def save_all_heatmaps_by_user(self):
"""
Saves all heatmaps in image files, this method compares all heatmaps associated to this user
and outputs images based on the minimums and maximums from all heatmaps. It also applies a logarithmic
normalizer because of some very high values compared to others which affects scaling
:return: None
"""
# prepare dirs
dir = config.WORKING_DIRECTORY + self.manager.project_name + "/users/"
if not os.path.exists(dir):
os.makedirs(dir)
dir = dir + "user_" + self.user_id + "/"
if not os.path.exists(dir):
os.makedirs(dir)
dir = dir + "gazemaps_image_method/"
if not os.path.exists(dir):
os.makedirs(dir)
max_val = 0
avg_val = 0
l = 0
# determines an average value for all the ln heatmaps
# determines the highest value found on all the heatmaps
for im_id in self.image_data:
pos = self.image_data[im_id].user_positions[self.user_id]
heatmap = np.copy(pos['heatmap'])
heatmap = heatmap + 1
heatmap = np.log(heatmap)
tmp = np.max(heatmap)
max_val = max(tmp, max_val)
if len(heatmap[heatmap > 0]) > 0:
avg_val = np.mean(heatmap[heatmap > 0])
l = l + 1
del heatmap
if l == 0:
gc.collect()
return
avg_val = avg_val / l
# Save all heatmaps while taking to account max and avg
for im_id in self.image_data:
rgb_im = self.image_data[im_id].image.convert('RGB')
rgb_im.save('converted_image.jpg')
out = dir + im_id + "_heatmap.png"
pos = self.image_data[im_id].user_positions[self.user_id]
heatmap = np.copy(pos['heatmap'])
heatmap = heatmap + 1
heatmap = np.log(heatmap)
heatmap[0][0] = max_val
save_heatmap(heatmap, (self.image_data[im_id].rescaled_width,
self.image_data[im_id].rescaled_height),
imagefile='converted_image.jpg',
savefilename=out,
alpha=0.5,
avg=avg_val,
annotations=self.image_data[im_id].ref_annotations)
del heatmap
os.remove('converted_image.jpg')
gc.collect()
def save_all_heatmaps_reduced(self, bar):
rgb_im = self.image.convert('RGB')
rgb_im.save('converted_image.jpg')
dir = self.image_dir + "gazemap_reduced/"
if not os.path.exists(dir):
os.makedirs(dir)
for u_id in self.user_positions:
out = dir + u_id + "_heatmap.png"
heatmap = generate_reduced_heatmap(self.user_positions[u_id],(self.rescaled_width, self.rescaled_height), self)
save_heatmap(heatmap, (self.rescaled_width, self.rescaled_height), imagefile='converted_image.jpg',
savefilename=out, alpha=0.5, annotations=self.ref_annotations)
gc.collect()
bar.next()
os.remove('converted_image.jpg')
def save_all_heatmaps(self):
"""
Save all heatmaps in images files, no normalization
:return: None
"""
rgb_im = self.image.convert('RGB')
rgb_im.save('converted_image.jpg')
dir = self.image_dir + "gazemaps_basic/"
if not os.path.exists(dir):
os.makedirs(dir)
for u_id in self.user_positions:
out = dir + u_id + "_heatmap.png"
pos = self.user_positions[u_id]
heatmap = np.copy(pos['heatmap'])
save_heatmap(heatmap, (self.rescaled_width, self.rescaled_height), imagefile='converted_image.jpg', savefilename=out, alpha=0.5, annotations=self.ref_annotations)
del heatmap
gc.collect()
os.remove('converted_image.jpg')
def save_all_heatmaps_ln(self):
"""
Saves all heatmaps in image files. It applies a logarithmic
normalizer because of some very high values compared to others which affects scaling
:return: None
"""
rgb_im = self.image.convert('RGB')
rgb_im.save('converted_image.jpg')
dir = self.image_dir + "gazemaps_ln_method/"
if not os.path.exists(dir):
os.makedirs(dir)
for u_id in self.user_positions:
out = dir + u_id + "_heatmap.png"
pos = self.user_positions[u_id]
heatmap = np.copy(pos['heatmap'])
heatmap = heatmap + 1
heatmap = np.log10(heatmap)
save_heatmap(heatmap, (self.rescaled_width, self.rescaled_height), imagefile='converted_image.jpg', savefilename=out, alpha=0.5, annotations=self.ref_annotations)
del heatmap
gc.collect()
os.remove('converted_image.jpg')