add 1 and 2

.vscode/launch.json

@@ -0,0 +1,12 @@
+{
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "Python Debugger: Current File",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "${file}",
+            "console": "integratedTerminal"
+        }
+    ]
+}

.vscode/settings.json

@@ -0,0 +1,3 @@
+{
+    "python.analysis.autoImportCompletions": true
+}

1/4.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hellohellohello\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"hello\" * 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "x = 2, y = 3.000000\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"x = %d, y = %f\" % (2, 3.0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2-3j)\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = 3 + 2j\n",
+    "y = -1j\n",
+    "print(x * y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "abcdefg\n",
+      "bc\n",
+      "bcdefg\n",
+      "abc\n",
+      "abcdef\n",
+      "ef\n",
+      "efg\n",
+      "abcdefg\n",
+      "aceg\n",
+      "gfedcba\n"
+     ]
+    }
+   ],
+   "source": [
+    "strs = \"abcdefg\"\n",
+    "print(strs[0:7:1])\n",
+    "print(strs[1:3])\n",
+    "print(strs[1:])\n",
+    "print(strs[:3])\n",
+    "print(strs[:-1])\n",
+    "print(strs[-3:-1])\n",
+    "print(strs[-3:])\n",
+    "print(strs[:])\n",
+    "print(strs[::2])\n",
+    "print(strs[::-1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "较小值为：  5.7\n"
+     ]
+    }
+   ],
+   "source": [
+    "def minimal(x, y):\n",
+    "    if x > y:\n",
+    "        print(\"较小值为： \", y)\n",
+    "    else:\n",
+    "        print(\"较小值为： \", x)\n",
+    "\n",
+    "\n",
+    "a = float(input(\"请输入第一个数据： \"))  # 输入了 5.4\n",
+    "b = float(input(\"请输入第二个数据： \"))  # 输入了 6.8\n",
+    "minimal(a, b)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

1/5.py

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+import cv2
+
+lenna = cv2.imread(r"img\Lenna.png")
+print(type(lenna))
+cv2.namedWindow("Lena", cv2.WINDOW_AUTOSIZE)
+cv2.imshow("Lena", lenna)
+cv2.waitKey(0)
+cv2.destroyWindow("Lena")
+cv2.imwrite(r"img\test_imwrite.png", lenna, (cv2.IMWRITE_PNG_COMPRESSION, 5))

1/6.py

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+import cv2
+import matplotlib.pyplot as plt
+
+plt.rcParams["font.family"] = ["SimHei"]
+plt.rcParams["axes.unicode_minus"] = False
+img_BGR = cv2.imread(r"img\iris.jpg")
+img_RGB = cv2.cvtColor(img_BGR, cv2.COLOR_BGR2RGB)
+plt.imshow(img_RGB)
+plt.show()

1/7.py

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+import cv2
+import matplotlib.pyplot as plt
+import math
+import numpy as np
+
+
+def createBox():
+    box = np.zeros((100, 100), np.uint8) + 255
+    print(type(box))
+    shape = box.shape
+    box = cv2.circle(box, (30, 50), 25, 0, -1)
+    for i in range(shape[0]):
+        for j in range(shape[1]):
+            if j in range(45, 95) and i in range(25, 75):
+                box[i, j] = 195
+    return box
+
+
+def histogram(image):
+    (row, col) = image.shape
+    hist = [0] * 256
+    for i in range(row):
+        for j in range(col):
+            hist[image[i, j]] += 1
+    return hist
+
+
+image0 = createBox()
+plt.figure()
+plt.subplot(1, 2, 1)
+plt.imshow(image0, vmin=0, vmax=255, cmap=plt.cm.gray)
+plt.title("idel image")
+image_hist0 = histogram(image0)
+plt.subplot(1, 2, 2)
+plt.bar(range(256), image_hist0)
+plt.show()

2/1.py

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+import numpy as np
+import cv2 as cv
+import matplotlib.pyplot as plt
+from sympy import im
+
+
+def global_linear_transmation(im, c=0, d=255):
+    img = im.copy()
+    maxV = img.max()
+    minV = img.min()
+    if maxV == minV:
+        return np.uint8(img)
+    for i in range(img.shape[0]):
+        for j in range(img.shape[1]):
+            img[i, j] = ((d - c) / (maxV - minV)) * (img[i, j] - minV) + c
+    return np.uint8(img)
+
+
+def histogram_equalization(im):
+    return np.uint8(cv.equalizeHist(im))
+
+
+if __name__ == "__main__":
+    im = cv.imread(r"E:\OneDrive\Code\Python\2\iris.jpg", cv.IMREAD_GRAYSCALE)
+    im1 = global_linear_transmation(im, 0, 150)
+    im2 = global_linear_transmation(im, 100)
+    im3 = global_linear_transmation(im, 50, 150)
+    im4 = histogram_equalization(im)
+    plt.figure()
+    plt.subplot(241)
+    plt.imshow(im1, cmap="gray")
+    plt.title("darker")
+    plt.subplot(242)
+    plt.imshow(im2, cmap="gray")
+    plt.title("brighter")
+    plt.subplot(243)
+    plt.imshow(im3, cmap="gray")
+    plt.title("lower contrast")
+    plt.subplot(244)
+    plt.imshow(im4, cmap="gray")
+    plt.title("equalized")
+    plt.subplot(245)
+    plt.hist(im1.flatten(), 256, [0, 256])
+    plt.subplot(246)
+    plt.hist(im2.flatten(), 256, [0, 256])
+    plt.subplot(247)
+    plt.hist(im3.flatten(), 256, [0, 256])
+    plt.subplot(248)
+    plt.hist(im4.flatten(), 256, [0, 256])
+    plt.show()

2/2.py

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+import numpy as np
+import cv2 as cv
+import matplotlib.pyplot as plt
+
+
+def gamma_trans(img, gamma=1.0):
+    gamma_table = [np.power(x / 255.0, gamma) * 255.0 for x in range(256)]
+    gamma_table = np.round(np.array(gamma_table)).astype(np.uint8)
+    return cv.LUT(img, gamma_table)
+
+
+if __name__ == "__main__":
+    im = cv.imread(r"E:\OneDrive\Code\Python\2\iris.jpg", cv.IMREAD_GRAYSCALE)
+    im1 = gamma_trans(im, 0.5)
+    im2 = gamma_trans(im, 1.5)
+    plt.figure()
+    plt.subplot(131)
+    plt.imshow(im, cmap="gray")
+    plt.title("original")
+    plt.subplot(132)
+    plt.imshow(im1, cmap="gray")
+    plt.title("gamma = 0.5")
+    plt.subplot(133)
+    plt.imshow(im2, cmap="gray")
+    plt.title("gamma = 1.5")
+    plt.show()

2/3.py

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+import cv2 as cv
+from matplotlib import pyplot as plt
+import numpy as np
+
+img = cv.imread(r"E:\OneDrive\Code\Python\2\iris.jpg", 0)
+fil1 = 1 / 16 * np.array([[1, 2, 1], [2, 4, 2], [1, 2, 1]])
+fil2 = 1 / 9 * np.array([[1, 1, 1], [1, 1, 1], [1, 1, 1]])
+fil3 = 1 / 10 * np.array([[1, 1, 1], [1, 2, 1], [1, 1, 1]])
+fil4 = np.array([[-1, -1, -1], [-1, 9, -1], [-1, -1, -1]])
+ImgSmoothed1 = cv.filter2D(img, -1, fil1, borderType=cv.BORDER_DEFAULT)
+ImgSmoothed2 = cv.filter2D(img, -1, fil2, borderType=cv.BORDER_DEFAULT)
+ImgSmoothed3 = cv.filter2D(img, -1, fil3, borderType=cv.BORDER_DEFAULT)
+ImgSharp = cv.filter2D(img, -1, fil4, borderType=cv.BORDER_DEFAULT)
+
+plt.figure()
+plt.subplot(221)
+plt.imshow(ImgSmoothed1, cmap="gray")
+plt.title("smoothed1")
+plt.subplot(222)
+plt.imshow(ImgSmoothed2, cmap="gray")
+plt.title("smoothed2")
+plt.subplot(223)
+plt.imshow(ImgSmoothed3, cmap="gray")
+plt.title("smoothed3")
+plt.subplot(224)
+plt.imshow(ImgSharp, cmap="gray")
+plt.title("sharp")
+plt.show()

2/4.py

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+import random as rd
+import numpy as np
+import cv2 as cv
+import matplotlib.pyplot as plt
+
+
+def addSaltAndPepper(src, percentage):
+    NoiseImg = src.copy()
+    NoiseNum = int(percentage * src.shape[0] * src.shape[1])
+    for i in range(NoiseNum):
+        randX = rd.randint(0, src.shape[0] - 1)
+        randY = rd.randint(0, src.shape[1] - 1)
+        if rd.randint(0, 1) == 0:
+            NoiseImg[randX, randY] = 0
+        else:
+            NoiseImg[randX, randY] = 255
+    return NoiseImg
+
+
+def addGaussianNoise(src, means, sigma):
+    NoiseImg = src / src.max()
+    rows = NoiseImg.shape[0]
+    cols = NoiseImg.shape[1]
+    for i in range(rows):
+        for j in range(cols):
+            NoiseImg[i, j] = NoiseImg[i, j] + rd.gauss(means, sigma)
+            if NoiseImg[i, j] < 0:
+                NoiseImg[i, j] = 0
+            if NoiseImg[i, j] > 1:
+                NoiseImg[i, j] = 1
+    NoiseImg = np.uint8(NoiseImg * 255)
+    return NoiseImg
+
+
+if __name__ == "__main__":
+    im = cv.imread(r"E:\OneDrive\Code\Python\2\iris.jpg", cv.IMREAD_GRAYSCALE)
+    im1 = addSaltAndPepper(im, 0.1)
+    im11 = cv.blur(im1, (3, 3))
+    im12 = cv.medianBlur(im1, 3)
+    im13 = cv.GaussianBlur(im1, (3, 3), 1)
+    im2 = addGaussianNoise(im, 0, 0.1)
+    im21 = cv.blur(im2, (3, 3))
+    im22 = cv.medianBlur(im2, 3)
+    im23 = cv.GaussianBlur(im2, (3, 3), 1)
+    plt.figure()
+    plt.subplot(241)
+    plt.imshow(im1, cmap="gray")
+    plt.title("salt and pepper")
+    plt.subplot(242)
+    plt.imshow(im11, cmap="gray")
+    plt.title("blur")
+    plt.subplot(243)
+    plt.imshow(im12, cmap="gray")
+    plt.title("median")
+    plt.subplot(244)
+    plt.imshow(im13, cmap="gray")
+    plt.title("gaussian")
+    plt.subplot(245)
+    plt.imshow(im2, cmap="gray")
+    plt.title("gaussian noise")
+    plt.subplot(246)
+    plt.imshow(im21, cmap="gray")
+    plt.title("blur")
+    plt.subplot(247)
+    plt.imshow(im22, cmap="gray")
+    plt.title("median")
+    plt.subplot(248)
+    plt.imshow(im23, cmap="gray")
+    plt.title("gaussian")
+    plt.show()

2/5.py

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+import cv2 as cv
+from matplotlib import pyplot as plt
+import numpy as np
+
+img = cv.imread(r"E:\OneDrive\Code\Python\2\iris.jpg", 0)
+lplc = np.array([[0, -1, 0], [-1, 4, -1], [0, -1, 0]])
+lplcEnhance = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]])
+ImgLplc = cv.filter2D(img, -1, lplc, borderType=cv.BORDER_DEFAULT)
+ImgLplcEnhance = cv.filter2D(img, -1, lplcEnhance, borderType=cv.BORDER_DEFAULT)
+
+plt.figure()
+plt.subplot(131)
+plt.imshow(img, cmap="gray")
+plt.title("original")
+plt.subplot(132)
+plt.imshow(ImgLplc, cmap="gray")
+plt.title("laplacian")
+plt.subplot(133)
+plt.imshow(ImgLplcEnhance, cmap="gray")
+plt.title("laplacian enhanced")
+plt.show()