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from PIL import Image, ImageTk
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from PIL import ImageFilter
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import tkinter as tk # 导入 Tkinter 库
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from tkinter.filedialog import askopenfilename
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import tkinter.messagebox
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import tkinter.ttk
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import tkinter.messagebox
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from PIL import ImageDraw
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from PIL import ImageFont
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from PIL import ImageEnhance
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import numpy as np
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import cv2 as cv
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import matplotlib.pyplot as plt
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from tkinter import simpledialog
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import collections
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import os
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class wlw(object):
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"""description of class"""
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# 加了滤镜的拓展功能，用的ImageFilter库
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def blurPic(Imf):
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Im2 = Imf.filter(ImageFilter.BLUR) # 图像模糊
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return Im2
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def edge(Imf):
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Im4 = Imf.filter(ImageFilter.EDGE_ENHANCE) # 边界增强
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return Im4
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def gaussianBlur(Imf):
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Im6 = Imf.filter(ImageFilter.GaussianBlur) # 高斯模糊
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return Im6
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def emboss(Imf):
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Im8 = Imf.filter(ImageFilter.EMBOSS) # 浮雕滤镜，
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return Im8
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# 以下为自己编写函数实现的功能
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# 线性灰度转换
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def linearization(Imf, a=1.5, c=1.5):
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Im12 = np.array(Imf)
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r, g, b = Im12[:, :, 0], Im12[:, :, 1], Im12[:, :, 2]
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Im12 = 0.2989 * r + 0.5870 * g + 0.1140 * b
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Im12 = float(a) * Im12 + float(c) # 对矩阵类型计算,a是对比度，c是亮度，由k和b传入
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# 进行数据截断，大于255的值要截断为255
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Im12[Im12 > 255] = 255
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# 数据类型转化
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Im12 = np.round(Im12)
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Im12 = Im12.astype(np.uint8)
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return Image.fromarray(Im12)
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# 非线性log灰度转换
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def tologpic(Imf, c=30.0):
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Im14 = np.array(Imf)
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r, g, b = Im14[:, :, 0], Im14[:, :, 1], Im14[:, :, 2]
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Im14 = 0.2989 * r + 0.5870 * g + 0.1140 * b
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Im14 = c * np.log(1.0 + Im14) # 对数运算
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Im14[Im14 > 255] = 255 # 这里应该是不用截断的
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Im14 = np.uint8(Im14 + 0.5)
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return Image.fromarray(Im14)
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# n值灰度转换
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def tonpic(Imf, n=1.5):
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Im16 = np.array(Imf) # 转换矩阵
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r, g, b = Im16[:, :, 0], Im16[:, :, 1], Im16[:, :, 2] # 灰度变换
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Im16 = 0.2989 * r + 0.5870 * g + 0.1140 * b
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Im16 = float(n) * Im16 # 对矩阵类型计算
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# 进行数据截断，大于255的值要截断为255
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Im16[Im16 > 255] = 255
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# 数据类型转化
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Im16 = np.round(Im16)
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Im16 = Im16.astype(np.uint8)
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return Image.fromarray(Im16)
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# 计算直方图
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def showhist(image):
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def clczhifangtu(gray):
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# 计算彩色图单通道的直方图
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hist_new = []
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num = []
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hist_result = []
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hist_key = []
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gray1 = list(gray.ravel()) # 将读取出来的数组转化为一维列表方便循环遍历
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obj = dict(collections.Counter(gray1)) # 计算每个灰度级出现的次数
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obj = sorted(obj.items(), key=lambda item: item[0])
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# 初始化hist数组
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for each in obj:
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hist1 = []
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key = list(each)[0]
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each = list(each)[1]
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hist_key.append(key)
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hist1.append(each)
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hist_new.append(hist1)
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# 检查从0-255每个通道是否都有个数，没有的话添加并将值设为0
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for i in range(0, 256):
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if i in hist_key:
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num = hist_key.index(i)
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hist_result.append(hist_new[num])
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else:
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hist_result.append([0])
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# 检查循环后的列表中是不是已经包含所有的灰度级
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if len(hist_result) < 256:
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for i in range(0, 256 - len(hist_result)):
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hist_new.append([0])
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hist_result = np.array(hist_result)
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return hist_result
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image = np.array(image)
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r, g, b = image[:, :, 0], image[:, :, 1], image[:, :, 2]
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hist_new_b = clczhifangtu(b)
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hist_new_g = clczhifangtu(g)
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hist_new_r = clczhifangtu(r)
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# 绘制直方图
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plt.plot(hist_new_b, color='b')
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plt.plot(hist_new_g, color='g')
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plt.plot(hist_new_r, color='r')
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plt.show()
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# 图像相加
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def Add(img1, img2):
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# 图像相加函数
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def IMG_PLUS(img1, img2):
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# 先修改img1尺寸和img2相同
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img1 = cv.resize(img1, (img2.shape[1], img2.shape[0]))
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# 矩阵相加
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newimg = img1 * 0.5 + img2 * 0.5
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newimg = newimg.astype(np.uint8)
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return newimg
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first = np.array(img1)
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second = np.array(img2)
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newimg = IMG_PLUS(first, second)
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return ImageTk.PhotoImage(Image.fromarray(newimg))
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# 均值滤波
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def filter1(img):
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def filter(img, b=3):
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padnum = (b - 1) // 2 # 填充数量
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pad = ((padnum, padnum), (padnum, padnum), (0, 0)) # 填充格式
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Filter = np.ones((b, b, img.shape[2]), img.dtype) # 方阵滤波器
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padnumImg = np.pad(img, pad, 'constant', constant_values=(0, 0))
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# 用滤波器对图像中像素依次计算取均值
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for i in range(padnum, padnumImg.shape[0] - padnum):
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for j in range(padnum, padnumImg.shape[1] - padnum):
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padnumImg[i][j] = (Filter * padnumImg[i - padnum:i + padnum + 1, j - padnum:j + padnum + 1]).sum(
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axis=0).sum(axis=0) // (b ** 2)
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newimg = padnumImg[padnum:padnumImg.shape[0] - padnum, padnum:padnumImg.shape[1] - padnum] # 剪切使尺寸一样
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return newimg
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l = simpledialog.askinteger(title='滤波核size', prompt='边长L', initialvalue=3, minvalue=0, maxvalue=99)
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img = filter(np.array(img), l)
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return ImageTk.PhotoImage(Image.fromarray(img))
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# 中值滤波
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def filter2(img):
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# 中值滤波处理函数
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def filter(img, b=3):
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padnum = (b - 1) // 2 # 填充数量
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pad = ((padnum, padnum), (padnum, padnum), (0, 0)) # 填充格式
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padImg = np.pad(img, pad, 'constant', constant_values=(0, 0)) # 方阵滤波器
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# 按通道计算中值函数
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def DimensionAdd(img):
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blank = np.zeros((img.shape[2]))
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for i in range(img.shape[2]):
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blank[i] = np.median(img[:, :, i])
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return blank
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# 用滤波器对图像中像素依次计算中值
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for i in range(padnum, padImg.shape[0] - padnum):
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for j in range(padnum, padImg.shape[1] - padnum):
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padImg[i][j] = DimensionAdd(padImg[i - padnum:i + padnum + 1, j - padnum:j + padnum + 1])
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newimg = padImg[padnum:padImg.shape[0] - padnum, padnum:padImg.shape[1] - padnum] # 把操作完多余的0去除，保证尺寸一样大
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return newimg
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l = simpledialog.askinteger(title='滤波核size', prompt='边长L', initialvalue=3, minvalue=0, maxvalue=99)
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img = filter(np.array(img), l)
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return ImageTk.PhotoImage(Image.fromarray(img))
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# sobel锐化
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def sharpen(img):
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img = np.array(img)
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r, g, b = img[:, :, 0], img[:, :, 1], img[:, :, 2]
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img = 0.2989 * r + 0.5870 * g + 0.1140 * b
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# sobel算子
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G_x = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]])
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G_y = np.array([[-1, -2, -1], [0, 0, 0], [1, 2, 1]])
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rows = np.size(img, 0)
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columns = np.size(img, 1)
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mag = np.zeros(img.shape)
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# 分别检测水平和垂直，在计算每个pixel的时候，将水平和垂直的值作一次平方和的处理
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for i in range(0, rows - 2):
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for j in range(0, columns - 2):
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v = sum(sum(G_x * img[i:i + 3, j:j + 3])) # vertical
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h = sum(sum(G_y * img[i:i + 3, j:j + 3])) # horizon
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mag[i + 1, j + 1] = np.sqrt((v ** 2) + (h ** 2))
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# 设置阈值
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threshold = 120
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mag[mag < threshold] = 0
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mag = mag.astype(np.uint8)
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return Image.fromarray(mag)
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# 人脸检测
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def face_detect(img, path):
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img = cv.imread(path)
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name = path.split('.')[0]
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print(img)
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if (img.ndim == 3):
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gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
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else:
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gray = img
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face_cas = cv.CascadeClassifier('haarcascade_frontalface_default.xml')
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face_scale = face_cas.detectMultiScale(gray, 1.1, 5, 0, (100, 100), (400, 400))
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print(face_scale)
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for (x, y, w, h) in face_scale:
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cv.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 1)
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cv.putText(img, name, (x + 10, y - 10), cv.FONT_HERSHEY_SIMPLEX, 1.2, (0, 255, 0), 1)
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return Image.fromarray(img)
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