本文整理匯總了Python中cv2.COLOR_YUV2BGR屬性的典型用法代碼示例。如果您正苦於以下問題：Python cv2.COLOR_YUV2BGR屬性的具體用法？Python cv2.COLOR_YUV2BGR怎麽用？Python cv2.COLOR_YUV2BGR使用的例子？那麽, 這裏精選的屬性代碼示例或許可以為您提供幫助。您也可以進一步了解該屬性所在類cv2的用法示例。
在下文中一共展示了cv2.COLOR_YUV2BGR屬性的7個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: convert_to_original_colors
# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import COLOR_YUV2BGR [as 別名]
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 示例2: visualize
# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import COLOR_YUV2BGR [as 別名]
def visualize(image, mask):
# cast image from yuv to brg.
image = cv2.cvtColor(image, cv2.COLOR_YUV2BGR)
max_val = np.max(mask)
min_val = np.min(mask)
mask = (mask - min_val) / (max_val - min_val)
mask = (mask * 255.0).astype(np.uint8)
overlay = np.copy(image)
overlay[:, :, 1] = cv2.add(image[:, :, 1], mask)
return image, mask, overlay 示例3: __call__
# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import COLOR_YUV2BGR [as 別名]
def __call__(self, im):
img_yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV)
clahe = cv2.createCLAHE(clipLimit=self.clipLimit, tileGridSize=self.tileGridSize)
img_yuv[:, :, 0] = clahe.apply(img_yuv[:, :, 0])
img_output = cv2.cvtColor(img_yuv, cv2.COLOR_YUV2BGR)
return img_output 示例4: histogram_equalization2
# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import COLOR_YUV2BGR [as 別名]
def histogram_equalization2(img: np.ndarray):
if len(np.shape(img)) == 3:
img_yuv = cv2.cvtColor(img, cv2.COLOR_BGR2YUV)
# equalize the histogram of the Y channel
img_yuv[:, :, 0] = cv2.equalizeHist(img_yuv[:, :, 0])
# convert the YUV image back to RGB format
img = cv2.cvtColor(img_yuv, cv2.COLOR_YUV2BGR)
return img 示例5: equalize_clahe_color_yuv
# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import COLOR_YUV2BGR [as 別名]
def equalize_clahe_color_yuv(img):
"""Equalize the image splitting it after conversion to YUV and applying CLAHE
to the Y channel and merging the channels and convert back to BGR
"""
cla = cv2.createCLAHE(clipLimit=4.0)
Y, U, V = cv2.split(cv2.cvtColor(img, cv2.COLOR_BGR2YUV))
eq_Y = cla.apply(Y)
eq_image = cv2.cvtColor(cv2.merge([eq_Y, U, V]), cv2.COLOR_YUV2BGR)
return eq_image 開發者ID:PacktPublishing，項目名稱:Mastering-OpenCV-4-with-Python，代碼行數:12，代碼來源:clahe_histogram_equalization.py
示例6: computeForwardPasses
# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import COLOR_YUV2BGR [as 別名]
def computeForwardPasses(nets, alexnet, im, transformer, transformer_alex, resize_net):
"""
Compute the forward passes for CALC and optionallly alexnet
"""
img_yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV)
img_yuv[:,:,0] = cv2.equalizeHist(img_yuv[:,:,0])
im = cv2.cvtColor(img_yuv, cv2.COLOR_YUV2BGR)
alex_conv3 = None
t_alex = -1
imcp = np.copy(im) # for AlexNet
if im.shape[2] > 1:
im = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
if not resize_net:
im = cv2.resize(im, (160, 120), interpolation = cv2.INTER_CUBIC)
else:
transformer = caffe.io.Transformer({'X1':(1,1,im.shape[0],im.shape[1])})
transformer.set_raw_scale('X1',1./255)
for net in nets:
x1 = net.blobs['X1']
x1.reshape(1,1,im.shape[0],im.shape[1])
net.reshape()
descr = []
t_calc = []
for net in nets:
t0 = time()
net.blobs['X1'].data[...] = transformer.preprocess('X1', im)
net.forward()
d = np.copy(net.blobs['descriptor'].data[...])
t_calc.append(time() - t0)
d /= np.linalg.norm(d)
descr.append(d)
if alexnet is not None:
im2 = cv2.resize(imcp, (227,227), interpolation=cv2.INTER_CUBIC)
t0 = time()
alexnet.blobs['data'].data[...] = transformer_alex.preprocess('data', im2)
alexnet.forward()
alex_conv3 = np.copy(alexnet.blobs['conv3'].data[...])
alex_conv3 = np.reshape(alex_conv3, (alex_conv3.size, 1))
global first_it
global A
if first_it:
np.random.seed(0)
A = np.random.randn(descr[0].size, alex_conv3.size) # For Gaussian random projection
first_it = False
alex_conv3 = np.matmul(A, alex_conv3)
alex_conv3 = np.reshape(alex_conv3, (1, alex_conv3.size))
t_alex = time() - t0
alex_conv3 /= np.linalg.norm(alex_conv3)
return descr, alex_conv3, t_calc, t_alex 示例7: embed
# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import COLOR_YUV2BGR [as 別名]
def embed(self,filename):
embed_ha_Y_block=self.ha_Y_block.copy()
embed_ha_U_block=self.ha_U_block.copy()
embed_ha_V_block=self.ha_V_block.copy()
self.random_dct = np.random.RandomState(self.random_seed_dct)
index = np.arange(self.block_shape[0]*self.block_shape[1])
for i in range(self.length):
self.random_dct.shuffle(index)
embed_ha_Y_block[self.block_add_index0[i],self.block_add_index1[i]] = self.block_add_wm(embed_ha_Y_block[self.block_add_index0[i],self.block_add_index1[i]],index,i)
embed_ha_U_block[self.block_add_index0[i],self.block_add_index1[i]] = self.block_add_wm(embed_ha_U_block[self.block_add_index0[i],self.block_add_index1[i]],index,i)
embed_ha_V_block[self.block_add_index0[i],self.block_add_index1[i]] = self.block_add_wm(embed_ha_V_block[self.block_add_index0[i],self.block_add_index1[i]],index,i)
embed_ha_Y_part = np.concatenate(embed_ha_Y_block,1)
embed_ha_Y_part = np.concatenate(embed_ha_Y_part,1)
embed_ha_U_part = np.concatenate(embed_ha_U_block,1)
embed_ha_U_part = np.concatenate(embed_ha_U_part,1)
embed_ha_V_part = np.concatenate(embed_ha_V_block,1)
embed_ha_V_part = np.concatenate(embed_ha_V_part,1)
embed_ha_Y = self.ha_Y.copy()
embed_ha_Y[:self.part_shape[0],:self.part_shape[1]] = embed_ha_Y_part
embed_ha_U = self.ha_U.copy()
embed_ha_U[:self.part_shape[0],:self.part_shape[1]] = embed_ha_U_part
embed_ha_V = self.ha_V.copy()
embed_ha_V[:self.part_shape[0],:self.part_shape[1]] = embed_ha_V_part
for i in range(self.dwt_deep):
(cH, cV, cD) = self.coeffs_Y[-1*(i+1)]
embed_ha_Y = idwt2((embed_ha_Y.copy(), (cH, cV, cD)),"haar") #其idwt得到父級的ha
(cH, cV, cD) = self.coeffs_U[-1*(i+1)]
embed_ha_U = idwt2((embed_ha_U.copy(), (cH, cV, cD)),"haar") #其idwt得到父級的ha
(cH, cV, cD) = self.coeffs_V[-1*(i+1)]
embed_ha_V = idwt2((embed_ha_V.copy(), (cH, cV, cD)),"haar") #其idwt得到父級的ha
#最上級的ha就是嵌入水印的圖,即for運行完的ha
embed_img_YUV = np.zeros(self.ori_img_YUV.shape,dtype=np.float32)
embed_img_YUV[:,:,0] = embed_ha_Y
embed_img_YUV[:,:,1] = embed_ha_U
embed_img_YUV[:,:,2] = embed_ha_V
embed_img_YUV=embed_img_YUV[:self.ori_img_shape[0],:self.ori_img_shape[1]]
if self.color_mod == 'RGB':
embed_img = embed_img_YUV
elif self.color_mod == 'YUV':
embed_img = cv2.cvtColor(embed_img_YUV,cv2.COLOR_YUV2BGR)
embed_img[embed_img>255]=255
embed_img[embed_img<0]=0
cv2.imwrite(filename,embed_img)