Hugging Face's logo

Spaces:
dbuscombe
/
DigitalGrainSize
Sleeping

App Files Community
DigitalGrainSize
File size: 5,054 Bytes 
d5f12de
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
 
# Written by Dr Daniel Buscombe, Marda Science LLC
#
# MIT License
#
# Copyright (c) 2022, Marda Science LLC
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.

import gradio as gr
import os
import pandas as pd
import matplotlib.pyplot as plt
# from skimage.transform import resize
plt.style.use('fivethirtyeight')

from funcs import *

#================================================================
def do_dgs(input_img):

   maxscale=5 #20
   verbose=True
   x=0 #-0.5
   resolution=1

   w=input_img.shape[0]
   h=input_img.shape[1]

   # input_img = resize(input_img, (int(w/2), int(h/2)), preserve_range=True, clip=True) 
   # resolution = 2

   data_out = dgs(input_img, resolution, maxscale, verbose, x)

   ## parse out dict into three separate dictionaries
   stats = dict(list(data_out.items())[:4])
   percentiles = dict(list(data_out.items())[4:6])
   freqs_bins = dict(list(data_out.items())[6:])

   if resolution!=1:
      freqs_bins['grain size bins']*=resolution
      percentiles['percentile_values']*=resolution

      for k in stats.keys():
            stats[k] = stats[k]*resolution

   tmp = list(stats.keys())
   d = {} 
   for k in range(len(tmp)):
       d.update( {tmp[k]: str(stats[tmp[k]])} )

   pd.DataFrame(data=d.values(), index=d.keys()).to_csv('stats.csv')

   tmp = list(percentiles.keys())
   d2 = {} 
   for k in range(len(tmp)):
       d2.update( {tmp[k]: str(percentiles[tmp[k]])} )

   pd.DataFrame(data=d2.values(), index=d2.keys()).to_csv('percentiles.csv')

   # write each to csv file
   pd.DataFrame.from_dict(freqs_bins).to_csv('freqs_bins.csv')

   # plt.clf()
   fig1=plt.figure(figsize=(4,4))
   plt.plot(freqs_bins['grain size bins'], freqs_bins['grain size frequencies'], lw=2) 
   plt.xlabel('Grain Size (pixels)')
   plt.ylabel('Frequency')
   plt.axvline(x=percentiles['percentile_values'][5], color="black", linestyle="--")
   plt.text(percentiles['percentile_values'][5], .1, 'd50')
   # plt.title('Grain Size Distribution')
   # plt.xlim(np.maximum(0,stats['mean grain size']-(2*stats['grain size sorting'])), stats['mean grain size']+(2*stats['grain size sorting']))
   # plt.savefig('psd.png', dpi=300, bbox_inches='tight')

   # plt.clf()
   fig2=plt.figure(figsize=(4,4))
   plt.imshow(input_img,cmap='gray')
   plt.plot([50, 50+stats['mean grain size']], [50, 50], 'r')
   try:
      plt.xlim(0,200)
      plt.ylim(0,200)
   except:
      pass
   plt.axis("off")

   out = {}
   out.update( {'mean grain size': float(str(stats['mean grain size'])[:4]) } )
   out.update( {'grain size sorting': float(str(stats['grain size sorting'])[:4]) } )
   out.update( {'d16': float(str(percentiles['percentile_values'][2])[:4]) } )
   out.update( {'d50': float(str(percentiles['percentile_values'][5])[:4]) } )
   out.update( {'d84': float(str(percentiles['percentile_values'][7])[:4]) } )
   
   return pd.DataFrame(data=out.values(), index=out.keys()).transpose(), fig1, fig2, 'stats.csv', 'percentiles.csv', 'freqs_bins.csv'

 
  
title = "Digital Grain Size" 
description = "Upload an image of sediment. Download grain size stats. Clear between images. See https://github.com/dbuscombe-usgs/pyDGS. May take a very long time for large images" 
    

examples= [[l] for l in glob('examples/*.jpg')]

inp = gr.Image(label='Upload images one-by-one')
out1 = gr.Dataframe(label='Summary statistics', headers=["mean (px)", "sorting (px)", "d16 (px)", "d50 (px)", "d84 (px)"], type='pandas')
#out1 = gr.outputs.Image(type='numpy')
out2a = gr.Plot(label='Grain Size Distribution', type='auto') #'matplotlib')
out2b = gr.Plot(label='Red Line = Mean Grain Length', type='auto') #'matplotlib')

out4 = gr.File(label='Click to download grain size summary stats (px)')
out5 = gr.File(label='Click to download grain size percentiles (px)')
out6 = gr.File(label='Click to download grain size distribution: bins (px) and frequencies')

Segapp = gr.Interface(do_dgs, inp, [out1, out2a, out2b, out4, out5, out6], title = title, description = description, examples=examples)

Segapp.launch(enable_queue=True)