new file: coins.jpg

	new file:   jpg_dft.py
	new file:   jpg_fft.py
	new file:   jpg_image_segmentation.py

jpg_dft.py

@@ -0,0 +1,16 @@
+import cv2
+import numpy as np
+from matplotlib import pyplot as plt
+
+img = cv2.imread('messi5.jpg',0)
+
+dft = cv2.dft(np.float32(img),flags = cv2.DFT_COMPLEX_OUTPUT)
+dft_shift = np.fft.fftshift(dft)
+
+magnitude_spectrum = 20*np.log(cv2.magnitude(dft_shift[:,:,0],dft_shift[:,:,1]))
+
+plt.subplot(121),plt.imshow(img, cmap = 'gray')
+plt.title('Input Image'), plt.xticks([]), plt.yticks([])
+plt.subplot(122),plt.imshow(magnitude_spectrum, cmap = 'gray')
+plt.title('Magnitude Spectrum'), plt.xticks([]), plt.yticks([])
+plt.show()

jpg_fft.py

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+import cv2
+import numpy as np
+from matplotlib import pyplot as plt
+
+img = cv2.imread('messi5.jpg',0)
+f = np.fft.fft2(img)
+fshift = np.fft.fftshift(f)
+magnitude_spectrum = 20*np.log(np.abs(fshift))
+
+plt.subplot(121),plt.imshow(img, cmap = 'gray')
+plt.title('Input Image'), plt.xticks([]), plt.yticks([])
+plt.subplot(122),plt.imshow(magnitude_spectrum, cmap = 'gray')
+plt.title('Magnitude Spectrum'), plt.xticks([]), plt.yticks([])
+plt.show()

jpg_image_segmentation.py

@@ -0,0 +1,36 @@
+import numpy as np
+import cv2
+from matplotlib import pyplot as plt
+
+img = cv2.imread('coins.jpg')
+
+gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
+ret, thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
+# noise removal
+kernel = np.ones((3,3),np.uint8)
+opening = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel, iterations = 2)
+
+# sure background area
+sure_bg = cv2.dilate(opening,kernel,iterations=3)
+
+# Finding sure foreground area
+dist_transform = cv2.distanceTransform(opening,cv2.DIST_L2,5)
+ret, sure_fg = cv2.threshold(dist_transform,0.7*dist_transform.max(),255,0)
+
+# Finding unknown region
+sure_fg = np.uint8(sure_fg)
+unknown = cv2.subtract(sure_bg,sure_fg)
+
+# Marker labelling
+ret, markers = cv2.connectedComponents(sure_fg)
+
+# Add one to all labels so that sure background is not 0, but 1
+markers = markers+1
+
+# Now, mark the region of unknown with zero
+markers[unknown==255] = 0
+markers = cv2.watershed(img,markers)
+img[markers == -1] = [255,0,0]
+
+plt.imshow(img)
+plt.show()