diff --git a/python/p/kcsj.py b/python/p/kcsj.py index bc99217..405b4f7 100644 --- a/python/p/kcsj.py +++ b/python/p/kcsj.py @@ -6,304 +6,342 @@ import numpy as np import tkinter as tk from tkinter import ttk from PIL import ImageTk, Image +from tkinter import filedialog + # plt显示彩色图片 -def plt_show0(img): - # cv2与plt的图像通道不同：cv2为[b,g,r];plt为[r, g, b] - b, g, r = cv2.split(img) - img = cv2.merge([r, g, b]) - plt.imshow(img) - plt.show() +def eaowej(file_path): + global history -# plt显示灰度图片 -def plt_show(img): - plt.imshow(img, cmap='gray') - plt.show() + # plt显示灰度图片 + def plt_show(img): + plt.imshow(img, cmap='gray') + plt.show() -# 图像去噪灰度处理 -def gray_guss(image): - image = cv2.GaussianBlur(image, (3, 3), 0) - gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) - return gray_image + # 图像去噪灰度处理 + def gray_guss(image): + image = cv2.GaussianBlur(image, (3, 3), 0) + gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + return gray_image -# 读取待检测图片 -origin_image = cv2.imread('../xiangA.jpg') + # 读取待检测图片 + origin_image = cv2.imread(file_path) -# 复制一张图片，在复制图上进行图像操作，保留原图 -image = origin_image.copy() + # 复制一张图片，在复制图上进行图像操作，保留原图 + image = origin_image.copy() -# 图像去噪灰度处理 -gray_image = gray_guss(image) + # 图像去噪灰度处理 + gray_image = gray_guss(image) -# x方向上的边缘检测（增强边缘信息） -Sobel_x = cv2.Sobel(gray_image, cv2.CV_16S, 1, 0) -absX = cv2.convertScaleAbs(Sobel_x) -image = absX + # x方向上的边缘检测（增强边缘信息） + Sobel_x = cv2.Sobel(gray_image, cv2.CV_16S, 1, 0) + absX = cv2.convertScaleAbs(Sobel_x) + image = absX -# 图像阈值化操作——获得二值化图 -ret, image = cv2.threshold(image, 0, 255, cv2.THRESH_OTSU) + # 图像阈值化操作——获得二值化图 + ret, image = cv2.threshold(image, 0, 255, cv2.THRESH_OTSU) -# 显示灰度图像 -plt_show(image) - -# 形态学（从图像中提取对表达和描绘区域形状有意义的图像分量）——闭操作 -kernelX = cv2.getStructuringElement(cv2.MORPH_RECT, (30, 10)) -image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernelX,iterations = 1) - -# 显示灰度图像 -plt_show(image) - -# 腐蚀（erode）和膨胀（dilate） -kernelX = cv2.getStructuringElement(cv2.MORPH_RECT, (50, 1)) -kernelY = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 20)) - -#x方向进行闭操作（抑制暗细节） -image = cv2.dilate(image, kernelX) -image = cv2.erode(image, kernelX) - -#y方向的开操作 -image = cv2.erode(image, kernelY) -image = cv2.dilate(image, kernelY) - -# 中值滤波（去噪） -image = cv2.medianBlur(image, 21) - -# 显示灰度图像 -plt_show(image) - -# 获得轮廓 -contours, hierarchy = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) - -for item in contours: - rect = cv2.boundingRect(item) - x = rect[0] - y = rect[1] - weight = rect[2] - height = rect[3] - # 根据轮廓的形状特点，确定车牌的轮廓位置并截取图像 - if (weight > (height * 3.5)) and (weight < (height * 4)): - image = origin_image[y:y + height, x:x + weight] - plt_show0(image) - -#车牌字符分割 -# 图像去噪灰度处理 -gray_image = gray_guss(image) - -# 图像阈值化操作——获得二值化图 -ret, image = cv2.threshold(gray_image, 0, 255, cv2.THRESH_OTSU) -plt_show(image) - -#膨胀操作，使“津”字膨胀为一个近似的整体，为分割做准备 -kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (2, 2)) -image = cv2.dilate(image, kernel) -plt_show(image) - -# 查找轮廓 -contours, hierarchy = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) -words = [] -word_images = [] - -#对所有轮廓逐一操作 -for item in contours: - word = [] - rect = cv2.boundingRect(item) - x = rect[0] - y = rect[1] - weight = rect[2] - height = rect[3] - - word.append(x) - - word.append(y) - - word.append(weight) - - word.append(height) - - words.append(word) - -# 排序，车牌号有顺序。words是一个嵌套列表 -words = sorted(words,key=lambda s:s[0],reverse=False) -i = 0 - -#word中存放轮廓的起始点和宽高 -for word in words: - # 筛选字符的轮廓 - if (word[3] > (word[2] * 1.5)) and (word[3] < (word[2] * 3.5)) and (word[2] > 25): - i = i+1 - splite_image = image[word[1]:word[1] + word[3], word[0]:word[0] + word[2]] - word_images.append(splite_image) - #print(i) -#print(words) - -for i,j in enumerate(word_images): - plt.subplot(1,7,i+1) - plt.imshow(word_images[i],cmap='gray') -plt.show() - -#模版匹配 -# 准备模板(template[0-9]为数字模板；) -template = ['0','1','2','3','4','5','6','7','8','9', - 'A','B','C','D','E','F','G','H','J','K', - 'L','M','N','P','Q','R','S','T','U','V', - 'W','X','Y','Z','藏','川','鄂','甘','赣', - '贵','桂','黑','沪','吉','冀','津','晋','京', - '辽','鲁','蒙','闽','宁','青','琼','陕','苏', - '皖','湘','新','渝','豫','粤','云','浙'] - -# 读取一个文件夹下的所有图片，输入参数是文件名，返回模板文件地址列表 -def read_directory(directory_name): - referImg_list = [] - for filename in os.listdir(directory_name): - referImg_list.append(directory_name + "/" + filename) - return referImg_list - -# 获得中文模板列表（只匹配车牌的第一个字符） -def get_chinese_words_list(): - chinese_words_list = [] - for i in range(34,64): - #将模板存放在字典中 - c_word = read_directory('./refer1/'+ template[i]) - chinese_words_list.append(c_word) - return chinese_words_list -chinese_words_list = get_chinese_words_list() - - -# 获得英文模板列表（只匹配车牌的第二个字符） -def get_eng_words_list(): - eng_words_list = [] - for i in range(10,34): - e_word = read_directory('./refer1/'+ template[i]) - eng_words_list.append(e_word) - return eng_words_list -eng_words_list = get_eng_words_list() - - -# 获得英文和数字模板列表（匹配车牌后面的字符） -def get_eng_num_words_list(): - eng_num_words_list = [] - for i in range(0,34): - word = read_directory('./refer1/'+ template[i]) - eng_num_words_list.append(word) - return eng_num_words_list -eng_num_words_list = get_eng_num_words_list() - - -# 读取一个模板地址与图片进行匹配，返回得分 -def template_score(template,image): - #将模板进行格式转换 - template_img=cv2.imdecode(np.fromfile(template,dtype=np.uint8),1) - template_img = cv2.cvtColor(template_img, cv2.COLOR_RGB2GRAY) - #模板图像阈值化处理——获得黑白图 - ret, template_img = cv2.threshold(template_img, 0, 255, cv2.THRESH_OTSU) -# height, width = template_img.shape -# image_ = image.copy() -# image_ = cv2.resize(image_, (width, height)) - image_ = image.copy() - #获得待检测图片的尺寸 - height, width = image_.shape - # 将模板resize至与图像一样大小 - template_img = cv2.resize(template_img, (width, height)) - # 模板匹配，返回匹配得分 - result = cv2.matchTemplate(image_, template_img, cv2.TM_CCOEFF) - return result[0][0] - - -# 对分割得到的字符逐一匹配 -def template_matching(word_images): - results = [] - for index,word_image in enumerate(word_images): - if index==0: - best_score = [] - for chinese_words in chinese_words_list: - score = [] - for chinese_word in chinese_words: - result = template_score(chinese_word,word_image) - score.append(result) - best_score.append(max(score)) - i = best_score.index(max(best_score)) - # print(template[34+i]) - r = template[34+i] - results.append(r) - continue - if index==1: - best_score = [] - for eng_word_list in eng_words_list: - score = [] - for eng_word in eng_word_list: - result = template_score(eng_word,word_image) - score.append(result) - best_score.append(max(score)) - i = best_score.index(max(best_score)) - # print(template[10+i]) - r = template[10+i] - results.append(r) - continue - else: - best_score = [] - for eng_num_word_list in eng_num_words_list: - score = [] - for eng_num_word in eng_num_word_list: - result = template_score(eng_num_word,word_image) - score.append(result) - best_score.append(max(score)) - i = best_score.index(max(best_score)) - # print(template[i]) - r = template[i] - results.append(r) - continue - return results - - -word_images_ = word_images.copy() -# 调用函数获得结果 - -result = template_matching(word_images_) -#print(result) -# "".join(result)函数将列表转换为拼接好的字符串，方便结果显示 -#print( "".join(result)) - - -from PIL import ImageFont, ImageDraw, Image - -height,weight = origin_image.shape[0:2] -#print(height) -#print(weight) - -image_1 = origin_image.copy() -cv2.rectangle(image_1, (int(0.2*weight), int(0.75*height)), (int(weight*0.9), int(height*0.95)), (0, 255, 0), 5) - -#设置需要显示的字体 -fontpath = "font/simsun.ttc" -font = ImageFont.truetype(fontpath,64) -img_pil = Image.fromarray(image_1) -draw = ImageDraw.Draw(img_pil) - -#绘制文字信息 -draw.text((int(0.2*weight)+25, int(0.75*height)), "".join(result), font = font, fill = (255, 255, 0)) -bk_img = np.array(img_pil) -#print(result) -print( "".join(result)) -plt_show0(bk_img) - -# 使用PIL将numpy数组转换为Image对象 -image_tk = Image.fromarray(cv2.cvtColor(bk_img, cv2.COLOR_BGR2RGB)) - -# 初始化Tkinter窗口 -root = tk.Tk() -root.title("车牌识别结果") + # 显示灰度图像 + plt_show(image) + + # 形态学（从图像中提取对表达和描绘区域形状有意义的图像分量）——闭操作 + kernelX = cv2.getStructuringElement(cv2.MORPH_RECT, (30, 10)) + image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernelX,iterations = 1) + + # 显示灰度图像 + plt_show(image) + + # 腐蚀（erode）和膨胀（dilate） + kernelX = cv2.getStructuringElement(cv2.MORPH_RECT, (50, 1)) + kernelY = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 20)) + + #x方向进行闭操作（抑制暗细节） + image = cv2.dilate(image, kernelX) + image = cv2.erode(image, kernelX) + + #y方向的开操作 + image = cv2.erode(image, kernelY) + image = cv2.dilate(image, kernelY) + + # 中值滤波（去噪） + image = cv2.medianBlur(image, 21) + + # 显示灰度图像 + plt_show(image) + + # 获得轮廓 + contours, hierarchy = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + + for item in contours: + rect = cv2.boundingRect(item) + x = rect[0] + y = rect[1] + weight = rect[2] + height = rect[3] + # 根据轮廓的形状特点，确定车牌的轮廓位置并截取图像 + if (weight > (height * 3.5)) and (weight < (height * 4)): + image = origin_image[y:y + height, x:x + weight] + plt_show0(image) + + #车牌字符分割 + # 图像去噪灰度处理 + gray_image = gray_guss(image) -# 将Image对象转换为Tkinter支持的PhotoImage对象 -photo_image = ImageTk.PhotoImage(image_tk) + # 图像阈值化操作——获得二值化图 + ret, image = cv2.threshold(gray_image, 0, 255, cv2.THRESH_OTSU) + plt_show(image) -# 创建标签以显示图像 -label = ttk.Label(root, image=photo_image) -label.pack() # 将标签添加到窗口中并自动调整大小 + #膨胀操作，使“津”字膨胀为一个近似的整体，为分割做准备 + kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (2, 2)) + image = cv2.dilate(image, kernel) + plt_show(image) + + # 查找轮廓 + contours, hierarchy = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + words = [] + word_images = [] + + #对所有轮廓逐一操作 + for item in contours: + word = [] + rect = cv2.boundingRect(item) + x = rect[0] + y = rect[1] + weight = rect[2] + height = rect[3] + + word.append(x) + + word.append(y) + + word.append(weight) + + word.append(height) + + words.append(word) + + # 排序，车牌号有顺序。words是一个嵌套列表 + words = sorted(words,key=lambda s:s[0],reverse=False) + i = 0 + + #word中存放轮廓的起始点和宽高 + for word in words: + # 筛选字符的轮廓 + if (word[3] > (word[2] * 1.5)) and (word[3] < (word[2] * 3.5)) and (word[2] > 25): + i = i+1 + splite_image = image[word[1]:word[1] + word[3], word[0]:word[0] + word[2]] + word_images.append(splite_image) + #print(i) + #print(words) + + for i,j in enumerate(word_images): + plt.subplot(1,7,i+1) + plt.imshow(word_images[i],cmap='gray') + plt.show() + + #模版匹配 + # 准备模板(template[0-9]为数字模板；) + template = ['0','1','2','3','4','5','6','7','8','9', + 'A','B','C','D','E','F','G','H','J','K', + 'L','M','N','P','Q','R','S','T','U','V', + 'W','X','Y','Z','藏','川','鄂','甘','赣', + '贵','桂','黑','沪','吉','冀','津','晋','京', + '辽','鲁','蒙','闽','宁','青','琼','陕','苏', + '皖','湘','新','渝','豫','粤','云','浙'] + + # 读取一个文件夹下的所有图片，输入参数是文件名，返回模板文件地址列表 + def read_directory(directory_name): + referImg_list = [] + for filename in os.listdir(directory_name): + referImg_list.append(directory_name + "/" + filename) + return referImg_list + + # 获得中文模板列表（只匹配车牌的第一个字符） + def get_chinese_words_list(): + chinese_words_list = [] + for i in range(34,64): + #将模板存放在字典中 + c_word = read_directory('./refer1/'+ template[i]) + chinese_words_list.append(c_word) + return chinese_words_list + chinese_words_list = get_chinese_words_list() + + + # 获得英文模板列表（只匹配车牌的第二个字符） + def get_eng_words_list(): + eng_words_list = [] + for i in range(10,34): + e_word = read_directory('./refer1/'+ template[i]) + eng_words_list.append(e_word) + return eng_words_list + eng_words_list = get_eng_words_list() + + + # 获得英文和数字模板列表（匹配车牌后面的字符） + def get_eng_num_words_list(): + eng_num_words_list = [] + for i in range(0,34): + word = read_directory('./refer1/'+ template[i]) + eng_num_words_list.append(word) + return eng_num_words_list + eng_num_words_list = get_eng_num_words_list() + + + # 读取一个模板地址与图片进行匹配，返回得分 + def template_score(template,image): + #将模板进行格式转换 + template_img=cv2.imdecode(np.fromfile(template,dtype=np.uint8),1) + template_img = cv2.cvtColor(template_img, cv2.COLOR_RGB2GRAY) + #模板图像阈值化处理——获得黑白图 + ret, template_img = cv2.threshold(template_img, 0, 255, cv2.THRESH_OTSU) + # height, width = template_img.shape + # image_ = image.copy() + # image_ = cv2.resize(image_, (width, height)) + image_ = image.copy() + #获得待检测图片的尺寸 + height, width = image_.shape + # 将模板resize至与图像一样大小 + template_img = cv2.resize(template_img, (width, height)) + # 模板匹配，返回匹配得分 + result = cv2.matchTemplate(image_, template_img, cv2.TM_CCOEFF) + return result[0][0] + + + # 对分割得到的字符逐一匹配 + def template_matching(word_images): + results = [] + for index,word_image in enumerate(word_images): + if index==0: + best_score = [] + for chinese_words in chinese_words_list: + score = [] + for chinese_word in chinese_words: + result = template_score(chinese_word,word_image) + score.append(result) + best_score.append(max(score)) + i = best_score.index(max(best_score)) + # print(template[34+i]) + r = template[34+i] + results.append(r) + continue + if index==1: + best_score = [] + for eng_word_list in eng_words_list: + score = [] + for eng_word in eng_word_list: + result = template_score(eng_word,word_image) + score.append(result) + best_score.append(max(score)) + i = best_score.index(max(best_score)) + # print(template[10+i]) + r = template[10+i] + results.append(r) + continue + else: + best_score = [] + for eng_num_word_list in eng_num_words_list: + score = [] + for eng_num_word in eng_num_word_list: + result = template_score(eng_num_word,word_image) + score.append(result) + best_score.append(max(score)) + i = best_score.index(max(best_score)) + # print(template[i]) + r = template[i] + results.append(r) + continue + return results + + + word_images_ = word_images.copy() + # 调用函数获得结果 + + result = template_matching(word_images_) + #print(result) + # "".join(result)函数将列表转换为拼接好的字符串，方便结果显示 + #print( "".join(result)) + history.append("".join(result)) + + + from PIL import ImageFont, ImageDraw, Image + + height,weight = origin_image.shape[0:2] + #print(height) + #print(weight) + + image_1 = origin_image.copy() + cv2.rectangle(image_1, (int(0.2*weight), int(0.75*height)), (int(weight*0.9), int(height*0.95)), (0, 255, 0), 5) + + #设置需要显示的字体 + fontpath = "font/simsun.ttc" + font = ImageFont.truetype(fontpath,64) + img_pil = Image.fromarray(image_1) + draw = ImageDraw.Draw(img_pil) + + #绘制文字信息 + draw.text((int(0.2*weight)+25, int(0.75*height)), "".join(result), font = font, fill = (255, 255, 0)) + bk_img = np.array(img_pil) + #print(result) + + return bk_img + + +root = tk.Tk() +root.title("图片导入器") +root.geometry("1000x1000") + +# 用于存储历史记录的列表 +history = [] +current_image_label = None +# 全局变量，用于存储加载的图像 +# 显示图像的函数 +def show_image(image): + global current_image_label # 引用全局变量 + if current_image_label is not None: # 如果存在先前的Label，先删除它 + current_image_label.pack_forget() # 使用pack_forget()移除组件 + + pil_image = ImageTk.PhotoImage(image=Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))) + lbl_image = tk.Label(root, image=pil_image) + lbl_image.image = pil_image # 保持对图像的引用，防止垃圾回收 + lbl_image.pack() + current_image_label = lbl_image # 更新全局变量，存储新的Label引用 +def open_image(): + # 弹出文件选择对话框并返回用户选择的文件路径 + file_path = filedialog.askopenfilename() + return file_path +def load_and_show_image(): + file_path = open_image() # 调用函数获取文件路径 + show_image(eaowej(file_path)) # 传递文件路径给加载图像的函数 + +# 显示历史记录的函数 + +def plt_show0(img): + # cv2与plt的图像通道不同：cv2为[b,g,r];plt为[r, g, b] + b, g, r = cv2.split(img) + img = cv2.merge([r, g, b]) + plt.imshow(img) + plt.show() -# 防止Tkinter在图像数据被垃圾回收时删除图像 -label.image = photo_image -# 运行Tkinter事件循环 -root.mainloop() \ No newline at end of file +def show_history(): + # 创建一个新的窗口来显示历史记录 + history_window = tk.Toplevel(root) + history_window.title("历史记录") + history_window.geometry("600x400") # 根据需要调整大小 + + # 创建一个文本框来显示历史记录 + txt_history = tk.Text(history_window, height=10, width=50) + txt_history.pack(pady=20) + + # 将历史记录数据添加到文本框中 + for record in history: + txt_history.insert(tk.END, record + "\n") +button_history = tk.Button(root, text="查看历史记录", command=show_history) +button_history.pack() +# 创建一个按钮，点击时调用 load_and_show_image 函数 +button = tk.Button(root, text="导入图片", command=load_and_show_image) +button.pack() +root.mainloop() +print(history) \ No newline at end of file