{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "62916051-266e-439b-9f14-5a16a9017622",
   "metadata": {},
   "source": [
    "## 1. 回顾\n",
    "\n",
    ">1. Git上传\n",
    ">2. 图像的处理\n",
    ">   |-访问像素（元素）\n",
    ">   |-修改像素（元素访问，块访问）"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f5fbc7c-16f8-4800-9162-5f756a258f9f",
   "metadata": {},
   "source": [
    "## 2.图像的像素\n",
    "\n",
    ">图像是矩阵（矩阵表达成一个数组，图像==矩阵，图像是三维矩阵（H,W,C））\n",
    ">BMP:C=3, JPG:C=3, PNG:C=4(alpha通道), 灰度图像:C=1(结论：图像通道1,3,4:matplotlib把矩阵作为图像显示，只显示三维，第三维度必须是1,3,4)\n",
    "\n",
    ">图像的块操作\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d4884373-e046-4a0c-832c-1e9923c892ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np              #数学运算：线性代数：向量运算，矩阵运算\n",
    "import cv2                      #图像处理：调用numpy\n",
    "import matplotlib.pyplot as plt #可视化：图像可视化，文本可视化，数据可视化（曲线，折线柱状图，饼图）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5c7ea9d2-edc6-49e3-a09c-fad2a08ae7d4",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'NoneType' object does not support item assignment",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[41], line 4\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# %matplotlib inline\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;66;03m# 加载图像\u001b[39;00m\n\u001b[0;32m      3\u001b[0m img \u001b[38;5;241m=\u001b[39m cv2\u001b[38;5;241m.\u001b[39mimread(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgpu.bmp\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m----> 4\u001b[0m \u001b[43mimg\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m:\u001b[49m\u001b[38;5;241;43m100\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m200\u001b[39;49m\u001b[43m:\u001b[49m\u001b[38;5;241;43m500\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m255\u001b[39m   \u001b[38;5;66;03m#对图像连续块进行访问：获取数据，修改数据\u001b[39;00m\n\u001b[0;32m      5\u001b[0m \u001b[38;5;66;03m# plt.imshow(img)\u001b[39;00m\n\u001b[0;32m      6\u001b[0m \u001b[38;5;66;03m# 把图像的红色通道像素全部设置为0\u001b[39;00m\n\u001b[0;32m      7\u001b[0m \u001b[38;5;66;03m# img[要修改的像素][条件] = 值\u001b[39;00m\n\u001b[0;32m      8\u001b[0m \u001b[38;5;66;03m# 注意：opencv的图像格式是：BGR\u001b[39;00m\n\u001b[0;32m      9\u001b[0m img[\u001b[38;5;241m200\u001b[39m:\u001b[38;5;241m500\u001b[39m, :, \u001b[38;5;241m1\u001b[39m:\u001b[38;5;241m3\u001b[39m][img[\u001b[38;5;241m200\u001b[39m:\u001b[38;5;241m500\u001b[39m, :, \u001b[38;5;241m1\u001b[39m:\u001b[38;5;241m3\u001b[39m] \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m255\u001b[39m  \u001b[38;5;66;03m# 0:red, 1:green, 2:blue\u001b[39;00m\n",
      "\u001b[1;31mTypeError\u001b[0m: 'NoneType' object does not support item assignment"
     ]
    }
   ],
   "source": [
    "# %matplotlib inline\n",
    "# 加载图像\n",
    "img = cv2.imread(\"gpu.bmp\")\n",
    "img[0:100, 200:500, :] = 255   #对图像连续块进行访问：获取数据，修改数据\n",
    "# plt.imshow(img)\n",
    "# 把图像的红色通道像素全部设置为0\n",
    "# img[要修改的像素][条件] = 值\n",
    "# 注意：opencv的图像格式是：BGR\n",
    "img[200:500, :, 1:3][img[200:500, :, 1:3] >= 0] = 255  # 0:red, 1:green, 2:blue\n",
    "plt.imshow(img)\n",
    "cv2.imwrite(\"2.jpg\",img)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6057527b-29d4-411a-a6bc-fb9301837a8c",
   "metadata": {},
   "source": [
    "## 3. 图像的操作\n",
    "> 图像的操作==矩阵操作（转置，维度转换……)b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e4a4a09c-7e27-4ba8-ba53-8ae0be53e05b",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute 'T'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[34], line 7\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[38;5;66;03m# 读取图像\u001b[39;00m\n\u001b[0;32m      6\u001b[0m img \u001b[38;5;241m=\u001b[39m cv2\u001b[38;5;241m.\u001b[39mimread(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgpu.bmp\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m----> 7\u001b[0m img_T \u001b[38;5;241m=\u001b[39m \u001b[43mimg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mT\u001b[49m    \u001b[38;5;66;03m#图像的转置\u001b[39;00m\n\u001b[0;32m      8\u001b[0m \u001b[38;5;28mprint\u001b[39m(img_T\u001b[38;5;241m.\u001b[39mshape)   \u001b[38;5;66;03m#  (H, W, C)\u001b[39;00m\n\u001b[0;32m      9\u001b[0m \u001b[38;5;28mprint\u001b[39m(img\u001b[38;5;241m.\u001b[39mshape)\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'T'"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 读取图像\n",
    "img = cv2.imread(\"gpu.bmp\")\n",
    "img_T = img.T    #图像的转置\n",
    "print(img_T.shape)   #  (H, W, C)\n",
    "print(img.shape)\n",
    "\n",
    "# 图像的像素类型做转变：uint8 -> float\n",
    "img_type = img.astype(np.float)\n",
    "\n",
    "plt.imshow(img_type)\n",
    "\n",
    "# 如果图像是整数，其值： 0——255，如果是浮点数， 其值： 0——1  （对小数越界，采用截断（clipping）的方式）"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc4e944f-1e9c-403a-9888-400763560634",
   "metadata": {},
   "source": [
    "## 4.颜色格式转换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "506bfa5d-45a3-4535-bdb5-a4e1679b178e",
   "metadata": {},
   "outputs": [
    {
     "ename": "error",
     "evalue": "OpenCV(4.7.0) D:\\a\\opencv-python\\opencv-python\\opencv\\modules\\imgproc\\src\\color.cpp:182: error: (-215:Assertion failed) !_src.empty() in function 'cv::cvtColor'\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31merror\u001b[0m                                     Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[40], line 7\u001b[0m\n\u001b[0;32m      4\u001b[0m img \u001b[38;5;241m=\u001b[39m cv2\u001b[38;5;241m.\u001b[39mimread(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgpu.bmp\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m      6\u001b[0m \u001b[38;5;66;03m# 格式转换\u001b[39;00m\n\u001b[1;32m----> 7\u001b[0m img_out \u001b[38;5;241m=\u001b[39m \u001b[43mcv2\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcvtColor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcv2\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mCOLOR_RGB2BGR\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      8\u001b[0m \u001b[38;5;66;03m# img_out = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\u001b[39;00m\n\u001b[0;32m      9\u001b[0m plt\u001b[38;5;241m.\u001b[39mimshow(img_out)\n",
      "\u001b[1;31merror\u001b[0m: OpenCV(4.7.0) D:\\a\\opencv-python\\opencv-python\\opencv\\modules\\imgproc\\src\\color.cpp:182: error: (-215:Assertion failed) !_src.empty() in function 'cv::cvtColor'\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "img = cv2.imread(\"gpu.bmp\")\n",
    "\n",
    "# 格式转换\n",
    "img_out = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\n",
    "# img_out = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n",
    "plt.imshow(img_out)\n",
    "# plt.imshow(img_out,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ec7c7d40-6754-4ea3-b9ef-b58570b9a2af",
   "metadata": {},
   "outputs": [
    {
     "ename": "error",
     "evalue": "OpenCV(4.7.0) D:\\a\\opencv-python\\opencv-python\\opencv\\modules\\imgproc\\src\\color.cpp:182: error: (-215:Assertion failed) !_src.empty() in function 'cv::cvtColor'\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31merror\u001b[0m                                     Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[36], line 7\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[0;32m      5\u001b[0m img \u001b[38;5;241m=\u001b[39m cv2\u001b[38;5;241m.\u001b[39mimread(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgpu.bmp\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m----> 7\u001b[0m img \u001b[38;5;241m=\u001b[39m \u001b[43mcv2\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcvtColor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcv2\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mCOLOR_RGB2BGR\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      8\u001b[0m \u001b[38;5;66;03m# 绘制几何图形（边框，圆框，文本）\u001b[39;00m\n\u001b[0;32m      9\u001b[0m \u001b[38;5;66;03m# cv:rectangle 绘制方框\u001b[39;00m\n\u001b[0;32m     10\u001b[0m img_rect \u001b[38;5;241m=\u001b[39m cv2\u001b[38;5;241m.\u001b[39mrectangle(img,  \u001b[38;5;66;03m# 绘制的图像\u001b[39;00m\n\u001b[0;32m     11\u001b[0m                          (\u001b[38;5;241m100\u001b[39m, \u001b[38;5;241m100\u001b[39m),  \u001b[38;5;66;03m# 矩形左上角顶点\u001b[39;00m\n\u001b[0;32m     12\u001b[0m                          (\u001b[38;5;241m500\u001b[39m, \u001b[38;5;241m500\u001b[39m),  \u001b[38;5;66;03m# 矩形右下角顶点\u001b[39;00m\n\u001b[0;32m     13\u001b[0m                          (\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m255\u001b[39m, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m255\u001b[39m),  \u001b[38;5;66;03m# 方框的颜色\u001b[39;00m\n\u001b[0;32m     14\u001b[0m                          \u001b[38;5;241m5\u001b[39m  \u001b[38;5;66;03m# 线的粗细\u001b[39;00m\n\u001b[0;32m     15\u001b[0m )\n",
      "\u001b[1;31merror\u001b[0m: OpenCV(4.7.0) D:\\a\\opencv-python\\opencv-python\\opencv\\modules\\imgproc\\src\\color.cpp:182: error: (-215:Assertion failed) !_src.empty() in function 'cv::cvtColor'\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "img = cv2.imread(\"gpu.bmp\")\n",
    "\n",
    "img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\n",
    "# 绘制几何图形（边框，圆框，文本）\n",
    "# cv:rectangle 绘制方框\n",
    "img_rect = cv2.rectangle(img,  # 绘制的图像\n",
    "                         (100, 100),  # 矩形左上角顶点\n",
    "                         (500, 500),  # 矩形右下角顶点\n",
    "                         (0, 255, 0, 255),  # 方框的颜色\n",
    "                         5  # 线的粗细\n",
    ")\n",
    "# void cv::circle 绘制圆\n",
    "img_circle = cv2.circle(img_rect, (500, 500), 100, (0, 0, 255), 20)\n",
    "# cv::putText 绘制文本\n",
    "img_text = cv2.putText(img_circle,  # 被绘制的图像\n",
    "                       \"This is OpenCV\",  # 绘制的文本内容\n",
    "                      (500, 500),  # 绘制的位置\n",
    "                       cv2.FONT_HERSHEY_SIMPLEX,  # 字体\n",
    "                       5.0,  # 字体放大倍数\n",
    "                       (255, 0, 0),  #绘制颜色\n",
    "                       20  # 字的粗细\n",
    ")\n",
    "plt.imshow(img_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c7e4c003-dd80-44c8-8b01-e1d7d65f6e43",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (2487568721.py, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  Cell \u001b[1;32mIn[37], line 1\u001b[1;36m\u001b[0m\n\u001b[1;33m    <matplotlib.image.AxesImage at 0x1a772d5a3d0>\u001b[0m\n\u001b[1;37m    ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "<matplotlib.image.AxesImage at 0x1a772d5a3d0>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc68b38e-4325-41bb-a298-6f9e2d5d750b",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ae4f5cea-63dd-4a52-be6d-3f5ab052dc71",
   "metadata": {},
   "source": [
    "## 6.利用矩阵分解压缩图像"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9b2b530d-82e6-4094-bb82-edad6554be1b",
   "metadata": {},
   "outputs": [
    {
     "ename": "error",
     "evalue": "OpenCV(4.7.0) D:\\a\\opencv-python\\opencv-python\\opencv\\modules\\imgproc\\src\\color.cpp:182: error: (-215:Assertion failed) !_src.empty() in function 'cv::cvtColor'\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31merror\u001b[0m                                     Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[38], line 10\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[38;5;66;03m# 读取图像\u001b[39;00m\n\u001b[0;32m      9\u001b[0m img \u001b[38;5;241m=\u001b[39m cv2\u001b[38;5;241m.\u001b[39mimread(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgpu.bmp\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m---> 10\u001b[0m img \u001b[38;5;241m=\u001b[39m \u001b[43mcv2\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcvtColor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcv2\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mCOLOR_RGB2BGR\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     11\u001b[0m new_img \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mndarray(img\u001b[38;5;241m.\u001b[39mshape)  \u001b[38;5;66;03m#生成一个空的图像,图像大小与加载的图像一样大小\u001b[39;00m\n\u001b[0;32m     12\u001b[0m \u001b[38;5;66;03m# 把图像当矩阵做奇异值分解\u001b[39;00m\n",
      "\u001b[1;31merror\u001b[0m: OpenCV(4.7.0) D:\\a\\opencv-python\\opencv-python\\opencv\\modules\\imgproc\\src\\color.cpp:182: error: (-215:Assertion failed) !_src.empty() in function 'cv::cvtColor'\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "n_dim = 200  #分解矩阵，只取特征值最大的5个合成新矩阵\n",
    "\n",
    "\n",
    "# 读取图像\n",
    "img = cv2.imread(\"gpu.bmp\")\n",
    "img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\n",
    "new_img = np.ndarray(img.shape)  #生成一个空的图像,图像大小与加载的图像一样大小\n",
    "# 把图像当矩阵做奇异值分解\n",
    "for i in range(3):\n",
    "    # 每个通道都做奇异值分解（对二维矩阵，对其他维数不支持奇异值分解）\n",
    "    img_tmp = img[:, :, i]\n",
    "    # print(img_tmp.shape)\n",
    "    # 分解\n",
    "    u, s, v = np.linalg.svd(img_tmp, full_matrices=False)    \n",
    "    # 利用奇异值，重新合成图像（只选择特征值大的酉阵，重新构建新的图像）\n",
    "    u[:, n_dim:] = 0  # 左，把酉阵的前5列保留，其它的设置为0\n",
    "    v[n_dim:, :] = 0  # 右\n",
    "    s[n_dim:]=0\n",
    "    \n",
    "    ds = np.diag(s)\n",
    "    img_tmp = np.dot(np.dot(u, ds), v) #u.s.v\n",
    "    new_img[:, :, i] = img_tmp\n",
    "    \n",
    "# 显示新的图像\n",
    "print(new_img.shape)\n",
    "new_img = new_img.astype(np.uint8)\n",
    "plt.imshow(new_img)"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "b86fc448-bf4e-4eb3-9499-456286d9f9fb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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