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 render_bounding_box(detection, inputFileName, outputFileName, lineWidth):
im = open_image(inputFileName)
d = detection.to_api_detection()
render_detection_bounding_boxes([d],
im,
thickness=lineWidth,
confidence_threshold=-10)
im.save(outputFileName)
def render_bounding_box(bbox, inputFileName, imageOutputFilename, linewidth):
im = open_image(inputFileName)
# bbox is [x, y, width, height] where x, y is the top left corner, normalized units wrt width and height of image
x_min, y_min, x_max, y_max = ct_utils.convert_coords_to_xyxy(bbox)
draw_bounding_box_on_image(im,
y_min,
x_min,
y_max,
x_max,
thickness=linewidth)
im.save(imageOutputFilename)
def render_bounding_boxes(image_base_dir,
image_relative_path,
display_name,
detections,
res,
detection_categories_map=None,
classification_categories_map=None,
options=None):
if options is None:
options = PostProcessingOptions()
# Leaving code in place for reading from blob storage, may support this
# in the future.
"""
stream = io.BytesIO()
_ = blob_service.get_blob_to_stream(container_name, image_id, stream)
image = Image.open(stream).resize(viz_size) # resize is to display them in this notebook or in the HTML more quickly
"""
image_full_path = os.path.join(image_base_dir, image_relative_path)
if not os.path.isfile(image_full_path):
print('Warning: could not find image file {}'.format(image_full_path))
return ''
image = vis_utils.open_image(image_full_path)
image = vis_utils.resize_image(image, options.viz_target_width)
vis_utils.render_detection_bounding_boxes(
detections,
image,
label_map=detection_categories_map,
classification_label_map=classification_categories_map,
confidence_threshold=options.confidence_threshold,
thickness=4)
# Render images to a flat folder... we can use os.sep here because we've
# already normalized paths
sample_name = res + '_' + image_relative_path.replace(os.sep, '~')
image.save(os.path.join(options.output_dir, res, sample_name))
# Use slashes regardless of os
file_name = '{}/{}'.format(res, sample_name)
return {
'filename':
file_name,
'title':
display_name,
'textStyle':
'font-family:verdana,arial,calibri;font-size:80%;text-align:left;margin-top:20;margin-bottom:5'
}
def render_image_info(rendering, args):
blob_service = rendering['blob_service']
image_obj = io.BytesIO()
_ = blob_service.get_blob_to_stream(rendering['container_name'], rendering['blob_path'], image_obj)
# resize is for displaying them more quickly
image = vis_utils.resize_image(vis_utils.open_image(image_obj), args.output_image_width)
vis_utils.render_megadb_bounding_boxes(rendering['bbox'], image)
annotated_img_name = rendering['annotated_img_name']
annotated_img_path = os.path.join(args.output_dir, 'rendered_images', annotated_img_name)
image.save(annotated_img_path)
def render_image_info(rendering, args):
blob_service = rendering['blob_service']
image_obj = io.BytesIO()
try:
_ = blob_service.get_blob_to_stream(rendering['container_name'], rendering['blob_path'], image_obj)
except Exception as e:
print(f'Image not found in blob storage: {rendering["blob_path"]}')
print(e)
return
# resize is for displaying them more quickly
image = vis_utils.resize_image(vis_utils.open_image(image_obj), args.output_image_width)
vis_utils.render_megadb_bounding_boxes(rendering['bbox'], image)
annotated_img_name = rendering['annotated_img_name']
annotated_img_path = os.path.join(args.output_dir, 'rendered_images', annotated_img_name)
image.save(annotated_img_path)
def render_image_info(rendering, args):
storage_client = rendering['storage_client']
image_obj = io.BytesIO()
try:
storage_client.download_blob(
rendering['blob_path']).readinto(image_obj)
except Exception as e:
print(f'Image not found in blob storage: {rendering["blob_path"]}')
print(e)
return
# resize is for displaying them more quickly
image = vis_utils.resize_image(vis_utils.open_image(image_obj),
args.output_image_width)
vis_utils.render_megadb_bounding_boxes(rendering['bbox'], image)
annotated_img_name = rendering['annotated_img_name']
annotated_img_path = os.path.join(args.output_dir, 'rendered_images',
annotated_img_name)
image.save(annotated_img_path)
def render_image_pair(fn):
input_image_path = os.path.join(options.image_folder,fn)
assert os.path.isfile(input_image_path), 'Image {} does not exist'.format(input_image_path)
im = visualization_utils.open_image(input_image_path)
image_pair = image_pairs[fn]
detections_a = image_pair[0]['detections']
detections_b = image_pair[1]['detections']
"""
def render_detection_bounding_boxes(detections, image,
label_map={},
classification_label_map={},
confidence_threshold=0.8, thickness=4, expansion=0,
classification_confidence_threshold=0.3,
max_classifications=3,
colormap=COLORS):
"""
if options.target_width is not None:
im = visualization_utils.resize_image(im, options.target_width)
visualization_utils.render_detection_bounding_boxes(detections_a,im,
confidence_threshold=pairwise_options.rendering_confidence_threshold_a,
thickness=4,expansion=0,
colormap=options.colormap_a,
textalign=visualization_utils.TEXTALIGN_LEFT)
visualization_utils.render_detection_bounding_boxes(detections_b,im,
confidence_threshold=pairwise_options.rendering_confidence_threshold_b,
thickness=2,expansion=0,
colormap=options.colormap_b,
textalign=visualization_utils.TEXTALIGN_RIGHT)
output_image_fn = path_utils.flatten_path(fn)
output_image_path = os.path.join(category_folder,output_image_fn)
im.save(output_image_path)
return output_image_path
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
def visualize_detector_output(detector_output_path: str,
out_dir: str,
images_dir: str,
is_azure: bool = False,
confidence: float = 0.8,
sample: int = -1,
output_image_width: int = 700,
random_seed: Optional[int] = None) -> List[str]:
"""Draw bounding boxes on images given the output of the detector.
Args:
detector_output_path: str, path to detector output json file
out_dir: str, path to directory for saving annotated images
images_dir: str, path to local images dir, or a SAS URL to an Azure Blob
Storage container
is_azure: bool, whether images_dir points to an Azure URL
confidence: float, threshold above which annotations will be rendered
sample: int, maximum number of images to annotate, -1 for all
output_image_width: int, width in pixels to resize images for display,
set to -1 to use original image width
random_seed: int, for deterministic image sampling when sample != -1
Returns: list of str, paths to annotated images
"""
# arguments error checking
assert confidence > 0 and confidence < 1, (
f'Confidence threshold {confidence} is invalid, must be in (0, 1).')
assert os.path.exists(detector_output_path), (
f'Detector output file does not exist at {detector_output_path}.')
if is_azure:
# we don't import sas_blob_utils at the top of this file in order to
# accommodate the MegaDetector Colab notebook which does not have
# the azure-storage-blob package installed
import sas_blob_utils
else:
assert os.path.isdir(images_dir)
os.makedirs(out_dir, exist_ok=True)
#%% Load detector output
with open(detector_output_path) as f:
detector_output = json.load(f)
assert 'images' in detector_output, (
'Detector output file should be a json with an "images" field.')
images = detector_output['images']
detector_label_map = DEFAULT_DETECTOR_LABEL_MAP
if 'detection_categories' in detector_output:
print('detection_categories provided')
detector_label_map = detector_output['detection_categories']
num_images = len(images)
print(f'Detector output file contains {num_images} entries.')
if sample > 0:
assert num_images >= sample, (
f'Sample size {sample} greater than number of entries '
f'({num_images}) in detector result.')
if random_seed is not None:
images = sorted(images, key=lambda x: x['file'])
random.seed(random_seed)
random.shuffle(images)
images = sorted(images[:sample], key=lambda x: x['file'])
print(f'Sampled {len(images)} entries from the detector output file.')
#%% Load images, annotate them and save
print('Starting to annotate the images...')
num_saved = 0
annotated_img_paths = []
image_obj: Any # str for local images, BytesIO for Azure images
for entry in tqdm(images):
image_id = entry['file']
if 'failure' in entry:
print(f'Skipping {image_id}, failure: "{entry["failure"]}"')
continue
# max_conf = entry['max_detection_conf']
if is_azure:
blob_uri = sas_blob_utils.build_blob_uri(container_uri=images_dir,
blob_name=image_id)
if not sas_blob_utils.check_blob_exists(blob_uri):
container = sas_blob_utils.get_container_from_uri(images_dir)
print(f'Image {image_id} not found in blob container '
f'{container}; skipped.')
continue
image_obj, _ = sas_blob_utils.download_blob_to_stream(blob_uri)
else:
image_obj = os.path.join(images_dir, image_id)
if not os.path.exists(image_obj):
print(f'Image {image_id} not found in images_dir; skipped.')
continue
# resize is for displaying them more quickly
image = vis_utils.resize_image(vis_utils.open_image(image_obj),
output_image_width)
vis_utils.render_detection_bounding_boxes(
entry['detections'],
image,
label_map=detector_label_map,
confidence_threshold=confidence)
for char in ['/', '\\', ':']:
image_id = image_id.replace(char, '~')
annotated_img_path = os.path.join(out_dir, f'anno_{image_id}')
annotated_img_paths.append(annotated_img_path)
image.save(annotated_img_path)
num_saved += 1
if is_azure:
image_obj.close() # BytesIO object
print(f'Rendered detection results on {num_saved} images, '
f'saved to {out_dir}.')
return annotated_img_paths
def render_bounding_boxes(image_base_dir, image_relative_path, display_name, detections, res,
detection_categories_map=None, classification_categories_map=None, options=None):
"""
Renders detection bounding boxes on a single image.
The source image is:
image_base_dir / image_relative_path
The target image is, for example:
[options.output_dir] / ['detections' or 'non-detections'] / [filename with slashes turned into tildes]
Returns the html info struct for this image in the form that's used for
write_html_image_list.
"""
if options is None:
options = PostProcessingOptions()
# Leaving code in place for reading from blob storage, may support this
# in the future.
"""
stream = io.BytesIO()
_ = blob_service.get_blob_to_stream(container_name, image_id, stream)
image = Image.open(stream).resize(viz_size) # resize is to display them in this notebook or in the HTML more quickly
"""
image_full_path = os.path.join(image_base_dir, image_relative_path)
# isfile() is slow when mounting remote directories; much faster to just try/except
# on the image open.
if False:
if not os.path.isfile(image_full_path):
print('Warning: could not find image file {}'.format(image_full_path))
return ''
try:
image = vis_utils.open_image(image_full_path)
except:
print('Warning: could not open image file {}'.format(image_full_path))
return ''
if options.viz_target_width is not None:
image = vis_utils.resize_image(image, options.viz_target_width)
vis_utils.render_detection_bounding_boxes(detections, image,
label_map=detection_categories_map,
classification_label_map=classification_categories_map,
confidence_threshold=options.confidence_threshold,
thickness=options.line_thickness,expansion=options.box_expansion)
# Render images to a flat folder... we can use os.sep here because we've
# already normalized paths
sample_name = res + '_' + path_utils.flatten_path(image_relative_path)
fullpath = os.path.join(options.output_dir, res, sample_name)
try:
image.save(fullpath)
except OSError as e:
# errno.ENAMETOOLONG doesn't get thrown properly on Windows, so
# we awkwardly check against a hard-coded limit
if (e.errno == errno.ENAMETOOLONG) or (len(fullpath) >= 259):
extension = os.path.splitext(sample_name)[1]
sample_name = res + '_' + str(uuid.uuid4()) + extension
image.save(os.path.join(options.output_dir, res, sample_name))
else:
raise
# Use slashes regardless of os
file_name = '{}/{}'.format(res, sample_name)
return {
'filename': file_name,
'title': display_name,
'textStyle': 'font-family:verdana,arial,calibri;font-size:80%;text-align:left;margin-top:20;margin-bottom:5'
}
if not os.path.exists(image_obj):
print('Image {} is not found at local images_dir; skipped.'.format(
image_id))
continue
else:
if not blob_service.exists(container_name, blob_name=image_id):
print('Image {} is not found in the blob container {}; skipped.'.
format(image_id, container_name))
continue
image_obj = io.BytesIO()
_ = blob_service.get_blob_to_stream(container_name, image_id,
image_obj)
# resize is for displaying them more quickly
image = vis_utils.resize_image(vis_utils.open_image(image_obj),
args.output_image_width)
vis_utils.render_detection_bounding_boxes(
detections,
image,
label_map=detector_label_map,
confidence_threshold=args.confidence)
annotated_img_name = 'anno_' + image_id.replace('/', '~').replace(
'\\', '~')
annotated_img_path = os.path.join(args.out_dir, annotated_img_name)
image.save(annotated_img_path)
num_saved += 1
print('Rendered detection results on {} images, saved to {}.'.format(
def visualize_detector_output(args):
#%% Load detector output
os.makedirs(args.out_dir, exist_ok=True)
images_local = True if args.images_dir is not None else False
detector_output = json.load(open(args.detector_output_path))
assert 'images' in detector_output, 'Detector output file should be a json with an "images" field.'
images = detector_output['images']
detector_label_map = DEFAULT_DETECTOR_LABEL_MAP
if 'detection_categories' in detector_output:
print('detection_categories provided')
detector_label_map = detector_output['detection_categories']
num_images = len(images)
print('Detector output file contains {} entries.'.format(num_images))
if args.sample > 0:
assert num_images >= args.sample, \
'Sample size {} specified greater than number of entries ({}) in detector result.'.format(args.sample, num_images)
if args.random_seed:
images = sorted(images, key=lambda x: x['file'])
random.seed(args.random_seed)
random.shuffle(images)
images = sorted(images[:args.sample], key=lambda x: x['file'])
print('Sampled {} entries from the detector output file.'.format(
len(images)))
#%% Load images, annotate them and save
if not images_local:
blob_service = get_service_from_uri(args.sas_url)
container_name = get_container_from_uri(args.sas_url)
print('Starting to annotate the images...')
num_saved = 0
annotated_img_paths = []
for entry in tqdm(images):
if 'failure' in entry:
print('Skipping {}, which failed because of "{}"'.format(
entry['file'], entry['failure']))
continue
image_id = entry['file']
# max_conf = entry['max_detection_conf']
detections = entry['detections']
if images_local:
image_obj = os.path.join(args.images_dir, image_id)
if not os.path.exists(image_obj):
print('Image {} is not found at local images_dir; skipped.'.
format(image_id))
continue
else:
if not blob_service.exists(container_name, blob_name=image_id):
print(
'Image {} is not found in the blob container {}; skipped.'.
format(image_id, container_name))
continue
image_obj = io.BytesIO()
_ = blob_service.get_blob_to_stream(container_name, image_id,
image_obj)
# resize is for displaying them more quickly
image = vis_utils.resize_image(vis_utils.open_image(image_obj),
args.output_image_width)
vis_utils.render_detection_bounding_boxes(
detections,
image,
label_map=detector_label_map,
confidence_threshold=args.confidence)
annotated_img_name = 'anno_' + image_id.replace('/', '~').replace(
'\\', '~').replace(':', '~')
annotated_img_path = os.path.join(args.out_dir, annotated_img_name)
annotated_img_paths.append(annotated_img_path)
image.save(annotated_img_path)
num_saved += 1
print('Rendered detection results on {} images, saved to {}.'.format(
num_saved, args.out_dir))
return annotated_img_paths
def render_bounding_boxes(image_base_dir,
image_relative_path,
display_name,
detections,
res,
detection_categories_map=None,
classification_categories_map=None,
options=None):
"""
Renders detection bounding boxes on a single image. Returns the html info struct
for this image in the form that's used for write_html_image_list.
"""
if options is None:
options = PostProcessingOptions()
# Leaving code in place for reading from blob storage, may support this
# in the future.
"""
stream = io.BytesIO()
_ = blob_service.get_blob_to_stream(container_name, image_id, stream)
image = Image.open(stream).resize(viz_size) # resize is to display them in this notebook or in the HTML more quickly
"""
image_full_path = os.path.join(image_base_dir, image_relative_path)
# isfile() is slow when mounting remote directories; much faster to just try/except
# on the image open.
if False:
if not os.path.isfile(image_full_path):
print('Warning: could not find image file {}'.format(
image_full_path))
return ''
try:
image = vis_utils.open_image(image_full_path)
except:
print('Warning: could not open image file {}'.format(image_full_path))
return ''
image = vis_utils.resize_image(image, options.viz_target_width)
vis_utils.render_detection_bounding_boxes(
detections,
image,
label_map=detection_categories_map,
classification_label_map=classification_categories_map,
confidence_threshold=options.confidence_threshold,
thickness=4)
# Render images to a flat folder... we can use os.sep here because we've
# already normalized paths
sample_name = res + '_' + image_relative_path.replace(os.sep, '~')
try:
image.save(os.path.join(options.output_dir, res, sample_name))
except OSError as e:
if e.errno == errno.ENAMETOOLONG:
sample_name = res + '_' + str(uuid.uuid4()) + '.jpg'
image.save(os.path.join(options.output_dir, res, sample_name))
else:
raise
# Use slashes regardless of os
file_name = '{}/{}'.format(res, sample_name)
return {
'filename':
file_name,
'title':
display_name,
'textStyle':
'font-family:verdana,arial,calibri;font-size:80%;text-align:left;margin-top:20;margin-bottom:5'
}
def visualize_incoming_annotations(args):
print('Connecting to MegaDB to get the datasets table...')
megadb_utils = MegadbUtils()
datasets_table = megadb_utils.get_datasets_table()
print('Loading the MegaDB entries...')
with open(args.megadb_entries) as f:
sequences = json.load(f)
print(f'Total number of sequences: {len(sequences)}')
dataset_seq_images = defaultdict(dict)
for seq in sequences:
dataset_seq_images[seq['dataset']][seq['seq_id']] = seq['images']
print('Loading incoming annotation entries...')
incoming = IndexedJsonDb(args.incoming_annotation)
print(
f'Number of images in this annotation file: {len(incoming.image_id_to_image)}'
)
if args.num_to_visualize != -1 and args.num_to_visualize <= len(
incoming.image_id_to_image):
incoming_id_to_anno = sample(
list(incoming.image_id_to_annotations.items()),
args.num_to_visualize)
else:
incoming_id_to_anno = incoming.image_id_to_annotations.items()
# The file_name field in the incoming json looks like alka_squirrels.seq2020_05_07_25C.frame119221.jpg
# we need to use the dataset, sequence and frame info to find the actual path in blob storage
# using the sequences
images_html = []
for image_id, annotations in tqdm(incoming_id_to_anno):
if args.trim_to_images_bboxes_labeled and annotations[0][
'category_id'] == 5:
# category_id 5 is No Object Visible
continue
anno_file_name = incoming.image_id_to_image[image_id]['file_name']
parts = anno_file_name.split('.')
dataset_name = parts[0]
seq_id = parts[1].split('seq')[1]
frame_num = int(parts[2].split('frame')[1])
im_rel_path = get_image_rel_path(dataset_seq_images, dataset_name,
seq_id, frame_num)
if im_rel_path is None:
print(f'Not found in megadb entries: dataset {dataset_name},'
f' seq_id {seq_id}, frame_num {frame_num}')
continue
im_full_path = megadb_utils.get_full_path(datasets_table, dataset_name,
im_rel_path)
# download the image
container_client = megadb_utils.get_storage_client(
datasets_table, dataset_name)
downloader = container_client.download_blob(im_full_path)
image_file = io.BytesIO()
blob_props = downloader.download_to_stream(image_file)
image = vis_utils.open_image(image_file)
boxes = [anno['bbox'] for anno in annotations]
classes = [anno['category_id'] for anno in annotations]
vis_utils.render_iMerit_boxes(boxes,
classes,
image,
label_map=incoming.cat_id_to_name)
file_name = '{}_gtbbox.jpg'.format(
os.path.splitext(anno_file_name)[0].replace('/', '~'))
image = vis_utils.resize_image(image, args.output_image_width)
image.save(os.path.join(args.output_dir, 'rendered_images', file_name))
images_html.append({
'filename':
'{}/{}'.format('rendered_images', file_name),
'title':
'{}, number of boxes: {}'.format(
anno_file_name, len([b for b in boxes if len(b) > 0])),
'textStyle':
'font-family:verdana,arial,calibri;font-size:80%;text-align:left;margin-top:20;margin-bottom:5'
})
# Write to HTML
images_html = sorted(images_html, key=lambda x: x['filename'])
write_html_image_list(filename=os.path.join(args.output_dir, 'index.html'),
images=images_html,
options={
'headerHtml':
'<h1>Sample annotations from {}</h1>'.format(
args.incoming_annotation)
})
print('Visualized {} images.'.format(len(images_html)))
