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.gitlab-ci.yml

@@ -0,0 +1,7 @@
+stages:
+  - test
+sonarqube_master_job:
+  stage: test
+  script:
+    - export ENCRYPT_KEY=`echo $CI_PROJECT_URL.git | md5sum | cut -d' ' -f1`
+    - /opt/sonar-scanner-4.7.0/bin/sonar-scanner -Dsonar.sources=. -Dsonar.projectKey=p$ENCRYPT_KEY

Blue_noise_sampling.py

@@ -4,47 +4,63 @@
 # @Email   : derighoid@gmail.com
 # @File    : Blue_noise_sampling.py
 # @Software: PyCharm
+from pathlib import Path
 
-'''
+import matplotlib
-计算新图形（放大后或缩小后）的坐标点像素值对应于原图像中哪一个像素点填充的。
-src是原图，dst是新图，原来的图像宽度/高度除以新图像的宽度/高度可以得到缩放比例，假如是缩小图片括号内的数字小于1，放大则大于1，相当于系数，再乘以新图片的宽度/高度，就实现了缩放。
-'''
-
-from PIL import Image
 import matplotlib.pyplot as plt
 import numpy as np
-import math
+from PIL import Image
+
+'''
+最近邻插值法：
+在放大图像时，多出来的像素点由最近邻的像素点构成
+计算新图形（放大后或缩小后）的坐标点像素值对应于原图像中哪一个像素点填充的。
+srcX=newX*(srcW/newW)
+srcY=newY*(srcH/newH)
+src是原图，dst是新图，原来的图像宽度/高度除以新图像的宽度/高度可以得到缩放比例，
+假如是缩小图片括号内的数字小于1，放大则大于1，相当于系数，再乘以新图片的宽度/高度，就实现了缩放。
+'''
+
+def blueNoiseSampl(img, newH, newW):
+    '''
+
+    :param img: 图片
+    :param newH:  新图的高
+    :param newW: 新图的宽
+    :return: 新图
+    '''
+    scrH, scrW, t = img.shape  # 原图的长宽
+    retimg = np.zeros((newH, newW, 3), dtype=np.uint8)  # 生成 newH* newW *3 的零矩阵
+    for i in range(newH - 1):
+        for j in range(newW - 1):
+            scrx = round(i * (scrH / newH))  # round对其四舍五入
+            scry = round(j * (scrW / newW))
+            retimg[i, j] = img[scrx, scry]  # new image
+    return retimg
 
 
-# # 最近邻插值算法
+# 图片展示函数
-# # dstH为新图的高;dstW为新图的宽
+def showImage(picPath):
-# def blueNoiseSampl(img, dstH, dstW):
+    '''
-#     scrH, scrW, t = img.shape  # src原图的长宽
+
-#     retimg = np.zeros((dstH, dstW, 3), dtype=np.uint8)
+    :param picPath: 图片地址
-#     for i in range(dstH - 1):
+    :return: 样图
-#         for j in range(dstW - 1):
+    '''
-#             scrx = round(i * (scrH / dstH))
+    # 获取图片矩阵
-#             scry = round(j * (scrW / dstW))
+    image = np.array(Image.open(picPath))
-#             retimg[i, j] = img[scrx, scry]
+    # 设置画布
-#
+    plt.figure(figsize=(16, 8))
-#     return retimg
+    # 合并
-#
+    plt.subplot(121)
-#
+    plt.imshow(image)
-# im_path = './data/th.png'
+    # 调用采样函数
-# image = np.array(Image.open(im_path))
+    image1 = blueNoiseSampl(image, image.shape[0] * 2, image.shape[1] * 2)
-#
+    # 图片保存
-# plt.figure(figsize=(16, 8))
+    Image.fromarray(np.uint8(image1)).save("./data/picture13.png")
-#
+    plt.subplot(122)
-# plt.subplot(1, 2, 1)
+    plt.imshow(image1)
-# plt.imshow(image)
+    plt.show()
-#
+    return image1
-# image1 = blueNoiseSampl(image, image.shape[0] * 2, image.shape[1] * 2)
-# # 从array转换成image
-# image1 = Image.fromarray(image1.astype('uint8')).convert('RGB')
-# image1.save('./data/picture13.png')
-# plt.subplot(1, 2, 2)
-# plt.imshow(image1)
-# plt.show()
 
 
 

GUI.py

@@ -1,12 +1,362 @@
 # -*- coding: utf-8 -*-
-# @Time    : 2022-7-25 0025 15:03
+# @Time    : 2022-7-27 0027 15:21
 # @Author  : Qing
 # @Email   : derighoid@gmail.com
 # @File    : GUI.py
 # @Software: PyCharm
+import os
 import tkinter
+import tkinter.filedialog
 
-# git checkout -b "main"
+import PIL
+import numpy as np
+import requests
+from PIL import Image, ImageTk
 
-import tkinter
+root = tkinter.Tk()
-root =tkinter.Tk()
+
+root.title("基于Python的图像处理")
+
+root.geometry('640x500')
+root.resizable(width=0, height=0)
+
+# 文件选择
+def choose_fiel():
+    selectFileName = tkinter.filedialog.askopenfilename(title='选择文件')  # 选择文件
+    e.set(selectFileName)
+
+#文件选择标签
+label1 = tkinter.Label(root, text="文件路径:")
+label1.place(x=40, y=0)
+
+e = tkinter.StringVar()
+e_entry = tkinter.Entry(root, width=60, textvariable=e)
+e_entry.place(x=100, y=0)
+
+lable2 = tkinter.Label(root, text="URL:")
+lable2.place(x=40, y=30)
+
+# 设置输入框
+entry = tkinter.Entry(root, width=60)
+#设置默认值
+entry.insert(0,
+             "https://tse2-mm.cn.bing.net/th/id/OIP-C.fT7uKiT7V7YO2PPINFeOdQHaJ4?w=186&h=248&c=7&r=0&o=5&dpr=1.25&pid=1.7")
+entry.place(x=100, y=30)
+
+# 选择文件按钮
+submit_button = tkinter.Button(root, text="选择文件", command=choose_fiel,activeforeground="red")
+submit_button.place(x=520, y=0)
+
+
+# 爬取函数
+def pictureCrawl(url):
+    # 文件保存地址
+    root = "E://桌面//Python_Picture_Analysis//data//"
+    # 文件的保存地址以及格式
+    path = root + url.split('/')[-3] + '.png'
+    try:
+        if not os.path.exists(root):  # 判断是否存在文件夹
+            os.mkdir(root)
+        if not os.path.exists(path):  # 判断是否存在该文件
+            r = requests.get(url)
+            # 文件写入
+            with open(path, 'wb') as fp:
+                fp.write(r.content)
+                print("OK")
+        else:
+            print("File already exists")
+    except:
+        print("Crawl successful")
+
+
+# 爬取按钮
+imgCrawling_button = tkinter.Button(root, text="开始爬取", command=lambda: pictureCrawl(entry.get()),activeforeground="red")
+imgCrawling_button.place(x=520, y=30)
+
+
+# 图片变灰
+def grayPic(e_entry):
+    img = Image.open(e_entry.get())
+    img = img.convert('L')
+    showPic = ImageTk.PhotoImage(img)
+    img = tkinter.Label(image=showPic)
+    img.image = showPic
+    img.place(x=350, y=150)
+
+
+# 灰色图按钮
+grayPic_button = tkinter.Button(root, text="灰色图", command=lambda: grayPic(e_entry),activeforeground="red")
+grayPic_button.place(x=110, y=75)
+
+
+# 图像水平镜面对称
+def mirrorSymmetry(e_entry):
+    img = Image.open(e_entry.get())
+    img = np.asarray(img)
+    img1 = np.flipud(img)
+    img2 = Image.fromarray(img1)
+    pic = ImageTk.PhotoImage(img2)
+    img = tkinter.Label(image=pic)
+    img.image = pic
+    img.place(x=350, y=150)
+
+
+# 镜面按钮
+mirrorSymmetry_button = tkinter.Button(root, text="镜面对称", command=lambda: mirrorSymmetry(e_entry),activeforeground="red")
+mirrorSymmetry_button.place(x=170, y=75)
+
+
+# 右旋转
+def verticalSummetry_right(e_tery):
+    img = Image.open(e_entry.get())
+
+    img2 = img.transpose(Image.ROTATE_270)
+
+    pic = ImageTk.PhotoImage(img2)
+    img = tkinter.Label(image=pic)
+    img.image = pic
+    img.place(x=300, y=150)
+
+
+# 右旋转按钮
+verticalSummetry_right_button = tkinter.Button(root, text="右旋转", command=lambda: verticalSummetry_right(e_entry),activeforeground="red")
+verticalSummetry_right_button.place(x=240, y=75)
+
+
+# 左旋转
+def verticalSummetry_lift(e_entry):
+    img = Image.open(e_entry.get())
+
+    img2 = img.transpose(Image.ROTATE_90)
+    pic = ImageTk.PhotoImage(img2)
+    img = tkinter.Label(image=pic)
+    img.image = pic
+    img.place(x=300, y=150)
+
+
+# 左旋转按钮
+verticalSummetry_lift_button = tkinter.Button(root, text="左旋转", command=lambda: verticalSummetry_lift(e_entry),activeforeground="red")
+verticalSummetry_lift_button.place(x=300, y=75)
+
+
+# 像素反转函数
+def pixInversion(e_entry):
+    img = Image.open(e_entry.get())
+    img2 = 255 - np.array(img)
+    img2 = Image.fromarray(img2)
+    img3 = ImageTk.PhotoImage(img2)
+    img = tkinter.Label(image=img3)
+    img.image = img3
+    img.place(x=350, y=150)
+
+
+# 像素反转
+pixInversion_button = tkinter.Button(root, text="像素反转", command=lambda: pixInversion(e_entry),activeforeground="red")
+pixInversion_button.place(x=360, y=75)
+
+
+def redImage(e_entry):
+    img = Image.open(e_entry.get())
+    img = np.array(img)
+    img[:, :, 1:3] = 0
+    img2 = Image.fromarray(img)
+    img3 = ImageTk.PhotoImage(img2)
+    img = tkinter.Label(image=img3)
+    img.image = img3
+    img.place(x=350, y=150)
+
+
+# 红色图按钮
+red_button = tkinter.Button(root, text="红色图", command=lambda: redImage(e_entry),activeforeground="red")
+red_button.place(x=430, y=75)
+
+
+def blueImage(e_entry):
+    img = Image.open(e_entry.get())
+    img = np.array(img)
+    img[:, :, :2] = 0
+    img2 = Image.fromarray(img)
+    img3 = ImageTk.PhotoImage(img2)
+    img = tkinter.Label(image=img3)
+    img.image = img3
+    img.place(x=350, y=150)
+
+
+# 蓝色图按钮
+blue_button = tkinter.Button(root, text="蓝色图", command=lambda: blueImage(e_entry),activeforeground="red")
+blue_button.place(x=490, y=75)
+
+
+# 绿色图函数
+def greenImage(e_entry):
+    img = Image.open(e_entry.get())
+    img = np.array(img)
+    img[:, :, ::2] = 0
+    img2 = Image.fromarray(img)
+    img3 = ImageTk.PhotoImage(img2)
+    img = tkinter.Label(image=img3)
+    img.image = img3
+    img.place(x=350, y=150)
+
+
+# 绿色按钮
+green_button = tkinter.Button(root, text='绿色图', command=lambda: greenImage(e_entry),activeforeground="red")
+green_button.place(x=550, y=75)
+
+
+# 图像混合函数
+
+def imageMix(e_entry):
+    img1 = Image.open("./data/picture.png")
+    img2 = Image.open(e_entry.get())
+    img3 = PIL.Image.blend(img1, img2, 0.5)
+
+    img4 = ImageTk.PhotoImage(img3)
+    img = tkinter.Label(image=img4)
+    img.image = img4
+    img.place(x=350, y=150)
+
+
+# 图像混合按钮
+imageMix_button = tkinter.Button(root, text="图像混合", command=lambda: imageMix(e_entry),activeforeground="red")
+imageMix_button.place(x=40, y=105)
+
+
+# 图像剪切函数
+def imageCut(e_entry,Entrybutton): # 一个Entrybutton代表四个值(20, 10, 190, 190)
+    img1 = Image.open(e_entry.get())
+    part = (eval(Entrybutton.get()))
+    img2 = img1.crop(part)
+    img3 = ImageTk.PhotoImage(img2)
+    img = tkinter.Label(image=img3)
+    img.image = img3
+    img.place(x=350, y=150)
+
+
+# 剪切按钮
+cut_button = tkinter.Button(root, text="剪   切", command=lambda: imageCut(e_entry,Entrybutton),activeforeground="red")
+cut_button.place(x=110, y=105)
+
+
+# 部分替换函数
+def imgReplace(e_entry,Entrybutton): # Entrybutton 代表4个值(10, 50, 250, 250)
+    part = (eval(Entrybutton.get()))
+    img1 = Image.open(e_entry.get())
+    img2 = Image.open("./data/picture.png")
+    img3 = img2.crop(part)
+    img1.paste(img3, (10, 50, 250, 250))
+
+    img4 = ImageTk.PhotoImage(img1)
+    img = tkinter.Label(image=img4)
+    img.image = img4
+    img.place(x=350, y=150)
+
+
+# 部分替换按钮
+imgReplace_button = tkinter.Button(root, text="部分替换", command=lambda: imgReplace(e_entry,Entrybutton),activeforeground="red")
+imgReplace_button.place(x=170, y=105)
+
+# 输入框
+Entrybutton = tkinter.Entry(root, width=15)
+Entrybutton.insert(0, 0.5)
+Entrybutton.place(x=490, y=110)
+
+lab5 = tkinter.Label(root, text="Gamma:")
+lab5.place(x=430, y=110)
+
+lab6=tkinter.Label(root,text=":<-other")
+lab6.place(x=585,y=110)
+# 伽马矫正函数(默认1/1,5)
+def gammaCorrect(e_entry, Entrybutton):
+    img = Image.open(e_entry.get())
+    img = np.array(img)
+    img1 = np.power(img / float(np.max(img)), float(Entrybutton.get()))
+    img2 = Image.fromarray(np.uint8(img1 * 255))
+    img3 = ImageTk.PhotoImage(img2)
+    img = tkinter.Label(image=img3)
+    img.image = img3
+    img.place(x=350, y=150)
+
+
+# 伽马矫正按钮
+gammaCorrect_button = tkinter.Button(root, text="伽马矫正", command=lambda: gammaCorrect(e_entry, Entrybutton),activeforeground="red")
+gammaCorrect_button.place(x=240, y=105)
+
+
+def decrease_color(e_entry):
+    img = Image.open(e_entry.get())
+    img = np.array(img)
+    H = img.shape[0]  # 获取图片的高
+    W = img.shape[1]  # 获取图片的宽
+    newImg = np.zeros((H, W, 3), np.uint8)
+
+    for i in range(H):
+        for j in range(W):
+            for k in range(3):  # RGB三分量
+                if img[i, j][k] < 128:
+                    gray = 0
+                else:
+                    gray = 128
+                newImg[i, j][k] = np.uint8(gray)  # 传给新图片
+    newImg = Image.fromarray(newImg)
+    img3 = ImageTk.PhotoImage(newImg)
+    img = tkinter.Label(image=img3)
+    img.image = img3
+    img.place(x=350, y=150)
+
+
+# 减色按钮
+decrease_color_button = tkinter.Button(root, text="减色", command=lambda: decrease_color(e_entry),activeforeground="red")
+decrease_color_button.place(x=310, y=105)
+
+
+# 采样函数
+def picSampl(img, newH, newW):
+    scrH, scrW, t = img.shape  # 原图的长宽
+
+    retimg = np.zeros((newH, newW, 3), dtype=np.uint8)  # 生成 newH* newW *3 的零矩阵
+    for i in range(newH - 1):
+        for j in range(newW - 1):
+            scrx = round(i * (scrH / newH))  # round对其四舍五入
+            scry = round(j * (scrW / newW))
+            retimg[i, j] = img[scrx, scry]  # new image
+    return retimg
+
+
+# 采样展示
+def showImage(e_entry):
+    img = np.array(Image.open(e_entry.get()))
+
+    img1 = picSampl(img, img.shape[0] * 2, img.shape[1] * 2)
+    img1 = Image.fromarray(img1)
+    img2 = ImageTk.PhotoImage(img1)
+    img = tkinter.Label(image=img2)
+    img.image = img2
+    img.place(x=350, y=150)
+
+
+# 采样按钮
+picSampl_button = tkinter.Button(root, text='图片采样', command=lambda: showImage(e_entry),activeforeground="red")
+picSampl_button.place(x=360, y=105)
+
+
+# 图片展示
+def showImg(e_entry):
+    load = Image.open(e_entry.get())
+    render = ImageTk.PhotoImage(load)
+
+    img = tkinter.Label(image=render)
+    img.image = render
+    img.place(x=50, y=150)
+
+
+
+# 图片展示按钮
+submit_button = tkinter.Button(root, text="显示原图", command=lambda: showImg(e_entry),activeforeground="red")
+submit_button.place(x=40, y=75)
+
+lable3 = tkinter.Label(root, text='原图', bg='red')
+lable3.place(x=140, y=460)
+lable4 = tkinter.Label(root, text='处理后', bg='red')
+lable4.place(x=450, y=460)
+root.mainloop()

GammaCorrection.py

@@ -6,34 +6,45 @@
 # @Software: PyCharm
 '''
 gamma矫正公式：
-f(x)=xγ
+f(x)=x^γ
 即输出是输入的幂函数，指数为γ．
 '''
 import matplotlib.pyplot as plt
 import numpy as np
 from PIL import Image
 
+# 文件保存的地址及文件夹
 root = "E:\\桌面\\Python_Picture_Analysis\\data\\"
 
 
-def gammaCorrect(filePath,val):
+# 定义伽马矫正函数
+def gammaCorrect(filePath, val):
+    '''
+
+    :param filePath:  图片路径
+    :param val: 伽马值
+    :return: 图片
+    '''
+
+    # 获取图片
     im = Image.open(filePath)
-    img = np.array(im)
+    img = np.array(im)  # 图片转矩阵
 
-    img1 = np.power(img / float(np.max(img)), 1 / 1.5)
+    # 伽马函数使用
-    img2 = np.power(img / float(np.max(img)), val)
+    img1 = np.power(img / float(np.max(img)), 1 / 1.5)  # gamma值为1/1.5
+    img2 = np.power(img / float(np.max(img)), val)  # 自定义gamma值
 
+    # 多图合并
     plt.subplot(131)
     plt.imshow(img)
-    plt.title("origin")
+    plt.title("origin")  # 展示原图
     plt.subplot(132)
     plt.imshow(img1)
-    plt.title("gammar=1/1.5")
+    plt.title("gammar=1/1.5")  # 展示gamma =1/1.5
     plt.subplot(133)
     plt.imshow(img2)
-    plt.title("user-defined")
+    plt.title("user-defined")  # 展示自定义 gammat Image
     plt.show()
+    # 图片保存
     Image.fromarray(np.uint8(img2)).save(root + 'picture10' + '.jpg')
     return img2
-
-

Image_Capture.py

@@ -9,9 +9,11 @@ import matplotlib.pyplot as plt
 import numpy as np
 from PIL import Image
 
+# 图片保存的地址文件夹
 root = "E:\\桌面\\Python_Picture_Analysis\\data\\"
 
 
+# 定义图片剪切函数
 def imgCut(path, left, upper, right, lower):
     """
     :param path: 图片路径
@@ -21,27 +23,34 @@ def imgCut(path, left, upper, right, lower):
     :param lower:与底部边界的距离
     :return:
     """
+    # 获取图片
     img = Image.open(path)
-    print(img.size)
+    # print(img.size) Picture Size
-    part = (left, upper, right, lower)
+    part = (left, upper, right, lower)  # 设置剪切的大小
 
+    # 函数的调用
     cut_img = img.crop(part)
 
+    # 展示图片
+    # 展示原图
     plt.figure("image Cut")
     plt.subplot(121)
     plt.title("origin")
     plt.imshow(img)
 
+    # 展示剪切后的图片
     plt.subplot(122)
     plt.title("CUT part")
     plt.imshow(cut_img)
     plt.show()
 
+    # 图片保存
     Image.fromarray(np.uint8(cut_img)).save(root + "picture11.jpg")
 
     return cut_img
 
 
+#定义图片部分替换函数
 def imgReplace(path1, path2, left2, upper2, right2, lower2):
     """
     :param path1: 被替换图片的路径
@@ -49,21 +58,27 @@ def imgReplace(path1, path2, left2, upper2, right2, lower2):
     :param left2, upper2, right2, lower2:替换图片的大小
     :return:
     """
-
+    #图片的获取
-    img1 = Image.open(path1)
+    img1 = Image.open(path1)  # 被替换图
-    img2 = Image.open(path2)
+    img2 = Image.open(path2)  #替换图
 
     part2 = (left2, upper2, right2, lower2)
     cut_img2 = img2.crop(part2)
 
+    #调用替换函数
     img1.paste(cut_img2, (10, 50, 250, 250))
 
+    #定义画布
     plt.figure("picture replace")
     plt.subplot(121)
     plt.imshow(img2)
+    plt.title("Origin")  # 展示原图
 
     plt.subplot(122)
     plt.imshow(img1)
+    plt.title("replaced picture")  #展示替换后的图
     plt.show()
+
+    # 保存 替换后的图片
     Image.fromarray(np.uint8(img1)).save(root + 'picture12.jpg')
     return img1

Image_Mix.py

@@ -4,22 +4,36 @@
 # @Email   : derighoid@gmail.com
 # @File    : Image_Mix.py
 # @Software: PyCharm
-
+import PIL
-from PIL import Image
 import matplotlib.pyplot as plt
 import numpy as np
-import cv2
+from PIL import Image
-# 阿尔法图像混合
+
+'''
+img1：图片对象1
+img2：图片对象2
+alpha：透明度 ，取值范围为 0 到 1，当取值为 0 时，输出图像相当于 image1 的拷贝，而取值为 1 时，
+则是 image2 的拷贝，只有当取值为 0.5 时，才为两个图像的中合。因此改值的大小决定了两个图像的混合程度'''
+
+
+# 图像混合
 def imageMix(imagePath1, imagePath2):
+    '''
+
+    :param imagePath1: 混合图片1的地址
+    :param imagePath2: 混合图像2的地址
+    :return:  混合后的图像
+    '''
+
+    # 获取两张图片
     img1 = Image.open(imagePath1)
-    im1 = np.array(img1)
     img2 = Image.open(imagePath2)
-    im2 = np.array(img2)
+    # 调用图片混合函数
-
+    img3 = PIL.Image.blend(img1, img2, 0.5)  # 0.5 为gamma 值
-    img3 = cv2.addWeighted(im1, 0.8, im2, 0.3, 0)
 
+    # 展示混合后的图片
     plt.imshow(img3)
+
+    # 保存图片
     Image.fromarray(np.uint8(img3)).save("./data/picture14.png")
     plt.show()
-
-

Modify_Pixels.py

@@ -41,17 +41,21 @@ class modfifyImage():
     # 显示图像三通道颜色
     def img_RGB(self):
         root="E:\\桌面\\Python_Picture_Analysis\\data\\"   # 图片保存地址
-        red = self.imageMatrix().copy()
+        red = self.imageMatrix().copy() # 图片复制
         red[:, :, 1:3] = 0
         green = self.imageMatrix().copy()
         green[:, :, ::2] = 0
         blue = self.imageMatrix().copy()
         blue[:, :, :2] = 0
+        # 定义画布 多图合并显示
         x, y = plt.subplots(2, 2)
         x.set_size_inches(10, 10)
         y[0, 0].imshow(self.imageMatrix())
+
         y[0, 1].imshow(red)
+
         y[1, 0].imshow(green)
+
         y[1, 1].imshow(blue)
         # 图片保存
         Image.fromarray(np.uint8(red)).save(root + 'picture1' + '.jpg')

Orientation_Modification.py

@@ -11,7 +11,7 @@ import os
 import matplotlib.pyplot as plt
 import numpy as np
 from PIL import Image
-
+#定义 图片方向改变类
 class directionChanges():
     def __init__(self, filePath):
         self.filePath = filePath
@@ -42,32 +42,32 @@ class directionChanges():
 
     # 图像水平镜面对称
     def mirrorSymmetry(self):
-        plt6 = self.imageMatrix()[::-1]
+        # plt6 = self.imageMatrix()[::-1]
+        plt6=self.imageRead().transpose(Image.ROTATE_180)
         plt.imshow(plt6)
         plt.show()
         return plt6
-
     # 垂直对称(右旋转)
     def verticalSummetry_right(self):
-        plt5 = self.imageMatrix().swapaxes(1, 0)
+
-        plt.imshow(plt5[:, ::-1])
+        plt5=self.imageRead().transpose(Image.ROTATE_270)
+
+        plt.imshow(plt5)
         plt.show()
         return plt5
-
     # 垂直镜面(左旋转)
-    # transpose函数的作用就是调换数组的行列值的索引值，类似于求矩阵的转置
     def verticalSummetry_lift(self):
-        plt7 = self.imageMatrix().transpose(1, 0, 2)  # x,y轴转置
+        plt7 = self.imageRead().transpose(Image.ROTATE_90)
-        plt.imshow(plt7[::-1])
+        plt.imshow(plt7)
         plt.show()
         return plt7
-
     # 保存图片
     def img_Save(self):
-        root = "E:\\桌面\\Python_Picture_Analysis\\data\\"
+        root = "E:\\桌面\\Python_Picture_Analysis\\data\\"  # 图片保存地址
-        if not os.path.exists(root):
+        if not os.path.exists(root): # 判断是否已经存在该文件
-            os.mkdir(root)
+            os.mkdir(root) #没有就创建
         else:
+            #图片保存
             Image.fromarray(np.uint8(self.imageRepetition())).save(root+'picture4'+'.jpg')
             Image.fromarray(np.uint8(self.mirrorSymmetry())).save(root+'picture5'+'.jpg')
             Image.fromarray(np.uint8(self.verticalSummetry_right())).save(root+'picture6'+'.jpg')

PictureCrawling/Picture _Crawling.py

@@ -31,6 +31,6 @@ def pictureCrawl(url):
     except:
         print("Crawl successful")
 
-
+#图片URL
-# url = "https://tse2-mm.cn.bing.net/th/id/OIP-C.fT7uKiT7V7YO2PPINFeOdQHaJ4?w=186&h=248&c=7&r=0&o=5&dpr=1.25&pid=1.7"
+url = "https://tse2-mm.cn.bing.net/th/id/OIP-C.fT7uKiT7V7YO2PPINFeOdQHaJ4?w=186&h=248&c=7&r=0&o=5&dpr=1.25&pid=1.7"
-# pictureCrawl(url)
+pictureCrawl(url)

Picture_Reading.py

@@ -7,17 +7,13 @@
 
 # 导入库
 import os.path
-
 import matplotlib.pyplot as plt
 import numpy as np
 from PIL import Image
-
-
 # 建立图片读取类
 class pictureRead(object):
     def __init__(self, filePath):
         self.filePath = filePath
-
     # 图片读取
     def fileRead(self):
         if self.filePath == " ":
@@ -25,8 +21,7 @@ class pictureRead(object):
         else:
             face = Image.open(self.filePath)
         return face
-
+    # 图片数字化
-    # 图片转矩阵
     def imageMatrix(self):
         im = np.array(self.fileRead())
         return im
@@ -50,11 +45,14 @@ class pictureRead(object):
 
     # 图片plt展示
     def plt_image(self):
+        plt.subplot(121)
         # 原图片显示
         plt.imshow(self.imageMatrix())
-        plt.show()
+        plt.title("Original")
+        plt.subplot(122)
         # 灰色图片显示
         plt.imshow(self.imageGrey())
+        plt.title('Gray')
         plt.show()
 
     # 图片保存

Subtractive_Algorithm.py

@@ -6,17 +6,17 @@
 # @Software: PyCharm
 import os.path
 
-import numpy as np
 import matplotlib.pyplot as plt
-from PIL import Image
+
 from Picture_Reading import *
 
+
 class ReduceColor(pictureRead):
-    def __init__(self,filePath):
+    def __init__(self, filePath):
-        self.filePath=filePath
+        self.filePath = filePath
 
     def readImg(self):
-        face4=Image.open(self.filePath)
+        face4 = Image.open(self.filePath)
         return face4
 
     def imageMatrix(self):
@@ -25,36 +25,105 @@ class ReduceColor(pictureRead):
 
     # 减色算法
     def decrease_color(self):
-        out=self.imageMatrix().copy()
+        '''
-        out=out//64*64+32
+        val=|32  (0<=var<64)
-        Image.fromarray((np.uint8(out))).save("E:\\桌面\\Python_Picture_Analysis\\data\\picture8.jpg")
+            |96 (64<=var<128)
-        return out
+            |160 (128<=var<192)
+            |224 (192<=var<256)
+        :return:
+        '''
+        img = self.imageMatrix().copy()
+        # img=img//64*64+32
+        H = img.shape[0]  # 获取图片的高
+        W = img.shape[1]  # 获取图片的宽
 
-    # 展示图片
+        # 创建新的图
-    def plt_Img(self):
+        newImg = np.zeros((H, W, 3), np.uint8)
-        plt.imshow(self.decrease_color())
+        newImg2 = np.zeros((H, W, 3), np.uint8)
+        newImg3 = np.zeros((H, W, 3), np.uint8)
+
+        # 量化操作
+        for i in range(H):
+            for j in range(W):
+                for k in range(3):  # RGB三分量
+                    if img[i, j][k] < 128:
+                        gray = 0
+                    else:
+                        gray = 128
+                    newImg[i, j][k] = np.uint8(gray)  # 传给新图片
+
+        for i in range(H):
+            for j in range(W):
+                for k in range(3):
+                    if img[i, j][k] < 64:
+                        gray = 0
+                    elif img[i, j][k] < 128:
+                        gray = 64
+                    elif img[i, j][k] < 192:
+                        gray = 128
+                    else:
+                        gray = 192
+                    newImg2[i, j][k] = np.uint8(gray)
+
+        for i in range(H):
+            for j in range(W):
+                for k in range(3):
+                    if newImg3[i, j][k] < 32:
+                        gray = 0
+                    elif newImg3[i, j][k] < 96:
+                        gray = 32
+                    elif newImg3[i, j][k] < 96:
+                        gray = 96
+                    elif newImg3[i, j][k] < 128:
+                        gray = 96
+                    elif newImg3[i,j][k]<160:
+                        gray=128
+                    elif newImg3[i,j][k]<192:
+                        gray=160
+                    elif newImg3[i,j][k]<244:
+                        gray=192
+                    else:
+                        gray = 224
+                    newImg3[i, j][k] = np.uint8(gray)
+
+        #定义画布
+        plt.figure(figsize=(16,8))
+        plt.subplot(131)
+        plt.imshow(img)
+        plt.title("origin")
+
+        plt.subplot(132)
+        plt.imshow(newImg)
+        plt.title("1")
+
+        plt.subplot(133)
+        plt.imshow(newImg2)
+        plt.title("2")
+
+        # plt.subplot(144)
+        # plt.imshow(newImg3)
+        # plt.title("3")
         plt.show()
+        return newImg,newImg2,newImg3
 
-
+    # 图像二值化(以128为阈值进行二值化)
-    #图像二值化(以128为阈值进行二值化)
     def imgBinarization(self):
-        a=self.imageGrey()-np.array([[128]])
+        a = self.imageGrey() - np.array([[128]])
-        b=np.floor(a/np.array([[256]]))
+        b = np.floor(a / np.array([[256]]))
-        c=b+np.array([[1]],dtype=np.int16)
+        c = b + np.array([[1]], dtype=np.int16)
-        bfilter=c.astype("uint8")
+        bfilter = c.astype("uint8")
-        result=bfilter*np.array([[255]],dtype=np.uint8)
+        result = bfilter * np.array([[255]], dtype=np.uint8)
         # result=np.array(self.readImg().convert("1"))
         plt.imshow(result)
         plt.show()
         return result
 
-    #保存图片
+    # 保存图片
     def img_Save(self):
         root = "E:\\桌面\\Python_Picture_Analysis\\data\\"
         if not os.path.exists(root):
             os.mkdir(root)
         else:
-            Image.fromarray(np.uint8(self.decrease_color())).save(root + 'picture8'+'.jpg')
+            Image.fromarray(np.uint8(self.decrease_color)).save(root + 'picture8' + '.jpg')
-            Image.fromarray(np.uint8(self.imgBinarization())).save(root + 'picture9'+'.jpg')
+            Image.fromarray(np.uint8(self.imgBinarization())).save(root + 'picture9' + '.jpg')
             print("Picture Save successfully")
-

main.py

@@ -10,40 +10,52 @@ from Modify_Pixels import *
 from Orientation_Modification import *
 from Subtractive_Algorithm import *
 from Image_Mix import *
+from Blue_noise_sampling import *
+from Image_Capture import *
+
 if __name__ == '__main__':
     # 图片读取
-    picture_read = pictureRead('E:\\桌面\\Python_Picture_Analysis\\data\\th.jpg')
+    picture_read = pictureRead('E:\\桌面\\Python_Picture_Analysis\\data\\th.png')
+    #图片展示
     # picture_read.plt_image()
 
+
     # 图片修改
-    modif_image = modfifyImage('E:\\桌面\\Python_Picture_Analysis\\data\\th.jpg')
+    modif_image = modfifyImage('E:\\桌面\\Python_Picture_Analysis\\data\\th.png')
-    # print(modif_image.img_RGB())
+    # modif_image.img_RGB()
+    # modif_image.pixInversion()
 
     # 图片方向修改
-    direction_Changes = directionChanges('E:\\桌面\\Python_Picture_Analysis\\data\\th.jpg')
+    direction_Changes = directionChanges('E:\\桌面\\Python_Picture_Analysis\\data\\th.png')
-    # direction_Changes.verticalSummetry_right()
+    # direction_Changes.verticalSummetry_right() # 右旋转
-    # direction_Changes.verticalSummetry_lift()
+    # direction_Changes.verticalSummetry_lift()  # 左旋转
-    # direction_Changes.mirrorSymmetry()
+    # direction_Changes.mirrorSymmetry()   # 镜像对称
-    # direction_Changes.imageRepetition()
+    # direction_Changes.imageRepetition()  # 图像重复
-    # direction_Changes.img_Save()
+    # # direction_Changes.img_Save()     # 图像保存
 
     # 减色算法
-    reduce_color = ReduceColor('E:\\桌面\\Python_Picture_Analysis\\data\\th.jpg')
+    reduce_color = ReduceColor('E:\\桌面\\Python_Picture_Analysis\\data\\th.png')
-    # reduce_color.plt_Img()
+    # reduce_color.decrease_color()  #  减色算法
-    # reduce_color.img_Save()
+    # # reduce_color.img_Save()
-    # reduce_color.imgBinarization()
+    # reduce_color.imgBinarization()  #图像二值化
+
 
     # Gamma矫正
     # gammaCorrect("./data/th.png", 1.5)
-
+    #
-    # 图片截取
+    # # 图片截取
-    # left, upper, right, lower
+    # # left, upper, right, lower
-    # imgCut('E:\\桌面\\Python_Picture_Analysis\\data\\th.jpg',20,10,190,190)
+    # imgCut('E:\\桌面\\Python_Picture_Analysis\\data\\th.png',20,10,190,190)
-    # imgCut('E:\\桌面\\Python_Picture_Analysis\\data\\picture.jpg',30,70,230,230)
+    # # imgCut('E:\\桌面\\Python_Picture_Analysis\\data\\picture.png',30,70,230,230)
-
+    #
-    # 图片替换
+    # # 图片替换
-    # imgReplace('E:\\桌面\\Python_Picture_Analysis\\data\\th.jpg', "E:\\桌面\\Python_Picture_Analysis\\data\\picture.jpg",
+    # imgReplace('E:\\桌面\\Python_Picture_Analysis\\data\\th.png', "E:\\桌面\\Python_Picture_Analysis\\data\\picture.png",
     #            10, 50, 250, 250)
-
+    #
-    # 阿尔法混合
+    # # 阿尔法混合
     # imageMix('E:\\桌面\\Python_Picture_Analysis\\data\\th.png','E:\\桌面\\Python_Picture_Analysis\\data\\picture.png')
+    #
+    # #采样
+    showImage("./data/th.png")
+
+