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Merge pull request tensorflow#2146 to fix data augmentation functions.
Removing unnecessary clipping in preprocessor.py (fix tensorflow#2066)
2 parents 31f7f41 + e627d39 commit c86f791

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‎research/object_detection/core/preprocessor.py

+21-21
Original file line numberDiff line numberDiff line change
@@ -736,7 +736,7 @@ def random_pixel_value_scale(image,
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Args:
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image: rank 3 float32 tensor contains 1 image -> [height, width, channels]
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with pixel values varying between [0, 1].
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with pixel values varying between [0, 255].
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minval: lower ratio of scaling pixel values.
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maxval: upper ratio of scaling pixel values.
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seed: random seed.
@@ -759,7 +759,7 @@ def random_pixel_value_scale(image,
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preprocess_vars_cache)
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image = tf.multiply(image, color_coef)
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image = tf.clip_by_value(image, 0.0, 1.0)
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image = tf.clip_by_value(image, 0.0, 255.0)
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return image
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@@ -825,7 +825,7 @@ def random_rgb_to_gray(image,
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Args:
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image: rank 3 float32 tensor contains 1 image -> [height, width, channels]
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with pixel values varying between [0, 1].
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with pixel values varying between [0, 255].
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probability: the probability of returning a grayscale image.
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The probability should be a number between [0, 1].
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seed: random seed.
@@ -862,11 +862,11 @@ def random_adjust_brightness(image,
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preprocess_vars_cache=None):
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"""Randomly adjusts brightness.
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Makes sure the output image is still between 0 and 1.
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Makes sure the output image is still between 0 and 255.
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Args:
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image: rank 3 float32 tensor contains 1 image -> [height, width, channels]
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with pixel values varying between [0, 1].
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with pixel values varying between [0, 255].
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max_delta: how much to change the brightness. A value between [0, 1).
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seed: random seed.
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preprocess_vars_cache: PreprocessorCache object that records previously
@@ -886,8 +886,8 @@ def random_adjust_brightness(image,
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preprocessor_cache.PreprocessorCache.ADJUST_BRIGHTNESS,
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preprocess_vars_cache)
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image = tf.image.adjust_brightness(image, delta)
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image = tf.clip_by_value(image, clip_value_min=0.0, clip_value_max=1.0)
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image = tf.image.adjust_brightness(image / 255, delta) * 255
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image = tf.clip_by_value(image, clip_value_min=0.0, clip_value_max=255.0)
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return image
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@@ -898,11 +898,11 @@ def random_adjust_contrast(image,
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preprocess_vars_cache=None):
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"""Randomly adjusts contrast.
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Makes sure the output image is still between 0 and 1.
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Makes sure the output image is still between 0 and 255.
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Args:
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image: rank 3 float32 tensor contains 1 image -> [height, width, channels]
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with pixel values varying between [0, 1].
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with pixel values varying between [0, 255].
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min_delta: see max_delta.
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max_delta: how much to change the contrast. Contrast will change with a
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value between min_delta and max_delta. This value will be
@@ -923,8 +923,8 @@ def random_adjust_contrast(image,
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generator_func,
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preprocessor_cache.PreprocessorCache.ADJUST_CONTRAST,
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preprocess_vars_cache)
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image = tf.image.adjust_contrast(image, contrast_factor)
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image = tf.clip_by_value(image, clip_value_min=0.0, clip_value_max=1.0)
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image = tf.image.adjust_contrast(image / 255, contrast_factor) * 255
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image = tf.clip_by_value(image, clip_value_min=0.0, clip_value_max=255.0)
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return image
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@@ -934,11 +934,11 @@ def random_adjust_hue(image,
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preprocess_vars_cache=None):
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"""Randomly adjusts hue.
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Makes sure the output image is still between 0 and 1.
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Makes sure the output image is still between 0 and 255.
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Args:
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image: rank 3 float32 tensor contains 1 image -> [height, width, channels]
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with pixel values varying between [0, 1].
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with pixel values varying between [0, 255].
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max_delta: change hue randomly with a value between 0 and max_delta.
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seed: random seed.
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preprocess_vars_cache: PreprocessorCache object that records previously
@@ -955,8 +955,8 @@ def random_adjust_hue(image,
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delta = _get_or_create_preprocess_rand_vars(
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generator_func, preprocessor_cache.PreprocessorCache.ADJUST_HUE,
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preprocess_vars_cache)
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image = tf.image.adjust_hue(image, delta)
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image = tf.clip_by_value(image, clip_value_min=0.0, clip_value_max=1.0)
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image = tf.image.adjust_hue(image / 255, delta) * 255
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image = tf.clip_by_value(image, clip_value_min=0.0, clip_value_max=255.0)
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return image
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@@ -967,11 +967,11 @@ def random_adjust_saturation(image,
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preprocess_vars_cache=None):
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"""Randomly adjusts saturation.
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Makes sure the output image is still between 0 and 1.
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Makes sure the output image is still between 0 and 255.
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Args:
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image: rank 3 float32 tensor contains 1 image -> [height, width, channels]
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with pixel values varying between [0, 1].
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with pixel values varying between [0, 255].
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min_delta: see max_delta.
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max_delta: how much to change the saturation. Saturation will change with a
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value between min_delta and max_delta. This value will be
@@ -992,20 +992,20 @@ def random_adjust_saturation(image,
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generator_func,
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preprocessor_cache.PreprocessorCache.ADJUST_SATURATION,
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preprocess_vars_cache)
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image = tf.image.adjust_saturation(image, saturation_factor)
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image = tf.clip_by_value(image, clip_value_min=0.0, clip_value_max=1.0)
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image = tf.image.adjust_saturation(image / 255, saturation_factor) * 255
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image = tf.clip_by_value(image, clip_value_min=0.0, clip_value_max=255.0)
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return image
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def random_distort_color(image, color_ordering=0, preprocess_vars_cache=None):
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"""Randomly distorts color.
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Randomly distorts color using a combination of brightness, hue, contrast
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and saturation changes. Makes sure the output image is still between 0 and 1.
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and saturation changes. Makes sure the output image is still between 0 and 255.
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Args:
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image: rank 3 float32 tensor contains 1 image -> [height, width, channels]
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with pixel values varying between [0, 1].
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with pixel values varying between [0, 255].
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color_ordering: Python int, a type of distortion (valid values: 0, 1).
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preprocess_vars_cache: PreprocessorCache object that records previously
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performed augmentations. Updated in-place. If this

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