# In[16]:
import cv2
import numpy as np
from save import result
from filtering import rgb2grayscale
img = cv2.imread("imori.jpg").astype(np.float)
H, W, C = img.shape
b = img[:, :, 0].copy()
g = img[:, :, 1].copy()
r = img[:, :, 2].copy()
gray_img = rgb2grayscale(r, g, b).reshape(128, -1)
result(gray_img.astype(np.uint8), "38_grayscale")
# In[10]:
# Discrete cosine transformation
T = 8
K = 8
F = np.zeros_like(gray_img, dtype=np.float32)
def weight(x, y, u, v):
if u == 0:
cu = 1 / np.sqrt(2)
else:
cu = 1
# coding: utf-8
# In[1]:
import cv2
import numpy as np
from save import result
from filtering import filtering, rgb2grayscale
img = cv2.imread("imori.jpg")
b = img[:, :, 0].copy()
g = img[:, :, 1].copy()
r = img[:, :, 2].copy()
gray_img = rgb2grayscale(r, g, b)
# In[5]:
# diferrential filter
## vertical
K_v = np.array([[0, -1, 0], [0, 1, 0], [0, 0, 0]])
## horizontal
K_h = np.array([[0, 0, 0], [-1, 1, 0], [0, 0, 0]])
out_v = filtering(gray_img, K_v, padding=True)
out_h = filtering(gray_img, K_h, padding=True)
# In[6]:
result(out_v, "14_v")
result(out_h, "14_h")