tf.image.stateless_random_jpeg_quality
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Deterministically radomize jpeg encoding quality for inducing jpeg noise.
tf.image.stateless_random_jpeg_quality(
image, min_jpeg_quality, max_jpeg_quality, seed
)
Guarantees the same results given the same seed independent of how many
times the function is called, and independent of global seed settings (e.g.
tf.random.set_seed).
min_jpeg_quality must be in the interval [0, 100] and less than
max_jpeg_quality.
max_jpeg_quality must be in the interval [0, 100].
Usage Example:
x = tf.constant([[[1, 2, 3],
[4, 5, 6]],
[[7, 8, 9],
[10, 11, 12]]], dtype=tf.uint8)
seed = (1, 2)
tf.image.stateless_random_jpeg_quality(x, 75, 95, seed)
<tf.Tensor: shape=(2, 2, 3), dtype=uint8, numpy=
array([[[ 0, 4, 5],
[ 1, 5, 6]],
[[ 5, 9, 10],
[ 5, 9, 10]]], dtype=uint8)>
Args |
|---|
image
|
3D image. Size of the last dimension must be 1 or 3.
|
min_jpeg_quality
|
Minimum jpeg encoding quality to use.
|
max_jpeg_quality
|
Maximum jpeg encoding quality to use.
|
seed
|
A shape [2] Tensor, the seed to the random number generator. Must have
dtype int32 or int64. (When using XLA, only int32 is allowed.)
|
Returns |
|---|
Adjusted image(s), same shape and DType as image.
|
Raises |
|---|
ValueError
|
if min_jpeg_quality or max_jpeg_quality is invalid.
|
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Last updated 2024-04-26 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-04-26 UTC."],[],[],null,["# tf.image.stateless_random_jpeg_quality\n\n\u003cbr /\u003e\n\n|------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.16.1/tensorflow/python/ops/image_ops_impl.py#L2866-L2919) |\n\nDeterministically radomize jpeg encoding quality for inducing jpeg noise. \n\n tf.image.stateless_random_jpeg_quality(\n image, min_jpeg_quality, max_jpeg_quality, seed\n )\n\nGuarantees the same results given the same `seed` independent of how many\ntimes the function is called, and independent of global seed settings (e.g.\n[`tf.random.set_seed`](../../tf/random/set_seed)).\n\n`min_jpeg_quality` must be in the interval `[0, 100]` and less than\n`max_jpeg_quality`.\n`max_jpeg_quality` must be in the interval `[0, 100]`.\n\n#### Usage Example:\n\n x = tf.constant([[[1, 2, 3],\n [4, 5, 6]],\n [[7, 8, 9],\n [10, 11, 12]]], dtype=tf.uint8)\n seed = (1, 2)\n tf.image.stateless_random_jpeg_quality(x, 75, 95, seed)\n \u003ctf.Tensor: shape=(2, 2, 3), dtype=uint8, numpy=\n array([[[ 0, 4, 5],\n [ 1, 5, 6]],\n [[ 5, 9, 10],\n [ 5, 9, 10]]], dtype=uint8)\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|--------------------|-----------------------------------------------------------------------------------------------------------------------------------------------|\n| `image` | 3D image. Size of the last dimension must be 1 or 3. |\n| `min_jpeg_quality` | Minimum jpeg encoding quality to use. |\n| `max_jpeg_quality` | Maximum jpeg encoding quality to use. |\n| `seed` | A shape \\[2\\] Tensor, the seed to the random number generator. Must have dtype `int32` or `int64`. (When using XLA, only `int32` is allowed.) |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| Adjusted image(s), same shape and DType as `image`. ||\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Raises ------ ||\n|--------------|---------------------------------------------------------|\n| `ValueError` | if `min_jpeg_quality` or `max_jpeg_quality` is invalid. |\n\n\u003cbr /\u003e"]]
View source on GitHub