output_json = input_json
image_filenames = crablip.get_person_face_image_filenames_from_json(input_json)
if image_filenames:
with file(outfn, "w") as outfile:
cnn_classify.run(
image_filenames,
args.model_path,
args.model_snapshot,
model_deploy=args.model_deploy,
labels_name=args.model_labels,
mean_pixel_name=args.mean_pixel_name,
gray_range=args.gray_range,
channel_swap=args.channel_swap,
batch_size=args.batch_size,
gpu_id=args.gpu_id,
verbose=args.verbose,
json=args.json,
outfile=outfile,
)
with file(outfn, "r") as fp:
gender_classification = json.load(fp)
output_json = crablip.generate_output_json_face(
input_json, gender_classification, probability_threshold, classifier_key, tag_name_translation
)
json.dump(output_json, args.workflow_out, indent=4)
outfn = "/tmp/age_classification.json"
input_json = json.load(args.json_input_file)
output_json = input_json
image_filenames = crablip.get_person_face_image_filenames_from_json(input_json)
if image_filenames:
with file(outfn, "w") as outfile:
cnn_classify.run(image_filenames, args.model_path,
args.model_snapshot,
model_deploy=args.model_deploy,
labels_name=args.model_labels,
mean_pixel_name=args.mean_pixel_name,
gray_range=args.gray_range,
channel_swap=args.channel_swap,
batch_size=args.batch_size,
gpu_id=args.gpu_id, verbose=args.verbose,
json=args.json, outfile=outfile)
with file(outfn, "r") as fp:
classification = json.load(fp)
output_json = crablip.generate_output_json_face(input_json,
classification,
probability_threshold,
classifier_key)
json.dump(output_json, args.workflow_out, indent=4)
