def test_rgb_lch_roundtrip(self):
rgb = img_as_float(self.img_rgb)
lab = rgb2lab(rgb)
lch = lab2lch(lab)
lab2 = lch2lab(lch)
rgb2 = lab2rgb(lab2)
assert_array_almost_equal(rgb, rgb2)
def test_rgb_lch_roundtrip(self):
rgb = img_as_float(self.img_rgb)
lab = rgb2lab(rgb)
lch = lab2lch(lab)
lab2 = lch2lab(lch)
rgb2 = lab2rgb(lab2)
assert_array_almost_equal(rgb, rgb2)
def change_hue(img, hue):
lab_img = rgb2lab(img)
lch_img = lab2lch(lab_img)
lch_img[:, :, 2] = hue
lab_img = lch2lab(lch_img)
rgb_img = lab2rgb(lab_img)
return rgb_img
def test_lab_lch_roundtrip_dtypes(dtype):
rgb = img_as_float(data.colorwheel()).astype(dtype=dtype, copy=False)
lab = rgb2lab(rgb)
float_dtype = _supported_float_type(dtype)
assert lab.dtype == float_dtype
lab2 = lch2lab(lab2lch(lab))
decimal = 4 if float_dtype == np.float32 else 7
assert_array_almost_equal(lab2, lab, decimal=decimal)
def test_lab_lch_roundtrip(self, channel_axis):
rgb = img_as_float(self.img_rgb)
rgb = np.moveaxis(rgb, source=-1, destination=channel_axis)
lab = rgb2lab(rgb, channel_axis=channel_axis)
lab2 = lch2lab(
lab2lch(lab, channel_axis=channel_axis),
channel_axis=channel_axis,
)
assert_array_almost_equal(lab2, lab)
def lch2rgb(lch):
"""Convert LCH to RGB colorspace (via LAB)
Input and output are in (bands, cols, rows) order
"""
# reshape for skimage (bands, cols, rows) -> (cols, rows, bands)
slch = np.swapaxes(lch, 0, 2)
# convert colorspace
rgb = lab2rgb(lch2lab(slch))
# return in (bands, cols, rows) order
return np.swapaxes(rgb, 2, 0)
def change_color_lch(batch_id, pal_lab, pal_name):
file_path = './samples/img/'
result_path = './samples/result_lch/'
file_list = os.listdir(file_path)
pal_lch = lab2lch(pal_lab)
for _, name in enumerate(file_list):
img_ori = plt.imread(file_path + name)
img_lab = rgb2lab(img_ori)
img_lch = lab2lch(img_lab)
counter_lch = [0, 0, 0, 0, 0, 0, 0]
for i in range(len(img_lch)):
for j in range(len(img_lch[i])):
point_ori = img_lch[i][j][2]
total_diff_last = 11 # lot((1-0)^2 + (1-0)^2) +1
for k in range(5):
point_pal = pal_lch[k][2]
total_diff = abs(point_ori - point_pal)
# 180 도 이상 처리 예각으로 대체
if total_diff > 5:
total_diff = 10 - total_diff
counter_lch[6] += 1
if total_diff < total_diff_last:
point_ori_tmp = point_pal
total_diff_last = total_diff
counter_fin = k
if img_lch[i][j][1] <= 0.2 and img_lch[i][j][
0] >= 9.8: # 채도 3 이하 명도 98 이상 패스(흰색)
counter_lch[5] += 1
# elif total_diff_last >= 1.2: #1.41
# counter_hsv[6] += 1
else:
counter_lch[counter_fin] += 1
img_lch[i][j][2] = point_ori_tmp
sum_total = counter_lch[0] + counter_lch[1] + counter_lch[
2] + counter_lch[3] + counter_lch[4] + counter_lch[5]
print(
f'[{pal_name}의 {name[0:-4]} 적용 내역] 팔레트1:{counter_lch[0]:,}회, 팔레트2:{counter_lch[1]:,}회, '
f'팔레트3:{counter_lch[2]:,}회, 팔레트4:{counter_lch[3]:,}회, 팔레트5:{counter_lch[4]:,}회, '
f'흰색 패스:{counter_lch[5]:,}회, 팔레트 변환 :{sum_total-counter_lch[5]:,}회'
f'({sum_total-counter_lch[5]-counter_lch[6]:,}회), 총 변환 :{sum_total:,}회'
)
img_trans = lch2lab(img_lch)
img_trans = lab2rgb(img_trans, illuminant='D55')
plt.imsave(
os.path.join(result_path,
f'{batch_id}_{pal_name}_{name[0:-4]}.png'), img_trans)
def preserve_color_cielch(content, stylized, image_name):
# extract info and convert to CIE-LCh for each image
rgbContent = io.imread(content)
labContent = color.lab2lch(
color.xyz2lab(color.rgb2xyz(numpy.asarray(rgbContent))))
labContentArray = numpy.array(labContent)
rgbStyle = io.imread(stylized)
labStyle = color.lab2lch(
color.xyz2lab(color.rgb2xyz(numpy.asarray(rgbStyle))))
labStyleArray = numpy.array(labStyle)
# color transfer
for i in range(len(labContentArray)):
for j in range(len(labContentArray[0])):
labContentArray[i][j][0] = labStyleArray[i][j][0]
labContentArray = color.xyz2rgb(
color.lab2xyz(color.lch2lab(labContentArray)))
viewer = ImageViewer(labContentArray)
viewer.show()
def test_lab_lch_roundtrip(self):
rgb = img_as_float(self.img_rgb)
lab = rgb2lab(rgb)
lab2 = lch2lab(lab2lch(lab))
assert_array_almost_equal(lab2, lab)
REEXP_FOLDER_SCALE = r'\S*scale-(\d+)pc'
# ERROR:root:error: Image size (... pixels) exceeds limit of ... pixels,
# could be decompression bomb DOS attack.
# SEE: https://gitlab.mister-muffin.de/josch/img2pdf/issues/42
Image.MAX_IMAGE_PIXELS = None
#: maximal image size for visualisations, larger images will be downscaled
MAX_IMAGE_SIZE = 5000
#: define pair of forward and backward color space conversion
CONVERT_RGB = {
'rgb': (lambda img: img, lambda img: img),
'hsv': (rgb2hsv, hsv2rgb),
'lab': (rgb2lab, lab2rgb),
'luv': (rgb2luv, luv2rgb),
'hed': (rgb2hed, hed2rgb),
'lch':
(lambda img: lab2lch(rgb2lab(img)), lambda img: lab2rgb(lch2lab(img))),
}
def detect_binary_blocks(vec_bin):
""" detect the binary object by beginning, end and length in !d signal
:param list(bool) vec_bin: binary vector with 1 for an object
:return tuple(list(int),list(int),list(int)):
>>> vec = np.array([1] * 15 + [0] * 5 + [1] * 20)
>>> detect_binary_blocks(vec)
([0, 20], [15, 39], [14, 19])
"""
begins, ends, lengths = [], [], []
return color.xyz2lab(tmp_xyz)
def hsb2lab(shb_pic):
tmp_rgb = color.hsv2rgb(hsv)
tmp_xyz = color.rgb2xyz(tmp_rgb)
return color.xyz2lab(tmp_xyz)
def hsi2lab(shi_pic):
tmp_rgb = color.hsv2rgb(hsv)
tmp_xyz = color.rgb2xyz(tmp_rgb)
return color.xyz2lab(tmp_xyz)
show_lch2lab = color.lch2lab(lch)
print("show_lch2lab")
print_pic(show_lch2lab)
show_rgb2lab = color.rgb2lab(Rgb)
print("show_rgb2lab")
print_pic(show_rgb2lab)
show_hsv2lab = hsv2lab(hsv)
print("show_hsv2lab")
print_pic(show_hsv2lab)
show_hsb2lab = hsb2lab(hsb)
print("show_hsb2lab")
print_pic(show_hsb2lab)
def test_lab_lch_roundtrip(self):
rgb = img_as_float(self.img_rgb)
lab = rgb2lab(rgb)
lab2 = lch2lab(lab2lch(lab))
assert_array_almost_equal(lab2, lab)
def lch2rgb(lch):
lab = color.lch2lab(lch)
return color.lab2rgb(lab)
def surface(s, t): LCH = np.stack((L(s, t, grid=False), C(s, t, grid=False), H(s, t, grid=False)), axis=-1) return color.lch2lab(LCH)
def curve(t): LCH = np.stack((L(t), C(t), H(t)), axis=-1) return color.lch2lab(LCH)
def lch2rgb(lch):
return color.lab2rgb(color.lch2lab(lch.astype(float))) * 255
