def loadpreds(dataset, predfile, pred_opts, get_hms=False, dotrain=False):
num_parts, vert, obs = pred_opts
hms = None
with h5py.File(predfile, 'r+') as f:
# Choose appropriate key
if vert: k = 'preds_v'
else: k = 'preds_tf'
# Load predictions
if k in f.keys():
preds = np.array(f[k])
else:
preds = eval.transformpreds(dataset, f['preds'],
[64, 64], rot=vert)
f[k] = preds
# Ignore additional predictions from segmentation (soon to be unnecessary)
if preds.shape[1] > num_parts:
preds = preds[:,:num_parts,:]
# Also load heatmaps if necessary
if get_hms:
hms = np.array(f['preds_raw'])
# Load distances
dist_key = 'dist_'
if vert: dist_key += 'v'
if obs: dist_key += 'o'
if dist_key in f.keys():
dists = np.array(f[dist_key])
else:
# Evaluate distances to ground truth
dists = eval.getdists(preds, dotrain)
f[dist_key] = dists
return preds, dists, hms
def loadpreds(dataset, predfile, pred_opts, get_hms=False, dotrain=False):
num_parts, vert, obs = pred_opts
hms = None
with h5py.File(predfile, 'r+') as f:
# Choose appropriate key
if vert: k = 'preds_v'
else: k = 'preds_tf'
# Load predictions
if k in f.keys():
preds = np.array(f[k])
else:
preds = eval.transformpreds(dataset, f['preds'],
[64, 64], rot=vert)
f[k] = preds
# Ignore additional predictions from segmentation (soon to be unnecessary)
if preds.shape[1] > num_parts:
preds = preds[:,:num_parts,:]
# Also load heatmaps if necessary
if get_hms:
hms = np.array(f['preds_raw'])
# Load distances
dist_key = 'dist_'
if vert: dist_key += 'v'
if obs: dist_key += 'o'
if dist_key in f.keys():
dists = np.array(f[dist_key])
else:
# Evaluate distances to ground truth
dists = eval.getdists(preds, dotrain)
f[dist_key] = dists
return preds, dists, hms
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 partheatmaps(annot, preds, preds_raw, dists=None, partnames=None, filt=None, num_rows=7, vert=False, num_cols=2, res=default_res, get_worst=False, page_num=1, pdf=None, title='Prediction Examples'):
# Dataset name
ds = annot.attrs['name']
# Initialize blank page
plt.clf()
fig = plt.figure(figsize=(8.5,11), dpi=100, facecolor='w')
ax = fig.add_subplot(111)
# If no specific parts have been chosen, use them all for scoring
if partnames is None:
partnames = ref.parts[ds]
part_idxs = [ref.parts[ds].index(part) if type(part) is str else part for part in partnames]
part_filt = [i in part_idxs for i in xrange(len(ref.parts[ds]))]
page = np.zeros((res[0]*num_rows, res[1]*num_cols*(1+len(part_idxs)), 3), np.uint8)
# If no filter is provided create entirely true array
if filt is None:
filt = np.array([True for _ in xrange(len(preds))])
else:
filt = filt.copy()
# If no precalculated distances are provided, calculate them
if dists is None:
dists = eval.getdists(preds)
# Determine scores from which we'll sort the images
scores = np.zeros(len(preds))
for i in xrange(len(preds)):
# A bit of an interesting line below, gets the mean distance for a particular image
# while only considering the parts we want and ignoring any parts where there's no annotation
vals = dists[i, part_filt * (annot['part'][i,:,0] > 0)]
if len(vals) > 0:
scores[i] = vals.mean()
else:
# If no valid annotation to make a score, filter out this example
filt[i] = False
if get_worst:
# Flip the scores if we're getting the worst images
scores = -scores
best_idxs = scores.argsort()
if title[:4] == 'head' and get_worst:
np.save('worst_head_idxs',best_idxs[:200])
curr_idx = 0
# Start filling in the overall image
for i in xrange(page_num * num_rows * num_cols):
while curr_idx < len(best_idxs) and not filt[best_idxs[curr_idx]]:
curr_idx += 1
if curr_idx >= len(best_idxs): break
# If we're doing multiple pages, pass over the images that have already been used
if i >= (page_num - 1) * num_rows * num_cols:
idx = best_idxs[curr_idx]
if vert:
inp, _ = data.gensample(annot, idx, chg={'rotate':-1})
else:
inp, _ = data.gensample(annot, idx)
new_img = img.sample_with_heatmap(ds, inp, preds_raw[idx], num_rows=1, parts_to_show=part_idxs)
row = ((i % (num_rows * num_cols)) / num_cols) * res[0]
col = ((i % (num_rows * num_cols)) % num_cols) * res[1] * (1+len(part_idxs))
page[row:row+res[0], col:col+(res[1]*(1+len(part_idxs)))] = new_img
curr_idx += 1
# Plot management
if not get_worst:
title += ' - Best - '
else:
title += ' - Worst - '
title += 'Page %d' % page_num
ax.set_title(title)
ax.imshow(page)
ax.axis('off')
fig.subplots_adjust(left=0.05,right=.95,bottom=0.05,top=.95)
if pdf:
pdf.savefig()
else:
plt.show()
fig.clf()
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 partheatmaps(annot, preds, preds_raw, dists=None, partnames=None, filt=None, num_rows=7, vert=False, num_cols=2, res=default_res, get_worst=False, page_num=1, pdf=None, title='Prediction Examples'):
# Dataset name
ds = annot.attrs['name']
# Initialize blank page
plt.clf()
fig = plt.figure(figsize=(8.5,11), dpi=100, facecolor='w')
ax = fig.add_subplot(111)
# If no specific parts have been chosen, use them all for scoring
if partnames is None:
partnames = ref.parts[ds]
part_idxs = [ref.parts[ds].index(part) if type(part) is str else part for part in partnames]
part_filt = [i in part_idxs for i in xrange(len(ref.parts[ds]))]
page = np.zeros((res[0]*num_rows, res[1]*num_cols*(1+len(part_idxs)), 3), np.uint8)
# If no filter is provided create entirely true array
if filt is None:
filt = np.array([True for _ in xrange(len(preds))])
else:
filt = filt.copy()
# If no precalculated distances are provided, calculate them
if dists is None:
dists = eval.getdists(preds)
# Determine scores from which we'll sort the images
scores = np.zeros(len(preds))
for i in xrange(len(preds)):
# A bit of an interesting line below, gets the mean distance for a particular image
# while only considering the parts we want and ignoring any parts where there's no annotation
vals = dists[i, part_filt * (annot['part'][i,:,0] > 0)]
if len(vals) > 0:
scores[i] = vals.mean()
else:
# If no valid annotation to make a score, filter out this example
filt[i] = False
if get_worst:
# Flip the scores if we're getting the worst images
scores = -scores
best_idxs = scores.argsort()
if title[:4] == 'head' and get_worst:
np.save('worst_head_idxs',best_idxs[:200])
curr_idx = 0
# Start filling in the overall image
for i in xrange(page_num * num_rows * num_cols):
while curr_idx < len(best_idxs) and not filt[best_idxs[curr_idx]]:
curr_idx += 1
if curr_idx >= len(best_idxs): break
# If we're doing multiple pages, pass over the images that have already been used
if i >= (page_num - 1) * num_rows * num_cols:
idx = best_idxs[curr_idx]
if vert:
inp, _ = data.gensample(annot, idx, chg={'rotate':-1})
else:
inp, _ = data.gensample(annot, idx)
new_img = img.sample_with_heatmap(ds, inp, preds_raw[idx], num_rows=1, parts_to_show=part_idxs)
row = ((i % (num_rows * num_cols)) / num_cols) * res[0]
col = ((i % (num_rows * num_cols)) % num_cols) * res[1] * (1+len(part_idxs))
page[row:row+res[0], col:col+(res[1]*(1+len(part_idxs)))] = new_img
curr_idx += 1
# Plot management
if not get_worst:
title += ' - Best - '
else:
title += ' - Worst - '
title += 'Page %d' % page_num
ax.set_title(title)
ax.imshow(page)
ax.axis('off')
fig.subplots_adjust(left=0.05,right=.95,bottom=0.05,top=.95)
if pdf:
pdf.savefig()
else:
plt.show()
fig.clf()
