如何正确可视化RAW（ARW,DNG,raw等格式）图像？

为了正确可视化RAW图像，需要做好：白平衡、提亮以及色彩映射。

 import numpy as np
 import struct
 from PIL import Image
 import rawpy
 import glob
 import os

 def conv(v):
     s = 0
     for i in range(len(v)):
         s += i * v[i]
         v[i] = s
     return v

 def diff(v):
     n = len(v)
     v_diff = np.zeros([len(v)])
     for i in range(n-1):
         v_diff[n-1-i] = v[n-1-i] - v[n-1-i-1]
     return v_diff

 def 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-7

 def 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 gray_world_balance(x):
     mean_ = np.maximum(np.mean(x, axis=(0, 1)), 1e-6)
     ratio_ = mean_.mean() / mean_
     return np.minimum(x * ratio_, 1.0)

 def show_bbf(path):
     print('====== %s =====' % path)
     max_level = 1023
     black_level = 64
     height = 3024
     width = 4032
     if path.endswith('dng') or path.endswith('DNG'):
         raw = rawpy.imread(path)
         im = raw.raw_image_visible.astype(np.float32)
         res_path = str.replace(path, '.dng', '.jpg')
     elif path.endswith('raw') or path.endswith('RAW'):
         raw = open(path, 'rb').read()
         raw = struct.unpack('H'*int(len(raw)/2), raw)
         im = np.float32(raw)
         res_path = str.replace(path, '.raw', '.jpg')
     else:
         assert False
     im = im.reshape(height, width)
     im = np.maximum(im - black_level, 0) / (max_level - black_level)
     # AMPLIFICATION
     # n = 256
     # std = 0.05
     # sample_rate = 1
     # extreme_dark_ratio = 13 * std / 0.05
     # light_scene_ratio = 4.0 * std / 0.05
     # light_threshold = 0.04
     # decay_in_light_ratio = 0.3
     # light_radius = 5
     # total = height * width / (sample_rate*sample_rate) / 4
     #
     # bins = np.arange(n + 1) / (n - 1)
     # hists = [None] * 5
     # hists[0], _ = np.histogram(im[0:1512:sample_rate, 0:2016:sample_rate], bins)
     # hists[1], _ = np.histogram(im[0:1512:sample_rate, 2016:4032:sample_rate], bins)
     # hists[2], _ = np.histogram(im[1512:3024:sample_rate, 0:2016:sample_rate], bins)
     # hists[3], _ = np.histogram(im[1512:3024:sample_rate, 2016:4032:sample_rate], bins)
     # hists[4] = (hists[0] + hists[1] + hists[2] + hists[3])/4
     # convs = [None] * 5
     # min_conv = 255
     # max_conv = 0
     # final_ratio = 1.0
     # is_dark = False
     #
     # for i in range(5):
     #     hists[i] = hists[i] / total
     #     convs[i] = conv(hists[i][0:n])
     #     print(convs[i][-1])
     #     if convs[i][-1]<min_conv:
     #         min_conv = convs[i][-1]
     #     if convs[i][-1]>max_conv:
     #         max_conv = convs[i][-1]
     #
     # print('min=%.6f, max=%.6f' % (min_conv, max_conv))
     #
     # hist_conv = convs[4]
     # hist_diff = diff(hist_conv)
     # ratio = std / (hist_conv[-1] / n)
     # print("Normal ratio=%.6f" % ratio)
     # if ratio < 1:
     #     print("Daylight scene found!")
     #     final_ratio = 1.0
     # # check if exists high contrast scene
     # if min_conv < 3 and max_conv/(min_conv + 1e-7) > 2.2:
     #     print('high contrast scene detected!')
     #     final_ratio = min(2.0, ratio)
     # else:
     #     if max(hist_diff[-light_radius:]) > light_threshold:
     #         print('Light Found')
     #         if ratio > extreme_dark_ratio:
     #             final_ratio = ratio * decay_in_light_ratio
     #             print('Extreme Dark')
     #         else:
     #             final_ratio = min(light_scene_ratio, ratio)
     #     else:
     #         if ratio > extreme_dark_ratio:
     #             print('Extreme Dark')
     #             is_dark = True
     #             final_ratio = extreme_dark_ratio*extreme_dark_ratio*extreme_dark_ratio/(ratio*ratio)
     #             if 4 < final_ratio < 6:
     #                 final_ratio = 4
     #         else:
     #             final_ratio = ratio
     #     if ratio < 1.0:
     #         final_ratio = 1.0
     # if 5 < final_ratio < 12 and not is_dark:
     #     final_ratio *= 0.7
     # im = np.minimum(im * final_ratio, 1.0)
     # print('ratio=%.6f' % final_ratio)
     im = np.expand_dims(im, axis=2)
     H = im.shape[0]
     W = im.shape[1]
     out = np.concatenate((
         im[0:H:2, 1:W:2, :],
         (im[0:H:2, 0:W:2, :] + im[1:H:2, 1:W:2, :])/2.0,
         im[1:H:2, 0:W:2, :]),
         axis=2)
     if path.endswith('dng') or path.endswith('DNG'):
         wb = np.array(raw.camera_whitebalance[:3], np.float32)
         wb = wb / wb[1]
         out = np.minimum(out * wb, 1.0)
     else:
         out = gray_world_balance(out)
     out = np.minimum(out * 0.2 / out[:, :, 1].mean(), 1.0)
     out = HDR(out, 0.35)
     Image.fromarray(np.uint8(out*255)).save(res_path)

 def dng2raw(path):
     raw = rawpy.imread(path)
     im = raw.raw_image_visible.astype(np.ushort)
     h, w = im.shape
     res_path = str.replace(path, '.dng', '.raw')
     with open(res_path, 'wb')as fp:
         for i in range(h):
             for j in range(w):
                 a = struct.pack('<H', im[i, j])
                 fp.write(a)

 def decode_sony(path):
     if os.path.split(path)[-1].split('.')[-1] != 'ARW':
         print('Error: image type not match!')
         exit(1)
     im_ = rawpy.imread(path)
     data_ = im_.raw_image_visible.astype(np.float32)
     h_, w_ = data_.shape
     save_path_ = str.replace(path, '.ARW', '.JPG')
     data_ = np.maximum(data_ - 512, 0) / (16383 - 512)
     data_ = np.expand_dims(data_, axis=2)
     data_ = np.concatenate((
         data_[0:h_:2, 0:w_:2, :],
         (data_[0:h_:2, 1:w_:2, :] + data_[1:h_:2, 0:w_:2, :]) / 2.0,
         data_[1:h_:2, 1:w_:2, :]),
         axis=2)
     # white balance
     wb = np.array(im_.camera_whitebalance[:3], np.float32)
     wb = wb / wb[1]
     data_ = np.minimum(data_ * wb, 1.0)
     data_ = np.minimum(data_ * 0.2 / data_[:, :, 1].mean(), 1.0)
     data_ = HDR(data_, 0.35)
     Image.fromarray(np.uint8(data_ * 255)).save(save_path_)

 def decode_sony_rawpy(path):
     if os.path.split(path)[-1].split('.')[-1] != 'ARW':
         print('Error: image type not match!')
         exit(1)
     im_ = rawpy.imread(path)
     save_path_ = str.replace(path, '.ARW', '.JPG')
     Image.fromarray(im_.postprocess(use_camera_wb=True)).save(save_path_)

 if __name__ == '__main__':
     # files = glob.glob('D:/data/report_dng/*.dng')
     # for i in range(len(files)):
     #     show_bbf(files[i])

     # files = glob.glob('D:/data/Sony/long/00057_00_10s.ARW')
     # im = rawpy.imread(files[0])
     # Image.fromarray(im.postprocess(use_camera_wb=True)).show()

     # files = glob.glob('D:/data/Sony/long/*.ARW')
     # for i in range(len(files)):
     #     decode_sony_rawpy(files[i])

     # files = glob.glob('D:/data/Sony/long/RAW/30s/*.ARW')
     # for i in range(len(files)):
     #     decode_sony(files[i])

     # files = glob.glob('D:/data/Sony/dataset/illu_detect/day/*.dng')
     # for i in range(len(files)):
     #     dng2raw(files[i])

     # files = glob.glob('C:/Users/Administrator/Desktop/ILLU/*.dng')
     files = glob.glob('D:/data/LightOnOff/*.dng')
     for i in range(len(files)):
         # dng2raw(files[i])
         show_bbf(files[i])

展示图片：