habana_frameworks.mediapipe.fn.Hue
habana_frameworks.mediapipe.fn.Hue¶
- Class:
habana_frameworks.mediapipe.fn.Hue(**kwargs)
- Define graph call:
__call__(input, degree_tensor)
- Parameter:
input - Input tensor to operator. Supported dimensions: minimum = 4, maximum = 4. Supported data types: UINT8, UINT16.
(optional) degree_tensor - Tensor containing degree value for each image in the batch. Supported dimensions: minimum = 1, maximum = 1. Supported data types: FLOAT32.
Description:
Hue operator modifies the hue of an image by specified degrees. It applies the HSV space modification to an RGB value to get the modified RGB value.
- Supported backend:
HPU
Keyword Arguments
kwargs |
Description |
---|---|
degree |
Angle by which hue of an image needs to be rotated.
|
dtype |
Output data type.
|
Note
Input/output tensor must be of the same data type.
Input 1 and output tensor should be in NCHW layout. Input 2 is one dimensional tensor of size N.
Angle value can be provided either as scalar or 1D tensor.
Number of channels in input and output should be 3.
Example: Hue Operator
Following code snippet shows usage of hue operator.
from habana_frameworks.mediapipe import fn
from habana_frameworks.mediapipe.mediapipe import MediaPipe
from habana_frameworks.mediapipe.media_types import imgtype as it
from habana_frameworks.mediapipe.media_types import dtype as dt
import matplotlib.pyplot as plt
# Create media pipeline derived class
class myMediaPipe(MediaPipe):
def __init__(self, device, dir, queue_depth, batch_size, img_h, img_w):
super(
myMediaPipe,
self).__init__(
device,
queue_depth,
batch_size,
self.__class__.__name__)
self.input = fn.ReadImageDatasetFromDir(shuffle=False,
dir=dir,
format="jpg")
# WHCN
self.decode = fn.ImageDecoder(device="hpu",
output_format=it.RGB_P,
resize=[img_w, img_h])
self.hue = fn.Hue(degree=50,
dtype=dt.UINT8)
# WHCN -> CWHN
self.transpose = fn.Transpose(permutation=[2, 0, 1, 3],
tensorDim=4,
dtype=dt.UINT8)
def definegraph(self):
images, labels = self.input()
images = self.decode(images)
images = self.hue(images)
images = self.transpose(images)
return images, labels
def display_images(images, batch_size, cols):
rows = (batch_size + 1) // cols
plt.figure(figsize=(10, 10))
for i in range(batch_size):
ax = plt.subplot(rows, cols, i + 1)
plt.imshow(images[i])
plt.axis("off")
plt.show()
def main():
batch_size = 6
img_width = 200
img_height = 200
img_dir = "/path/to/images"
queue_depth = 2
columns = 3
# Create media pipeline object
pipe = myMediaPipe('hpu', img_dir, queue_depth, batch_size,
img_height, img_width)
# Build media pipeline
pipe.build()
# Initialize media pipeline iterator
pipe.iter_init()
# Run media pipeline
images, labels = pipe.run()
# Copy data to host from device as numpy array
images = images.as_cpu().as_nparray()
labels = labels.as_cpu().as_nparray()
# Display images
display_images(images, batch_size, columns)
if __name__ == "__main__":
main()
Images with Changed Hue 1
- 1
Licensed under a CC BY SA 4.0 license. The images used here are taken from https://data.caltech.edu/records/mzrjq-6wc02.