关于计算PSNR的时候报错

符明东233

请大佬们看一下 为什么我图片处理后的格式不在区间内 怎样解决呢 谢谢

1. import os
   
2. import sys
   
3. import time
   
4. import math
   
5. from datetime import datetime
   
6. import random
   
7. import logging
   
8. from collections import OrderedDict
   
9. import numpy as np
   
10. import cv2
    
11. import torch
    
12. from torchvision.utils import make_grid
    
13. from shutil import get_terminal_size
    
14. 
    
15. import yaml
    
16. try:
    
17.     from yaml import CLoader as Loader, CDumper as Dumper
    
18. except ImportError:
    
19.     from yaml import Loader, Dumper
    
20. 
    
21. 
    
22. def OrderedYaml():
    
23.     '''yaml orderedDict support'''
    
24.     _mapping_tag = yaml.resolver.BaseResolver.DEFAULT_MAPPING_TAG
    
25. 
    
26.     def dict_representer(dumper, data):
    
27.         return dumper.represent_dict(data.items())
    
28. 
    
29.     def dict_constructor(loader, node):
    
30.         return OrderedDict(loader.construct_pairs(node))
    
31. 
    
32.     Dumper.add_representer(OrderedDict, dict_representer)
    
33.     Loader.add_constructor(_mapping_tag, dict_constructor)
    
34.     return Loader, Dumper
    
35. 
    
36. 
    
37. ####################
    
38. # miscellaneous
    
39. ####################
    
40. 
    
41. 
    
42. def get_timestamp():
    
43.     return datetime.now().strftime('%y%m%d-%H%M%S')
    
44. 
    
45. 
    
46. def mkdir(path):
    
47.     if not os.path.exists(path):
    
48.         os.makedirs(path)
    
49. 
    
50. 
    
51. def mkdirs(paths):
    
52.     if isinstance(paths, str):
    
53.         mkdir(paths)
    
54.     else:
    
55.         for path in paths:
    
56.             mkdir(path)
    
57. 
    
58. 
    
59. def mkdir_and_rename(path):
    
60.     if os.path.exists(path):
    
61.         new_name = path + '_archived_' + get_timestamp()
    
62.         print('Path already exists. Rename it to [{:s}]'.format(new_name))
    
63.         logger = logging.getLogger('base')
    
64.         logger.info('Path already exists. Rename it to [{:s}]'.format(new_name))
    
65.         os.rename(path, new_name)
    
66.     os.makedirs(path)
    
67. 
    
68. 
    
69. def set_random_seed(seed):
    
70.     random.seed(seed)
    
71.     np.random.seed(seed)
    
72.     torch.manual_seed(seed)
    
73.     torch.cuda.manual_seed_all(seed)
    
74. 
    
75. 
    
76. def setup_logger(logger_name, root, phase, level=logging.INFO, screen=False, tofile=False):
    
77.     '''set up logger'''
    
78.     lg = logging.getLogger(logger_name)
    
79.     formatter = logging.Formatter('%(asctime)s.%(msecs)03d - %(levelname)s: %(message)s',
    
80.                                   datefmt='%y-%m-%d %H:%M:%S')
    
81.     lg.setLevel(level)
    
82.     if tofile:
    
83.         log_file = os.path.join(root, phase + '_{}.log'.format(get_timestamp()))
    
84.         fh = logging.FileHandler(log_file, mode='w')
    
85.         fh.setFormatter(formatter)
    
86.         lg.addHandler(fh)
    
87.     if screen:
    
88.         sh = logging.StreamHandler()
    
89.         sh.setFormatter(formatter)
    
90.         lg.addHandler(sh)
    
91. 
    
92. 
    
93. ####################
    
94. # image convert
    
95. ####################
    
96. 
    
97. 
    
98. def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)):
    
99.     '''
    
100.     Converts a torch Tensor into an image Numpy array
     
101.     Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order
     
102.     Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default)
     
103.     '''
     
104.     tensor = tensor.squeeze().float().cpu().clamp_(*min_max)  # clamp
     
105.     tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0])  # to range [0,1]
     
106.     n_dim = tensor.dim()
     
107.     if n_dim == 4:
     
108.         n_img = len(tensor)
     
109.         img_np = make_grid(tensor, nrow=int(math.sqrt(n_img)), normalize=False).numpy()
     
110.         img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0))  # HWC, BGR
     
111.     elif n_dim == 3:
     
112.         img_np = tensor.numpy()
     
113.         img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0))  # HWC, BGR
     
114.     elif n_dim == 2:
     
115.         img_np = tensor.numpy()
     
116.     else:
     
117.         raise TypeError(
     
118.             'Only support 4D, 3D and 2D tensor. But received with dimension: {:d}'.format(n_dim))
     
119.     if out_type == np.uint8:
     
120.         img_np = (img_np * 255.0).round()
     
121.         # Important. Unlike matlab, numpy.unit8() WILL NOT round by default.
     
122.     return img_np.astype(out_type)
     
123. 
     
124. 
     
125. def save_img(img, img_path, mode='RGB'):
     
126.     cv2.imwrite(img_path, img)
     
127. 
     
128. 
     
129. ####################
     
130. # metric
     
131. ####################
     
132. 
     
133. 
     
134. def calculate_psnr(img1, img2):
     
135.     # img1 and img2 have range [0, 255]
     
136.     img1 = img1.astype(np.float64)
     
137.     img2 = img2.astype(np.float64)
     
138.     mse = np.mean((img1 - img2)**2)
     
139.     if mse == 0:
     
140.         return float('inf')
     
141.     return 20 * math.log10(255.0 / math.sqrt(mse))
     
142. 
     
143. 
     
144. def ssim(img1, img2):
     
145.     C1 = (0.01 * 255)**2
     
146.     C2 = (0.03 * 255)**2
     
147. 
     
148.     img1 = img1.astype(np.float64)
     
149.     img2 = img2.astype(np.float64)
     
150.     kernel = cv2.getGaussianKernel(11, 1.5)
     
151.     window = np.outer(kernel, kernel.transpose())
     
152. 
     
153.     mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5]  # valid
     
154.     mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
     
155.     mu1_sq = mu1**2
     
156.     mu2_sq = mu2**2
     
157.     mu1_mu2 = mu1 * mu2
     
158.     sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq
     
159.     sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq
     
160.     sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2
     
161. 
     
162.     ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) *
     
163.                                                             (sigma1_sq + sigma2_sq + C2))
     
164.     return ssim_map.mean()
     
165. 
     
166. 
     
167. def calculate_ssim(img1, img2):
     
168.     '''calculate SSIM
     
169.     the same outputs as MATLAB's
     
170.     img1, img2: [0, 255]
     
171.     '''
     
172.     if not img1.shape == img2.shape:
     
173.         raise ValueError('Input images must have the same dimensions.')
     
174.     if img1.ndim == 2:
     
175.         return ssim(img1, img2)
     
176.     elif img1.ndim == 3:
     
177.         if img1.shape[2] == 3:
     
178.             ssims = []
     
179.             for i in range(3):
     
180.                 ssims.append(ssim(img1, img2))
     
181.             return np.array(ssims).mean()
     
182.         elif img1.shape[2] == 1:
     
183.             return ssim(np.squeeze(img1), np.squeeze(img2))
     
184.     else:
     
185.         raise ValueError('Wrong input image dimensions.')
     
186. 
     
187. 
     
188. class ProgressBar(object):
     
189.     '''A progress bar which can print the progress
     
190.     modified from https://github.com/hellock/cvbase/blob/master/cvbase/progress.py
     
191.     '''
     
192. 
     
193.     def __init__(self, task_num=0, bar_width=50, start=True):
     
194.         self.task_num = task_num
     
195.         max_bar_width = self._get_max_bar_width()
     
196.         self.bar_width = (bar_width if bar_width <= max_bar_width else max_bar_width)
     
197.         self.completed = 0
     
198.         if start:
     
199.             self.start()
     
200. 
     
201.     def _get_max_bar_width(self):
     
202.         terminal_width, _ = get_terminal_size()
     
203.         max_bar_width = min(int(terminal_width * 0.6), terminal_width - 50)
     
204.         if max_bar_width < 10:
     
205.             print('terminal width is too small ({}), please consider widen the terminal for better '
     
206.                   'progressbar visualization'.format(terminal_width))
     
207.             max_bar_width = 10
     
208.         return max_bar_width
     
209. 
     
210.     def start(self):
     
211.         if self.task_num > 0:
     
212.             sys.stdout.write('[{}] 0/{}, elapsed: 0s, ETA:\n{}\n'.format(
     
213.                 ' ' * self.bar_width, self.task_num, 'Start...'))
     
214.         else:
     
215.             sys.stdout.write('completed: 0, elapsed: 0s')
     
216.         sys.stdout.flush()
     
217.         self.start_time = time.time()
     
218. 
     
219.     def update(self, msg='In progress...'):
     
220.         self.completed += 1
     
221.         elapsed = time.time() - self.start_time
     
222.         fps = self.completed / elapsed
     
223.         if self.task_num > 0:
     
224.             percentage = self.completed / float(self.task_num)
     
225.             eta = int(elapsed * (1 - percentage) / percentage + 0.5)
     
226.             mark_width = int(self.bar_width * percentage)
     
227.             bar_chars = '>' * mark_width + '-' * (self.bar_width - mark_width)
     
228.             sys.stdout.write('\033[2F')  # cursor up 2 lines
     
229.             sys.stdout.write('\033[J')  # clean the output (remove extra chars since last display)
     
230.             sys.stdout.write('[{}] {}/{}, {:.1f} task/s, elapsed: {}s, ETA: {:5}s\n{}\n'.format(
     
231.                 bar_chars, self.completed, self.task_num, fps, int(elapsed + 0.5), eta, msg))
     
232.         else:
     
233.             sys.stdout.write('completed: {}, elapsed: {}s, {:.1f} tasks/s'.format(
     
234.                 self.completed, int(elapsed + 0.5), fps))
     
235.         sys.stdout.flush()
     

复制代码

chenyiyun

代码太多，看不懂，能告诉我这是干啥的么？

later1025

看不懂