Sliding_identification_program.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 滑面识别程序 / Sliding surface identification program"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0 一些准备工作 / Some preparation work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 0.1 导入相关库 / Import related libraries\n",
    "导入一些运行所必须的库 / Import some necessary libraries for operation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "# import sklearn.metrics as sm\n",
    "from skimage import io, measure, morphology\n",
    "from sklearn.cluster import KMeans, MiniBatchKMeans\n",
    "import copy\n",
    "from PIL import Image, ImageChops\n",
    "import time\n",
    "\n",
    "import alphashape\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "from sklearn import metrics\n",
    "from sklearn.cluster import DBSCAN\n",
    "\n",
    "import shapely.geometry\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 0.2 设置相关参数 / Set the relevant parameters\n",
    "\n",
    "其中的 A, B, C, D, E, F 坐标如图所示，使用的是像素坐标，可用画图等软件获取该坐标。\n",
    "\n",
    "The coordinates of A, B, C, D, E and F are shown in the figure. The pixel coordinates are used, and the coordinates can be obtained by software such as *Microsoft Paint*.\n",
    "\n",
    "![alt text](坐标.svg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 总位移云图所在目录 / The directory of the total displkacement contour map\n",
    "image_path = \"program_process_files\"\n",
    "\n",
    "# A_x, A_y = 600, 1687\n",
    "# B_x, B_y = 3262, 1687\n",
    "# C_x, C_y = 3262, 90\n",
    "# D_x, D_y = 2197, 90\n",
    "# E_x, E_y = 865, 1421\n",
    "# F_x, F_y = 600, 1421\n",
    "\n",
    "p_A = [  63, 635]\n",
    "p_B = [1470, 635]\n",
    "p_C = [1470, 196]\n",
    "p_D = [1106, 196]\n",
    "p_E = [ 570, 465]\n",
    "p_F = [  63, 465]\n",
    "\n",
    "\n",
    "\n",
    "slope_boundary_array = [p_A, p_B, p_C, p_D, p_E, p_F]\n",
    "\n",
    "# 保存的颜色为类似红色,其意义为只保存最接近该种颜色的颜色\n",
    "# The color preserved is similar to red, which means that only the color closest to the color is preserved.\n",
    "keep_of_color = np.array([0, 0, 0]) / 255.0\n",
    "# 其他颜色直接设置other_color\n",
    "# Other colors set other_color directly.\n",
    "other_color = np.array([255, 255, 255]) / 255.0\n",
    "\n",
    "\n",
    "# 像素尺寸与实际尺寸比例 / The ratio of pixel size to actual size\n",
    "k = 21.93548\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 滑块识别 / Sliding surface identification"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 使用图像分割识别包含其他块体的滑块 / Image segmentation is used to identify sliding block containing other blocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义函数用于识别包含其他块体的滑块\n",
    "# A function is defined to identify the sliding block containing other blocks.\n",
    "def get_block_with_noise(imgae_path, str_of_picture):\n",
    "    filename_of_image = image_path + \"\\\\\" + \"Slice_slide_\" + str_of_picture + \".png\"\n",
    "\n",
    "    num_of_color = 2\n",
    "    color_of_keep = np.array([0, 0, 0]) / 255.0\n",
    "    other_color = np.array([255, 255, 255]) / 255.0\n",
    "\n",
    "    # 读取图片并显示\n",
    "    image = io.imread(filename_of_image)\n",
    "\n",
    "    # 读取图片每个像素点的信息(RGB值)\n",
    "    data = image / 255.0\n",
    "    # print(\"pre_data: \\n\", data)\n",
    "    data = data.reshape(-1, 3)\n",
    "    # print(\"lat_data: \\n\", data)\n",
    "\n",
    "    # 开始对图片中的像素进行聚类\n",
    "    # 最要想要留下的颜色个数\n",
    "    color_use = num_of_color\n",
    "    # 开始进行聚类\n",
    "    # km = MiniBatchKMeans(color_use)\n",
    "    km = KMeans(init='k-means++', n_clusters=color_use, random_state=0)\n",
    "    km.fit(data)\n",
    "    # 利用np.array的整数索引，用聚类中心点值，代替原来点的值\n",
    "    new_data = km.cluster_centers_[km.predict(data)]\n",
    "    # 获取查看剩下的color_use 种颜色\n",
    "    # print(\"new_data: \\n\", new_data)\n",
    "    color_set = np.array(list(set([tuple(t) for t in new_data])))\n",
    "    # print(\"color_set:\\n\", color_set)\n",
    "\n",
    "    # 计算聚类后的每种颜色与保留色的距离\n",
    "    dist = []\n",
    "    for i in range(len(color_set)):\n",
    "        dist.append(np.sqrt(np.sum(np.square(color_set[i] - color_of_keep))))\n",
    "\n",
    "    # print(\"dist:\\n\", dist)\n",
    "\n",
    "    # 获取距离最小的下标\n",
    "    index_min = dist.index(min(dist))\n",
    "    # print(\"index_min: \", index_min)\n",
    "\n",
    "    # 将new_data中除最小下标所对应的值之外的值变为指定颜色\n",
    "    for i in range(len(new_data)):\n",
    "        if tuple(new_data[i]) != tuple(color_set[index_min]):\n",
    "            new_data[i] = other_color\n",
    "        else:\n",
    "            # new_data[i] = np.array([0, 0, 0])\n",
    "            new_data[i] = color_of_keep\n",
    "\n",
    "    # 重新设置图片\n",
    "    image_new = new_data.reshape(image.shape)\n",
    "    image_new_convert = np.array(np.round(image_new * 255), dtype='uint8')\n",
    "    save_name_of_image = image_path + \"\\\\cluster_image_\" + str_of_picture + \".jpg\"\n",
    "    io.imsave(save_name_of_image, image_new_convert)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 函数“get_block_with_noise”检查，可不运行 \\ Code for checking the function 'get_block_with_noise', you can not run\n",
    "get_block_with_noise(image_path, \"47.50\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 使用连通区域标识面积去除其他块体 \\ Use connected domain identification to remove other blocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义一个函数，去除多余滑块\n",
    "# Define a function to remove redundant sliding blocks\n",
    "def remove_noise_block(image_path, str_of_picture):\n",
    "    save_name_of_image = image_path + \"\\\\cluster_image_\" + str_of_picture + \".jpg\"\n",
    "    # 使用cv2读取图片\n",
    "    image = cv2.imread(save_name_of_image)\n",
    "    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "    thresh = cv2.threshold(\n",
    "        gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]\n",
    "    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))\n",
    "    opening = 255 - \\\n",
    "        cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=1)\n",
    "    \n",
    "    save_name_of_image = image_path + \"\\\\cluster_image_without_noise_\" + str_of_picture + \".jpg\"\n",
    "\n",
    "    io.imsave(save_name_of_image, opening)\n",
    "\n",
    "    # 再将图片反色 #\n",
    "    im = Image.open(save_name_of_image)\n",
    "    im_inverted = ImageChops.invert(im)\n",
    "    im_inverted.save(save_name_of_image)\n",
    "\n",
    "    # 对输入图像进行降噪，去除噪声干扰，然后再二值化 #\n",
    "    # ----------- 第一个调试点, 用于出图 -----------# 出 反色之后的图\n",
    "    im = cv2.imread(save_name_of_image)\n",
    "    # print(im)\n",
    "    blurred = cv2.GaussianBlur(im, (5, 5), 0)\n",
    "    gray = cv2.cvtColor(blurred, cv2.COLOR_BGR2GRAY)\n",
    "    t, binary = cv2.threshold(\n",
    "        gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)\n",
    "\n",
    "    # 将数组还原成图片 Image.fromarray方法 传入数组 和 通道\n",
    "    img = Image.fromarray(binary)\n",
    "    img.save(save_name_of_image)\n",
    "\n",
    "    # 使用连通域阈值对图片进行去噪 #\n",
    "    threshold = 35000  # ----------- 第二个调试点, 用于出图 -----------#\n",
    "    # 读入图片\n",
    "    img = cv2.imread(save_name_of_image, cv2.IMREAD_COLOR)\n",
    "    GrayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  # 图片灰度化处理\n",
    "    # 二值化图片\n",
    "    ret, binary = cv2.threshold(GrayImage, 40, 255, cv2.THRESH_BINARY)\n",
    "    # cv2.fingContours寻找图片轮廓信息\n",
    "    \"\"\"\n",
    "    提取二值化后图片中的轮廓信息 ，返回值contours存储的即是图片中的轮廓信息，是一个向量，内每个元素保存了一组由连续的Point点构成的点的集合的向量，每一组Point点集就是一个轮廓，有多少轮廓，向量contours就有多少元素\n",
    "    \"\"\"\n",
    "    contours, hierarch = cv2.findContours(\n",
    "        binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)\n",
    "\n",
    "    if len(contours) == 1:\n",
    "        pass\n",
    "    else:\n",
    "        for i in range(len(contours)):\n",
    "            area = cv2.contourArea(contours[i])  # 计算轮廓所占面积\n",
    "            if area < threshold:  # 将area小于阈值区域填充背景色，由于OpenCV读出的是BGR值\n",
    "                # 原始图片背景BGR值(84,1,68)\n",
    "                cv2.drawContours(img, [contours[i]], -1, (0, 0, 0), thickness=-1)\n",
    "                continue\n",
    "\n",
    "    # cv2.imshow('Output',img)\n",
    "    cv2.waitKey(0)\n",
    "    cv2.destroyAllWindows()\n",
    "\n",
    "    # 保存图片\n",
    "    cv2.imwrite(save_name_of_image, img)\n",
    "\n",
    "    # 在执行一次反色\n",
    "    im = Image.open(save_name_of_image)\n",
    "    im_inverted = ImageChops.invert(im)\n",
    "\n",
    "    im_inverted.save(save_name_of_image)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 函数“remove_noise_block”检查，可不运行 \\ Code for checking the function 'remove_noise_block', you can not run\n",
    "remove_noise_block(image_path, \"47.50\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 滑面识别 \\ Sliding surface identification"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 识别滑块边界 \\ Identify the slider boundary"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.1.1 使用alpha shapes算法获取滑块边界 \\ Use the alpha shapes algorithm to obtain the sliding block boundary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_black_point(image_file):\n",
    "    # 打开图片文件\n",
    "    img = Image.open(image_file).convert(\"RGB\")\n",
    "    array_of_image = np.asarray(img)\n",
    "    black_point_array = []\n",
    "    # print(array_of_image[0][0][0])\n",
    "\n",
    "    for i in range(len(array_of_image)):\n",
    "        for j in range(len(array_of_image[0])):\n",
    "            if array_of_image[i][j][0] < 128:\n",
    "                temp_point = [j, i]\n",
    "                black_point_array.append(temp_point)\n",
    "    # print(black_point_array[0])\n",
    "\n",
    "    alpha_shape = alphashape.alphashape(black_point_array, 0.5)\n",
    "    # print(alpha_shape)\n",
    "\n",
    "    boundary_array = alpha_shape.exterior.coords[:]\n",
    "    # print(shape(boundary_array))\n",
    "\n",
    "    return boundary_array"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 去除滑块边界中边坡面部分 \\ Remove the slope boundary in the slider boundary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # 这是原来使用的方法，现在不再使用该方法\n",
    "# # This was the original method, which is no longer used\n",
    "# # 2. 利用边坡边界的线段方程，去除大部分边坡边界\n",
    "# # 2. Most of the slope boundaries are removed by using the line segment equation of the slope boundary\n",
    "# def get_init_slide(boundary_array):\n",
    "#     # print(\"boundary_array len is\", len(boundary_array))\n",
    "#     slide_array = []\n",
    "#     for i in range(len(boundary_array)):\n",
    "#         k = (D_y - E_y) / (D_x - E_x)\n",
    "#         b = E_y - k * E_x\n",
    "#         x = boundary_array[i][0]\n",
    "#         y = boundary_array[i][1]\n",
    "#         error_range = 6\n",
    "#         if (y - error_range) <= (k * x + b) <= (y + error_range):\n",
    "#             # print(\"({:4.1f}, {:4.1f})\".format(x, y))\n",
    "#             continue\n",
    "#         if y - 1 <= D_y <= y + 1:\n",
    "#             # print(\"({:4.1f}, {:4.1f})\".format(x, y))\n",
    "#             continue\n",
    "#         if (F_x <= x <= E_x) and (y - 1 <= F_y <= y + 1):\n",
    "#             continue\n",
    "\n",
    "#         slide_array.append(boundary_array[i])\n",
    "#     return slide_array\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2_. 定义一个去除边界的新函数 / Define a new function to remove the boundary\n",
    "# 2_1. 判断一个点是否在多边形内 / To determine whether a point is in a polygon\n",
    "def judge_point_in_polygon(point_, polygon_array_):\n",
    "    point_func = shapely.geometry.Point(point_)\n",
    "\n",
    "    poly_context = {\"type\": \"MULTIPOLYGON\",\n",
    "                    \"coordinates\": [[polygon_array_]]}\n",
    "    poly_shape = shapely.geometry.shape(poly_context)\n",
    "    return poly_shape.intersects(point_func)\n",
    "\n",
    "# 2_2. 判断一系列点是否在多边形内 / Determines whether a series of points are within a polygon\n",
    "def judge_points_in_polygon(point_array_, polygon_array_):\n",
    "    print(\"slope_before:\", polygon_array_)\n",
    "    # 由于不能包含边界上的点，所以将非0的边界点减去6\n",
    "    for i in range(len(polygon_array_)):\n",
    "        if polygon_array_[i][0] != 0:\n",
    "            polygon_array_[i][0] += 1\n",
    "        if polygon_array_[i][1] != 0:\n",
    "            polygon_array_[i][1] += 1\n",
    "\n",
    "    print(\"slope_after:\", polygon_array_)\n",
    "    judge_in_polygon = np.array([False] * len(point_array_))\n",
    "    # print(point_array_)\n",
    "    for i in range(len(point_array_)):\n",
    "        # print(i)\n",
    "        judge_in_polygon[i] = judge_point_in_polygon(point_array_[i], polygon_array_)\n",
    "    # print(judge_in_polygon)\n",
    "    return judge_in_polygon\n",
    "\n",
    "# 2_3. 获取只在多边形内的点 / Get the points only in the polygon\n",
    "def get_in_points(point_array_, polygon_array_):\n",
    "    # print(point_array_)\n",
    "    judge_in_polygon = judge_points_in_polygon(point_array_, polygon_array_)\n",
    "    # print(judge_in_polygon)\n",
    "    points_in_polygon_ = []\n",
    "    for i in range(len(judge_in_polygon)):\n",
    "        if judge_in_polygon[i]:\n",
    "            points_in_polygon_.append(point_array_[i])\n",
    "\n",
    "    return np.array(points_in_polygon_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3. 定义去除滑面噪点的函数\n",
    "# Define the function to remove the noise of the sliding surface\n",
    "def remove_noise_form_silde(slide_array):\n",
    "    # 定义dbscan函数\n",
    "    def dbscan_(data, r, min_samples):\n",
    "        \"\"\"实现DBSCAN算法\n",
    "\n",
    "        Args:\n",
    "            data (ndarray): 输入数据集\n",
    "            r (float): 阈值半径\n",
    "            min_samples (int): 点数阈值\n",
    "        \"\"\"\n",
    "        db = DBSCAN(eps=r, min_samples=min_samples)\n",
    "        data = data.reshape(-1, 1)\n",
    "        clustering = db.fit(data)\n",
    "        # 计算分簇\n",
    "        labels = clustering.labels_.tolist()\n",
    "        # # 计算分簇数\n",
    "        # if -1 in labels:\n",
    "        #     k = len(set(labels)) - 1\n",
    "        # else:\n",
    "        #     k = len(set(labels))\n",
    "        # # 计算轮廓系数\n",
    "        # if k == 0:\n",
    "        #     dbscan_score = 0\n",
    "        # else:\n",
    "        #     dbscan_score = metrics.silhouette_score(X=data, labels=labels)\n",
    "\n",
    "        return labels\n",
    "    result = dbscan_(slide_array, 10, 10)\n",
    "    # print(result[0])\n",
    "\n",
    "    slide_label = result\n",
    "    print(\"set slide is \", set(slide_label))\n",
    "    label_sum = [0 for i in range(len(set(slide_label)) - 1)]\n",
    "    slide_array_db = []\n",
    "    # # 获取除噪点之外最短的一组\n",
    "    # for i_ in range(len(slide_array)):\n",
    "    #     if slide_label[i_] == -1:\n",
    "    #         continue\n",
    "    #     label_sum[slide_label[i_]] += 1\n",
    "    \n",
    "    # min_num_of_label = min(label_sum)\n",
    "    # index_min_label = label_sum.index(min_num_of_label)\n",
    "\n",
    "    for i_ in range(len(slide_array)):        \n",
    "        if slide_label[i_] == -1: # or slide_array[i_] == index_min_label:\n",
    "            continue\n",
    "        slide_array_db.append(slide_array[i_])\n",
    "    print(\"num_of_labels is \", label_sum)\n",
    "    return slide_array_db\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 4. 对数组进行重新排序\n",
    "# Reorder an array\n",
    "def resort_slide(slide_array_w_noise):\n",
    "    silde_left = []\n",
    "    slide_right = []\n",
    "\n",
    "    label = 1\n",
    "    for i in range(1, len(slide_array_w_noise)):\n",
    "        d = abs(slide_array_w_noise[i][1] - slide_array_w_noise[i - 1][1])\n",
    "        if d >= 100:\n",
    "            # print(\"ge\")\n",
    "            label = 0\n",
    "\n",
    "        # print(label)\n",
    "        if label == 0:\n",
    "            silde_left.append(slide_array_w_noise[i])\n",
    "        elif label == 1:\n",
    "            slide_right.append(slide_array_w_noise[i])\n",
    "\n",
    "    slide_array_resort = silde_left + slide_right\n",
    "\n",
    "    slide_array = []\n",
    "\n",
    "    return slide_array_resort\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 定义函数，获取滑动面 \\ Define the function to obtain the sliding surface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义一个函数，获取单张图片的滑动面\n",
    "# Define a function to obtain the sliding surface of a single picture\n",
    "def get_curve_of_slide(image_path, str_of_picture):\n",
    "    image_file = image_path + \"\\\\cluster_image_without_noise_\" + str_of_picture + \".jpg\"\n",
    "    boundary_array = get_black_point(image_file=image_file)\n",
    "    # print(boundary_array)\n",
    "    # slide_array = get_init_slide(boundary_array=boundary_array)\n",
    "    slide_array = get_in_points(boundary_array, slope_boundary_array)\n",
    "    print(len(slide_array))\n",
    "    slide_array_w_noise = remove_noise_form_silde(slide_array=slide_array)\n",
    "    slide_array_resort = resort_slide(slide_array_w_noise=slide_array_w_noise)\n",
    "    x_of_slide = []\n",
    "    y_of_slide = []\n",
    "    for i_ in slide_array_resort:\n",
    "        x_of_slide.append(i_[0])\n",
    "        y_of_slide.append(i_[1])\n",
    "    return x_of_slide, y_of_slide\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义一个保存x,y的函数\n",
    "def get_and_save_x_y_array(image_path, str_of_picture, float_of_picture):\n",
    "    x_i_, y_i_ = get_curve_of_slide(image_path, str_of_picture)\n",
    "    x_i_array = np.array(x_i_)\n",
    "    y_i_array = np.array(y_i_)\n",
    "    z_i_array = np.array([float_of_picture * k for i in range(len(x_i_))])\n",
    "    np.savetxt(image_path + \"\\\\x_array_\" + str_of_picture + \".txt\", x_i_array, fmt=\"%4d\")\n",
    "    np.savetxt(image_path + \"\\\\y_array_\" + str_of_picture + \".txt\", y_i_array, fmt=\"%4d\")\n",
    "    np.savetxt(image_path + \"\\\\z_array_\" + str_of_picture + \".txt\", z_i_array, fmt=\"%4.5f\")\n",
    "    return x_i_, y_i_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "slope_before: [[63, 635], [1470, 635], [1470, 196], [1106, 196], [570, 465], [63, 465]]\n",
      "slope_after: [[64, 636], [1471, 636], [1471, 197], [1107, 197], [571, 466], [64, 466]]\n",
      "175\n",
      "set slide is  {0, 1}\n",
      "num_of_labels is  [0]\n"
     ]
    }
   ],
   "source": [
    "# 函数“get_and_save_x_y_array”检查，可不运行 \\ Code for checking the function 'get_and_save_x_y_array', you can not run\n",
    "x_i, y_i = get_and_save_x_y_array(image_path, \"47.50\", 47.5)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 绘制滑动面 \\ Draw the sliding surface\n",
    "### 3.1 绘制单独的滑动面 \\ Draw single sliding surface\n",
    "> 未运行上一个单元格无法绘图 \\ Unable to draw without running the previous cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'y_i' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[12], line 30\u001b[0m\n\u001b[0;32m     27\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(slope_x_array, slope_y_array, linewidth\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1.5\u001b[39m, color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m#621661\u001b[39m\u001b[38;5;124m\"\u001b[39m, zorder\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m)\n\u001b[0;32m     29\u001b[0m y_i_ \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m---> 30\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[43my_i\u001b[49m:\n\u001b[0;32m     31\u001b[0m     y_i_\u001b[38;5;241m.\u001b[39mappend(max_y \u001b[38;5;241m-\u001b[39m i)\n\u001b[0;32m     32\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(x_i, y_i_, linewidth\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m, zorder\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'y_i' is not defined"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.axis('off')\n",
    "plt.axis(\"equal\")\n",
    "# plt.xlim(-10, 2732)\n",
    "# plt.ylim(-10, 1677)\n",
    "plt.gca().xaxis.set_major_locator(plt.NullLocator())\n",
    "plt.gca().yaxis.set_major_locator(plt.NullLocator())\n",
    "fig = plt.gcf()\n",
    "\n",
    "slope_x_array = np.array([])\n",
    "slope_y_array = np.array([])\n",
    "\n",
    "for i in range(len(slope_boundary_array)):\n",
    "    slope_x_array = np.append(slope_x_array, slope_boundary_array[i][0])\n",
    "    slope_y_array = np.append(slope_y_array, slope_boundary_array[i][1])\n",
    "slope_x_array = np.append(slope_x_array, slope_boundary_array[0][0])\n",
    "slope_y_array = np.append(slope_y_array, slope_boundary_array[0][1])\n",
    "\n",
    "max_y = max(slope_y_array)\n",
    "slope_y_array = max_y - slope_y_array\n",
    "\n",
    "# max_y = max(A_y, B_y, C_y, D_y, E_y, F_y)\n",
    "# A_y_, B_y_, C_y_, D_y_, E_y_, F_y_ = max_y - A_y, max_y - B_y, max_y - C_y, max_y - D_y, max_y - E_y, max_y - F_y\n",
    "\n",
    "# plt.plot((A_x, B_x, C_x, D_x, E_x, F_x, A_x), (A_y_, B_y_, C_y_,\n",
    "#                                                D_y_, E_y_, F_y_, A_y_), linewidth=1.5, color=\"#621661\", zorder=2)\n",
    "\n",
    "plt.plot(slope_x_array, slope_y_array, linewidth=1.5, color=\"#621661\", zorder=2)\n",
    "\n",
    "y_i_ = []\n",
    "for i in y_i:\n",
    "    y_i_.append(max_y - i)\n",
    "plt.plot(x_i, y_i_, linewidth=1, zorder=1)\n",
    "plt.gca().xaxis.set_major_locator(plt.NullLocator())\n",
    "plt.gca().yaxis.set_major_locator(plt.NullLocator())\n",
    "plt.margins(0, 0)\n",
    "fig.savefig(image_path + \"\\\\slide_surface.pdf\",\n",
    "            format='pdf', dpi=1200, pad_inches=0, transparent=1)\n",
    "plt.show()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 将滑动面与总位移云图、最大剪切应变云图分别绘制 \\ Draw the sliding surface with the total displacement contour map and the maximum shear strain contour map respectively\n",
    "\n",
    "> 用于批量识别时，不执行这里的代码，即是不绘制滑动面-最大剪切应变云图和滑动面-总位移云图\n",
    ">\n",
    "> When used for batch identification, the code here is not executed, that is, the Sliding surface-maximum shear strain contour map and the Sliding surface-total displacement contour map are not drawn.\n",
    "\n",
    "#### 3.2.1 滑动面-最大剪切应变云图 \\ Sliding surface-maximum shear strain contour map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# maximum_shear_strain_image = image_path + \"\\\\maximum_shear_strain.jpg\"\n",
    "# # 为了避免每次都要重新运行上面的代码，这里选择从文件中读取x, y的数据\n",
    "# x_i = np.loadtxt(image_path + \"\\\\x_array.txt\", dtype=float)\n",
    "# y_i = np.loadtxt(image_path + \"\\\\y_array.txt\", dtype=float)\n",
    "\n",
    "# img = Image.open(maximum_shear_strain_image)\n",
    "# plt.figure(\"Image\", figsize=(19.16, 8.65))  # 图像窗口名称\n",
    "# plt.imshow(img)\n",
    "# plt.plot(x_i, y_i, linewidth=3, color=\"red\")\n",
    "# plt.savefig(image_path + \"\\\\mss_slide.pdf\",\n",
    "#             format='pdf', dpi=1200, pad_inches=0)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.2.2 滑动面-总位移云图 \\ Sliding surface-total displacement contour map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# total_displacement_image = image_path + \"\\\\total_displacement_.jpg\"\n",
    "# img = Image.open(total_displacement_image)\n",
    "# plt.figure(\"Image\", figsize=(19.16, 8.65))  # 图像窗口名称\n",
    "# plt.imshow(img)\n",
    "# plt.plot(x_i, y_i, linewidth=3, color=\"red\")\n",
    "# plt.savefig(image_path + \"\\\\td_silde.pdf\",\n",
    "#             format='pdf', dpi=1200, pad_inches=0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 循环识别每个图的滑面 \\ Circularly identify the sliding surface of each graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 必要的一些参数 \\ Some necessary parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 进行批量识别的一些必要参数 \\ Some necessary parameters for batch identification\n",
    "# 除了前缀之外和后缀之外的文件名 \\ File names other than prefixes and suffixes\n",
    "# 下面为z坐标为区分 \\ The following is the z coordinate for the distinction\n",
    "z_str_of_current_in = np.array([\"02.50\", \"05.00\", \"10.00\", \"15.00\", \"20.00\", \"25.00\", \"30.00\", \"35.00\", \"40.00\", \"45.00\", \"47.50\"])\n",
    "# 获取浮点型的z坐标 \\ Obtain the z coordinate of the float type\n",
    "z_float_of_current_in = np.array([float(str_i) for str_i in z_str_of_current_in])\n",
    "# z_float_of_current_in\n",
    "# 像素尺寸与实际尺寸比例 / The ratio of pixel size to actual size\n",
    "k = 21.93548\n",
    "# x,y 在实际尺寸中的相对坐标 / The relative coordinates of x, y in the actual size\n",
    "x = 2.85799\n",
    "y = 8.94853"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 循环获取每个截面的滑面 \\ The sliding surface of each section is obtained circularly\n",
    "> 我不能理解为什么循环到最后一个(\"47.50\")程序会报错，如果发生报错的请单独运行下下个单元格。\n",
    ">\n",
    "> I can't understand why the loop to the last (\"47.50\") program will report an error. If an error occurs, please run the next cell of the next."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "slope_before: [[63, 635], [1470, 635], [1470, 196], [1106, 196], [570, 465], [63, 465]]\n",
      "slope_after: [[64, 636], [1471, 636], [1471, 197], [1107, 197], [571, 466], [64, 466]]\n",
      "175\n",
      "set slide is  {0, 1}\n",
      "num_of_labels is  [0]\n",
      "slope_before: [[64, 636], [1471, 636], [1471, 197], [1107, 197], [571, 466], [64, 466]]\n",
      "slope_after: [[65, 637], [1472, 637], [1472, 198], [1108, 198], [572, 467], [65, 467]]\n",
      "371\n",
      "set slide is  {0, 1}\n",
      "num_of_labels is  [0]\n",
      "slope_before: [[65, 637], [1472, 637], [1472, 198], [1108, 198], [572, 467], [65, 467]]\n",
      "slope_after: [[66, 638], [1473, 638], [1473, 199], [1109, 199], [573, 468], [66, 468]]\n",
      "468\n",
      "set slide is  {0, 1}\n",
      "num_of_labels is  [0]\n",
      "slope_before: [[66, 638], [1473, 638], [1473, 199], [1109, 199], [573, 468], [66, 468]]\n",
      "slope_after: [[67, 639], [1474, 639], [1474, 200], [1110, 200], [574, 469], [67, 469]]\n",
      "504\n",
      "set slide is  {0, 1}\n",
      "num_of_labels is  [0]\n",
      "slope_before: [[67, 639], [1474, 639], [1474, 200], [1110, 200], [574, 469], [67, 469]]\n",
      "slope_after: [[68, 640], [1475, 640], [1475, 201], [1111, 201], [575, 470], [68, 470]]\n",
      "512\n",
      "set slide is  {0, 1}\n",
      "num_of_labels is  [0]\n",
      "slope_before: [[68, 640], [1475, 640], [1475, 201], [1111, 201], [575, 470], [68, 470]]\n",
      "slope_after: [[69, 641], [1476, 641], [1476, 202], [1112, 202], [576, 471], [69, 471]]\n",
      "517\n",
      "set slide is  {0, 1}\n",
      "num_of_labels is  [0]\n",
      "slope_before: [[69, 641], [1476, 641], [1476, 202], [1112, 202], [576, 471], [69, 471]]\n",
      "slope_after: [[70, 642], [1477, 642], [1477, 203], [1113, 203], [577, 472], [70, 472]]\n",
      "509\n",
      "set slide is  {0, 1}\n",
      "num_of_labels is  [0]\n",
      "slope_before: [[70, 642], [1477, 642], [1477, 203], [1113, 203], [577, 472], [70, 472]]\n",
      "slope_after: [[71, 643], [1478, 643], [1478, 204], [1114, 204], [578, 473], [71, 473]]\n",
      "496\n",
      "set slide is  {0, 1}\n",
      "num_of_labels is  [0]\n",
      "slope_before: [[71, 643], [1478, 643], [1478, 204], [1114, 204], [578, 473], [71, 473]]\n",
      "slope_after: [[72, 644], [1479, 644], [1479, 205], [1115, 205], [579, 474], [72, 474]]\n",
      "453\n",
      "set slide is  {0, 1}\n",
      "num_of_labels is  [0]\n",
      "slope_before: [[72, 644], [1479, 644], [1479, 205], [1115, 205], [579, 474], [72, 474]]\n",
      "slope_after: [[73, 645], [1480, 645], [1480, 206], [1116, 206], [580, 475], [73, 475]]\n",
      "352\n",
      "set slide is  {0, 1}\n",
      "num_of_labels is  [0]\n",
      "slope_before: [[73, 645], [1480, 645], [1480, 206], [1116, 206], [580, 475], [73, 475]]\n",
      "slope_after: [[74, 646], [1481, 646], [1481, 207], [1117, 207], [581, 476], [74, 476]]\n",
      "121\n",
      "set slide is  {0, 1}\n",
      "num_of_labels is  [0]\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(z_str_of_current_in)):\n",
    "    # 1.1 获取所有块体\n",
    "    get_block_with_noise(image_path, z_str_of_current_in[i])\n",
    "    # 1.2 使用连通域去除其他块体\n",
    "    remove_noise_block(image_path, z_str_of_current_in[i])\n",
    "    # 2. 获取滑动面\n",
    "    get_and_save_x_y_array(image_path, z_str_of_current_in[i], z_float_of_current_in[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "47.50\n",
      "47.5\n",
      "slope_before: [[63, 635], [1470, 635], [1470, 196], [1106, 196], [570, 465], [63, 465]]\n",
      "slope_after: [[65, 637], [1472, 637], [1472, 198], [1108, 198], [572, 467], [65, 467]]\n",
      "172\n",
      "set slide is  {0, 1}\n",
      "num_of_labels is  [0]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "([676.0,\n",
       "  677.0,\n",
       "  678.0,\n",
       "  679.0,\n",
       "  680.0,\n",
       "  682.0,\n",
       "  683.0,\n",
       "  684.0,\n",
       "  685.0,\n",
       "  686.0,\n",
       "  688.0,\n",
       "  689.0,\n",
       "  690.0,\n",
       "  692.0,\n",
       "  693.0,\n",
       "  694.0,\n",
       "  695.0,\n",
       "  697.0,\n",
       "  698.0,\n",
       "  699.0,\n",
       "  701.0,\n",
       "  702.0,\n",
       "  703.0,\n",
       "  705.0,\n",
       "  706.0,\n",
       "  707.0,\n",
       "  709.0,\n",
       "  710.0,\n",
       "  711.0,\n",
       "  713.0,\n",
       "  714.0,\n",
       "  715.0,\n",
       "  717.0,\n",
       "  718.0,\n",
       "  719.0,\n",
       "  721.0,\n",
       "  722.0,\n",
       "  723.0,\n",
       "  725.0,\n",
       "  726.0,\n",
       "  727.0,\n",
       "  729.0,\n",
       "  730.0,\n",
       "  732.0,\n",
       "  733.0,\n",
       "  734.0,\n",
       "  736.0,\n",
       "  737.0,\n",
       "  739.0,\n",
       "  740.0,\n",
       "  741.0,\n",
       "  743.0,\n",
       "  744.0,\n",
       "  746.0,\n",
       "  747.0,\n",
       "  749.0,\n",
       "  750.0,\n",
       "  752.0,\n",
       "  753.0,\n",
       "  755.0,\n",
       "  756.0,\n",
       "  758.0,\n",
       "  759.0,\n",
       "  761.0,\n",
       "  762.0,\n",
       "  764.0,\n",
       "  765.0,\n",
       "  767.0,\n",
       "  768.0,\n",
       "  770.0,\n",
       "  771.0,\n",
       "  773.0,\n",
       "  775.0,\n",
       "  776.0,\n",
       "  778.0,\n",
       "  779.0,\n",
       "  781.0,\n",
       "  783.0,\n",
       "  784.0,\n",
       "  786.0,\n",
       "  787.0,\n",
       "  789.0,\n",
       "  791.0,\n",
       "  793.0,\n",
       "  794.0,\n",
       "  796.0,\n",
       "  798.0,\n",
       "  800.0,\n",
       "  801.0,\n",
       "  803.0,\n",
       "  805.0,\n",
       "  807.0,\n",
       "  808.0,\n",
       "  810.0,\n",
       "  812.0,\n",
       "  814.0,\n",
       "  816.0,\n",
       "  817.0,\n",
       "  819.0,\n",
       "  821.0,\n",
       "  823.0,\n",
       "  825.0,\n",
       "  826.0,\n",
       "  828.0,\n",
       "  830.0,\n",
       "  832.0,\n",
       "  834.0,\n",
       "  836.0,\n",
       "  838.0,\n",
       "  840.0,\n",
       "  842.0,\n",
       "  844.0,\n",
       "  846.0,\n",
       "  848.0,\n",
       "  849.0,\n",
       "  851.0,\n",
       "  852.0,\n",
       "  854.0,\n",
       "  855.0,\n",
       "  857.0,\n",
       "  859.0,\n",
       "  861.0,\n",
       "  863.0,\n",
       "  865.0,\n",
       "  866.0,\n",
       "  868.0,\n",
       "  869.0,\n",
       "  871.0,\n",
       "  872.0,\n",
       "  873.0,\n",
       "  874.0,\n",
       "  876.0,\n",
       "  877.0,\n",
       "  878.0,\n",
       "  880.0,\n",
       "  881.0,\n",
       "  883.0,\n",
       "  885.0,\n",
       "  886.0,\n",
       "  888.0,\n",
       "  889.0,\n",
       "  890.0,\n",
       "  891.0,\n",
       "  892.0,\n",
       "  893.0,\n",
       "  894.0,\n",
       "  895.0,\n",
       "  896.0,\n",
       "  897.0,\n",
       "  898.0,\n",
       "  900.0,\n",
       "  901.0,\n",
       "  903.0,\n",
       "  904.0,\n",
       "  905.0,\n",
       "  906.0,\n",
       "  907.0,\n",
       "  909.0,\n",
       "  910.0,\n",
       "  911.0,\n",
       "  912.0,\n",
       "  913.0,\n",
       "  914.0,\n",
       "  915.0,\n",
       "  916.0,\n",
       "  917.0,\n",
       "  918.0,\n",
       "  919.0,\n",
       "  919.0,\n",
       "  919.0,\n",
       "  919.0],\n",
       " [416.0,\n",
       "  416.0,\n",
       "  416.0,\n",
       "  416.0,\n",
       "  416.0,\n",
       "  415.0,\n",
       "  415.0,\n",
       "  415.0,\n",
       "  415.0,\n",
       "  415.0,\n",
       "  414.0,\n",
       "  414.0,\n",
       "  414.0,\n",
       "  413.0,\n",
       "  413.0,\n",
       "  413.0,\n",
       "  413.0,\n",
       "  412.0,\n",
       "  412.0,\n",
       "  412.0,\n",
       "  411.0,\n",
       "  411.0,\n",
       "  411.0,\n",
       "  410.0,\n",
       "  410.0,\n",
       "  410.0,\n",
       "  409.0,\n",
       "  409.0,\n",
       "  409.0,\n",
       "  408.0,\n",
       "  408.0,\n",
       "  408.0,\n",
       "  407.0,\n",
       "  407.0,\n",
       "  407.0,\n",
       "  406.0,\n",
       "  406.0,\n",
       "  406.0,\n",
       "  405.0,\n",
       "  405.0,\n",
       "  405.0,\n",
       "  404.0,\n",
       "  404.0,\n",
       "  403.0,\n",
       "  403.0,\n",
       "  403.0,\n",
       "  402.0,\n",
       "  402.0,\n",
       "  401.0,\n",
       "  401.0,\n",
       "  401.0,\n",
       "  400.0,\n",
       "  400.0,\n",
       "  399.0,\n",
       "  399.0,\n",
       "  398.0,\n",
       "  398.0,\n",
       "  397.0,\n",
       "  397.0,\n",
       "  396.0,\n",
       "  396.0,\n",
       "  395.0,\n",
       "  395.0,\n",
       "  394.0,\n",
       "  394.0,\n",
       "  393.0,\n",
       "  393.0,\n",
       "  392.0,\n",
       "  392.0,\n",
       "  391.0,\n",
       "  391.0,\n",
       "  390.0,\n",
       "  389.0,\n",
       "  389.0,\n",
       "  388.0,\n",
       "  388.0,\n",
       "  387.0,\n",
       "  386.0,\n",
       "  386.0,\n",
       "  385.0,\n",
       "  385.0,\n",
       "  384.0,\n",
       "  383.0,\n",
       "  382.0,\n",
       "  382.0,\n",
       "  381.0,\n",
       "  380.0,\n",
       "  379.0,\n",
       "  379.0,\n",
       "  378.0,\n",
       "  377.0,\n",
       "  376.0,\n",
       "  376.0,\n",
       "  375.0,\n",
       "  374.0,\n",
       "  373.0,\n",
       "  372.0,\n",
       "  372.0,\n",
       "  371.0,\n",
       "  370.0,\n",
       "  369.0,\n",
       "  368.0,\n",
       "  367.0,\n",
       "  366.0,\n",
       "  365.0,\n",
       "  364.0,\n",
       "  363.0,\n",
       "  362.0,\n",
       "  361.0,\n",
       "  360.0,\n",
       "  359.0,\n",
       "  358.0,\n",
       "  357.0,\n",
       "  356.0,\n",
       "  355.0,\n",
       "  354.0,\n",
       "  353.0,\n",
       "  352.0,\n",
       "  351.0,\n",
       "  350.0,\n",
       "  349.0,\n",
       "  348.0,\n",
       "  347.0,\n",
       "  346.0,\n",
       "  345.0,\n",
       "  344.0,\n",
       "  343.0,\n",
       "  342.0,\n",
       "  341.0,\n",
       "  340.0,\n",
       "  339.0,\n",
       "  338.0,\n",
       "  337.0,\n",
       "  336.0,\n",
       "  335.0,\n",
       "  334.0,\n",
       "  333.0,\n",
       "  332.0,\n",
       "  331.0,\n",
       "  330.0,\n",
       "  329.0,\n",
       "  328.0,\n",
       "  327.0,\n",
       "  326.0,\n",
       "  325.0,\n",
       "  324.0,\n",
       "  323.0,\n",
       "  322.0,\n",
       "  321.0,\n",
       "  320.0,\n",
       "  319.0,\n",
       "  318.0,\n",
       "  317.0,\n",
       "  315.0,\n",
       "  314.0,\n",
       "  312.0,\n",
       "  310.0,\n",
       "  309.0,\n",
       "  308.0,\n",
       "  307.0,\n",
       "  306.0,\n",
       "  305.0,\n",
       "  304.0,\n",
       "  303.0,\n",
       "  302.0,\n",
       "  300.0,\n",
       "  298.0,\n",
       "  296.0,\n",
       "  295.0,\n",
       "  294.0,\n",
       "  293.0])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 如果上一个单元格没有报错，则没有必要运行这个单元格 / If there is no error in the previous cell, there is no need to run this cell\n",
    "print(z_str_of_current_in[-1])\n",
    "print(z_float_of_current_in[-1])\n",
    "# 1.1 获取所有块体\n",
    "get_block_with_noise(image_path, z_str_of_current_in[-1])\n",
    "# 1.2 使用连通域去除其他块体\n",
    "remove_noise_block(image_path, z_str_of_current_in[-1])\n",
    "# 2. 获取滑动面\n",
    "get_and_save_x_y_array(image_path, z_str_of_current_in[-1], z_float_of_current_in[-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 绘制3D图 \\ Draw a 3D drawing\n",
    "> 这部分为了在使用其他IDE(PyCharm等)也能绘制，因此会重复写一下上面已经出现的变量及导入已导入过的库。\n",
    "> \n",
    "> In order to be able to draw in other IDEs (PyCharm, etc.), this part will repeat the variables that have appeared above and import the libraries that have been imported."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1 导入需要的库和定义画图需要的变量\n",
    "from matplotlib import pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import numpy as np\n",
    "\n",
    "z_str_of_current_in = np.array([\"02.50\", \"05.00\", \"10.00\", \"15.00\", \"20.00\", \"25.00\", \"30.00\", \"35.00\", \"40.00\", \"45.00\", \"47.50\"])\n",
    "z_float_of_current_in = np.array([float(str_i) for str_i in z_str_of_current_in])\n",
    "image_path = \"program_process_files\"\n",
    "\n",
    "p_A = [  63, 635, 0]\n",
    "p_B = [1470, 635, 0]\n",
    "p_C = [1470, 196, 0]\n",
    "p_D = [1106, 196, 0]\n",
    "p_E = [ 570, 465, 0]\n",
    "p_F = [  63, 465, 0]\n",
    "\n",
    "k = 21.93548\n",
    "\n",
    "z_ = 50 * k\n",
    "\n",
    "p_A_z = [  63, 635, z_]\n",
    "p_B_z = [1470, 635, z_]\n",
    "p_C_z = [1470, 196, z_]\n",
    "p_D_z = [1106, 196, z_]\n",
    "p_E_z = [ 570, 465, z_]\n",
    "p_F_z = [  63, 465, z_]\n",
    "\n",
    "x_array_array = [0 for i in range(len(z_str_of_current_in))]\n",
    "y_array_array = [0 for i in range(len(z_str_of_current_in))]\n",
    "z_array_array = [0 for i in range(len(z_str_of_current_in))]\n",
    "\n",
    "for i in range(len(z_str_of_current_in)):\n",
    "    x_array_array[i] = np.loadtxt(image_path + \"\\\\x_array_\" + z_str_of_current_in[i] + \".txt\", dtype=float)\n",
    "    y_array_array[i] = np.loadtxt(image_path + \"\\\\y_array_\" + z_str_of_current_in[i] + \".txt\", dtype=float)\n",
    "    z_array_array[i] = np.loadtxt(image_path + \"\\\\z_array_\" + z_str_of_current_in[i] + \".txt\", dtype=float)\n",
    "# 2 开始绘图\n",
    "#定义坐标轴\n",
    "fig = plt.figure()\n",
    "ax1 = plt.axes(projection='3d')\n",
    "# 设置坐标轴标签\n",
    "ax1.set_xlabel(\"x_axes\")\n",
    "ax1.set_ylabel(\"y_axes\")\n",
    "ax1.set_zlabel(\"z_axes\")\n",
    "\n",
    "\n",
    "# 先画边坡\n",
    "slope_zero_x_array = np.array([p_A[0], p_B[0], p_C[0], p_D[0], p_E[0], p_F[0], p_A[0]])\n",
    "slope_zero_y_array = np.array([p_A[1], p_B[1], p_C[1], p_D[1], p_E[1], p_F[1], p_A[1]])\n",
    "slope_zero_z_array = np.array([p_A[2], p_B[2], p_C[2], p_D[2], p_E[2], p_F[2], p_A[2]])\n",
    "\n",
    "slope_50_x_array = np.array([p_A_z[0], p_B_z[0], p_C_z[0], p_D_z[0], p_E_z[0], p_F_z[0], p_A_z[0]])\n",
    "slope_50_y_array = np.array([p_A_z[1], p_B_z[1], p_C_z[1], p_D_z[1], p_E_z[1], p_F_z[1], p_A_z[1]])\n",
    "slope_50_z_array = np.array([p_A_z[2], p_B_z[2], p_C_z[2], p_D_z[2], p_E_z[2], p_F_z[2], p_A_z[2]])\n",
    "\n",
    "# 按正常的视图设置坐标\n",
    "max_y_ = max(slope_zero_y_array)\n",
    "slope_zero_y_array = max_y_ - slope_zero_y_array\n",
    "slope_50_y_array = max_y_ - slope_50_y_array\n",
    "slope_zero_y_array, slope_zero_z_array  = slope_zero_z_array, slope_zero_y_array\n",
    "slope_50_y_array, slope_50_z_array = slope_50_z_array, slope_50_y_array\n",
    "\n",
    "ax1.plot(slope_zero_x_array, slope_zero_y_array, slope_zero_z_array, linewidth=1.5, color=\"#621661\", zorder=2)\n",
    "ax1.plot(slope_50_x_array, slope_50_y_array, slope_50_z_array, linewidth=1.5, color=\"#621661\", zorder=2)\n",
    "\n",
    "for i in range(len(slope_50_x_array)):\n",
    "    x_array_ = np.array([slope_zero_x_array[i], slope_50_x_array[i]])\n",
    "    y_array_ = np.array([slope_zero_y_array[i], slope_50_y_array[i]])\n",
    "    z_array_ = np.array([slope_zero_z_array[i], slope_50_z_array[i]])\n",
    "\n",
    "    ax1.plot(x_array_, y_array_, z_array_, linewidth=1.5, color=\"#621661\", zorder=2)\n",
    "\n",
    "# 绘制滑动面\n",
    "for i in range(len(x_array_array)):\n",
    "    temp_y = y_array_array[i]\n",
    "    temp_z = z_array_array[i]\n",
    "    \n",
    "    temp_y = max_y_ - temp_y\n",
    "    temp_y, temp_z = temp_z, temp_y\n",
    "\n",
    "    ax1.plot(x_array_array[i], temp_y, temp_z, linewidth=1, color=\"#2188ff\", zorder=1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4 绘制切片示意图 / Draw a schematic diagram of the slice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 开始绘图\n",
    "# 定义坐标轴\n",
    "fig = plt.figure()\n",
    "ax1 = plt.axes()\n",
    "# 设置坐标轴标签\n",
    "ax1.set_xlabel(\"x_axes\")\n",
    "ax1.set_ylabel(\"y_axes\")\n",
    "\n",
    "\n",
    "# 先画边坡\n",
    "slope_zero_x_array = np.array([p_A[0], p_B[0], p_C[0], p_D[0], p_E[0], p_F[0], p_A[0]])\n",
    "slope_zero_y_array = np.array([p_A[1], p_B[1], p_C[1], p_D[1], p_E[1], p_F[1], p_A[1]])\n",
    "\n",
    "image_file = image_path + r\"\\Slice_slide_15.00.png\"\n",
    "\n",
    "img = Image.open(image_file)\n",
    "plt.imshow(img)\n",
    "plt.plot(slope_zero_x_array, slope_zero_y_array, linewidth=1, color=\"blue\")\n",
    "plt.savefig(image_path + \"\\\\cut_show.pdf\", format=\"pdf\", dpi=1200, pad_inches=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.5 绘制该切片上的滑面 / Draw the sliding surface on the slice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 开始绘图\n",
    "# 定义坐标轴\n",
    "fig = plt.figure()\n",
    "ax1 = plt.axes()\n",
    "# 设置坐标轴标签\n",
    "ax1.set_xlabel(\"x_axes\")\n",
    "ax1.set_ylabel(\"y_axes\")\n",
    "ax1.set_aspect(\"equal\")\n",
    "\n",
    "# 先画边坡\n",
    "slope_zero_x_array = np.array([p_A[0], p_B[0], p_C[0], p_D[0], p_E[0], p_F[0], p_A[0]])\n",
    "slope_zero_y_array = np.array([p_A[1], p_B[1], p_C[1], p_D[1], p_E[1], p_F[1], p_A[1]])\n",
    "max_y_ = max(slope_zero_y_array)\n",
    "slope_zero_y_array = max_y_ - slope_zero_y_array\n",
    "\n",
    "plt.plot(slope_zero_x_array, slope_zero_y_array, linewidth=1, color=\"blue\")\n",
    "\n",
    "x_array_array_15 = np.loadtxt(image_path + \"\\\\x_array_\" + \"15.00\" + \".txt\", dtype=float)\n",
    "y_array_array_15 = np.loadtxt(image_path + \"\\\\y_array_\" + \"15.00\" + \".txt\", dtype=float)\n",
    "y_array_array_15 = max_y_ - y_array_array_15\n",
    "\n",
    "plt.plot(x_array_array_15, y_array_array_15, linewidth=1, color=\"red\")\n",
    "plt.savefig(image_path + \"\\\\sliding_15.00.pdf\", format=\"pdf\", dpi=1200, pad_inches=0)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# END"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Phase2_Env",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.16"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}