Marmelator/image-classification-service
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feat: parameterize image stiching tolerance

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Also sets image stitching tolerance default to one (pixel) and adds
informative log of which settings are loaded when initializing the
image classification pipeline.
This commit is contained in:
Julius Unverfehrt 2024-09-06 15:51:14 +02:00
parent d23034e38a
commit a192e05be2
4 changed files with 5 additions and 2 deletions
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1
config/settings.toml
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@ -5,6 +5,7 @@ level = "INFO"
# Print document processing progress to stdout # Print document processing progress to stdout
verbose = false verbose = false
batch_size = 16 batch_size = 16
image_stiching_tolerance = 1 # in pixels
mlflow_run_id = "fabfb1f192c745369b88cab34471aba7" mlflow_run_id = "fabfb1f192c745369b88cab34471aba7"
# These variables control filters that are applied to either images, image metadata or service_estimator predictions. # These variables control filters that are applied to either images, image metadata or service_estimator predictions.

2
scripts/run_pipeline.py
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@ -36,7 +36,7 @@ def process_pdf(pipeline, pdf_path, page_range=None):
def main(args): def main(args):
pipeline = load_pipeline(verbose=True, batch_size=CONFIG.service.batch_size) pipeline = load_pipeline(verbose=CONFIG.service.verbose, batch_size=CONFIG.service.batch_size, tolerance=CONFIG.service.image_stiching_tolerance)
if os.path.isfile(args.input): if os.path.isfile(args.input):
pdf_paths = [args.input] pdf_paths = [args.input]

2
src/image_prediction/pipeline.py
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@ -3,6 +3,7 @@ from functools import lru_cache, partial
from itertools import chain, tee from itertools import chain, tee
from funcy import rcompose, first, compose, second, chunks, identity, rpartial from funcy import rcompose, first, compose, second, chunks, identity, rpartial
from kn_utils.logging import logger
from tqdm import tqdm from tqdm import tqdm
from image_prediction.config import CONFIG from image_prediction.config import CONFIG
@ -21,6 +22,7 @@ os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
@lru_cache(maxsize=None) @lru_cache(maxsize=None)
def load_pipeline(**kwargs): def load_pipeline(**kwargs):
logger.info(f"Loading pipeline with kwargs: {kwargs}")
model_loader = get_mlflow_model_loader(MLRUNS_DIR) model_loader = get_mlflow_model_loader(MLRUNS_DIR)
model_identifier = CONFIG.service.mlflow_run_id model_identifier = CONFIG.service.mlflow_run_id

2
src/serve.py
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@ -18,7 +18,7 @@ logger.reconfigure(sink=stdout, level=CONFIG.logging.level)
# FIXME: Find more fine-grained solution or if the problem occurs persistently for python services, # FIXME: Find more fine-grained solution or if the problem occurs persistently for python services,
@wrap_in_process @wrap_in_process
def process_data(data: bytes, _message: dict) -> list: def process_data(data: bytes, _message: dict) -> list:
pipeline = load_pipeline(verbose=CONFIG.service.verbose, batch_size=CONFIG.service.batch_size) pipeline = load_pipeline(verbose=CONFIG.service.verbose, batch_size=CONFIG.service.batch_size, tolerance=CONFIG.service.image_stiching_tolerance)
return list(pipeline(data)) return list(pipeline(data))