from collections import namedtuple
from pprint import pprint
# "jpeg_psnr,jpeg_ssim,our_ssim,our_q,jpeg_psnrhvs,png_size,model_number,our_size,filename,jpeg_vifp,jpeg_q,jpeg_msssim,our_psnrhvsm,jpeg_psnrhvsm,our_vifp,our_psnr,our_msssim,our_psnrhvs,jpeg_size"
def process_one(eg):
value_map = {}
eg_s = eg.split('|')
metrics = "PSNR SSIM MSSSIM VIFP PSNRHVS PSNRHVSM".lower().split(' ')
meta = "filename model_number png_size jpeg_size our_size jpeg_q our_q".split()
first_line = eg_s[0].strip().split()
for index, m in enumerate(meta):
value_map[m] = first_line[index]
for typ, v in zip(['jpeg', 'our'], [eg_s[1], eg_s[2]]):
for m, value in zip(metrics, v.strip().split()):
value_map[typ + '_' + m] = float(value)
return value_map
def process_log(filename):
f = open(filename).read().splitlines()
values = [process_one(l) for l in f]
return values
def print_given_average_metrics(values, metric):
total = len(values)
avg = sum([float(v[metric]) for v in values])/total
print metric, str(avg)
def print_all_average_metrics(values, silent=False):
out = {}
kk = values[0].keys()
total = len(values)
for k in kk:
if k == 'filename': continue
avg = sum([float(v[k]) for v in values])/total
out[str(k)] = avg
if not silent:
print str(k), str(avg)
return out
def pprint_metrics(avg_metrics):
out = {}
for k,v in avg_metrics.iteritems():
model, metric = k.split('_')
if metric not in out:
out[metric] = {}
out[metric][model] = v
return out
def pprint_by_categories(values, metric=None, data_type='mit'):
from itertools import product
if data_type == 'mit':
categories = json.load(open('./categories.json'))
else:
categories = ['01', '02', '03', '04', '05', '06', '07', '08', '09'] + map(str, range(10,25))
out = []
for cat in categories:
filtered_values = filter(lambda x: cat in x['filename'], values)
avg_metrics = print_all_average_metrics(filtered_values, silent=True)
if metric is not None:
res = pprint_metrics(avg_metrics)[metric]
else:
res = pprint_metrics(avg_metrics)
pprint ( res )
out.append((cat,res))
return out
def plot(values, metric_name):
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import sys
plt.style.use('ggplot')
fig, ax = plt.subplots(1, 1, figsize=(25, 3))
ax.margins(0)
x = []
y = []
for index,v in enumerate( values ):
# if not index: continue
# plt.plot(x, new_recall, linewidth=2, label='Condensed Mem Network')
x.append(index)
y.append(v[1]['our']-v[1]['jpeg'])
# plt.plot(x,y, 'o')
# plt.semilogy(x,y)
y_neg = [max(0,i) for i in y]
y_pos = [min(0,i) for i in y]
plt.bar(x,y_neg)
plt.bar(x,y_pos, color='r')
plt.tick_params(axis='x', which='both', bottom='off', top='off', labelbottom='off')
plt.title(metric_name.upper(), x=0.5, y=0.8, fontsize=14)
plt.legend(loc='')
ax.get_xaxis().set_visible(False)
ax.xaxis.set_major_formatter(plt.NullFormatter())
fig.tight_layout()
# plt.savefig('plot_size_' + metric_name + '.png', bbox_inches='tight_layout', pad_inches=0)
plt.savefig('plot_kodak_' + metric_name + '.png')
if __name__ == '__main__':
import sys
import json
print_average = True
print_categories = False
if len(sys.argv) > 1:
filename = sys.argv[1]
else:
# filename = 'logs/log_small_ALL_model_69.log'
filename = 'logs/log_small_100_model_6_79.log'
values = process_log(filename)
metrics = "PSNR SSIM MSSSIM VIFP PSNRHVS PSNRHVSM".lower().split(' ')
if print_categories:
for metric_name in metrics:
out = pprint_by_categories(values, metric_name, data_type='size')
print metric_name, len(out)
plot(out, metric_name)
if print_average:
avg_metrics = print_all_average_metrics(values, silent=True)
avg_metrics = pprint_metrics(avg_metrics)
pprint ( avg_metrics )
print(filename, avg_metrics['ssim'])
print_given_average_metrics(values, 'filename')
print_given_average_metrics(values, 'our_q')
print_given_average_metrics(values, 'jpeg_q')
print_given_average_metrics(values, 'our_size')
print_given_average_metrics(values, 'jpeg_size')
print_given_average_metrics(values, 'jpeg_psnr')
