car_size = 3 # meters
GSD = 0.15 # meters
yolt_box_size = np.rint(car_size / GSD) # size in pixels
print("yolt_box_size (pixels):", yolt_box_size)
##############################
##############################
# slicing variables
slice_overlap = 0.1
zero_frac_thresh = 0.2
sliceHeight, sliceWidth = 544, 544 # for for 82m windows
##############################
##############################
# set yolt category params from pbtxt
label_map_dict = preprocess_tfrecords.load_pbtxt(label_map_path, verbose=False)
print("label_map_dict:", label_map_dict)
# get ordered keys
key_list = sorted(label_map_dict.keys())
category_num = len(key_list)
print key_list
# category list for yolt
cat_list = [label_map_dict[k] for k in key_list]
cat_list = 'car'
print("cat list:", cat_list)
yolt_cat_str = ','.join(cat_list)
print("yolt cat str:", yolt_cat_str)
# create yolt_category dictionary (should start at 0, not 1!)
yolt_cat_dict = {x: i for i, x in enumerate(cat_list)}
print("yolt_cat_dict:", yolt_cat_dict)
# conversion between yolt and pbtxt numbers (just increase number by 1)
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 gen_data(self):
sys.path.append(os.path.join(self.path_simrdwn_utils, '..', 'core'))
import preprocess_tfrecords
##############################
# set yolt category params from pbtxt
label_map_dict = preprocess_tfrecords.load_pbtxt(self.label_map_path,
verbose=False)
print("label_map_dict:", label_map_dict)
# get ordered keys
key_list = sorted(label_map_dict.keys())
category_num = len(key_list)
# category list for yolt
cat_list = [label_map_dict[k] for k in key_list]
print("cat list:", cat_list)
yolt_cat_str = ','.join(cat_list)
print("yolt cat str:", yolt_cat_str)
# create yolt_category dictionary (should start at 0, not 1!)
yolt_cat_dict = {x: i for i, x in enumerate(cat_list)}
print("yolt_cat_dict:", yolt_cat_dict)
# conversion between yolt and pbtxt numbers (just increase number by 1)
convert_dict = {x: x + 1 for x in range(100)}
print("convert_dict:", convert_dict)
##############################
##############################
# Slice large images into smaller chunks
##############################
print("self.im_list_name:", self.im_list_name)
if os.path.exists(self.im_list_name):
run_slice = False
else:
run_slice = True
for i, d in enumerate(self.train_dirs):
dtot = os.path.join(self.ground_truth_dir, d)
print("dtot:", dtot)
# get label files
files = os.listdir(dtot)
annotate_files = [
f for f in files if f.endswith(self.annotation_suffix)
]
# print ("annotate_files:", annotate_files
img_files = [
f for f in files if f.endswith('.jpg') or f.endswith('.png')
]
for imfile in img_files:
ext = imfile.split('.')[-1]
name_root = imfile.split(ext)[0]
annotate_file = name_root + self.annotation_suffix
annotate_file_tot = os.path.join(dtot, annotate_file)
imfile_tot = os.path.join(dtot, imfile)
outroot = d + '_' + imfile.split('.' + ext)[0]
print("\nName_root", name_root)
print(" annotate_file:", annotate_file)
print(" imfile:", imfile)
print(" imfile_tot:", imfile_tot)
print(" outroot:", outroot)
if run_slice:
if os.path.exists(annotate_file_tot):
parse_cowc.slice_im_cowc(
imfile_tot,
annotate_file_tot,
outroot,
self.images_dir,
self.labels_dir,
yolt_cat_dict,
cat_list[0],
self.yolt_box_size,
sliceHeight=self.sliceHeight,
sliceWidth=self.sliceWidth,
zero_frac_thresh=self.zero_frac_thresh,
overlap=self.slice_overlap,
pad=0,
verbose=self.verbose)
else:
parse_cowc.slice_im_no_mask(
imfile_tot,
outroot,
self.images_dir,
self.labels_dir,
yolt_cat_dict,
cat_list[0],
self.yolt_box_size,
sliceHeight=self.sliceHeight,
sliceWidth=self.sliceWidth,
zero_frac_thresh=self.zero_frac_thresh,
overlap=self.slice_overlap,
pad=0,
verbose=self.verbose)
##############################
##############################
# Get list for simrdwn/data/, copy to data dir
##############################
train_ims = [
os.path.join(self.images_dir, f)
for f in os.listdir(self.images_dir)
]
f = open(self.im_list_name, 'w')
for item in train_ims:
f.write("%s\n" % item)
f.close()
# copy to data dir
print("Copying", self.im_list_name, "to:", self.simrdwn_data_dir)
shutil.copy(self.im_list_name, self.simrdwn_data_dir)
##############################
##############################
# Ensure labels were created correctly by plotting a few
##############################
max_plots = 50
thickness = 2
yolt_data_prep_funcs.plot_training_bboxes(
self.labels_dir,
self.images_dir,
ignore_augment=False,
sample_label_vis_dir=self.sample_label_vis_dir,
max_plots=max_plots,
thickness=thickness,
ext='.png',
verbose=True)
##############################
# Make a .tfrecords file
##############################
importlib.reload(preprocess_tfrecords)
preprocess_tfrecords.yolt_imlist_to_tf(
self.im_list_name,
label_map_dict,
TF_RecordPath=self.tfrecord_train,
TF_PathVal='',
val_frac=0.0,
convert_dict=convert_dict,
verbose=True)
# copy train file to data dir
print("Copying", self.tfrecord_train, "to:", self.simrdwn_data_dir)
shutil.copy(self.tfrecord_train, self.simrdwn_data_dir)
##############################
# Copy test images to test dir
print("Copying test images to:", self.test_out_dir)
for td in self.test_dirs:
td_tot_in = os.path.join(self.ground_truth_dir, td)
td_tot_out = os.path.join(self.test_out_dir, td)
if not os.path.exists(td_tot_out):
os.makedirs(td_tot_out)
# copy non-label files
for f in os.listdir(td_tot_in):
if (f.endswith('.png')
or f.endswith('.jpg')) and not f.endswith(
('_Cars.png', '_Negatives.png', '.xcf')):
shutil.copy2(os.path.join(td_tot_in, f), td_tot_out)
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")