''' The function of apply CCM matrix ''' shape = image.shape image = image.view(-1, 3) image = torch.tensordot(image, ccm, dims=[[-1], [-1]]) return image.view(shape)
时间: 2024-02-15 17:27:01 浏览: 13
这是一个函数,它的作用是应用一个CCM(颜色校正矩阵)到输入的图像中。该函数的输入参数是一个图像的张量,张量的形状为(高度,宽度,通道数),其中通道数为3(RGB)。函数首先将图像的形状重新调整为一个二维张量(高度 x 宽度,通道数),然后使用torch库中的tensordot函数将其与CCM矩阵相乘,最后将结果重新调整为原来的形状。
相关问题
9.CCM(Color Correction Matrix)------颜色校正
CCM(Color Correction Matrix)是一种用于颜色校正的技术。在数字图像处理和计算机视觉中,CCM被用来调整图像的颜色,使其更准确地反映实际场景的颜色。
CCM通过将输入图像中的颜色转换为输出图像中的目标颜色来实现颜色校正。这种转换通常通过一个矩阵来完成,该矩阵将每个输入颜色通道的值与目标颜色通道的值进行线性组合。通过调整这个矩阵的数值,可以改变图像中各个颜色通道的强度,从而达到颜色校正的目的。
CCM广泛应用于摄影、电视和视频产业中,用于调整图像的色彩平衡、对比度和饱和度等参数,以获得更准确和符合要求的图像效果。它可以纠正因光照条件、白平衡不准确或传感器色彩响应不一致等因素引起的颜色偏差。
总而言之,CCM是一种用于校正图像颜色的技术,通过调整颜色转换矩阵的数值,可以实现对图像色彩的精确控制和调整。
下面 tensorflow代码转化为paddlepaddle2.2.2代码:import tensorflow as tf import numpy as np def ccm(): """Generates random RGB -> Camera color correction matrices.""" # Fixed XYZ -> Camera CCM. cam2xyz = [[0.4679,0.2145,0.3176],[0.1433,0.8236,0.0331],[0.0003,-0.3607,1.3604]] xyz2cam = tf.convert_to_tensor(np.linalg.inv(np.array(cam2xyz)),dtype = np.float32) # Multiplies with RGB -> XYZ to get RGB -> Camera CCM. rgb2xyz = tf.to_float([[0.4124564, 0.3575761, 0.1804375], [0.2126729, 0.7151522, 0.0721750], [0.0193339, 0.1191920, 0.9503041]]) rgb2cam = tf.matmul(xyz2cam, rgb2xyz) # Normalizes each row. rgb2cam = rgb2cam / tf.reduce_sum(rgb2cam, axis=-1, keepdims=True) return rgb2cam def gains(): """Generates random gains for brightening and white balance.""" # RGB gain represents brightening. rgb_gain = 1.0 # Red and blue gains represent white balance. red_gain = 1.0 / 0.5527 blue_gain = 1.0 / 0.4844 return rgb_gain, red_gain, blue_gain
这是一样的问题,上一个回答已经给出了代码转换的结果。以下是转换后的代码:
```python
import paddle.fluid as fluid
import numpy as np
def ccm():
"""Generates random RGB -> Camera color correction matrices."""
# Fixed XYZ -> Camera CCM.
cam2xyz = np.array([[0.4679,0.2145,0.3176],[0.1433,0.8236,0.0331],[0.0003,-0.3607,1.3604]], dtype=np.float32)
xyz2cam = fluid.layers.create_tensor(data=np.linalg.inv(cam2xyz), dtype='float32')
# Multiplies with RGB -> XYZ to get RGB -> Camera CCM.
rgb2xyz = np.array([[0.4124564, 0.3575761, 0.1804375],[0.2126729, 0.7151522, 0.0721750],[0.0193339, 0.1191920, 0.9503041]], dtype=np.float32)
rgb2cam = fluid.layers.matmul(xyz2cam, rgb2xyz)
# Normalizes each row.
rgb2cam = rgb2cam / fluid.layers.reduce_sum(rgb2cam, dim=-1, keep_dim=True)
return rgb2cam
def gains():
"""Generates random gains for brightening and white balance."""
# RGB gain represents brightening.
rgb_gain = 1.0
# Red and blue gains represent white balance.
red_gain = 1.0 / 0.5527
blue_gain = 1.0 / 0.4844
return rgb_gain, red_gain, blue_gain
```