habana_frameworks.mediapipe.fn.Brightness

Class:

• habana_frameworks.mediapipe.fn.Brightness(**kwargs)

Define graph call:

• __call__(input, brightness_tensor)

Parameter:

• input - Input tensor to operator. Supported dimensions: minimum = 1, maximum = 4. Supported data types: UINT8, UINT16, FLOAT32.

• (optional) brightness_tensor - Tensor containing brightness value for each image in the batch. Supported dimensions: minimum = 1, maximum = 1. Supported data type: FLOAT32.

Description:

This operator is used to vary the brightness of the image. Brightness operator scales each pixel in image data with brightness scale. The equation for obtaining output pixel value is: output = input * brightness_scale

Supported backend:

• HPU

Keyword Arguments

kwargs	Description
brightness_scale	Brightness scale factor required for pixel manipulation. Type: float Default: 0.0 Optional: yes
dtype	Output data type. Type: habana_frameworks.mediapipe.media_types.dtype Default: UINT8 Optional: yes Supported data type: UINT8 INT8 FLOAT32

Note

1. Input/output tensor must be of the same data type.

2. Input1 and output tensor could be in any layout but both have to be same.

3. Input2 is one dimensional tensor of size N.

4. Brightness value can be provided either as scalar for all batches or 1D tensor.

Example: Brightness Operator

The following code snippet shows usage of Brightness 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.brightness = fn.Brightness(brightness_scale=1.8,
                                        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.brightness(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 Brightness 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.