Python cv2.COLOR_LAB2BGR Examples
The following are 18
code examples of cv2.COLOR_LAB2BGR().
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Example #1
| Source File: neural_style.py From neural-style-tf with GNU General Public License v3.0 | 7 votes |
|
def convert_to_original_colors(content_img, stylized_img):
content_img = postprocess(content_img)
stylized_img = postprocess(stylized_img)
if args.color_convert_type == 'yuv':
cvt_type = cv2.COLOR_BGR2YUV
inv_cvt_type = cv2.COLOR_YUV2BGR
elif args.color_convert_type == 'ycrcb':
cvt_type = cv2.COLOR_BGR2YCR_CB
inv_cvt_type = cv2.COLOR_YCR_CB2BGR
elif args.color_convert_type == 'luv':
cvt_type = cv2.COLOR_BGR2LUV
inv_cvt_type = cv2.COLOR_LUV2BGR
elif args.color_convert_type == 'lab':
cvt_type = cv2.COLOR_BGR2LAB
inv_cvt_type = cv2.COLOR_LAB2BGR
content_cvt = cv2.cvtColor(content_img, cvt_type)
stylized_cvt = cv2.cvtColor(stylized_img, cvt_type)
c1, _, _ = cv2.split(stylized_cvt)
_, c2, c3 = cv2.split(content_cvt)
merged = cv2.merge((c1, c2, c3))
dst = cv2.cvtColor(merged, inv_cvt_type).astype(np.float32)
dst = preprocess(dst)
return dst
Example #2
| Source File: utils.py From PHiSeg-code with Apache License 2.0 | 6 votes |
|
def histogram_equalization(img):
lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
# -----Splitting the LAB image to different channels-------------------------
l, a, b = cv2.split(lab)
# -----Applying CLAHE to L-channel-------------------------------------------
clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8, 8))
cl = clahe.apply(l)
# -----Merge the CLAHE enhanced L-channel with the a and b channel-----------
limg = cv2.merge((cl, a, b))
# -----Converting image from LAB Color model to RGB model--------------------
final = cv2.cvtColor(limg, cv2.COLOR_LAB2BGR)
return final
Example #3
| Source File: omnirobot_simulator_server.py From robotics-rl-srl with MIT License | 6 votes |
|
def renderEnvLuminosityNoise(self, origin_image, noise_var=0.1, in_RGB=False, out_RGB=False):
"""
render the different environment luminosity
"""
# variate luminosity and color
origin_image_LAB = cv2.cvtColor(
origin_image, cv2.COLOR_RGB2LAB if in_RGB else cv2.COLOR_BGR2LAB, cv2.CV_32F)
origin_image_LAB[:, :, 0] = origin_image_LAB[:,
:, 0] * (np.random.randn() * noise_var + 1.0)
origin_image_LAB[:, :, 1] = origin_image_LAB[:,
:, 1] * (np.random.randn() * noise_var + 1.0)
origin_image_LAB[:, :, 2] = origin_image_LAB[:,
:, 2] * (np.random.randn() * noise_var + 1.0)
out_image = cv2.cvtColor(
origin_image_LAB, cv2.COLOR_LAB2RGB if out_RGB else cv2.COLOR_LAB2BGR, cv2.CV_8UC3)
return out_image
Example #4
| Source File: phiseg_makegif_samples.py From PHiSeg-code with Apache License 2.0 | 6 votes |
|
def histogram_equalization(img):
lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
# -----Splitting the LAB image to different channels-------------------------
l, a, b = cv2.split(lab)
# -----Applying CLAHE to L-channel-------------------------------------------
clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8, 8))
cl = clahe.apply(l)
# -----Merge the CLAHE enhanced L-channel with the a and b channel-----------
limg = cv2.merge((cl, a, b))
# -----Converting image from LAB Color model to RGB model--------------------
final = cv2.cvtColor(limg, cv2.COLOR_LAB2BGR)
return final
Example #5
| Source File: image.py From surface-crack-detection with MIT License | 6 votes |
|
def equalize_light(image, limit=3, grid=(7,7), gray=False):
if (len(image.shape) == 2):
image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
gray = True
clahe = cv2.createCLAHE(clipLimit=limit, tileGridSize=grid)
lab = cv2.cvtColor(image, cv2.COLOR_BGR2LAB)
l, a, b = cv2.split(lab)
cl = clahe.apply(l)
limg = cv2.merge((cl,a,b))
image = cv2.cvtColor(limg, cv2.COLOR_LAB2BGR)
if gray:
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
return np.uint8(image)
Example #6
| Source File: image.py From OverwatchDataAnalysis with GNU General Public License v3.0 | 5 votes |
|
def increase_contrast(img):
"""
Increase contrast of an RGB image
@Author: Appcell
@param img: image to be processed
@return: a numpy.ndarray object of this image
"""
lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
l, a, b = cv2.split(lab)
clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(4, 4))
cl = clahe.apply(l)
limg = cv2.merge((cl,a,b))
final = cv2.cvtColor(limg, cv2.COLOR_LAB2BGR)
return final
Example #7
| Source File: image_processing.py From kaggle-dsb2018 with Apache License 2.0 | 5 votes |
|
def rgb_clahe(in_rgb_img):
bgr = in_rgb_img[:,:,[2,1,0]] # flip r and b
lab = cv2.cvtColor(bgr, cv2.COLOR_BGR2LAB)
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
lab[:,:,0] = clahe.apply(lab[:,:,0])
bgr = cv2.cvtColor(lab, cv2.COLOR_LAB2BGR)
return bgr[:,:,[2,1,0]]
Example #8
| Source File: color_transfer.py From pyimagesearch with GNU General Public License v3.0 | 5 votes |
|
def color_transfer(source, target):
source = cv2.cvtColor(source, cv2.COLOR_BGR2LAB).astype("float32")
target = cv2.cvtColor(target, cv2.COLOR_BGR2LAB).astype("float32")
(lMeanSrc, lStdSrc, aMeanSrc, aStdSrc, bMeanSrc, bStdSrc) = image_stats(source)
(lMeanTar, lStdTar, aMeanTar, aStdTar, bMeanTar, bStdTar) = image_stats(target)
(l, a, b) = cv2.split(target)
l -= lMeanTar
a -= aMeanTar
b -= bMeanTar
l = (lStdTar / lStdSrc) * l
a = (aStdTar / aStdSrc) * a
b = (bStdTar / bStdSrc) * b
l += lMeanSrc
a += aMeanSrc
b += bMeanSrc
l = np.clip(l, 0, 255)
a = np.clip(a, 0, 255)
b = np.clip(b, 0, 255)
transfer = cv2.merge([l, a, b])
transfer = cv2.cvtColor(transfer.astype("uint8"), cv2.COLOR_LAB2BGR)
return transfer
Example #9
| Source File: preprocess.py From pytorch-segmentation with MIT License | 5 votes |
|
def clahe(img, clip=2, grid=8):
img_yuv = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
_clahe = cv2.createCLAHE(clipLimit=clip, tileGridSize=(grid, grid))
img_yuv[:, :, 0] = _clahe.apply(img_yuv[:, :, 0])
img_equ = cv2.cvtColor(img_yuv, cv2.COLOR_LAB2BGR)
return img_equ
Example #10
| Source File: inference_utils.py From rpg_e2vid with GNU General Public License v3.0 | 5 votes |
|
def upsample_color_image(grayscale_highres, color_lowres_bgr, colorspace='LAB'):
"""
Generate a high res color image from a high res grayscale image, and a low res color image,
using the trick described in:
http://www.planetary.org/blogs/emily-lakdawalla/2013/04231204-image-processing-colorizing-images.html
"""
assert(len(grayscale_highres.shape) == 2)
assert(len(color_lowres_bgr.shape) == 3 and color_lowres_bgr.shape[2] == 3)
if colorspace == 'LAB':
# convert color image to LAB space
lab = cv2.cvtColor(src=color_lowres_bgr, code=cv2.COLOR_BGR2LAB)
# replace lightness channel with the highres image
lab[:, :, 0] = grayscale_highres
# convert back to BGR
color_highres_bgr = cv2.cvtColor(src=lab, code=cv2.COLOR_LAB2BGR)
elif colorspace == 'HSV':
# convert color image to HSV space
hsv = cv2.cvtColor(src=color_lowres_bgr, code=cv2.COLOR_BGR2HSV)
# replace value channel with the highres image
hsv[:, :, 2] = grayscale_highres
# convert back to BGR
color_highres_bgr = cv2.cvtColor(src=hsv, code=cv2.COLOR_HSV2BGR)
elif colorspace == 'HLS':
# convert color image to HLS space
hls = cv2.cvtColor(src=color_lowres_bgr, code=cv2.COLOR_BGR2HLS)
# replace lightness channel with the highres image
hls[:, :, 1] = grayscale_highres
# convert back to BGR
color_highres_bgr = cv2.cvtColor(src=hls, code=cv2.COLOR_HLS2BGR)
return color_highres_bgr
Example #11
| Source File: color_transfer.py From DeepFaceLab with GNU General Public License v3.0 | 5 votes |
|
def color_transfer_mix(img_src,img_trg):
img_src = np.clip(img_src*255.0, 0, 255).astype(np.uint8)
img_trg = np.clip(img_trg*255.0, 0, 255).astype(np.uint8)
img_src_lab = cv2.cvtColor(img_src, cv2.COLOR_BGR2LAB)
img_trg_lab = cv2.cvtColor(img_trg, cv2.COLOR_BGR2LAB)
rct_light = np.clip ( linear_color_transfer(img_src_lab[...,0:1].astype(np.float32)/255.0,
img_trg_lab[...,0:1].astype(np.float32)/255.0 )[...,0]*255.0,
0, 255).astype(np.uint8)
img_src_lab[...,0] = (np.ones_like (rct_light)*100).astype(np.uint8)
img_src_lab = cv2.cvtColor(img_src_lab, cv2.COLOR_LAB2BGR)
img_trg_lab[...,0] = (np.ones_like (rct_light)*100).astype(np.uint8)
img_trg_lab = cv2.cvtColor(img_trg_lab, cv2.COLOR_LAB2BGR)
img_rct = color_transfer_sot( img_src_lab.astype(np.float32), img_trg_lab.astype(np.float32) )
img_rct = np.clip(img_rct, 0, 255).astype(np.uint8)
img_rct = cv2.cvtColor(img_rct, cv2.COLOR_BGR2LAB)
img_rct[...,0] = rct_light
img_rct = cv2.cvtColor(img_rct, cv2.COLOR_LAB2BGR)
return (img_rct / 255.0).astype(np.float32)
Example #12
| Source File: merge_faces_larger.py From df with Mozilla Public License 2.0 | 5 votes |
|
def transfer_avg_color(img_old,img_new):
assert(img_old.shape==img_new.shape)
source = cv2.cvtColor(img_old, cv2.COLOR_BGR2LAB).astype("float32")
target = cv2.cvtColor(img_new, cv2.COLOR_BGR2LAB).astype("float32")
(lMeanSrc, lStdSrc, aMeanSrc, aStdSrc, bMeanSrc, bStdSrc) = image_stats(source)
(lMeanTar, lStdTar, aMeanTar, aStdTar, bMeanTar, bStdTar) = image_stats(target)
(l, a, b) = cv2.split(target)
l -= lMeanTar
a -= aMeanTar
b -= bMeanTar
l = (lStdTar / lStdSrc) * l
a = (aStdTar / aStdSrc) * a
b = (bStdTar / bStdSrc) * b
l += lMeanSrc
a += aMeanSrc
b += bMeanSrc
l = numpy.clip(l, 0, 255)
a = numpy.clip(a, 0, 255)
b = numpy.clip(b, 0, 255)
transfer = cv2.merge([l, a, b])
transfer = cv2.cvtColor(transfer.astype("uint8"), cv2.COLOR_LAB2BGR)
return transfer
Example #13
| Source File: common.py From pytorch-faster-rcnn with MIT License | 5 votes |
|
def light(im1_name, im2_name):
# im1
im = cv2.imread(im1_name)
im = im.astype(np.float32)
im /= 255.
im_lab = cv2.cvtColor(im, cv2.COLOR_BGR2LAB)
l = im_lab[:, :, 0]
L1_mean = np.mean(l)
L1_std = np.std(l)
# im2
im = cv2.imread(im2_name)
im = im.astype(np.float32)
im /= 255.
im_lab = cv2.cvtColor(im, cv2.COLOR_BGR2LAB)
l = im_lab[:, :, 0]
L2_mean = np.mean(l)
L2_std = np.std(l)
if L2_std != 0:
l = (l - L2_mean) / L2_std * L1_std + L1_mean
l = l[:, :, np.newaxis]
im_lab = np.concatenate((l, im_lab[:, :, 1:]), axis=2)
im = cv2.cvtColor(im_lab, cv2.COLOR_LAB2BGR)
im *= 255.
return im
Example #14
| Source File: img_util.py From CvStudio with MIT License | 5 votes |
|
def correct_lightness(img: np.ndarray):
if len(np.shape(img)) == 3:
img_lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
l, a, b = cv2.split(img_lab)
clahe = cv2.createCLAHE(clipLimit=40.0, tileGridSize=(4, 4))
l = clahe.apply(l)
img = cv2.merge((l, a, b))
img = cv2.cvtColor(img, cv2.COLOR_LAB2BGR)
return img
Example #15
| Source File: image.py From BirdCLEF-Baseline with MIT License | 5 votes |
|
def lightness(img, amount=0.25):
try:
# Only works with BGR images
lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
lab[:, :, 0] *= RANDOM.uniform(1 - amount, 1 + amount)
lab[:, :, 0].clip(0, 255)
img = cv2.cvtColor(lab, cv2.COLOR_LAB2BGR)
except:
pass
return img
Example #16
| Source File: Demo_OpenCV_Simple_GUI.py From PySimpleGUI with GNU Lesser General Public License v3.0 | 4 votes |
|
def main():
sg.theme('LightGreen')
# define the window layout
layout = [
[sg.Text('OpenCV Demo', size=(60, 1), justification='center')],
[sg.Image(filename='', key='-IMAGE-')],
[sg.Radio('None', 'Radio', True, size=(10, 1))],
[sg.Radio('threshold', 'Radio', size=(10, 1), key='-THRESH-'),
sg.Slider((0, 255), 128, 1, orientation='h', size=(40, 15), key='-THRESH SLIDER-')],
[sg.Radio('canny', 'Radio', size=(10, 1), key='-CANNY-'),
sg.Slider((0, 255), 128, 1, orientation='h', size=(20, 15), key='-CANNY SLIDER A-'),
sg.Slider((0, 255), 128, 1, orientation='h', size=(20, 15), key='-CANNY SLIDER B-')],
[sg.Radio('blur', 'Radio', size=(10, 1), key='-BLUR-'),
sg.Slider((1, 11), 1, 1, orientation='h', size=(40, 15), key='-BLUR SLIDER-')],
[sg.Radio('hue', 'Radio', size=(10, 1), key='-HUE-'),
sg.Slider((0, 225), 0, 1, orientation='h', size=(40, 15), key='-HUE SLIDER-')],
[sg.Radio('enhance', 'Radio', size=(10, 1), key='-ENHANCE-'),
sg.Slider((1, 255), 128, 1, orientation='h', size=(40, 15), key='-ENHANCE SLIDER-')],
[sg.Button('Exit', size=(10, 1))]
]
# create the window and show it without the plot
window = sg.Window('OpenCV Integration', layout, location=(800, 400))
cap = cv2.VideoCapture(0)
while True:
event, values = window.read(timeout=20)
if event == 'Exit' or event == sg.WIN_CLOSED:
break
ret, frame = cap.read()
if values['-THRESH-']:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2LAB)[:, :, 0]
frame = cv2.threshold(frame, values['-THRESH SLIDER-'], 255, cv2.THRESH_BINARY)[1]
elif values['-CANNY-']:
frame = cv2.Canny(frame, values['-CANNY SLIDER A-'], values['-CANNY SLIDER B-'])
elif values['-BLUR-']:
frame = cv2.GaussianBlur(frame, (21, 21), values['-BLUR SLIDER-'])
elif values['-HUE-']:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
frame[:, :, 0] += int(values['-HUE SLIDER-'])
frame = cv2.cvtColor(frame, cv2.COLOR_HSV2BGR)
elif values['-ENHANCE-']:
enh_val = values['-ENHANCE SLIDER-'] / 40
clahe = cv2.createCLAHE(clipLimit=enh_val, tileGridSize=(8, 8))
lab = cv2.cvtColor(frame, cv2.COLOR_BGR2LAB)
lab[:, :, 0] = clahe.apply(lab[:, :, 0])
frame = cv2.cvtColor(lab, cv2.COLOR_LAB2BGR)
imgbytes = cv2.imencode('.png', frame)[1].tobytes()
window['-IMAGE-'].update(data=imgbytes)
window.close()
Example #17
| Source File: Demo_OpenCV_Simple_GUI.py From PySimpleGUI with GNU Lesser General Public License v3.0 | 4 votes |
|
def main():
sg.ChangeLookAndFeel('LightGreen')
# define the window layout
layout = [[sg.Text('OpenCV Demo', size=(40, 1), justification='center')],
[sg.Image(filename='', key='image')],
[sg.Radio('None', 'Radio', True, size=(10, 1))],
[sg.Radio('threshold', 'Radio', size=(10, 1), key='thresh'),
sg.Slider((0, 255), 128, 1, orientation='h', size=(40, 15), key='thresh_slider')],
[sg.Radio('canny', 'Radio', size=(10, 1), key='canny'),
sg.Slider((0, 255), 128, 1, orientation='h', size=(20, 15), key='canny_slider_a'),
sg.Slider((0, 255), 128, 1, orientation='h', size=(20, 15), key='canny_slider_b')],
[sg.Radio('contour', 'Radio', size=(10, 1), key='contour'),
sg.Slider((0, 255), 128, 1, orientation='h', size=(20, 15), key='contour_slider'),
sg.Slider((0, 255), 80, 1, orientation='h', size=(20, 15), key='base_slider')],
[sg.Radio('blur', 'Radio', size=(10, 1), key='blur'),
sg.Slider((1, 11), 1, 1, orientation='h', size=(40, 15), key='blur_slider')],
[sg.Radio('hue', 'Radio', size=(10, 1), key='hue'),
sg.Slider((0, 225), 0, 1, orientation='h', size=(40, 15), key='hue_slider')],
[sg.Radio('enhance', 'Radio', size=(10, 1), key='enhance'),
sg.Slider((1, 255), 128, 1, orientation='h', size=(40, 15), key='enhance_slider')],
[sg.Button('Exit', size=(10, 1))]]
# create the window and show it without the plot
window = sg.Window('Demo Application - OpenCV Integration',
location=(800, 400))
window.Layout(layout).Finalize()
cap = cv2.VideoCapture(0)
while True:
event, values = window.Read(timeout=0, timeout_key='timeout')
if event == 'Exit' or event is None:
sys.exit(0)
ret, frame = cap.read()
if values['thresh']:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2LAB)[:, :, 0]
_, frame = cv2.threshold(frame, values['thresh_slider'], 255, cv2.THRESH_BINARY)
if values['canny']:
frame = cv2.Canny(frame, values['canny_slider_a'], values['canny_slider_b'])
if values['blur']:
frame = cv2.GaussianBlur(frame, (21, 21), values['blur_slider'])
if values['hue']:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
frame[:, :, 0] += values['hue_slider']
frame = cv2.cvtColor(frame, cv2.COLOR_HSV2BGR)
if values['enhance']:
enh_val = values['enhance_slider'] / 40
clahe = cv2.createCLAHE(clipLimit=enh_val, tileGridSize=(8, 8))
lab = cv2.cvtColor(frame, cv2.COLOR_BGR2LAB)
lab[:, :, 0] = clahe.apply(lab[:, :, 0])
frame = cv2.cvtColor(lab, cv2.COLOR_LAB2BGR)
if values['contour']:
hue = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
hue = cv2.GaussianBlur(hue, (21, 21), 1)
hue = cv2.inRange(hue, np.array([values['contour_slider'], values['base_slider'], 40]),
np.array([values['contour_slider'] + 30, 255, 220]))
_, cnts, _ = cv2.findContours(hue, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cv2.drawContours(frame, cnts, -1, (0, 0, 255), 2)
imgbytes = cv2.imencode('.png', frame)[1].tobytes() # ditto
window.FindElement('image').Update(data=imgbytes)
Example #18
| Source File: fixed_size.py From Detectron-PYTORCH with Apache License 2.0 | 4 votes |
|
def distort_color(im):
# distort brightness
hsv = cv2.cvtColor(im, cv2.COLOR_BGR2HSV)
h_, s_, v_ = cv2.split(hsv)
v_ += np.random.randint(-16, 16)
v_[v_ > 255] = 255
v_[v_ < 0] = 0
hsv = cv2.merge((h_, s_, v_))
im = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
# distort contrast
# """
# From TF source code
# For each channel, this Op computes the mean of the image pixels in the
# channel and then adjusts each component `x` of each pixel to
# `(x - mean) * contrast_factor + mean`.
# """
im = im.astype(np.float32)
b, g, r = cv2.split(im)
# factor = (np.random.rand() + 0.5)
factor = np.random.uniform(0.75, 1.25)
b = (b - b.mean()) * factor + b.mean()
b[b > 255] = 255
b[b < 0] = 0
# factor = (np.random.rand() + 0.5)
g = (g - g.mean()) * factor + g.mean()
g[g > 255] = 255
g[g < 0] = 0
# factor = (np.random.rand() + 0.5)
r = (r - r.mean()) * factor + r.mean()
r[r > 255] = 255
r[r < 0] = 0
im = cv2.merge((b, g, r))
# im = im.astype(np.uint8)
# clip_value = np.random.rand() * 3.0
# clahe = cv2.createCLAHE(clipLimit=clip_value, tileGridSize=(8, 8))
# lab = cv2.cvtColor(im, cv2.COLOR_BGR2LAB) # convert from BGR to LAB color space
# l, a, b = cv2.split(lab) # split on 3 different channels
# l2 = clahe.apply(l) # apply CLAHE to the L-channel
# lab = cv2.merge((l2, a, b)) # merge channels
# im = cv2.cvtColor(lab, cv2.COLOR_LAB2BGR) # convert from LAB to BGR
# im = im.astype(np.float32)
return im
