上面两个公式中,L*,a*,b*是最终的LAB色彩空间三个通道的值。X,Y,Z是RGB转XYZ后计算出来的值,Xn,Yn,Zn一般默认是95.047,100.0,108.883。 2.代码实现 (1)完全按照算法无优化实现 inlinefloatgamma(floatx){returnx>0.04045?pow((x+0.055f)/1.055f,2.4f):x/12.92;};voidRGBToLab(unsigned char*rgbImg,...
MAT_RGB2XYZ = np.array([[0.412453, 0.357580, 0.180423], [0.212671, 0.715160, 0.072169], [0.019334, 0.119193, 0.950227]]) MAT_XYZ2RGB = np.linalg.inv(MAT_RGB2XYZ) XYZ_REF_WHITE = np.array([0.95047, 1.0, 1.08883]) def rgb_to_lab(rgb): """ Convert color space from rgb to lab P...
# 将图像从RGB颜色转换为Lab颜色 lab_img = cv2.cvtColor(img, cv2.COLOR_RGB2LAB) # 显示图像 cv2.imshow('Lab Image', lab_img) cv2.waitKey(0) cv2.destroyAllWindows() 转换后的Lab图像与原始图像具有相同的尺寸,但颜色空间不同。Lab颜色空间将图像的亮度(intensity)和色彩平衡(hue)信息保留下来,因此...
V = [0,255] Lab:颜色之间的欧式距离有具体含义–距离越大,人眼感官两种颜色差距越远 L 通道:像素亮度,上白下黑 中间灰 a 通道:左绿 右红 b 通道:一端纯蓝,一端纯黄 灰度图:每个像素[0,255] 根据人眼敏感度,把RGB图片转换为灰度图,不是简单把RGB每个通道取平均值 而是:Y = 0.299R + 0.587G + 0....
#BGR转LAB img_LAB = cv2.cvtColor(img_BGR, cv2.COLOR_BGR2LAB) #BGR转YUV img_YUV = cv2.cvtColor(img_BGR, cv2.COLOR_BGR2YUV) #调用matplotlib显示处理结果 titles = ['BGR', 'RGB', 'GRAY', 'HSV', 'YCrCb', 'HLS', 'XYZ', 'LAB', 'YUV'] ...
lab = Image.open(label[30]).convert("RGB") img, lab = image_transforms(img, lab, 224, 224) print(img.shape) print(lab.shape) torch.Size([3, 224, 224]) torch.Size([224, 224]) (2)数据读取 定义一个数据读取的类VOCSegDataset类,继承torch.utils.data.Dataset ...
32 rgb_arr = color.lab2rgb(lab_arr)33 io.imsave(path, rgb_arr)34 35def make_cluster(self, row, col):36 row=int(row)37 col=int(col)38return Cluster(row, col,39 self.data[row][col][0],40 self.data[row][col][1],41 self.data[row][col][2])42 43def_...
lab = color.rgb2lab(image) a, b = lab[:, :, 1], lab[:, :, 2] metrics['colorfulness'] = np.sqrt(np.mean(a ** 2 + b ** 2)) # Get dimenstions of the image height, width, _ = image.shape metrics['height'] = height ...
先看一组公式: RMSRCR(x,y)'=G⋅RMSRCR(x,y)+b RMSRCR (x,y)=C(x,y)RMSR(x,y) C(x,y)=f[I'(x,y)]=f[I(x,y)/∑I(x,y)]Ci(x,y)=f[Ii′(x,y)]=f[Ii(x,y)∑j=1NIj(x,y)] f[I'(x,y)]=βlog[αI'(x,y)]=β{log[αI'(x,y)]−log[∑I(x,y)]} ...