def draw_qualitative() :
print "drawing qualitative"
# draw 300, 600, full mean accuracy for both winder and uwiii
for p in itertools.product(dataset_list,stripsize_list) :
print p
# populate a hash keyed by dataset_algo, e.g., "winder_rast"
# values will be arrays of npdata (300,600,fullpage) of the metrics
qual_data = {}
for dataset in dataset_list :
for algo in algorithm_list :
key = "{0}_{1}".format( dataset, algo )
qual_data[key] = []
for stripsize in stripsize_list :
data_list = datfile.loaddb( dataset=dataset, stripsize=stripsize, algorithm=algo )
# join all the metrics together
all_metric = np.concatenate( [ d["metrics"] for d in data_list ] )
# array, in order 300,600,1200
# this is the data we will plot
qual_data[key].append( all_metric )
# break the reference
all_metric = None
fig = Figure()
ax = fig.add_subplot(111)
ax.grid()
ax.set_ylim(0,1.0)
line_fmt_list = ( '-', '--', '-.', ':' )
line_fmt = iter(line_fmt_list)
key_list = sorted(qual_data.keys())
for key in key_list :
# plot the 300,600,fullpage
print key
metric_means = [np.mean(m) for m in qual_data[key]]
metric_errs = [np.std(m) for m in qual_data[key]]
print metric_means, metric_errs
# ax.errorbar( (0,1,2), metric_means, yerr=metric_errs, fmt="o-" )
ax.plot( metric_means, "kx"+line_fmt.next(), linewidth=2.0 )
# draw legend in upper left
ax.legend(key_list,loc=2)
ax.set_xticklabels( ("300","","600","","full"))
ax.set_xlabel( "Image Size" )
ax.set_ylabel( "Match Metric" )
outfilename = "all_qual.png"
canvas = FigureCanvasAgg(fig)
canvas.print_figure(outfilename)
print "wrote", outfilename
def graph_all_results() :
result_list = [
{ "dir" : "winder_fullpage_rast",
"outfile": "winder_fullpage_rast.png",
"title" : "Winder Full Page RAST" },
{ "dir" : "winder_fullpage_vor",
"outfile": "winder_fullpage_vor.png",
"title" : "Winder Full Page Voronoi" },
{ "dir" : "300_winder_rast",
"outfile": "300_winder_rast.png",
"title" : "Winder 300 RAST" },
{ "dir" : "300_winder_rast",
"outfile": "300_winder_rast.png",
"title" : "Winder 300 RAST" },
{ "dir" : "300_winder_vor",
"outfile": "300_winder_vor.png",
"title" : "Winder 300 Voronoi" },
{ "dir" : "300_vor",
"outfile": "300_uwiii_vor.png",
"title" : "UW-III 300 Voronoi" },
{ "dir" : "300_rast",
"outfile": "300_uwiii_rast.png",
"title" : "UW-III 300 RAST" },
]
# add each image class of Amy Winder's data set
for c in winder_imgclass_list :
result_list.append({"dir": os.path.join("winder_fullpage_rast", c),
"outfile" : "winder_"+c+"_rast.png",
"title": "Winder " + c.replace("_"," " ) + " RAST"})
for c in winder_imgclass_list :
result_list.append({"dir": os.path.join("winder_fullpage_vor", c),
"outfile" : "winder_"+c+"_vor.png",
"title": "Winder " + c.replace("_"," " ) + " Voronoi"})
for result in result_list :
print "result=",result
if not os.path.exists( result["outfile"] ) :
print result["dir"]
ndata = load_all_datfiles(result["dir"])
np.save('metrics.npy',ndata)
make_histogram( ndata, result["outfile"], title=result["title"] )
# UW-III Full Page Histograms
uwiii_fullpage_rast = datfile.loaddb( dataset="uwiii", stripsize="fullpage", algorithm="rast" )
ndata = np.concatenate( [ d["metrics"] for d in uwiii_fullpage_rast ] )
make_histogram( ndata, "uwiii_fullpage_rast.png", title="UW-III Full Page RAST" )
nz = np.where( ndata != 0 )
make_histogram( ndata[nz], "uwiii_fullpage_rast_nonzero.png",
title="UW-III Full Page RAST (No Zeros)" )
# UW-III Full Page with Zeros removed (shows better histogram detail)
uwiii_fullpage_vor = datfile.loaddb( dataset="uwiii", stripsize="fullpage", algorithm="vor" )
ndata = np.concatenate( [ d["metrics"] for d in uwiii_fullpage_vor ] )
make_histogram( ndata, "uwiii_fullpage_vor.png", title="UW-III Full Page Voronoi" )
nz = np.where( ndata != 0 )
make_histogram( ndata[nz], "uwiii_fullpage_vor_nonzero.png",
title="UW-III Full Page Voronoi (No Zeros)" )
uwiii_300_rast = datfile.loaddb( dataset="uwiii", stripsize="300", algorithm="rast" )
ndata = np.concatenate( [ d["metrics"] for d in uwiii_300_rast ] )
make_histogram( ndata, "uwiii_300_rast.png", title="UW-III 300 RAST" )
nz = np.where( ndata != 0 )
make_histogram( ndata[nz], "uwiii_300_rast_nonzero.png",
title="UW-III 300 RAST (No Zeros)" )
uwiii_300_vor = datfile.loaddb( dataset="uwiii", stripsize="300", algorithm="vor" )
ndata = np.concatenate( [ d["metrics"] for d in uwiii_300_vor ] )
make_histogram( ndata, "uwiii_300_vor.png", title="UW-III 300 Voronoi" )
nz = np.where( ndata != 0 )
make_histogram( ndata[nz], "uwiii_300_vor_nonzero.png",
title="UW-III 300 Voronoi (No Zeros)" )
# draw a few single page graphs
draw_single_pages()
# draw a graph of each class of the winder dataset
draw_winder_class_results()
# draw ALL THE RESULTS on one graph
draw_qualitative()
draw_class_results_barchart("uwiii","UW-III")
draw_class_results_barchart("winder","Winder")
draw_four_up_histogram("winder")
draw_four_up_histogram("uwiii")
def draw_class_results_barchart(dataset,dataset_title) :
outfilename = "{0}_class_rast_vs_vor.png".format(dataset)
if dataset=="uwiii" :
imgclass_list = uwiii_imgclass_list
label_list = uwiii_imgclass_list
elif dataset=="winder" :
imgclass_list = winder_imgclass_list
label_list = winder_imgclass_list_shorthand
else:
assert 0, dataset
fig = Figure()
ax = fig.add_subplot(111)
fig.suptitle( "{0} RAST vs Voronoi Class Performance".format(dataset_title) )
ind = np.arange(len(imgclass_list),dtype="float")
width = .20
means_hash = { "rast_fullpage": [],
"vor_fullpage": [] ,
"rast_300": [],
"vor_300": []
}
for algo in algorithm_list :
for imgclass in imgclass_list :
for stripsize in ("300","fullpage") :
data_list = datfile.loaddb( dataset=dataset, stripsize=stripsize,
algorithm=algo, imgclass=imgclass )
all_metric = np.concatenate( [ d["metrics"] for d in data_list ] )
key = "{0}_{1}".format( algo, stripsize )
means_hash[key].append( np.mean(all_metric) )
# break the ref
all_metric = None
print means_hash
ax.set_ylim(0,1.0)
ax.set_ylabel( "Match Metric" )
ax.set_xlabel( "Image Class" )
rects = []
# citer = iter( ("r","g","y","b"))
# copied the hatch list from http://matplotlib.org/api/axes_api.html
hiter = iter( ("/" , "\\" , "x" , "o" , "O" , "." , "*") )
for key in ("rast_fullpage","vor_fullpage","rast_300","vor_300") :
print ind
r = ax.bar( ind, means_hash[key], width, hatch=hiter.next(), color="w" )
# r = ax.bar( ind, means_hash[key], width, color=citer.next() )
ind += 0.2
rects.append( r )
# break the ref
r = None
# rects2 = ax.bar( ind, means_hash["vor_fullpage"], width, color='y' )
# ind += width
# rects3 = ax.bar( ind, means_hash["rast_300"], width, color='r' )
# ind += width
# rects4 = ax.bar( ind, means_hash["vor_300"], width, color='y' )
# http://stackoverflow.com/questions/13515471/matplotlib-how-to-prevent-x-axis-labels-from-overlapping-each-other
ax.set_xticks( range(len(imgclass_list)) )
tlist = ax.set_xticklabels( label_list, ha='left' )
for t in tlist :
# t.set_horizontalalignment('center')
# t.set_bbox(dict(facecolor="white", alpha=0.5))
# print t.get_position()
pass
# ax.set( xticks=range(len(imgclass_list)), xticklabels=label_list )
# ax.set_xticklabels( imgclass_list )
leg = ax.legend( rects, ('RAST full','Vor full', 'RAST 300', 'Vor 300'),
frameon=False )
print leg, ax.get_legend()
canvas = FigureCanvasAgg(fig)
canvas.print_figure(outfilename)
print "wrote", outfilename
stretch_width(outfilename)
# # load the image we just wrote, resize 50% wider
# img = Image.open(outfilename)
# img.load()
# print img.size
# img.resize( (img.size[0]+img.size[0]/2,img.size[1]), Image.BILINEAR ).save(outfilename)
## print img.size
## img.save(outfilename)
print "wrote", outfilename