Image Decoder examples (CPU)¶
In this notebook we will show usage examples of different flavors of ImageDecoder
for CPU backend.
Common code¶
[1]:
from nvidia.dali.pipeline import Pipeline
import nvidia.dali.ops as ops
import nvidia.dali.types as types
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
%matplotlib inline
seed = 1549361629
def show_images(image_batch):
columns = 4
rows = (batch_size + 1) // (columns)
fig = plt.figure(figsize = (32,(32 // columns) * rows))
gs = gridspec.GridSpec(rows, columns)
for j in range(rows*columns):
plt.subplot(gs[j])
plt.axis("off")
plt.imshow(image_batch.at(j))
image_dir = "../data/images"
batch_size = 4
Image Decoder (CPU)¶
‘ImageDecoder’ decodes the whole image (no cropping).
[2]:
class ImageDecoderPipeline(Pipeline):
def __init__(self, batch_size, num_threads, device_id):
super(ImageDecoderPipeline, self).__init__(batch_size, num_threads, device_id, seed = seed)
self.input = ops.FileReader(file_root = image_dir)
self.decode = ops.ImageDecoder(device = 'cpu', output_type = types.RGB)
def define_graph(self):
jpegs, labels = self.input()
images = self.decode(jpegs)
return (images, labels)
pipe = ImageDecoderPipeline(batch_size, 1, 0)
pipe.build()
images, _ = pipe.run()
show_images( images )
Image Decoder (CPU) with random cropping window size and anchor¶
‘ImageDecoderRandomCrop’ produces a randomly cropped image. The random cropping window is produced based on a given aspect ratio and area distributions.
[3]:
class ImageDecoderRandomCropPipeline(Pipeline):
def __init__(self, batch_size, num_threads, device_id):
super(ImageDecoderRandomCropPipeline, self).__init__(batch_size, num_threads, device_id, seed = seed)
self.input = ops.FileReader(file_root = image_dir)
self.decode = ops.ImageDecoderRandomCrop(
device="cpu",
output_type=types.RGB,
random_aspect_ratio=[0.8, 1.25],
random_area=[0.1, 1.0],
num_attempts=100)
def define_graph(self):
jpegs, labels = self.input()
images = self.decode(jpegs)
return (images, labels)
pipe = ImageDecoderRandomCropPipeline(batch_size, 1, 0)
pipe.build()
images, _ = pipe.run()
show_images( images )
Image Decoder with fixed cropping window size and external anchor¶
‘ImageDecoderCrop’ produces a cropped image with a fixed cropping window size and a variable position
[4]:
class ImageDecoderCropPipeline(Pipeline):
def __init__(self, batch_size, num_threads, device_id):
super(ImageDecoderCropPipeline, self).__init__(batch_size, num_threads, device_id, seed = seed)
self.input = ops.FileReader(file_root = image_dir)
self.pos_rng_x = ops.Uniform(range = (0.0, 1.0))
self.pos_rng_y = ops.Uniform(range = (0.0, 1.0))
self.decode = ops.ImageDecoderCrop(device = 'cpu', output_type = types.RGB, crop = (224, 224))
def define_graph(self):
jpegs, labels = self.input()
pos_x = self.pos_rng_x()
pos_y = self.pos_rng_y()
images = self.decode(jpegs, crop_pos_x=pos_x, crop_pos_y=pos_y)
return (images, labels)
pipe = ImageDecoderCropPipeline(batch_size, 1, 0)
pipe.build()
images, _ = pipe.run()
show_images( images )
Image Decoder (CPU) with external window size and anchor¶
‘ImageDecoderSlice’ produces a cropped image based on additional tensor inputs describing the cropping window size and position
[5]:
import numpy as np
class ExternalInputIterator(object):
def __init__(self, batch_size):
self.batch_size = batch_size
def __iter__(self):
self.i = 0
self.n = self.batch_size
return self
def __next__(self):
pos = []
size = []
for _ in range(self.batch_size):
pos.append(np.asarray([0.4, 0.2], dtype=np.float32))
size.append(np.asarray([0.3, 0.5], dtype=np.float32))
self.i = (self.i + 1) % self.n
return (pos, size)
next = __next__
eii = ExternalInputIterator(batch_size)
pos_size_iter = iter(eii)
class ImageDecoderSlicePipeline(Pipeline):
def __init__(self, batch_size, num_threads, device_id):
super(ImageDecoderSlicePipeline, self).__init__(batch_size, num_threads, device_id, seed = seed)
self.input = ops.FileReader(file_root = image_dir)
self.input_crop_pos = ops.ExternalSource()
self.input_crop_size = ops.ExternalSource()
self.input_crop = ops.ExternalSource()
self.decode = ops.ImageDecoderSlice(device = 'cpu', output_type = types.RGB)
def define_graph(self):
jpegs, labels = self.input()
self.crop_pos = self.input_crop_pos()
self.crop_size = self.input_crop_size()
images = self.decode(jpegs, self.crop_pos, self.crop_size)
return (images, labels)
def iter_setup(self):
(crop_pos, crop_size) = pos_size_iter.next()
self.feed_input(self.crop_pos, crop_pos)
self.feed_input(self.crop_size, crop_size)
pipe = ImageDecoderSlicePipeline(batch_size, 1, 0)
pipe.build()
images, _ = pipe.run()
show_images( images )
Image Decoder (Hybrid)¶
ImageDecoder
decodes the whole image (no cropping)
[6]:
class ImageDecoderPipeline(Pipeline):
def __init__(self, batch_size, num_threads, device_id):
super(ImageDecoderPipeline, self).__init__(batch_size, num_threads, device_id, seed = seed)
self.input = ops.FileReader(file_root = image_dir)
self.decode = ops.ImageDecoder(device = 'mixed', output_type = types.RGB)
def define_graph(self):
jpegs, labels = self.input()
images = self.decode(jpegs)
return (images, labels)
pipe = ImageDecoderPipeline(batch_size, 1, 0)
pipe.build()
images, _ = pipe.run()
show_images( images.as_cpu() )
Image Decoder (Hybrid) with random cropping window size and anchor¶
‘ImageDecoderRandomCrop’ produces a randomly cropped image. The random cropping window is produced based on a given aspect ratio and area distributions.
[7]:
class ImageDecoderRandomCropPipeline(Pipeline):
def __init__(self, batch_size, num_threads, device_id):
super(ImageDecoderRandomCropPipeline, self).__init__(batch_size, num_threads, device_id, seed = seed)
self.input = ops.FileReader(file_root = image_dir)
self.decode = ops.ImageDecoderRandomCrop(
device="mixed",
output_type=types.RGB,
random_aspect_ratio=[0.8, 1.25],
random_area=[0.1, 1.0],
num_attempts=100)
def define_graph(self):
jpegs, labels = self.input()
images = self.decode(jpegs)
return (images, labels)
pipe = ImageDecoderRandomCropPipeline(batch_size, 1, 0)
pipe.build()
images, _ = pipe.run()
show_images( images.as_cpu() )
Image Decoder (Hybrid) with fixed cropping window size and external anchor¶
‘ImageDecoderCrop’ produces a cropped image with a fixed cropping window size and a variable position
[8]:
class ImageDecoderCropPipeline(Pipeline):
def __init__(self, batch_size, num_threads, device_id):
super(ImageDecoderCropPipeline, self).__init__(batch_size, num_threads, device_id, seed = seed)
self.input = ops.FileReader(file_root = image_dir)
self.pos_rng_x = ops.Uniform(range = (0.0, 1.0))
self.pos_rng_y = ops.Uniform(range = (0.0, 1.0))
self.decode = ops.ImageDecoderCrop(device = 'mixed', output_type = types.RGB, crop = (224, 224))
def define_graph(self):
jpegs, labels = self.input()
pos_x = self.pos_rng_x()
pos_y = self.pos_rng_y()
images = self.decode(jpegs, crop_pos_x=pos_x, crop_pos_y=pos_y)
return (images, labels)
pipe = ImageDecoderCropPipeline(batch_size, 1, 0)
pipe.build()
images, _ = pipe.run()
show_images( images.as_cpu() )
Image Decoder (Hybrid) with external window size and anchor¶
‘ImageDecoderSlice’ produces a cropped image based on additional tensor inputs describing the cropping window size and position
[9]:
import numpy as np
class ExternalInputIterator(object):
def __init__(self, batch_size):
self.batch_size = batch_size
def __iter__(self):
self.i = 0
self.n = self.batch_size
return self
def __next__(self):
pos = []
size = []
for _ in range(self.batch_size):
pos.append(np.asarray([0.4, 0.2], dtype=np.float32))
size.append(np.asarray([0.3, 0.5], dtype=np.float32))
self.i = (self.i + 1) % self.n
return (pos, size)
next = __next__
eii = ExternalInputIterator(batch_size)
pos_size_iter = iter(eii)
class ImageDecoderSlicePipeline(Pipeline):
def __init__(self, batch_size, num_threads, device_id):
super(ImageDecoderSlicePipeline, self).__init__(batch_size, num_threads, device_id, seed = seed)
self.input = ops.FileReader(file_root = image_dir)
self.input_crop_pos = ops.ExternalSource()
self.input_crop_size = ops.ExternalSource()
self.input_crop = ops.ExternalSource()
self.decode = ops.ImageDecoderSlice(device = 'mixed', output_type = types.RGB)
def define_graph(self):
jpegs, labels = self.input()
self.crop_pos = self.input_crop_pos()
self.crop_size = self.input_crop_size()
images = self.decode(jpegs, self.crop_pos, self.crop_size)
return (images, labels)
def iter_setup(self):
(crop_pos, crop_size) = pos_size_iter.next()
self.feed_input(self.crop_pos, crop_pos)
self.feed_input(self.crop_size, crop_size)
pipe = ImageDecoderSlicePipeline(batch_size, 1, 0)
pipe.build()
images, _ = pipe.run()
show_images( images.as_cpu() )
[ ]: