import numpy as np
from six.moves import xrange
import ref
import img
# Reference for other predictions
other_preds = {'nyu': {'flic': 'nyu_pred', 'mpii': 'nyu_pred'}}
def get_path(dataset_name, file_name):
return ref.posedir + '/data/' + dataset_name + '/ref/' + file_name + '.npy'
# Load ground truth annotations
annot = {
'flic': ref.load('flic', 'test'),
'mpii': ref.load('mpii', 'valid'),
'mpii_train': ref.load('mpii', 'train'),
'mpii_test': ref.load('mpii', 'test')
}
def getdists(pred, dotrain=False):
# Get normalized distances between predictions and ground truth
# Automatically figures out dataset based on number of parts
if pred.shape[1] == 11:
dataset = 'flic'
elif pred.shape[1] == 16:
dataset = 'mpii'
else:
def main(argv):
dataset = None
exp_id = None
extra = []
prev = []
other_dists = {}
vert = False
images = False
obs = False
limb = False
# Process command line arguments
try:
opts, args = getopt.getopt(argv, "hd:e:c:p:viol", ["help", "dataset=", "expID=", "compare=", "prev=",
"vert", "images", "obs", "limb"])
except getopt.GetoptError:
print "Incorrect arguments"
helpmessage()
sys.exit(2)
for opt, arg in opts:
if opt in ('-h', '--help'):
helpmessage()
elif opt in ('-d', '--dataset'): dataset = arg
elif opt in ('-e', '--expID'): exp_id = arg
elif opt in ('-c', '--compare'): extra += arg.split(',')
elif opt in ('-p', '--prev'): prev += arg.split(',')
elif opt in ('-v', '--vert'): vert = True
elif opt in ('-i', '--images'): images = True
elif opt in ('-o', '--obs'): obs = True
elif opt in ('-l', '--limb'): limb = True
if dataset is None:
print "No dataset chosen."
helpmessage()
if not (dataset in ['mpii','flic']):
print "Bad argument for --dataset"
helpmessage()
if exp_id is None:
print "No experiment number provided."
helpmessage()
expdir = ref.posedir + '/exp/' + dataset + '/' + exp_id
# Generate the simple report for mini limb networks
if limb:
pdf = PdfPages(expdir + '/report.pdf')
limbreport(dataset, exp_id, pdf)
pdf.close()
return
# Load in dataset information
num_parts = len(ref.parts[dataset])
if obs:
annot = ref.load(dataset, 'test_obs')
eval.annot[dataset] = annot
else:
annot = ref.load(dataset, 'valid')
# Load predictions
print "Loading predictions"
pred_opts = [num_parts, vert, obs]
preds, dists, _ = loadpreds(dataset, expdir + '/preds.h5', pred_opts, images)
# Load previous predictions
for prv in prev:
_,d,_ = loadpreds(dataset, expdir + '/preds_%s.h5' % prv, pred_opts)
other_dists[prv] = [d, None]
# Load comparison predictions
for ext in extra:
predfile = ref.posedir + '/exp/' + dataset + '/' + ext + '/preds.h5'
_,d,_ = loadpreds(dataset, predfile, pred_opts)
other_dists[ext] = [d, None]
# Load previous best
if vert: predfile = expdir + '/../best/preds_vert.h5'
else: predfile = expdir + '/../best/preds.h5'
_,best_dists,_ = loadpreds(dataset, predfile, pred_opts)
#other_dists["Kaiyu's best model"] = [best_dists, None]
# Load NYU predictions
if dataset == 'mpii':
nyu_dists = np.load(eval.get_path(dataset, 'nyu_dists'))
else:
if not obs: nyu_preds = np.load(eval.get_path(dataset, 'nyu_pred'))
else: nyu_preds = np.load(eval.get_path(dataset, 'nyu_pred_obs'))
nyu_dists = eval.getdists(nyu_preds)
np.save('nyu_dists_%s%s'%(dataset,'_obs' if obs else ''),nyu_dists)
other_dists['Tompson'] = [nyu_dists, None]
# Load training set predictions
if False:
_,d,_ = loadpreds(dataset, expdir + '/preds_train.h5', pred_opts, dotrain=True)
other_dists['Train'] = [d, None]
filt = None
print "Creating overview page"
# Main report creation
pdf = PdfPages(expdir + '/report.pdf')
# Training overview page
trainingoverview(dataset, dists, [filt], [exp_id], exp_id=exp_id,
other_dists=other_dists, pdf=pdf)
if images:
print "Creating prediction examples page"
# Overall performance examples
num_good_exs = 2
num_bad_exs = 6
for i in xrange(num_good_exs):
sampleimages(annot,preds,dists,pdf=pdf,page_num=i+1)
for i in xrange(num_bad_exs):
sampleimages(annot,preds,dists,get_worst=True,pdf=pdf,page_num=i+1)
# print "Creating part heatmap examples"
# # Heatmap examples
# for i in xrange(len(ref.part_pairs[dataset])):
# title = ref.pair_names[dataset][i]
# pt_names = ref.part_pairs[dataset][i]
# if not title == 'face':
# partheatmaps(annot,preds,preds_raw,dists=dists,partnames=pt_names,title='%s Heatmap Examples'%title,
# pdf=pdf, page_num=1, vert=vert)
# for j in xrange(1,3):
# partheatmaps(annot,preds,preds_raw,dists=dists,partnames=pt_names,title='%s Heatmap Examples'%title,
# pdf=pdf, page_num=j, vert=vert, get_worst=True)
pdf.close()
def generateset(dataset, settype, filename, numsamples, datadir=None, chg=None, sampletype='default', idxs=None, extra_args=None):
# Generate full hdf5 dataset
# Path to output file
if datadir is None:
filepath = ref.posedir + '/data/' + dataset + '/' + filename + '.h5'
else:
filepath = datadir + '/' + dataset + '/' + filename + '.h5'
# Load in annotations
annot = ref.load(dataset, settype)
# Option to strictly follow the order of the provided annotations
# Useful for generating test sets.
if idxs is None:
numavailable = len(annot['index']) # Number of available samples
else:
numavailable = len(idxs)
inorder = False
if numsamples == -1:
numsamples = numavailable
inorder = True
print ""
print "Generating %s %s set: %s" % (dataset, sampletype, settype)
print "Path to dataset: %s" % filepath
print "Number of samples: %d" % numsamples
print "Data augmentation: %s" % (str(chg))
# Data/label sizes can be all over the place, this is the easiest way to check
ex_in, ex_out = gensample(annot, 0, chg=chg, sampletype=sampletype, extra_args=extra_args)
# Initialize numpy arrays to hold data
data = np.zeros((numsamples, ex_in.shape[0], ex_in.shape[1], ex_in.shape[2]), np.float32)
label = np.zeros((numsamples, ex_out.shape[0], ex_out.shape[1], ex_out.shape[2]), np.float32)
ref_idxs = np.zeros((numsamples, 1), np.float32)
# Loop to generate new samples
print ''
print '| Progress |'
print '|',
sys.stdout.flush()
starttime = time.time()
for i in xrange(numsamples):
if idxs is not None: idx = idxs[i]
elif inorder: idx = i
else: idx = np.random.randint(numavailable)
data[i], label[i] = gensample(annot, idx, chg=chg, sampletype=sampletype, extra_args=extra_args)
ref_idxs[i] = idx
if i % (numsamples/10) == 0:
print '=',
sys.stdout.flush()
print '|'
print ''
print 'Done!',
print '(%.2f seconds to complete.)' % (time.time() - starttime)
print ''
# Write out to hdf5 files
with h5py.File(filepath, 'w') as f:
f['data'] = data
f['label'] = label
f['index'] = ref_idxs
def generateset(dataset,
settype,
filename,
numsamples,
datadir=None,
chg=None,
sampletype='default',
idxs=None,
extra_args=None):
# Generate full hdf5 dataset
# Path to output file
if datadir is None:
filepath = ref.posedir + '/data/' + dataset + '/' + filename + '.h5'
else:
filepath = datadir + '/' + dataset + '/' + filename + '.h5'
# Load in annotations
annot = ref.load(dataset, settype)
# Option to strictly follow the order of the provided annotations
# Useful for generating test sets.
if idxs is None:
numavailable = len(annot['index']) # Number of available samples
else:
numavailable = len(idxs)
inorder = False
if numsamples == -1:
numsamples = numavailable
inorder = True
print ""
print "Generating %s %s set: %s" % (dataset, sampletype, settype)
print "Path to dataset: %s" % filepath
print "Number of samples: %d" % numsamples
print "Data augmentation: %s" % (str(chg))
# Data/label sizes can be all over the place, this is the easiest way to check
ex_in, ex_out = gensample(annot,
0,
chg=chg,
sampletype=sampletype,
extra_args=extra_args)
# Initialize numpy arrays to hold data
data = np.zeros(
(numsamples, ex_in.shape[0], ex_in.shape[1], ex_in.shape[2]),
np.float32)
label = np.zeros(
(numsamples, ex_out.shape[0], ex_out.shape[1], ex_out.shape[2]),
np.float32)
ref_idxs = np.zeros((numsamples, 1), np.float32)
# Loop to generate new samples
print ''
print '| Progress |'
print '|',
sys.stdout.flush()
starttime = time.time()
for i in xrange(numsamples):
if idxs is not None: idx = idxs[i]
elif inorder: idx = i
else: idx = np.random.randint(numavailable)
data[i], label[i] = gensample(annot,
idx,
chg=chg,
sampletype=sampletype,
extra_args=extra_args)
ref_idxs[i] = idx
if i % (numsamples / 10) == 0:
print '=',
sys.stdout.flush()
print '|'
print ''
print 'Done!',
print '(%.2f seconds to complete.)' % (time.time() - starttime)
print ''
# Write out to hdf5 files
with h5py.File(filepath, 'w') as f:
f['data'] = data
f['label'] = label
f['index'] = ref_idxs
def main(argv):
dataset = None
exp_id = None
extra = []
prev = []
other_dists = {}
vert = False
images = False
obs = False
limb = False
# Process command line arguments
try:
opts, args = getopt.getopt(argv, "hd:e:c:p:viol", ["help", "dataset=", "expID=", "compare=", "prev=",
"vert", "images", "obs", "limb"])
except getopt.GetoptError:
print("Incorrect arguments")
helpmessage()
sys.exit(2)
for opt, arg in opts:
if opt in ('-h', '--help'):
helpmessage()
elif opt in ('-d', '--dataset'): dataset = arg
elif opt in ('-e', '--expID'): exp_id = arg
elif opt in ('-c', '--compare'): extra += arg.split(',')
elif opt in ('-p', '--prev'): prev += arg.split(',')
elif opt in ('-v', '--vert'): vert = True
elif opt in ('-i', '--images'): images = True
elif opt in ('-o', '--obs'): obs = True
elif opt in ('-l', '--limb'): limb = True
if dataset is None:
print("No dataset chosen.")
helpmessage()
if not (dataset in ['mpii','flic']):
print("Bad argument for --dataset")
helpmessage()
if exp_id is None:
print("No experiment number provided.")
helpmessage()
expdir = ref.posedir + '/exp/' + dataset + '/' + exp_id
# Generate the simple report for mini limb networks
if limb:
pdf = PdfPages(expdir + '/report.pdf')
limbreport(dataset, exp_id, pdf)
pdf.close()
return
# Load in dataset information
num_parts = len(ref.parts[dataset])
if obs:
annot = ref.load(dataset, 'test_obs')
eval.annot[dataset] = annot
else:
annot = ref.load(dataset, 'valid')
# Load predictions
print("Loading predictions")
pred_opts = [num_parts, vert, obs]
preds, dists, _ = loadpreds(dataset, expdir + '/preds.h5', pred_opts, images)
# Load previous predictions
for prv in prev:
_,d,_ = loadpreds(dataset, expdir + '/preds_%s.h5' % prv, pred_opts)
other_dists[prv] = [d, None]
# Load comparison predictions
for ext in extra:
predfile = ref.posedir + '/exp/' + dataset + '/' + ext + '/preds.h5'
_,d,_ = loadpreds(dataset, predfile, pred_opts)
other_dists[ext] = [d, None]
# Load previous best
if vert: predfile = expdir + '/../best/preds_vert.h5'
else: predfile = expdir + '/../best/preds.h5'
_,best_dists,_ = loadpreds(dataset, predfile, pred_opts)
#other_dists["Kaiyu's best model"] = [best_dists, None]
# Load NYU predictions
if dataset == 'mpii':
nyu_dists = np.load(eval.get_path(dataset, 'nyu_dists'))
else:
if not obs: nyu_preds = np.load(eval.get_path(dataset, 'nyu_pred'))
else: nyu_preds = np.load(eval.get_path(dataset, 'nyu_pred_obs'))
nyu_dists = eval.getdists(nyu_preds)
np.save('nyu_dists_%s%s'%(dataset,'_obs' if obs else ''),nyu_dists)
other_dists['Tompson'] = [nyu_dists, None]
# Load training set predictions
if False:
_,d,_ = loadpreds(dataset, expdir + '/preds_train.h5', pred_opts, dotrain=True)
other_dists['Train'] = [d, None]
filt = None
print("Creating overview page")
# Main report creation
pdf = PdfPages(expdir + '/report.pdf')
# Training overview page
trainingoverview(dataset, dists, [filt], [exp_id], exp_id=exp_id,
other_dists=other_dists, pdf=pdf)
if images:
print("Creating prediction examples page")
# Overall performance examples
num_good_exs = 2
num_bad_exs = 6
for i in xrange(num_good_exs):
sampleimages(annot,preds,dists,pdf=pdf,page_num=i+1)
for i in xrange(num_bad_exs):
sampleimages(annot,preds,dists,get_worst=True,pdf=pdf,page_num=i+1)
# print "Creating part heatmap examples"
# # Heatmap examples
# for i in xrange(len(ref.part_pairs[dataset])):
# title = ref.pair_names[dataset][i]
# pt_names = ref.part_pairs[dataset][i]
# if not title == 'face':
# partheatmaps(annot,preds,preds_raw,dists=dists,partnames=pt_names,title='%s Heatmap Examples'%title,
# pdf=pdf, page_num=1, vert=vert)
# for j in xrange(1,3):
# partheatmaps(annot,preds,preds_raw,dists=dists,partnames=pt_names,title='%s Heatmap Examples'%title,
# pdf=pdf, page_num=j, vert=vert, get_worst=True)
pdf.close()
import numpy as np
import ref
import img
# Reference for other predictions
other_preds = {'nyu':{'flic':'nyu_pred', 'mpii':'nyu_pred'}}
def get_path(dataset_name, file_name):
return ref.posedir + '/data/' + dataset_name + '/ref/' + file_name + '.npy'
# Load ground truth annotations
annot = {'flic':ref.load('flic','test'),
'mpii':ref.load('mpii','valid'),
'mpii_train':ref.load('mpii','train'),
'mpii_test':ref.load('mpii','test')}
def getdists(pred, dotrain=False):
# Get normalized distances between predictions and ground truth
# Automatically figures out dataset based on number of parts
if pred.shape[1] == 11:
dataset = 'flic'
elif pred.shape[1] == 16:
dataset = 'mpii'
else:
print "Error: Bad prediction file."
return 0
idx_ref = []
if dotrain:
idx_ref = np.load(get_path(dataset,'train_idxs'))
dataset += '_train'
