def run_document_conversion(data, _context=None):
"""Converts document passed in to PDF and triggers PDF extraction."""
data = get_pubsub_data(data)
doc_id = data["doc_id"]
slug = data["slug"]
extension = data["extension"]
logger.info("[DOCUMENT CONVERSION] doc_id %s extension %s", doc_id,
extension)
# Ensure whitelisted file extension
if extension.lower().strip() not in SUPPORTED_DOCUMENT_EXTENSIONS:
raise DocumentExtensionError()
input_file = path.original_path(doc_id, slug, extension)
# Ensure non-PDF document size is within the limit
if storage.size(input_file) > DOCUMENT_SIZE_LIMIT:
# If not, remove the PDF
storage.delete(path.path(doc_id))
raise DocumentSizeError()
# Run conversion
convert(input_file, doc_id, slug)
# Delete the original file
storage.delete(input_file)
# Trigger PDF processing (output file should be expected doc path)
publisher.publish(PDF_PROCESS_TOPIC, data=encode_pubsub_data(data))
def trigger_processing():
"""Triggers PDF processing via pubsub."""
publisher.publish(
PDF_PROCESS_TOPIC,
encode_pubsub_data({
"doc_id": ID,
"slug": SLUG,
"access": Access.private
}),
)
def process_doc(request, _context=None):
"""Central command to run processing on a doc"""
data = get_http_data(request)
doc_id = data["doc_id"]
job_type = data["method"]
extension = data.get("extension", "pdf").lower()
# Initialize the processing environment
utils.initialize(REDIS, doc_id)
# Launch PDF processing via pubsub
if job_type == "process_pdf":
if extension == "pdf":
publisher.publish(PDF_PROCESS_TOPIC, data=encode_pubsub_data(data))
else:
# Non-PDF files require conversion first
publisher.publish(DOCUMENT_CONVERT_TOPIC,
data=encode_pubsub_data(data))
elif job_type == "redact_doc":
publisher.publish(REDACT_TOPIC, data=encode_pubsub_data(data))
elif job_type == "modify_doc":
publisher.publish(MODIFY_TOPIC, data=encode_pubsub_data(data))
elif job_type == "cancel_doc_processing":
utils.clean_up(REDIS, doc_id)
else:
logger.error("Invalid doc processing type: %s",
job_type,
exc_info=sys.exc_info())
return "Error"
return encode_response("Ok")
def trigger_redacting(page_numbers):
"""Triggers redaction processing via pubsub."""
publisher.publish(
REDACT_TOPIC,
encode_pubsub_data({
"doc_id":
ID,
"slug":
SLUG,
"access":
Access.private,
"redactions": [{
"page_number": page_number
} for page_number in page_numbers],
}),
)
def flush(queue):
if not queue:
return
# Trigger text position extraction pipeline
publisher.publish(
TEXT_POSITION_EXTRACT_TOPIC,
encode_pubsub_data(
{
"paths_and_numbers": queue,
"doc_id": doc_id,
"slug": slug,
"access": access,
"ocr_code": ocr_code,
"partial": partial,
"in_memory": True,
}
),
)
queue.clear()
def run_tesseract(data, _context=None):
"""Runs OCR on the images passed in, storing the extracted text.
"""
# pylint: disable=too-many-locals, too-many-statements
overall_start = time.time()
data = get_pubsub_data(data)
doc_id = data["doc_id"]
slug = data["slug"]
access = data.get("access", access_choices.PRIVATE)
ocr_code = data.get("ocr_code", "eng")
paths_and_numbers = data["paths_and_numbers"]
partial = data["partial"] # Whether it is a partial update (e.g. redaction) or not
force_ocr = data["force_ocr"]
if force_ocr:
ocr_version = f"{OCR_VERSION}_force"
else:
ocr_version = OCR_VERSION
logger.info(
"[RUN TESSERACT] doc_id %s ocr_code %s ocr_version %s page_numbers %s",
doc_id,
ocr_code,
ocr_version,
",".join([str(number[0]) for number in paths_and_numbers]),
)
result = {}
if PROFILE_CPU:
# Perform speed thresholding to prevent running OCR on a slow CPU
speed = profile_cpu(CPU_DIFFICULTY)
if speed > SPEED_THRESHOLD:
# Resubmit to queue
publisher.publish(
OCR_TOPIC,
data=encode_pubsub_data(
{
"paths_and_numbers": paths_and_numbers,
"doc_id": doc_id,
"slug": slug,
"access": access,
"ocr_code": ocr_code,
"partial": partial,
"force_ocr": force_ocr,
}
),
)
logging.warning("Too slow (speed: %f)", speed)
return "Too slow, retrying"
result["speed"] = speed
# Keep track of how long OCR takes (useful for profiling)
elapsed_times = []
if not paths_and_numbers:
logging.warning("No paths/numbers")
return "Ok"
# Queue up text position extraction tasks
queue = []
def flush(queue):
if not queue:
return
# Trigger text position extraction pipeline
publisher.publish(
TEXT_POSITION_EXTRACT_TOPIC,
encode_pubsub_data(
{
"paths_and_numbers": queue,
"doc_id": doc_id,
"slug": slug,
"access": access,
"ocr_code": ocr_code,
"partial": partial,
"in_memory": True,
}
),
)
queue.clear()
def check_and_flush(queue):
if len(queue) >= TEXT_POSITION_BATCH:
flush(queue)
# Loop through all paths and numbers
for page_number, image_path in paths_and_numbers:
ocrd = utils.page_ocrd(REDIS, doc_id, page_number)
logger.info(
"[RUN TESSERACT] doc_id %s page_number %s ocrd %s",
doc_id,
page_number,
ocrd,
)
text_path = path.page_text_path(doc_id, slug, page_number)
# Benchmark OCR speed
start_time = time.time()
logger.info(
"[RUN TESSERACT] doc_id %s page %s start_time %s",
doc_id,
page_number,
start_time,
)
text, pdf_contents = ocr_page(doc_id, image_path, text_path, access, ocr_code)
elapsed_time = time.time() - start_time
elapsed_times.append(elapsed_time)
logger.info(
"[RUN TESSERACT] doc_id %s page %s elapsed_time %s",
doc_id,
page_number,
elapsed_time,
)
# Write the output text and pdf to Redis
utils.write_page_text(REDIS, doc_id, page_number, text, ocr_version, ocr_code)
utils.write_page_text_pdf(REDIS, doc_id, page_number, pdf_contents)
# Decrement the texts remaining
utils.register_page_ocrd(REDIS, doc_id, page_number)
# Queue text position extraction tasks
queue.append(page_number)
check_and_flush(queue)
# Flush the remaining queue
flush(queue)
result["doc_id"] = doc_id
result["elapsed"] = elapsed_times
result["status"] = "Ok"
result["overall_elapsed"] = time.time() - overall_start
if PROFILE_CPU:
result["speed_after"] = profile_cpu()
return json.dumps(result)
def sidekick(request, _context=None):
"""Kick off sidekick processing lambda"""
data = get_http_data(request)
publisher.publish(SIDEKICK_PREPROCESS_TOPIC, data=encode_pubsub_data(data))
return encode_response("Ok")
def import_documents(request, _context=None):
"""Command to start the import process on an organization"""
data = get_http_data(request)
publisher.publish(START_IMPORT_TOPIC, data=encode_pubsub_data(data))