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Digit_Image_Process_System
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fireandstar 2 years ago
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.idea/.gitignore vendored
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# 默认忽略的文件
/shelf/
/workspace.xml
# 基于编辑器的 HTTP 客户端请求
/httpRequests/
# Datasource local storage ignored files
/dataSources/
/dataSources.local.xml

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.idea/Digit_Image_Process_System.iml
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<?xml version="1.0" encoding="UTF-8"?>
<module type="PYTHON_MODULE" version="4">
<component name="NewModuleRootManager">
<content url="file://$MODULE_DIR$" />
<orderEntry type="inheritedJdk" />
<orderEntry type="sourceFolder" forTests="false" />
</component>
</module>

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.idea/inspectionProfiles/profiles_settings.xml
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<component name="InspectionProjectProfileManager">
<settings>
<option name="USE_PROJECT_PROFILE" value="false" />
<version value="1.0" />
</settings>
</component>

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.idea/misc.xml
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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10" project-jdk-type="Python SDK" />
</project>

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.idea/modules.xml
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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="ProjectModuleManager">
<modules>
<module fileurl="file://$PROJECT_DIR$/.idea/Digit_Image_Process_System.iml" filepath="$PROJECT_DIR$/.idea/Digit_Image_Process_System.iml" />
</modules>
</component>
</project>

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.idea/other.xml
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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="PySciProjectComponent">
<option name="PY_SCI_VIEW_SUGGESTED" value="true" />
</component>
</project>

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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="VcsDirectoryMappings">
<mapping directory="$PROJECT_DIR$" vcs="Git" />
</component>
</project>

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Basic.py
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import math
import random
import cv2
import numpy as np
import matplotlib.pyplot as plt
counter = 1
def turnToBinary(img):
if len(img.shape) == 3:
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
Binary = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2)
return Binary
def segmented_linear_transformation(img):
if len(img.shape) == 3:
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
hist = cv2.calcHist([img], [0], None, [256], [0, 256])
left = -1
right = 256
for i in range(1, 256):
hist[i] = hist[i - 1] + hist[i]
for i in range(0, 256):
if hist[i] >= hist[255] * 0.1 and left == -1:
left = i
elif hist[i] >= hist[255] * 0.9 and right == 256:
right = i
h, w = img.shape
out = np.zeros(img.shape, np.uint8)
for i in range(h):
for j in range(w):
pix = img[i][j]
if pix < left:
out[i][j] = 0.5 * pix
elif left <= pix < right:
out[i][j] = 3.6 * pix - 310
else:
out[i][j] = 0.238 * pix + 194
out = np.around(out)
out = out.astype(np.uint8)
return out
def hsv_ajust(img, k, t):
img = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
for i in range(img.shape[0]):
for j in range(img.shape[1]):
tmp = float(img[i][j][t] * k)
if tmp > 255:
tmp = 255
img[i][j][t] = int(tmp)
img = cv2.cvtColor(img, cv2.COLOR_HSV2RGB)
return img
def size_change(img, x, y, z): # x weight y height z interpolation
itp = {'最近邻插值': cv2.INTER_NEAREST, '双线性插值': cv2.INTER_LINEAR, '基于局部像素的重采样': cv2.INTER_AREA,
'基于4x4像素邻域的3次插值法': cv2.INTER_CUBIC, '基于8x8像素邻域的Lanczos插值': cv2.INTER_LANCZOS4}
return cv2.resize(img, (int(y), int(x)), itp.get(z))
def image_panning(img, x, y): # 图片平移
M = np.float32([[1, 0, y], [0, 1, x]])
return cv2.warpAffine(img, M, (img.shape[1], img.shape[0]))
def image_rotate(img, x):
height, width, channel = img.shape
M = cv2.getRotationMatrix2D((width / 2, height / 2), x, 1)
return cv2.warpAffine(img, M, (width, height))
def get_gray_hist(img):
global counter
filename = 'src/' + str(counter) + '_gray.jpg'
counter = counter + 1
if len(img.shape) == 3:
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
plt.figure(counter)
plt.hist(img.ravel(), 256)
plt.savefig(filename)
return cv2.cvtColor(cv2.imread(filename, 1), cv2.COLOR_BGR2RGB)
def get_colorful_hist(img):
global counter
filename = 'src/' + str(counter) + '_colorful.jpg'
counter = counter + 1
plt.figure(counter)
color = ['b', 'g', 'r']
for index, c in enumerate(color):
hist = cv2.calcHist([img], [index], None, [256], [0, 256])
print(hist)
plt.plot(hist, color=c)
plt.xlim([0, 256])
plt.savefig(filename)
return cv2.cvtColor(cv2.imread(filename, 1), cv2.COLOR_BGR2RGB)
def Hough_transform(img):
img = cv2.GaussianBlur(img, (3, 3), 0) # 高斯模糊
edges = cv2.Canny(img, 50, 150, apertureSize=3) # Canny边缘检测
lines = cv2.HoughLinesP(edges, 1, np.pi / 180, 30)
img_empty = np.zeros((img.shape[0], img.shape[1], 3), np.uint8)
for i in lines:
for x1, y1, x2, y2 in i:
cv2.line(img_empty, (x1, y1), (x2, y2), (255, 255, 255), 1)
return img_empty
def gradient_img_sharpen(img):
if len(img.shape) == 3:
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
h, w = img.shape
img.astype('float')
gradient = np.zeros([h, w], dtype='float')
for x in range(h):
for y in range(w):
if x == h - 1:
gx = abs(float(img[x][y]) - float(img[x - 1][y]))
else:
gx = abs(float(img[x + 1][y]) - float(img[x][y]))
if y == w - 1:
gy = abs(float(img[x][y]) - float(img[x][y - 1]))
else:
gy = abs(float(img[x][y + 1]) - float(img[x][y]))
gradient[x][y] = float(gx) + float(gy)
sharp = gradient
sharp = np.where(sharp > 255, 255, sharp)
sharp = np.where(sharp < 0, 0, sharp)
sharp = sharp.astype('uint8')
return sharp
def Roberts_img_sharpen(img):
if len(img.shape) == 3:
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
kernelx = np.array([[-1, 0], [0, 1]], dtype=int)
kernely = np.array([[0, -1], [1, 0]], dtype=int)
x = cv2.filter2D(img, cv2.CV_16S, kernelx)
y = cv2.filter2D(img, cv2.CV_16S, kernely)
absX = cv2.convertScaleAbs(x)
absY = cv2.convertScaleAbs(y)
Roberts = cv2.addWeighted(absX, 0.5, absY, 0.5, 0)
return Roberts
def Prewitt_img_sharpen(img):
if len(img.shape) == 3:
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
kernelx = np.array([[1, 0, -1], [1, 0, -1], [1, 0, -1]], dtype=int)
kernely = np.array([[-1, -1, -1], [0, 0, 0], [1, 1, 1]], dtype=int)
x = cv2.filter2D(img, cv2.CV_16S, kernelx)
y = cv2.filter2D(img, cv2.CV_16S, kernely)
absX = cv2.convertScaleAbs(x)
absY = cv2.convertScaleAbs(y)
Prewitt = cv2.addWeighted(absX, 0.5, absY, 0.5, 0)
return Prewitt
def Sobel_img_sharpen(img):
if len(img.shape) == 3:
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
x = cv2.Sobel(img, cv2.CV_16S, 1, 0)
y = cv2.Sobel(img, cv2.CV_16S, 0, 1)
absX = cv2.convertScaleAbs(x)
absY = cv2.convertScaleAbs(y)
Sobel = cv2.addWeighted(absX, 0.5, absY, 0.5, 0)
return Sobel
def Laplacian_img_sharpen(img):
if len(img.shape) == 3:
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
gray = cv2.GaussianBlur(img, (5, 5), 0)
dst = cv2.Laplacian(gray, cv2.CV_16S, ksize=3)
Laplacian = cv2.convertScaleAbs(dst)
return Laplacian
def LoG_img_sharpen(img):
if len(img.shape) == 3:
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
gray = cv2.copyMakeBorder(img, 2, 2, 2, 2, borderType=cv2.BORDER_REPLICATE)
gray = cv2.GaussianBlur(gray, (3, 3), 0, 0)
LoG = np.array([[0, 0, -1, 0, 0], [0, -1, -2, -1, 0], [-1, -2, 16, -2, -1], [0, -1, -2, -1, 0], [0, 0, -1, 0, 0]])
h = gray.shape[0]
w = gray.shape[1]
image = np.zeros([h, w], dtype=int)
for i in range(2, h - 2):
for j in range(2, w - 2):
image[i, j] = np.sum(LoG * gray[i - 2:i + 3, j - 2:j + 3, 1])
image = cv2.convertScaleAbs(image)
return image
def Canny_img_sharpen(img):
img = cv2.GaussianBlur(img, (3, 3), 0)
if len(img.shape) == 3:
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
gx = cv2.Sobel(img, cv2.CV_16SC1, 1, 0)
gy = cv2.Sobel(img, cv2.CV_16SC1, 0, 1)
edge_output = cv2.Canny(gx, gy, 50, 100)
return edge_output
def neighberhood_average_smooth(img):
if len(img.shape) == 3:
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
h, w = img.shape
out = np.zeros((h, w))
for i in range(h):
for j in range(w):
if i == 0 or j == 0 or i == h - 1 or j == w - 1:
out[i][j] = img[i][j]
else:
ans = 0
for k in range(-1, 2):
for s in range(-1, 2):
ans = ans + img[i + k][j + s]
ans = ans / 9
out[i][j] = ans
return out
def median_filtering_smooth(img):
if len(img.shape) == 3:
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
h, w = img.shape
out = np.zeros((h, w))
for i in range(h):
for j in range(w):
if i == 0 or j == 0 or i == h - 1 or j == w - 1:
out[i][j] = img[i][j]
else:
pix = []
for k in range(-1, 2):
for s in range(-1, 2):
pix.append(img[i + k][j + s])
pix.sort()
out[i][j] = pix[4]
return out
def Ideal_low_pass_filtering_smooth(img):
if len(img.shape) == 3:
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
img = np.float32(img)
dft = cv2.dft(img, flags=cv2.DFT_COMPLEX_OUTPUT)
dft = np.fft.fftshift(dft)
h, w = img.shape
mask = np.zeros((h, w, 2), dtype=np.uint8)
mask[int(h / 2) - 20:int(h / 2) + 20, int(w / 2) - 20:int(w / 2) + 20] = 1
f = dft * mask
f = np.fft.ifftshift(f)
img = cv2.idft(f)
img = cv2.magnitude(img[:, :, 0], img[:, :, 1])
global counter
filename = 'src/' + str(counter) + '_ideal_low_pass.png'
plt.imsave(filename, img, cmap='gray')
counter = counter + 1
img = cv2.imread(filename, 1)
return img
def ButterWorth_filtering_smooth(img):
if len(img.shape) == 3:
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
img = np.float32(img)
dft = cv2.dft(img, flags=cv2.DFT_COMPLEX_OUTPUT)
dft = np.fft.fftshift(dft)
h, w = img.shape
mask = np.zeros((h, w, 2), dtype=np.float32)
for i in range(h):
for j in range(w):
x = i - h / 2
y = j - w / 2
D = np.sqrt(x ** 2 + y ** 2)
k = D / 20
k = k ** 4
k = k + 1.0
k = 1.0 / k
mask[i, j, 0] = mask[i, j, 1] = k
f = dft * mask
f = np.fft.ifftshift(f)
img = cv2.idft(f)
img = cv2.magnitude(img[:, :, 0], img[:, :, 1])
global counter
filename = 'src/' + str(counter) + '_butterworth_low_pass.png'
plt.imsave(filename, img, cmap='gray')
counter = counter + 1
img = cv2.imread(filename, 1)
return img
def Gaussian_filtering_smooth(img):
if len(img.shape) == 3:
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
img = np.float32(img)
dft = cv2.dft(img, flags=cv2.DFT_COMPLEX_OUTPUT)
dft = np.fft.fftshift(dft)
h, w = img.shape
mask = np.zeros((h, w, 2), dtype=np.float32)
for i in range(h):
for j in range(w):
x = i - h / 2
y = j - w / 2
D = np.sqrt(x ** 2 + y ** 2)
k = D / 20
k = k * k
k = k * - 0.5
k = 2.7182818 ** k
mask[i][j][0] = mask[i][j][1] = k
f = dft * mask
f = np.fft.ifftshift(f)
img = cv2.idft(f)
img = cv2.magnitude(img[:, :, 0], img[:, :, 1])
global counter
filename = 'src/' + str(counter) + '_gaussian_low_pass.png'
plt.imsave(filename, img, cmap='gray')
counter = counter + 1
img = cv2.imread(filename, 1)
return img
def produce_pepper_salt_noise(img):
h, w, t = img.shape
for i in range(h):
for j in range(w):
rnd = random.random()
if rnd < 0.05:
img[i][j] = [0, 0, 0]
elif rnd > 0.95:
img[i][j] = [255, 255, 255]
return img
def produce_Gaussian_noise(img):
img = np.array(img / 255, dtype=float)
noise = np.random.normal(0, 0.1, img.shape)
out = img + noise
out = np.clip(out, 0.0, 1.0)
out = np.uint8(out * 255)
return out
def image_erode(img):
if len(img.shape) == 3:
img = turnToBinary(img)
kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (5, 5), (-1, -1))
img = cv2.erode(img, kernel)
return img
def image_dilate(img):
if len(img.shape) == 3:
img = turnToBinary(img)
kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (5, 5), (-1, -1))
img = cv2.dilate(img, kernel)
return img

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advanced.py
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import cv2
import numpy as np
def face_detect_function(img):
if len(img.shape) == 3:
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
face_detect = cv2.CascadeClassifier(
'haarcascade_frontalface_default.xml') # 人脸识别分类器
face = face_detect.detectMultiScale(img)
for x, y, w, h in face:
cv2.rectangle(img, (x, y, w, h), color=(0, 0, 255), thickness=2)
return img
def removeBG(img, back):
imgt = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
back = cv2.resize(back, (imgt.shape[1], imgt.shape[0]))
if len(back.shape) == 3:
back = cv2.cvtColor(back, cv2.COLOR_RGB2GRAY)
bgModel = cv2.createBackgroundSubtractorMOG2(0, 50)
img = cv2.bilateralFilter(img, 5, 50, 100) # smoothing filter
fgmask = bgModel.apply(img, learningRate=0.01)
kernel = np.ones((3, 3), np.uint8)
fgmask = cv2.erode(fgmask, kernel, iterations=1)
img = cv2.bitwise_and(img, img, mask=fgmask)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img = cv2.GaussianBlur(gray, (41, 41), 0)
_, img = cv2.threshold(img, 0, 255, cv2.THRESH_OTSU)
kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (17, 17), (-1, -1))
img = cv2.dilate(img, kernel, iterations=5)
result = imgt & img
img = ~img
result = (back & img) + result
result = cv2.GaussianBlur(result, (5, 5), 0)
return result

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haarcascade_frontalface_default.xml
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main.exe
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main.py
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import tkinter as tk
import tkinter.filedialog
from tkinter.ttk import *
import cv2
import numpy as np
from PIL import Image, ImageTk
import Basic
import advanced
root = tk.Tk()
root.title('Galaxy_Eyes')
img = cv2.imread('origin.jpg')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img2 = Image.fromarray(img)
photo = ImageTk.PhotoImage(img2)
imageLabel = tk.Label(root, image=photo)
imageLabel.pack(side=tk.LEFT)
logical_var = tk.IntVar()
arithmetic_var = tk.IntVar()
imgx = np.zeros((img.shape[1], img.shape[0]))
def img_change(img_t):
global img
global img2
global photo
global imageLabel
img = img_t
img2 = Image.fromarray(img)
photo = ImageTk.PhotoImage(img2)
imageLabel.config(image=photo)
def ope():
filename = tkinter.filedialog.askopenfilename()
if filename != '':
i = cv2.imread(filename)
i = cv2.cvtColor(i, cv2.COLOR_BGR2RGB)
img_change(i)
def sav(imgt):
filename = tkinter.filedialog.asksaveasfilename(defaultextension='.png',
filetypes=(('PNG文件', '*.png'),
('JPEG文件', '*.jpg'),
('全部类型文件', '*.*')))
if filename != '':
img_t = imgt
if len(imgt.shape) >= 3:
img_t = cv2.cvtColor(img_t, cv2.COLOR_RGB2BGR)
cv2.imwrite(filename, img_t)
def otsu():
global img
img_change(Basic.turnToBinary(img))
def logical_operation(winNew, imx, imy):
if logical_var.get() == 1:
result = imx & imy
elif logical_var.get() == 2:
result = imx | imy
elif logical_var.get() == 3:
result = imx ^ imy
else:
return
result = cv2.resize(result, (600, 600))
img_change(result)
winNew.destroy()
def logical_image_choose(fr, ph):
filename = tkinter.filedialog.askopenfilename()
if filename != '':
imgt = cv2.imread(filename)
imgt = cv2.cvtColor(imgt, cv2.COLOR_BGR2RGB)
imgt = Basic.turnToBinary(imgt)
imgt = cv2.resize(imgt, (150, 150))
fr.photot = ImageTk.PhotoImage(Image.fromarray(imgt))
ph.config(image=fr.photot)
fr.imgy = imgt
def logical_window_setup():
global logical_var
logical_winNew = tk.Toplevel(root)
logical_winNew.wm_attributes('-topmost', 1)
logical_winNew.title('Logical_operation')
logical_winNew.geometry('400x400')
frame1 = tk.Frame(logical_winNew, relief=tkinter.RAISED, borderwidth=2)
operation_and = tk.Radiobutton(frame1, text='逻辑与', variable=logical_var, value=1)
operation_or = tk.Radiobutton(frame1, text='逻辑或', variable=logical_var, value=2)
operation_xor = tk.Radiobutton(frame1, text='逻辑异或', variable=logical_var, value=3)
operation_and.pack()
operation_or.pack()
operation_xor.pack()
frame1.pack()
frame2 = tk.Frame(logical_winNew, relief=tkinter.RAISED, borderwidth=2)
global img
img_Binary = Basic.turnToBinary(img)
img_Binary = cv2.resize(img_Binary, (150, 150)) # 缩小至100x100
frame2.imgx = img_Binary
frame2.imgy = img_Binary
frame2.photo1 = ImageTk.PhotoImage(Image.fromarray(img_Binary))
frame2.photo2 = frame2.photo1
photo1 = tk.Label(frame2, image=frame2.photo1)
photo2 = tk.Label(frame2, image=frame2.photo2)
photo1.pack(side=tk.LEFT)
photo2.pack(side=tk.RIGHT)
frame2.pack()
frame3 = tk.Frame(logical_winNew, relief=tkinter.RAISED, borderwidth=2)
choose_image = tk.Button(frame3, text='请选择操作图', command=lambda: logical_image_choose(frame2, photo2))
operation_button = tk.Button(frame3, text='执行操作',
command=lambda: logical_operation(logical_winNew, frame2.imgx, frame2.imgy))
choose_image.pack(side=tk.LEFT)
operation_button.pack(side=tk.RIGHT)
frame3.pack()
def arithmetic_operation(winNew, imx, imy):
if arithmetic_var.get() == 1:
result = cv2.add(imx, imy)
elif arithmetic_var.get() == 2:
result = cv2.subtract(imx, imy)
elif arithmetic_var.get() == 3:
result = cv2.multiply(imx, imy)
elif arithmetic_var.get() == 4:
result = cv2.divide(imx, imy)
else:
return
result = cv2.resize(result, (600, 600))
img_change(result)
winNew.destroy()
def arithmetic_image_choose(fr, ph):
filename = tkinter.filedialog.askopenfilename()
if filename != '':
imgt = cv2.imread(filename)
imgt = cv2.cvtColor(imgt, cv2.COLOR_BGR2RGB)
imgt = cv2.resize(imgt, (150, 150))
fr.photot = ImageTk.PhotoImage(Image.fromarray(imgt))
ph.config(image=fr.photot)
fr.imgy = imgt
def arithmetic_window_setup():
global arithmetic_var
arithmetic_winNew = tk.Toplevel(root)
arithmetic_winNew.wm_attributes('-topmost', 1)
arithmetic_winNew.title('Logical_operation')
arithmetic_winNew.geometry('400x400')
frame1 = tk.Frame(arithmetic_winNew, relief=tkinter.RAISED, borderwidth=2)
operation_add = tk.Radiobutton(frame1, text='算术加', variable=arithmetic_var, value=1)
operation_sub = tk.Radiobutton(frame1, text='算术减', variable=arithmetic_var, value=2)
operation_mul = tk.Radiobutton(frame1, text='算术乘', variable=arithmetic_var, value=3)
operation_div = tk.Radiobutton(frame1, text='算术除', variable=arithmetic_var, value=4)
operation_add.pack(side=tk.LEFT)
operation_sub.pack(side=tk.LEFT)
operation_mul.pack(side=tk.LEFT)
operation_div.pack(side=tk.LEFT)
frame1.pack()
frame2 = tk.Frame(arithmetic_winNew, relief=tkinter.RAISED, borderwidth=2)
global img
img = cv2.resize(img, (150, 150))
frame2.imgx = img
frame2.imgy = img
frame2.photo1 = ImageTk.PhotoImage(Image.fromarray(img))
frame2.photo2 = frame2.photo1
photo1 = tk.Label(frame2, image=frame2.photo1)
photo2 = tk.Label(frame2, image=frame2.photo2)
photo1.pack(side=tk.LEFT)
photo2.pack(side=tk.RIGHT)
frame2.pack()
frame3 = tk.Frame(arithmetic_winNew, relief=tkinter.RAISED, borderwidth=2)
choose_image = tk.Button(frame3, text='请选择操作图', command=lambda: arithmetic_image_choose(frame2, photo2))
operation_button = tk.Button(frame3, text='执行操作',
command=lambda: arithmetic_operation(arithmetic_winNew, frame2.imgx, frame2.imgy))
choose_image.pack(side=tk.LEFT)
operation_button.pack(side=tk.RIGHT)
frame3.pack()
def size_change_window_setup():
size_winNew = tk.Toplevel(root)
textlabel = tk.Label(size_winNew, text='选择放缩后高度和宽度的大小 和 使用的线性插值')
textlabel.pack()
frame1 = tk.Frame(size_winNew, relief=tkinter.RAISED, borderwidth=2)
frame1_hw = tk.Frame(frame1, relief=tkinter.RAISED, borderwidth=2) # 宽高
frame1_itp = tk.Frame(frame1, relief=tkinter.RAISED, borderwidth=2) # 线性插值
height_entry = tk.Entry(frame1_hw)
weight_entry = tk.Entry(frame1_hw)
global img
height_entry.insert(0, img.shape[0])
weight_entry.insert(0, img.shape[1])
frame1.pack()
frame1_hw.pack(side=tk.LEFT)
frame1_itp.pack(side=tk.LEFT)
height_entry.pack()
weight_entry.pack()
itp_var = tk.StringVar()
itp_comb = Combobox(size_winNew, textvariable=itp_var,
values=['最近邻插值', '双线性插值', '基于局部像素的重采样', '基于4x4像素邻域的3次插值法', '基于8x8像素邻域的Lanczos插值'])
itp_comb.pack()
tk.Button(size_winNew, text='放缩！',
command=lambda: img_change(
Basic.size_change(img, height_entry.get(), weight_entry.get(),
itp_comb.current()))).pack()
def panning_window_setup():
pan_winNew = tk.Toplevel(root)
framex = tk.Frame(pan_winNew, relief=tkinter.RAISED, borderwidth=2)
x_direction = tk.StringVar()
x_direction_comb = Combobox(framex, textvariable=x_direction, value=['上', '下'])
x_direction_comb.pack(side=tk.LEFT)
x_direction_comb.current(0)
x_direction_entry = tk.Entry(framex)
x_direction_entry.insert(0, 0)
x_direction_entry.pack(side=tk.LEFT)
framex.pack()
framey = tk.Frame(pan_winNew, relief=tkinter.RAISED, borderwidth=2)
y_direction = tk.StringVar()
y_direction_comb = Combobox(framey, textvariable=y_direction, value=['左', '右'])
y_direction_comb.pack(side=tk.LEFT)
y_direction_comb.current(0)
y_direction_entry = tk.Entry(framey)
y_direction_entry.pack(side=tk.LEFT)
y_direction_entry.insert(0, 0)
framey.pack()
global img
tk.Button(pan_winNew, text='平移！', command=lambda: img_change(
Basic.image_panning(img, int(x_direction_entry.get()) * (1 if x_direction.get() == '下' else -1),
int(y_direction_entry.get()) * (1 if y_direction.get() == '右' else -1)))).pack()
def rotate_window_setup():
rotate_winNew = tk.Toplevel(root)
rotate_direction = tk.StringVar()
rotate_direction_comb = Combobox(rotate_winNew, textvariable=rotate_direction, value=['逆时针', '顺时针'])
rotate_direction_comb.pack(side=tk.LEFT)
rotate_direction_comb.current(0)
rotate_angle = tk.Entry(rotate_winNew)
rotate_angle.insert(0, 0)
rotate_angle.pack(side=tk.LEFT)
global img
tk.Button(rotate_winNew, text='旋转！', command=lambda: img_change(
Basic.image_rotate(img, int(rotate_angle.get()) if rotate_direction.get() == '逆时针' else (
360 - int(rotate_angle.get()))))).pack(side=tk.BOTTOM)
def Matrix_operation(MA):
M = [[float for i in range(3)] for i in range(2)]
for i in range(0, 2):
for j in range(0, 3):
M[i][j] = float(MA[i][j].get())
M = np.float32(M)
global img
img_change(cv2.warpAffine(img, M, (img.shape[1], img.shape[0])))
def Matrix_window_setup():
Matrix_winNew = tk.Toplevel(root)
Matrix_A = [[tk.Entry() for i in range(3)] for i in range(2)]
for i in range(0, 2):
for j in range(0, 3):
Matrix_A[i][j] = tk.Entry(Matrix_winNew)
Matrix_A[i][j].grid(row=i, column=j)
Matrix_A[i][j].insert(0, (1 if i == j else 0))
for i in range(0, 3):
tk.Label(Matrix_winNew, text=(1 if i == 2 else 0)).grid(row=2, column=i)
tk.Button(Matrix_winNew, text='仿射变换', command=lambda: Matrix_operation(Matrix_A)).grid(row=3, column=0,
columnspan=3)
def flip_window_setup():
flip_winNew = tk.Toplevel(root)
flip_direction = tk.StringVar()
flip_direction_comb = Combobox(flip_winNew, textvariable=flip_direction, value=['水平翻转', '垂直翻转', '对角翻转'])
flip_direction_comb.current(0)
flip_direction_comb.pack()
flip_dic = {'水平翻转': 1, '垂直翻转': 0, '对角翻转': -1}
global img
tk.Button(flip_winNew, text='翻转！',
command=lambda: img_change(cv2.flip(img, flip_dic[flip_direction.get()], dst=None))).pack()
def img_sharp(x, winNew):
if x == 1:
img_change(Basic.gradient_img_sharpen(img))
elif x == 2:
img_change(Basic.Roberts_img_sharpen(img))
elif x == 3:
img_change(Basic.Prewitt_img_sharpen(img))
elif x == 4:
img_change(Basic.Sobel_img_sharpen(img))
elif x == 5:
img_change(Basic.Laplacian_img_sharpen(img))
elif x == 6:
img_change(Basic.LoG_img_sharpen(img))
elif x == 7:
img_change(Basic.Canny_img_sharpen(img))
winNew.destroy()
def sharp_window_setup():
sharp_winNew = tk.Toplevel(root)
sharp_way = tk.StringVar()
sharp_way_comb = Combobox(sharp_winNew, textvariable=sharp_way,
value=['梯度', 'Roberts算子', 'Prewitt算子', 'Sobel算子', 'Laplacian算子', 'LoG算子', 'Canny边缘检测'])
sharp_way_comb.current(0)
sharp_way_comb.pack()
sharp_dic = {'梯度': 1, 'Roberts算子': 2, 'Prewitt算子': 3, 'Sobel算子': 4, 'Laplacian算子': 5, 'LoG算子': 6, 'Canny边缘检测': 7}
tk.Button(sharp_winNew, text='检测！', command=lambda: img_sharp(sharp_dic[sharp_way.get()], sharp_winNew)).pack()
def maintain_face_detection(camera, winNew, panel, bk):
global imgx
success, imgx = camera.read() # 从摄像头读取照片
if success:
global img
imgx = cv2.cvtColor(imgx, cv2.COLOR_BGR2RGB) # 转换颜色从BGR到RGB
if bk.get() == 1:
imgx = advanced.face_detect_function(imgx)
elif bk.get() == 2:
imgx = advanced.removeBG(imgx, img)
current_image = Image.fromarray(imgx) # 将图像转换成Image对象
imgtk = ImageTk.PhotoImage(image=current_image)
panel.imgtk1 = imgtk
panel.config(image=imgtk)
winNew.after(1, lambda: maintain_face_detection(camera, winNew, panel, bk))
def real_time_face_detection():
global imgx
face_winNew = tk.Toplevel(root)
imageLabelt = tk.Label(face_winNew)
imageLabelt.pack()
frame1 = tk.Frame(face_winNew)
frame1.pack()
bk = tk.IntVar()
no_background = tk.Radiobutton(frame1, text='无背景,人脸检测', variable=bk, value=1)
with_background = tk.Radiobutton(frame1, text='以软件当前显示图片为背景', variable=bk, value=2)
no_background.pack(side=tk.LEFT)
with_background.pack(side=tk.LEFT)
tk.Button(face_winNew, text='拍照', command=lambda: sav(imgx)).pack()
camera = cv2.VideoCapture(0)
maintain_face_detection(camera, face_winNew, imageLabelt, bk)
face_winNew.mainloop()
camera.release()
mainmenu = tk.Menu(root) # 菜单栏
filemenu = tk.Menu(mainmenu) # 文件菜单
processmenu = tk.Menu(mainmenu) # 处理菜单
calculationmenu = tk.Menu(mainmenu) # 运算菜单
histmenu = tk.Menu(mainmenu) # 直方图
smoothmenu = tk.Menu(processmenu)
noisemenu = tk.Menu(processmenu)
structuringmenu = tk.Menu(mainmenu)
advancedmenu = tk.Menu(mainmenu)
mainmenu.add_cascade(label='文件', menu=filemenu)
mainmenu.add_cascade(label='基本处理', menu=processmenu)
mainmenu.add_cascade(label='运算', menu=calculationmenu)
mainmenu.add_cascade(label='直方图', menu=histmenu)
mainmenu.add_cascade(label='形态学操作', menu=structuringmenu)
mainmenu.add_cascade(label='进阶操作', menu=advancedmenu)
filemenu.add_command(label='打开', command=ope)
filemenu.add_command(label='保存', command=lambda: sav(img))
processmenu.add_command(label='自适应阈值法二值化', command=otsu)
processmenu.add_command(label='缩放', command=size_change_window_setup)
processmenu.add_command(label='平移', command=panning_window_setup)
processmenu.add_command(label='旋转', command=rotate_window_setup)
processmenu.add_command(label='翻转', command=flip_window_setup)
processmenu.add_command(label='平移和旋转（矩阵）——仿射变换', command=Matrix_window_setup)
processmenu.add_command(label='Hough变换检测直线', command=lambda: img_change(Basic.Hough_transform(img)))
processmenu.add_command(label='边缘检测', command=sharp_window_setup)
processmenu.add_cascade(label='平滑/滤噪', menu=smoothmenu)
processmenu.add_cascade(label='添加噪声', menu=noisemenu)
smoothmenu.add_command(label='空域平滑-邻域平均法', command=lambda: img_change(Basic.neighberhood_average_smooth(img)))
smoothmenu.add_command(label='空域平滑-中值滤波法', command=lambda: img_change(Basic.median_filtering_smooth(img)))
smoothmenu.add_command(label='频域平滑-理想低通滤波', command=lambda: img_change(Basic.Ideal_low_pass_filtering_smooth(img)))
smoothmenu.add_command(label='频域平滑-巴特沃斯低通滤波', command=lambda: img_change(Basic.ButterWorth_filtering_smooth(img)))
smoothmenu.add_command(label='频域平滑-高斯低通滤波', command=lambda: img_change(Basic.Gaussian_filtering_smooth(img)))
noisemenu.add_command(label='添加椒盐噪声', command=lambda: img_change(Basic.produce_pepper_salt_noise(img)))
noisemenu.add_command(label='添加高斯噪声', command=lambda: img_change(Basic.produce_Gaussian_noise(img)))
calculationmenu.add_command(label='逻辑运算', command=logical_window_setup)
calculationmenu.add_command(label='算术运算', command=arithmetic_window_setup)
histmenu.add_command(label='灰度直方图', command=lambda: img_change(Basic.get_gray_hist(img)))
histmenu.add_command(label='彩色直方图', command=lambda: img_change(Basic.get_colorful_hist(img)))
histmenu.add_command(label='分段线性变化', command=lambda: img_change(Basic.segmented_linear_transformation(img)))
structuringmenu.add_command(label='膨胀', command=lambda: img_change(Basic.image_erode(img)))
structuringmenu.add_command(label='腐蚀', command=lambda: img_change(Basic.image_dilate(img)))
structuringmenu.add_command(label='开运算', command=lambda: img_change(Basic.image_dilate(Basic.image_erode(img))))
structuringmenu.add_command(label='闭运算', command=lambda: img_change(Basic.image_erode(Basic.image_dilate(img))))
advancedmenu.add_command(label='人脸检测', command=lambda: img_change(advanced.face_detect_function(img)))
advancedmenu.add_command(label='实时更换背景/拍照', command=real_time_face_detection)
root.config(menu=mainmenu)
# root.bind('Button-3', popupmenu)
root.mainloop()

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