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Converted visualise to clean code standards

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Signed-off-by: Jim Martens <github@2martens.de>
This commit is contained in:
Jim Martens
2019-07-17 15:41:08 +02:00
parent cd79be4307
commit 71928650d3
1 changed files with 84 additions and 48 deletions
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132
src/twomartens/masterthesis/cli.py
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@ -80,57 +80,15 @@ def evaluate(args: argparse.Namespace) -> None:
def visualise(args: argparse.Namespace) -> None:
import pickle
from matplotlib import pyplot
import numpy as np
from PIL import Image
from twomartens.masterthesis.ssd_keras.eval_utils import coco_utils
with open(f"{args.ground_truth_path}/photo_paths.bin", "rb") as file:
file_names = pickle.load(file)
with open(f"{args.ground_truth_path}/instances.bin", "rb") as file:
instances = pickle.load(file)
output_path, coco_path, ground_truth_path = _visualise_get_config_values(conf.get_property)
output_path, annotation_file_train = _visualise_prepare_paths(args, output_path, coco_path)
file_names, instances, \
cats_to_classes, cats_to_names = _visualise_load_gt(ground_truth_path, annotation_file_train,
coco_utils.get_coco_category_maps)
output_path = f"{args.output_path}/visualise/{args.trajectory}"
annotation_file_train = f"{args.coco_path}/annotations/instances_train2014.json"
cats_to_classes, _, cats_to_names, _ = coco_utils.get_coco_category_maps(annotation_file_train)
colors = pyplot.cm.hsv(np.linspace(0, 1, 81)).tolist()
i = 0
nr_images = len(file_names[args.trajectory])
nr_digits = math.ceil(math.log10(nr_images))
for file_name, labels in zip(file_names[args.trajectory], instances[args.trajectory]):
if not labels:
continue
# only loop through selected trajectory
with Image.open(file_name) as image:
figure = pyplot.figure(figsize=(20, 12))
pyplot.imshow(image)
current_axis = pyplot.gca()
for instance in labels:
bbox = instance['bbox']
# Transform the predicted bounding boxes for the 300x300 image to the original image dimensions.
xmin = bbox[0]
ymin = bbox[1]
xmax = bbox[2]
ymax = bbox[3]
color = colors[cats_to_classes[int(instance['coco_id'])]]
label = f"{cats_to_names[int(instance['coco_id'])]}: {instance['wordnet_class_name']}, " \
f"{instance['wordnet_id']}"
current_axis.add_patch(
pyplot.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin, color=color, fill=False, linewidth=2))
current_axis.text(xmin, ymin, label, size='x-large', color='white',
bbox={'facecolor': color, 'alpha': 1.0})
pyplot.savefig(f"{output_path}/{str(i).zfill(nr_digits)}")
pyplot.close(figure)
i += 1
_visualise_gt(args, file_names, instances, cats_to_classes, cats_to_names, output_path)
def measure_mapping(args: argparse.Namespace) -> None:
@ -390,6 +348,49 @@ def _ssd_evaluate_save_images(filenames: Sequence[str], labels: Sequence[np.ndar
save_images(filenames, labels, output_path, coco_path, image_size, get_coco_cat_maps_func)
def _visualise_gt(args: argparse.Namespace,
file_names: Sequence[Sequence[str]], instances: Sequence[Sequence[Sequence[dict]]],
cats_to_classes: Dict[int, int], cats_to_names: Dict[int, str],
output_path: str):
from matplotlib import pyplot
from PIL import Image
colors = pyplot.cm.hsv(np.linspace(0, 1, 81)).tolist()
i = 0
nr_images = len(file_names[args.trajectory])
nr_digits = math.ceil(math.log10(nr_images))
for file_name, labels in zip(file_names[args.trajectory], instances[args.trajectory]):
if not labels:
continue
# only loop through selected trajectory
with Image.open(file_name) as image:
figure = pyplot.figure()
pyplot.imshow(image)
current_axis = pyplot.gca()
for instance in labels:
bbox = instance['bbox']
# Transform the predicted bounding boxes for the 300x300 image to the original image dimensions.
xmin = bbox[0]
ymin = bbox[1]
xmax = bbox[2]
ymax = bbox[3]
color = colors[cats_to_classes[int(instance['coco_id'])]]
label = f"{cats_to_names[int(instance['coco_id'])]}: {instance['wordnet_class_name']}, " \
f"{instance['wordnet_id']}"
current_axis.add_patch(
pyplot.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin, color=color, fill=False, linewidth=2))
current_axis.text(xmin, ymin, label, size='x-large', color='white',
bbox={'facecolor': color, 'alpha': 1.0})
pyplot.savefig(f"{output_path}/{str(i).zfill(nr_digits)}")
pyplot.close(figure)
i += 1
def _init_eager_mode() -> None:
tf.enable_eager_execution()
@ -582,6 +583,15 @@ def _measure_get_config_values(config_get: Callable[[str], Union[str, int, float
return output_path, coco_path, ground_truth_path
def _visualise_get_config_values(config_get: Callable[[str], Union[str, int, float, bool]]
) -> Tuple[str, str, str]:
output_path = config_get("Paths.output")
coco_path = config_get("Paths.coco")
ground_truth_path = config_get("Paths.scenenet_gt")
return output_path, coco_path, ground_truth_path
def _ssd_is_dropout(args: argparse.Namespace) -> bool:
return False if args.network == "ssd" else True
@ -656,6 +666,15 @@ def _measure_prepare_paths(args: argparse.Namespace,
return output_path, annotation_file_train, ground_truth_path
def _visualise_prepare_paths(args: argparse.Namespace,
output_path: str, coco_path: str) -> Tuple[str, str]:
output_path = f"{output_path}/visualise/{args.trajectory}"
annotation_file_train = f"{coco_path}/annotations/instances_train2014.json"
return output_path, annotation_file_train
def _ssd_train_load_gt(train_gt_path: str, val_gt_path: str
) -> Tuple[Sequence[Sequence[str]],
Sequence[Sequence[Sequence[dict]]],
@ -701,6 +720,23 @@ def _measure_load_gt(gt_path: str, annotation_file_train: str,
return instances, cats_to_classes, cats_to_names
def _visualise_load_gt(gt_path: str, annotation_file_train: str,
get_coco_cat_maps_func: callable) -> Tuple[Sequence[Sequence[str]],
Sequence[Sequence[Sequence[dict]]],
Dict[int, int],
Dict[int, str]]:
import pickle
with open(f"{gt_path}/photo_paths.bin", "rb") as file:
file_names = pickle.load(file)
with open(f"{gt_path}/instances.bin", "rb") as file:
instances = pickle.load(file)
cats_to_classes, _, cats_to_names, _ = get_coco_cat_maps_func(annotation_file_train)
return file_names, instances, cats_to_classes, cats_to_names
def _ssd_train_get_generators(args: argparse.Namespace,
load_data: callable,
file_names_train: Sequence[Sequence[str]],