commit a190933e3518d5196bbdbd0999df459d6be4ee25 Author: nizhangdaye <2508022039@qq.com> Date: Mon Jun 17 18:08:24 2024 +0800 第一次递交：能够实现图片分割，开头数字识别 diff --git a/.idea/.gitignore b/.idea/.gitignore new file mode 100644 index 0000000..359bb53 --- /dev/null +++ b/.idea/.gitignore @@ -0,0 +1,3 @@ +# 默认忽略的文件 +/shelf/ +/workspace.xml diff --git a/.idea/.name b/.idea/.name new file mode 100644 index 0000000..ac0be0d --- /dev/null +++ b/.idea/.name @@ -0,0 +1 @@ +TLD_Code \ No newline at end of file diff --git a/.idea/TLD_Code.iml b/.idea/TLD_Code.iml new file mode 100644 index 0000000..e61883f --- /dev/null +++ b/.idea/TLD_Code.iml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/.idea/deployment.xml b/.idea/deployment.xml new file mode 100644 index 0000000..fef5aab --- /dev/null +++ b/.idea/deployment.xml @@ -0,0 +1,14 @@ + + + + + + + + + + + + + + \ No newline at end of file diff --git a/.idea/inspectionProfiles/Project_Default.xml 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cv +import numpy as np +from matplotlib import pyplot as plt +import number_processing + + +def cv_show(name, img): + """定义cv_show函数，用于显示图像""" + cv.namedWindow(name, cv.WINDOW_NORMAL) # 窗口大小可调 + cv.resizeWindow(name, img.shape[1], img.shape[0]) # 窗口大小可调 + cv.imshow(name, img) + cv.waitKey(0) + cv.destroyAllWindows() + + +def cv_info(name, img): + """ + 定义cv_info函数，确认图像是否成功读取，并且打印图像的基本信息 + """ + print(f"图像 {name} 信息如下：") + if img is None: + print("无法读取图像") + else: + print("图像数据类型：", img.dtype) + print("图像类型：", type(img)) + print("图像尺寸：", img.shape) + print("图像行数：", img.shape[0]) + print("图像列数：", img.shape[1]) + print() + + +def find_line(img): + """ + 定义find_line函数，用于寻找分界线 + 返回一段图像的宽度 + """ + for i in range(img.shape[1]): # 遍历 a 列 + if np.all(img[0, i] >= [230, 230, 230]): + # 求该列像素均值，判断是否为白色 + mean = np.mean(img[:, i]) + if mean >= 240: # 像素均值大于 230 判断为白色 + print(f"分界线位置：{i}, 像素均值：{mean}", end="\n\n") + return i + return -1 # 如果没有找到符合条件的分界线，则返回图像的宽度 + + +def split_img(img): + """ + 定义split_img函数，用于将图像分割并保存 + 无返回值 + """ + img_width = find_line(img) # 返回图像的宽度 + n = 0 # 用于计数分割得到的图像数 + for i in range(0, img.shape[1], img_width + 1): # 遍历 img 的每一列 + image = img[:, i: i + img_width] + n += 1 + cv.imwrite(f"data/saved_img/image_{n}.PNG", image) # 保存图片 无损压缩格式 PNG + + +def split_num(img): + """ + 定义split_num函数，用于分割数字并保存 + 返回数字 + """ + # 将彩色图像转化为灰度图像 + img_gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY) + # 二值化处理，你可以根据需要调整阈值 + _, thresh = cv.threshold(img_gray, 128, 255, cv.THRESH_BINARY_INV) + # 查找轮廓 + contours, _ = cv.findContours(thresh, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE) + # 对轮廓进行排序，以便从左到右处理数字（可选） + contours = sorted(contours, key=lambda x: cv.boundingRect(x)[0]) + + # 用于存储识别出的数字字符串 + recognized_digits = [] + + # 遍历每个轮廓，并保存每个数字的图片 + for i, contour in enumerate(contours): + x, y, w, h = cv.boundingRect(contour) + digit = thresh[y:y + h, x:x + w] + # print(f"第{i+1}个数字的尺寸：{digit.shape[0]}") + if digit.shape[0] < 5: # 小数点的轮廓太小，跳过 + break + + recognized_digits.append(str(number_processing.num_list.index(digit.tolist()))) + + # # 保存数字图片 + # cv.imwrite(f'data/saved_img/digit_{i}.png', digit) + return int("".join(recognized_digits)) diff --git a/mian.py b/mian.py new file mode 100644 index 0000000..8a0f857 --- /dev/null +++ b/mian.py @@ -0,0 +1,30 @@ +import cv2 as cv +import numpy as np +from matplotlib import pyplot as plt +import img_processing as ip + +print(cv.__version__) + +img_path = 'data/1-D2K286+520-300GD P_1.JPG' +img = cv.imread(img_path, cv.IMREAD_COLOR) # 读取图像 三通道彩色图 + +# 起始点假设为固定位置，即 44 行，65 列 +data_interest = img[44:, 65:-2] # 感兴趣的数据区域 +number_interest = img[8:20, 64:175] # 感兴趣的数字区域 横向 + +ip.cv_info(f"data_interest", data_interest) +ip.cv_info(f"number_interest", number_interest) + +# 切割图像 +ip.split_img(data_interest) + +# 获取数字 +num = ip.split_num(number_interest) +print(type(num)) +print(num) + +# gi.cv_show('data', data_interest) +# gi.cv_show('number', a) + +cv.imwrite('data/saved_img/data_interest.PNG', data_interest) # 保存图片 +cv.imwrite('data/saved_img/number_interest.PNG', number_interest) # 保存图片 diff --git a/number_processing.py b/number_processing.py new file mode 100644 index 0000000..e4fa947 --- /dev/null +++ b/number_processing.py @@ -0,0 +1,61 @@ +import cv2 +import os + + +def get_num_list(folder_path): + """ + 读取文件夹中的所有.png格式图片，并进行二值化处理，并返回包含二值化图片的列表。 + """ + binary_images = [] + # 遍历文件夹中的所有文件 + for filename in sorted(os.listdir(folder_path)): + # 检查文件是否是.png格式 + if filename.endswith('.png'): + # 构建图片的完整路径 + img_path = os.path.join(folder_path, filename) + + # 使用OpenCV读取图片为灰度图 + img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE) + + # 检查图片是否成功读取 + if img is not None: + # 使用阈值进行二值化处理 + # 假设我们使用127作为阈值，但这可以根据你的需求进行调整 + _, binary_img = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY) + + # 将二值化后的图片添加到列表中 + binary_images.append(binary_img.tolist()) + + return binary_images + + +num_list = [ + [[0, 255, 255, 255, 255, 0], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255], + [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255], + [255, 255, 0, 0, 255, 255], [0, 255, 255, 255, 255, 0]], + [[0, 0, 255, 255], [255, 255, 255, 255], [0, 0, 255, 255], [0, 0, 255, 255], [0, 0, 255, 255], [0, 0, 255, 255], + [0, 0, 255, 255], [0, 0, 255, 255], [0, 0, 255, 255], [0, 0, 255, 255]], + [[0, 255, 255, 255, 255, 0], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255], [0, 0, 0, 0, 255, 255], + [0, 0, 0, 255, 255, 0], [0, 0, 255, 255, 0, 0], [0, 255, 255, 0, 0, 0], [255, 255, 0, 0, 0, 0], + [255, 255, 0, 0, 0, 0], [255, 255, 255, 255, 255, 255]], + [[0, 255, 255, 255, 255, 0], [255, 255, 0, 0, 255, 255], [0, 0, 0, 0, 255, 255], [0, 0, 0, 0, 255, 255], + [0, 0, 255, 255, 255, 0], [0, 0, 0, 0, 255, 255], [0, 0, 0, 0, 255, 255], [0, 0, 0, 0, 255, 255], + [255, 255, 0, 0, 255, 255], [0, 255, 255, 255, 255, 0]], + [[0, 0, 0, 0, 255, 255], [0, 0, 0, 255, 255, 255], [0, 0, 255, 255, 255, 255], [0, 0, 255, 255, 255, 255], + [0, 255, 255, 0, 255, 255], [0, 255, 255, 0, 255, 255], [255, 255, 0, 0, 255, 255], [255, 255, 255, 255, 255, 255], + [0, 0, 0, 0, 255, 255], [0, 0, 0, 0, 255, 255]], + [[255, 255, 255, 255, 255, 255], [255, 255, 0, 0, 0, 0], [255, 255, 0, 0, 0, 0], [255, 255, 0, 0, 0, 0], + [255, 255, 255, 255, 255, 0], [255, 255, 0, 0, 255, 255], [0, 0, 0, 0, 255, 255], [0, 0, 0, 0, 255, 255], + [255, 255, 0, 0, 255, 255], [0, 255, 255, 255, 255, 0]], + [[0, 255, 255, 255, 255, 0], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 0, 0], [255, 255, 0, 0, 0, 0], + [255, 255, 255, 255, 255, 0], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255], + [255, 255, 0, 0, 255, 255], [0, 255, 255, 255, 255, 0]], + [[255, 255, 255, 255, 255, 255], [0, 0, 0, 0, 255, 255], [0, 0, 0, 255, 255, 0], [0, 0, 0, 255, 255, 0], + [0, 0, 255, 255, 0, 0], [0, 0, 255, 255, 0, 0], [0, 0, 255, 255, 0, 0], [0, 255, 255, 0, 0, 0], + [0, 255, 255, 0, 0, 0], [0, 255, 255, 0, 0, 0]], + [[0, 255, 255, 255, 255, 0], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255], + [0, 255, 255, 255, 255, 0], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255], + [255, 255, 0, 0, 255, 255], [0, 255, 255, 255, 255, 0]], + [[0, 255, 255, 255, 255, 0], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255], + [255, 255, 0, 0, 255, 255], [0, 255, 255, 255, 255, 255], [0, 0, 0, 0, 255, 255], [0, 0, 0, 0, 255, 255], + [255, 255, 0, 0, 255, 255], [0, 255, 255, 255, 255, 0]]]