def main():
if len(sys.argv) < 3:
usage()
url = sys.argv[-2]
command = sys.argv[-1]
args = sys.argv[:-2]
if command not in ['baseline', 'new', 'compare', 'rebaseline']:
usage()
num = 10
model_type = 'original'
x = 1
while x < len(args):
if args[x] == '-n':
x += 1
num = int(args[x])
elif args[x] == '-t':
x += 1
model_type = args[x]
else:
usage()
x += 1
items = grab_list(url, num, 'model', model_type, 'meerkat')
if command in ['baseline', 'new']:
generate_images(items, command)
elif command == 'compare':
compute_diffs(items)
elif command == 'rebaseline':
rebaseline(items)
def main():
if len(sys.argv) < 3:
usage()
url = sys.argv[-2]
command = sys.argv[-1]
args = sys.argv[:-2]
if command not in ['baseline', 'new', 'compare', 'rebaseline']:
usage()
num = 10
model_type = 'original'
x = 1
while x < len(args):
if args[x] == '-n':
x += 1
num = int(args[x])
elif args[x] == '-t':
x += 1
model_type = args[x]
else:
usage()
x += 1
items = grab_list(url, num, 'model', model_type, 'meerkat')
if command in ['baseline', 'new']:
generate_images(items, command)
elif command == 'compare':
compute_diffs(items)
elif command == 'rebaseline':
rebaseline(items)
def main():
if len(sys.argv) != 2 or not os.path.isdir(sys.argv[1]):
print 'usage: python meshtool_regression.py directory'
sys.exit(1)
base_dir = sys.argv[1]
diffs = []
cdn_list = list.grab_list(SERVER, 1000, '', '', 'dump')
pool = Pool(processes=NUM_PROCS)
results = []
for f in cdn_list:
results.append(pool.apply_async(process_file, args=(base_dir, f)))
for r in results:
r.wait()
print '\t'.join(['id',
'orig_size',
'orig_num_triangles',
'orig_num_draw_raw',
'orig_num_draw_with_instances',
'orig_num_draw_with_batching',
'orig_texture_ram',
'opt_size',
'opt_num_triangles',
'opt_num_draw_raw',
'opt_num_draw_with_instances',
'opt_num_draw_with_batching',
'opt_texture_ram'])
for r in results:
unique_id, orig_size, opt_size, orig_render_info, opt_render_info = r.get()
print '\t'.join(map(str,[
unique_id,
orig_size,
orig_render_info['num_triangles'],
orig_render_info['num_draw_raw'],
orig_render_info['num_draw_with_instances'],
orig_render_info['num_draw_with_batching'],
orig_render_info['texture_ram'],
opt_size,
opt_render_info['num_triangles'],
opt_render_info['num_draw_raw'],
opt_render_info['num_draw_with_instances'],
opt_render_info['num_draw_with_batching'],
opt_render_info['texture_ram']
]))
def main():
if len(sys.argv) != 2 or not os.path.isdir(sys.argv[1]):
print 'usage: python meshtool_regression.py directory'
sys.exit(1)
base_dir = sys.argv[1]
cdn_list = list.grab_list(SERVER, 10000, '', '', 'dump')
#print 'got', len(cdn_list), 'results'
pool = Pool(processes=NUM_PROCS)
results = []
for f in cdn_list:
if 'original' in f['metadata']['types'] and 'progressive' in f['metadata']['types']:
results.append((f['full_path'], pool.apply_async(process_file, args=(base_dir, f))))
for r in results:
r[1].wait()
if not r[1].successful():
print 'FAILURE', r[0]
def main():
if len(sys.argv) != 2 or not os.path.isdir(sys.argv[1]):
print 'usage: python meshtool_regression.py directory'
sys.exit(1)
base_dir = sys.argv[1]
diffs = []
cdn_list = list.grab_list(SERVER, 1000, '', '', 'dump')
pool = Pool(processes=NUM_PROCS)
results = []
for f in cdn_list:
results.append(pool.apply_async(process_file, args=(base_dir, f)))
for r in results:
r.wait()
print '\t'.join([
'id', 'orig_size', 'orig_num_triangles', 'orig_num_draw_raw',
'orig_num_draw_with_instances', 'orig_num_draw_with_batching',
'orig_texture_ram', 'opt_size', 'opt_num_triangles',
'opt_num_draw_raw', 'opt_num_draw_with_instances',
'opt_num_draw_with_batching', 'opt_texture_ram'
])
for r in results:
unique_id, orig_size, opt_size, orig_render_info, opt_render_info = r.get(
)
print '\t'.join(
map(str, [
unique_id, orig_size, orig_render_info['num_triangles'],
orig_render_info['num_draw_raw'],
orig_render_info['num_draw_with_instances'],
orig_render_info['num_draw_with_batching'],
orig_render_info['texture_ram'], opt_size,
opt_render_info['num_triangles'],
opt_render_info['num_draw_raw'],
opt_render_info['num_draw_with_instances'],
opt_render_info['num_draw_with_batching'],
opt_render_info['texture_ram']
]))
def main():
if len(sys.argv) != 2 or not os.path.isdir(sys.argv[1]):
print 'usage: python meshtool_regression.py directory'
sys.exit(1)
base_dir = sys.argv[1]
cdn_list = list.grab_list(SERVER, 10000, '', '', 'dump')
#print 'got', len(cdn_list), 'results'
pool = Pool(processes=NUM_PROCS)
results = []
for f in cdn_list:
if 'original' in f['metadata']['types'] and 'progressive' in f[
'metadata']['types']:
results.append((f['full_path'],
pool.apply_async(process_file,
args=(base_dir, f))))
for r in results:
r[1].wait()
if not r[1].successful():
print 'FAILURE', r[0]
def main():
if len(sys.argv) != 2 or not os.path.isdir(sys.argv[1]):
print 'usage: python meshtool_regression.py directory'
sys.exit(1)
base_dir = sys.argv[1]
diffs = []
cdn_list = list.grab_list(SERVER, 2000, '', '', 'dump')
size_pairs = []
draw_calls_original = []
for f in cdn_list:
types = f['metadata']['types']
if 'original' not in types or 'progressive' not in types:
continue
gzip_orig_size = get_gzip_size(base_dir, types['original']['hash'])
gzip_progressive_size = get_gzip_size(base_dir, types['progressive']['hash'])
for subfile in types['original']['subfiles']:
mesh_path = f['base_path']
subfile_split = subfile.split('/')
subfile_name = subfile_split[-2]
version_num = f['version_num']
subfile_url = '%s%s/%s/%s/%s' % (DNS, mesh_path, 'original', version_num, subfile_name)
hash_cache_name = base64.urlsafe_b64encode(subfile_url)
hash_cache_file = os.path.join(base_dir, hash_cache_name)
if not os.path.isfile(hash_cache_file):
try:
subfile_data = urllib2.urlopen(subfile_url).read()
except urllib2.HTTPError:
continue
sf = open(hash_cache_file, 'w')
sf.write(subfile_data)
else:
sf = open(hash_cache_file, 'r')
subfile_data = sf.read()
sf.close()
subfile_info = json.loads(subfile_data)
subfile_hash = subfile_info['Hash']
subfile_size = get_gzip_size(base_dir, subfile_hash)
gzip_orig_size += subfile_size
mipmap_levels = next(types['progressive']['mipmaps'].itervalues())['byte_ranges']
progressive_mipmap_size = 0
for mipmap in mipmap_levels:
progressive_mipmap_size = mipmap['length']
if mipmap['width'] >= 128 or mipmap['height'] >= 128:
break
gzip_progressive_size += progressive_mipmap_size
size_pairs.append((gzip_orig_size, gzip_progressive_size))
draw_calls_original.append(types['original']['metadata']['num_draw_calls'])
reductions = [(p[1] - p[0]) / 1024.0 for p in size_pairs]
reductions = sorted(reductions)
x_vals = range(len(reductions))
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
ax.set_yscale('symlog')
ax.scatter(x_vals,reductions,color='b', marker='+', s=1)
plt.ylim(min(reductions),max(reductions))
plt.xlim(0, len(reductions))
ax.set_title('Change in Download Size for First Display')
ax.set_xlabel('Mesh Number')
ax.set_ylabel('Change in KiB')
plt.savefig('mesh_size_change.pdf', format='pdf')
hist, bins = numpy.histogram(draw_calls_original, bins=[1,2,3,4,5,6,7,8,9,10,15,20,25,100,max(draw_calls_original)])
bin_labels = [1,2,3,4,5,6,7,8,9,10,'11-15','16-20','21-25','26+']
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
#ax.set_xscale('symlog')
#ax.hist(draw_calls_original, bins=[0,1,2,3,4,5,10,15,20,25,100,1500])
ax.bar(range(1,len(hist)+1), hist, align='center')
#plt.ylim(0,max(draw_calls_original))
plt.xlim(0,len(hist)+1)
plt.xticks(range(1,len(hist)+1), bin_labels, rotation='vertical')
ax.set_title('Original Number of Draw Calls to Display Mesh')
ax.set_xlabel('Number of Draw Calls')
ax.set_ylabel('Frequency')
plt.gcf().subplots_adjust(bottom=0.15)
plt.savefig('num_draw_calls_hist.pdf', format='pdf')
def main():
if len(sys.argv) != 2 or not os.path.isdir(sys.argv[1]):
print 'usage: python meshtool_regression.py directory'
sys.exit(1)
base_dir = sys.argv[1]
diffs = []
cdn_list = list.grab_list(SERVER, 2000, '', '', 'dump')
size_pairs = []
draw_calls_original = []
num_triangles = []
num_images = []
ram_usage = []
num_to_check = len([f for f in cdn_list if 'original' in f['metadata']['types'] and 'progressive' in f['metadata']['types']])
size_table = numpy.zeros((num_to_check, 5, 5), dtype=numpy.float32)
table_index = 0
for f in cdn_list:
types = f['metadata']['types']
if 'original' not in types or 'progressive' not in types:
continue
gzip_orig_size = get_gzip_size(base_dir, types['original']['hash'])
gzip_progressive_size = get_gzip_size(base_dir, types['progressive']['hash'])
for subfile in types['original']['subfiles']:
mesh_path = f['base_path']
subfile_split = subfile.split('/')
subfile_name = subfile_split[-2]
version_num = f['version_num']
subfile_url = '%s%s/%s/%s/%s' % (DNS, mesh_path, 'original', version_num, subfile_name)
hash_cache_name = base64.urlsafe_b64encode(subfile_url)
hash_cache_file = os.path.join(base_dir, hash_cache_name)
if not os.path.isfile(hash_cache_file):
try:
subfile_data = urllib2.urlopen(subfile_url).read()
except urllib2.HTTPError:
continue
sf = open(hash_cache_file, 'w')
sf.write(subfile_data)
else:
sf = open(hash_cache_file, 'r')
subfile_data = sf.read()
sf.close()
subfile_info = json.loads(subfile_data)
subfile_hash = subfile_info['Hash']
subfile_size = get_gzip_size(base_dir, subfile_hash)
gzip_orig_size += subfile_size
gzip_progressive_stream_size = get_gzip_size(base_dir, types['progressive']['progressive_stream']) if types['progressive']['progressive_stream'] else 0
mipmap_levels = next(types['progressive']['mipmaps'].itervalues())['byte_ranges']
for stream_percentage in range(5):
for mipmap_level in range(5):
current_stream_size = gzip_progressive_stream_size * (stream_percentage / 4.0)
current_mipmap_size = [128,256,512,1024,2048][mipmap_level]
all_mipmaps_size = 0
for mipmap in mipmap_levels:
all_mipmaps_size += mipmap['length']
if mipmap['width'] >= current_mipmap_size or mipmap['height'] >= current_mipmap_size:
break
entrysize = gzip_progressive_size + current_stream_size + all_mipmaps_size
size_table[table_index, stream_percentage, mipmap_level] = entrysize / gzip_orig_size
progressive_mipmap_size = 0
for mipmap in mipmap_levels:
progressive_mipmap_size = mipmap['length']
if mipmap['width'] >= 128 or mipmap['height'] >= 128:
break
gzip_progressive_size += progressive_mipmap_size
size_pairs.append((gzip_orig_size, gzip_progressive_size))
draw_calls_original.append(types['original']['metadata']['num_draw_calls'])
num_triangles.append(types['original']['metadata']['num_triangles'])
num_images.append(types['original']['metadata']['num_images'])
ram_usage.append(types['original']['metadata']['texture_ram_usage'])
table_index += 1
#print numpy.mean(size_table, axis=0)
#print numpy.min(size_table, axis=0)
#print numpy.max(size_table, axis=0)
size_table = numpy.median(size_table, axis=0)
print 'Relative Median Size (rows=0,25,50,75,100, cols=128,256,512,1024,2048)'
print size_table
reductions = [(p[1] - p[0]) / 1024.0 for p in size_pairs]
reductions = sorted(reductions)
x_vals = numpy.arange(0, 1, 1.0 / len(reductions))
fig = plt.figure(figsize=(4,1.5))
ax = fig.add_subplot(1,1,1)
ax.set_yscale('symlog')
ax.scatter(x_vals,reductions,color='b', marker='+', s=1)
plt.ylim(min(reductions),max(reductions))
plt.xlim(0, 1)
#ax.set_title('Change in Download Size for First Display')
ax.set_xlabel('Mesh')
ax.set_ylabel('Change in KB')
plt.gcf().subplots_adjust(bottom=0.26, left=0.18, right=0.95, top=0.98)
plt.savefig('mesh_size_change.pdf', format='pdf', dpi=150)
hist, bins = numpy.histogram(draw_calls_original, bins=[1,2,3,4,5,6,7,8,9,10,15,20,25,100,max(draw_calls_original)])
bin_labels = [1,2,3,4,5,6,7,8,9,10,'11-15','16-20','21-25','26+']
fig = plt.figure(figsize=(4,2))
ax = fig.add_subplot(1,1,1)
#ax.set_xscale('symlog')
#ax.hist(draw_calls_original, bins=[0,1,2,3,4,5,10,15,20,25,100,1500])
hist = hist / float(numpy.sum(hist))
ax.bar(range(1,len(hist)+1), hist, align='center')
#plt.ylim(0,max(draw_calls_original))
plt.xlim(0,len(hist)+1)
plt.xticks(range(1,len(hist)+1), bin_labels, rotation='vertical')
plt.yticks([0,0.05,0.10,0.15,0.20], ['0','.05','.10','.15','.20'])
#ax.set_title('Original Number of Draw Calls to Display Mesh')
ax.set_xlabel('Number of Draw Calls')
ax.set_ylabel('Frequency')
plt.gcf().subplots_adjust(bottom=0.35, left=0.15, right=0.95, top=0.96)
plt.savefig('num_draw_calls_hist.pdf', format='pdf', dpi=150)
hist, bins = numpy.histogram(num_triangles, bins=[0,1000,10000,100000,max(num_triangles)])
bin_labels = ['\\textless1K','\\textless10K','\\textless100K','100K+']
fig = plt.figure(figsize=(4,2))
ax = fig.add_subplot(1,1,1)
#ax.set_xscale('symlog')
#ax.hist(draw_calls_original, bins=[0,1,2,3,4,5,10,15,20,25,100,1500])
hist = hist / float(numpy.sum(hist))
ax.bar(range(1,len(hist)+1), hist, align='center')
#plt.ylim(0,max(draw_calls_original))
plt.xlim(0.4,len(hist)+0.6)
plt.xticks(range(1,len(hist)+1), bin_labels) #, rotation=17)
plt.yticks([0,0.05,0.10,0.15,0.20,0.25,0.3,0.35,0.4], ['0','.05','.10','.15','.20','.25','.30','.35','.40'])
#ax.set_title('Original Number of Draw Calls to Display Mesh')
ax.set_xlabel('Number of Triangles')
ax.set_ylabel('Frequency')
plt.gcf().subplots_adjust(bottom=0.21, left=0.15, right=0.95, top=0.96)
plt.savefig('num_triangles_hist.pdf', format='pdf', dpi=150)
hist, bins = numpy.histogram(ram_usage, bins=[0,1,100*1024,1024*1024,10*1024*1024,max(ram_usage)])
bin_labels = ['0','\\textless100KB','\\textless1MB','\\textless10MB','10MB+']
fig = plt.figure(figsize=(4,2))
ax = fig.add_subplot(1,1,1)
#ax.set_xscale('symlog')
#ax.hist(draw_calls_original, bins=[0,1,2,3,4,5,10,15,20,25,100,1500])
hist = hist / float(numpy.sum(hist))
ax.bar(range(1,len(hist)+1), hist, align='center')
#plt.ylim(0,max(draw_calls_original))
plt.xlim(0.4,len(hist)+0.6)
plt.xticks(range(1,len(hist)+1), bin_labels, rotation=17)
plt.yticks([0,0.05,0.10,0.15,0.20,0.25,0.3,0.35], ['0','.05','.10','.15','.20','.25','.30','.35'])
#ax.set_title('Original Number of Draw Calls to Display Mesh')
ax.set_xlabel('Texture RAM Usage')
ax.set_ylabel('Frequency')
plt.gcf().subplots_adjust(bottom=0.3, left=0.15, right=0.95, top=0.96)
plt.savefig('texture_ram_hist.pdf', format='pdf', dpi=150)
chart_error_dir = os.path.join(os.path.dirname(__file__), 'chart_errors')
for errdir in os.listdir(chart_error_dir):
if errdir != 'duck':
continue
errfile = os.path.join(chart_error_dir, errdir, 'err.txt')
maxerrfile = os.path.join(chart_error_dir, errdir, 'maxerr.txt')
chart_errors = numpy.fromfile(errfile, sep='\n')
chart_errors = numpy.sort(chart_errors)
max_errors = numpy.fromfile(maxerrfile, sep='\n')
max_errors = numpy.sort(max_errors)
heuristic = numpy.log(chart_errors+1) / numpy.log(max_errors+1)
stop_point = numpy.where(heuristic >= 0.90)[0][0]
x_vals = numpy.arange(0, len(chart_errors))
fig = plt.figure(figsize=(4,3))
ax = fig.add_subplot(1,1,1)
ax.set_yscale('symlog')
ax.scatter(x_vals, chart_errors, color='b', marker='+', s=1)
ax.scatter(x_vals, max_errors, color='r', marker='x', s=1)
#ax.set_title('Change in Download Size for First Display')
ax.set_xlabel('Merge Step')
ax.set_ylabel('Error Value')
plt.ylim(min(chart_errors),max(chart_errors))
axR = fig.add_subplot(1,1,1, sharex=ax, frameon=False)
axR.yaxis.tick_right()
axR.yaxis.set_label_position("right")
axR.scatter(x_vals, heuristic, color='g', marker='s', s=1)
axR.set_ylabel("Heuristic Value")
plt.axvline(stop_point, linestyle='-', color='#333333')
plt.xticks([0,1000,2000,3000,4000])
plt.xlim(0, len(chart_errors))
p1 = Line2D([0],[1], color="g", linewidth=2)
p2 = Line2D([0],[1], color="r", linewidth=2)
p3 = Line2D([0],[1], color="b", linewidth=2)
prop = FontProperties(size=11)
plt.legend((p1, p2, p3), ['Heuristic', 'Maximum', 'Current'], loc=0, prop=prop)
plt.gcf().subplots_adjust(bottom=0.14, left=0.13, right=0.87, top=0.98)
plt.savefig('chart_merge_error_%s.pdf' % errdir, format='pdf', dpi=150)
percep_lines = DATA_OUT.split("\n")
y_vals = []
y_vals2 = []
perc90_vals = []
perc10_vals = []
for line in percep_lines:
ct, tot, avg, median, perc90, perc10 = map(float, line.split(" "))
y_vals.append(avg)
y_vals2.append(median)
perc10_vals.append(perc10)
perc90_vals.append(perc90)
x_vals = range(0,101,10)
fig = plt.figure(figsize=(4,2))
ax = fig.add_subplot(1,1,1)
#ax.set_yscale('symlog')
ax.plot(x_vals, y_vals, color='b', marker='s', label='Mean')
ax.plot(x_vals, y_vals2, color='r', marker='o', label='Median')
plt.ylim(0, max(y_vals) * 1.05)
plt.xlim(0, 100)
plt.xticks(range(0, 101, 10))
#ax.set_title('Perceptual Difference from Original Mesh')
ax.set_xlabel('Progressive Stream %')
ax.set_ylabel('Delta E (CIE2000)')
ax.legend()
plt.gcf().subplots_adjust(bottom=0.2, left=0.13, right=0.95, top=0.96)
plt.savefig('progressive_perceptual_difference.pdf', format='pdf', dpi=150)
