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@PulkitS01
Last active Apr 7, 2020
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cluster_pic = pic2show.reshape(pic.shape[0], pic.shape[1], pic.shape[2])
plt.imshow(cluster_pic)
out_l = ndimage.convolve(gray, kernel_laplace, mode='reflect')
plt.imshow(out_l, cmap='gray')
out_h = ndimage.convolve(gray, sobel_horizontal, mode='reflect')
out_v = ndimage.convolve(gray, sobel_vertical, mode='reflect')
# here mode determines how the input array is extended when the filter overlaps a border.
gray_r = gray.reshape(gray.shape[0]*gray.shape[1])
for i in range(gray_r.shape[0]):
if gray_r[i] > gray_r.mean():
gray_r[i] = 1
else:
gray_r[i] = 0
gray = gray_r.reshape(gray.shape[0],gray.shape[1])
plt.imshow(gray, cmap='gray')
gray = rgb2gray(image)
plt.imshow(gray, cmap='gray')
from skimage.color import rgb2gray
import numpy as np
import cv2
import matplotlib.pyplot as plt
%matplotlib inline
from scipy import ndimage
from sklearn.cluster import KMeans
kmeans = KMeans(n_clusters=5, random_state=0).fit(pic_n)
pic2show = kmeans.cluster_centers_[kmeans.labels_]
kernel_laplace = np.array([np.array([1, 1, 1]), np.array([1, -8, 1]), np.array([1, 1, 1])])
print(kernel_laplace, 'is a laplacian kernel')
gray = rgb2gray(image)
gray_r = gray.reshape(gray.shape[0]*gray.shape[1])
for i in range(gray_r.shape[0]):
if gray_r[i] > gray_r.mean():
gray_r[i] = 3
elif gray_r[i] > 0.5:
gray_r[i] = 2
elif gray_r[i] > 0.25:
gray_r[i] = 1
else:
gray_r[i] = 0
gray = gray_r.reshape(gray.shape[0],gray.shape[1])
plt.imshow(gray, cmap='gray')
plt.imshow(out_h, cmap='gray')
plt.imshow(out_v, cmap='gray')
image = plt.imread('1.jpeg')
image.shape
plt.imshow(image)
image = plt.imread('index.png')
plt.imshow(image)
pic = plt.imread('1.jpeg')/255 # dividing by 255 to bring the pixel values between 0 and 1
print(pic.shape)
plt.imshow(pic)
pic_n = pic.reshape(pic.shape[0]*pic.shape[1], pic.shape[2])
pic_n.shape
# converting to grayscale
gray = rgb2gray(image)
# defining the sobel filters
sobel_horizontal = np.array([np.array([1, 2, 1]), np.array([0, 0, 0]), np.array([-1, -2, -1])])
print(sobel_horizontal, 'is a kernel for detecting horizontal edges')
sobel_vertical = np.array([np.array([-1, 0, 1]), np.array([-2, 0, 2]), np.array([-1, 0, 1])])
print(sobel_vertical, 'is a kernel for detecting vertical edges')
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