フーリエ変換

31.py

import cv2
import numpy as np
import pandas as pd
from tqdm import tqdm
from numba import jit
from sklearn.preprocessing import MinMaxScaler
img = cv2.imread("/home/yuma/PycharmProjects/deeplearning/Gasyori100knock-master/Question_31_40/imori.jpg")

print(img.shape)
shape = img.shape

img2 = img[:, :, 2] * 0.2126 + img[:, :, 1] * 0.7152 + img[:, :, 0] * 0.0722
print(img2.shape)

out = np.zeros_like(img2).astype(np.complex)
out2 = np.zeros_like(out)

@jit
def G(k, l, img):
    res = 0
    for x in range(128):
        for y in range(128):
            memo = np.exp(-2j * np.pi * (k * x / 128 + l * y / 128) / 128) * img[x, y]
            res += memo
    return res


@jit
def F(out, img2):
    for i in range(128):
        for j in range(128):
            out[i, j] = G(i, j, img2)
    return out


@jit
def Gr(k, l, img):
    res = 0
    for x in range(128):
        for y in range(128):
            memo = np.exp(2j * np.pi * (k * x / 128 + l * y / 128) / np.sqrt(128 * 128))
            res += memo * img[x, y]
    return res


@jit
def Fr(out, out2):
    for i in range(128):
        for j in range(128):
            out2[i, j] = np.abs(Gr(i, j, out))
    return out2


Gt = F(out, img2)
ps = (np.abs(Gt) / np.abs(Gt).max() * 255).astype(np.uint8)
print(np.max(np.abs(Gt)))
cv2.imwrite("out_ps.jpg", ps)

out = Fr(Gt, out)
print(out)
# mmsc = MinMaxScaler(feature_range=(0, 255))
# out = mmsc.fit_transform(out)
# out = normalize(out, 0, 255)
print(np.max(out))
print(np.min(out))

out[out < 0] = 0
out[out > 255] = 255
out = out.astype(np.uint8)
# # 出力
cv2.imshow("result", out)
cv2.waitKey(0)
cv2.imwrite("out.jpg", out)