检测照度/噪声水平 

  1 from __future__ import division
  2 import os, time, scipy.io
  3 import tensorflow as tf
  4 import numpy as np
  5 import rawpy
  6 import glob
  7 from model_sid_latest import network_my_unet
  8 import platform
  9 from PIL import Image
 10 
 11 # BBF100-2
 12 bbf_w = 4032 // 2
 13 bbf_h = 3024 // 2
 14 
 15 input_dir = 'D:/data/Sony/dataset/illu_detect/'
 16 
 17 
 18 def preprocess(raw, bl, wl):
 19     im = raw.raw_image_visible.astype(np.float32)
 20     im = np.maximum(im - bl, 0)
 21     return im / (wl - bl)
 22 
 23 
 24 def pack_raw_bbf(path):
 25     raw = rawpy.imread(path)
 26     bl = 64
 27     wl = 1023
 28     im = preprocess(raw, bl, wl)
 29     im = np.expand_dims(im, axis=2)
 30     H = im.shape[0]
 31     W = im.shape[1]
 32     if raw.raw_pattern[0, 0] == 0: # CFA=RGGB
 33         out = np.concatenate((im[0:H:2, 0:W:2, :],
 34                               im[0:H:2, 1:W:2, :],
 35                               im[1:H:2, 1:W:2, :],
 36                               im[1:H:2, 0:W:2, :]), axis=2)
 37     elif raw.raw_pattern[0,0] == 2: # BGGR
 38         out = np.concatenate((im[1:H:2, 1:W:2, :],
 39                               im[0:H:2, 1:W:2, :],
 40                               im[0:H:2, 0:W:2, :],
 41                               im[1:H:2, 0:W:2, :]), axis=2)
 42     elif raw.raw_pattern[0,0] == 1 and raw.raw_pattern[0,1] == 0: # GRBG
 43         out = np.concatenate((im[0:H:2, 1:W:2, :],
 44                               im[0:H:2, 0:W:2, :],
 45                               im[1:H:2, 0:W:2, :],
 46                               im[1:H:2, 1:W:2, :]), axis=2)
 47     elif raw.raw_pattern[0,0] == 1 and raw.raw_pattern[0,1] == 2: # GBRG
 48         out = np.concatenate((im[1:H:2, 0:W:2, :],
 49                               im[0:H:2, 0:W:2, :],
 50                               im[0:H:2, 1:W:2, :],
 51                               im[1:H:2, 1:W:2, :]), axis=2)
 52     else:
 53         assert False
 54     # wb = np.array(raw.camera_whitebalance)
 55     # wb[3] = wb[1]
 56     # wb = wb / wb[1]
 57     # out = np.minimum(out * wb, 1.0)
 58     return out
 59 
 60 
 61 in_im = tf.placeholder(tf.float32, [1, bbf_h, bbf_w, 4], name='input')
 62 
 63 # Obtain the noise level map
 64 radius = 3
 65 strides = 32
 66 
 67 # in_patches_r = tf.extract_image_patches(
 68 #     tf.expand_dims(
 69 #         in_im[:, :, :, 0], axis=3),
 70 #     (1, radius, radius, 1),
 71 #     (1, strides, strides, 1),
 72 #     (1, 1, 1, 1),
 73 #     'VALID')
 74 # miu_r = tf.maximum(tf.reduce_mean(in_patches_r, axis=-1, keepdims=True), 1e-7)
 75 # sigma_r = tf.reduce_sum(tf.abs((in_patches_r - miu_r) / miu_r), axis=-1, keepdims=True)/(radius * radius - 1)
 76 
 77 in_patches_g = tf.extract_image_patches(
 78     tf.expand_dims(
 79         in_im[:, :, :, 3], axis=3),
 80     (1, radius, radius, 1),
 81     (1, strides, strides, 1),
 82     (1, 1, 1, 1),
 83     'VALID')
 84 miu_g = tf.maximum(tf.reduce_mean(in_patches_g, axis=-1, keepdims=True), 1e-7)
 85 sigma_g = tf.reduce_sum(tf.abs((in_patches_g - miu_g) / miu_g), axis=-1, keepdims=True)/(radius * radius - 1)
 86 
 87 # in_patches_b = tf.extract_image_patches(
 88 #     tf.expand_dims(
 89 #         in_im[:, :, :, 2], axis=3),
 90 #     (1, radius, radius, 1),
 91 #     (1, strides, strides, 1),
 92 #     (1, 1, 1, 1),
 93 #     'VALID')
 94 # miu_b = tf.maximum(tf.reduce_mean(in_patches_b, axis=-1, keepdims=True), 1e-7)
 95 # sigma_b = tf.reduce_sum(tf.abs((in_patches_b - miu_b) / miu_b), axis=-1, keepdims=True)/(radius * radius - 1)
 96 #
 97 # sigma_level = tf.reduce_mean(tf.minimum(tf.minimum(sigma_r, sigma_g), sigma_b))
 98 
 99 sigma_level = tf.reduce_mean(sigma_g)
100 illu_level = tf.reduce_mean(miu_g)
101 print(in_patches_g.shape)
102 
103 sess = tf.Session()
104 # print('============= DAY ============')
105 # files = glob.glob(input_dir + '/day/*.dng')
106 # files.sort()
107 # for i in range(len(files)):
108 #     noise_level = sess.run(sigma_level, feed_dict={in_im: np.expand_dims(pack_raw_bbf(files[i]), axis=0)})
109 #     illu = 0.3 / noise_level
110 #     print(illu)
111 #
112 # print('============= NIGHT ============')
113 # files = glob.glob(input_dir + '/night/*.dng')
114 # files.sort()
115 # for i in range(len(files)):
116 #     noise_level = sess.run(sigma_level, feed_dict={in_im: np.expand_dims(pack_raw_bbf(files[i]), axis=0)})
117 #     illu = 0.3 / noise_level
118 #     print(illu)
119 
120 print('============= NIGHT ============')
121 files = glob.glob('C:/Users/Administrator/Desktop/ILLU/*.dng')
122 files.sort()
123 for i in range(len(files)):
124     sigma_, illu_ = sess.run([sigma_level, illu_level], feed_dict={in_im: np.expand_dims(pack_raw_bbf(files[i]), axis=0)})
125     illu = 0.3 / sigma_
126     print(illu)
127     print(illu_)
128     print('--------------')

通过检测每个采样像素的邻域均方差来确定画面的整体噪声水平。

这里需要注意的是每个点的明暗程度不一致，因此需要对均方差进行亮度归一化。

【上述程序有个BUG】

在存在明显的光源时，会出现照度测量不正确的情形。为了解决这一问题，引入全局画面亮度作为参考，即：平均照度应该考量总体像素值波动幅度的同时对全局亮度的归一。

 1 from __future__ import division
 2 import os, time, scipy.io
 3 import tensorflow as tf
 4 import numpy as np
 5 import rawpy
 6 import glob
 7 from model_sid_latest import network_my_unet
 8 import platform
 9 from PIL import Image
10 
11 # BBF100-2
12 bbf_w = 4032 // 2
13 bbf_h = 3024 // 2
14 
15 input_dir = 'D:/data/Sony/dataset/illu_detect/'
16 
17 
18 def preprocess(raw, bl, wl):
19     im = raw.raw_image_visible.astype(np.float32)
20     im = np.maximum(im - bl, 0)
21     return im / (wl - bl)
22 
23 
24 def pack_raw_bbf(path):
25     raw = rawpy.imread(path)
26     bl = 64
27     wl = 1023
28     im = preprocess(raw, bl, wl)
29     im = np.expand_dims(im, axis=2)
30     H = im.shape[0]
31     W = im.shape[1]
32     if raw.raw_pattern[0, 0] == 0: # CFA=RGGB
33         out = np.concatenate((im[0:H:2, 0:W:2, :],
34                               im[0:H:2, 1:W:2, :],
35                               im[1:H:2, 1:W:2, :],
36                               im[1:H:2, 0:W:2, :]), axis=2)
37     elif raw.raw_pattern[0,0] == 2: # BGGR
38         out = np.concatenate((im[1:H:2, 1:W:2, :],
39                               im[0:H:2, 1:W:2, :],
40                               im[0:H:2, 0:W:2, :],
41                               im[1:H:2, 0:W:2, :]), axis=2)
42     elif raw.raw_pattern[0,0] == 1 and raw.raw_pattern[0,1] == 0: # GRBG
43         out = np.concatenate((im[0:H:2, 1:W:2, :],
44                               im[0:H:2, 0:W:2, :],
45                               im[1:H:2, 0:W:2, :],
46                               im[1:H:2, 1:W:2, :]), axis=2)
47     elif raw.raw_pattern[0,0] == 1 and raw.raw_pattern[0,1] == 2: # GBRG
48         out = np.concatenate((im[1:H:2, 0:W:2, :],
49                               im[0:H:2, 0:W:2, :],
50                               im[0:H:2, 1:W:2, :],
51                               im[1:H:2, 1:W:2, :]), axis=2)
52     else:
53         assert False
54     # wb = np.array(raw.camera_whitebalance)
55     # wb[3] = wb[1]
56     # wb = wb / wb[1]
57     # out = np.minimum(out * wb, 1.0)
58     return out
59 
60 
61 in_im = tf.placeholder(tf.float32, [1, bbf_h, bbf_w, 4], name='input')
62 
63 # Obtain the noise level map
64 radius = 3
65 strides = 32
66 
67 in_patches_g = tf.extract_image_patches(
68     tf.expand_dims(
69         in_im[:, :, :, 3], axis=3),
70     (1, radius, radius, 1),
71     (1, strides, strides, 1),
72     (1, 1, 1, 1),
73     'VALID')
74 miu_g = tf.maximum(tf.reduce_mean(in_patches_g, axis=-1, keepdims=True), 1e-7)
75 illu_level = tf.reduce_mean(miu_g)
76 # sigma_g = tf.reduce_sum(tf.abs((in_patches_g - miu_g) / miu_g), axis=-1, keepdims=True)/(radius * radius - 1)
77 sigma_g = tf.reduce_sum(tf.abs((in_patches_g - miu_g) / illu_level), axis=-1, keepdims=True)/(radius * radius - 1)
78 
79 sigma_level = tf.reduce_mean(sigma_g)
80 print(in_patches_g.shape)
81 
82 sess = tf.Session()
83 
84 files = glob.glob('C:/Users/Administrator/Desktop/ILLU/*.dng')
85 
86 files.sort()
87 for i in range(len(files)):
88     sigma_, illu_ = sess.run([sigma_level, illu_level], feed_dict={in_im: np.expand_dims(pack_raw_bbf(files[i]), axis=0)})
89     illu = 0.25 / sigma_
90     print(illu)

 

posted @ 2019-02-21 13:55  xchk138  阅读(530)  评论(0)    收藏  举报