ReflectionEraser/ReflectionEraserApp
0
1import numpy as np
2import os
3import ntpath #for handling Windows paths.
4import time
5from . import util # . = same package
6from . import html
7import visdom # for visualizing data.
8
9
10class Visualizer():
11 def __init__(self, opt):
12 self.display_id = opt.display_id #In Visdom, you can create multiple windows to display various types of information, such as images, graphs, or tables, during the training and testing of machine learning models. The display_id specifies which window (by ID) the visualizations should be displayed in.
13 self.use_html = opt.isTrain and not opt.no_html
14 #opt.isTrain==True means using train_options.py subclass
15 #HTML visualization in the context of deep learning involves generating web-based visual representations of the training process, model performance, and data. This can be particularly useful for monitoring, debugging, and sharing results.
16 self.win_size = opt.display_winsize
17 self.name = opt.name
18 self.opt = opt
19 self.saved = False
20 if self.display_id > 0: #if diplay_id==0, disable Visdom
21 self.vis = visdom.Visdom(port=opt.display_port, ipv6=False)
22
23 if self.use_html: #logging training loss
24 self.web_dir = os.path.join(opt.checkpoints_dir, opt.name, 'web')
25 self.img_dir = os.path.join(self.web_dir, 'images')
26 print('create web directory %s...' % self.web_dir)
27 util.mkdirs([self.web_dir, self.img_dir])
28 self.log_name = os.path.join(opt.checkpoints_dir, opt.name, 'loss_log.txt')
29 with open(self.log_name, "a") as log_file:
30 now = time.strftime("%c")
31 log_file.write('================ Training Loss (%s) ================\n' % now)
32
33 def reset(self):
34 self.saved = False
35
36 # (mingcv)|visuals|: dictionary of images to display or save
37 def display_current_results(self, visuals, epoch, save_result):
38 if self.display_id > 0: #(mingcv)show images in the browser
39 ncols = self.opt.display_single_pane_ncols
40 if ncols > 0:
41 h, w = next(iter(visuals.values())).shape[:2]
42 table_css = """<style>
43 table {border-collapse: separate; border-spacing:4px; white-space:nowrap; text-align:center}
44 table td {width: %dpx; height: %dpx; padding: 4px; outline: 4px solid black}
45 </style>""" % (w, h)
46 title = self.name
47 label_html = ''
48 label_html_row = ''
49 nrows = int(np.ceil(len(visuals.items()) / ncols))
50 images = []
51 idx = 0
52 for label, image_numpy in visuals.items():
53 label_html_row += '<td>%s</td>' % label
54 images.append(image_numpy.transpose([2, 0, 1]))
55 idx += 1
56 if idx % ncols == 0:
57 label_html += '<tr>%s</tr>' % label_html_row
58 label_html_row = ''
59 white_image = np.ones_like(image_numpy.transpose([2, 0, 1])) * 255
60 while idx % ncols != 0:
61 images.append(white_image)
62 label_html_row += '<td></td>'
63 idx += 1
64 if label_html_row != '':
65 label_html += '<tr>%s</tr>' % label_html_row
66 # pane col = image row
67 self.vis.images(images, nrow=ncols, env=self.opt.name, win=self.display_id + 1,
68 padding=2, opts=dict(title=title + ' images'))
69 label_html = '<table>%s</table>' % label_html
70 self.vis.text(table_css + label_html, env=self.opt.name, win=self.display_id + 2,
71 opts=dict(title=title + ' labels'))
72 else:
73 idx = 1
74 for label, image_numpy in visuals.items():
75 self.vis.image(image_numpy.transpose([2, 0, 1]), opts=dict(title=label),
76 env=self.opt.name,
77 win=self.display_id + idx)
78 idx += 1
79
80 if self.use_html and (save_result or not self.saved): # save images to a html file
81 self.saved = True
82 for label, image_numpy in visuals.items():
83 img_path = os.path.join(self.img_dir, 'epoch%.3d_%s.png' % (epoch, label))
84 util.save_image(image_numpy, img_path)
85 # update website
86 webpage = html.HTML(self.web_dir, 'Experiment name = %s' % self.name, reflesh=1)
87 for n in range(epoch, 0, -1):
88 webpage.add_header('epoch [%d]' % n)
89 ims = []
90 txts = []
91 links = []
92
93 for label, image_numpy in visuals.items():
94 img_path = 'epoch%.3d_%s.png' % (n, label)
95 ims.append(img_path)
96 txts.append(label)
97 links.append(img_path)
98 webpage.add_images(ims, txts, links, height=self.win_size)
99 webpage.save()
100
101 #(mingcv) errors: dictionary of error labels and values
102 def plot_current_errors(self, epoch, counter_ratio, opt, errors):
103 if not hasattr(self, 'plot_data'):
104 self.plot_data = {'X': [], 'Y': [], 'legend': list(errors.keys())}
105 self.plot_data['X'].append(epoch + counter_ratio)
106 self.plot_data['Y'].append([errors[k] for k in self.plot_data['legend']])
107 self.vis.line(
108 X=np.stack([np.array(self.plot_data['X'])] * len(self.plot_data['legend']), 1),
109 Y=np.array(self.plot_data['Y']),
110 opts={
111 'title': self.name + ' loss over time',
112 'legend': self.plot_data['legend'],
113 'xlabel': 'epoch',
114 'ylabel': 'loss'},
115 win=self.display_id,
116 env=self.opt.name)
117
118 # errors: same format as |errors| of plotCurrentErrors
119 def print_current_errors(self, epoch, i, errors, t):
120 message = '(epoch: %d, iters: %d, time: %.3f) ' % (epoch, i, t)
121 for k, v in errors.items():
122 message += '%s: %.3f ' % (k, v)
123
124 print(message)
125 with open(self.log_name, "a") as log_file:
126 log_file.write('%s\n' % message)
127
128 # (mingcv) save image to the disk
129 def save_images(self, webpage, visuals, image_path, aspect_ratio=1.0):
130 image_dir = webpage.get_image_dir()
131 short_path = ntpath.basename(image_path[0])
132 name = os.path.splitext(short_path)[0]
133
134 webpage.add_header(name)
135 ims = []
136 txts = []
137 links = []
138
139 for label, im in visuals.items():
140 image_name = '%s_%s.png' % (name, label)
141 save_path = os.path.join(image_dir, image_name)
142 h, w, _ = im.shape
143 if aspect_ratio > 1.0:
144 im = np.array(Image.fromarray(im).resize((h, int(w * aspect_ratio))))
145 if aspect_ratio < 1.0:
146 im = np.array(Image.fromarray(im).resize((h, int(h / aspect_ratio))))
147 util.save_image(im, save_path)
148
149 ims.append(image_name)
150 txts.append(label)
151 links.append(image_name)
152 webpage.add_images(ims, txts, links, height=self.win_size)
153