def main():
parser = argparse.ArgumentParser()
parser.add_argument(
'--tfrecords_filename',
type=str,
default='/cosmiq/simrdwn/tmp/val_detections_ssd.tfrecord',
help="tfrecords file")
parser.add_argument('--outdir',
type=str,
default='/cosmiq/simrdwn/tmp/images_ssd',
help="Output file location")
parser.add_argument(
'--pbtxt_filename',
type=str,
default='/cosmiq/simrdwn/data/class_labels_airplane_boat_car.pbtxt',
help="Class dictionary")
parser.add_argument('--tf_type',
type=str,
default='test',
help="weather the tfrecord is for test or train")
parser.add_argument('--slice_val_images',
type=int,
default=0,
help="Switch for if validaion images are sliced")
parser.add_argument('--verbose',
type=int,
default=0,
help="Print a lot o stuff?")
#### Plotting settings
parser.add_argument(
'--plot_thresh',
type=float,
default=0.33,
help="Threshold for plotting boxes, set < 0 to skip plotting")
parser.add_argument(
'--nms_overlap_thresh',
type=float,
default=0.5,
help="IOU threshold for non-max-suppresion, skip if < 0")
parser.add_argument('--make_box_labels',
type=int,
default=1,
help="If 1, make print label above each box")
parser.add_argument('--scale_alpha',
type=int,
default=1,
help="If 1, scale box opacity with confidence")
parser.add_argument('--plot_line_thickness',
type=int,
default=1,
help="If 1, scale box opacity with confidence")
args = parser.parse_args()
print("args:", args)
t0 = time.time()
if not os.path.exists(args.outdir):
os.mkdir(args.outdir)
# make label_map_dic (key=int, value=str), and reverse
label_map_dict = preprocess_tfrecords.load_pbtxt(args.pbtxt_filename,
verbose=False)
#label_map_dict_rev = {v: k for k,v in label_map_dict.iteritems()}
# convert tfrecord to dataframe
df_init0 = tf_to_df(tfrecords_filename=args.tfrecords_filename,
label_map_dict=label_map_dict,
tf_type=args.tf_type)
#df_init = tf_to_df(tfrecords_filename=args.tfrecords_filename,
# outdir=args.outdir, plot_thresh=args.plot_thresh,
# label_map_dict=label_map_dict,
# show_labels = bool(args.make_box_labels),
# alpha_scaling = bool(args.scale_alpha),
# plot_line_thickness=args.plot_line_thickness)
t1 = time.time()
print("Time to run tf_to_df():", t1 - t0, "seconds")
print("df_init.columns:", df_init0.columns)
# filter out low confidence detections
df_init = df_init0.copy()[df_init0['Prob'] >= args.plot_thresh]
# augment dataframe columns
df = post_process.augment_df(df_init,
valid_testims_dir_tot='',
slice_sizes=[0],
valid_slice_sep='__',
edge_buffer_valid=0,
max_edge_aspect_ratio=4,
valid_box_rescale_frac=1.0,
rotate_boxes=False,
verbose=bool(args.verbose))
print("len df:", len(df))
print("df.columns:", df_init.columns)
print("df.iloc[0[:", df.iloc[0])
outfile_df = os.path.join(args.outdir, '00_dataframe.csv')
df.to_csv(outfile_df)
# plot
if args.plot_thresh > 0:
post_process.refine_and_plot_df(
df,
label_map_dict=label_map_dict,
outdir=args.outdir,
#slice_sizes=[0],
sliced=bool(args.slice_val_images),
plot_thresh=args.plot_thresh,
nms_overlap_thresh=args.nms_overlap_thresh,
show_labels=args.make_box_labels,
alpha_scaling=args.scale_alpha,
plot_line_thickness=args.plot_line_thickness,
verbose=bool(args.verbose))
print("Plots output to:", args.outdir)
print("Time to get and plot records:", time.time() - t0, "seconds")
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--outdir',
type=str,
default='/cosmiq/simrdwn/tmp/images_ssd',
help="Output file location")
parser.add_argument(
'--pbtxt_filename',
type=str,
default='/cosmiq/simrdwn/data/class_labels_airplane_boat_car.pbtxt',
help="Class dictionary")
parser.add_argument('--df_csv', type=str, default='', help="dataframe csv")
parser.add_argument('--df_csv_out',
type=str,
default='',
help="output dataframe csv")
parser.add_argument('--verbose',
type=int,
default=0,
help="Print a lot o stuff?")
#### Plotting settings
parser.add_argument('--slice_val_images',
type=int,
default=0,
help="Switch for if validaion images are sliced")
parser.add_argument(
'--plot_thresh',
type=float,
default=0.33,
help="Threshold for plotting boxes, set < 0 to skip plotting")
parser.add_argument(
'--nms_overlap_thresh',
type=float,
default=0.5,
help="IOU threshold for non-max-suppresion, skip if < 0")
parser.add_argument('--make_box_labels',
type=int,
default=1,
help="If 1, make print label above each box")
parser.add_argument('--scale_alpha',
type=int,
default=1,
help="If 1, scale box opacity with confidence")
parser.add_argument('--plot_line_thickness',
type=int,
default=1,
help="If 1, scale box opacity with confidence")
args = parser.parse_args()
print("args:", args)
t0 = time.time()
header = [
'Loc_Tmp', u'Prob', u'Xmin', u'Ymin', u'Xmax', u'Ymax', u'Category'
]
# make label_map_dic (key=int, value=str), and reverse
label_map_dict = preprocess_tfrecords.load_pbtxt(args.pbtxt_filename,
verbose=False)
#label_map_dict_rev = {v: k for k,v in label_map_dict.iteritems()}
if not os.path.exists(args.outdir):
os.mkdir(args.outdir)
# read dataframe
df_init = pd.read_csv(args.df_csv, names=header)
# tf_infer_cmd outputs integer categories, update to strings
df_init['Category'] = [
label_map_dict[ktmp] for ktmp in df_init['Category'].values
]
# augment dataframe columns
df = post_process.augment_df(df_init,
valid_testims_dir_tot='',
slice_sizes=[0],
valid_slice_sep='__',
edge_buffer_valid=0,
max_edge_aspect_ratio=4,
valid_box_rescale_frac=1.0,
rotate_boxes=False,
verbose=bool(args.verbose))
print("df.columns:", df_init.columns)
print("df.iloc[0[:", df.iloc[0])
outfile_df = args.df_csv_out
#outfile_df = args.df_csv.split('.')[0] + '_aug.csv'
#outfile_df = os.path.join(args.outdir, '00_dataframe.csv')
df.to_csv(outfile_df)
# plot
if args.plot_thresh > 0:
post_process.refine_and_plot_df(
df,
label_map_dict=label_map_dict,
outdir=args.outdir,
#slice_sizes=[0],
sliced=bool(args.slice_val_images),
plot_thresh=args.plot_thresh,
nms_overlap_thresh=args.nms_overlap_thresh,
show_labels=args.make_box_labels,
alpha_scaling=args.scale_alpha,
plot_line_thickness=args.plot_line_thickness,
verbose=bool(args.verbose))
print("Plots output to:", args.outdir)
print("Time to get and plot records:", time.time() - t0, "seconds")