habana_frameworks.mediapipe.fn.Crop

Class:

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

Define graph call:

• __call__(input)

Parameter:

• input - Input tensor to operator. Supported dimensions: minimum = 4, maximum = 5. Supported data types: UINT8, UINT16.

Description:

This operator crops input tensor with specified window dimensions and window position. Crop kernel can crop input along W/H/C dimensions. It can also cast output to FLOAT32 or FLOAT16 data type.

Supported backend:

• HPU, CPU

Keyword Arguments

kwargs	Description
crop_w	Specify width of crop window in pixels. crop_w should be non zero value and less than or equal to input tensor width. Type: int Default: 100 Optional: yes
crop_h	Specify height of crop window in pixels. crop_h should be non zero value and less than or equal to input tensor height. Type: int Default: 100 Optional: yes
crop_d	Cropping along depth axis is optional. crop_d should be set to 0 if there are no cropping along depth axis. crop_d specify depth of crop window in pixels, its default set to zero, only for volumetric data crop_d should be non zero value and less than or equal to input tensor depth. Type: int Default: 0 Optional: yes
crop_pos_x	Normalized (0.0 - 1.0) position of the cropping window along width. Actual position is calculated as crop_x = crop_pos_x * (w - crop_w), where crop_pos_x is the normalized position, w is the width of the input tensor and crop_w is the width of the cropping window. Type: float Default: 0.0 Optional: yes
crop_pos_y	Normalized (0.0 - 1.0) position of the cropping window along height. Actual position is calculated as crop_y = crop_pos_y * (h - crop_h), where crop_pos_y is the normalized position, h is the height of the input tensor and crop_h is the height of the cropping window. Type: float Default: 0.0 Optional: yes
crop_pos_z	Only for volumetric data, normalized (0.0 - 1.0) position of the cropping window along depth. Actual position is calculated as crop_z = crop_pos_z * (d - crop_d), where crop_pos_z is the normalized position, d is the depth of the input tensor and crop_d is the depth of the cropping window. Type: float Default: 0.0 Optional: yes
dtype	Output data type. Type: habana_frameworks.mediapipe.media_types.dtype Default: UINT8 Optional: yes Note: User should manually set dtype same as input data type. Otherwise output tensor’s data type will get changed to default UINT8. Supported data types: UINT8

Example: Crop Operator

The following code snippet shows usage of Crop operator. The first decoder produces images of width=200, height=200. These are fed to crop operator with crop_w=150, crop_h=150.

from habana_frameworks.mediapipe import fn
from habana_frameworks.mediapipe.mediapipe import MediaPipe
from habana_frameworks.mediapipe.media_types import dtype as dt

# Create media pipeline derived class
class myMediaPipe(MediaPipe):
    def __init__(self, device, queue_depth, batch_size, num_threads, dir):
        super(myMediaPipe, self).__init__(
            device,
            queue_depth,
            batch_size,
            num_threads,
            self.__class__.__name__)

        self.inp = fn.ReadNumpyDatasetFromDir(num_outputs=1,
                                              shuffle=False,
                                              dir=dir,
                                              pattern="inp_x_*.npy",
                                              dense=True,
                                              dtype=dt.UINT8,
                                              device=device)

        self.crop = fn.Crop(crop_h=2,
                            crop_w=2,
                            dtype=dt.UINT8,
                            device=device)


    def definegraph(self):
        inp = self.inp()
        out = self.crop(inp)
        return out, inp


def main():
    batch_size = 2
    queue_depth = 2
    num_threads = 1
    dir = '/path/to/numpy/files'
    device = 'hpu'

    # Create media pipeline object
    pipe = myMediaPipe(device, queue_depth, batch_size, num_threads, dir)

    # Build media pipeline
    pipe.build()

    # Initialize media pipeline iterator
    pipe.iter_init()

    # Run media pipeline
    out, inp = pipe.run()

    if (device == 'cpu'):
        # Copy data as numpy array
        out = out.as_nparray()
        inp = inp.as_nparray()
    else:
        # Copy data to host from device as numpy array
        out = out.as_cpu().as_nparray()
        inp = inp.as_cpu().as_nparray()

    print("\ninp tensor shape:", inp.shape)
    print("inp tensor dtype:", inp.dtype)
    print("inp tensor data:\n", inp)

    print("\nout tensor shape:", out.shape)
    print("out tensor dtype:", out.dtype)
    print("out tensor data:\n", out)

    pipe.del_iter()


if __name__ == "__main__":
    main()

The following is the output of Crop operator:

inp tensor shape: (2, 3, 2, 3)
inp tensor dtype: uint8
inp tensor data:
[[[[149 187 232]
  [160 201 202]]

  [[ 80 147 153]
  [199 174 158]]

  [[200 124 139]
  [  3 161 216]]]


[[[106  93  83]
  [ 57 253  52]]

  [[222 189  26]
  [174  60 118]]

  [[218  84  43]
  [251  75  73]]]]

out tensor shape: (2, 3, 2, 2)
out tensor dtype: uint8
out tensor data:
[[[[149 187]
  [160 201]]

  [[ 80 147]
  [199 174]]

  [[200 124]
  [  3 161]]]


[[[106  93]
  [ 57 253]]

  [[222 189]
  [174  60]]

  [[218  84]
  [251  75]]]]