def render_image_info(rendering_info):
img_path = rendering_info['img_path']
bboxes = rendering_info['bboxes']
bboxClasses = rendering_info['boxClasses']
output_file_name = rendering_info['output_file_name']
if not os.path.exists(img_path):
print('Image {} cannot be found'.format(img_path))
return
try:
original_image = vis_utils.open_image(img_path)
original_size = original_image.size
image = vis_utils.resize_image(original_image, options.viz_size[0],
options.viz_size[1])
except Exception as e:
print('Image {} failed to open. Error: {}'.format(img_path, e))
return
vis_utils.render_db_bounding_boxes(boxes=bboxes,
classes=bboxClasses,
image=image,
original_size=original_size,
label_map=label_map)
image.save(
os.path.join(output_dir, 'rendered_images', output_file_name))
def processImages(db_path,
output_dir,
image_base_dir,
options=None,
bbox_db=None):
"""
Writes images and html to output_dir to visualize the annotations in the json file
db_path.
Returns the html filename and the bbox database.
"""
if options is None:
options = DbVizOptions()
print(options.__dict__)
os.makedirs(os.path.join(output_dir, 'rendered_images'), exist_ok=True)
assert (os.path.isfile(db_path))
assert (os.path.isdir(image_base_dir))
if bbox_db is None:
print('Loading the database...')
bbox_db = json.load(open(db_path))
print('...done')
annotations = bbox_db['annotations']
images = bbox_db['images']
categories = bbox_db['categories']
# Optionally remove all images without bounding boxes, *before* sampling
if options.trim_to_images_with_bboxes:
bHasBbox = [False] * len(annotations)
for iAnn, ann in enumerate(annotations):
if 'bbox' in ann:
assert isinstance(ann['bbox'], list)
bHasBbox[iAnn] = True
annotationsWithBboxes = list(compress(annotations, bHasBbox))
imageIDsWithBboxes = [x['image_id'] for x in annotationsWithBboxes]
imageIDsWithBboxes = set(imageIDsWithBboxes)
bImageHasBbox = [False] * len(images)
for iImage, image in enumerate(images):
imageID = image['id']
if imageID in imageIDsWithBboxes:
bImageHasBbox[iImage] = True
imagesWithBboxes = list(compress(images, bImageHasBbox))
images = imagesWithBboxes
# put the annotations in a dataframe so we can select all annotations for a given image
df_anno = pd.DataFrame(annotations)
df_img = pd.DataFrame(images)
# construct label map
label_map = {}
for cat in categories:
label_map[int(cat['id'])] = cat['name']
# take a sample of images
if options.num_to_visualize is not None:
df_img = df_img.sample(n=options.num_to_visualize,
random_state=options.random_seed)
images_html = []
# iImage = 0
for iImage in tqdm(range(len(df_img))):
img_id = df_img.iloc[iImage]['id']
img_relative_path = df_img.iloc[iImage]['file_name']
img_path = os.path.join(
image_base_dir,
imageFilenameToPath(img_relative_path, image_base_dir))
if not os.path.exists(img_path):
print('Image {} cannot be found'.format(img_path))
continue
annos_i = df_anno.loc[df_anno['image_id'] ==
img_id, :] # all annotations on this image
try:
originalImage = vis_utils.open_image(img_path)
original_size = originalImage.size
image = vis_utils.resize_image(originalImage, options.viz_size[0],
options.viz_size[1])
except Exception as e:
print('Image {} failed to open. Error: {}'.format(img_path, e))
continue
bboxes = []
boxClasses = []
# All the class labels we've seen for this image (with out without bboxes)
imageCategories = set()
annotationLevelForImage = ''
# Iterate over annotations for this image
# iAnn = 0; anno = annos_i.iloc[iAnn]
for iAnn, anno in annos_i.iterrows():
if 'sequence_level_annotation' in anno:
bSequenceLevelAnnotation = anno['sequence_level_annotation']
if bSequenceLevelAnnotation:
annLevel = 'sequence'
else:
annLevel = 'image'
if annotationLevelForImage == '':
annotationLevelForImage = annLevel
elif annotationLevelForImage != annLevel:
annotationLevelForImage = 'mixed'
categoryID = anno['category_id']
categoryName = label_map[categoryID]
if options.add_search_links:
categoryName = categoryName.replace('"', '')
categoryName = '<a href="https://www.bing.com/images/search?q={}">{}</a>'.format(
categoryName, categoryName)
imageCategories.add(categoryName)
if 'bbox' in anno:
bbox = anno['bbox']
if isinstance(bbox, float):
assert math.isnan(
bbox
), "I shouldn't see a bbox that's neither a box nor NaN"
continue
bboxes.append(bbox)
boxClasses.append(anno['category_id'])
imageClasses = ', '.join(imageCategories)
# render bounding boxes in-place
vis_utils.render_db_bounding_boxes(bboxes, boxClasses, image,
original_size, label_map)
file_name = '{}_gtbbox.jpg'.format(img_id.lower().split('.jpg')[0])
file_name = file_name.replace('/', '~')
image.save(os.path.join(output_dir, 'rendered_images', file_name))
labelLevelString = ''
if len(annotationLevelForImage) > 0:
labelLevelString = ' (annotation level: {})'.format(
annotationLevelForImage)
images_html.append({
'filename':
'{}/{}'.format('rendered_images', file_name),
'title':
'{}<br/>{}, number of boxes: {}, class labels: {}{}'.format(
img_relative_path, img_id, len(bboxes), imageClasses,
labelLevelString),
'textStyle':
'font-family:verdana,arial,calibri;font-size:80%;text-align:left;margin-top:20;margin-bottom:5'
})
# ...for each image
if options.sort_by_filename:
images_html = sorted(images_html, key=lambda x: x['filename'])
htmlOutputFile = os.path.join(output_dir, 'index.html')
htmlOptions = options.htmlOptions
htmlOptions['headerHtml'] = '<h1>Sample annotations from {}</h1>'.format(
db_path)
write_html_image_list(filename=htmlOutputFile,
images=images_html,
options=htmlOptions)
print('Visualized {} images, wrote results to {}'.format(
len(images_html), htmlOutputFile))
return htmlOutputFile, bbox_db