def main(args=None):
"""
The main method for parsing command-line arguments and labeling.
:param args: the commandline arguments, uses sys.argv if not supplied
:type args: list
"""
parser = argparse.ArgumentParser()
parser.add_argument("--image", help="image to be processed", required=True)
parser.add_argument("--graph",
help="graph/model to be executed",
required=True)
parser.add_argument("--labels",
help="name of file containing labels",
required=True)
parser.add_argument("--input_height",
type=int,
help="input height",
default=299)
parser.add_argument("--input_width",
type=int,
help="input width",
default=299)
parser.add_argument("--input_mean", type=int, help="input mean", default=0)
parser.add_argument("--input_std", type=int, help="input std", default=255)
parser.add_argument("--input_layer",
help="name of input layer",
default="Placeholder")
parser.add_argument("--output_layer",
help="name of output layer",
default="final_result")
parser.add_argument("--top_x",
type=int,
help="output only the top K labels; use <1 for all",
default=5)
args = parser.parse_args(args=args)
graph = load_graph(args.graph)
labels = load_labels(args.labels)
with tf.compat.v1.Session(graph=graph) as sess:
tensor = read_tensor_from_image_file(args.image,
input_height=args.input_height,
input_width=args.input_width,
input_mean=args.input_mean,
input_std=args.input_std,
sess=sess)
results = tensor_to_probs(graph, args.input_layer, args.output_layer,
tensor, sess)
top_x = top_k_probs(results, args.top_x)
if args.top_x > 0:
print("Top " + str(args.top_x) + " labels")
else:
print("All labels")
for i in top_x:
print("- " + labels[i] + ":", results[i])
def main(args=None):
"""
The main method for parsing command-line arguments and labeling.
:param args: the commandline arguments, uses sys.argv if not supplied
:type args: list
"""
parser = argparse.ArgumentParser()
parser.add_argument("--in_dir",
help="the input directory to poll for images",
required=True)
parser.add_argument(
"--out_dir",
help="the output directory for processed images and predictions",
required=True)
parser.add_argument(
'--delete',
default=False,
help=
"Whether to delete images rather than move them to the output directory.",
action='store_true')
parser.add_argument("--graph",
help="graph/model to be executed",
required=True)
parser.add_argument("--labels",
help="name of file containing labels",
required=True)
parser.add_argument("--input_height",
type=int,
help="input height",
default=299)
parser.add_argument("--input_width",
type=int,
help="input width",
default=299)
parser.add_argument("--input_mean", type=int, help="input mean", default=0)
parser.add_argument("--input_std", type=int, help="input std", default=255)
parser.add_argument("--input_layer",
help="name of input layer",
default="Placeholder")
parser.add_argument("--output_layer",
help="name of output layer",
default="final_result")
parser.add_argument("--top_x",
type=int,
help="output only the top K labels; use <1 for all",
default=5)
args = parser.parse_args(args=args)
graph = load_graph(args.graph)
labels = load_labels(args.labels)
with tf.compat.v1.Session(graph=graph) as sess:
poll(sess, graph, args.input_layer, args.output_layer, labels,
args.in_dir, args.out_dir, args.input_height, args.input_width,
args.input_mean, args.input_std, args.top_x, args.delete)
def main(args=None):
"""
The main method for parsing command-line arguments and labeling.
:param args: the commandline arguments, uses sys.argv if not supplied
:type args: list
"""
parser = argparse.ArgumentParser(
description="For bulk or continuous prediction output using a trained model.",
prog="tfic-poll",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("--in_dir", metavar="DIR", help="the input directory to poll for images", required=True)
parser.add_argument("--out_dir", metavar="DIR", help="the output directory for processed images and predictions", required=True)
parser.add_argument('--continuous', action='store_true', help='Whether to continuously load test images and perform prediction', required=False, default=False)
parser.add_argument('--delete', default=False, help="Whether to delete images rather than move them to the output directory.", action='store_true')
parser.add_argument("--graph", metavar="FILE", help="graph/model to be executed", required=True)
parser.add_argument("--info", help="name of json file with model info (dimensions, layers); overrides input_height/input_width/labels/input_layer/output_layer options", default=None)
parser.add_argument("--labels", metavar="FILE", help="name of file containing labels", required=False)
parser.add_argument("--input_height", metavar="INT", type=int, help="input height", default=299)
parser.add_argument("--input_width", metavar="INT", type=int, help="input width", default=299)
parser.add_argument("--input_layer", metavar="NAME", help="name of input layer", default="Placeholder")
parser.add_argument("--output_layer", metavar="NAME", help="name of output layer", default="final_result")
parser.add_argument("--input_mean", metavar="INT", type=int, help="input mean", default=0)
parser.add_argument("--input_std", metavar="INT", type=int, help="input std", default=255)
parser.add_argument("--top_x", metavar="INT", type=int, help="output only the top K labels; use <1 for all", default=5)
parser.add_argument("--grid_size", metavar="INT", type=int, help="the number of columns and rows to divide the image in, passing each sub-image through the model to obtain predictions", default=None)
parser.add_argument("--grid_threshold", metavar="0-1", type=float, help="the minimum probability threshold for predictions in the grid to show up in the output", default=0.9)
parser.add_argument("--grid_ignored", metavar="label1,label2,...", help="the labels to ignore when in grid prediction mode (comma-separated list)", default=None)
parser.add_argument("--reset_session", metavar="INT", type=int, help="The number of processed images after which to reinitialize the Tensorflow session to reduce memory leaks.", default=50)
args = parser.parse_args(args=args)
# values from options
labels = None
input_height = args.input_height
input_width = args.input_width
input_layer = args.input_layer
output_layer = args.output_layer
# override from info file?
if args.info is not None:
input_height, input_width, input_layer, output_layer, labels = load_info_file(args.info)
if (labels is None) and (args.labels is not None):
labels = load_labels(args.labels)
if labels is None:
raise Exception("No labels determined, either supply --info or --labels!")
graph = load_graph(args.graph)
poll(graph, input_layer, output_layer, labels, args.in_dir, args.out_dir, args.continuous,
input_height, input_width, args.input_mean, args.input_std, args.top_x, args.delete,
grid_size=args.grid_size, grid_threshold=args.grid_threshold, grid_ignored=args.grid_ignored,
reset_session=args.reset_session)
def main(args=None):
"""
The main method for parsing command-line arguments and labeling.
:param args: the commandline arguments, uses sys.argv if not supplied
:type args: list
"""
parser = argparse.ArgumentParser(
description=
"Generates statistics in CSV format by recording predictions on images list files.",
prog="tfic-stats",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--image_dir',
type=str,
default='',
help='Path to folders of labeled images.')
parser.add_argument('--image_list',
type=str,
required=False,
help='The JSON file with images per sub-directory.')
parser.add_argument("--graph",
help="graph/model to be executed",
required=True)
parser.add_argument(
"--info",
help=
"name of json file with model info (dimensions, layers); overrides input_height/input_width/labels/input_layer/output_layer options",
default=None)
parser.add_argument("--labels",
help="name of file containing labels",
required=False)
parser.add_argument("--input_height",
type=int,
help="input height",
default=299)
parser.add_argument("--input_width",
type=int,
help="input width",
default=299)
parser.add_argument("--input_layer",
help="name of input layer",
default="Placeholder")
parser.add_argument("--output_layer",
help="name of output layer",
default="final_result")
parser.add_argument("--input_mean", type=int, help="input mean", default=0)
parser.add_argument("--input_std", type=int, help="input std", default=255)
parser.add_argument('--output_preds',
type=str,
required=True,
help='The CSV file to store the predictions in.')
parser.add_argument('--output_stats',
type=str,
required=True,
help='The CSV file to store the statistics in.')
parser.add_argument('--logging_verbosity',
type=str,
default='INFO',
choices=['DEBUG', 'INFO', 'WARN', 'ERROR', 'FATAL'],
help='How much logging output should be produced.')
args = parser.parse_args(args=args)
# values from options
labels = None
input_height = args.input_height
input_width = args.input_width
input_layer = args.input_layer
output_layer = args.output_layer
# override from info file?
if args.info is not None:
input_height, input_width, input_layer, output_layer, labels = load_info_file(
args.info)
if (labels is None) and (args.labels is not None):
labels = load_labels(args.labels)
if labels is None:
raise Exception(
"No labels determined, either supply --info or --labels!")
graph = load_graph(args.graph)
with tf.compat.v1.Session(graph=graph) as sess:
generate_stats(sess, graph, input_layer, output_layer, labels,
args.image_dir, args.image_list, input_height,
input_width, args.input_mean, args.input_std,
args.output_preds, args.output_stats,
args.logging_verbosity)
def main(args=None):
"""
The main method for parsing command-line arguments and labeling.
:param args: the commandline arguments, uses sys.argv if not supplied
:type args: list
"""
parser = argparse.ArgumentParser()
parser.add_argument('--image_dir',
type=str,
default='',
help='Path to folders of labeled images.')
parser.add_argument('--image_list',
type=str,
required=False,
help='The JSON file with images per .')
parser.add_argument("--graph",
help="graph/model to be executed",
required=True)
parser.add_argument("--labels",
help="name of file containing labels",
required=True)
parser.add_argument("--input_height",
type=int,
help="input height",
default=299)
parser.add_argument("--input_width",
type=int,
help="input width",
default=299)
parser.add_argument("--input_mean", type=int, help="input mean", default=0)
parser.add_argument("--input_std", type=int, help="input std", default=255)
parser.add_argument("--input_layer",
help="name of input layer",
default="Placeholder")
parser.add_argument("--output_layer",
help="name of output layer",
default="final_result")
parser.add_argument('--output_preds',
type=str,
required=True,
help='The CSV file to store the predictions in.')
parser.add_argument('--output_stats',
type=str,
required=True,
help='The CSV file to store the statistics in.')
parser.add_argument('--logging_verbosity',
type=str,
default='INFO',
choices=['DEBUG', 'INFO', 'WARN', 'ERROR', 'FATAL'],
help='How much logging output should be produced.')
args = parser.parse_args(args=args)
graph = load_graph(args.graph)
labels = load_labels(args.labels)
with tf.compat.v1.Session(graph=graph) as sess:
generate_stats(sess, graph, args.input_layer, args.output_layer,
labels, args.image_dir, args.image_list,
args.input_height, args.input_width, args.input_mean,
args.input_std, args.output_preds, args.output_stats,
args.logging_verbosity)
def main(args=None):
"""
The main method for parsing command-line arguments and labeling.
:param args: the commandline arguments, uses sys.argv if not supplied
:type args: list
"""
parser = argparse.ArgumentParser(
description=
"Outputs predictions for single image using a trained model.",
prog="tfic-labelimage",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("--image", help="image to be processed", required=True)
parser.add_argument("--graph",
help="graph/model to be executed",
required=True)
parser.add_argument(
"--info",
help=
"name of json file with model info (dimensions, layers); overrides input_height/input_width/labels/input_layer/output_layer options",
default=None)
parser.add_argument("--labels",
help="name of file containing labels",
required=False)
parser.add_argument("--input_height",
type=int,
help="input height",
default=299)
parser.add_argument("--input_width",
type=int,
help="input width",
default=299)
parser.add_argument("--input_layer",
help="name of input layer",
default="Placeholder")
parser.add_argument("--output_layer",
help="name of output layer",
default="final_result")
parser.add_argument("--input_mean", type=int, help="input mean", default=0)
parser.add_argument("--input_std", type=int, help="input std", default=255)
parser.add_argument("--top_x",
type=int,
help="output only the top K labels; use <1 for all",
default=5)
args = parser.parse_args(args=args)
# values from options
labels = None
input_height = args.input_height
input_width = args.input_width
input_layer = args.input_layer
output_layer = args.output_layer
# override from info file?
if args.info is not None:
input_height, input_width, input_layer, output_layer, labels = load_info_file(
args.info)
if (labels is None) and (args.labels is not None):
labels = load_labels(args.labels)
if labels is None:
raise Exception(
"No labels determined, either supply --info or --labels!")
graph = load_graph(args.graph)
with tf.compat.v1.Session(graph=graph) as sess:
tensor = read_tensor_from_image_file(args.image,
input_height=input_height,
input_width=input_width,
input_mean=args.input_mean,
input_std=args.input_std,
sess=sess)
results = tensor_to_probs(graph, input_layer, output_layer, tensor,
sess)
top_x = top_k_probs(results, args.top_x)
if args.top_x > 0:
print("Top " + str(args.top_x) + " labels")
else:
print("All labels")
for i in top_x:
print("- " + labels[i] + ":", results[i])