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40 changes: 40 additions & 0 deletions example/augmentation_visualisation.py
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import tensorflow as tf
import matplotlib.pyplot as plt
import yaml

image_path = "images/image0000002.jpg"
config_path = "augmentations_example.yaml"

# Decode image into tensor
image = open(image_path, 'rb').read()
image_tensor = tf.image.decode_jpeg(image)

# Get augmentations
with open(config_path, "r") as file:
data = yaml.safe_load(file)
augmentations = data["example"]["config"]["augmentations"]

# Apply the augmentations that were listed
if "brightness" in augmentations:
v = augmentations["brightness"]
image_tensor = tf.image.adjust_brightness(image_tensor, tf.random.truncated_normal(shape=(), **v))
if "contrast" in augmentations:
v = augmentations["contrast"]
image_tensor = tf.image.adjust_contrast(image_tensor, tf.random.truncated_normal(shape=(), **v))
if "hue" in augmentations:
v = augmentations["hue"]
image_tensor = tf.image.adjust_hue(image_tensor, tf.random.truncated_normal(shape=(), **v))
if "saturation" in augmentations:
v = augmentations["saturation"]
image_tensor = tf.image.adjust_saturation(image_tensor, tf.random.truncated_normal(shape=(), **v))
if "gamma" in augmentations:
v_gamma = augmentations["gamma"]["gamma"]
v_gain = augmentations["gamma"]["gain"]
image_tensor = tf.image.adjust_gamma(
image_tensor, tf.random.truncated_normal(shape=(), **v_gamma), tf.random.truncated_normal(shape=(), **v_gain)
)

# Display image using matplotlib
plt.imshow(image_tensor)
plt.axis('off')
plt.show()
17 changes: 17 additions & 0 deletions example/augmentations_example.yaml
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example:
type: Image
config:
augmentations:
# Adjust the brightness `x + delta`
brightness: { mean: 0, stddev: 0.05 }
# Adjust the contrast `(x - mean) * delta + mean`
contrast: { mean: 1, stddev: 0.05 }
# Convert to hsv, adjust the hue by a value from [-1 -> 1] and back to rgb
hue: { mean: 0, stddev: 0.05 }
# Convert to hsv, multiply saturation by value and convert back to rgb
saturation: { mean: 1, stddev: 0.05 }
# Adjust the gamma `gain * x**gamma`
gamma:
gamma: { mean: 1, stddev: 0.05 }
gain: { mean: 1, stddev: 0.05 }