CCM 标定的原理是，使用 sensor 抓拍到的 24 色卡场景下前 18 个色块的实际颜色信息和其期望值，计算 3x3 的 CCM 矩阵。输入颜色经 CCM 矩阵处理得到的颜色与其期望值差距越小，则 CCM 矩阵就越理想。
海思CCM矩阵：8bit 小数精度。bit 15是符号位，0 表示正数，1表示负数。典型的三组 CCM 为D50，TL84，A 三个光源下的 CCM。典型的五组 CCM为 10K，D65，D50，TL84，A 五个光源下的CCM。优先红、绿、蓝纯色；然后肤色块
【资料图】
采集步骤：(1)标准 X-Rite 24 色卡，照度为 600Lux 均匀光源；(2)调整 AE 目标亮度，最亮灰阶(Block 19)的 G 分量亮度在饱和值的 0.8 倍左右
手动调试CCM步骤：
(1)优先确认颜色问题是由于AWB模块引入还是CCM引入
(2)过灯箱客观指标，确认是否需要重新标定
(3)最后再考虑手动调整CCM
a.一般都是针对单一颜色进行微调整（比如车牌天蓝，想要深蓝；人脸偏红或是偏黄绿），所以单一颜色都会有个当前RGB值，想要调整的目标RGB值，其实调整CCM的目的就是通过CCM使得当前RGB值趋于目标RGB值
b.针对a所述，调整RGB值，调整R：a00、a01、a02（RinR、GinR、BinR）；调整G：a10、a11、a12（RinG、GinG、BinG）；调整B：a20、a21、a22（RinB、GinB、BinB）。
CCM矩阵色彩校正的方法_一只特立独行的zhu..-CSDN博客_ccm调试
WDR模式下标定CCM，CCM 容易受到 DRC 的影响，容易造成颜色难以矫正：
(1)曝光比手动最大，同时也要调整亮度值，避免长帧过曝，采集长帧的 RAW 数据进行 CCM 的标定。标定过程中可以适当的降低饱和度，不能选择开启 autoGain 功能。
(2)适当减少 DRC 曲线对图像亮度的大幅度提升，这样 DRC 对颜色的改变会较弱。此时，图像的亮度会有所降低达不到想要的亮度，这时，可以用 gamma 对亮度进行适当的提升。这样联调 DRC 和 gamma 模块，可以让整体的颜色调节更准确一些。
(3)对于 WDR 模式，因为大多场景是混合光源场景，容易出现亮处颜色偏色，人脸颜色偏红等问题，除了可以降低饱和度值以外，还可以使用 CA 模块对这些区域适当的降低饱和度。
反gamma后图像和gamma 1.0存在些许差异：
(1)CSC模块
CCM模块代码：
https://github.com/WaterdropsKun/isppipeline_Python/tree/main/lecture14
Python实现ISP pipeline代码：
GitHub - WaterdropsKun/isppipeline_Python: isppipeline_Python
如有帮助，希望帮忙github帮忙点个star
def degamma_hisi(data, clip_range, gamma_txt): # gamma degamma hisi_gamma_x = 1024-1 hisi_gamma_y = 4096-1 hisi_degamma_x = 256-1 hisi_degamma_y = 1.0 gamma_hisi_x1023_y4095 = [] degamma_x255_y1 = [] with open(gamma_txt, "r") as f: for i, line in enumerate(f.readlines()): line = line.split(",") gamma_hisi_x1023_y4095 = [float(x) for x in line] # for j, value in enumerate(line): # print(j, value) # x = np.arange(0, 1024+1, 1) # np.arange(start, end+step, step) [start, end] end/step+1 # plt.plot(x, gamma_hisi_x1023_y4095) # plt.show() for i in range(hisi_degamma_x+1): # for i in range(0, hisi_degamma_x+1, 1): for j, value in enumerate(gamma_hisi_x1023_y4095): if (value / hisi_gamma_y * hisi_degamma_x) >= i: degamma_x255_y1.append(j/hisi_gamma_x) break # x = np.arange(0, hisi_degamma_x+1, 1) # plt.plot(x, degamma_x255_y1) # plt.show() # degamma data = np.clip(data, clip_range[0], clip_range[1]) data = np.divide(data, clip_range[1]) height = data.shape[0] weight = data.shape[1] channels = data.shape[2] for row in range(height): # 遍历高 for col in range(weight): # 遍历宽 pv0 = data[row, col, 0] pv1 = data[row, col, 1] pv2 = data[row, col, 2] data[row, col, 0] = degamma_x255_y1[int(pv0*255)] data[row, col, 1] = degamma_x255_y1[int(pv1*255)] data[row, col, 2] = degamma_x255_y1[int(pv2*255)] data_show = data.copy() data_show = np.clip(data_show * clip_range[1], clip_range[0], clip_range[1]) # gbr = rgb[...,[2,0,1]] # data_show = data_show[..., ::-1] data_show = data_show[..., [2,1,0]] cv2.imshow("data", data_show.astype(np.uint8)) cv2.waitKey(0) return np.clip(data * clip_range[1], clip_range[0], clip_range[1])def gamma_hisi(data, clip_range, gamma_txt): # gamma degamma hisi_gamma_x = 1024-1 hisi_gamma_y = 4096-1 hisi_degamma_x = 256-1 hisi_degamma_y = 1.0 gamma_hisi_x1023_y4095 = [] degamma_x255_y1 = [] with open(gamma_txt, "r") as f: for i, line in enumerate(f.readlines()): line = line.split(",") gamma_hisi_x1023_y4095 = [float(x) for x in line] # for j, value in enumerate(line): # print(j, value) # x = np.arange(0, 1024+1, 1) # np.arange(start, end+step, step) [start, end] end/step+1 # plt.plot(x, gamma_hisi_x1023_y4095) # plt.show() for i in range(hisi_degamma_x+1): # for i in range(0, hisi_degamma_x+1, 1): for j, value in enumerate(gamma_hisi_x1023_y4095): if (value / hisi_gamma_y * hisi_degamma_x) >= i: degamma_x255_y1.append(j/hisi_gamma_x) break # x = np.arange(0, hisi_degamma_x+1, 1) # plt.plot(x, degamma_x255_y1) # plt.show() # gamma data = np.clip(data, clip_range[0], clip_range[1]) data = np.divide(data, clip_range[1]) height = data.shape[0] weight = data.shape[1] channels = data.shape[2] for row in range(height): # 遍历高 for col in range(weight): # 遍历宽 pv0 = data[row, col, 0] pv1 = data[row, col, 1] pv2 = data[row, col, 2] data[row, col, 0] = gamma_hisi_x1023_y4095[int(pv0*1023)] / 4095.0 data[row, col, 1] = gamma_hisi_x1023_y4095[int(pv1*1023)] / 4095.0 data[row, col, 2] = gamma_hisi_x1023_y4095[int(pv2*1023)] / 4095.0 data_show = data.copy() data_show = np.clip(data_show * clip_range[1], clip_range[0], clip_range[1]) # gbr = rgb[...,[2,0,1]] # data_show = data_show[..., ::-1] data_show = data_show[..., [2,1,0]] cv2.imshow("data", data_show.astype(np.uint8)) cv2.waitKey(0) return np.clip(data * clip_range[1], clip_range[0], clip_range[1])def CCM_convert(data, CCM, color_space="srgb", clip_range=[0, 255]): # CCM工作在线性RGB因此需要先进行degamma if (color_space == "srgb"): data = color.degamma_srgb(data, clip_range) data = np.float32(data) data = np.divide(data, clip_range[1]) # 归一化 elif (color_space == "hisi"): data = degamma_hisi(data, clip_range, "./gamma_hisi_int.txt") data = np.float32(data) data = np.divide(data, clip_range[1]) # 归一化 # matrix multiplication output = np.empty(np.shape(data), dtype=np.float32) output[:, :, 0] = data[:, :, 0] * CCM[0,0] + data[:, :, 1] * CCM[0,1] + data[:, :, 2] * CCM[0,2] output[:, :, 1] = data[:, :, 0] * CCM[1,0] + data[:, :, 1] * CCM[1,1] + data[:, :, 2] * CCM[1,2] output[:, :, 2] = data[:, :, 0] * CCM[2,0] + data[:, :, 1] * CCM[2,1] + data[:, :, 2] * CCM[2,2] # gamma if (color_space == "srgb"): output = output*clip_range[1] output = color.gamma_srgb(output, clip_range) elif (color_space == "hisi"): output = output*clip_range[1] output = gamma_hisi(output, clip_range, "./gamma_hisi_int.txt") return outputif __name__ == "__main__": # CCM = np.array([ # [1.507812, -0.546875, 0.039062], # [-0.226562, 1.085938, 0.140625], # [-0.062500, -0.648438, 1.718750], # ]) CCM = np.array([ [1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0], ]) maxvalue = 255 # image = plt.imread("kodim19.png") image = plt.imread("test02.png") if (np.max(image) <= 1): image = image * maxvalue new_image = CCM_convert(image, CCM, color_space="hisi", clip_range=[0, maxvalue]) color.rgb_show(image / 255) color.rgb_show(new_image / 255)
