def getCrowdCount(self, img_path):
# Read image files
im = loadImage(img_path, color=self.is_colored)
if self.resize_im > 0:
# Resize image
im = utl.resizeMaxSize(im, self.resize_im)
s = time.time()
# print 'pw:',self.pw
npred, resImg, realmap = testOnImg(self.CNN, im, self.pw, self.mask)
return npred, time.time() - s
def main(argv):
# Init parameters
use_cpu = False
gpu_dev = 0
# GAME max level
mx_game = 4 # Max game target
# Batch size
b_size = -1
# CNN vars
prototxt_path = 'models/trancos/hydra2/hydra_deploy.prototxt'
caffemodel_path = 'models/trancos/hydra2/trancos_hydra2.caffemodel'
# Get parameters
try:
opts, _ = getopt.getopt(
argv, "h:",
["prototxt=", "caffemodel=", "cpu_only", "dev=", "cfg="])
except getopt.GetoptError as err:
print "Error while parsing parameters: ", err
dispHelp(argv[0])
return
for opt, arg in opts:
if opt == '-h':
dispHelp(argv[0])
return
elif opt in ("--prototxt"):
prototxt_path = arg
elif opt in ("--caffemodel"):
caffemodel_path = arg
elif opt in ("--cpu_only"):
use_cpu = True
elif opt in ("--dev"):
gpu_dev = int(arg)
elif opt in ("--cfg"):
cfg_file = arg
print "Loading configuration file: ", cfg_file
(dataset, use_mask, mask_file, test_names_file, im_folder, dot_ending, pw,
sigmadots, n_scales, perspective_path, use_perspective, is_colored,
results_file, resize_im) = initTestFromCfg(cfg_file)
print "Choosen parameters:"
print "-------------------"
print "Use only CPU: ", use_cpu
print "GPU devide: ", gpu_dev
print "Dataset: ", dataset
print "Results files: ", results_file
print "Test data base location: ", im_folder
print "Test inmage names: ", test_names_file
print "Dot image ending: ", dot_ending
print "Use mask: ", use_mask
print "Mask pattern: ", mask_file
print "Patch width (pw): ", pw
print "Sigma for each dot: ", sigmadots
print "Number of scales: ", n_scales
print "Perspective map: ", perspective_path
print "Use perspective:", use_perspective
print "Prototxt path: ", prototxt_path
print "Caffemodel path: ", caffemodel_path
print "Batch size: ", b_size
print "Resize images: ", resize_im
print "==================="
print "----------------------"
print "Preparing for Testing"
print "======================"
# Set GPU CPU setting
if use_cpu:
caffe.set_mode_cpu()
else:
# Use GPU
caffe.set_device(gpu_dev)
caffe.set_mode_gpu()
print "Reading perspective file"
if use_perspective:
pers_file = h5py.File(perspective_path, 'r')
pmap = np.array(pers_file['pmap'])
pers_file.close()
mask = None
if dataset == 'UCSD':
print "Reading mask"
if use_mask:
mask_f = h5py.File(mask_file, 'r')
mask = np.array(mask_f['mask'])
mask_f.close()
print "Reading image file names:"
im_names = np.loadtxt(test_names_file, dtype='str')
# Perform test
ntrueall = []
npredall = []
# Init GAME
n_im = len(im_names)
game_table = np.zeros((n_im, mx_game))
# Init CNN
CNN = CaffePredictor(prototxt_path, caffemodel_path, n_scales)
print
print "Start prediction ..."
count = 0
gt_vector = np.zeros((len(im_names)))
pred_vector = np.zeros((len(im_names)))
for ix, name in enumerate(im_names):
# Get image paths
im_path = utl.extendName(name, im_folder)
dot_im_path = utl.extendName(name,
im_folder,
use_ending=True,
pattern=dot_ending)
# Read image files
im = loadImage(im_path, color=is_colored)
dot_im = loadImage(dot_im_path, color=True)
# Generate features
if use_perspective:
dens_im = genPDensity(dot_im, sigmadots, pmap)
else:
dens_im = genDensity(dot_im, sigmadots)
if resize_im > 0:
# Resize image
im = utl.resizeMaxSize(im, resize_im)
gt_sum = dens_im.sum()
dens_im = utl.resizeMaxSize(dens_im, resize_im)
dens_im = dens_im * gt_sum / dens_im.sum()
# Get mask if needed
if dataset != 'UCSD':
if use_mask:
mask_im_path = utl.extendName(name,
im_folder,
use_ending=True,
pattern=mask_file)
mask = sio.loadmat(mask_im_path,
chars_as_strings=1,
matlab_compatible=1)
mask = mask.get('BW')
s = time.time()
ntrue, npred, resImg, gtdots = testOnImg(CNN, im, dens_im, pw, mask)
print "image : %d , ntrue = %.2f ,npred = %.2f , time =%.2f sec" % (
count, ntrue, npred, time.time() - s)
# Keep individual predictions
gt_vector[ix] = ntrue
pred_vector[ix] = npred
# Hold predictions and originasl
ntrueall.append(ntrue)
npredall.append(npred)
# Compute game metric
for l in range(mx_game):
game_table[count, l] = gameMetric(resImg, gtdots, l)
count = count + 1
ntrueall = np.asarray(ntrueall)
npredall = np.asarray(npredall)
print "done ! mean absolute error %.2f" % np.mean(
np.abs(ntrueall - npredall))
# Print Game results
results = np.zeros(mx_game)
for l in range(mx_game):
results[l] = np.mean(game_table[:, l])
print "GAME for level %d: %.2f " % (l, np.mean(game_table[:, l]))
# Dump results into a txt file
np.savetxt(results_file + '_pred.txt', npredall)
np.savetxt(results_file + '_gt.txt', ntrueall)
return 0
def main(argv):
# Init cfg file
cfg_file = ''
# Get parameters
try:
opts, _ = getopt.getopt(argv, "h:", ["cfg="])
except getopt.GetoptError:
dispHelp()
return
for opt, arg in opts:
if opt == '-h':
dispHelp(argv[0])
return
elif opt in ("--cfg"):
cfg_file = arg
print "Loading configuration file: ", cfg_file
(dataset, im_folder, im_list_file, output_file, feature_file_path,
dot_ending, pw_base, pw_norm, pw_dens, sigmadots, Nr, n_scales,
split_size, do_flip, perspective_path, use_perspective, is_colored,
resize_im) = initGenFeatFromCfg(cfg_file)
print "Choosen parameters:"
print "-------------------"
print "Dataset: ", dataset
print "Data base location: ", im_folder
print "Image names file: ", im_list_file
print "Output file:", output_file
print "Output feature names file:", feature_file_path
print "Dot image ending: ", dot_ending
print "Patch width (pw_base): ", pw_base
print "Patch width (pw_norm): ", pw_norm
print "Patch width (pw_dens): ", pw_dens
print "Number of patches per image: ", Nr
print "Perspective map: ", perspective_path
print "Use perspective:", use_perspective
print "Sigma for each dot: ", sigmadots
print "Number of scales: ", n_scales
print "Split size: ", split_size
print "Flip images: ", do_flip
print "Resize images: ", resize_im
print "==================="
print "Reading perspective file"
if use_perspective:
pers_file = h5py.File(perspective_path, 'r')
pmap = np.array(pers_file['pmap'])
pers_file.close()
print "Creating feature names file:"
feature_file = open(feature_file_path, 'w')
feature_file.close() # Create empty file
print "Reading image file names:"
im_names = np.loadtxt(im_list_file, dtype='str')
ldens = []
lpos = []
lpatches = []
file_count = 0
for ix, name in enumerate(im_names):
print "Processing image: ", name
# Get image paths
im_path = utl.extendName(name, im_folder)
dot_im_path = utl.extendName(name,
im_folder,
use_ending=True,
pattern=dot_ending)
# Read image files
im = loadImage(im_path, color=is_colored)
dot_im = loadImage(dot_im_path, color=True)
# Do ground truth
if use_perspective:
dens_im = genPDensity(dot_im, sigmadots, pmap)
else:
dens_im = genDensity(dot_im, sigmadots)
if resize_im > 0:
# Resize image
im = utl.resizeMaxSize(im, resize_im)
gt_sum = dens_im.sum()
dens_im = utl.resizeMaxSize(dens_im, resize_im)
dens_im = dens_im * gt_sum / dens_im.sum()
# Collect features from random locations
# height, width, _ = im.shape
# pos = get_dense_pos(height, width, pw_base=pw_base, stride = 5 )
pos = utl.genRandomPos(im.shape, pw_base, Nr)
# Collect original patches
patch = cropAtPos(im, pos, pw_base)
# patch = cropPerspective(im, pos, pmap, pw_base)
# Collect dens patches
dpatch = cropAtPos(dens_im, pos, pw_base)
# dpatch = cropPerspective(dens_im, pos, pmap, pw_base)
# Resize images
patch = utl.resizePatches(patch, (pw_norm, pw_norm))
dpatch = utl.resizeListDens(
dpatch, (pw_dens, pw_dens)) # 18 is the output size of the paper
# Flip function
if do_flip:
fpatch = hFlipImages(patch)
fdpatch = hFlipImages(dpatch)
fscales = extractEscales(fpatch, n_scales)
# Add flipped data
lpatches.append(fscales)
ldens.append(fdpatch)
lpos.append(pos)
# Store features and densities
ldens.append(dpatch)
lpos.append(pos)
lpatches.append(extractEscales(patch, n_scales))
# Save it into a file
if split_size > 0 and (ix + 1) % split_size == 0:
# Prepare for saving
ldens = np.vstack(ldens)
lpos = np.vstack(lpos)
patches_list = np.vstack(lpatches[:])
opt_num_name = output_file + str(file_count) + ".h5"
print "Saving data file: ", opt_num_name
print "Saving {} examples".format(len(ldens))
# Compress data and save
feature_file = open(feature_file_path, 'a')
comp_kwargs = {'compression': 'gzip', 'compression_opts': 1}
with h5py.File(opt_num_name, 'w') as f:
f.create_dataset('label', data=ldens, **comp_kwargs)
# Save all scales data
for s in range(n_scales):
dataset_name = 'data_s{}'.format(s)
print "Creating dataset: ", dataset_name
f.create_dataset(dataset_name,
data=trasposeImages(patches_list[:, s,
...]),
**comp_kwargs)
f.close()
feature_file.write(opt_num_name + '\n')
feature_file.close()
# Increase file counter
file_count += 1
# Clean memory
ldens = []
lpos = []
lpatches = []
## Last save
if len(lpatches) > 0:
# Prepare for saving
ldens = np.vstack(ldens)
lpos = np.vstack(lpos)
patches_list = np.vstack(lpatches[:])
opt_num_name = output_file + ".h5"
print "Saving data file: ", opt_num_name
print "Saving {} examples".format(len(ldens))
# Compress data and save
feature_file = open(feature_file_path, 'a')
comp_kwargs = {'compression': 'gzip', 'compression_opts': 1}
with h5py.File(opt_num_name, 'w') as f:
f.create_dataset('label', data=ldens, **comp_kwargs)
# Save all scales data
for s in range(n_scales):
dataset_name = 'data_s{}'.format(s)
print "Creating dataset: ", dataset_name
f.create_dataset(dataset_name,
data=trasposeImages(patches_list[:, s, ...]),
**comp_kwargs)
f.close()
feature_file.write(opt_num_name + '\n')
feature_file.close()
print "--------------------"
print "Finish!"
def main(argv):
# Init cfg file
cfg_file = ''
# Get parameters
try:
opts, _ = getopt.getopt(argv, "h:", ["cfg="])
except getopt.GetoptError:
dispHelp()
return
for opt, arg in opts:
if opt == '-h':
dispHelp(argv[0])
return
elif opt in ("--cfg"):
cfg_file = arg
print "Loading configuration file: ", cfg_file
(dataset, im_folder, im_list_file, output_file, feature_file_path,
dot_ending, pw_base, pw_norm, pw_dens, sigmadots, Nr, n_scales, split_size,
do_flip, perspective_path, use_perspective, is_colored, resize_im) = initGenFeatFromCfg(cfg_file)
print "Choosen parameters:"
print "-------------------"
print "Dataset: ", dataset
print "Data base location: ", im_folder
print "Image names file: ", im_list_file
print "Output file:", output_file
print "Output feature names file:", feature_file_path
print "Dot image ending: ", dot_ending
print "Patch width (pw_base): ", pw_base
print "Patch width (pw_norm): ", pw_norm
print "Patch width (pw_dens): ", pw_dens
print "Number of patches per image: ", Nr
print "Perspective map: ", perspective_path
print "Use perspective:", use_perspective
print "Sigma for each dot: ", sigmadots
print "Number of scales: ", n_scales
print "Split size: ", split_size
print "Flip images: ", do_flip
print "Resize images: ", resize_im
print "==================="
print "Reading perspective file"
if use_perspective:
pers_file = h5py.File(perspective_path,'r')
pmap = np.array( pers_file['pmap'] )
pers_file.close()
print "Creating feature names file:"
feature_file = open(feature_file_path, 'w')
feature_file.close() # Create empty file
print "Reading image file names:"
im_names = np.loadtxt(im_list_file, dtype='str')
ldens = []
lpos = []
lpatches = []
file_count = 0
for ix, name in enumerate(im_names):
print "Processing image: ", name
# Get image paths
im_path = utl.extendName(name, im_folder)
dot_im_path = utl.extendName(name, im_folder, use_ending=True, pattern=dot_ending)
# Read image files
im = loadImage(im_path, color = is_colored)
dot_im = loadImage(dot_im_path, color = True)
# Do ground truth
if use_perspective:
dens_im = genPDensity(dot_im, sigmadots, pmap)
else:
dens_im = genDensity(dot_im, sigmadots)
if resize_im > 0:
# Resize image
im = utl.resizeMaxSize(im, resize_im)
gt_sum = dens_im.sum()
dens_im = utl.resizeMaxSize(dens_im, resize_im)
dens_im = dens_im * gt_sum / dens_im.sum()
# Collect features from random locations
# height, width, _ = im.shape
# pos = get_dense_pos(height, width, pw_base=pw_base, stride = 5 )
pos = utl.genRandomPos(im.shape, pw_base, Nr)
# Collect original patches
patch = cropAtPos(im, pos, pw_base)
# patch = cropPerspective(im, pos, pmap, pw_base)
# Collect dens patches
dpatch = cropAtPos(dens_im, pos, pw_base)
# dpatch = cropPerspective(dens_im, pos, pmap, pw_base)
# Resize images
patch = utl.resizePatches(patch, (pw_norm,pw_norm))
dpatch = utl.resizeListDens(dpatch, (pw_dens, pw_dens)) # 18 is the output size of the paper
# Flip function
if do_flip:
fpatch = hFlipImages(patch)
fdpatch = hFlipImages(dpatch)
fscales = extractEscales(fpatch, n_scales)
# Add flipped data
lpatches.append( fscales )
ldens.append( fdpatch )
lpos.append( pos )
# Store features and densities
ldens.append( dpatch )
lpos.append(pos)
lpatches.append( extractEscales(patch, n_scales) )
# Save it into a file
if split_size > 0 and (ix + 1) % split_size == 0:
# Prepare for saving
ldens = np.vstack(ldens)
lpos = np.vstack(lpos)
patches_list = np.vstack(lpatches[:])
opt_num_name = output_file + str(file_count) + ".h5"
print "Saving data file: ", opt_num_name
print "Saving {} examples".format(len(ldens))
# Compress data and save
feature_file = open(feature_file_path, 'a')
comp_kwargs = {'compression': 'gzip', 'compression_opts': 1}
with h5py.File(opt_num_name, 'w') as f:
f.create_dataset('label', data=ldens, **comp_kwargs)
# Save all scales data
for s in range(n_scales):
dataset_name = 'data_s{}'.format(s)
print "Creating dataset: ", dataset_name
f.create_dataset(dataset_name, data=trasposeImages(patches_list[:,s,...]), **comp_kwargs)
f.close()
feature_file.write(opt_num_name + '\n')
feature_file.close()
# Increase file counter
file_count += 1
# Clean memory
ldens = []
lpos = []
lpatches = []
## Last save
if len(lpatches) >0:
# Prepare for saving
ldens = np.vstack(ldens)
lpos = np.vstack(lpos)
patches_list = np.vstack(lpatches[:])
opt_num_name = output_file + ".h5"
print "Saving data file: ", opt_num_name
print "Saving {} examples".format(len(ldens))
# Compress data and save
feature_file = open(feature_file_path, 'a')
comp_kwargs = {'compression': 'gzip', 'compression_opts': 1}
with h5py.File(opt_num_name, 'w') as f:
f.create_dataset('label', data=ldens, **comp_kwargs)
# Save all scales data
for s in range(n_scales):
dataset_name = 'data_s{}'.format(s)
print "Creating dataset: ", dataset_name
f.create_dataset(dataset_name, data=trasposeImages(patches_list[:,s,...]), **comp_kwargs)
f.close()
feature_file.write(opt_num_name + '\n')
feature_file.close()
print "--------------------"
print "Finish!"
def main(argv):
# Init parameters
use_cpu = True
gpu_dev = 0
# Batch size
b_size = -1
# CNN vars
prototxt_path = 'models/ucsd/hydra3/hydra3_deploy.prototxt'
caffemodel_path = 'models/ucsd/hydra3/trancos_hydra2.caffemodel'
# Get parameters
try:
opts, _ = getopt.getopt(
argv, "h:",
["prototxt=", "caffemodel=", "cpu_only", "dev=", "cfg=", "img="])
except getopt.GetoptError as err:
print "Error while parsing parameters: ", err
dispHelp(argv[0])
return
for opt, arg in opts:
if opt == '-h':
dispHelp(argv[0])
return
elif opt in ("--prototxt"):
prototxt_path = arg
elif opt in ("--caffemodel"):
caffemodel_path = arg
elif opt in ("--cpu_only"):
use_cpu = True
elif opt in ("--dev"):
gpu_dev = int(arg)
elif opt in ("--cfg"):
cfg_file = arg
elif opt in ("--img"):
image_file = arg
print "Loading configuration file: ", cfg_file
(dataset, use_mask, mask_file, test_names_file, im_folder, dot_ending, pw,
sigmadots, n_scales, perspective_path, use_perspective, is_colored,
results_file, resize_im) = initTestFromCfg(cfg_file)
print "Choosen parameters:"
print "-------------------"
print "Use only CPU: ", use_cpu
print "GPU devide: ", gpu_dev
print "Dataset: ", dataset
print "Results files: ", results_file
print "Test data base location: ", im_folder
print "Test inmage names: ", test_names_file
print "Dot image ending: ", dot_ending
print "Use mask: ", use_mask
print "Mask pattern: ", mask_file
print "Patch width (pw): ", pw
print "Sigma for each dot: ", sigmadots
print "Number of scales: ", n_scales
print "Perspective map: ", perspective_path
print "Use perspective:", use_perspective
print "Prototxt path: ", prototxt_path
print "Caffemodel path: ", caffemodel_path
print "Batch size: ", b_size
print "Resize images: ", resize_im
print "==================="
print "----------------------"
print "Preparing for Testing"
print "======================"
# Set GPU CPU setting
if use_cpu:
caffe.set_mode_cpu()
else:
# Use GPU
caffe.set_device(gpu_dev)
caffe.set_mode_gpu()
print "Reading perspective file"
if use_perspective:
pers_file = h5py.File(perspective_path, 'r')
pers_file.close()
mask = None
if dataset == 'UCSD':
print "Reading mask"
if use_mask:
mask_f = h5py.File(mask_file, 'r')
mask = np.array(mask_f['mask'])
mask_f.close()
# Init CNN
CNN = CaffePredictor(prototxt_path, caffemodel_path, n_scales)
# Read image files
im = loadImage(image_file, color=is_colored)
if resize_im > 0:
# Resize image
im = utl.resizeMaxSize(im, resize_im)
s = time.time()
npred, resImg, realImg = testOnImg(CNN, im, pw, mask)
print "npred = %.2f , time =%.2f sec" % (npred, time.time() - s)
sio.savemat('predictionmap.mat', {'d_map': realImg})
return 0
def main(argv, image_name):
use_cpu = False
gpu_dev = 0
prototxt_path = 'models/trancos/hydra2/hydra_deploy.prototxt'
caffemodel_path = 'models/trancos/hydra2/trancos_hydra2.caffemodel'
try:
opts, _ = getopt.getopt(
argv, "h:",
["prototxt=", "caffemodel=", "cpu_only", "dev=", "cfg="])
except getopt.GetoptError as err:
print("Error while parsing parameters: ", err)
return
for opt, arg in opts:
if opt in ("--prototxt"):
prototxt_path = arg
elif opt in ("--caffemodel"):
caffemodel_path = arg
elif opt in ("--cpu_only"):
use_cpu = True
elif opt in ("--dev"):
gpu_dev = int(arg)
elif opt in ("--cfg"):
cfg_file = arg
(dataset, use_mask, mask_file, test_names_file, im_folder, dot_ending, pw,
sigmadots, n_scales, perspective_path, use_perspective, is_colored,
results_file, resize_im) = init_parameters_from_config(cfg_file)
if use_cpu:
caffe.set_mode_cpu()
else:
# Use GPU
caffe.set_device(gpu_dev)
caffe.set_mode_gpu()
# Init CNN
CNN = CaffePredictor(prototxt_path, caffemodel_path, n_scales)
print("\nStart prediction for " + image_name)
im_path = utl.extendName(image_name, im_folder)
im = load_image(im_path, color=is_colored)
if resize_im > 0:
im = utl.resizeMaxSize(im, resize_im)
mask = None
if use_mask:
mask_im_path = utl.extendName(image_name,
im_folder,
use_ending=True,
pattern=mask_file)
mask = sio.loadmat(mask_im_path,
chars_as_strings=1,
matlab_compatible=1)
mask = mask.get('BW')
s = time.time()
npred, resImg = count_objects(CNN, im, pw, mask)
print("image : %s, npred = %.2f , time =%.2f sec" %
(image_name, npred, time.time() - s))
return npred
def main(argv):
# Init parameters
use_cpu = False
gpu_dev = 0
# GAME max level
mx_game = 4 # Max game target
# Batch size
b_size = -1
# CNN vars
prototxt_path = 'models/trancos/hydra2/hydra_deploy.prototxt'
caffemodel_path = 'models/trancos/hydra2/trancos_hydra2.caffemodel'
# Get parameters
try:
opts, _ = getopt.getopt(argv, "h:", ["prototxt=", "caffemodel=",
"cpu_only", "dev=", "cfg="])
except getopt.GetoptError as err:
print "Error while parsing parameters: ", err
dispHelp(argv[0])
return
for opt, arg in opts:
if opt == '-h':
dispHelp(argv[0])
return
elif opt in ("--prototxt"):
prototxt_path = arg
elif opt in ("--caffemodel"):
caffemodel_path = arg
elif opt in ("--cpu_only"):
use_cpu = True
elif opt in ("--dev"):
gpu_dev = int(arg)
elif opt in ("--cfg"):
cfg_file = arg
print "Loading configuration file: ", cfg_file
(dataset, use_mask, mask_file, test_names_file, im_folder,
dot_ending, pw, sigmadots, n_scales, perspective_path,
use_perspective, is_colored, results_file, resize_im) = initTestFromCfg(cfg_file)
print "Choosen parameters:"
print "-------------------"
print "Use only CPU: ", use_cpu
print "GPU devide: ", gpu_dev
print "Dataset: ", dataset
print "Results files: ", results_file
print "Test data base location: ", im_folder
print "Test inmage names: ", test_names_file
print "Dot image ending: ", dot_ending
print "Use mask: ", use_mask
print "Mask pattern: ", mask_file
print "Patch width (pw): ", pw
print "Sigma for each dot: ", sigmadots
print "Number of scales: ", n_scales
print "Perspective map: ", perspective_path
print "Use perspective:", use_perspective
print "Prototxt path: ", prototxt_path
print "Caffemodel path: ", caffemodel_path
print "Batch size: ", b_size
print "Resize images: ", resize_im
print "==================="
print "----------------------"
print "Preparing for Testing"
print "======================"
# Set GPU CPU setting
if use_cpu:
caffe.set_mode_cpu()
else:
# Use GPU
caffe.set_device(gpu_dev)
caffe.set_mode_gpu()
print "Reading perspective file"
if use_perspective:
pers_file = h5py.File(perspective_path,'r')
pmap = np.array( pers_file['pmap'] )
pers_file.close()
mask = None
if dataset == 'UCSD':
print "Reading mask"
if use_mask:
mask_f = h5py.File(mask_file,'r')
mask = np.array(mask_f['mask'])
mask_f.close()
print "Reading image file names:"
im_names = np.loadtxt(test_names_file, dtype='str')
# Perform test
ntrueall=[]
npredall=[]
# Init GAME
n_im = len( im_names )
game_table = np.zeros( (n_im, mx_game) )
# Init CNN
CNN = CaffePredictor(prototxt_path, caffemodel_path, n_scales)
print
print "Start prediction ..."
count = 0
gt_vector = np.zeros((len(im_names)))
pred_vector = np.zeros((len(im_names)))
for ix, name in enumerate(im_names):
# Get image paths
im_path = utl.extendName(name, im_folder)
dot_im_path = utl.extendName(name, im_folder, use_ending=True, pattern=dot_ending)
# Read image files
im = loadImage(im_path, color = is_colored)
dot_im = loadImage(dot_im_path, color = True)
# Generate features
if use_perspective:
dens_im = genPDensity(dot_im, sigmadots, pmap)
else:
dens_im = genDensity(dot_im, sigmadots)
if resize_im > 0:
# Resize image
im = utl.resizeMaxSize(im, resize_im)
gt_sum = dens_im.sum()
dens_im = utl.resizeMaxSize(dens_im, resize_im)
dens_im = dens_im * gt_sum / dens_im.sum()
# Get mask if needed
if dataset != 'UCSD':
if use_mask:
mask_im_path = utl.extendName(name, im_folder, use_ending=True, pattern=mask_file)
mask = sio.loadmat(mask_im_path, chars_as_strings=1, matlab_compatible=1)
mask = mask.get('BW')
s=time.time()
ntrue,npred,resImg,gtdots=testOnImg(CNN, im, dens_im, pw, mask)
print "image : %d , ntrue = %.2f ,npred = %.2f , time =%.2f sec"%(count,ntrue,npred,time.time()-s)
# Keep individual predictions
gt_vector[ix] = ntrue
pred_vector[ix] = npred
# Hold predictions and originasl
ntrueall.append(ntrue)
npredall.append(npred)
# Compute game metric
for l in range(mx_game):
game_table[count, l] = gameMetric(resImg, gtdots, l)
count = count +1
ntrueall=np.asarray(ntrueall)
npredall=np.asarray(npredall)
print "done ! mean absolute error %.2f" % np.mean(np.abs(ntrueall-npredall))
# Print Game results
results = np.zeros(mx_game)
for l in range(mx_game):
results[l] = np.mean( game_table[:,l] )
print "GAME for level %d: %.2f " % (l, np.mean( game_table[:,l] ))
# Dump results into a txt file
np.savetxt(results_file + '_pred.txt', npredall)
np.savetxt(results_file + '_gt.txt', ntrueall)
return 0