wehub-resource-sync
307 lines
10 KiB
Python
307 lines
10 KiB
Python
from __future__ import print_function, division
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import time
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import numpy as np
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from skimage import data
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import imgaug as ia
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import imgaug.multicore as multicore
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from imgaug import augmenters as iaa
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def main():
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augseq = iaa.Sequential([
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iaa.Fliplr(0.5),
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iaa.CoarseDropout(p=0.1, size_percent=0.1)
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])
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def func_images(images, random_state, parents, hooks):
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time.sleep(0.2)
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return images
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def func_heatmaps(heatmaps, random_state, parents, hooks):
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return heatmaps
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def func_keypoints(keypoints_on_images, random_state, parents, hooks):
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return keypoints_on_images
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augseq_slow = iaa.Sequential([
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iaa.Fliplr(0.5),
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iaa.Lambda(
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func_images=func_images,
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func_heatmaps=func_heatmaps,
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func_keypoints=func_keypoints
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)
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])
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print("------------------")
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print("augseq.augment_batches(batches, background=True)")
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print("------------------")
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batches = list(load_images())
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batches_aug = augseq.augment_batches(batches, background=True)
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images_aug = []
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keypoints_aug = []
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for batch_aug in batches_aug:
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images_aug.append(batch_aug.images_aug)
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keypoints_aug.append(batch_aug.keypoints_aug)
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ia.imshow(draw_grid(images_aug, keypoints_aug))
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print("------------------")
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print("augseq.augment_batches(batches, background=True) -> only images")
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print("------------------")
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batches = list(load_images())
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batches = [batch.images_unaug for batch in batches]
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batches_aug = augseq.augment_batches(batches, background=True)
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images_aug = []
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keypoints_aug = None
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for batch_aug in batches_aug:
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images_aug.append(batch_aug)
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ia.imshow(draw_grid(images_aug, keypoints_aug))
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print("------------------")
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print("BackgroundAugmenter")
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print("------------------")
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batch_loader = multicore.BatchLoader(load_images)
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bg_augmenter = multicore.BackgroundAugmenter(batch_loader, augseq)
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images_aug = []
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keypoints_aug = []
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while True:
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print("Next batch...")
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batch = bg_augmenter.get_batch()
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if batch is None:
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print("Finished.")
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break
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images_aug.append(batch.images_aug)
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keypoints_aug.append(batch.keypoints_aug)
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ia.imshow(draw_grid(images_aug, keypoints_aug))
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print("------------------")
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print("BackgroundAugmenter with generator in BL")
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print("------------------")
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batch_loader = multicore.BatchLoader(load_images())
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bg_augmenter = multicore.BackgroundAugmenter(batch_loader, augseq)
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images_aug = []
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keypoints_aug = []
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while True:
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print("Next batch...")
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batch = bg_augmenter.get_batch()
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if batch is None:
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print("Finished.")
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break
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images_aug.append(batch.images_aug)
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keypoints_aug.append(batch.keypoints_aug)
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ia.imshow(draw_grid(images_aug, keypoints_aug))
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print("------------------")
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print("Long running BackgroundAugmenter at BL-queue_size=12")
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print("------------------")
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batch_loader = multicore.BatchLoader(load_images(n_batches=1000), queue_size=12)
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bg_augmenter = multicore.BackgroundAugmenter(batch_loader, augseq)
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i = 0
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while True:
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if i % 100 == 0:
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print("batch=%d..." % (i,))
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batch = bg_augmenter.get_batch()
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if batch is None:
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print("Finished.")
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break
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i += 1
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print("------------------")
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print("Long running BackgroundAugmenter at BL-queue_size=2")
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print("------------------")
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batch_loader = multicore.BatchLoader(load_images(n_batches=1000), queue_size=2)
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bg_augmenter = multicore.BackgroundAugmenter(batch_loader, augseq)
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i = 0
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while True:
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if i % 100 == 0:
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print("batch=%d..." % (i,))
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batch = bg_augmenter.get_batch()
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if batch is None:
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print("Finished.")
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break
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i += 1
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print("------------------")
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print("Long running BackgroundAugmenter (slow loading)")
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print("------------------")
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batch_loader = multicore.BatchLoader(load_images(n_batches=100, sleep=0.2))
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bg_augmenter = multicore.BackgroundAugmenter(batch_loader, augseq)
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i = 0
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while True:
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if i % 10 == 0:
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print("batch=%d..." % (i,))
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batch = bg_augmenter.get_batch()
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if batch is None:
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print("Finished.")
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break
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i += 1
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print("------------------")
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print("Long running BackgroundAugmenter (slow aug) at BL-queue_size=12")
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print("------------------")
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batch_loader = multicore.BatchLoader(load_images(n_batches=100), queue_size=12)
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bg_augmenter = multicore.BackgroundAugmenter(batch_loader, augseq_slow)
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i = 0
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while True:
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if i % 10 == 0:
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print("batch=%d..." % (i,))
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batch = bg_augmenter.get_batch()
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if batch is None:
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print("Finished.")
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break
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i += 1
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print("------------------")
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print("Long running BackgroundAugmenter (slow aug) at BL-queue_size=2")
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print("------------------")
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batch_loader = multicore.BatchLoader(load_images(n_batches=100), queue_size=2)
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bg_augmenter = multicore.BackgroundAugmenter(batch_loader, augseq_slow)
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i = 0
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while True:
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if i % 10 == 0:
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print("batch=%d..." % (i,))
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batch = bg_augmenter.get_batch()
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if batch is None:
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print("Finished.")
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break
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i += 1
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for augseq_i in [augseq, augseq_slow]:
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print("------------------")
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print("Many very small runs (batches=1)")
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print("------------------")
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for i in range(100):
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batch_loader = multicore.BatchLoader(load_images(n_batches=1), queue_size=100)
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bg_augmenter = multicore.BackgroundAugmenter(batch_loader, augseq_i)
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while True:
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batch = bg_augmenter.get_batch()
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if batch is None:
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print("Finished (%d/%d)." % (i+1, 100))
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break
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print("------------------")
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print("Many very small runs (batches=2)")
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print("------------------")
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for i in range(100):
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batch_loader = multicore.BatchLoader(load_images(n_batches=2), queue_size=100)
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bg_augmenter = multicore.BackgroundAugmenter(batch_loader, augseq_i)
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while True:
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batch = bg_augmenter.get_batch()
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if batch is None:
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print("Finished (%d/%d)." % (i+1, 100))
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break
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print("------------------")
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print("Many very small runs, separate function (batches=1)")
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print("------------------")
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def _augment_small_1():
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batch_loader = multicore.BatchLoader(load_images(n_batches=1), queue_size=100)
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bg_augmenter = multicore.BackgroundAugmenter(batch_loader, augseq_i)
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i = 0
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while True:
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batch = bg_augmenter.get_batch()
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if batch is None:
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break
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i += 1
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for i in range(100):
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_augment_small_1()
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print("Finished (%d/%d)." % (i+1, 100))
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print("------------------")
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print("Many very small runs, separate function (batches=2)")
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print("------------------")
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def _augment_small_2():
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batch_loader = multicore.BatchLoader(load_images(n_batches=2), queue_size=100)
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bg_augmenter = multicore.BackgroundAugmenter(batch_loader, augseq_i)
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i = 0
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while True:
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batch = bg_augmenter.get_batch()
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if batch is None:
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break
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i += 1
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for i in range(100):
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_augment_small_2()
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print("Finished (%d/%d)." % (i+1, 100))
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print("------------------")
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print("Many very small runs, separate function, incomplete fetching (batches=2)")
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print("------------------")
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def _augment_small_3():
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batch_loader = multicore.BatchLoader(load_images(n_batches=2), queue_size=100)
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bg_augmenter = multicore.BackgroundAugmenter(batch_loader, augseq_i)
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batch = bg_augmenter.get_batch()
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for i in range(100):
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_augment_small_3()
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print("Finished (%d/%d)." % (i+1, 100))
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#for augseq_i in [augseq, augseq_slow]:
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print("------------------")
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print("Many very small runs, separate function, incomplete fetching (batches=10)")
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print("------------------")
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def _augment_small_4():
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batch_loader = multicore.BatchLoader(load_images(n_batches=10), queue_size=100)
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bg_augmenter = multicore.BackgroundAugmenter(batch_loader, augseq_i)
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batch = bg_augmenter.get_batch()
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#bg_augmenter.terminate()
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for i in range(100):
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_augment_small_4()
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print("Finished (%d/%d)." % (i+1, 100))
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def load_images(n_batches=10, sleep=0.0):
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batch_size = 4
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astronaut = data.astronaut()
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astronaut = ia.imresize_single_image(astronaut, (64, 64))
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kps = ia.KeypointsOnImage([ia.Keypoint(x=15, y=25)], shape=astronaut.shape)
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counter = 0
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for i in range(n_batches):
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batch_images = []
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batch_kps = []
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for b in range(batch_size):
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astronaut_text = ia.draw_text(astronaut, x=0, y=0, text="%d" % (counter,), color=[0, 255, 0], size=16)
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batch_images.append(astronaut_text)
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batch_kps.append(kps)
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counter += 1
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batch = ia.Batch(
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images=np.array(batch_images, dtype=np.uint8),
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keypoints=batch_kps
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)
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yield batch
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if sleep > 0:
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time.sleep(sleep)
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def draw_grid(images_aug, keypoints_aug):
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if keypoints_aug is None:
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keypoints_aug = []
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for bidx in range(len(images_aug)):
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keypoints_aug.append([None for image in images_aug[bidx]])
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images_kps_batches = []
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for bidx in range(len(images_aug)):
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images_kps_batch = []
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for image, kps in zip(images_aug[bidx], keypoints_aug[bidx]):
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if kps is None:
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image_kps = image
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else:
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image_kps = kps.draw_on_image(image, size=5, color=[255, 0, 0])
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images_kps_batch.append(image_kps)
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images_kps_batches.extend(images_kps_batch)
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grid = ia.draw_grid(images_kps_batches, cols=len(images_aug[0]))
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return grid
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if __name__ == "__main__":
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main()
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