import cv2
import base as bs
import numpy as np
def conv_to_gray(img):
return (0.0722 * img[:, :, 0] + 0.7152 * img[:, :, 1] + 0.2126 * img[:, :, 2]).astype(np.uint8)
img = cv2.imread("imori.jpg").astype(np.float)
img_gray = conv_to_gray(img)
bs.show_img(img_gray)
H = hsv[..., 0]
S = hsv[..., 1]
V = hsv[..., 2]
C = S
H_ = H / 60.
X = C * (1 - np.abs(H_ % 2 - 1))
Z = np.zeros_like(H)
vals = [[Z, X, C], [Z, C, X], [X, C, Z], [C, X, Z], [C, Z, X], [X, Z, C]]
for i in range(6):
ind = np.where((i <= H_) & (H_ < (i + 1)))
out[..., 0][ind] = (V - C)[ind] + vals[i][0][ind]
out[..., 1][ind] = (V - C)[ind] + vals[i][1][ind]
out[..., 2][ind] = (V - C)[ind] + vals[i][2][ind]
out[np.where(max_v == min_v)] = 0
out = np.clip(out, 0, 1)
out = (out * 255).astype(np.uint8)
return out
img = cv2.imread("imori.jpg").astype(np.float)
#hsv = BGR2HSV(img)
hsv = rgb2hsv(img)
bs.show_img(hsv)
out = HSV2BGR(img, hsv)
bs.show_img(out)
out_v = np.clip(out_v, 0, 255)
out_v = out_v[pad:pad + H, pad:pad + W].astype(np.uint8)
out_h = np.clip(out_h, 0, 255)
out_h = out_h[pad:pad + H, pad:pad + W].astype(np.uint8)
return out_v, out_h
def pool2d(_img, w_size=8):
out = img.copy()
h, w, c = _img.shape
nh = int(h / w_size)
nw = int(w / w_size)
for y in range(nh):
for x in range(nw):
for c_i in range(c):
out[w_size * y:w_size * (y + 1), w_size * x:w_size * (x + 1),
c_i] = np.max(_img[w_size * y:w_size * (y + 1),
w_size * x:w_size * (x + 1), c_i])
return out
img = cv2.imread("imori.jpg")
img_gray = bs.conv_to_gray(img)
q_img_v, q_img_h = sobel_filter(img_gray)
bs.show_img(q_img_v)
bs.show_img(q_img_h)
for x in range(W):
for c_i in range(C):
out[pad + y, pad + x, c_i] = np.mean(tmp[y:y + K_size,
x:x + K_size, c_i])
out = np.clip(out, 0, 255)
out = out[pad:pad + H, pad:pad + W].astype(np.uint8)
return out
def pool2d(_img, w_size=8):
out = img.copy()
h, w, c = _img.shape
nh = int(h / w_size)
nw = int(w / w_size)
for y in range(nh):
for x in range(nw):
for c_i in range(c):
out[w_size * y:w_size * (y + 1), w_size * x:w_size * (x + 1),
c_i] = np.max(_img[w_size * y:w_size * (y + 1),
w_size * x:w_size * (x + 1), c_i])
return out
img = cv2.imread("imori.jpg")
q_img = mean_filter(img)
bs.show_img(q_img)
def Otsu_binarize(img):
img = img.astype(np.float)
thres_set = range(255)
argmin_thr = 0
max_sigma_b = None
for thr in thres_set:
c_1 = np.array(img[img <= thr])
c_2 = np.array(img[img > thr])
w_1 = len(c_1)
w_2 = len(c_2)
if (len(c_1) == 0 or len(c_2) == 0):
continue
else:
tmp_sigma_b = w_1 * w_2 * np.power(np.mean(c_1) - np.mean(c_2),
2) / np.power((w_1 + w_2), 2)
if (max_sigma_b is None or max_sigma_b < tmp_sigma_b):
argmin_thr = thr
max_sigma_b = tmp_sigma_b
return binarize(img.astype(np.uint8), argmin_thr), argmin_thr
img = cv2.imread("imori.jpg").astype(np.float)
img_gray = conv_to_gray(img)
img_bin, thr = Otsu_binarize(img_gray)
print("thr:", thr)
bs.show_img(img_bin)
