See In The Dark之RAW图片读取-rawpy

本文主要是介绍See In The Dark之RAW图片读取-rawpy，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

前言：处理不同相机的RAW格式图片，并放入卷积运算，输入到see in the dark模型进行夜间识别

1. 保存RAW图片为RGB的JPG格式

import rawpy
import imageio
import numpy as np# .ARW为索尼Sony相机RAW格式
# .CR2为佳能canon相机RAW格式
raw = rawpy.imread('IMG_0001.CR2')# use_camera_wb 是否执行自动白平衡，如果不执行白平衡，一般图像会偏色
# half_size 是否图像减半
# no_auto_bright 不自动调整亮度
# output_bps bit数据， 8或16
img = raw.postprocess(use_camera_wb=True, half_size=False, no_auto_bright=False, output_bps=16)# img = np.float32(img / (2**16-1)*255.0)
# img = np.asarray(img,np.uint8)imageio.imsave('take.jpg', img)

2. bayer阵列解析

拜耳阵列（Bayer pattern）分为GBRG、GRBG、BGGR、RGGB四种模式：
在这里插入图片描述
使用rawpy把不同模式下raw解析为四通道：

import rawpy
import numpy as np
from PIL import Image
import imageio
import exifreaddef gray_ps(rgb):return np.power(np.power(rgb[:, :, 0], 2.2) * 0.2973 + np.power(rgb[:, :, 1], 2.2) * 0.6274+ np.power(rgb[:, :, 2], 2.2) * 0.0753, 1 / 2.2) + 1e-7def do_HDR(x, curve_ratio):gray_scale = np.expand_dims(gray_ps(x), axis=-1)gray_scale_new = np.power(gray_scale, curve_ratio)return np.minimum(x * gray_scale_new / gray_scale, 1.0)def adjust_out_matrix(RAW_path, out=None):raw = open(RAW_path, 'rb')exif_info = exifread.process_file(raw, details=False, strict=True)orientation_str = 'EXIF Orientation'if exif_info.__contains__('Image Orientation'):orientation_str = 'Image Orientation'orientation_info = exif_info[orientation_str].printableif orientation_info == 'Rotated 180':if out is None:return Trueelse:if out.shape[2] == 3:out0 = out[:, :, :1]out1 = out[:, :, 1:2]out2 = out[:, :, 2:3]out = np.concatenate((out2, out1, out0), 2)elif out.shape[2] == 4:out0 = out[:, :, :1]out1 = out[:, :, 1:2]out2 = out[:, :, 2:3]out3 = out[:, :, 3:4]out = np.concatenate((out3, out2, out1, out0), 2)else:raisereturn np.flip(out)elif orientation_info == 'Horizontal (normal)':if out is None:return Falseelse:return outelse:raise# pack Bayer image to 4 channels
def pack_raw(raw_path, white_balance=True, auto_bright=True, HDR=True, save_JPEG=True):raw = rawpy.imread(raw_path)im = raw.raw_image_visible.astype(np.float32)# subtract the black level# 16383(2^14) is the camera's maximal pixel value, you can get it by "np.max(raw.raw_image)" . Ensure full exposure!im = np.maximum(im - raw.black_level_per_channel[0], 0) / (16383 - raw.black_level_per_channel[0])im = np.expand_dims(im, axis=2)H = im.shape[0]W = im.shape[1]if raw.raw_pattern[0, 0] == 0:  # RGGBout = np.concatenate((im[0:H:2, 0:W:2, :],im[0:H:2, 1:W:2, :],im[1:H:2, 1:W:2, :],im[1:H:2, 0:W:2, :]), axis=2)elif raw.raw_pattern[0, 0] == 2:  # BGGRout = np.concatenate((im[1:H:2, 1:W:2, :],im[0:H:2, 1:W:2, :],im[0:H:2, 0:W:2, :],im[1:H:2, 0:W:2, :]), axis=2)elif raw.raw_pattern[0, 0] == 1 and raw.raw_pattern[0, 1] == 0:  # GRBGout = np.concatenate((im[0:H:2, 1:W:2, :],im[0:H:2, 0:W:2, :],im[1:H:2, 0:W:2, :],im[1:H:2, 1:W:2, :]), axis=2)elif raw.raw_pattern[0, 0] == 1 and raw.raw_pattern[0, 1] == 2:  # GBRGout = np.concatenate((im[1:H:2, 0:W:2, :],im[0:H:2, 0:W:2, :],im[0:H:2, 1:W:2, :],im[1:H:2, 1:W:2, :]), axis=2)# 启用白平衡可以防止图片偏蓝或者偏红if white_balance:wb = np.array(raw.camera_whitebalance, np.float32)wb[3] = wb[1]wb = wb / wb[1]out = np.minimum(out * wb, 1.0)if auto_bright:mean_G = (out[:, :, 1].mean() + out[:, :, 3].mean()) / 2.0out = np.minimum(out*0.2/mean_G, 1.0)out = adjust_out_matrix(raw_path, out)# 检测RAW格式解析后的图片是否正常if save_JPEG:out0 = out[:, :, 0:1]out1 = out[:, :, 1:2]out2 = out[:, :, 2:3]out3 = out[:, :, 3:4]out_JPEG = np.concatenate((out0, (out1 + out3) / 2., out2), axis=2)if HDR:out_JPEG = do_HDR(out_JPEG, 0.35)Image.fromarray(np.uint8(out_JPEG * 255)).save('result.jpg')return outif __name__ == '__main__':raw = rawpy.imread('IMG_0001.CR2')np_channel = pack_raw('IMG_0001.CR2', auto_bright=False, HDR=False)img = raw.postprocess(use_camera_wb=True, half_size=False, no_auto_bright=True, output_bps=16)imageio.imsave('rawpy.jpg', img)

3. 苹果相机iphone 13 pro max 的RAW图片

	raw = rawpy.imread("IMG_0001.DNG")im = raw.raw_image_visible.astype(np.float32)  # （h, w, c） = (h, w, 4)# 手机竖直拍摄需要加上下面两行，横向拍摄不需要if vertical:out = np.swapaxes(im,1,0)		# (h w) axis change 20220808out = out[:,::-1,:]   			# Horizontal mirror 20220810

4. 小米12pro 的RAW图片

def pack_raw(raw):#pack Bayer image to 4 channelsim = raw.raw_image_visible.astype(np.float32)im = np.maximum(im - 63,0)/ (255 - 63) #subtract the black levelim = np.expand_dims(im,axis=2)img_shape = im.shapeH = img_shape[0]W = img_shape[1]out = np.concatenate((im[0:H:2,0:W:2,:],im[0:H:2,1:W:2,:],im[1:H:2,1:W:2,:],im[1:H:2,0:W:2,:]), axis=2)# 手机竖直拍摄需要加上下面两行，横向拍摄不需要if vertical:out = np.swapaxes(out,1,0)    # (h w) axis change 20220808out = out[:,::-1,:]           # Horizontal mirror 20220810print("pack_raw out.shape: ", out.shape)return outraw = rawpy.imread("IMG_0001.DNG")
im = raw.raw_image_visible.astype(np.float32)  # (h, w)
input_images = np.expand_dims(pack_raw(raw),axis=0)