108 lines
2.7 KiB
Python
108 lines
2.7 KiB
Python
|
from skimage.exposure import rescale_intensity
|
||
|
|
import numpy as np
|
||
|
|
import cv2 as cv
|
||
|
|
import matplotlib.pyplot as plt
|
||
|
|
import math
|
||
|
|
def convolve(image, kernel):
|
||
|
|
(iH, iW) = image.shape[:2]
|
||
|
|
(kH, kW) = kernel.shape[:2]
|
||
|
|
kernel = kernel[::-1]
|
||
|
|
pad = (kW - 1) // 2
|
||
|
|
image = cv.copyMakeBorder(image, pad, pad, pad, pad, cv.BORDER_REPLICATE)
|
||
|
|
output = np.zeros((iH,iW), dtype="float32")
|
||
|
|
|
||
|
|
for y in np.arange(pad, iH + pad):
|
||
|
|
for x in np.arange(pad, iW + pad):
|
||
|
|
roi = image[y - pad:y+pad+1, x - pad:x + pad + 1]
|
||
|
|
k =(roi * kernel).sum()
|
||
|
|
output[y - pad, x - pad] = k
|
||
|
|
|
||
|
|
output = rescale_intensity(output, in_range=(0,255))
|
||
|
|
output = (output * 255).astype("uint8")
|
||
|
|
return output
|
||
|
|
|
||
|
|
I = np.array([ [5,0,0,1,2],
|
||
|
|
[2,1,5,1,2],
|
||
|
|
[7,1,5,1,2],
|
||
|
|
[7,4,5,4,3],
|
||
|
|
[7,1,6,1,3] ])
|
||
|
|
|
||
|
|
sobelX = np.array((
|
||
|
|
[-1,0,1],
|
||
|
|
[-2,0,2],
|
||
|
|
[-1,0,1]), dtype="int")
|
||
|
|
|
||
|
|
sobelY = np.array((
|
||
|
|
[-1,-1,-1],
|
||
|
|
[-2, 0, 2],
|
||
|
|
[-1, 0, 1]), dtype="int")
|
||
|
|
|
||
|
|
H,W = I.shape
|
||
|
|
D_x = convolve(I,sobelX)
|
||
|
|
D_y = convolve(I,sobelY)
|
||
|
|
result_a = []
|
||
|
|
for i in range(H):
|
||
|
|
temp = []
|
||
|
|
for j in range(W):
|
||
|
|
temp += [(D_x[i,j],D_y[i,j])]
|
||
|
|
result_a.append(temp)
|
||
|
|
print("1A\n",result_a)
|
||
|
|
|
||
|
|
def create_histogram(img):
|
||
|
|
assert len(img.shape) == 2
|
||
|
|
H,W = img.shape
|
||
|
|
sum = H * W
|
||
|
|
histogram = np.zeros(shape=(8,), dtype = float)
|
||
|
|
for row in range(img.shape[0]):
|
||
|
|
for col in range(img.shape[1]):
|
||
|
|
histogram[img[row,col]] += 1 / sum
|
||
|
|
return histogram
|
||
|
|
|
||
|
|
|
||
|
|
def visualize_histogram(histogram, name):
|
||
|
|
index = np.arange(len(histogram))
|
||
|
|
plt.bar(index,histogram)
|
||
|
|
plt.xlabel('Intensity', fontsize = 5)
|
||
|
|
plt.ylabel('Frequency', fontsize = 5)
|
||
|
|
plt.title(name)
|
||
|
|
plt.show()
|
||
|
|
|
||
|
|
def histogram_equation(histogram, img):
|
||
|
|
c = np.cumsum(histogram)
|
||
|
|
print(c)
|
||
|
|
m_table = np.array([]).astype(np.uint8)
|
||
|
|
m_table = c * 7
|
||
|
|
for row in range(img.shape[0]):
|
||
|
|
for col in range(img.shape[1]):
|
||
|
|
img[row,col] = m_table[img[row,col]]
|
||
|
|
return img
|
||
|
|
|
||
|
|
|
||
|
|
visualize_histogram(create_histogram(I),"1B")
|
||
|
|
|
||
|
|
print("1C\n",histogram_equation(create_histogram(I),I))
|
||
|
|
|
||
|
|
def DFT1D(array):
|
||
|
|
N = array.shape[0]
|
||
|
|
# (a[x, y], b[x, y]) = (x, y)
|
||
|
|
a = np.tile(np.arange(0, N), (N, 1))
|
||
|
|
b = a.copy().T
|
||
|
|
W = np.exp(-2j*np.pi/N*a*b)
|
||
|
|
return np.around(np.dot(W, array), 2)
|
||
|
|
|
||
|
|
def DCT1D(array):
|
||
|
|
N = array.shape[0]
|
||
|
|
factor = math.pi / N
|
||
|
|
C = np.zeros((N, N), dtype = np.float32)
|
||
|
|
for x in range(N):
|
||
|
|
C[0][x] = math.sqrt(1/N) * math.cos((x + 0.5) * 0 * factor)
|
||
|
|
for u in range(N)[1:]:
|
||
|
|
for x in range(N):
|
||
|
|
C[u][x] = math.sqrt(2/N) * math.cos((x + 0.5) * u * factor)
|
||
|
|
return C, np.matmul(C, array)
|
||
|
|
|
||
|
|
print("2A\n",DFT1D(np.array([1,3])))
|
||
|
|
C, F = DCT1D(np.array([1,0,1,0]))
|
||
|
|
print("2B\nC=\n", C)
|
||
|
|
print("F=",F)
|