def custom_check_existing_pipeline():
for url in IMAGE_URLS:
outpath = keep_filename_save_png_in_tempdir(url)
delete_file_if_exists(outpath)
yield Pipeline(
load_func=load_image_from_url,
write_func=write_image,
)
for url in IMAGE_URLS:
outpath = keep_filename_save_png_in_tempdir(url)
delete_file_if_exists(outpath)
def trim_resize_pipeline():
for url in IMAGE_URLS:
outpath = keep_filename_save_png_in_tempdir(url)
delete_file_if_exists(outpath)
trim_bottom_100 = lambda image: image[:-100, :] # noqa: 29
resize_224 = partial(resize, shape=IMAGE_RESIZE_SHAPE)
trim_resize_pipeline = DownloadImagePipeline()
trim_resize_pipeline.ops = [trim_bottom_100, resize_224]
yield trim_resize_pipeline
for url in IMAGE_URLS:
outpath = keep_filename_save_png_in_tempdir(url)
delete_file_if_exists(outpath)
def record_mean_brightness_pipeline():
for url in IMAGE_URLS:
outpath = keep_filename_save_png_in_tempdir(url)
delete_file_if_exists(outpath)
record_mean_brightness_pipeline = CustomReportingPipeline(
load_func=load_image_from_url,
ops=record_mean_brightness,
write_func=write_image,
)
yield record_mean_brightness_pipeline
for url in IMAGE_URLS:
outpath = keep_filename_save_png_in_tempdir(url)
delete_file_if_exists(outpath)
def test_trim_resize_pipeline_str_paths(trim_resize_pipeline):
inpaths = [str(path) for path in IMAGE_URLS]
trim_resize_pipeline(inpaths=inpaths,
path_func=keep_filename_save_png_in_tempdir,
n_jobs=6)
for path in inpaths:
outpath = keep_filename_save_png_in_tempdir(path)
image = plt.imread(str(outpath))
assert image.shape[:2] == IMAGE_RESIZE_SHAPE
def test_duplicate_outpath_pipeline():
inpaths = [SAMPLE_DATA_DIR / 'blue.png'] * 1_000
outpath = keep_filename_save_png_in_tempdir(inpaths[0])
delete_file_if_exists(outpath)
pipeline = Pipeline(load_func=load_image_from_disk, write_func=write_image)
pipeline(inpaths=inpaths,
path_func=keep_filename_save_png_in_tempdir,
n_jobs=100)
delete_file_if_exists(outpath)
assert pipeline.run_report_.loc[:, "error"].isna().all()
