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6 Commits
| Author | SHA1 | Date | |
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298633d90f | ||
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0157fe648c | ||
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3d78b6edb3 |
7
.gitlab-ci.yml
Normal file
7
.gitlab-ci.yml
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@ -0,0 +1,7 @@
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stages:
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- test
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sonarqube_master_job:
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stage: test
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script:
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- export ENCRYPT_KEY=`echo $CI_PROJECT_URL.git | md5sum | cut -d' ' -f1`
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- /opt/sonar-scanner-4.7.0/bin/sonar-scanner -Dsonar.sources=. -Dsonar.projectKey=p$ENCRYPT_KEY
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@ -5,46 +5,55 @@
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# @File : Blue_noise_sampling.py
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# @Software: PyCharm
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'''
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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。
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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。
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'''
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from PIL import Image
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import matplotlib.pyplot as plt
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import numpy as np
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import math
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# # 最近邻插值算法
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# # dstH为新图的高;dstW为新图的宽
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# def blueNoiseSampl(img, dstH, dstW):
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# scrH, scrW, t = img.shape # src原图的长宽
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# retimg = np.zeros((dstH, dstW, 3), dtype=np.uint8)
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# for i in range(dstH - 1):
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# for j in range(dstW - 1):
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# scrx = round(i * (scrH / dstH))
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# scry = round(j * (scrW / dstW))
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# retimg[i, j] = img[scrx, scry]
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#
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# return retimg
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#
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#
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# im_path = './data/th.png'
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# image = np.array(Image.open(im_path))
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#
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# plt.figure(figsize=(16, 8))
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#
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# plt.subplot(1, 2, 1)
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# plt.imshow(image)
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#
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# image1 = blueNoiseSampl(image, image.shape[0] * 2, image.shape[1] * 2)
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# # 从array转换成image
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# image1 = Image.fromarray(image1.astype('uint8')).convert('RGB')
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# image1.save('./data/picture13.png')
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# plt.subplot(1, 2, 2)
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# plt.imshow(image1)
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# plt.show()
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from PIL import Image
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'''
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srcX=newX*(srcW/newW)
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srcY=newY*(srcH/newH)
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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，
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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。
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'''
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def blueNoiseSampl(img, newH, newW):
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'''
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:param img: 图片
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:param newH: 新图的高
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:param newW: 新图的宽
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:return: 新图
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'''
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scrH, scrW, t = img.shape # 原图的长宽
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retimg = np.zeros((newH, newW, 3), dtype=np.uint8) # 生成 newH* newW *3 的零矩阵
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for i in range(newH - 1):
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for j in range(newW - 1):
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scrx = round(i * (scrH / newH)) # round对其四舍五入
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scry = round(j * (scrW / newW))
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retimg[i, j] = img[scrx, scry] # new image
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return retimg
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# 图片展示函数
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def showImage(picPath):
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'''
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:param picPath: 图片地址
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:return: 样图
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'''
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# 获取图片矩阵
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image = np.array(Image.open(picPath))
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# 设置画布
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plt.figure(figsize=(16, 8))
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# 合并
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plt.subplot(121)
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plt.imshow(image)
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# 调用采样函数
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image1 = blueNoiseSampl(image, image.shape[0] * 2, image.shape[1] * 2)
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# 图片保存
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Image.fromarray(np.uint8(image1)).save("./data/picture13.png")
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plt.subplot(122)
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plt.imshow(image1)
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plt.show()
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return image1
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357
GUI.py
357
GUI.py
@ -1,12 +1,363 @@
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# -*- coding: utf-8 -*-
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# @Time : 2022-7-25 0025 15:03
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# @Time : 2022-7-27 0027 15:21
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# @Author : Qing
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# @Email : derighoid@gmail.com
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# @File : GUI.py
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# @Software: PyCharm
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import os
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import tkinter
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import tkinter.filedialog
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import tkinter.messagebox # 弹窗库
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# git checkout -b "main"
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import PIL
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import numpy as np
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import requests
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from PIL import Image, ImageTk
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import tkinter
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root = tkinter.Tk()
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root.title = 'new'
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root.geometry('640x500')
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# 文件选择
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def choose_fiel():
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selectFileName = tkinter.filedialog.askopenfilename(title='选择文件') # 选择文件
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e.set(selectFileName)
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label1 = tkinter.Label(root, text="文件路径:")
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label1.place(x=40, y=0)
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e = tkinter.StringVar()
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e_entry = tkinter.Entry(root, width=60, textvariable=e)
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e_entry.place(x=100, y=0)
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lable2 = tkinter.Label(root, text="URL:")
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lable2.place(x=40, y=30)
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# 设置输入框
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entry = tkinter.Entry(root, width=60)
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entry.insert(0,
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"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")
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entry.place(x=100, y=30)
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# 选择文件按钮
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submit_button = tkinter.Button(root, text="选择文件", command=choose_fiel)
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submit_button.place(x=520, y=0)
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# 爬取函数
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def pictureCrawl(url):
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# 文件保存地址
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root = "E://桌面//Python_Picture_Analysis//data//"
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# 文件的保存地址以及格式
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path = root + url.split('/')[-3] + '.png'
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try:
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if not os.path.exists(root): # 判断是否存在文件夹
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os.mkdir(root)
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if not os.path.exists(path): # 判断是否存在该文件
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r = requests.get(url)
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# 文件写入
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with open(path, 'wb') as fp:
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fp.write(r.content)
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print("OK")
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else:
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print("File already exists")
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except:
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print("Crawl successful")
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# 爬取按钮
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imgCrawling_button = tkinter.Button(root, text="开始爬取", command=lambda: pictureCrawl(entry.get()))
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imgCrawling_button.place(x=520, y=30)
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# 图片变灰
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def grayPic(e_entry):
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img = Image.open(e_entry.get())
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img = img.convert('L')
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showPic = ImageTk.PhotoImage(img)
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img = tkinter.Label(image=showPic)
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img.image = showPic
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img.place(x=350, y=150)
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# 灰色图按钮
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grayPic_button = tkinter.Button(root, text="灰色图", command=lambda: grayPic(e_entry))
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grayPic_button.place(x=110, y=75)
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# 图像水平镜面对称
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def mirrorSymmetry(e_entry):
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img = Image.open(e_entry.get())
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img = np.asarray(img)
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img1 = np.flipud(img)
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img2 = Image.fromarray(img1)
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pic = ImageTk.PhotoImage(img2)
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img = tkinter.Label(image=pic)
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img.image = pic
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img.place(x=350, y=150)
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# 镜面按钮
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mirrorSymmetry_button = tkinter.Button(root, text="镜面对称", command=lambda: mirrorSymmetry(e_entry))
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mirrorSymmetry_button.place(x=170, y=75)
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# 右旋转
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def verticalSummetry_right(e_tery):
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img = Image.open(e_entry.get())
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img = np.array(img)
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img1 = img.swapaxes(1, 0)
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img2 = img1[:, ::-1]
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img2 = Image.fromarray(img2)
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pic = ImageTk.PhotoImage(img2)
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img = tkinter.Label(image=pic)
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img.image = pic
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img.place(x=300, y=150)
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# 右旋转按钮
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verticalSummetry_right_button = tkinter.Button(root, text="右旋转", command=lambda: verticalSummetry_right(e_entry))
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verticalSummetry_right_button.place(x=240, y=75)
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# 左旋转
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def verticalSummetry_lift(e_entry):
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img = Image.open(e_entry.get())
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img = np.array(img)
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img1 = img.transpose(1, 0, 2) # x,y轴转置
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img2 = img1[::-1]
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img2 = Image.fromarray(img2)
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pic = ImageTk.PhotoImage(img2)
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img = tkinter.Label(image=pic)
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img.image = pic
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img.place(x=300, y=150)
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# 左旋转按钮
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verticalSummetry_lift_button = tkinter.Button(root, text="左旋转", command=lambda: verticalSummetry_lift(e_entry))
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verticalSummetry_lift_button.place(x=300, y=75)
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# 像素反转函数
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def pixInversion(e_entry):
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img = Image.open(e_entry.get())
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img2 = 255 - np.array(img)
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img2 = Image.fromarray(img2)
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img3 = ImageTk.PhotoImage(img2)
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img = tkinter.Label(image=img3)
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img.image = img3
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img.place(x=350, y=150)
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# 像素反转
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pixInversion_button = tkinter.Button(root, text="像素反转", command=lambda: pixInversion(e_entry))
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pixInversion_button.place(x=360, y=75)
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def redImage(e_entry):
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img = Image.open(e_entry.get())
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img = np.array(img)
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img[:, :, 1:3] = 0
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img2 = Image.fromarray(img)
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img3 = ImageTk.PhotoImage(img2)
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img = tkinter.Label(image=img3)
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img.image = img3
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img.place(x=350, y=150)
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# 红色图按钮
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red_button = tkinter.Button(root, text="红色图", command=lambda: redImage(e_entry))
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red_button.place(x=430, y=75)
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def blueImage(e_entry):
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img = Image.open(e_entry.get())
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img = np.array(img)
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img[:, :, :2] = 0
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img2 = Image.fromarray(img)
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img3 = ImageTk.PhotoImage(img2)
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img = tkinter.Label(image=img3)
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img.image = img3
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img.place(x=350, y=150)
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# 蓝色图按钮
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blue_button = tkinter.Button(root, text="蓝色图", command=lambda: blueImage(e_entry))
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blue_button.place(x=490, y=75)
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#绿色图函数
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def greenImage(e_entry):
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img = Image.open(e_entry.get())
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img = np.array(img)
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img[:, :, ::2] = 0
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img2 = Image.fromarray(img)
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img3 = ImageTk.PhotoImage(img2)
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img = tkinter.Label(image=img3)
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img.image = img3
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img.place(x=350, y=150)
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# 绿色按钮
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green_button = tkinter.Button(root, text='绿色图', command=lambda: greenImage(e_entry))
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green_button.place(x=550, y=75)
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# 图像混合函数
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def imageMix(e_entry):
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img1 = Image.open("./data/picture.png")
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img2 = Image.open(e_entry.get())
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img3 = PIL.Image.blend(img1, img2, 0.5)
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img4 = ImageTk.PhotoImage(img3)
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img = tkinter.Label(image=img4)
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img.image = img4
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img.place(x=350, y=150)
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# 图像混合按钮
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imageMix_button = tkinter.Button(root, text="图像混合", command=lambda: imageMix(e_entry))
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imageMix_button.place(x=40, y=105)
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|
||||
|
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# 图像剪切函数
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def imageCut(e_entry):
|
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img1 = Image.open(e_entry.get())
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part = (20, 10, 190, 190)
|
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img2 = img1.crop(part)
|
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img3 = ImageTk.PhotoImage(img2)
|
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img = tkinter.Label(image=img3)
|
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img.image = img3
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img.place(x=350, y=150)
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# 剪切按钮
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cut_button = tkinter.Button(root, text="剪 切", command=lambda: imageCut(e_entry))
|
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cut_button.place(x=110, y=105)
|
||||
|
||||
|
||||
# 部分替换函数
|
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def imgReplace(e_entry):
|
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part = (10, 50, 250, 250)
|
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img1 = Image.open(e_entry.get())
|
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img2 = Image.open("./data/picture.png")
|
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img3 = img2.crop(part)
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img1.paste(img3, (10, 50, 250, 250))
|
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|
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img4 = ImageTk.PhotoImage(img1)
|
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img = tkinter.Label(image=img4)
|
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img.image = img4
|
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img.place(x=350, y=150)
|
||||
|
||||
|
||||
# 部分替换按钮
|
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imgReplace_button = tkinter.Button(root, text="部分替换", command=lambda: imgReplace(e_entry))
|
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imgReplace_button.place(x=170, y=105)
|
||||
|
||||
# 输入框
|
||||
Entrybutton = tkinter.Entry(root, width=15)
|
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Entrybutton.insert(0, 2)
|
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Entrybutton.place(x=490, y=110)
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||||
|
||||
lab5 = tkinter.Label(root, text="Gamma:")
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lab5.place(x=430, y=110)
|
||||
|
||||
|
||||
# 伽马矫正函数(默认1/1,5)
|
||||
def gammaCorrect(e_entry, Entrybutton):
|
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img = Image.open(e_entry.get())
|
||||
img = np.array(img)
|
||||
img1 = np.power(img / float(np.max(img)), int(Entrybutton.get()))
|
||||
img2 = Image.fromarray(np.uint8(img1 * 255))
|
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img3 = ImageTk.PhotoImage(img2)
|
||||
img = tkinter.Label(image=img3)
|
||||
img.image = img3
|
||||
img.place(x=350, y=150)
|
||||
|
||||
|
||||
# 伽马矫正按钮
|
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gammaCorrect_button = tkinter.Button(root, text="伽马矫正", command=lambda: gammaCorrect(e_entry, Entrybutton))
|
||||
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))
|
||||
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))
|
||||
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))
|
||||
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()
|
||||
|
||||
@ -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\\"
|
||||
|
||||
|
||||
# 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)
|
||||
img2 = np.power(img / float(np.max(img)), val)
|
||||
# 伽马函数使用
|
||||
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
|
||||
|
||||
|
||||
|
||||
@ -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)
|
||||
part = (left, upper, right, lower)
|
||||
# print(img.size) Picture Size
|
||||
part = (left, upper, right, lower) # 设置剪切的大小
|
||||
|
||||
# 剪切函数的调用
|
||||
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:
|
||||
"""
|
||||
|
||||
img1 = Image.open(path1)
|
||||
img2 = Image.open(path2)
|
||||
#图片的获取
|
||||
img1 = Image.open(path1) # 被替换图
|
||||
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
|
||||
|
||||
32
Image_Mix.py
32
Image_Mix.py
@ -4,22 +4,36 @@
|
||||
# @Email : derighoid@gmail.com
|
||||
# @File : Image_Mix.py
|
||||
# @Software: PyCharm
|
||||
|
||||
from PIL import Image
|
||||
import PIL
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
import cv2
|
||||
from PIL import Image
|
||||
|
||||
'''
|
||||
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'''
|
||||
|
||||
|
||||
# 阿尔法图像混合
|
||||
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)
|
||||
|
||||
img3 = cv2.addWeighted(im1, 0.8, im2, 0.3, 0)
|
||||
# 调用图片混合函数
|
||||
img3 = PIL.Image.blend(img1, img2, 0.5) # 0.5 为gamma 值
|
||||
|
||||
# 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()
|
||||
|
||||
|
||||
|
||||
@ -41,12 +41,13 @@ 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())
|
||||
|
||||
@ -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
|
||||
@ -64,10 +64,11 @@ class directionChanges():
|
||||
|
||||
# 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)
|
||||
root = "E:\\桌面\\Python_Picture_Analysis\\data\\" # 图片保存地址
|
||||
if not os.path.exists(root): # 判断是否已经存在该文件
|
||||
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')
|
||||
|
||||
36
PictureCrawling/Picture _Crawling.py
Normal file
36
PictureCrawling/Picture _Crawling.py
Normal file
@ -0,0 +1,36 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
# @Time : 2022-7-24 0024 11:18
|
||||
# @Author : Qing
|
||||
# @Email : derighoid@gmail.com
|
||||
# @File : Picture _Crawling.py
|
||||
# @Software: PyCharm
|
||||
|
||||
# 导入库
|
||||
import os
|
||||
import requests
|
||||
|
||||
|
||||
# path = '(?<=<img(?:.*)\s+src=")[^"]+(\.png|jpg|jpeg)'
|
||||
# 定义函数
|
||||
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")
|
||||
|
||||
#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"
|
||||
pictureCrawl(url)
|
||||
@ -50,9 +50,10 @@ 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.subplot(122)
|
||||
# 灰色图片显示
|
||||
plt.imshow(self.imageGrey())
|
||||
plt.show()
|
||||
|
||||
@ -6,11 +6,11 @@
|
||||
# @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):
|
||||
self.filePath = filePath
|
||||
@ -25,16 +25,86 @@ class ReduceColor(pictureRead):
|
||||
|
||||
# 减色算法
|
||||
def decrease_color(self):
|
||||
out=self.imageMatrix().copy()
|
||||
out=out//64*64+32
|
||||
Image.fromarray((np.uint8(out))).save("E:\\桌面\\Python_Picture_Analysis\\data\\picture8.jpg")
|
||||
return out
|
||||
'''
|
||||
val=|32 (0<=var<64)
|
||||
|96 (64<=var<128)
|
||||
|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
|
||||
def plt_Img(self):
|
||||
plt.imshow(self.decrease_color())
|
||||
# 创建新的图
|
||||
newImg = np.zeros((H, W, 3), np.uint8)
|
||||
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(141)
|
||||
plt.imshow(img)
|
||||
plt.title("origin")
|
||||
|
||||
plt.subplot(142)
|
||||
plt.imshow(newImg)
|
||||
plt.title("1")
|
||||
|
||||
plt.subplot(143)
|
||||
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)
|
||||
def imgBinarization(self):
|
||||
@ -54,7 +124,6 @@ class ReduceColor(pictureRead):
|
||||
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')
|
||||
print("Picture Save successfully")
|
||||
|
||||
|
||||
32
main.py
32
main.py
@ -10,17 +10,22 @@ 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.plt_image()
|
||||
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())
|
||||
|
||||
# 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_lift()
|
||||
# direction_Changes.mirrorSymmetry()
|
||||
@ -28,8 +33,8 @@ if __name__ == '__main__':
|
||||
# direction_Changes.img_Save()
|
||||
|
||||
# 减色算法
|
||||
reduce_color = ReduceColor('E:\\桌面\\Python_Picture_Analysis\\data\\th.jpg')
|
||||
# reduce_color.plt_Img()
|
||||
reduce_color = ReduceColor('E:\\桌面\\Python_Picture_Analysis\\data\\th.png')
|
||||
# reduce_color.decrease_color()
|
||||
# reduce_color.img_Save()
|
||||
# reduce_color.imgBinarization()
|
||||
|
||||
@ -38,12 +43,17 @@ if __name__ == '__main__':
|
||||
|
||||
# 图片截取
|
||||
# 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\\picture.jpg',30,70,230,230)
|
||||
imgCut('E:\\桌面\\Python_Picture_Analysis\\data\\th.png',20,10,190,190)
|
||||
imgCut('E:\\桌面\\Python_Picture_Analysis\\data\\picture.png',30,70,230,230)
|
||||
|
||||
# 图片替换
|
||||
# imgReplace('E:\\桌面\\Python_Picture_Analysis\\data\\th.jpg', "E:\\桌面\\Python_Picture_Analysis\\data\\picture.jpg",
|
||||
# 10, 50, 250, 250)
|
||||
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
|
||||
# imageMix('E:\\桌面\\Python_Picture_Analysis\\data\\th.png','E:\\桌面\\Python_Picture_Analysis\\data\\picture.png')
|
||||
imageMix('E:\\桌面\\Python_Picture_Analysis\\data\\th.png','E:\\桌面\\Python_Picture_Analysis\\data\\picture.png')
|
||||
|
||||
#蓝噪
|
||||
showImage("./data/th.png")
|
||||
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user