def test_build_blob_uri(self):
generated = build_blob_uri(container_uri=PUBLIC_CONTAINER_URI,
blob_name=PUBLIC_BLOB_NAME)
self.assertEqual(generated, PUBLIC_BLOB_URI)
generated = build_blob_uri(container_uri=PUBLIC_CONTAINER_URI_SAS,
blob_name=PUBLIC_BLOB_NAME)
self.assertEqual(generated, PUBLIC_BLOB_URI_SAS)
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