{"id":6691,"date":"2019-04-15T10:30:59","date_gmt":"2019-04-15T01:30:59","guid":{"rendered":"http:\/\/www.gisdeveloper.co.kr\/?p=6691"},"modified":"2020-05-28T12:24:25","modified_gmt":"2020-05-28T03:24:25","slug":"python%ea%b3%bc-opencv-24-fourier-transform","status":"publish","type":"post","link":"http:\/\/www.gisdeveloper.co.kr\/?p=6691","title":{"rendered":"Python\uacfc OpenCV \u2013 25 : Fourier Transform"},"content":{"rendered":"

\uc774 \uae00\uc758 \uc6d0\ubb38\uc740 https:\/\/opencv-python-tutroals.readthedocs.io\/en\/latest\/py_tutorials\/py_imgproc\/py_transforms\/py_fourier_transform\/py_fourier_transform.html#fourier-transform \uc785\ub2c8\ub2e4.<\/p>\n

\ud4e8\ub9ac\uc5d0 \ubcc0\ud658\uc774\ub780 \uc5b4\ub5a4 \uc785\ub825 \uc2e0\ud638\ub97c \ub2e4\uc218\uc758 sin\uacfc cos \ud568\uc218\uc758 \ud569\uc73c\ub85c \ubcc0\ud658\ud55c \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4. sin\uacfc cos \ud568\uc218\uc758 \ud569\uc774\ubbc0\ub85c \uadf8\ub798\ud504\ub97c \uadf8\ub824\ubcf4\uba74 \uc5b4\ub5a4 \ubc18\ubcf5\uc801\uc778 \uc8fc\uae30\ub97c \uac00\uc9c0\uace0 \uc788\uc744 \uac83\uc785\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ud4e8\ub9ac\uc5d0 \ubcc0\ud658\uc744 \uc774\ubbf8\uc9c0\uc5d0 \uc801\uc6a9\ud560 \uc218 \uc788\ub294\ub370, \uc774\ubbf8\uc9c0\uc5d0 \uc801\uc6a9\ud55c \ud4e8\ub9ac\uc5d0 \ubcc0\ud658\uc758 \uacb0\uacfc\ub97c \uc5bb\ub294 \uac83\uc744 2D DFT(Discrete Fourier Transform)\uc774\ub77c\uace0 \ud569\ub2c8\ub2e4. \ub610\ud55c \uc774\ub807\uac8c \uc5bb\uc740 \ud4e8\ub9ac\uc5d0 \ubcc0\ud658 \uacb0\uacfc\ub97c \uc774\uc6a9\ud574 \ub2e4\uc2dc \uc5ed\uc73c\ub85c \uc774\ubbf8\uc9c0\ub97c \uc5bb\ub294 \uac83\uc744 \uc5ed \ud4e8\ub9ac\uc5d0 \ubcc0\ud658\uc774\ub77c\uace0 \ud569\ub2c8\ub2e4. \ud4e8\ub9ac\uc5d0 \ubcc0\ud658\uc740 Numpy\uc640 OpenCV \ubaa8\ub450\uc5d0\uc11c \ud568\uc218\ub85c \uc81c\uacf5\ud569\ub2c8\ub2e4. \uba3c\uc800 Numpy\uc5d0\uc11c \uc81c\uacf5\ud558\ub294 \ud4e8\ub9ac\uc5d0 \ubcc0\ud658\uc5d0 \ub300\ud574\uc11c \uc0b4\ud3b4\ubcf4\uba74..<\/p>\n

\r\nimport cv2\r\nimport numpy as np\r\nfrom matplotlib import pyplot as plt\r\n\r\nimg = cv2.imread('.\/data\/messi5.jpg',0)\r\nf = np.fft.fft2(img)\r\nfshift = np.fft.fftshift(f)\r\nmagnitude_spectrum = 20*np.log(np.abs(fshift))\r\n\r\nplt.subplot(121),plt.imshow(img, cmap = 'gray')\r\nplt.title('Input Image'), plt.xticks([]), plt.yticks([])\r\nplt.subplot(122),plt.imshow(magnitude_spectrum, cmap = 'gray')\r\nplt.title('Magnitude Spectrum'), plt.xticks([]), plt.yticks([])\r\nplt.show()\r\n<\/pre>\n
\uc2e4\ud589 \uacb0\uacfc\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/p>\n
\"\"<\/p>\n
\uc67c\ucabd\uc740 \uc785\ub825 \uc774\ubbf8\uc9c0\uc774\uace0 \uc624\ub978\ucabd\uc740 \uc774\ubbf8\uc9c0\uc5d0 \ub300\ud55c \ud478\ub9ac\uc5d0 \ubcc0\ud658\uc758 \uacb0\uacfc\ub97c \uc2dc\uac01\ud654\ud55c \uac83\uc785\ub2c8\ub2e4. 6\ubc88 \ucf54\ub4dc\uc758 np.fft.fft2 \ud568\uc218\uac00 \uc774\ubbf8\uc9c0\uc5d0 \ub300\ud55c \ud478\ub9ac\uc5d0 \ubcc0\ud658\uc744 \uc2e4\ud589\ud558\ub294 \uac83\uc774\uace0, 7\ubc88 \ucf54\ub4dc\uc758 np.fft.fftshift \ud568\uc218\ub294 \ud478\ub9ac\uc5d0 \ubcc0\ud658\uc758 \uacb0\uacfc\ub97c \uc911\uc2ec\uc73c\ub85c \uc774\ub3d9\uc2dc\ud0b5\ub2c8\ub2e4. \ud478\ub9ac\uc5d0 \ubcc0\ud658\uc740 \ubc18\ubcf5\uc801\uc778 \uc8fc\uae30\ub97c \uac16\uc73c\ubbc0\ub85c \uc774\ucc98\ub7fc \uadf8 \uacb0\uacfc\ub97c \uc911\uc2ec\uc73c\ub85c \uc774\ub3d9\uc2dc\ud0a4\ub294 \uac83\uc774 \uc2dc\uac01\ud654 \uad00\uc810\uc5d0\uc11c \uc88b\uc2b5\ub2c8\ub2e4. \uadf8\ub9ac\uace0 8\ubc88 \ucf54\ub4dc\ub294 \ud478\ub9ac\uc5d0 \ubcc0\ud658\uc758 \uacb0\uacfc\uc758 \uac12\uc744 \uc99d\ud3ed\uc2dc\ud0b5\ub2c8\ub2e4. \uc6cc\ub099 \ud478\ub9ac\uc5d0 \ubcc0\ud658\uc758 \uac12\uc774 \uc791\uae30\ub54c\ubb38\uc5d0 \uc774\ub97c \uc2dc\uac01\ud654\ud558\uba74 \ub450\ub4dc\ub7ec\uc9c4 \ud2b9\uc9d5\uc744 \uc0b4\ud3b4\ubcfc \uc218 \uc5c6\uc5b4 \uac12\uc744 \uc99d\ud3ed\uc2dc\ud0b5\ub2c8\ub2e4.<\/p>\n
\uc774\uc81c \ud478\ub9ac\uc5d0 \ubcc0\ud658\uc758 \uacb0\uacfc\ub97c \ubcc0\uacbd\ud574\uc11c, \ubcc0\uacbd\ub41c \ud478\ub9ac\uc5d0 \ubcc0\ud658\uc744 \ud1b5\ud574 \uc774\ubbf8\uc9c0\ub85c \uc5ed \ubcc0\ud658\uc2dc\ud0a4\ub294 \ucf54\ub4dc\ub97c \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
\r\nimport cv2\r\nimport numpy as np\r\nfrom matplotlib import pyplot as plt\r\n\r\nimg = cv2.imread('.\/data\/messi5.jpg',0)\r\nf = np.fft.fft2(img)\r\nfshift = np.fft.fftshift(f)\r\n\r\nrows,cols = img.shape\r\ncrow,ccol = (int)(rows\/2),(int)(cols\/2)\r\nfshift[crow-30:crow+30, ccol-30:ccol+30] = 0\r\nf_ishift = np.fft.ifftshift(fshift)\r\nimg_back = np.fft.ifft2(f_ishift)\r\nimg_back = np.abs(img_back)\r\n\r\nplt.subplot(131),plt.imshow(img, cmap = 'gray')\r\nplt.title('Input Image'), plt.xticks([]), plt.yticks([])\r\nplt.subplot(132),plt.imshow(img_back, cmap = 'gray')\r\nplt.title('Image after HPF'), plt.xticks([]), plt.yticks([])\r\nplt.subplot(133),plt.imshow(img_back)\r\nplt.title('Result in JET'), plt.xticks([]), plt.yticks([])\r\n\r\nplt.show()\r\n<\/pre>\n
\ud478\ub9ac\uc5d0 \ubcc0\ud658 \uacb0\uacfc\uc5d0 \uc5b4\ub5a4 \ubcc0\uacbd\uc744 \uac00\ud588\ub294\uc9c0 \uc5b8\uae09\ud558\uba74, 11\ubc88 \ucf54\ub4dc\uc5d0\uc11c \ud478\ub9ac\uc5d0 \ubcc0\ud658 \uacb0\uacfc \uc774\ubbf8\uc9c0\uc758 \uc911\uc2ec\uc744 \uae30\uc900\uc73c\ub85c \uc88c\uc6b0\ub85c 30\ud53d\uc140 \ubc94\uc704 \uc601\uc5ed\uc758 \uac12\uc744 0\uc73c\ub85c \ubcc0\uacbd\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub807\uac8c \ubcc0\uacbd\ub41c \ud478\ub9ac\uc5d0 \ubcc0\ud658 \uacb0\uacfc\ub97c \ud1b5\ud574 \uc5ed \ubcc0\ud658\uc2dc\ud0a4\ub294 \ucf54\ub4dc\uac00 12\ubc88\uacfc 13\ubc88 \ucf54\ub4dc\uc785\ub2c8\ub2e4. \uacb0\uacfc\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/p>\n
\"\"<\/p>\n
\uc774\ubbf8\uc9c0\uc758 Edge\ub97c \ucd94\ucd9c\ud55c \uacb0\uacfc\uac00 \ub3c4\ucd9c\ub418\uc5c8\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c\uc73c\ub85c OpenCV\ub97c \ud1b5\ud55c \ud478\ub9ac\uc5d0 \ubcc0\ud658\uc5d0 \ub300\ud574 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
\r\nimport numpy as np\r\nimport cv2\r\nfrom matplotlib import pyplot as plt\r\n\r\nimg = cv2.imread('.\/data\/messi5.jpg',0)\r\n\r\ndft = cv2.dft(np.float32(img),flags = cv2.DFT_COMPLEX_OUTPUT)\r\ndft_shift = np.fft.fftshift(dft)\r\n\r\nmagnitude_spectrum = 20*np.log(cv2.magnitude(dft_shift[:,:,0],dft_shift[:,:,1]))\r\n\r\nplt.subplot(121),plt.imshow(img, cmap = 'gray')\r\nplt.title('Input Image'), plt.xticks([]), plt.yticks([])\r\nplt.subplot(122),plt.imshow(magnitude_spectrum, cmap = 'gray')\r\nplt.title('Magnitude Spectrum'), plt.xticks([]), plt.yticks([])\r\nplt.show()\r\n<\/pre>\n
\uc2e4\ud589 \uacb0\uacfc\ub294 \uc544\ub798\uc640 \uac19\uace0 Numpy\ub97c \ud1b5\ud55c \ud478\ub9ac\uc5d0 \ubcc0\ud658\uacfc \ub3d9\uc77c\ud569\ub2c8\ub2e4.<\/p>\n
\"\"<\/p>\n
\uc774\uc81c \uc5ec\uae30\uc11c \ud478\ub9ac\uc5d0 \ubcc0\ud658 \uacb0\uacfc\uc5d0 \uc5b4\ub5a4 \ubcc0\uacbd\uc744 \uac00\ud558\uace0 \ub09c\ub4a4 \ud478\ub9ac\uc5d0 \uc5ed\ubcc0\ud658\uc744 \ud1b5\ud574 \uc774\ubbf8\uc9c0\ub97c \uc5bb\uc5b4 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc774\ubc88 \ubcc0\uacbd\uc740 \uc55e\uc11c\uc11c \uac00\uc6b4\ub370 \uc601\uc5ed\uc744 0\uc73c\ub85c \uc124\uc815\ud588\ub294\ub370, \uc774\ubc88\uc5d0\ub294 1\ub85c \uc124\uc815\ud569\ub2c8\ub2e4.<\/p>\n
\r\nimport numpy as np\r\nimport cv2\r\nfrom matplotlib import pyplot as plt\r\n\r\nimg = cv2.imread('.\/data\/messi5.jpg',0)\r\n\r\ndft = cv2.dft(np.float32(img),flags = cv2.DFT_COMPLEX_OUTPUT)\r\ndft_shift = np.fft.fftshift(dft)\r\n\r\nrows, cols = img.shape\r\ncrow,ccol = int(rows\/2), int(cols\/2)\r\n\r\n# create a mask first, center square is 1, remaining all zeros\r\nmask = np.zeros((rows,cols,2),np.uint8)\r\nmask[crow-30:crow+30, ccol-30:ccol+30] = 1\r\n\r\n# apply mask and inverse DFT\r\nfshift = dft_shift*mask\r\nf_ishift = np.fft.ifftshift(fshift)\r\nimg_back = cv2.idft(f_ishift)\r\nimg_back = cv2.magnitude(img_back[:,:,0],img_back[:,:,1])\r\n\r\nplt.subplot(121),plt.imshow(img, cmap = 'gray')\r\nplt.title('Input Image'), plt.xticks([]), plt.yticks([])\r\nplt.subplot(122),plt.imshow(img_back, cmap = 'gray')\r\nplt.title('Magnitude Spectrum'), plt.xticks([]), plt.yticks([])\r\nplt.show()\r\n<\/pre>\n
\uc2e4\ud589 \uacb0\uacfc\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/p>\n
\"\"<\/p>\n
\uc774\ubbf8\uc9c0\uc5d0 \ube14\ub7ec\ub9c1 \ud544\ud130\uac00 \uc801\uc6a9\ub41c \uac83\uacfc \uac19\uc2b5\ub2c8\ub2e4. \uadf8\ub807\ub2e4\uba74 \uc65c \ud478\ub9ac\uc5d0 \ubcc0\ud658\uacfc \uadf8 \uc5ed\ubcc0\ud658\uc744 \ud1b5\ud574 \uc6d0\ub798\uc758 \uc774\ubbf8\uc9c0\uac00 Edge \ucd94\ucd9c\uc774\ub098 Blur \ud6a8\uacfc\uac00 \uc801\uc6a9\ub418\ub294\uc9c0 \uc9c1\uad00\uc801\uc73c\ub85c \uc774\ud574\ud558\uae30 \uc704\ud574 \ub2e4\uc74c \uc608\uc81c\ub97c \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
\r\nimport cv2\r\nimport numpy as np\r\nfrom matplotlib import pyplot as plt\r\n\r\n# simple averaging filter without scaling parameter\r\nmean_filter = np.ones((3,3))\r\n\r\n# creating a guassian filter\r\nx = cv2.getGaussianKernel(5,10)\r\ngaussian = x*x.T\r\n\r\n# different edge detecting filters\r\n# scharr in x-direction\r\nscharr = np.array([[-3, 0, 3],\r\n                   [-10,0,10],\r\n                   [-3, 0, 3]])\r\n# sobel in x direction\r\nsobel_x= np.array([[-1, 0, 1],\r\n                   [-2, 0, 2],\r\n                   [-1, 0, 1]])\r\n# sobel in y direction\r\nsobel_y= np.array([[-1,-2,-1],\r\n                   [0, 0, 0],\r\n                   [1, 2, 1]])\r\n# laplacian\r\nlaplacian=np.array([[0, 1, 0],\r\n                    [1,-4, 1],\r\n                    [0, 1, 0]])\r\n\r\nfilters = [mean_filter, gaussian, laplacian, sobel_x, sobel_y, scharr]\r\nfilter_name = ['mean_filter', 'gaussian','laplacian', 'sobel_x', \\\r\n                'sobel_y', 'scharr_x']\r\nfft_filters = [np.fft.fft2(x) for x in filters]\r\nfft_shift = [np.fft.fftshift(y) for y in fft_filters]\r\nmag_spectrum = [np.log(np.abs(z)+1) for z in fft_shift]\r\n\r\nfor i in range(6):\r\n    plt.subplot(2,3,i+1),plt.imshow(mag_spectrum[i],cmap = 'gray')\r\n    plt.title(filter_name[i]), plt.xticks([]), plt.yticks([])\r\n\r\nplt.show()\r\n<\/pre>\n
\uc704\uc758 \ucf54\ub4dc\ub97c \uc2e4\ud589\ud574\ubcf4\uba74 \uc544\ub798\uc640 \uac19\uc740\ub370.. \uac01 \ud589\ub82c\uc5d0 \ub300\ud574 \ud478\ub9ac\uc5d0 \ubcc0\ud658\uc744 \uc218\ud589\ud574 \uadf8 \uacb0\uacfc\ub97c \uc0b4\ud3b4\ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\"\"<\/p>\n
\ucc98\uc74c Numpy \ubc29\uc2dd\uc5d0\uc11c\ub294 \ud478\ub9ac\uc5d0 \ubcc0\ud658\uc5d0 \uc704\uc758 \uc608\uc81c \uacb0\uacfc\uc5d0\uc11c\uc758 laplacian\uc73c\ub85c \ubcc0\uacbd\ud588\uace0, \ub450\ubc88\uc9f8 OpenCV \ubc29\uc2dd\uc5d0\uc11c\ub294 mean_filter\ub098 gaussian\uc73c\ub85c \ubcc0\uacbd\ud558\uc5ec \ud478\ub9ac\uc5d0 \uc5ed\ubcc0\ud658\uc744 \uc218\ud589\ud588\ub2e4\ub294 \uac83\uc744 \uc0c1\uae30\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n","protected":false},"excerpt":{"rendered":"
\uc774 \uae00\uc758 \uc6d0\ubb38\uc740 https:\/\/opencv-python-tutroals.readthedocs.io\/en\/latest\/py_tutorials\/py_imgproc\/py_transforms\/py_fourier_transform\/py_fourier_transform.html#fourier-transform \uc785\ub2c8\ub2e4. \ud4e8\ub9ac\uc5d0 \ubcc0\ud658\uc774\ub780 \uc5b4\ub5a4 \uc785\ub825 \uc2e0\ud638\ub97c \ub2e4\uc218\uc758 sin\uacfc cos \ud568\uc218\uc758 \ud569\uc73c\ub85c \ubcc0\ud658\ud55c \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4. sin\uacfc cos \ud568\uc218\uc758 \ud569\uc774\ubbc0\ub85c \uadf8\ub798\ud504\ub97c \uadf8\ub824\ubcf4\uba74 \uc5b4\ub5a4 \ubc18\ubcf5\uc801\uc778 \uc8fc\uae30\ub97c \uac00\uc9c0\uace0 \uc788\uc744 \uac83\uc785\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ud4e8\ub9ac\uc5d0 \ubcc0\ud658\uc744 \uc774\ubbf8\uc9c0\uc5d0 \uc801\uc6a9\ud560 \uc218 \uc788\ub294\ub370, \uc774\ubbf8\uc9c0\uc5d0 \uc801\uc6a9\ud55c \ud4e8\ub9ac\uc5d0 \ubcc0\ud658\uc758 \uacb0\uacfc\ub97c \uc5bb\ub294 \uac83\uc744 2D DFT(Discrete Fourier Transform)\uc774\ub77c\uace0 \ud569\ub2c8\ub2e4. \ub610\ud55c \uc774\ub807\uac8c \uc5bb\uc740 \ud4e8\ub9ac\uc5d0 \ubcc0\ud658 \uacb0\uacfc\ub97c … <\/p>\n
\ub354 \ubcf4\uae30 “Python\uacfc OpenCV \u2013 25 : Fourier Transform”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[130,131],"tags":[],"class_list":["post-6691","post","type-post","status-publish","format-standard","hentry","category-opencv","category-python"],"_links":{"self":[{"href":"http:\/\/www.gisdeveloper.co.kr\/index.php?rest_route=\/wp\/v2\/posts\/6691","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/www.gisdeveloper.co.kr\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.gisdeveloper.co.kr\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.gisdeveloper.co.kr\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/www.gisdeveloper.co.kr\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=6691"}],"version-history":[{"count":11,"href":"http:\/\/www.gisdeveloper.co.kr\/index.php?rest_route=\/wp\/v2\/posts\/6691\/revisions"}],"predecessor-version":[{"id":9451,"href":"http:\/\/www.gisdeveloper.co.kr\/index.php?rest_route=\/wp\/v2\/posts\/6691\/revisions\/9451"}],"wp:attachment":[{"href":"http:\/\/www.gisdeveloper.co.kr\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6691"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.gisdeveloper.co.kr\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6691"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.gisdeveloper.co.kr\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6691"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}