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Modified debug module to conform to clean code standards

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Signed-off-by: Jim Martens <github@2martens.de>
This commit is contained in:
Jim Martens
2019-07-10 12:12:13 +02:00
parent 4cf6723c28
commit a90c6c7c92
1 changed files with 23 additions and 9 deletions
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30
src/twomartens/masterthesis/debug.py
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@ -19,7 +19,7 @@
Handle debug functionality. Handle debug functionality.
Functions: Functions:
save_ssd_train_images(images, labels, output_path): save_ssd_train_images(...):
saves the first batch of SSD train images with overlaid ground truth bounding boxes saves the first batch of SSD train images with overlaid ground truth bounding boxes
""" """
import os import os
@ -29,19 +29,33 @@ import numpy as np
from matplotlib import pyplot from matplotlib import pyplot
from PIL import Image from PIL import Image
from twomartens.masterthesis import config
from twomartens.masterthesis.ssd_keras.eval_utils import coco_utils
def save_ssd_train_images(images: np.ndarray, labels: np.ndarray,
output_path: str, coco_path: str,
image_size: int, get_coco_cat_maps_func: callable,
custom_string: str = None) -> None:
"""
Saves given images and labels to given output path.
def save_ssd_train_images(images: np.ndarray, labels: np.ndarray, output_path: str, custom_string: str = None) -> None: The images are saved both in a raw version and with bounding boxes printed on them.
annotation_file_train = f"{config.get_property('Paths.coco')}/annotations/instances_train2014.json"
_, _, _, classes_to_names = coco_utils.get_coco_category_maps(annotation_file_train) Args:
images: a NumPy array of images
labels: a NumPy array of labels
output_path: path to save the images in
coco_path: path to the COCO data set
image_size: size of the resized images
get_coco_cat_maps_func: callable that returns the COCO category maps for a given annotation file
custom_string: optional custom string that is prepended to file names
"""
annotation_file_train = f"{coco_path}/annotations/instances_train2014.json"
_, _, _, classes_to_names = get_coco_cat_maps_func(annotation_file_train)
colors = pyplot.cm.hsv(np.linspace(0, 1, 81)).tolist() colors = pyplot.cm.hsv(np.linspace(0, 1, 81)).tolist()
os.makedirs(output_path, exist_ok=True) os.makedirs(output_path, exist_ok=True)
nr_images = len(images) nr_images = len(images)
nr_digits = math.ceil(math.log10(nr_images)) nr_digits = math.ceil(math.log10(nr_images))
image_size = config.get_property("Parameters.ssd_image_size")
custom_string = f"{custom_string}_" if custom_string is not None else "" custom_string = f"{custom_string}_" if custom_string is not None else ""
for i, train_image in enumerate(images): for i, train_image in enumerate(images):
@ -70,5 +84,5 @@ def save_ssd_train_images(images: np.ndarray, labels: np.ndarray, output_path: s
linewidth=2)) linewidth=2))
current_axis.text(xmin, ymin, label, size='x-large', color='white', current_axis.text(xmin, ymin, label, size='x-large', color='white',
bbox={'facecolor': color, 'alpha': 1.0}) bbox={'facecolor': color, 'alpha': 1.0})
pyplot.savefig(f"{output_path}/bboxes{str(i).zfill(nr_digits)}.png") pyplot.savefig(f"{output_path}/{custom_string}bboxes{str(i).zfill(nr_digits)}.png")
pyplot.close(figure) pyplot.close(figure)