def plot_evaluation(res, run_id, epoch):
maybe_create_folder(join(dir_root, 'images', run_id))
for k in range(len(res['imgs_l'])):
img_gray = l_to_rgb(res['imgs_l'][k][:, :, 0])
img_output = lab_to_rgb(res['imgs_l'][k][:, :, 0], res['imgs_ab'][k])
img_true = lab_to_rgb(res['imgs_l'][k][:, :, 0],
res['imgs_true_ab'][k])
top_5 = np.argsort(res['imgs_emb'][k])[-5:]
try:
top_5 = ' / '.join(labels_to_categories[i] for i in top_5)
except:
ptop_5 = str(top_5)
plt.subplot(1, 3, 1)
plt.imshow(img_gray)
plt.title('Input (grayscale)')
plt.axis('off')
plt.subplot(1, 3, 2)
plt.imshow(img_output)
plt.title('Network output')
plt.axis('off')
plt.subplot(1, 3, 3)
plt.imshow(img_true)
plt.title('Target (original)')
plt.axis('off')
plt.suptitle(top_5, fontsize=7)
plt.savefig(
join(dir_root, 'images', run_id, '{}_{}.png'.format(epoch, k)))
plt.clf()
plt.close()
def __init__(self, links_source: str, dest_dir: str):
# Destination folder
maybe_create_folder(dest_dir)
self.dest_dir = dest_dir
# If the source is a link download it
if links_source.startswith('http://'):
print('Using urllib.request for the link archive is extremely',
'slow, it\'s better to download the tgz archive manualy',
'and pass its path to this constructor',
file=sys.stderr)
links_source, _ = urllib.request.urlretrieve(
links_source, join(dir_root, 'imagenet_fall11_urls.tgz'))
# If the source is an archive extract it
if links_source.endswith('.tgz'):
with tarfile.open(links_source, 'r:gz') as tar:
tar.extractall(path=dir_root)
links_source = join(dir_root, 'fall11_urls.txt')
if not isfile(links_source):
pass #raise Exception('Links source not valid: {}'.format(links_source))
self.links_source = links_source
def __init__(self, source_dir: str, dest_dir: str):
if not isdir(source_dir):
raise Exception(
'Input folder does not exists: {}'.format(source_dir))
self.source_dir = source_dir
# Destination folder
maybe_create_folder(dest_dir)
self.dest_dir = dest_dir
def __init__(self, links_source: str, dest_dir: str):
# Destination folder
maybe_create_folder(dest_dir)
self.dest_dir = dest_dir
if not isfile(links_source):
raise Exception('Links source not valid: {}'.format(links_source))
if links_source.endswith('.tgz'):
with tarfile.open(links_source, 'r:gz') as tar:
tar.extractall(path=dir_root)
links_source = join(dir_root, 'fall11_urls.txt')
self.links_source = links_source
def __init__(self, inputs_dir: str, records_dir: str,
checkpoint_source: str):
if not isdir(inputs_dir):
raise Exception(
'Input folder does not exists: {}'.format(inputs_dir))
self.inputs_dir = inputs_dir
# Destination folder
maybe_create_folder(records_dir)
self.records_dir = records_dir
# Inception checkpoint
self.checkpoint_file = maybe_download_inception(checkpoint_source)
# Utils
self._examples_count = 0
self.records_names_gen = progressive_filename_generator(
join(records_dir, 'lab_images_{}.tfrecord'))
def __init__(self, source_dir: str, dest_dir: str, verbose: int,
skip: int):
# 判断文件夹是否存在
if not isdir(source_dir):
raise Exception(
'Input folder does not exists: {}'.format(source_dir))
self.source_dir = source_dir
# 若输出文件夹不存在则创建
# Destination folder
maybe_create_folder(dest_dir)
self.dest_dir = dest_dir
# 是否启用啰嗦模式
self.verbose = verbose
#跳过文件
self.skip = skip
def plot_evaluation(res, run_id, epoch, is_eval=False):
maybe_create_folder(join(dir_root, 'images', run_id))
for k in range(len(res['imgs_l'])):
imgs_l = res['imgs_l'][k][:, :, 0]
img_gray = l_to_rgb(imgs_l)
zeros = np.zeros(res['imgs_ref_ab'][k][:, :, 0].shape)
img_ab = lab_to_rgb(zeros,
res['imgs_ab'][k])
img_fwd_ab = lab_to_rgb(zeros,
res['imgs_fwd_ab'][k])
img_ref_ab = lab_to_rgb(zeros,
res['imgs_ref_ab'][k])
img_output = lab_to_rgb(imgs_l,
res['imgs_ab'][k])
img_fwd_output = lab_to_rgb(imgs_l,
res['imgs_fwd_ab'][k])
img_ref_output = lab_to_rgb(imgs_l,
res['imgs_ref_ab'][k])
img_true = lab_to_rgb(imgs_l,
res['imgs_true_ab'][k])
# save simple single image output
if is_eval:
im = trim(img_ref_output)
im.save(join(dir_root, 'images', run_id, '{}.png'.format(k)), "PNG")
# display the colorfulness score on the image
C_output = image_colorfulness(img_output)
C_fwd_output = image_colorfulness(img_fwd_output)
C_ref_output = image_colorfulness(img_ref_output)
C_true = image_colorfulness(img_true)
# display the cost function(MSE) output of the image
cost = res['cost']
fig, axes = plt.subplots(2, 4)
# Colorization ab
axes[0,0].imshow(img_ab)
axes[0,0].set_title('Deep Koalarization ab')
axes[0,0].axis('off')
# Low res ab
axes[0,1].imshow(img_fwd_ab)
axes[0,1].set_title('Feedforward Colorization ab')
axes[0,1].axis('off')
# Refined ab
axes[0,2].imshow(img_ref_ab)
axes[0,2].set_title('Refined ab')
axes[0,2].axis('off')
# Input (grayscale)
axes[0,3].imshow(img_gray)
axes[0,3].set_title('Input (grayscale)')
axes[0,3].axis('off')
# Colorization output
axes[1,0].imshow(img_output)
axes[1,0].set_title('Deep Koalarization output\n' + ("{:.4f}".format(C_output)))
axes[1,0].axis('off')
# Low Resolution output
axes[1,1].imshow(img_fwd_output)
axes[1,1].set_title('Feedforward Colorization output\n' + ("{:.4f}".format(C_fwd_output)))
axes[1,1].axis('off')
# Refinement output
axes[1,2].imshow(img_ref_output)
axes[1,2].set_title('Refinement output\n' + ("{:.4f}".format(C_ref_output)))
axes[1,2].axis('off')
# Target (original)
axes[1,3].imshow(img_true)
axes[1,3].set_title('Target (original)\n' + ("{:.4f}".format(C_true)))
axes[1,3].axis('off')
plt.suptitle('Cost(MSE): ' + str(cost), fontsize=7)
plt.savefig(join(
dir_root, 'images', run_id, '{}_{}.png'.format(epoch, k)))
plt.clf()
plt.close()
# write on the output_colorfulness_*.txt file the colorfulness of the output image and the groundtruth image
with open('output_colorfulness_{}.txt'.format(run_id), mode='a') as f:
f.write('{},{}\n'.format(C_output, C_true))
