cap: cap.release() # 释放摄像头 #cap = None canvas1.delete("all") # 清空Canvas上的所有内容 time.sleep(1) cv2.destroyAllWindows() # 销毁窗口 def record_camera(): print('进入_保存视频') global cap,video_path #flag_save_camera = 1 def record_camera1(): fourcc = cv2.VideoWriter_fourcc('XVID') fps = 20.0 width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) # 获取当前时间 now = datetime.now() # 格式化时间 current_datetime = now.strftime("%Y%m%d_%H%M%S") print("当前日期和时间：", current_datetime) #file_name = 'pic_temp'+str(current_datetime)+'.avi' file_name = 'pic_temp_'+str(current_datetime)+'.mp4' out = cv2.VideoWriter(file_name, fourcc, fps, (width, height)) #out = cv2.VideoWriter('pic_temp.avi', fourcc, fps,(620,300)) # 捕获视频帧并保存 while cap.isOpened(): ret, frame = cap.read() if not ret: print("无法读取视频帧") break out.write(frame) cv2.imshow('Record_video', frame) if cv2.waitKey(10) & 0xFF == ord('q'): # 释放资源 #cap.release() #out.release() #cv2.destroyAllWindows() break #th_record_camera1 = threading.Thread(target=record_camera1,args=sstime) th_record_camera1 = threading.Thread(target=record_camera1) th_record_camera1.start() #threading.Thread(target=save_camera1).start() def select_file(): print('进入_选择文件') global cap,video_path global flag_picture,flag_video path_ = askopenfilename() path.set(path_) video_path = path_ print('video_path:',video_path) suffix = os.path.splitext(video_path)[1] #_, ext = os.path.splitext(video_path) #print('ext:',_, ext) ## 读取图像，解决imread不能读取中文路径的问题 def cv_imread(filePath): cv_img = cv2.imdecode(np.fromfile(filePath, dtype=np.uint8), -1) ## imdecode读取的是rgb，如果后续需要opencv处理的话，需要转换成bgr，转换后图片颜色会变化 ##cv_img=cv2.cvtColor(cv_img,cv2.COLOR_RGB2BGR) return cv_img if suffix in ['.jpg', '.png', '.jpeg']: # 根据需要添加其他图片格式 #cap = cv2.imread('87.jpg') cap = cv_imread(video_path) if cap is None: print("图像文件未找到或路径错误") flag_picture = 1 flag_video = 0 #cv2.imshow('Image', cap) #cv2.waitKey(0) #cv2.destroyAllWindows() else: flag_video = 1 flag_picture = 0 cap = cv2.VideoCapture(video_path) #cap = cv2.VideoCapture(0,cv2.CAP_DSHOW) # 显示画面 def play_display(): print('进入_显示画面') global flag_picture,flag_video,cap if flag_picture == 1 and flag_video == 0: play_pic() if flag_picture == 0 and flag_video==1: play_video1() def play_pic(): global cap #cap = cv2.imread(video_path) selected_option = radio_var.get() if selected_option == '3': # 定义矩形的左上角和右下角坐标 x11=entry_x41.get() y11=entry_y41.get() #x=int(x11)-130 # 存在偏差，进行校正 #y=int(y11)-260 x=int(x11) # 存在偏差，进行校正 y=int(y11) print('x_y:',x,'_',y) w=50 h=50 #x, y, w, h = 280, 240, 70,70 # 例如，从(100, 100)到(300, 300) # 在图片上绘制矩形框 #print('1111') cv2.rectangle(cap, (x, y), (x+w, y+h), (0, 255, 0), 2) # 颜色为绿色，线宽为2 # 将OpenCV的图像数据转换为PIL图像格式，因为Tkinter需要PIL图像 #img_pil = Image.fromarray(cap) # 使用Pillow将PIL图像转换为Tkinter兼容的PhotoImage格式 #img_tk = ImageTk.PhotoImage(img_pil) #img_tk = Image.fromarray(img_tk) #img_tk = img_tk.resize((width, height),Image.LANCZOS) # 调整视频帧大小以适应Canvas大小 #canvas1.create_image(0, 0, anchor=tk.NW, image=cap) # 保持对图像的引用，防止被垃圾回收 #canvas1.image = img_tk # 将视频帧显示在Canvas上 img = cv2.cvtColor(cap, cv2.COLOR_BGR2RGB) img = Image.fromarray(img) img = img.resize((width, height),Image.LANCZOS) # 调整视频帧大小以适应Canvas大小 # 将PIL图像转换为ImageTk图像 img = ImageTk.PhotoImage(image=img) canvas1.create_image(0, 0, anchor=tk.NW, image=img) canvas1.img = img # 显示图片 #cv2.imshow('Image with Rectangle', cap) #cv2.waitKey(0) #cv2.destroyAllWindows() # 如果需要保存图片 #cv2.imwrite('image_with_rectangle.jpg', cap) def play_video1(): global cap,frame1,video_path #global stop_event,thread_video #cap = cv2.VideoCapture(video_path) def play(): #print('cap:',cap) ret, frame = cap.read() #print('ret1:',ret) #print('frame:',frame) if ret: # 确定矩形的位置和大小 #print('ret2:',ret) selected_option = radio_var.get() if selected_option == '3': #x = 800 #y = 300 x11=entry_x41.get() y11=entry_y41.get() x=int(x11) # 存在偏差，进行校正 y=int(y11) w =50 h = 50 #print('1111+++') try: cv2.rectangle(frame,(x, y), (x+w, y+h), (0,255, 0), thickness=6) # 使用FILLED来填充矩形绿色 except OSError as err: print("OS error: {0}".format(err)) #print('2222') frame1=frame #out.write(frame) # 将视频帧显示在Canvas上 img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) img = Image.fromarray(img) img = img.resize((width, height),Image.LANCZOS) # 调整视频帧大小以适应Canvas大小 # 将PIL图像转换为ImageTk图像 img = ImageTk.PhotoImage(image=img) canvas1.create_image(0, 0, anchor=tk.NW, image=img) canvas1.img = img canvas1.after(10, play_video1) #threading.Thread(target=play).start() #stop_event = threading.Event() #thread_video = threading.Thread(target=play,args=(stop_event,)) thread_video = threading.Thread(target=play, daemon=True) #daemon=True 守护线程 thread_video.start() """ while thread.is_alive(): print("Thread is still running") time.sleep(1) print("Thread has finished") """ def stop_play(): print('进入_停止播放') global cap #global stop_event,thread_video #stop_event.set() #thread_video.join() cap.release() cv2.destroyAllWindows() """ def delayed_action(): time.sleep(3) # 延时5秒 get_rgb_point(event) print("延时结束") """ """ def on_canvas1_click(event): global cap,frame1 print('frame1:',frame1) if frame1 is not None: x, y = int(event.x), int(event.y) # 获取点击坐标（注意转换坐标系） pixel = frame1[y, x] # 获取RGB值，注意opencv的BGR顺序，此处已转换为RGB显示，所以直接取用即可 print(f"Clicked pixel RGB: {pixel}") # 打印RGB值，注意这里是BGR顺序，但因为我们用的是Pillow显示，所以已经是RGB顺序了。 # 注意：如果要保持与原始视频帧的一致性（BGR），则应使用pixel[::-1]来获取RGB顺序的元组。 print(f"Clicked pixel BGR: {pixel[::-1]}") # 打印原始BGR顺序的RGB值（如果需要） """ def get_rgb_at_mouse(event): print('进入_获取RGB') selected_option = radio_var.get() print('selected_option:',selected_option) if selected_option == '1': print('进入_鼠标“点击”获取RGB') get_rgb_point(event) elif selected_option == '2': print('进入_选定“点”获取RGB') def print_number(number): get_rgb_point(event) count21 = entry_c21.get() # 获取输入框内容 count21_int = int(count21) if number < count21_int-1: # 假设我们想输出10次 threading.Timer(3, print_number, args=[number+1]).start()# 3秒 print_number(0) elif selected_option == '3': print('进入_选定“面”获取RGB') def print_number1(number): get_rgb_point(event) count31 = entry_c31.get() # 获取输入框内容 count31_int = int(count31) if number < count31_int-1: # 假设我们想输出10次 threading.Timer(5, print_number1, args=[number+1]).start() # 5秒 print_number1(0) else: print('请选择选取方式') def get_rgb_point(event): print('----- 进入_获取RGB -----') global flag_picture,flag_video # 获取当前时间 #now = datetime.now() # 格式化时间 #current_datetime = now.strftime("%Y-%m-%d %H:%M:%S") #print("当前日期和时间：", current_datetime) try: #while True: # 获取当前鼠标的位置 #xx = int(event.x) # yy = int(event.y) #print('xx:',xx,'_yy:',yy) selected_option = radio_var.get() print('selected_option:',selected_option) if selected_option == '1': x, y = pyautogui.position() # 截取当前屏幕图像 screenshot = pyautogui.screenshot() # 获取鼠标位置的 RGB 值 rgb = screenshot.getpixel((x, y)) # 打印 RGB 值 print(f"鼠标位置: ({x}, {y}) -> RGB: {rgb}") #print('RGB:',rgb,type(rgb)) display_point_item(x,y,rgb) elif selected_option == '2': x21 = entry_x21.get() # 获取输入框内容 y21 = entry_y21.get() # 获取输入框内容 x=int(x21) y=int(y21) #pyautogui.click(x=x,y=y) pyautogui.moveTo(x=x,y=y) #x, y = pyautogui.position() # 截取当前屏幕图像 screenshot = pyautogui.screenshot() # 获取鼠标位置的 RGB 值 rgb = screenshot.getpixel((x, y)) # 打印 RGB 值 print(f"鼠标位置: ({x}, {y}) -> RGB: {rgb}") #print('RGB:',rgb,type(rgb)) display_point_item(x,y,rgb) elif selected_option == '3': if flag_picture == 1 and flag_video == 0: ccc = 40 else: ccc = 0 #print('3333') x1=0 x2=0 x3=0 x4=0 y1=0 y2=0 y3=0 y4=0 x31 = entry_x31.get() # 获取输入框内容 y31 = entry_y31.get() # 获取输入框内容 x1=int(x31)-25 # 方框点 y1=int(y31)-25 ## 第1点 ## pyautogui.moveTo(x=x1,y=y1) #pyautogui.moveRel(100,500,duration=4) # 第一个参数是左右移动像素值，第二个是上下移动像素值 #pyautogui.moveRel(x,y,duration=4) # 截取当前屏幕图像 screenshot = pyautogui.screenshot() # 获取鼠标位置的 RGB 值 rgb1 = screenshot.getpixel((x1, y1)) # 打印 RGB 值 #print(f"鼠标位置: ({x}, {y}) -> RGB: {rgb}") #print('RGB1:',rgb1,type(rgb1)) ## 第2点 ## x2=x1+50 # 方框点 y2=y1 pyautogui.moveTo(x=x2,y=y2) screenshot = pyautogui.screenshot() rgb2 = screenshot.getpixel((x2, y2)) #print('RGB2:',rgb2,type(rgb2)) ## 第3点 ## x3=int(x31)+25 # 方框点 y3=int(y31)+25 # 方框点 pyautogui.moveTo(x=x3,y=y3) screenshot = pyautogui.screenshot() rgb3 = screenshot.getpixel((x3, y3)) #print('RGB3:',rgb3,type(rgb3)) ## 第4点 ## x4=x1 y4=y1+50 # 方框点 pyautogui.moveTo(x=x4,y=y4) screenshot = pyautogui.screenshot() rgb4 = screenshot.getpixel((x4, y4)) #print('RGB4:',rgb4,type(rgb4)) ## 第5点 ## x5=int(x31) # 方框点中心 y5=int(y31) # 方框点中心 pyautogui.moveTo(x=x5,y=y5) screenshot = pyautogui.screenshot() rgb5 = screenshot.getpixel((x5, y5)) #print('RGB5:',rgb5,type(rgb5)) rgb1_list = list(rgb1) rgb2_list = list(rgb2) rgb3_list = list(rgb3) rgb4_list = list(rgb4) rgb5_list = list(rgb5) #print('1:',int(rgb1_list[0])) #print('2:',int(rgb2_list[0])) #print('3:',int(rgb3_list[0])) #print('4:',int(rgb4_list[0])) #print('5:',int(rgb5_list[0])) r_avg =(int(rgb1_list[0]) +int(rgb2_list[0]) +int(rgb3_list[0]) +int(rgb4_list[0]) +int(rgb5_list[0]) )/5 g_avg =(int(rgb1_list[1]) +int(rgb2_list[1]) +int(rgb3_list[1]) +int(rgb4_list[1]) +int(rgb5_list[1]) )/5 b_avg =( int(rgb1_list[2]) +int(rgb2_list[2]) +int(rgb3_list[2]) +int(rgb4_list[2]) +int(rgb5_list[2]) )/5 rgb_list = [] rgb_list.append(int(r_avg)) rgb_list.append(int(g_avg)) rgb_list.append(int(b_avg)) x=x5 y=y5 rgb=tuple(rgb_list) #print('rgb:',rgb,type(rgb)) # 截取当前屏幕图像 #screenshot = pyautogui.screenshot() # 获取鼠标位置的 RGB 值 #rgb = screenshot.getpixel((x, y)) # 打印 RGB 值 print(f"鼠标位置: ({x}, {y}) -> RGB: {rgb}") #print('RGB:',rgb,type(rgb)) display_point_item(x,y,rgb) # 暂停一段时间，避免过于频繁的输出 #time.sleep(0.5) except KeyboardInterrupt: print("\n程序已结束。") def display_point_item(x,y,rgb): #print('x_y_rgb:',x,'_',y,'_',rgb) ### 显示RGB ### selected_option = radio_var.get() #print('selected_option:',selected_option) if selected_option == '1': #text_11 =str(xx) text_11 =str(x) label_item_11.configure(text=text_11) #text_12 =str(yy) text_12 =str(y) label_item_12.configure(text=text_12) text_13 =str(rgb[0]) label_item_13.configure(text=text_13) text_14 =str(rgb[1]) label_item_14.configure(text=text_14) text_15 =str(rgb[2]) label_item_15.configure(text=text_15) r=int(text_13) g=int(text_14) b=int(text_15) elif selected_option == '2': """ text_11 =str(x) label_item_11.configure(text=text_11) #text_12 =str(yy) text_12 =str(y) label_item_12.configure(text=text_12) """ text_23 =str(rgb[0]) label_item_23.configure(text=text_23) text_24 =str(rgb[1]) label_item_24.configure(text=text_24) text_25 =str(rgb[2]) label_item_25.configure(text=text_25) r=int(text_23) g=int(text_24) b=int(text_25) elif selected_option == '3': """ text_11 =str(x) label_item_11.configure(text=text_11) #text_12 =str(yy) text_12 =str(y) label_item_12.configure(text=text_12) """ text_33 =str(rgb[0]) label_item_33.configure(text=text_33) text_34 =str(rgb[1]) label_item_34.configure(text=text_34) text_35 =str(rgb[2]) label_item_35.configure(text=text_35) r=int(text_33) g=int(text_34) b=int(text_35) #print('r1_g1_b1:',r,'_',g,'_',b,'_') temp_data_calculate(x,y,r,g,b) def temp_data_calculate(x,y,r,g,b): file_path = 'temp_data_spec.txt' # TXT文件路径 #read_txt_file(file_path) temp_spec = [] with open(file_path, 'r') as file: for line in file: parts = line.strip().split(',') if len(parts) == 6: temp_spec.append((parts[0], parts[1],parts[2],parts[3],parts[4],parts[5])) #print('temp_spec1:',temp_spec) #r=249 #g=100 #b=100 #print('rgb:',r,'-',g,'-',b) list_1 = [] list_2 = [] list_a=[] list_b=[] del temp_spec[0] #print('temp_spec2:',temp_spec) temp_spec_list = [] i=0 while i<len(temp_spec): temp_spec_list_0 = [] temp_spec_list_0.append(temp_spec[i][0]) # 序号 temp_spec_list_0.append(temp_spec[i][1]) # 温度标准 temp_spec_list_0.append(str(int(temp_spec[i][3])+int(temp_spec[i][4])/1000+int(temp_spec[i][5])/(10001000))) # 色度标准折算 temp_spec_list.append(temp_spec_list_0) i=i+1 #print('temp_spec_list:',temp_spec_list) rgb_real_data = int(r)+int(g)/1000+int(b)/(10001000) print('rgb_real_data:',rgb_real_data) rgb_real_data_str = str(rgb_real_data) # 色度实时值折算 #print('rgb_real_data_str:',rgb_real_data_str) if rgb_real_data < float(temp_spec_list[-1][2]): print('低于标准最小值') temp_real_data = float(temp_spec_list[-1][1]) rgb_real_data / float(temp_spec_list[-1][2]) elif rgb_real_data == float(temp_spec_list[-1][2]): print('等于标准最小值') temp_real_data = float(temp_spec_list[-1][1]) elif rgb_real_data > float(temp_spec_list[0][2]): print('高于标准最大值') temp_real_data = float(temp_spec_list[0][1]) * rgb_real_data / float(temp_spec_list[0][2]) elif rgb_real_data == float(temp_spec_list[0][2]): print('等于标准最大值') temp_real_data = float(temp_spec_list[0][1]) else: print('在最小值-最大值之间') """ i=0 while i <len(temp_spec_list): print('float(rgb_real_data_str) :',float(rgb_real_data_str) ) print('float(temp_spec_list[i][2]) :',float(temp_spec_list[i][2])) if float(rgb_real_data_str) == float(temp_spec_list[i][2]): print('111') temp_real_data = temp_spec_list[i][1] i=i+1 """ #print('222') a=0 list_a = [] while a < len(temp_spec_list): if float(rgb_real_data_str) < float(temp_spec_list[a][2]): #print('lower') list_a.append(temp_spec_list[a]) # 所有大于实际值的数值 #print('list_a:',list_a) a=a+1 temp_spec_higher = list_a[-1] # 最后1个，为上限值 print('======temp_spec_higher:',temp_spec_higher) c=0 while c<len(temp_spec_list): #print('temp_data[c][3]:',temp_data[i][3]) if (int(temp_spec_higher[0])+1) == int(temp_spec_list[c][0]): # 上限值+1，为下限值 temp_spec_lower_0 = temp_spec_list[c] c=c+1 temp_spec_lower = list(temp_spec_lower_0) print('======temp_spec_lower:',temp_spec_lower) rgb_spec_data_lower = float(temp_spec_lower[2]) rgb_spec_data_higher = float(temp_spec_higher[2]) temp_spec_data_lower = float(temp_spec_lower[1]) temp_spec_data_higher = float(temp_spec_higher[1]) data_middle = (float(rgb_real_data_str)-rgb_spec_data_lower)/(rgb_spec_data_higher-rgb_spec_data_lower) temp_real_data = (temp_spec_data_higher - temp_spec_data_lower)data_middle+temp_spec_data_lower print('temp_real_data:',temp_real_data) temp_display(x,y,r,g,b,temp_real_data) def temp_display(x,y,r,g,b,temp_real_data): print('进入_温度显示') #global tree ### 显示温度值 ### selected_option = radio_var.get() #print('selected_option:',selected_option) if selected_option == '1': text_16 =str(int(temp_real_data)) label_item_16.configure(text=text_16) elif selected_option == '2': text_26 =str(int(temp_real_data)) label_item_26.configure(text=text_26) if selected_option == '3': text_36 =str(int(temp_real_data)) label_item_36.configure(text=text_36) temp_spec_lower = (entry_lower11.get()) # 获取输入框内容 temp_spec_higher = (entry_higher11.get()) # 获取输入框内容 print('temp_spec_lower:',temp_spec_lower) temp_spec_higher_int = int(temp_spec_higher) temp_spec_lower_int = int(temp_spec_lower) flag_lower = 0 flag_higher = 0 if temp_real_data < temp_spec_lower_int : print('温度低异常') flag_lower = 1 ### 显示判定值&报警 ### selected_option = radio_var.get() #print('selected_option:',selected_option) if selected_option == '1': text_17 ='异常' #label_item_17.configure(bg='white',fg='red',text='异常') #label_item_17.configure(bg='white',fg='red',text=text_17) text_18 ='温度低' #label_item_18.configure(bg='red',fg='white',text='报警') label_item_18.configure(bg='red',fg='white',text=text_18) label_topic_4.configure(bg='red',fg='white',text='温度偏低') elif selected_option == '2': text_27 ='异常' #label_item_17.configure(bg='white',fg='red',text='异常') #label_item_27.configure(bg='white',fg='red',text=text_27) text_28 ='温度低' #label_item_18.configure(bg='red',fg='white',text='报警') label_item_28.configure(bg='red',fg='white',text=text_28) label_topic_4.configure(bg='red',fg='white',text='温度偏低') elif selected_option == '3': text_37 ='异常' #label_item_17.configure(bg='white',fg='red',text='异常') #label_item_37.configure(bg='white',fg='red',text=text_37) text_38 ='温度低' #label_item_18.configure(bg='red',fg='white',text='报警') label_item_38.configure(bg='red',fg='white',text=text_38) label_topic_4.configure(bg='red',fg='white',text='温度偏低') #messagebox.showwarning(title = "异常！", message="温度偏低......") # 播放系统默认警告声音 #winsound.Beep(1000, 1000) # 频率为1000Hz，时长为1000毫秒 elif temp_real_data > temp_spec_higher_int: print('温度高异常') flag_higher = 1 ### 显示判定值&报警 ### selected_option = radio_var.get() #print('selected_option:',selected_option) if selected_option == '1': text_17 ='异常' #label_item_17.configure(bg='white',fg='red',text='异常') #label_item_17.configure(bg='white',fg='red',text=text_17) text_18 ='温度高' #label_item_18.configure(bg='red',fg='white',text='报警') label_item_18.configure(bg='red',fg='white',text=text_18) label_topic_4.configure(bg='red',fg='white',text='温度偏高') elif selected_option == '2': text_27 ='异常' #label_item_17.configure(bg='white',fg='red',text='异常') #label_item_27.configure(bg='white',fg='red',text=text_27) text_28 ='温度高' #label_item_18.configure(bg='red',fg='white',text='报警') label_item_28.configure(bg='red',fg='white',text=text_28) label_topic_4.configure(bg='red',fg='white',text='温度偏高') elif selected_option == '3': text_37 ='异常' #label_item_17.configure(bg='white',fg='red',text='异常') #label_item_37.configure(bg='white',fg='red',text=text_37) text_38 ='温度高' #label_item_18.configure(bg='red',fg='white',text='报警') label_item_38.configure(bg='red',fg='white',text=text_38) label_topic_4.configure(bg='red',fg='white',text='温度偏高') else: selected_option = radio_var.get() #print('selected_option:',selected_option) if selected_option == '1': print('温度正常') text_17 ='正常' #label_item_17.configure(bg='white',fg='green',text=text_17) text_18 ='正常' label_item_18.configure(bg='green',fg='white',text=text_18) label_topic_4.configure(bg='green',fg='white',text='温度正常') elif selected_option == '2': print('温度正常') text_27 ='正常' #label_item_27.configure(bg='white',fg='green',text=text_27) text_28 ='正常' label_item_28.configure(bg='green',fg='white',text=text_28) label_topic_4.configure(bg='green',fg='white',text='温度正常') elif selected_option == '3': print('温度正常') text_37 ='正常' #label_item_37.configure(bg='white',fg='green',text=text_37) text_38 ='正常' label_item_38.configure(bg='green',fg='white',text=text_38) label_topic_4.configure(bg='green',fg='white',text='温度正常') data_ttl = [] data_ttl_0 = [] # 获取当前时间 now = datetime.now() # 格式化时间 current_datetime = now.strftime("%Y-%m-%d %H:%M:%S") print("当前日期和时间：", current_datetime) global point_count point_count = point_count+1 data_ttl_0.append(str(point_count)) data_ttl_0.append(str(current_datetime)) data_ttl_0.append(str(x)) data_ttl_0.append(str(y)) data_ttl_0.append(str(r)) data_ttl_0.append(str(g)) data_ttl_0.append(str(b)) data_ttl_0.append(str(int(temp_real_data))) data_ttl_0.append(str(temp_spec_lower)) data_ttl_0.append(str(temp_spec_higher)) selected_option = radio_var.get() #print('selected_option:',selected_option) if selected_option == '1': #data_ttl_0.append(str(text_17)) data_ttl_0.append(str(text_18)) elif selected_option == '2': #data_ttl_0.append(str(text_27)) data_ttl_0.append(str(text_28)) elif selected_option == '3': #data_ttl_0.append(str(text_37)) data_ttl_0.append(str(text_38)) data_ttl.append(data_ttl_0) print('data_ttl:',data_ttl) # 插入数据项 for item in data_ttl: tree.insert('', '0', values=item) #tree.insert('', 'end', values=item) """ # 获取第一列的所有数据 for item in tree.get_children(): date_tree = tree.item(item, 'values') # 获取第一列的值 print('date_tree:',date_tree) """ date_tree = [] data_tree = [] count_tree = [] for item in tree.get_children(): count_tree_0 = tree.set(item, 'c1') # 获取第一列的值 count_tree.append(int(count_tree_0)) #print('count_tree:',count_tree) for item in tree.get_children(): date_tree_0 = tree.set(item, 'c2') # 获取第一列的值 date_tree.append(date_tree_0) #print('date_tree:',date_tree) # 获取第一列的所有数据 for item in tree.get_children(): data_tree_0 = tree.set(item, 'c8') # 获取第一列的值 data_tree.append(int(data_tree_0)) #print('data_tree:',data_tree) plot_curve(count_tree,date_tree,data_tree) if selected_option == '1' and flag_higher == 1: # 播放系统默认警告声音 winsound.Beep(1000, 1000) # 频率为1000Hz，时长为1000毫秒 messagebox.showwarning(title = "异常！", message="温度偏高......") if selected_option == '1' and flag_lower == 1: # 播放系统默认警告声音 winsound.Beep(1000, 1000) # 频率为1000Hz，时长为1000毫秒 messagebox.showwarning(title = "异常！", message="温度偏低......") elif selected_option == '2' and flag_lower == 1: #messagebox.showwarning(title = "异常！", message="温度偏低......") # 播放系统默认警告声音 winsound.Beep(500, 500) # 频率为1000Hz，时长为1000毫秒 elif selected_option == '2' and flag_higher == 1: #messagebox.showwarning(title = "异常！", message="温度偏低......") # 播放系统默认警告声音 winsound.Beep(500, 500) # 频率为1000Hz，时长为1000毫秒 elif selected_option == '3' and flag_lower == 1: #messagebox.showwarning(title = "异常！", message="温度偏低......") # 播放系统默认警告声音 winsound.Beep(500, 500) # 频率为1000Hz，时长为1000毫秒 elif selected_option == '3' and flag_higher == 1: #messagebox.showwarning(title = "异常！", message="温度偏低......") # 播放系统默认警告声音 winsound.Beep(500, 500) # 频率为1000Hz，时长为1000毫秒 def plot_curve(count_tree,date_tree,data_tree): print('进入_曲线绘图') global output, fig output = None x = count_tree y= data_tree fig = Figure(figsize=(6.4, 3.1), dpi=100) # adding the subplot plot1 = fig.add_subplot(111) # plotting the graph #plot1.plot(x,y_higher,'ro-', label='H_Line', markersize=1) plot1.plot(x,y, 'bo-', label='Value',markersize=5) #plot1.plot(x,y_lower,'ro-', label='L_Line', markersize=1) # 添加图例 plot1.legend() # 设置字体，避免出现方块 plt.rcParams['font.sans-serif'] = ['SimHei'] # 指定默认字体为黑体 plt.rcParams['axes.unicode_minus'] = False # 解决保存图像是负号'-'显示为方块的问题 # 设置图表标题和坐标轴标签 #plot1.set_title('变化趋势图', fontsize=14) plot1.set_xlabel('序号') plot1.set_ylabel('数值') #plot1.set_rotation(180) #plot1.set_xticks(fontname='Times New Roman', fontsize=14) #plot1.set_yticks(fontname='Times New Roman', fontsize=14) output = FigureCanvasTkAgg(fig, master=canvas2) print('FigureCanvasTkAgg：',FigureCanvasTkAgg) #messagebox.showinfo("提示", "绘图中,请稍后......") output.draw() output.get_tk_widget().place(x=0, y=0) def clear_video_plot(): count_tree =[] date_tree=[] data_tree=[] plot_curve(count_tree,date_tree,data_tree) tree.delete(tree.get_children()) global point_count point_count=0 def page_to_main(): print('page_to_main') #window_video.place_forget() window_video.iconify() # 最小化窗口 #self.open_window_sql() def page_to_sql(): print('page_to_sql') #window_video.iconify() # 最小化窗口 window_video.destroy() NewWindow_sql(master) #self.open_window_sql() # 连接到MySQL数据库 def connect_to_database(): return mysql.connector.connect( host="localhost", user="root", password="123456", database="pic_temp" ) # 将Treeview中的数据保存到数据库 def save_to_database(): print('进入_保存数据到Mysql') # 连接到数据库并保存数据到数据库的按钮 conn = connect_to_database() database_name = "pic_temp" cursor = conn.cursor() items = tree.get_children() flag_save_ok = 0 for item in items[::-1] : # 从 tree 最后1条开始读取 values = tree.item(item, 'values') #print('values:',values) try: query = "SELECT COUNT(*) FROM temp_real WHERE date = %s" # 查询测试记录是否存在，避免重复保存 cursor.execute(query, (values[1],)) results = cursor.fetchone()[0] if results == 0: sql_01 = "INSERT INTO temp_real (date, x,y,r,g,b,temp,lower,higher,alarm) "+\ "VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s)" cursor.execute(sql_01, (values[1], values[2], values[3], values[4], values[5], values[6], values[7], values[8], values[9], values[10])) conn.commit() # 提交更改到数据库 flag_save_ok = 1 else: flag_save_ok = 2 print("数据重复保存!") messagebox.showinfo("提示", "数据重复保存！") except mysql.connector.Error as err: print("Error: ", err) flag_save_ok = 0 conn.rollback() if flag_save_ok == 1: print("保存数据库完成!") messagebox.showinfo("提示", "保存数据库完成！\n数据表名:"+database_name) elif flag_save_ok == 0: print("保存数据库异常!") messagebox.showinfo("提示", "保存数据库异常！\n异常名:"+err) cursor.close() conn.close() def output_excel(): #global tree print('进入_导出数据到EXCEL') #file_path = filedialog.asksaveasfilename(defaultextension='.xlsx') #if file_path: # 获取所有行数据 records = tree.get_children() data = [tree.item(r)['values'] for r in records] df = pd.DataFrame(data, columns=['序号', '时间', 'X','Y', 'R', 'G', 'B','温度', '下限', '上限','报警']) # 获取当前时间 now = datetime.now() # 格式化时间 current_datetime = now.strftime("%Y%m%d_%H%M%S") print("当前日期和时间：", current_datetime) # 将DataFrame导出到Excel文件 file_path = 'Temp_output_'+str(current_datetime)+'.xlsx' df.to_excel(file_path, index=False) print("导出Excel完成!") messagebox.showinfo("提示", "导出Excel完成！\n文件名:"+file_path) #else: #print("未选择保存文件路径") #messagebox.showinfo("提示", "未选择保存文件路径！") ### 界面 ### path = StringVar() ### 左侧 ### #radio_button_camera = tk.Radiobutton(window_video, text="摄像头",variable=radio_var_path, value="1",font=('宋体', 11)) #radio_button1.place(x=30, y=80) #radio_button_pic = tk.Radiobutton(window_video, text="照片图像",variable=radio_var_path, value="2",font=('宋体', 11)) #radio_button1.place(x=30, y=130) label_topic_2 = Label(window_video, text="照片图像", font=('宋体', 18)) label_topic_2.place(x=30, y=130) #button_camera_open = tk.Button(window_video, text="开启\n摄像头", command=open_camera,width=5, height=2) #button_camera_open.place(x=10, y=120) button_select = tk.Button(window_video, text="选择\n文件", command=select_file,width=5, height=2) button_select.place(x=150, y=120) label_path=tk.Label(window_video,text = "目标路径:",width=10, height=2) label_path.place(x=200, y=110) entry_path=tk.Entry(window_video, textvariable = path,width=25, font=('宋体', 10)) entry_path.place(x=200, y=145) button_play = tk.Button(window_video, text="显示\n画面", command=play_display,width=5, height=2) button_play.place(x=390, y=120) button_camera_open = tk.Button(window_video, text="开启\n摄像头", command=open_camera,width=5, height=2) button_camera_open.place(x=450, y=120) button_stop = tk.Button(window_video, text="停止\n播放", command=stop_play,width=5, height=2) button_stop.place(x=500, y=120) button_camera_save = tk.Button(window_video, text="录制\n视频", command=record_camera,width=5, height=2) button_camera_save.place(x=550, y=120) button_camera_stop = tk.Button(window_video, text="关闭\n摄像头", command=close_camera,width=5, height=2) button_camera_stop.place(x=600, y=120) width = 630 height = 300 # 调整窗口大小以适应视频 canvas1 = tk.Canvas(window_video, width=630, height=300,bg='lightgreen') canvas1.place(x=10, y=185) #canvas1.bind("<Button-1>", on_canvas1_click) canvas1.bind("<Button-1>", get_rgb_at_mouse) ### 第0行 ### label_item_01 = Label(window_video, text="选择方式", font=('宋体', 11)) label_item_01.place(x=10, y=500) label_item_0B = Label(window_video, text="次数", font=('宋体', 11)) label_item_0B.place(x=90, y=500) label_item_07 = Label(window_video, text="X", font=('宋体', 11)) label_item_07.place(x=150, y=500) label_item_08 = Label(window_video, text="Y", font=('宋体', 11)) label_item_08.place(x=200, y=500) label_item_09 = Label(window_video, text="R", font=('宋体', 11)) label_item_09.place(x=250, y=500) label_item_10 = Label(window_video, text="G", font=('宋体', 11)) label_item_10.place(x=300, y=500) label_item_0A = Label(window_video, text="B", font=('宋体', 11)) label_item_0A.place(x=350, y=500) label_item_02 = Label(window_video, text="温度", font=('宋体', 11)) label_item_02.place(x=400, y=500) label_item_03 = Label(window_video, text="下限值", font=('宋体', 11)) label_item_03.place(x=470, y=500) label_item_04 = Label(window_video, text="上限值", font=('宋体', 11)) label_item_04.place(x=540, y=500) #label_item_05 = Label(window_video, text="数据质量", font=('宋体', 11)) #label_item_05.place(x=710, y=500) label_item_06 = Label(window_video, text="报警", font=('宋体', 11)) label_item_06.place(x=610, y=500) #label_item_11 = Label(window, text="温度1", font=('宋体', 12)) #label_item_11.place(x=250, y=700) ### 第1行 ### label_item_1B = Label(window_video, text="1", font=('宋体', 11)) label_item_1B.place(x=90, y=550) label_item_11 = Label(window_video, text="X1", font=('宋体', 11)) label_item_11.place(x=150, y=550) label_item_12 = Label(window_video, text="Y1", font=('宋体', 11)) label_item_12.place(x=200, y=550) label_item_13 = Label(window_video, text="R1", font=('宋体', 11)) label_item_13.place(x=250, y=550) label_item_14 = Label(window_video, text="G1", font=('宋体', 11)) 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entry_x21.insert(0, "410") entry_y21 = Entry(window_video, width=5, font=('宋体', 11)) entry_y21.place(x=200, y=600) entry_y21.insert(0, "490") label_item_23 = Label(window_video, text="R2", font=('宋体', 11)) label_item_23.place(x=250, y=600) label_item_24 = Label(window_video, text="G2", font=('宋体', 11)) label_item_24.place(x=300, y=600) label_item_25 = Label(window_video, text="B2", font=('宋体', 11)) label_item_25.place(x=350, y=600) label_item_26 = Label(window_video, text="温度2", font=('宋体', 11)) label_item_26.place(x=400, y=600) entry_lower22 = Entry(window_video,width=5, font=('宋体', 11)) entry_lower22.place(x=470, y=600) entry_lower22.insert(0, "48") entry_higher22 = Entry(window_video, width=5, font=('宋体', 11)) entry_higher22.place(x=540, y=600) entry_higher22.insert(0, "58") #label_item_27 = Label(window_video, text="数据质量", font=('宋体', 11)) #label_item_27.place(x=750, y=600) label_item_28 = Label(window_video, text="报警", font=('宋体', 11)) label_item_28.place(x=610, y=600) ### 第3行 ### entry_c31 = Entry(window_video, width=5, font=('宋体', 11)) entry_c31.place(x=90, y=650) entry_c31.insert(0, "3") entry_x31 = Entry(window_video, width=5, font=('宋体', 11)) entry_x31.place(x=150, y=650) entry_x31.insert(0, "410") entry_y31 = Entry(window_video, width=5, font=('宋体', 11)) entry_y31.place(x=200, y=650) entry_y31.insert(0, "490") label_item_33 = Label(window_video, text="R3", font=('宋体', 11)) label_item_33.place(x=250, y=650) label_item_34 = Label(window_video, text="G3", font=('宋体', 11)) label_item_34.place(x=300, y=650) label_item_35 = Label(window_video, text="B3", font=('宋体', 11)) label_item_35.place(x=350, y=650) label_item_36 = Label(window_video, text="温度3", font=('宋体', 11)) label_item_36.place(x=400, y=650) entry_lower32 = Entry(window_video, width=5, font=('宋体', 11)) entry_lower32.place(x=470, y=650) entry_lower32.insert(0, "48") entry_higher32 = Entry(window_video, width=5, font=('宋体', 11)) entry_higher32.place(x=540, y=650) entry_higher32.insert(0, "58") 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