refactoring
This commit is contained in:
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c567ba6273
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291
lab2_1/lab2_1.py
291
lab2_1/lab2_1.py
@ -1,161 +1,162 @@
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import os
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import sys
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import traceback
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import cv2
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import numpy as np
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from matplotlib import pyplot as plt
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def menu():
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print("Выберите пункт меню:\n1.Linear filter\n2.Blur\n3.Median blur\n4.GaussianBlur\n5.Erode")
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print("6.Dilate\n7.MORPH_OPERATIONS\n8.Sobel\n9.Laplacian\n10.Canny\n11.CalcHist\n12.EqualizeHist")
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print("13.Save\n14.Add Figure\n15.Del Figure\n16.Exit")
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SOURCE_IMAGE = "test.jpg"
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def filter_linear(image):
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kernel = np.array([[-0.1, 0.2, -0.1], [0.2, 3.0, 0.2], [-0.1, 0.2, -0.1]])
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cv2.imshow("Исходное изображение", image)
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cv2.imshow("Результат", cv2.filter2D(image.copy(), -1, kernel))
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def filter_blur(image):
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cv2.imshow("Исходное изображение", image)
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cv2.imshow("Результат", cv2.blur(image.copy(), (5, 5)))
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def filter_median_blur(image):
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cv2.imshow("Исходное изображение", image)
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cv2.imshow("Результат", cv2.medianBlur(image.copy(), 5))
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def filter_gauss_blur(image):
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cv2.imshow("Исходное изображение", image)
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cv2.imshow("Результат", cv2.GaussianBlur(image.copy(), (9, 9), cv2.BORDER_DEFAULT))
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def filter_erode(image):
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cv2.imshow("Исходное изображение", image)
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cv2.imshow("Результат", cv2.erode(image.copy(), np.ones((11, 11))))
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def filter_dilate(image):
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cv2.imshow("Исходное изображение", image)
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cv2.imshow("Результат", cv2.dilate(image.copy(), np.ones((11, 11))))
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def filter_morph(image):
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# TODO переписать
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image2 = image.copy()
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image3 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY)
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cv2.imshow("Image_load", image)
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kernel = np.ones((6, 6), np.uint8)
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image3 = cv2.morphologyEx(image3, cv2.MORPH_OPEN, kernel, iterations=1)
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cv2.imshow("MORPH_OPEN", image3)
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image3 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY)
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image3 = cv2.morphologyEx(image3, cv2.MORPH_CLOSE, kernel, iterations=1)
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cv2.imshow("MORPH_CLOSE", image3)
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image3 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY)
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image3 = cv2.morphologyEx(image3, cv2.MORPH_GRADIENT, kernel, iterations=1)
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cv2.imshow("MORPH_GRADIENT", image3)
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image3 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY)
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image3 = cv2.morphologyEx(image3, cv2.MORPH_TOPHAT, kernel, iterations=1)
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cv2.imshow("MORPH_TOPHAT", image3)
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image3 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY)
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image3 = cv2.morphologyEx(image3, cv2.MORPH_BLACKHAT, kernel, iterations=1)
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cv2.imshow("MORPH_BLACKHAT", image3)
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image3 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY)
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def filter_sobel(image):
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image2 = image.copy()
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image3 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY)
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image3 = cv2.GaussianBlur(image3, (3, 3), 0)
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im3 = cv2.Sobel(image3, cv2.CV_64F, 1, 0, ksize=5)
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im4 = cv2.Sobel(image3, cv2.CV_64F, 0, 1, ksize=5)
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im5 = cv2.Sobel(image3, cv2.CV_8UC1, 0, 1, ksize=5)
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cv2.imshow("Image_load", image)
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cv2.imshow("Result_Image_X", im3) # x
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cv2.imshow("Result_Image_Y", im4) # y
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cv2.imshow("Result_Gradient", im5) # gradient
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def filter_laplacian(image):
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image2 = image.copy()
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image3 = cv2.cvtColor(image2, cv2.COLOR_RGB2GRAY)
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image3 = cv2.GaussianBlur(image3, (3, 3), 0)
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im3 = cv2.Laplacian(image3, cv2.CV_64F)
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cv2.imshow("Image_load", image)
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cv2.imshow("Result_Image", im3)
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def filter_canny(image):
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image2 = image.copy()
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image3 = cv2.blur(image2, (5, 5))
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image3 = cv2.Canny(image3, 100, 100)
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cv2.imshow("Image_load", image)
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cv2.imshow("Result_Image", image3)
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def filter_calc_hist(image):
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image2 = image.copy()
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color = ('b', 'g', 'r')
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cv2.imshow("Image_load", image)
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for i, col in enumerate(color):
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histr = cv2.calcHist([image2], [i], None, [256], [0, 256])
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plt.plot(histr, color=col)
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plt.xlim([0, 256])
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plt.show()
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plt.close()
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def filter_equalize_hist(image):
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cv2.imshow("Исходное изображение", image)
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cv2.imshow("Результат", cv2.equalizeHist(cv2.cvtColor(image.copy(), cv2.COLOR_RGB2GRAY)))
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functions_list = {
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"lf": {"func": filter_linear, "help": "Linear filter"},
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"bl": {"func": filter_blur, "help": "Blur"},
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"mb": {"func": filter_median_blur, "help": "Median blur"},
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"gb": {"func": filter_gauss_blur, "help": "GaussianBlur"},
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"er": {"func": filter_erode, "help": "Erode"},
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"di": {"func": filter_dilate, "help": "Dilate"},
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"mo": {"func": filter_morph, "help": "MORPH_OPERATIONS"},
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"so": {"func": filter_sobel, "help": "Sobel"},
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"la": {"func": filter_laplacian, "help": "Laplacian"},
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"ca": {"func": filter_canny, "help": "Canny"},
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"cl": {"func": filter_calc_hist, "help": "CalcHist"},
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"eh": {"func": filter_equalize_hist, "help": "EqualizeHist"},
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"q": {"func": lambda image: sys.exit(0), "help": "Exit"},
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}
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def print_help():
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print("Доступные функции:")
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for k in functions_list:
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print(f" {k} - {functions_list[k]['help']}")
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def main():
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image1 = cv2.imread("D:\\MACH\\LAB_2\\image0.jpg")
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image2 = image1.copy()
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im = image1.copy()
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image1 = cv2.imread(SOURCE_IMAGE)
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while True:
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menu()
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try:
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s = int(input())
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if s == 1:
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image2 = image1.copy()
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kernel = np.array([[-0.1, 0.2, -0.1], [0.2, 3.0, 0.2], [-0.1, 0.2, -0.1]])
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image2 = cv2.filter2D(image2, -1, kernel)
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cv2.imshow("Image_load", image1)
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cv2.imshow("Result_Image", image2)
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cv2.waitKey(0)
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if s == 2:
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image2 = image1.copy()
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image2 = cv2.blur(image2, (5, 5))
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cv2.imshow("Image_load", image1)
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cv2.imshow("Result_Image", image2)
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cv2.waitKey(0)
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if s == 3:
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image2 = image1.copy()
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image2 = cv2.medianBlur(image2, 5)
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cv2.imshow("Image_load", image1)
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cv2.imshow("Result_Image", image2)
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cv2.waitKey(0)
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if s == 4:
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image2 = image1.copy()
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image2 = cv2.GaussianBlur(image2, (9, 9), cv2.BORDER_DEFAULT)
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cv2.imshow("Image_load", image1)
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cv2.imshow("Result_Image", image2)
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cv2.waitKey(0)
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if s == 5:
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image2 = image1.copy()
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image2 = cv2.erode(image2, np.ones((11, 11)))
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cv2.imshow("Image_load", image1)
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cv2.imshow("Result_Image", image2)
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cv2.waitKey(0)
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if s == 6:
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image2 = image1.copy()
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image2 = cv2.dilate(image2, np.ones((11, 11)))
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cv2.imshow("Image_load", image1)
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cv2.imshow("Result_Image", image2)
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cv2.waitKey(0)
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if s == 7:
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image2 = image1.copy()
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image3 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY)
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cv2.imshow("Image_load", image1)
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kernel = np.ones((6, 6), np.uint8)
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image3 = cv2.morphologyEx(image3, cv2.MORPH_OPEN, kernel, iterations=1)
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cv2.imshow("MORPH_OPEN", image3)
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image3 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY)
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image3 = cv2.morphologyEx(image3, cv2.MORPH_CLOSE, kernel, iterations=1)
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cv2.imshow("MORPH_CLOSE", image3)
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image3 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY)
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image3 = cv2.morphologyEx(image3, cv2.MORPH_GRADIENT, kernel, iterations=1)
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cv2.imshow("MORPH_GRADIENT", image3)
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image3 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY)
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image3 = cv2.morphologyEx(image3, cv2.MORPH_TOPHAT, kernel, iterations=1)
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cv2.imshow("MORPH_TOPHAT", image3)
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image3 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY)
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image3 = cv2.morphologyEx(image3, cv2.MORPH_BLACKHAT, kernel, iterations=1)
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cv2.imshow("MORPH_BLACKHAT", image3)
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image3 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY)
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cv2.waitKey(0)
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if s == 8:
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image2 = image1.copy()
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image3 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY)
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image3 = cv2.GaussianBlur(image3, (3, 3), 0)
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im3 = cv2.Sobel(image3, cv2.CV_64F, 1, 0, ksize=5)
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im4 = cv2.Sobel(image3, cv2.CV_64F, 0, 1, ksize=5)
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im5 = cv2.Sobel(image3, cv2.CV_8UC1, 0, 1, ksize=5)
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cv2.imshow("Image_load", image1)
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cv2.imshow("Result_Image_X", im3) # x
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cv2.imshow("Result_Image_Y", im4) # y
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cv2.imshow("Result_Gradient", im5) # gradient
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cv2.waitKey(0)
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if s == 9:
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image2 = image1.copy()
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image3 = cv2.cvtColor(image2, cv2.COLOR_RGB2GRAY)
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image3 = cv2.GaussianBlur(image3, (3, 3), 0)
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im3 = cv2.Laplacian(image3, cv2.CV_64F)
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cv2.imshow("Image_load", image1)
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cv2.imshow("Result_Image", im3) # x
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cv2.waitKey(0)
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if s == 10:
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image2 = image1.copy()
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image3 = cv2.cvtColor(image2, cv2.COLOR_RGB2GRAY)
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image3 = cv2.blur(image2, (5, 5))
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image3 = cv2.Canny(image3, 100, 100)
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cv2.imshow("Image_load", image1)
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cv2.imshow("Result_Image", image3) # x
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cv2.waitKey(0)
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if s == 11:
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image2 = image1.copy()
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color = ('b', 'g', 'r')
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cv2.imshow("Image_load", image1)
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for i, col in enumerate(color):
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histr = cv2.calcHist([image2], [i], None, [256], [0, 256])
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plt.plot(histr, color=col)
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plt.xlim([0, 256])
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plt.show()
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plt.close()
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cv2.waitKey(0)
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if s == 12:
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image2 = image1.copy()
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image2 = cv2.cvtColor(image2, cv2.COLOR_RGB2GRAY)
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image2 = cv2.equalizeHist(image2)
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cv2.imshow("Image_load", image1)
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cv2.imshow("Result_Image", image2)
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cv2.waitKey(0)
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if s == 13:
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cv2.imshow("Image_load", image1)
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cv2.imshow("Result_Image", image2)
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cv2.imwrite("D:\\im1.jpg", image1)
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cv2.imwrite("D:\\im2.jpg", image2)
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cv2.waitKey(0)
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if s == 14:
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image2 = image1.copy()
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cv2.imshow("Image_load", image1)
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image2 = cv2.line(image2, (15, 15), (270, 270), (76, 187, 23), 10) # линия
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image2 = cv2.rectangle(image2, (20, 20), (100, 100), (0, 0, 255), 10) # нарисуем четырехугольник
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image2 = cv2.circle(image2, (100, 100), 40, (0, 255, 0), 10) # нарисуем круг
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cv2.imshow("Result_Image", image2)
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image1 = image2
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cv2.waitKey(0)
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if s == 15:
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cv2.imwrite("1.jpg", im)
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cv2.imwrite("2.jpg", image1)
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img = cv2.imread("1.jpg")
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mask = cv2.imread("2.jpg", 0)
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res = cv2.bitwise_and(img, img, mask=mask)
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image2 = res.copy()
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image1 = res.copy()
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cv2.imshow("Result_Image", res)
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cmd = input(">> ")
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if cmd == "":
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continue
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elif cmd == "?":
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print_help()
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else:
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if cmd in functions_list:
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try:
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functions_list[cmd]["func"](image=image1.copy())
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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except Exception:
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print("Произошла ошибка при выполнении функции")
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traceback.print_exc()
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else:
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print("Функция не найдена! введите '?' для получения справки")
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cv2.waitKey(0)
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os.remove("1.jpg")
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os.remove("2.jpg")
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if s == 16:
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cv2.destroyAllWindows()
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sys.exit(0)
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except ValueError:
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print("Неверный пункт меню!!!! Выберите другое!")
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except Exception:
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traceback.print_exc()
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if __name__ == "__main__":
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BIN
lab2_1/test.jpg
Normal file
BIN
lab2_1/test.jpg
Normal file
Binary file not shown.
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After Width: | Height: | Size: 110 KiB |
240
lab2_2/lab2_2.py
240
lab2_2/lab2_2.py
@ -1,13 +1,17 @@
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import sys
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import cv2
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import numpy
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import numpy
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from PyQt5.QtGui import QImage, QPixmap
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from PyQt5.QtWidgets import QWidget, QApplication, QLabel, QVBoxLayout, QHBoxLayout, QPushButton, QFileDialog,QComboBox,QMessageBox
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from PyQt5.QtWidgets import QWidget, QApplication, QLabel, QVBoxLayout, QHBoxLayout, QPushButton, QFileDialog, \
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QComboBox, QMessageBox
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from PyQt5.QtCore import Qt
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from matplotlib import pyplot as plt
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class Example(QWidget):
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def __init__(self):
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super().__init__()
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self.image2 = None
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self.image = None
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self.label = QLabel()
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self.initUI()
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@ -22,11 +26,10 @@ class Example(QWidget):
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btn_procesar = QPushButton('Сохранить изображение')
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btn_procesar.clicked.connect(self.saveImage)
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f=open("Operate.txt","r")
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attr=f.read().splitlines()
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f = open("Operate.txt", "r")
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attr = f.read().splitlines()
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f.close()
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self.oper=QComboBox()
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self.oper = QComboBox()
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self.oper.addItems(attr)
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self.oper.currentIndexChanged.connect(self.combobox)
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@ -43,25 +46,21 @@ class Example(QWidget):
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self.show()
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def openImage(self):
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self.filename, _ = QFileDialog.getOpenFileName(None, 'Buscar Imagen', '.', 'Image Files (*.png *.jpg *.jpeg *.bmp)')
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self.filename, _ = QFileDialog.getOpenFileName(None, 'Buscar Imagen', '.',
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'Image Files (*.png *.jpg *.jpeg *.bmp)')
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if self.filename:
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with open(self.filename, "rb") as file:
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data = numpy.array(bytearray(file.read()))
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self.image = cv2.imdecode(data, cv2.IMREAD_UNCHANGED)
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self.image = cv2.imdecode(data, cv2.IMREAD_UNCHANGED)
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self.mostrarImagen(self.image)
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self.image2=self.image
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self.image2 = self.image
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def saveImage(self):
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filename = QFileDialog.getSaveFileName(self,"QFileDialog.getSaveFileName()","","Image Files (*.jpg)")
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img=self.image2
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cv2.imwrite(filename[0],img)
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filename = QFileDialog.getSaveFileName(self, "QFileDialog.getSaveFileName()", "", "Image Files (*.jpg)")
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img = self.image2
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cv2.imwrite(filename[0], img)
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def mostrarImagen(self,s):
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def mostrarImagen(self, s):
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size = s.shape
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step = s.size / size[0]
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qformat = QImage.Format_Indexed8
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@ -74,198 +73,193 @@ class Example(QWidget):
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img = img.rgbSwapped()
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self.label.setPixmap(QPixmap.fromImage(img))
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self.resize(self.label.pixmap().size())
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||||
|
||||
|
||||
def combobox(self, index):
|
||||
if index==0:
|
||||
def combobox(self, index):
|
||||
if index == 0:
|
||||
self.i0()
|
||||
if index==1:
|
||||
if index == 1:
|
||||
self.i1()
|
||||
if index==2:
|
||||
if index == 2:
|
||||
self.i2()
|
||||
if index==3:
|
||||
if index == 3:
|
||||
self.i3()
|
||||
if index==4:
|
||||
if index == 4:
|
||||
self.i4()
|
||||
if index==5:
|
||||
if index == 5:
|
||||
self.i5()
|
||||
if index==6:
|
||||
if index == 6:
|
||||
self.i6()
|
||||
if index==7:
|
||||
if index == 7:
|
||||
self.i7()
|
||||
if index==8:
|
||||
if index == 8:
|
||||
self.i8()
|
||||
if index==9:
|
||||
if index == 9:
|
||||
self.i9()
|
||||
if index==10:
|
||||
if index == 10:
|
||||
self.i10()
|
||||
if index==11:
|
||||
if index == 11:
|
||||
self.i11()
|
||||
if index==12:
|
||||
if index == 12:
|
||||
self.i12()
|
||||
if index==13:
|
||||
if index == 13:
|
||||
self.i13()
|
||||
if index==14:
|
||||
if index == 14:
|
||||
self.i14()
|
||||
if index==15:
|
||||
if index == 15:
|
||||
self.i15()
|
||||
if index==16:
|
||||
if index == 16:
|
||||
self.i16()
|
||||
if index==17:
|
||||
if index == 17:
|
||||
self.i17()
|
||||
if index==18:
|
||||
if index == 18:
|
||||
self.i18()
|
||||
if index==19:
|
||||
if index == 19:
|
||||
self.i19()
|
||||
if index==20:
|
||||
if index == 20:
|
||||
self.i20()
|
||||
|
||||
|
||||
def i0(self):
|
||||
self.image2=self.image
|
||||
self.image2 = self.image
|
||||
self.mostrarImagen(self.image)
|
||||
|
||||
def i1(self):
|
||||
self.image2=self.image
|
||||
kernel = numpy.array([[-0.1,0.2,-0.1],[0.2,3.0,0.2],[-0.1,0.2,-0.1]])
|
||||
self.image2=cv2.filter2D(self.image2,-1,kernel)
|
||||
self.image2 = self.image
|
||||
kernel = numpy.array([[-0.1, 0.2, -0.1], [0.2, 3.0, 0.2], [-0.1, 0.2, -0.1]])
|
||||
self.image2 = cv2.filter2D(self.image2, -1, kernel)
|
||||
self.mostrarImagen(self.image2)
|
||||
|
||||
|
||||
|
||||
def i2(self):
|
||||
self.image2=self.image
|
||||
self.image2 = cv2.blur(self.image2,(5,5))
|
||||
self.image2 = self.image
|
||||
self.image2 = cv2.blur(self.image2, (5, 5))
|
||||
self.mostrarImagen(self.image2)
|
||||
|
||||
|
||||
def i3(self):
|
||||
self.image2=self.image
|
||||
self.image2 = cv2.medianBlur(self.image2,5)
|
||||
self.image2 = self.image
|
||||
self.image2 = cv2.medianBlur(self.image2, 5)
|
||||
self.mostrarImagen(self.image2)
|
||||
|
||||
|
||||
def i4(self):
|
||||
self.image2=self.image
|
||||
self.image2 = cv2.GaussianBlur(self.image2, (9,9),cv2.BORDER_DEFAULT)
|
||||
self.image2 = self.image
|
||||
self.image2 = cv2.GaussianBlur(self.image2, (9, 9), cv2.BORDER_DEFAULT)
|
||||
self.mostrarImagen(self.image2)
|
||||
|
||||
|
||||
def i5(self):
|
||||
self.image2=self.image
|
||||
self.image2 = self.image
|
||||
self.image2 = cv2.erode(self.image2, numpy.ones((11, 11)))
|
||||
self.mostrarImagen(self.image2)
|
||||
|
||||
|
||||
def i6(self):
|
||||
self.image2=self.image
|
||||
self.image2 = self.image
|
||||
self.image2 = cv2.dilate(self.image2, numpy.ones((11, 11)))
|
||||
self.mostrarImagen(self.image2)
|
||||
|
||||
|
||||
def i7(self):
|
||||
self.image2=self.image
|
||||
kernel = numpy.ones((6,6),numpy.uint8)
|
||||
self.image2=cv2.morphologyEx(self.image2, cv2.MORPH_OPEN, kernel,iterations = 1)
|
||||
self.image2 = self.image
|
||||
kernel = numpy.ones((6, 6), numpy.uint8)
|
||||
self.image2 = cv2.morphologyEx(self.image2, cv2.MORPH_OPEN, kernel, iterations=1)
|
||||
self.mostrarImagen(self.image2)
|
||||
|
||||
|
||||
def i8(self):
|
||||
self.image2=self.image
|
||||
kernel = numpy.ones((6,6),numpy.uint8)
|
||||
self.image2=cv2.morphologyEx(self.image2, cv2.MORPH_CLOSE, kernel,iterations = 1)
|
||||
self.image2 = self.image
|
||||
kernel = numpy.ones((6, 6), numpy.uint8)
|
||||
self.image2 = cv2.morphologyEx(self.image2, cv2.MORPH_CLOSE, kernel, iterations=1)
|
||||
self.mostrarImagen(self.image2)
|
||||
|
||||
|
||||
def i9(self):
|
||||
self.image2=self.image
|
||||
kernel = numpy.ones((6,6),numpy.uint8)
|
||||
self.image2=cv2.morphologyEx(self.image2, cv2.MORPH_GRADIENT, kernel,iterations = 1)
|
||||
self.image2 = self.image
|
||||
kernel = numpy.ones((6, 6), numpy.uint8)
|
||||
self.image2 = cv2.morphologyEx(self.image2, cv2.MORPH_GRADIENT, kernel, iterations=1)
|
||||
self.mostrarImagen(self.image2)
|
||||
|
||||
|
||||
def i10(self):
|
||||
self.image2=self.image
|
||||
kernel = numpy.ones((6,6),numpy.uint8)
|
||||
self.image2=cv2.morphologyEx(self.image2, cv2.MORPH_TOPHAT, kernel,iterations = 1)
|
||||
self.image2 = self.image
|
||||
kernel = numpy.ones((6, 6), numpy.uint8)
|
||||
self.image2 = cv2.morphologyEx(self.image2, cv2.MORPH_TOPHAT, kernel, iterations=1)
|
||||
self.mostrarImagen(self.image2)
|
||||
|
||||
|
||||
def i11(self):
|
||||
self.image2=self.image
|
||||
kernel = numpy.ones((6,6),numpy.uint8)
|
||||
self.image2=cv2.morphologyEx(self.image2, cv2.MORPH_BLACKHAT, kernel,iterations = 1)
|
||||
self.image2 = self.image
|
||||
kernel = numpy.ones((6, 6), numpy.uint8)
|
||||
self.image2 = cv2.morphologyEx(self.image2, cv2.MORPH_BLACKHAT, kernel, iterations=1)
|
||||
self.mostrarImagen(self.image2)
|
||||
|
||||
|
||||
def i12(self):
|
||||
self.image2=self.image
|
||||
self.image2 = cv2.cvtColor(self.image2,cv2.COLOR_BGR2GRAY)
|
||||
self.image2 = cv2.GaussianBlur(self.image2,(3,3),0)
|
||||
self.image2 = cv2.Sobel(self.image2,cv2.CV_64F,1,0,ksize=5)
|
||||
self.image2 = self.image
|
||||
self.image2 = cv2.cvtColor(self.image2, cv2.COLOR_BGR2GRAY)
|
||||
self.image2 = cv2.GaussianBlur(self.image2, (3, 3), 0)
|
||||
self.image2 = cv2.Sobel(self.image2, cv2.CV_64F, 1, 0, ksize=5)
|
||||
self.mostrarImagen(self.image2)
|
||||
|
||||
|
||||
def i13(self):
|
||||
self.image2=self.image
|
||||
self.image2 = cv2.cvtColor(self.image2,cv2.COLOR_BGR2GRAY)
|
||||
self.image2 = cv2.GaussianBlur(self.image2,(3,3),0)
|
||||
self.image2 = cv2.Sobel(self.image2,cv2.CV_64F,0,1,ksize=5)
|
||||
self.image2 = self.image
|
||||
self.image2 = cv2.cvtColor(self.image2, cv2.COLOR_BGR2GRAY)
|
||||
self.image2 = cv2.GaussianBlur(self.image2, (3, 3), 0)
|
||||
self.image2 = cv2.Sobel(self.image2, cv2.CV_64F, 0, 1, ksize=5)
|
||||
self.mostrarImagen(self.image2)
|
||||
|
||||
|
||||
def i14(self):
|
||||
self.image2=self.image
|
||||
self.image2 = cv2.cvtColor(self.image2,cv2.COLOR_BGR2GRAY)
|
||||
self.image2 = cv2.GaussianBlur(self.image2,(3,3),0)
|
||||
self.image2 = cv2.Sobel(self.image2,cv2.CV_8UC1,0,1,ksize=5)
|
||||
self.image2 = self.image
|
||||
self.image2 = cv2.cvtColor(self.image2, cv2.COLOR_BGR2GRAY)
|
||||
self.image2 = cv2.GaussianBlur(self.image2, (3, 3), 0)
|
||||
self.image2 = cv2.Sobel(self.image2, cv2.CV_8UC1, 0, 1, ksize=5)
|
||||
self.mostrarImagen(self.image2)
|
||||
|
||||
|
||||
def i15(self):
|
||||
self.image2=self.image
|
||||
self.image2 = cv2.cvtColor(self.image2,cv2.COLOR_RGB2GRAY)
|
||||
self.image2 = cv2.GaussianBlur(self.image2,(3,3),0)
|
||||
self.image2 = cv2.Laplacian(self.image2,cv2.CV_64F)
|
||||
self.image2 = self.image
|
||||
self.image2 = cv2.cvtColor(self.image2, cv2.COLOR_RGB2GRAY)
|
||||
self.image2 = cv2.GaussianBlur(self.image2, (3, 3), 0)
|
||||
self.image2 = cv2.Laplacian(self.image2, cv2.CV_64F)
|
||||
self.mostrarImagen(self.image2)
|
||||
|
||||
|
||||
def i16(self):
|
||||
self.image2=self.image
|
||||
self.image2 = cv2.cvtColor(self.image2,cv2.COLOR_RGB2GRAY)
|
||||
self.image2 = cv2.blur(self.image2,(5,5))
|
||||
self.image2 = self.image
|
||||
self.image2 = cv2.cvtColor(self.image2, cv2.COLOR_RGB2GRAY)
|
||||
self.image2 = cv2.blur(self.image2, (5, 5))
|
||||
self.image2 = cv2.Canny(self.image2, 100, 100)
|
||||
self.mostrarImagen(self.image2)
|
||||
|
||||
def i17(self):
|
||||
self.image2=self.image
|
||||
color = ('b','g','r')
|
||||
for i,col in enumerate(color):
|
||||
histr = cv2.calcHist([self.image2],[i],None,[256],[0,256])
|
||||
plt.plot(histr,color = col)
|
||||
plt.xlim([0,256])
|
||||
self.image2 = self.image
|
||||
color = ('b', 'g', 'r')
|
||||
for i, col in enumerate(color):
|
||||
histr = cv2.calcHist([self.image2], [i], None, [256], [0, 256])
|
||||
plt.plot(histr, color=col)
|
||||
plt.xlim([0, 256])
|
||||
plt.show()
|
||||
|
||||
|
||||
def i18(self):
|
||||
self.image2=self.image
|
||||
self.image2 = cv2.cvtColor(self.image2,cv2.COLOR_RGB2GRAY)
|
||||
self.image2 = self.image
|
||||
self.image2 = cv2.cvtColor(self.image2, cv2.COLOR_RGB2GRAY)
|
||||
self.image2 = cv2.equalizeHist(self.image2)
|
||||
self.mostrarImagen(self.image2)
|
||||
|
||||
def i19(self):
|
||||
|
||||
self.image2=self.image
|
||||
self.image2 = cv2.line(self.image2,(15,15),(270,270),(76,187,23),10)#линия
|
||||
self.image2 = cv2.rectangle(self.image2,(20,20),(100,100),(0,0,255),10)#нарисуем четырехугольник
|
||||
self.image2 = cv2.circle(self.image2,(100,100),40,(0,255,0),10)#нарисуем круг
|
||||
self.image=self.image2
|
||||
|
||||
self.image2 = self.image
|
||||
self.image2 = cv2.line(self.image2, (15, 15), (270, 270), (76, 187, 23), 10) # линия
|
||||
self.image2 = cv2.rectangle(self.image2, (20, 20), (100, 100), (0, 0, 255), 10) # нарисуем четырехугольник
|
||||
self.image2 = cv2.circle(self.image2, (100, 100), 40, (0, 255, 0), 10) # нарисуем круг
|
||||
self.image = self.image2
|
||||
self.mostrarImagen(self.image2)
|
||||
|
||||
def i20(self):
|
||||
if self.filename:
|
||||
with open(self.filename, "rb") as file:
|
||||
data = numpy.array(bytearray(file.read()))
|
||||
self.image = cv2.imdecode(data, cv2.IMREAD_UNCHANGED)
|
||||
self.image = cv2.imdecode(data, cv2.IMREAD_UNCHANGED)
|
||||
self.mostrarImagen(self.image)
|
||||
self.mostrarImagen(self.image)
|
||||
self.image2=self.image
|
||||
self.image2 = self.image
|
||||
|
||||
def closeEvent(self, event):
|
||||
close = QMessageBox.question(self,"Выход","Вы хотите завершить работу?",QMessageBox.Yes | QMessageBox.No)
|
||||
close = QMessageBox.question(self, "Выход", "Вы хотите завершить работу?", QMessageBox.Yes | QMessageBox.No)
|
||||
if close == QMessageBox.Yes:
|
||||
event.accept()
|
||||
else:
|
||||
event.ignore()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
app = QApplication(sys.argv)
|
||||
win = Example()
|
||||
sys.exit(app.exec_())
|
||||
sys.exit(app.exec_())
|
||||
|
||||
188
lab4/lab4.py
188
lab4/lab4.py
@ -1,32 +1,22 @@
|
||||
#import numpy as np
|
||||
#import cv2
|
||||
#img = cv2.imread("D:\MACH\LAB_4\crocodile\image_0001.jpg")
|
||||
#gray= cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
|
||||
#sift = cv2.SIFT_create()
|
||||
#kp = sift.detect(gray,None)
|
||||
#img=cv2.drawKeypoints(gray,kp,img)
|
||||
#cv2.imshow("R",img)
|
||||
#cv2.waitKey(0)
|
||||
#cv2.destroyAllWindows()
|
||||
import random
|
||||
import sys
|
||||
import cv2
|
||||
import numpy as np
|
||||
import random
|
||||
import math
|
||||
from PyQt5.QtCore import Qt
|
||||
from sklearn.ensemble import RandomForestClassifier
|
||||
from sklearn.metrics import accuracy_score
|
||||
from PyQt5.QtGui import QImage, QPixmap
|
||||
from PyQt5.QtWidgets import (QWidget, QApplication, QLabel, QVBoxLayout, QHBoxLayout, QPushButton,
|
||||
QFileDialog,QComboBox,QMessageBox,QTextBrowser)
|
||||
from PyQt5.QtCore import Qt
|
||||
|
||||
|
||||
class Example(QWidget):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.image = None
|
||||
self.textbrowser = QTextBrowser()
|
||||
self.initUI()
|
||||
|
||||
def initUI(self):
|
||||
|
||||
self.btn_open = QPushButton('Изображения folder1')
|
||||
self.btn_open.clicked.connect(self.openImages1)
|
||||
@ -34,11 +24,9 @@ class Example(QWidget):
|
||||
self.btn_open1 = QPushButton('Изображения folder2')
|
||||
self.btn_open1.clicked.connect(self.openImages2)
|
||||
|
||||
|
||||
self.btn_ = QPushButton('Обучить и создать матрицы')
|
||||
self.btn_.clicked.connect(self.matrix_and_train)
|
||||
|
||||
|
||||
self.btn1 = QPushButton('Детектировать тестовые изображения')
|
||||
self.btn1.clicked.connect(self.detect_image)
|
||||
|
||||
@ -49,7 +37,6 @@ class Example(QWidget):
|
||||
self.btn1.setVisible(False)
|
||||
self.btn2.setVisible(False)
|
||||
|
||||
|
||||
top_bar = QHBoxLayout()
|
||||
top_bar.addWidget(self.btn_open)
|
||||
top_bar.addWidget(self.btn_open1)
|
||||
@ -59,156 +46,159 @@ class Example(QWidget):
|
||||
root = QVBoxLayout(self)
|
||||
root.addLayout(top_bar)
|
||||
root.addWidget(self.textbrowser)
|
||||
|
||||
self.spisok=list()
|
||||
self.spisok2=list()
|
||||
self.spisok3=list()
|
||||
|
||||
self.spisok = list()
|
||||
self.spisok2 = list()
|
||||
self.spisok3 = list()
|
||||
|
||||
self.train=list()
|
||||
self.matrix=list()
|
||||
self.train = list()
|
||||
self.matrix = list()
|
||||
self.resize(540, 574)
|
||||
self.setWindowTitle('ST_4')
|
||||
self.show()
|
||||
|
||||
def openImages1(self):
|
||||
filenames1 = QFileDialog.getOpenFileNames(None, 'Открыть изображения', '.', 'Image Files (*.png *.jpg *.jpeg *.bmp)')
|
||||
lk=filenames1[0]
|
||||
self.erroropened(lk,"1")
|
||||
self.mass(lk,1)
|
||||
filenames1 = QFileDialog.getOpenFileNames(None, 'Открыть изображения', '.',
|
||||
'Image Files (*.png *.jpg *.jpeg *.bmp)')
|
||||
lk = filenames1[0]
|
||||
self.erroropened(lk, "1")
|
||||
self.mass(lk, 1)
|
||||
lk.clear()
|
||||
|
||||
|
||||
def openImages2(self):
|
||||
filenames2 = QFileDialog.getOpenFileNames(None, 'Открыть изображения', '.', 'Image Files (*.png *.jpg *.jpeg *.bmp)')
|
||||
lk=filenames2[0]
|
||||
self.erroropened(lk,"2")
|
||||
self.mass(lk,2)
|
||||
filenames2 = QFileDialog.getOpenFileNames(None, 'Открыть изображения', '.',
|
||||
'Image Files (*.png *.jpg *.jpeg *.bmp)')
|
||||
lk = filenames2[0]
|
||||
self.erroropened(lk, "2")
|
||||
self.mass(lk, 2)
|
||||
lk.clear()
|
||||
|
||||
def erroropened(self,s,s1):
|
||||
if len(s)!=0:
|
||||
q=QMessageBox.information(self,"Информация","Изображения из "+s1+" папки получены!")
|
||||
|
||||
def erroropened(self, s, s1):
|
||||
if len(s) != 0:
|
||||
q = QMessageBox.information(self, "Информация", "Изображения из " + s1 + " папки получены!")
|
||||
else:
|
||||
q=QMessageBox.information(self,"Информация","Вы не выбрали изображения!")
|
||||
q = QMessageBox.information(self, "Информация", "Вы не выбрали изображения!")
|
||||
|
||||
|
||||
def mass(self,p,s1):
|
||||
if s1==1:
|
||||
def mass(self, p, s1):
|
||||
if s1 == 1:
|
||||
self.spisok3.clear()
|
||||
for v in range(len(p)):
|
||||
self.spisok.append(str(p[v]))
|
||||
self.spisok3.append(str(p[v])+"SKT")
|
||||
self.appendos("Тестовый набор картинок",self.spisok)
|
||||
if s1==2:
|
||||
self.spisok3.append(str(p[v]) + "SKT")
|
||||
self.appendos("Тестовый набор картинок", self.spisok)
|
||||
if s1 == 2:
|
||||
for v in range(len(p)):
|
||||
self.spisok2.append(str(p[v]))
|
||||
self.spisok3.append(str(p[v])+"NO")
|
||||
self.spisok3=list(set(self.spisok3))
|
||||
self.appendos("Набор картинок для тренировки",self.spisok2)
|
||||
self.spisok3.append(str(p[v]) + "NO")
|
||||
self.spisok3 = list(set(self.spisok3))
|
||||
self.appendos("Набор картинок для тренировки", self.spisok2)
|
||||
self.btn_open.setVisible(False)
|
||||
self.btn_open1.setVisible(False)
|
||||
self.btn_.setVisible(True)
|
||||
q=QMessageBox.information(self,"Информация","Количество изображений тестовой категории: "+str(len(self.spisok))+"\nКоличество изображений основной категории: "+str(len(self.spisok2))+"\nОбщее количество изображений: "+str(int(len(self.spisok)+int(len(self.spisok2)))))
|
||||
|
||||
q = QMessageBox.information(self, "Информация", "Количество изображений тестовой категории: " + str(
|
||||
len(self.spisok)) + "\nКоличество изображений основной категории: " + str(
|
||||
len(self.spisok2)) + "\nОбщее количество изображений: " + str(
|
||||
int(len(self.spisok) + int(len(self.spisok2)))))
|
||||
|
||||
def matrix_and_train(self):
|
||||
for v in range(len(self.spisok3)):
|
||||
s=str(self.spisok3[v])
|
||||
s = str(self.spisok3[v])
|
||||
if s.endswith("SKT"):
|
||||
self.train.append(round(float(0.5),1))
|
||||
self.matrix.append(round(float(1.0),1))
|
||||
self.train.append(round(float(0.5), 1))
|
||||
self.matrix.append(round(float(1.0), 1))
|
||||
if s.endswith("NO"):
|
||||
q=round(float(random.uniform(1.0,3.0)),1)
|
||||
q = round(float(random.uniform(1.0, 3.0)), 1)
|
||||
self.train.append(q)
|
||||
self.matrix.append(round(float(-1.0),1))
|
||||
|
||||
self.appendos("Ваши тренировочные данные",self.train)
|
||||
self.appendos("Ваша матрица",self.matrix)
|
||||
train=np.array([self.train],dtype=int)
|
||||
labels = np.array(self.matrix,dtype=int)
|
||||
self.matrix.append(round(float(-1.0), 1))
|
||||
|
||||
self.appendos("Ваши тренировочные данные", self.train)
|
||||
self.appendos("Ваша матрица", self.matrix)
|
||||
train = np.array([self.train], dtype=int)
|
||||
labels = np.array(self.matrix, dtype=int)
|
||||
self.svm = cv2.ml.SVM_create()
|
||||
self.svm.train(train, cv2.ml.COL_SAMPLE, labels)
|
||||
self.svm.save("1.yml")
|
||||
self.textbrowser.append("Модель сохранена!")
|
||||
close = QMessageBox.question(self,"Поздравляем!","Ваша модель натренирована!",QMessageBox.Yes | QMessageBox.No)
|
||||
close = QMessageBox.question(self, "Поздравляем!", "Ваша модель натренирована!",
|
||||
QMessageBox.Yes | QMessageBox.No)
|
||||
if close == QMessageBox.Yes:
|
||||
pass
|
||||
self.btn_.setVisible(False)
|
||||
self.btn1.setVisible(True)
|
||||
|
||||
def appendos(self,s1,s2):
|
||||
def appendos(self, s1, s2):
|
||||
self.textbrowser.append(s1)
|
||||
for v in range(len(s2)):
|
||||
self.textbrowser.append(str(s2[v]))
|
||||
|
||||
def detect_image(self):
|
||||
self.model = RandomForestClassifier(n_estimators=100,bootstrap = True,max_features = 'sqrt')
|
||||
self.model = RandomForestClassifier(n_estimators=100, bootstrap=True, max_features='sqrt')
|
||||
self.spisok3.clear()
|
||||
for v in range(len(self.spisok)):
|
||||
lkst=list()
|
||||
lkst = list()
|
||||
img = cv2.imread(str(self.spisok[v]))
|
||||
gray= cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
|
||||
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
|
||||
sift = cv2.SIFT_create()
|
||||
kp = sift.detect(gray,None)
|
||||
kp = sift.detect(gray, None)
|
||||
for keyPoint in kp:
|
||||
#self.spisok3.append(keyPoint.pt[0])
|
||||
#self.spisok3.append(keyPoint.pt[1])
|
||||
#print(keyPoint.pt[0])
|
||||
#print(keyPoint.pt[1])
|
||||
# self.spisok3.append(keyPoint.pt[0])
|
||||
# self.spisok3.append(keyPoint.pt[1])
|
||||
# print(keyPoint.pt[0])
|
||||
# print(keyPoint.pt[1])
|
||||
lkst.append(keyPoint.pt[0])
|
||||
lkst.append(keyPoint.pt[1])
|
||||
|
||||
#self.spisok3.append(lkst)
|
||||
train=np.array([lkst],dtype=int)
|
||||
labels = np.array([lkst],dtype=int)
|
||||
|
||||
# self.spisok3.append(lkst)
|
||||
train = np.array([lkst], dtype=int)
|
||||
labels = np.array([lkst], dtype=int)
|
||||
self.model.fit(train, labels)
|
||||
self.textbrowser.append("Модель натренирована на рисунке "+str(v))
|
||||
#print(len(self.spisok3))
|
||||
self.textbrowser.append("Модель натренирована на рисунке " + str(v))
|
||||
# print(len(self.spisok3))
|
||||
self.textbrowser.append("Выявлены контурные точки на тестовых изображениях!")
|
||||
|
||||
#s = keyPoint.size
|
||||
#print(x,y,s)
|
||||
|
||||
# s = keyPoint.size
|
||||
# print(x,y,s)
|
||||
self.btn1.setVisible(False)
|
||||
self.btn2.setVisible(True)
|
||||
|
||||
def form_les_and_train(self):
|
||||
|
||||
#print(self.spisok3)
|
||||
|
||||
# print(self.spisok3)
|
||||
for v in range(len(self.spisok2)):
|
||||
lk=list()
|
||||
lkst = list()
|
||||
img = cv2.imread(str(self.spisok2[v]))
|
||||
gray= cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
|
||||
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
|
||||
sift = cv2.SIFT_create()
|
||||
kp = sift.detect(gray,None)
|
||||
kp = sift.detect(gray, None)
|
||||
for keyPoint in kp:
|
||||
#self.spisok3.append(keyPoint.pt[0])
|
||||
#self.spisok3.append(keyPoint.pt[1])
|
||||
#print(keyPoint.pt[0])
|
||||
#print(keyPoint.pt[1])
|
||||
# self.spisok3.append(keyPoint.pt[0])
|
||||
# self.spisok3.append(keyPoint.pt[1])
|
||||
# print(keyPoint.pt[0])
|
||||
# print(keyPoint.pt[1])
|
||||
lkst.append(keyPoint.pt[0])
|
||||
lkst.append(keyPoint.pt[1])
|
||||
train =np.array([lkst],dtype=int)
|
||||
train = np.array([lkst], dtype=int)
|
||||
self.model.predict(train)
|
||||
#self.model.
|
||||
# self.model.
|
||||
print("RED")
|
||||
#train = np.array([self.spisok3],dtype=int)
|
||||
#labels = np.array([self.spisok3],dtype=int)
|
||||
#model = RandomForestClassifier(n_estimators=100,bootstrap = True,max_features = 'sqrt')
|
||||
#model.fit(train, labels)
|
||||
#tree = DecisionTreeClassifier()
|
||||
#tree.fit(self.spisok3, self.spisok3)
|
||||
#accuracy = accuracy_score(len(self.spisok3), len(self.spisok3))
|
||||
#print('Model Accuracy:',accuracy)
|
||||
|
||||
# train = np.array([self.spisok3],dtype=int)
|
||||
# labels = np.array([self.spisok3],dtype=int)
|
||||
# model = RandomForestClassifier(n_estimators=100,bootstrap = True,max_features = 'sqrt')
|
||||
# model.fit(train, labels)
|
||||
# tree = DecisionTreeClassifier()
|
||||
# tree.fit(self.spisok3, self.spisok3)
|
||||
# accuracy = accuracy_score(len(self.spisok3), len(self.spisok3))
|
||||
# print('Model Accuracy:',accuracy)
|
||||
|
||||
def closeEvent(self, event):
|
||||
close = QMessageBox.question(self,"Выход","Вы хотите завершить работу?",QMessageBox.Yes | QMessageBox.No)
|
||||
close = QMessageBox.question(self, "Выход", "Вы хотите завершить работу?", QMessageBox.Yes | QMessageBox.No)
|
||||
if close == QMessageBox.Yes:
|
||||
event.accept()
|
||||
else:
|
||||
event.ignore()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
app = QApplication(sys.argv)
|
||||
win = Example()
|
||||
sys.exit(app.exec_())
|
||||
sys.exit(app.exec_())
|
||||
|
||||
Reference in New Issue
Block a user