def test_check_blob_exists(self):
print('PUBLIC_BLOB_URI')
self.assertTrue(check_blob_exists(PUBLIC_BLOB_URI))
print('PUBLIC_CONTAINER_URI + PUBLIC_BLOB_NAME')
self.assertTrue(
check_blob_exists(PUBLIC_CONTAINER_URI,
blob_name=PUBLIC_BLOB_NAME))
print('PUBLIC_CONTAINER_URI')
with self.assertRaises(IndexError):
check_blob_exists(PUBLIC_CONTAINER_URI)
print('PUBLIC_INVALID_BLOB_URI')
self.assertFalse(check_blob_exists(PUBLIC_INVALID_BLOB_URI))
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 check_local_then_azure(local_path: str, blob_url: str) -> bool:
return (os.path.exists(local_path)
or sas_blob_utils.check_blob_exists(blob_url))
def check_image_condition(img_path: str,
truncated_images_lock: threading.Lock,
account: Optional[str] = None,
container: Optional[str] = None,
sas_token: Optional[str] = None,
datasets_table: Optional[Mapping[str, Any]] = None
) -> Tuple[str, str]:
"""
Args:
img_path: str, either <blob_name> if datasets_table is None, or
<dataset>/<blob_name> if datasets_table is given
account: str, name of Azure Blob Storage account
container: str, name of Azure Blob Storage container
sas_token: str, optional SAS token (without leading '?') if the
container is not publicly accessible
datasets_table: dict, maps dataset name to dict of information
Returns: (img_file, status) tuple, where status is one of
'nonexistant': blob does not exist in the container
'non_image': img_file does not have valid file extension
'good': image exists and is able to be opened without setting
ImageFile.LOAD_TRUNCATED_IMAGES=True
'truncated': image exists but can only be opened by setting
ImageFile.LOAD_TRUNCATED_IMAGES=True
'bad': image exists, but cannot be opened even when setting
ImageFile.LOAD_TRUNCATED_IMAGES=True
"""
if (account is None) or (container is None) or (datasets_table is not None):
assert account is None
assert container is None
assert sas_token is None
assert datasets_table is not None
dataset, img_file = img_path.split('/', maxsplit=1)
account = datasets_table[dataset]['storage_account']
container = datasets_table[dataset]['container']
sas_token = datasets_table[dataset]['container_sas_key']
if sas_token[0] == '?': # strip leading '?' from SAS token
sas_token = sas_token[1:]
else:
img_file = img_path
if not path_utils.is_image_file(img_file):
return img_file, 'non_image'
blob_url = sas_blob_utils.build_azure_storage_uri(
account=account, container=container, sas_token=sas_token,
blob=img_file)
blob_exists = sas_blob_utils.check_blob_exists(blob_url)
if not blob_exists:
return img_file, 'nonexistant'
stream, _ = sas_blob_utils.download_blob_to_stream(blob_url)
stream.seek(0)
try:
with truncated_images_lock:
ImageFile.LOAD_TRUNCATED_IMAGES = False
with Image.open(stream) as img:
img.load()
return img_file, 'good'
except OSError as e: # PIL.UnidentifiedImageError is a subclass of OSError
try:
stream.seek(0)
with truncated_images_lock:
ImageFile.LOAD_TRUNCATED_IMAGES = True
with Image.open(stream) as img:
img.load()
return img_file, 'truncated'
except Exception as e: # pylint: disable=broad-except
exception_type = type(e).__name__
tqdm.write(f'Unable to load {img_file}. {exception_type}: {e}.')
return img_file, 'bad'
def request_detections():
"""
Checks that the input parameters to this endpoint are valid, starts a thread
to launch the batch processing job, and return the job_id/request_id to the user.
"""
if not request.is_json:
msg = 'Body needs to have a JSON mimetype (e.g., application/json).'
return make_error(415, msg)
try:
post_body = request.get_json()
except Exception as e:
return make_error(415,
f'Error occurred reading POST request body: {e}.')
app.logger.info(f'server, request_detections, post_body: {post_body}')
# required params
caller_id = post_body.get('caller', None)
if caller_id is None or caller_id not in app_config.get_allowlist():
msg = ('Parameter caller is not supplied or is not on our allowlist. '
'Please email [email protected] to request access.')
return make_error(401, msg)
use_url = post_body.get('use_url', False)
if use_url and isinstance(
use_url,
str): # in case it is included but is intended to be False
if use_url.lower() in ['false', 'f', 'no', 'n']:
use_url = False
input_container_sas = post_body.get('input_container_sas', None)
if not input_container_sas and not use_url:
msg = ('input_container_sas with read and list access is a required '
'field when not using image URLs.')
return make_error(400, msg)
if input_container_sas is not None:
if not sas_blob_utils.is_container_uri(input_container_sas):
return make_error(
400, 'input_container_sas provided is not for a container.')
result = check_data_container_sas(input_container_sas)
if result is not None:
return make_error(result[0], result[1])
images_requested_json_sas = post_body.get('images_requested_json_sas',
None)
if images_requested_json_sas is not None:
try:
exists = sas_blob_utils.check_blob_exists(
images_requested_json_sas)
except Exception as e:
return make_error(400, 'images_requested_json_sas is not valid.')
if not exists:
return make_error(
400,
'images_requested_json_sas points to a non-existent file.')
# if use_url, then images_requested_json_sas is required
if use_url:
if images_requested_json_sas is None:
msg = 'images_requested_json_sas is required since use_url is true.'
return make_error(400, msg)
# optional params
# check model_version is among the available model versions
model_version = post_body.get('model_version', '')
if model_version != '':
model_version = str(model_version) # in case user used an int
if model_version not in api_config.MD_VERSIONS_TO_REL_PATH: # TODO use AppConfig to store model version info
return make_error(
400, f'model_version {model_version} is not supported.')
# check request_name has only allowed characters
request_name = post_body.get('request_name', '')
if request_name != '':
if len(request_name) > 92:
return make_error(400,
'request_name is longer than 92 characters.')
allowed = set(string.ascii_letters + string.digits + '_' + '-')
if not set(request_name) <= allowed:
msg = ('request_name contains invalid characters (only letters, '
'digits, - and _ are allowed).')
return make_error(400, msg)
# optional params for telemetry collection - logged to status table for now as part of call_params
country = post_body.get('country', None)
organization_name = post_body.get('organization_name', None)
# All API instances / node pools share a quota on total number of active Jobs;
# we cannot accept new Job submissions if we are at the quota
try:
num_active_jobs = batch_job_manager.get_num_active_jobs()
if num_active_jobs >= api_config.MAX_BATCH_ACCOUNT_ACTIVE_JOBS:
return make_error(503,
f'Too many active jobs, please try again later')
except Exception as e:
return make_error(500, f'Error checking number of active jobs: {e}')
try:
job_id = uuid.uuid4().hex
job_status_table.create_job_status(
job_id=job_id,
status=get_job_status('created',
'Request received. Listing images next...'),
call_params=post_body)
except Exception as e:
return make_error(500, f'Error creating a job status entry: {e}')
try:
thread = threading.Thread(target=create_batch_job,
name=f'job_{job_id}',
kwargs={
'job_id': job_id,
'body': post_body
})
thread.start()
except Exception as e:
return make_error(
500,
f'Error creating or starting the batch processing thread: {e}')
return {'request_id': job_id}