def main():
# 読み込み＆初期画像表示
img = cv2.imread("./assets/imori.jpg")
showImg(img, QUESTION_ID)
# no openCV
ans1 = x_y_sharing(img, dx=30)
showImg(ans1, QUESTION_ID + "_noOpenCV_" + QUESTION_NAME)
ans1 = x_y_sharing(img, dy=30)
showImg(ans1, QUESTION_ID + "_noOpenCV_" + QUESTION_NAME)
ans1 = x_y_sharing(img, dx=30, dy=30)
showImg(ans1, QUESTION_ID + "_noOpenCV_" + QUESTION_NAME)
# openCV
h, w, d = img.shape
after_height, after_width = getAfterHeightAndWidth(h, w, theta=30)
img_r = np.insert(img, [0] * (after_width - w // 2 + 1), 0, axis=1)
img_r = np.insert(img_r, [0] * (after_height - h // 2 + 1), 0, axis=0)
img_r = np.insert(img_r, [img_r.shape[1]] * (after_width - w // 2 + 1),
0,
axis=1)
img_r = np.insert(img_r, [img_r.shape[0]] * (after_height - h // 2 + 1),
0,
axis=0)
M = cv2.getRotationMatrix2D((img_r.shape[1] // 2, img_r.shape[0] // 2), 30,
1)
ans2 = cv2.warpAffine(img_r, M, (img_r.shape[1], img_r.shape[0]))
showImg(ans2, QUESTION_ID + "_OpenCV_" + QUESTION_NAME)
def main():
# 読み込み＆初期画像表示
img = cv2.imread("./assets/imori.jpg")
showImg(img, "Q6")
# 平均プーリング no openCV
tileSize = 8
ans = mean_pooling(img, tileSize)
showImg(ans, "Q7_no_openCV")
def main():
# 読み込み＆初期画像表示
img = cv2.imread("./assets/imori_dark.jpg")
showImg(img, QUESTION_ID)
grayImg = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# ヒストグラム平坦化 no openCV
ans = histgramFlatten(img, m = 128, sigma = 52)
showImg(ans, QUESTION_ID + "_" + QUESTION_NAME)
def main():
# 読み込み＆初期画像表示
img = cv2.imread("./assets/imori_dark.jpg")
showImg(img, QUESTION_ID)
grayImg = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# ヒストグラム作成 no openCV
ans = histgramTransform(grayImg, 0, 255, grayImg.min(), grayImg.max())
# ans = histgramTransform(grayImg, 0, 255, 30, 220)
showImg(ans, QUESTION_ID + "_" + QUESTION_NAME)
def main():
# 読み込み＆初期画像表示
img = cv2.imread("./assets/imori_dark.jpg")
showImg(img, QUESTION_ID)
grayImg = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# ヒストグラム作成 no openCV
bins = np.arange(256)
plt.hist(grayImg.flatten(), bins=bins)
plt.title(QUESTION_ID + " " + QUESTION_NAME)
plt.xlim(0, 255)
plt.show()
def main():
# 読み込み＆初期画像表示
img = cv2.imread("./assets/imori.jpg")
showImg(img, "Q12")
grayImg = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 平滑化フィルタ処理 no openCV
ans = sobelFilter(grayImg, "V")
showImg(ans, "Q15_no_openCV_vertical")
ans = sobelFilter(grayImg, "H")
showImg(ans, "Q15_no_openCV_horizontal")
# openCVでの平滑化処理 padding処理は zero padding
ans = cv2.Sobel(grayImg, -1, 0, 1, ksize=3) #vertical
showImg(np.clip(ans, 0, 255), "Q15_openCV_vertical")
ans = cv2.Sobel(grayImg, -1, 1, 0, ksize=3) #horizontal
showImg(np.clip(ans, 0, 255), "Q15_openCV_horizontal")
def main():
# 読み込み＆初期画像表示
img = cv2.imread("./assets/imori.jpg")
showImg(img, QUESTION_ID)
grayImg = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 平滑化フィルタ処理 no openCV
ans = prewittFilter(grayImg, "V")
showImg(ans, QUESTION_ID + "no_openCV" + "vertical")
ans = prewittFilter(grayImg, "H")
showImg(ans, QUESTION_ID + "no_openCV" + "horizontal")
# openCVでの平滑化処理
ans = cv2.filter2D(grayImg, -1, kernel=getprewittFilter("V")) #vertical
showImg(np.clip(ans, 0, 255), QUESTION_ID + "openCV_vertical")
ans = cv2.filter2D(grayImg, -1, kernel=getprewittFilter("H")) #horizontal
showImg(np.clip(ans, 0, 255), QUESTION_ID + "openCV_horizontal")
def main():
# 読み込み＆初期画像表示
img = cv2.imread("./assets/imori.jpg")
showImg(img, "Q14")
grayImg = bgr2gray(img)
# 微分フィルタ処理 no openCV
ans = diffFilter(grayImg, "V")
showImg(ans, "Q14_no_openCV vertical")
ans = diffFilter(grayImg, "H")
showImg(ans, "Q14_no_openCV horizontal")
# 微分フィルタ処理 openCV
kernel = {
"V": np.array([[0, -1, 0], [0, 1, 0], [0, 0, 0]]),
"H": np.array([[0, 0, 0], [-1, 1, 0], [0, 0, 0]]),
}
ans = cv2.filter2D(grayImg, -1, kernel["V"])
showImg(np.clip(ans, 0, 255), "Q14_opencv_vertical")
ans = cv2.filter2D(grayImg, -1, kernel["H"])
showImg(np.clip(ans, 0, 255), "Q14_opencv_vertical")
def main():
# 読み込み＆初期画像表示
img = cv2.imread("./assets/imori_noise.jpg")
showImg(img, QUESTION_ID)
grayImg = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
showImg(grayImg, QUESTION_ID)
s = 3
kernelSize = 3
# 平滑化フィルタ処理 no openCV
ans = logFilter(grayImg, s=s, kernelSize=kernelSize)
showImg(ans, QUESTION_ID + "no_openCV" + QUESTION_NAME)
# openCVでの平滑化処理
ans = cv2.filter2D(grayImg, -1, kernel=getLoGFilter(s, kernelSize))
showImg(np.clip(ans, 0, 255), QUESTION_ID + "openCV" + QUESTION_NAME)
def main():
# 読み込み＆初期画像表示
img = cv2.imread("./assets/imori.jpg")
showImg(img, "Q11")
tileSize = 3
# 平滑化フィルタ処理 no openCV
ans = smoothingBlur(img, tileSize)
showImg(ans, "Q11_no_openCV")
# openCVでの平滑化処理 padding処理は zero padding
ans = cv2.blur(img, (tileSize, tileSize), borderType=cv2.BORDER_CONSTANT)
showImg(ans, "Q11_openCV")
def main():
# 読み込み＆初期画像表示
img = cv2.imread("./assets/imori_gamma.jpg")
showImg(img, QUESTION_ID)
c = 1
gamma = 2.2
# ガンマ補正 no openCV
ans = gammaCorrection(img, c, gamma)
showImg(ans, QUESTION_ID + "_noOpenCV_" + QUESTION_NAME)
# ガンマ補正 openCV
LUT = np.array([255 * (1 / c * x / 255)**(1 / gamma) for x in range(256)])
ans = cv2.LUT(img, LUT)
showImg(ans, QUESTION_ID + "_OpenCV_" + QUESTION_NAME)
def main():
# 読み込み＆初期画像表示
img = cv2.imread("./assets/imori.jpg")
showImg(img, "Q6")
# 減色処理 no openCV
ans = ((img - 1) // 64) * 64 + 32
showImg(ans, "Q6_numpy")
# 減色処理 no openCV
# kmeans法による減色ができる。
# 問題文と異なる減色方法にはなるが、サンプルとして処理をためす
k = 12
ans = genSubImg(img, k)
showImg(ans, "Q6_OpenCV, k = " + str(k))
def main():
# 読み込み＆初期画像表示
img = cv2.imread("./assets/imori.jpg")
showImg(img, QUESTION_ID)
grayImg = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
kernelSize = 3
# 平滑化フィルタ処理 no openCV
ans = lapracianFilter(grayImg)
showImg(ans, QUESTION_ID + "no_openCV" + QUESTION_NAME)
# openCVでの平滑化処理
ans = cv2.filter2D(grayImg, -1, kernel = getLapracianFilter())
showImg(np.clip(ans, 0, 255), QUESTION_ID + "openCV" + QUESTION_NAME)
def main():
# 読み込み＆初期画像表示
img = cv2.imread("./assets/imori.jpg")
showImg(img, QUESTION_ID)
# バイリニア no openCV
ans = bi_linear(img, ratio=1.5)
showImg(ans, QUESTION_ID + "_noOpenCV_" + QUESTION_NAME)
# バイリニア openCV
ans = cv2.resize(
img,
dsize=(int(img.shape[0] * 1.5), int(img.shape[1] * 1.5)),
interpolation=cv2.INTER_LINEAR,
)
showImg(ans, QUESTION_ID + "_OpenCV_" + QUESTION_NAME)
def main():
# 読み込み＆初期画像表示
img = cv2.imread("./assets/imori.jpg")
showImg(img, QUESTION_ID)
# no openCV
ans1 = bi_cubic(img, ratio = 1.5)
showImg(ans1, QUESTION_ID + "_noOpenCV_" + QUESTION_NAME)
# openCV
ans2 = cv2.resize(
img,
dsize = (int(img.shape[1] * 1.5), int(img.shape[0] * 1.5)),
interpolation = cv2.INTER_CUBIC,
)
showImg(ans2, QUESTION_ID + "_OpenCV_" + QUESTION_NAME)
def main():
# 読み込み＆初期画像表示
img = cv2.imread("./assets/imori.jpg")
showImg(img, QUESTION_ID)
grayImg = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# ヒストグラム平坦化 no openCV
ans = histgramFlatten_2(img)
showImg(ans, QUESTION_ID + "_noOpenCV_" + QUESTION_NAME)
# ヒストグラム平坦化
ans = img.copy()
for i in range(3):
ans[:, :, i] = cv2.equalizeHist(img[:, :, i])
showImg(ans, QUESTION_ID + "_OpenCV_" + QUESTION_NAME)
def main():
# 読み込み＆初期画像表示
img = cv2.imread("./assets/imori.jpg")
showImg(img, QUESTION_ID)
# no openCV
ans1 = translation(img, dx = 30, dy = -30)
showImg(ans1, QUESTION_ID + "_noOpenCV_" + QUESTION_NAME)
# openCV
M = np.array([[1,0,30],[0,1,-30]], dtype = np.float)
ans2 = cv2.warpAffine(
img,
M,
(img.shape[1], img.shape[0])
)
showImg(ans2, QUESTION_ID + "_OpenCV_" + QUESTION_NAME)
def main():
# 読み込み＆初期画像表示
img = cv2.imread("./assets/imori_noise.jpg")
showImg(img, "Q6")
tileSize = 3
sigma = 1.3
# ガウシアンフィルタ処理 no openCV
ans = medianBlur(img, tileSize)
showImg(ans, "Q7_no_openCV")
# openCVでのガウシアンフィルタ処理 padding処理はdefault(reflect)
ans = cv2.medianBlur(
img,
ksize=tileSize,
#⃣borderType = cv2.BORDER_CONSTANT,
)
showImg(ans, "Q7_openCV")
def main():
# 読み込み＆初期画像表示
img = cv2.imread("./assets/imori_noise.jpg")
showImg(img, "Q6")
tileSize = 3
sigma = 1.3
# ガウシアンフィルタ処理 no openCV
ans = gaussianBlur(img, tileSize, sigma)
showImg(ans, "Q7_no_openCV")
# openCVでのガウシアンフィルタ処理
ans = cv2.GaussianBlur(
img,
ksize=(tileSize, tileSize),
sigmaX=sigma,
sigmaY=sigma,
borderType=cv2.BORDER_CONSTANT,
)
showImg(ans, "Q7_openCV")
def main():
# 読み込み＆初期画像表示
img = cv2.imread("./assets/imori.jpg")
showImg(img, QUESTION_ID)
# no openCV
ans1 = bi_cubic(img, 1.3, 0.8, -1)
ans1 = translation(ans1, dx = 30, dy = -30)
showImg(ans1, QUESTION_ID + "_noOpenCV_" + QUESTION_NAME)
# openCV
ans2 = cv2.resize(img,
dsize = (int(img.shape[0] * 1.3), int(img.shape[1] * 0.8)),
interpolation = cv2.INTER_CUBIC,
)
M = np.array([[1,0,30],[0,1,-30]], dtype = np.float)
ans2 = cv2.warpAffine(
ans2,
M,
(ans2.shape[1], ans2.shape[0])
)
showImg(ans2, QUESTION_ID + "_OpenCV_" + QUESTION_NAME)
def main():
# 読み込み＆初期画像表示
img = cv2.imread("./assets/imori.jpg")
grayImg = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
showImg(grayImg, QUESTION_ID)
h, w = grayImg.shape
r1 = w / 2 * 0.1
r2 = w / 2 * 0.5
filt = np.array([[
1 if r2 > sqrt(min(x, w - x)**2 + min(y, h - y)**2) > r1 else 0
for x in range(w)
] for y in range(h)])
# filt[0,0] = 1 # 直流成分（バイアス）だけ通す
# # no openCV
spectrum = fourier_trans(grayImg)
spectrum *= filt
ps = np.abs(spectrum[1:, 1:]) / np.abs(spectrum[1:, 1:]).max() * 255
ps = ps.astype(np.uint8)
showImg(ps, QUESTION_ID + " no opencv " + "power spectrum")
ans1 = inv_fourier_trans(spectrum)
showImg(
ans1.clip(0, 255).astype(np.uint8),
QUESTION_ID + " no opencv " + "img")
# numpy
spectrum = np.fft.fft2(grayImg)
spectrum *= filt
ps = np.abs(spectrum[1:, 1:]) / np.abs(spectrum[1:, 1:]).max() * 255
ps = ps.astype(np.uint8)
showImg(ps, QUESTION_ID + " numpy " + "power spectrum")
ans2 = np.fft.ifft2(spectrum)
ans2 = np.abs(ans2)
showImg(
ans2.clip(0, 255).astype(np.uint8), QUESTION_ID + " numpy " + "img")
# openCV
spectrum = cv2.dft(grayImg.astype(np.float), flags=cv2.DFT_COMPLEX_OUTPUT)
spectrum[:, :, 0] *= filt
spectrum[:, :, 1] *= filt
ps = np.sqrt(spectrum[1:, 1:, 0]**2 + spectrum[1:, 1:, 1]**2)
ps = ps / ps.max() * 255
showImg(ps, QUESTION_ID + " opencv " + "power spectrum")
ans3 = cv2.idft(spectrum)
ans3 = cv2.magnitude(ans3[:, :, 0], ans3[:, :, 1]) / grayImg.size
showImg(
ans3.clip(0, 255).astype(np.uint8), QUESTION_ID + " opencv " + "img")
def main():
# 読み込み＆初期画像表示
img = cv2.imread("./assets/imori.jpg")
grayImg = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
showImg(grayImg, QUESTION_ID)
h, w = grayImg.shape
filt = np.array([[1 for x in range(w)] for y in range(h)])
# no openCV
spectrum = fourier_trans(grayImg)
spectrum *= filt
ps = np.abs(spectrum) / np.abs(spectrum).max() * 255
ps = ps.astype(np.uint8)
showImg(ps, QUESTION_ID + " no opencv " + "power spectrum")
ans1 = inv_fourier_trans(spectrum)
showImg(ans1, QUESTION_ID + " no opencv " + "img")
# numpy
spectrum = np.fft.fft2(grayImg)
spectrum *= filt
ps = np.abs(spectrum) / np.abs(spectrum).max() * 255
ps = ps.astype(np.uint8)
showImg(ps, QUESTION_ID + " numpy " + "power spectrum")
ans2 = np.fft.ifft2(spectrum)
ans2 = np.abs(ans2)
showImg(ans2, QUESTION_ID + " numpy " + "img")
# openCV
spectrum = cv2.dft(grayImg.astype(np.float), flags=cv2.DFT_COMPLEX_OUTPUT)
spectrum[:, :, 0] *= filt
spectrum[:, :, 1] *= filt
ps = np.sqrt(spectrum[:, :, 0]**2 + spectrum[:, :, 1]**2)
ps = ps / ps.max() * 255
showImg(ps, QUESTION_ID + " opencv " + "power spectrum")
ans3 = cv2.idft(spectrum)
ans3 = cv2.magnitude(ans3[:, :, 0], ans3[:, :, 1]) / grayImg.size
showImg(ans3, QUESTION_ID + " opencv " + "img")