def recolor(img, original, new_palette):
h, w, d = img.shape
newimg = np.empty((h, w, 3))
for i in range(h):
for j in range(w):
r, g, b = hex2rgb(new_palette[original.index(
get_color(img, i, j, original))])
newimg[i, j] = [r, g, b]
return np.uint8(newimg)
def perturb(palette, temp, fixed=None):
#fixed is list of palette indeces that should not be changed
unfixed = []
if fixed is None:
unfixed = [i for i in range(len(palette))]
else:
for i in range(len(palette)):
if i not in fixed:
unfixed.append(i)
num = len(unfixed)
#swap 2 colors
color1 = np.random.randint(0, num)
color2 = np.random.randint(0, num)
oopsies = palette[unfixed[color1]]
palette[unfixed[color1]] = palette[unfixed[color2]]
palette[unfixed[color2]] = oopsies
# perturb randomly chosen color
rate = 1
sigma = rate * temp
color = np.random.randint(0, num)
r, g, b = hex2rgb(palette[unfixed[color]])
rp = np.random.normal(0, sigma)
gp = np.random.normal(0, sigma)
bp = np.random.normal(0, sigma)
if r + rp > 255 or r + rp < 0:
r = r - rp
else:
r = r + rp
if g + gp > 255 or g + gp < 0:
g = g - gp
else:
g = g + gp
if b + bp > 255 or b + bp < 0:
b = b - bp
else:
b = b + bp
r = max(0, min(255, int(r)))
g = max(0, min(255, int(g)))
b = max(0, min(255, int(b)))
newhex = rgb2hex((r, g, b))
palette[unfixed[color]] = newhex
return palette
def hex2lab(color):
r, g, b = hex2rgb(color)
rgb = [[[r / 255, g / 255, b / 255]]]
return rgb2lab(rgb)[0][0] / 100.0
def compat_features(c1, c2, c3, c4, c5):
colors = [c1, c2, c3, c4, c5]
rgb = np.empty((5, 3))
lab = np.empty((5, 3))
hsv = np.empty((5, 3))
chsv = np.empty((5, 3))
for idx, c in enumerate(colors):
r, g, b = hex2rgb(c)
rgb1 = np.array([r / 255, g / 255, b / 255])
lab1 = hex2lab(c)
hsv1 = rgb2hsv([[rgb1]])[0][0]
chsv1 = [hsv1[1] * cos(hsv1[0]), hsv1[0] * sin(hsv1[0]), hsv1[2]]
rgb[idx] = rgb1
lab[idx] = lab1
hsv[idx] = hsv1
chsv[idx] = chsv1
sort_idx = np.argsort(lab[:, 0])
rgb_sorted = rgb[sort_idx].flatten()
lab_sorted = lab[sort_idx].flatten()
hsv_sorted = hsv[sort_idx].flatten()
chsv_sorted = chsv[sort_idx].flatten()
rgb_diff = np.zeros((3, 4))
lab_diff = np.zeros((3, 4))
hsv_diff = np.zeros((3, 4))
chsv_diff = np.zeros((3, 4))
for i in range(1, 5):
rgb_diff[0, i - 1] = rgb[i, 0] - rgb[i - 1, 0]
rgb_diff[1, i - 1] = rgb[i, 1] - rgb[i - 1, 1]
rgb_diff[2, i - 1] = rgb[i, 2] - rgb[i - 1, 2]
lab_diff[0, i - 1] = lab[i, 0] - lab[i - 1, 0]
lab_diff[1, i - 1] = lab[i, 1] - lab[i - 1, 1]
lab_diff[2, i - 1] = lab[i, 2] - lab[i - 1, 2]
minSatVal = min(np.concatenate((hsv[i - 1:i, 1], hsv[i - 1:i, 2])))
if minSatVal >= 0.2:
pts = np.sort([hsv[i, 1], hsv[i - 1, 1]])
hsv_diff[0, i - 1] = min(pts[1] - pts[0], 1 - (pts[1] - pts[0]))
hsv_diff[1, i - 1] = hsv[i, 1] - hsv[i - 1, 1]
hsv_diff[2, i - 1] = hsv[i, 2] - hsv[i - 1, 2]
chsv_diff[0, i - 1] = chsv[i, 0] - chsv[i - 1, 0]
chsv_diff[1, i - 1] = chsv[i, 1] - chsv[i - 1, 1]
chsv_diff[2, i - 1] = chsv[i, 2] - chsv[i - 1, 2]
sort_rgb_diff = np.concatenate(
(np.sort(rgb_diff[0]), np.sort(rgb_diff[1]), np.sort(rgb_diff[2])))
sort_lab_diff = np.concatenate(
(np.sort(lab_diff[0]), np.sort(lab_diff[1]), np.sort(lab_diff[2])))
sort_hsv_diff = np.concatenate(
(np.sort(hsv_diff[0]), np.sort(hsv_diff[1]), np.sort(hsv_diff[2])))
sort_chsv_diff = np.concatenate(
(np.sort(chsv_diff[0]), np.sort(chsv_diff[1]), np.sort(chsv_diff[2])))
rgb_mean = np.mean(rgb, axis=0)
lab_mean = np.mean(lab, axis=0)
hsv_mean = np.mean(hsv, axis=0)
chsv_mean = np.mean(chsv, axis=0)
rgb_std = np.std(rgb, axis=0)
lab_std = np.std(lab, axis=0)
hsv_std = np.std(hsv, axis=0)
chsv_std = np.std(chsv, axis=0)
rgb_median = np.median(rgb, axis=0)
lab_median = np.median(lab, axis=0)
hsv_median = np.median(hsv, axis=0)
chsv_median = np.median(chsv, axis=0)
rgb_max = np.amax(rgb, axis=0)
lab_max = np.amax(lab, axis=0)
hsv_max = np.amax(hsv, axis=0)
chsv_max = np.amax(chsv, axis=0)
rgb_min = np.amin(rgb, axis=0)
lab_min = np.amin(lab, axis=0)
hsv_min = np.amin(hsv, axis=0)
chsv_min = np.amin(chsv, axis=0)
rgb_range = rgb_max - rgb_min
lab_range = lab_max - lab_min
hsv_range = hsv_max - hsv_min
chsv_range = chsv_max - chsv_min
return np.concatenate(
(chsv.flatten(), chsv_sorted.flatten(), chsv_diff.flatten(),
sort_chsv_diff.flatten(), chsv_mean.flatten(), chsv_std.flatten(),
chsv_median.flatten(), chsv_max.flatten(), chsv_min.flatten(),
chsv_range.flatten(), lab.flatten(), lab_sorted.flatten(),
lab_diff.flatten(), sort_lab_diff.flatten(), lab_mean.flatten(),
lab_std.flatten(), lab_median.flatten(), lab_max.flatten(),
lab_min.flatten(), lab_range.flatten(), hsv.flatten(),
hsv_sorted.flatten(), hsv_diff.flatten(), sort_hsv_diff.flatten(),
hsv_mean.flatten(), hsv_std.flatten(), hsv_median.flatten(),
hsv_max.flatten(), hsv_min.flatten(), hsv_range.flatten(),
rgb.flatten(), rgb_sorted.flatten(), rgb_diff.flatten(),
sort_rgb_diff.flatten(), rgb_mean.flatten(), rgb_std.flatten(),
rgb_median.flatten(), rgb_max.flatten(), rgb_min.flatten(),
rgb_range.flatten()))