From 662c5ec7afe2b76637cf7b1d4aceccb6d995d2c1 Mon Sep 17 00:00:00 2001 From: Jim Martens Date: Thu, 4 Jul 2019 17:03:47 +0200 Subject: [PATCH] Extracted debug functionality into separate module Signed-off-by: Jim Martens --- src/twomartens/masterthesis/cli.py | 46 ++---------------- src/twomartens/masterthesis/debug.py | 71 ++++++++++++++++++++++++++++ 2 files changed, 76 insertions(+), 41 deletions(-) create mode 100644 src/twomartens/masterthesis/debug.py diff --git a/src/twomartens/masterthesis/cli.py b/src/twomartens/masterthesis/cli.py index fac769a..5979bb2 100644 --- a/src/twomartens/masterthesis/cli.py +++ b/src/twomartens/masterthesis/cli.py @@ -172,7 +172,8 @@ def _ssd_train(args: argparse.Namespace) -> None: batch_size = conf.get_property("Parameters.batch_size") image_size = conf.get_property("Parameters.ssd_image_size") use_dropout = False if args.network == "ssd" else True - + + summary_path = conf.get_property("Paths.summaries") weights_path = conf.get_property("Paths.weights") pre_trained_weights_file = f"{weights_path}/VGG_coco_SSD_300x300_iter_400000.h5" weights_path = f"{weights_path}/train/{args.network}/" @@ -213,57 +214,20 @@ def _ssd_train(args: argparse.Namespace) -> None: del file_names_train, instances_train, file_names_val, instances_val if args.debug and conf.get_property("Debug.train_images"): - from matplotlib import pyplot - import numpy as np - from PIL import Image - from twomartens.masterthesis.ssd_keras.eval_utils import coco_utils + from twomartens.masterthesis import debug train_data = next(train_generator) train_length -= batch_size train_images = train_data[0] train_labels = train_data[1] + output_path = f"{summary_path}/train/{args.network}/{args.iteration}" - annotation_file_train = f"{args.coco_path}/annotations/instances_train2014.json" - _, _, _, classes_to_names = coco_utils.get_coco_category_maps(annotation_file_train) - colors = pyplot.cm.hsv(np.linspace(0, 1, 81)).tolist() - summary_path = f"{args.summary_path}/train/{args.network}/{args.iteration}/" - os.makedirs(summary_path, exist_ok=True) - - nr_images = len(train_images) - nr_digits = math.ceil(math.log10(nr_images)) - - for i, train_image in enumerate(train_images): - instances = train_labels[i] - image = Image.fromarray(train_image) - image.save(f"{summary_path}" - f"train_image{str(i).zfill(nr_digits)}.png") - - figure = pyplot.figure(figsize=(20, 12)) - pyplot.imshow(image) - - current_axis = pyplot.gca() - - for instance in instances: - xmin = instance[-12] * image_size - ymin = instance[-11] * image_size - xmax = instance[-10] * image_size - ymax = instance[-9] * image_size - class_id = np.argmax(instance[:-12], axis=0) - color = colors[class_id] - label = f"{classes_to_names[class_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"{args.summary_path}/train/{args.network}/{args.iteration}/bboxes{str(i).zfill(nr_digits)}.png") - pyplot.close(figure) + debug.save_ssd_train_images(train_images, train_labels, output_path) nr_batches_train = int(math.floor(train_length / batch_size)) nr_batches_val = int(math.floor(val_length / batch_size)) - summary_path = conf.get_property("Paths.summaries") tensorboard_callback = tf.keras.callbacks.TensorBoard( log_dir=f"{summary_path}/train/{args.network}/{args.iteration}" ) diff --git a/src/twomartens/masterthesis/debug.py b/src/twomartens/masterthesis/debug.py new file mode 100644 index 0000000..8e254d4 --- /dev/null +++ b/src/twomartens/masterthesis/debug.py @@ -0,0 +1,71 @@ +# -*- coding: utf-8 -*- + +# Copyright 2018 Timon Brüning, Inga Kempfert, Anne Kunstmann, Jim Martens, +# Marius Pierenkemper, Yanneck Reiss +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Handle debug functionality. + +Functions: + save_ssd_train_images(images, labels, output_path): + saves the first batch of SSD train images with overlaid ground truth bounding boxes +""" +import os + +import math +import numpy as np +from matplotlib import pyplot +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) -> None: + 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) + colors = pyplot.cm.hsv(np.linspace(0, 1, 81)).tolist() + os.makedirs(output_path, exist_ok=True) + + nr_images = len(images) + nr_digits = math.ceil(math.log10(nr_images)) + image_size = config.get_property("Parameters.ssd_image_size") + + for i, train_image in enumerate(images): + instances = labels[i] + image = Image.fromarray(train_image) + image.save(f"{output_path}" + f"train_image{str(i).zfill(nr_digits)}.png") + + figure = pyplot.figure(figsize=(20, 12)) + pyplot.imshow(image) + + current_axis = pyplot.gca() + + for instance in instances: + xmin = instance[-12] * image_size + ymin = instance[-11] * image_size + xmax = instance[-10] * image_size + ymax = instance[-9] * image_size + class_id = np.argmax(instance[:-12], axis=0) + color = colors[class_id] + label = f"{classes_to_names[class_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}/bboxes{str(i).zfill(nr_digits)}.png") + pyplot.close(figure)