/home/docs/checkouts/readthedocs.org/user_builds/advanced-micro-devices-rocal/checkouts/docs-6.1.1/rocAL_pybind/amd/rocal/fn.py File Reference#
fn.py File Reference
File containing augmentation functions used in multiple trainings. More...
Functions | |
| def | rocAL_pybind.amd.rocal.fn.blend (*inputs, ratio=None, device=None, output_layout=types.NHWC, output_dtype=types.UINT8) |
| Blends two input images given the ratio: output = input1*ratio + input2*(1-ratio) More... | |
| def | rocAL_pybind.amd.rocal.fn.snow (*inputs, snow=0.5, device=None, output_layout=types.NHWC, output_dtype=types.UINT8) |
| Applies snow effect on images. More... | |
| def | rocAL_pybind.amd.rocal.fn.exposure (*inputs, exposure=0.5, device=None, output_layout=types.NHWC, output_dtype=types.UINT8) |
| Adjusts the exposure in images. More... | |
| def | rocAL_pybind.amd.rocal.fn.fish_eye (*inputs, device=None, fill_value=0.0, output_layout=types.NHWC, output_dtype=types.UINT8) |
| Applies fish eye effect on images. More... | |
| def | rocAL_pybind.amd.rocal.fn.fog (*inputs, fog=0.5, device=None, output_layout=types.NHWC, output_dtype=types.UINT8) |
| Applies fog effect on images. More... | |
| def | rocAL_pybind.amd.rocal.fn.brightness (*inputs, brightness=None, brightness_shift=None, device=None, output_layout=types.NHWC, output_dtype=types.UINT8) |
| Adjusts brightness of the image. More... | |
| def | rocAL_pybind.amd.rocal.fn.brightness_fixed (*inputs, brightness=1.0, brightness_shift=0.0, device=None, output_layout=types.NHWC, output_dtype=types.UINT8) |
| Adjusts brightness of the image with fixed parameters. More... | |
| def | rocAL_pybind.amd.rocal.fn.lens_correction (*inputs, strength=None, zoom=None, device=None, output_layout=types.NHWC, output_dtype=types.UINT8) |
| Applies lens correction effect on images. More... | |
| def | rocAL_pybind.amd.rocal.fn.blur (*inputs, window_size=None, sigma=0.0, device=None, output_layout=types.NHWC, output_dtype=types.UINT8) |
| Applies blur effect to images. More... | |
| def | rocAL_pybind.amd.rocal.fn.contrast (*inputs, contrast=None, contrast_center=None, device=None, output_layout=types.NHWC, output_dtype=types.UINT8) |
| Adjusts contrast of the image. More... | |
| def | rocAL_pybind.amd.rocal.fn.flip (*inputs, horizontal=0, vertical=0, device=None, output_layout=types.NHWC, output_dtype=types.UINT8) |
| Flip images horizontally and/or vertically based on inputs. More... | |
| def | rocAL_pybind.amd.rocal.fn.gamma_correction (*inputs, gamma=0.5, device=None, output_layout=types.NHWC, output_dtype=types.UINT8) |
| Applies gamma correction on image. More... | |
| def | rocAL_pybind.amd.rocal.fn.hue (*inputs, hue=None, device=None, seed=0, output_layout=types.NHWC, output_dtype=types.UINT8) |
| Adjust the hue in the images. More... | |
| def | rocAL_pybind.amd.rocal.fn.jitter (*inputs, kernel_size=None, seed=0, fill_value=0.0, device=None, output_layout=types.NHWC, output_dtype=types.UINT8) |
| Applies Jitter effect on images. More... | |
| def | rocAL_pybind.amd.rocal.fn.pixelate (*inputs, device=None, output_layout=types.NHWC, output_dtype=types.UINT8) |
| Applies pixelate effect on images. More... | |
| def | rocAL_pybind.amd.rocal.fn.rain (*inputs, rain=None, rain_width=None, rain_height=None, rain_transparency=None, device=None, output_layout=types.NHWC, output_dtype=types.UINT8) |
| Applies Rain effect on images. More... | |
| def | rocAL_pybind.amd.rocal.fn.resize (*inputs, max_size=[], resize_longer=0, resize_shorter=0, resize_width=0, resize_height=0, scaling_mode=types.SCALING_MODE_DEFAULT, interpolation_type=types.LINEAR_INTERPOLATION, antialias=True, device=None, output_layout=types.NHWC, output_dtype=types.UINT8) |
| Resizes the images. More... | |
| def | rocAL_pybind.amd.rocal.fn.resize_crop_mirror (*inputs, resize_width=0, resize_height=0, crop_w=0, crop_h=0, mirror=1, device=None, max_size=[], resize_longer=0, resize_shorter=0, scaling_mode=types.SCALING_MODE_DEFAULT, interpolation_type=types.LINEAR_INTERPOLATION, output_layout=types.NHWC, output_dtype=types.UINT8) |
| Fused function which performs resize, crop and flip on images. More... | |
| def | rocAL_pybind.amd.rocal.fn.resize_crop (*inputs, resize_width=0, resize_height=0, crop_area_factor=None, crop_aspect_ratio=None, x_drift=None, y_drift=None, device=None, max_size=[], resize_longer=0, resize_shorter=0, scaling_mode=types.SCALING_MODE_DEFAULT, interpolation_type=types.LINEAR_INTERPOLATION, output_layout=types.NHWC, output_dtype=types.UINT8) |
| Fused function which performs resize, crop on images. More... | |
| def | rocAL_pybind.amd.rocal.fn.resize_mirror_normalize (*inputs, max_size=[], resize_longer=0, resize_shorter=0, resize_width=0, resize_height=0, scaling_mode=types.SCALING_MODE_DEFAULT, interpolation_type=types.LINEAR_INTERPOLATION, mean=[0.0], std=[1.0], mirror=1, device=None, output_layout=types.NHWC, output_dtype=types.UINT8) |
| Fused function which performs resize, Normalize and flip on images. More... | |
| def | rocAL_pybind.amd.rocal.fn.random_crop (*inputs, crop_area_factor=[0.08, 1], crop_aspect_ratio=[0.75, 1.333333], crop_pox_x=0, crop_pox_y=0, num_attempts=20, device=None, all_boxes_above_threshold=True, allow_no_crop=True, ltrb=True, output_layout=types.NHWC, output_dtype=types.UINT8) |
| Crops images randomly. More... | |
| def | rocAL_pybind.amd.rocal.fn.rotate (*inputs, angle=None, dest_width=0, dest_height=0, interpolation_type=types.LINEAR_INTERPOLATION, device=None, fill_value=None, output_layout=types.NHWC, output_dtype=types.UINT8) |
| Rotates images. More... | |
| def | rocAL_pybind.amd.rocal.fn.saturation (*inputs, saturation=1.0, device=None, output_layout=types.NHWC, output_dtype=types.UINT8) |
| Adjusts the saturation in images. More... | |
| def | rocAL_pybind.amd.rocal.fn.ssd_random_crop (*inputs, p_threshold=None, crop_area_factor=None, crop_aspect_ratio=None, crop_pos_x=None, crop_pos_y=None, num_attempts=20, device=None, all_boxes_above_threshold=True, allow_no_crop=True, ltrb=True, output_layout=types.NHWC, output_dtype=types.UINT8) |
| Crops images randomly used for SSD training. More... | |
| def | rocAL_pybind.amd.rocal.fn.warp_affine (*inputs, dest_width=0, dest_height=0, matrix=[0, 0, 0, 0, 0, 0], interpolation_type=types.LINEAR_INTERPOLATION, device=None, output_layout=types.NHWC, output_dtype=types.UINT8) |
| Applies affine transformation to images. More... | |
| def | rocAL_pybind.amd.rocal.fn.vignette (*inputs, vignette=0.5, device=None, output_layout=types.NHWC, output_dtype=types.UINT8) |
| Applies Vignette effect. More... | |
| def | rocAL_pybind.amd.rocal.fn.crop_mirror_normalize (*inputs, crop=[0, 0], crop_pos_x=0.5, crop_pos_y=0.5, crop_w=0, crop_h=0, mean=[0.0], std=[1.0], mirror=1, device=None, output_layout=types.NHWC, output_dtype=types.UINT8) |
| Fused function which performs crop, normalize and flip on images. More... | |
| def | rocAL_pybind.amd.rocal.fn.center_crop (*inputs, crop=[0, 0], crop_h=0, crop_w=0, crop_d=1, device=None, output_layout=types.NHWC, output_dtype=types.UINT8) |
| Crops images at the center. More... | |
| def | rocAL_pybind.amd.rocal.fn.crop (*inputs, crop=[0, 0], crop_pos_x=0.5, crop_pos_y=0.5, crop_pos_z=0.5, crop_w=0, crop_h=0, crop_d=1, device=None, output_layout=types.NHWC, output_dtype=types.UINT8) |
| crops images More... | |
| def | rocAL_pybind.amd.rocal.fn.color_twist (*inputs, brightness=1.0, contrast=1.0, hue=0.0, saturation=1.0, device=None, output_layout=types.NHWC, output_dtype=types.UINT8) |
| Adjusts the brightness, hue and saturation of the images. More... | |
| def | rocAL_pybind.amd.rocal.fn.uniform (*inputs, range=[-1, 1], device=None) |
| Applies uniform random number generation to the input images. More... | |
| def | rocAL_pybind.amd.rocal.fn.random_bbox_crop (*inputs, all_boxes_above_threshold=True, allow_no_crop=True, aspect_ratio=None, bbox_layout="", threshold_type="iou", thresholds=None, crop_shape=None, num_attempts=1, scaling=None, seed=1, shape_layout="", input_shape=None, total_num_attempts=0, device=None, ltrb=True, labels=None) |
| Applies random bounding box cropping to the input images. More... | |
| def | rocAL_pybind.amd.rocal.fn.one_hot (*inputs, num_classes=0, device=None) |
| Applies one-hot encoding to the input images. More... | |
| def | rocAL_pybind.amd.rocal.fn.box_encoder (*inputs, anchors, criteria=0.5, means=None, offset=False, scale=1.0, stds=None, device=None) |
| Applies box encoding to the input bounding boxes. More... | |
| def | rocAL_pybind.amd.rocal.fn.color_temp (*inputs, adjustment_value=50, device=None, output_layout=types.NHWC, output_dtype=types.UINT8) |
| Adjusts the color temperature in images. More... | |
| def | rocAL_pybind.amd.rocal.fn.nop (*inputs, device=None) |
| Performs no operation. More... | |
| def | rocAL_pybind.amd.rocal.fn.copy (*inputs, device=None) |
| Copies input tensor to output tensor. More... | |
| def | rocAL_pybind.amd.rocal.fn.snp_noise (*inputs, p_noise=0.0, p_salt=0.0, noise_val=0.0, salt_val=0.0, seed=0, device=None, output_layout=types.NHWC, output_dtype=types.UINT8) |
| Applies salt-and-pepper noise to the input image. More... | |
| def | rocAL_pybind.amd.rocal.fn.box_iou_matcher (*inputs, anchors, high_threshold=0.5, low_threshold=0.4, allow_low_quality_matches=True, device=None) |
| Applies box IoU matching to the input image. More... | |
| def | rocAL_pybind.amd.rocal.fn.external_source (*inputs, source, device=None, color_format=types.RGB, random_shuffle=False, mode=types.EXTSOURCE_FNAME, max_width=2000, max_height=2000) |
Detailed Description
File containing augmentation functions used in multiple trainings.
Function Documentation
◆ blend()
| def rocAL_pybind.amd.rocal.fn.blend | ( | * | inputs, |
ratio = None, |
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device = None, |
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output_layout = types.NHWC, |
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output_dtype = types.UINT8 |
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| ) |
Blends two input images given the ratio: output = input1*ratio + input2*(1-ratio)
@param inputs list containing the input images
@param ratio (float, optional, default = None) ratio used for blending one image with another
@param device (string, optional, default = None) Parameter unused for augmentation
@param output_layout (int, optional, default = types.NHWC) tensor layout for the augmentation output
@param output_dtype (int, optional, default = types.UINT8) tensor dtype for the augmentation output
@return blended image
◆ blur()
| def rocAL_pybind.amd.rocal.fn.blur | ( | * | inputs, |
window_size = None, |
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sigma = 0.0, |
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device = None, |
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output_layout = types.NHWC, |
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output_dtype = types.UINT8 |
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| ) |
Applies blur effect to images.
@param inputs the input image passed to the augmentation
@param window_size (int, default = None) kernel size used for the filter
@param sigma (float, default = 0.0) sigma value for blur effect
@param device (string, optional, default = None) Parameter unused for augmentation
@param output_layout (int, optional, default = types.NHWC) tensor layout for the augmentation output
@param output_dtype (int, optional, default = types.UINT8) tensor dtype for the augmentation output
@return Image with Blur effect
◆ box_encoder()
| def rocAL_pybind.amd.rocal.fn.box_encoder | ( | * | inputs, |
| anchors, | |||
criteria = 0.5, |
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means = None, |
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offset = False, |
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scale = 1.0, |
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stds = None, |
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device = None |
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| ) |
Applies box encoding to the input bounding boxes.
@param inputs (list) The input bounding boxes to which box encoding is applied.
@param anchors (list of floats) Anchors to be used for encoding, as a list of floats in the ltrb format.
@param criteria (float, optional, default = 0.5) Threshold IoU for matching bounding boxes with anchors. The value needs to be between 0 and 1. Default is 0.5.
@param means (list of floats, optional, default = None) [x y w h] mean values for normalization. Default is [0.0, 0.0, 0.0, 0.0].
@param offset (bool, optional, default = False) Returns normalized offsets ((encoded_bboxes * scale - anchors * scale) - mean) / stds in Encoded bboxes that use std and the mean and scale arguments. Default is False.
@param scale (float, optional, default = 1.0) Rescales the box and anchor values before the offset is calculated (for example, to return to the absolute values). Default is 1.0.
@param stds (list of float, optional, default = None) Parameter unused for augmentation
@param device (string, optional, default = None) Parameter unused for augmentation
@return encoded bounding boxes.
◆ box_iou_matcher()
| def rocAL_pybind.amd.rocal.fn.box_iou_matcher | ( | * | inputs, |
| anchors, | |||
high_threshold = 0.5, |
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low_threshold = 0.4, |
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allow_low_quality_matches = True, |
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device = None |
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| ) |
Applies box IoU matching to the input image.
@param inputs (list) The input image to which box IoU matching is applied.
@param anchors (list of floats) Anchors to be used for encoding, in the ltrb format.
@param high_threshold (float, optional, default = 0.5) Upper threshold used for matching indices. Default is 0.5.
@param low_threshold (float, optional, default = 0.4) Lower threshold used for matching indices. Default is 0.4.
@param allow_low_quality_matches (bool, optional, default = True) Whether to allow low quality matches as output. Default is True.
@param device (string, optional, default = None) Parameter unused for augmentation
@return matched boxes and the list of matched indices.
◆ brightness()
| def rocAL_pybind.amd.rocal.fn.brightness | ( | * | inputs, |
brightness = None, |
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brightness_shift = None, |
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device = None, |
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output_layout = types.NHWC, |
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output_dtype = types.UINT8 |
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| ) |
Adjusts brightness of the image.
@param inputs the input image passed to the augmentation
@param brightness (float, optional, default = None): brightness multiplier. Values >= 0 are accepted. For example: 0 - black image, 1 - no change, 2 - increase brightness twice
@param brightness_shift (float, optional, default = None) brightness shift
@param device (string, optional, default = None) Parameter unused for augmentation
@param output_layout (int, optional, default = types.NHWC) tensor layout for the augmentation output
@param output_dtype (int, optional, default = types.UINT8) tensor dtype for the augmentation output
@return Image with Adjusted Brightness
◆ brightness_fixed()
| def rocAL_pybind.amd.rocal.fn.brightness_fixed | ( | * | inputs, |
brightness = 1.0, |
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brightness_shift = 0.0, |
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device = None, |
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output_layout = types.NHWC, |
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output_dtype = types.UINT8 |
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| ) |
Adjusts brightness of the image with fixed parameters.
@param inputs the input image passed to the augmentation
@param brightness (float, optional, default = 1.0) brightness multiplier. Values >= 0 are accepted. For example: 0 - black image, 1 - no change, 2 - increase brightness twice
@param brightness_shift (float, optional, default = 0.0) brightness shift
@param device (string, optional, default = None) Parameter unused for augmentation
@param output_layout (int, optional, default = types.NHWC) tensor layout for the augmentation output
@param output_dtype (int, optional, default = types.UINT8) tensor dtype for the augmentation output
@return Image with adjusted brightness
◆ center_crop()
| def rocAL_pybind.amd.rocal.fn.center_crop | ( | * | inputs, |
crop = [0, 0], |
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crop_h = 0, |
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crop_w = 0, |
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crop_d = 1, |
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device = None, |
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output_layout = types.NHWC, |
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output_dtype = types.UINT8 |
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| ) |
Crops images at the center.
@param inputs the input image passed to the augmentation
@param crop (list of ints, optional, default = [0, 0]) list containing the crop dimensions of the cropped image
@param crop_h (int, optional, default = 0) crop height
@param crop_w (int, optional, default = 0) crop width
@param crop_d (int, optional, default = 0) crop depth
@param device (string, optional, default = None) Parameter unused for augmentation
@param output_layout (int, optional, default = types.NHWC) tensor layout for the augmentation output
@param output_dtype (int, optional, default = types.UINT8) tensor dtype for the augmentation output
@return Center cropped Images
◆ color_temp()
| def rocAL_pybind.amd.rocal.fn.color_temp | ( | * | inputs, |
adjustment_value = 50, |
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device = None, |
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output_layout = types.NHWC, |
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output_dtype = types.UINT8 |
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| ) |
Adjusts the color temperature in images.
@param inputs the input image passed to the augmentation
@param adjustment_value (int, default = 50) value for adjusting the color temperature
@param device (string, optional, default = None) Parameter unused for augmentation
@param output_layout (int, optional, default = types.NHWC) tensor layout for the augmentation output
@param output_dtype (int, optional, default = types.UINT8) tensor dtype for the augmentation output
@return Images with Adjusted Color temperature
◆ color_twist()
| def rocAL_pybind.amd.rocal.fn.color_twist | ( | * | inputs, |
brightness = 1.0, |
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contrast = 1.0, |
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hue = 0.0, |
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saturation = 1.0, |
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device = None, |
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output_layout = types.NHWC, |
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output_dtype = types.UINT8 |
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| ) |
Adjusts the brightness, hue and saturation of the images.
@param inputs the input image passed to the augmentation
@param brightness (float, optional, default = 1.0) brightness multiplier. Values >= 0 are accepted. For example: 0 - black image, 1 - no change, 2 - increase brightness twice
@param contrast (float, optional, default = 1.0) contrast multiplier used for the augmentation. Values >= 0 are accepted. For example: 0 - gray image, 1 - no change, 2 - increase contrast twice
@param hue (float, optional, default = 0.0) hue change in degrees
@param saturation (float, optional, default = 1.0) The saturation change factor. Values must be non-negative. Example values: 0 - Completely desaturated image, 1 - No change to image's saturation.
@param device (string, optional, default = None) Parameter unused for augmentation
@param output_layout (int, optional, default = types.NHWC) tensor layout for the augmentation output
@param output_dtype (int, optional, default = types.UINT8): tensor dtype for the augmentation output
@return Images with Adjusted Brightness, Hue and Saturation
◆ contrast()
| def rocAL_pybind.amd.rocal.fn.contrast | ( | * | inputs, |
contrast = None, |
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contrast_center = None, |
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device = None, |
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output_layout = types.NHWC, |
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output_dtype = types.UINT8 |
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| ) |
Adjusts contrast of the image.
@param inputs: the input image passed to the augmentation
@param contrast (float, optional, default = None) contrast multiplier used for the augmentation. Values >= 0 are accepted. For example: 0 - gray image, 1 - no change, 2 - increase contrast twice
@param contrast_center (float, optional, default = None) intensity value unaffected by the augmentation
@param device (string, optional, default = None) Parameter unused for augmentation
@param output_layout (int, optional, default = types.NHWC) tensor layout for the augmentation output
@param output_dtype (int, optional, default = types.UINT8) tensor dtype for the augmentation output
@return Image with adjusted contrast
◆ copy()
| def rocAL_pybind.amd.rocal.fn.copy | ( | * | inputs, |
device = None |
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| ) |
Copies input tensor to output tensor.
@param inputs the input image passed to the augmentation
@param device (string, optional, default = None) Parameter unused for augmentation
@return Copied Image
◆ crop()
| def rocAL_pybind.amd.rocal.fn.crop | ( | * | inputs, |
crop = [0, 0], |
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crop_pos_x = 0.5, |
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crop_pos_y = 0.5, |
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crop_pos_z = 0.5, |
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crop_w = 0, |
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crop_h = 0, |
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crop_d = 1, |
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device = None, |
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output_layout = types.NHWC, |
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output_dtype = types.UINT8 |
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| ) |
crops images
@param inputs the input image passed to the augmentation
@param crop (list of ints, optional, default = [0, 0]) list containing the crop dimensions of the cropped image
@param crop_pox_x (float, optional, default = 0.5) crop_x position used for crop generation
@param crop_pox_y (float, optional, default = 0.5) crop_y position used for crop generation
@param crop_pox_z (float, optional, default = 0.5) crop_z position used for crop generation
@param crop_w (int, optional, default = 0) crop width
@param crop_h (int, optional, default = 0) crop height
@param crop_d (int, optional, default = 1) crop depth
@param device (string, optional, default = None) Parameter unused for augmentation
@param output_layout (int, optional, default = types.NHWC) tensor layout for the augmentation output
@param output_dtype (int, optional, default = types.UINT8) tensor dtype for the augmentation output
@return Cropped Images
◆ crop_mirror_normalize()
| def rocAL_pybind.amd.rocal.fn.crop_mirror_normalize | ( | * | inputs, |
crop = [0, 0], |
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crop_pos_x = 0.5, |
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crop_pos_y = 0.5, |
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crop_w = 0, |
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crop_h = 0, |
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mean = [0.0], |
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std = [1.0], |
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mirror = 1, |
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device = None, |
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output_layout = types.NHWC, |
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output_dtype = types.UINT8 |
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| ) |
Fused function which performs crop, normalize and flip on images.
@param inputs the input image passed to the augmentation
@param crop (list of ints, optional, default = [0, 0]) list containing the crop dimensions of the cropped image
@param crop_pox_x (float, optional, default = 0.5) crop_x position used for crop generation
@param crop_pox_y (float, optional, default = 0.5) crop_y position used for crop generation
@param crop_w (int, optional, default = 0) crop width
@param crop_h (int, optional, default = 0) crop height
@param mean (list of floats, optional, default = [0.0]) mean used for normalization
@param std (list of floats, optional, default = [1.0]) standard deviation used for normalization
@param mirror (int, optional, default = 1) flag for the horizontal flip.
@param device (string, optional, default = None) Parameter unused for augmentation
@param output_layout (int, optional, default = types.NHWC) tensor layout for the augmentation output
@param output_dtype (int, optional, default = types.UINT8) tensor dtype for the augmentation output
@return Transformed Images after perform crop , normalize and flip operations
◆ exposure()
| def rocAL_pybind.amd.rocal.fn.exposure | ( | * | inputs, |
exposure = 0.5, |
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device = None, |
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output_layout = types.NHWC, |
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output_dtype = types.UINT8 |
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| ) |
Adjusts the exposure in images.
@param inputs the input image passed to the augmentation
@param exposure (float, default = 0.5) exposure fill value used for the augmentation
@param device (string, optional, default = None) Parameter unused for augmentation
@param output_layout (int, optional, default = types.NHWC) tensor layout for the augmentation output
@param output_dtype (int, optional, default = types.UINT8) tensor dtype for the augmentation output
@return Image with adjusted exposure
◆ fish_eye()
| def rocAL_pybind.amd.rocal.fn.fish_eye | ( | * | inputs, |
device = None, |
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fill_value = 0.0, |
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output_layout = types.NHWC, |
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output_dtype = types.UINT8 |
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| ) |
Applies fish eye effect on images.
@param inputs the input image passed to the augmentation
@param fill_value (float, optional, default = 0.0) Parameter unused for augmentation
@param device (string, optional, default = None) Parameter unused for augmentation
@param output_layout (int, optional, default = types.NHWC) tensor layout for the augmentation output
@param output_dtype (int, optional, default = types.UINT8) tensor dtype for the augmentation output
@return Image with fish eye effect
◆ flip()
| def rocAL_pybind.amd.rocal.fn.flip | ( | * | inputs, |
horizontal = 0, |
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vertical = 0, |
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device = None, |
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output_layout = types.NHWC, |
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output_dtype = types.UINT8 |
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| ) |
Flip images horizontally and/or vertically based on inputs.
@param inputs the input image passed to the augmentation
@param horizontal (int, optional, default = 0) flip the horizontal dimension
@param vertical (int, optional, default = 0) flip the vertical dimension
@param device (string, optional, default = None) Parameter unused for augmentation
@param output_layout (int, optional, default = types.NHWC) tensor layout for the augmentation output
@param output_dtype (int, optional, default = types.UINT8) tensor dtype for the augmentation output
@return Flipped Image
◆ fog()
| def rocAL_pybind.amd.rocal.fn.fog | ( | * | inputs, |
fog = 0.5, |
|||
device = None, |
|||
output_layout = types.NHWC, |
|||
output_dtype = types.UINT8 |
|||
| ) |
Applies fog effect on images.
@param inputs the input image passed to the augmentation
@param fog (float, default = 0.5) fog fill value used for the augmentation
@param device (string, optional, default = None) Parameter unused for augmentation
@param output_layout (int, optional, default = types.NHWC) tensor layout for the augmentation output
@param output_dtype (int, optional, default = types.UINT8) tensor dtype for the augmentation output
@return Image with fog effect
◆ gamma_correction()
| def rocAL_pybind.amd.rocal.fn.gamma_correction | ( | * | inputs, |
gamma = 0.5, |
|||
device = None, |
|||
output_layout = types.NHWC, |
|||
output_dtype = types.UINT8 |
|||
| ) |
Applies gamma correction on image.
@param inputs the input image passed to the augmentation
@param gamma (float, default = 0.5) gamma correction value used for the augmentation
@param device (string, optional, default = None) Parameter unused for augmentation
@param output_layout (int, optional, default = types.NHWC) tensor layout for the augmentation output
@param output_dtype (int, optional, default = types.UINT8) tensor dtype for the augmentation output
@return Image with Gamma Correction
◆ hue()
| def rocAL_pybind.amd.rocal.fn.hue | ( | * | inputs, |
hue = None, |
|||
device = None, |
|||
seed = 0, |
|||
output_layout = types.NHWC, |
|||
output_dtype = types.UINT8 |
|||
| ) |
Adjust the hue in the images.
@param inputs the input image passed to the augmentation
@param hue (float, default = None) hue change in degrees
@param seed (int, optional, default = 0) seed used for randomization in the augmentation
@param device (string, optional, default = None) Parameter unused for augmentation
@param output_layout (int, optional, default = types.NHWC) tensor layout for the augmentation output
@param output_dtype (int, optional, default = types.UINT8) tensor dtype for the augmentation output
@return Image with Hue effect
◆ jitter()
| def rocAL_pybind.amd.rocal.fn.jitter | ( | * | inputs, |
kernel_size = None, |
|||
seed = 0, |
|||
fill_value = 0.0, |
|||
device = None, |
|||
output_layout = types.NHWC, |
|||
output_dtype = types.UINT8 |
|||
| ) |
Applies Jitter effect on images.
@param inputs the input image passed to the augmentation
@param kernel_size (int, optional, default = None) kernel size used for the augmentation
@param seed (int, optional, default = 0) seed used for randomization in the augmentation
@param fill_value (float, optional, default = 0.0) Value to fill areas outside image.
@param device (string, optional, default = None) Parameter unused for augmentation
@param output_layout (int, optional, default = types.NHWC) tensor layout for the augmentation output
@param output_dtype (int, optional, default = types.UINT8) tensor dtype for the augmentation output
@return Image with Jitter effect
◆ lens_correction()
| def rocAL_pybind.amd.rocal.fn.lens_correction | ( | * | inputs, |
strength = None, |
|||
zoom = None, |
|||
device = None, |
|||
output_layout = types.NHWC, |
|||
output_dtype = types.UINT8 |
|||
| ) |
Applies lens correction effect on images.
@param inputs the input image passed to the augmentation
@param strength (float, optional, default = None) strength value used for the augmentation
@param zoom (float, optional, default = None) zoom value used for the augmentation
@param device (string, optional, default = None) Parameter unused for augmentation
@param output_layout (int, optional, default = types.NHWC) tensor layout for the augmentation output
@param output_dtype (int, optional, default = types.UINT8) tensor dtype for the augmentation output
@return Image with lens correction effect
◆ nop()
| def rocAL_pybind.amd.rocal.fn.nop | ( | * | inputs, |
device = None |
|||
| ) |
Performs no operation.
@param inputs the input image passed to the augmentation
@param device (string, optional, default = None) Parameter unused for augmentation
@return Nop Output
◆ one_hot()
| def rocAL_pybind.amd.rocal.fn.one_hot | ( | * | inputs, |
num_classes = 0, |
|||
device = None |
|||
| ) |
Applies one-hot encoding to the input images.
@param inputs (list) The input images to which one-hot encoding is applied.
@param num_classes (int, optional, default = 0) Number of classes used for one-hot encoding. Default is 0.
@param device (string, optional, default = None) Parameter unused for augmentation
@return an empty list
◆ pixelate()
| def rocAL_pybind.amd.rocal.fn.pixelate | ( | * | inputs, |
device = None, |
|||
output_layout = types.NHWC, |
|||
output_dtype = types.UINT8 |
|||
| ) |
Applies pixelate effect on images.
@param inputs the input image passed to the augmentation
@param device (string, optional, default = None) Parameter unused for augmentation
@param output_layout (int, optional, default = types.NHWC) tensor layout for the augmentation output
@param output_dtype (int, optional, default = types.UINT8) tensor dtype for the augmentation output
@return Images with pixelate effect
◆ rain()
| def rocAL_pybind.amd.rocal.fn.rain | ( | * | inputs, |
rain = None, |
|||
rain_width = None, |
|||
rain_height = None, |
|||
rain_transparency = None, |
|||
device = None, |
|||
output_layout = types.NHWC, |
|||
output_dtype = types.UINT8 |
|||
| ) |
Applies Rain effect on images.
@param inputs the input image passed to the augmentation
@param rain (float, optional, default = None) rain fill value used for the augmentation
@param rain_width (int, optional, default = None) width of the rain pixels for the augmentation
@param rain_height (int, optional, default = None) height of the rain pixels for the augmentation
@param rain_transparency (float, optional, default = None) transparency value used for the augmentation
@param device (string, optional, default = None) Parameter unused for augmentation
@param output_layout (int, optional, default = types.NHWC): tensor layout for the augmentation output
@param output_dtype (int, optional, default = types.UINT8) tensor dtype for the augmentation output
@return Images with Rain effect
◆ random_bbox_crop()
| def rocAL_pybind.amd.rocal.fn.random_bbox_crop | ( | * | inputs, |
all_boxes_above_threshold = True, |
|||
allow_no_crop = True, |
|||
aspect_ratio = None, |
|||
bbox_layout = "", |
|||
threshold_type = "iou", |
|||
thresholds = None, |
|||
crop_shape = None, |
|||
num_attempts = 1, |
|||
scaling = None, |
|||
seed = 1, |
|||
shape_layout = "", |
|||
input_shape = None, |
|||
total_num_attempts = 0, |
|||
device = None, |
|||
ltrb = True, |
|||
labels = None |
|||
| ) |
Applies random bounding box cropping to the input images.
@param inputs (list) The input images to which random cropping is applied.
@param all_boxes_above_threshold (bool, optional, default = True) If set to True, all bounding boxes in a sample should overlap with the cropping window. Default is True.
@param allow_no_crop (bool, optional, default = True) If set to True, one of the possible outcomes of the random process will be to not crop. Default is True.
@param aspect_ratio (list of floats, optional, default = None) Aspect ratio range [min, max] used for crop generation. Default is None.
@param crop_shape (list of ints, optional, default = None) Crop shape [width, height] used for crop generation. Default is None.
@param num_attempts (int, optional, default = 1) Number of attempts to get a crop window that matches the aspect_ratio and threshold. Default is 1.
@param scaling (list of int, optional, default = None) Scaling range [min, max] for the crop size with respect to the original image dimensions. Default is None.
@param seed (int, optional, default = 1) Random seed. Default is 1.
@param total_num_attempts (int, optional, default = 0) If provided, it indicates the total maximum number of attempts to get a crop window that matches the aspect_ratio and the threshold. After total_num_attempts attempts, the best candidate will be selected. If this value is not specified, the crop search will continue indefinitely until a valid crop is found. Default is 0.
@param device (string, optional, default = None) Parameter unused for augmentation
@param ltrb (bool, optional, default = True) Parameter unused for augmentation
@return cropped images
◆ random_crop()
| def rocAL_pybind.amd.rocal.fn.random_crop | ( | * | inputs, |
crop_area_factor = [0.08, 1], |
|||
crop_aspect_ratio = [0.75, 1.333333], |
|||
crop_pox_x = 0, |
|||
crop_pox_y = 0, |
|||
num_attempts = 20, |
|||
device = None, |
|||
all_boxes_above_threshold = True, |
|||
allow_no_crop = True, |
|||
ltrb = True, |
|||
output_layout = types.NHWC, |
|||
output_dtype = types.UINT8 |
|||
| ) |
Crops images randomly.
@param inputs: the input image passed to the augmentation
@param crop_area_factor (list of floats, optional, default = [0.08, 1]) area factor used for crop generation
@param crop_aspect_ratio (list of floats, optional, default = [0.75, 1.333333]) valid range of aspect ratio of the cropping windows
@param crop_pox_x (int, optional, default = 0) crop_x position used for crop generation
@param crop_pox_y (int, optional, default = 0) crop_y position used for crop generation
@param num_attempts (int, optional, default = 20) number of attempts to get a crop window that matches the area factor and aspect ratio conditions
@param device (string, optional, default = None) Parameter unused for augmentation
@param all_boxes_above_threshold (bool, optional, default = True) Parameter unused for augmentation
@param allow_no_crop (bool, optional, default = True) Parameter unused for augmentation
@param ltrb (bool, optional, default = True) Parameter unused for augmentation
@param output_layout (int, optional, default = types.NHWC) tensor layout for the augmentation output
@param output_dtype (int, optional, default = types.UINT8) tensor dtype for the augmentation output
@return cropped Image
◆ resize()
| def rocAL_pybind.amd.rocal.fn.resize | ( | * | inputs, |
max_size = [], |
|||
resize_longer = 0, |
|||
resize_shorter = 0, |
|||
resize_width = 0, |
|||
resize_height = 0, |
|||
scaling_mode = types.SCALING_MODE_DEFAULT, |
|||
interpolation_type = types.LINEAR_INTERPOLATION, |
|||
antialias = True, |
|||
device = None, |
|||
output_layout = types.NHWC, |
|||
output_dtype = types.UINT8 |
|||
| ) |
Resizes the images.
@param inputs the input image passed to the augmentation
@param max_size (int or list of int, optional, default = []) Maximum size of the longer dimension when resizing with resize_shorter. When set with resize_shorter, the shortest dimension will be resized to resize_shorter if the longest dimension is smaller or equal to max_size. If not, the shortest dimension is resized to satisfy the constraint longest_dim == max_size. Can be also an array of size 2, where the two elements are maximum size per dimension (H, W). Example: Original image = 400x1200. Resized with: resize_shorter = 200 (max_size not set) => 200x600 resize_shorter = 200, max_size = 400 => 132x400 resize_shorter = 200, max_size = 1000 => 200x600
@param resize_longer (int, optional, default = 0) The length of the longer dimension of the resized image. This option is mutually exclusive with resize_shorter,`resize_x` and resize_y. The op will keep the aspect ratio of the original image.
@param resize_shorter (int, optional, default = 0) The length of the shorter dimension of the resized image. This option is mutually exclusive with resize_longer, resize_x and resize_y. The op will keep the aspect ratio of the original image. The longer dimension can be bounded by setting the max_size argument. See max_size argument doc for more info.
@param resize_width (int, optional, default = 0) The length of the X dimension of the resized image. This option is mutually exclusive with resize_shorter. If the resize_y is left at 0, then the op will keep the aspect ratio of the original image.
@param resize_height (int, optional, default = 0) The length of the Y dimension of the resized image. This option is mutually exclusive with resize_shorter. If the resize_x is left at 0, then the op will keep the aspect ratio of the original image.
@param scaling_mode (int, optional, default = types.SCALING_MODE_DEFAULT) resize scaling mode.
@param interpolation_type (int, optional, default = types.LINEAR_INTERPOLATION) Type of interpolation to be used.
@param antialias (bool, optional, default = True) Parameter unused for augmentation
@param device (string, optional, default = None) Parameter unused for augmentation
@param output_layout (int, optional, default = types.NHWC) tensor layout for the augmentation output
@param output_dtype (int, optional, default = types.UINT8) tensor dtype for the augmentation output
@return Resized Image
◆ resize_crop()
| def rocAL_pybind.amd.rocal.fn.resize_crop | ( | * | inputs, |
resize_width = 0, |
|||
resize_height = 0, |
|||
crop_area_factor = None, |
|||
crop_aspect_ratio = None, |
|||
x_drift = None, |
|||
y_drift = None, |
|||
device = None, |
|||
max_size = [], |
|||
resize_longer = 0, |
|||
resize_shorter = 0, |
|||
scaling_mode = types.SCALING_MODE_DEFAULT, |
|||
interpolation_type = types.LINEAR_INTERPOLATION, |
|||
output_layout = types.NHWC, |
|||
output_dtype = types.UINT8 |
|||
| ) |
Fused function which performs resize, crop on images.
@param inputs: the input image passed to the augmentation
@param resize_width (int, optional, default = 0) The length of the X dimension of the resized image
@param resize_height (int, optional, default = 0) The length of the Y dimension of the resized image
@param crop_area_factor (float, optional, default = None) area factor used for crop generation
@param crop_aspect_ratio (float, optional, default = None) aspect ratio used for crop generation
@param device (string, optional, default = None) Parameter unused for augmentation
@param x_drift (float, optional, default = None) x_drift used for crop generation
@param y_drift (float, optional, default = None) y_drift used for crop generation
@param max_size (int or list of int, optional, default = []) Maximum size of the longer dimension when resizing with resize_shorter. When set with resize_shorter, the shortest dimension will be resized to resize_shorter if the longest dimension is smaller or equal to max_size. If not, the shortest dimension is resized to satisfy the constraint longest_dim == max_size. Can be also an array of size 2, where the two elements are maximum size per dimension (H, W). Example: Original image = 400x1200. Resized with: resize_shorter = 200 (max_size not set) => 200x600 resize_shorter = 200, max_size = 400 => 132x400 resize_shorter = 200, max_size = 1000 => 200x600
@param resize_longer (int, optional, default = 0) The length of the longer dimension of the resized image. This option is mutually exclusive with resize_shorter,`resize_x` and resize_y. The op will keep the aspect ratio of the original image.
@param resize_shorter (int, optional, default = 0) The length of the shorter dimension of the resized image. This option is mutually exclusive with resize_longer, resize_x and resize_y. The op will keep the aspect ratio of the original image. The longer dimension can be bounded by setting the max_size argument. See max_size argument doc for more info.
@param scaling_mode (int, optional, default = types.SCALING_MODE_DEFAULT) resize scaling mode.
@param interpolation_type (int, optional, default = types.LINEAR_INTERPOLATION) Type of interpolation to be used.
@param output_layout (int, optional, default = types.NHWC) tensor layout for the augmentation output
@param output_dtype (int, optional, default = types.UINT8) tensor dtype for the augmentation output
@return Resized and cropped Image
◆ resize_crop_mirror()
| def rocAL_pybind.amd.rocal.fn.resize_crop_mirror | ( | * | inputs, |
resize_width = 0, |
|||
resize_height = 0, |
|||
crop_w = 0, |
|||
crop_h = 0, |
|||
mirror = 1, |
|||
device = None, |
|||
max_size = [], |
|||
resize_longer = 0, |
|||
resize_shorter = 0, |
|||
scaling_mode = types.SCALING_MODE_DEFAULT, |
|||
interpolation_type = types.LINEAR_INTERPOLATION, |
|||
output_layout = types.NHWC, |
|||
output_dtype = types.UINT8 |
|||
| ) |
Fused function which performs resize, crop and flip on images.
@param inputs the input image passed to the augmentation
@param resize_width (int, optional, default = 0) The length of the X dimension of the resized image
@param resize_height (int, optional, default = 0) The length of the Y dimension of the resized image
@param crop_w (int, optional, default = 0) Cropping window width (in pixels).
@param crop_h (int, optional, default = 0) Cropping window height (in pixels).
@param mirror (int, optional, default = 1) flag for the horizontal flip.
@param device (string, optional, default = None) Parameter unused for augmentation
@param max_size (int or list of int, optional, default = []) Parameter unused for augmentation
@param resize_longer (int, optional, default = 0) Parameter unused for augmentation
@param resize_shorter (int, optional, default = 0) Parameter unused for augmentation
@param scaling_mode (int, optional, default = types.SCALING_MODE_DEFAULT) Parameter unused for augmentation
@param interpolation_type (int, optional, default = types.LINEAR_INTERPOLATION) Parameter unused for augmentation
@param output_layout (int, optional, default = types.NHWC) tensor layout for the augmentation output
@param output_dtype (int, optional, default = types.UINT8) tensor dtype for the augmentation output
@return Resized crop mirror Image
◆ resize_mirror_normalize()
| def rocAL_pybind.amd.rocal.fn.resize_mirror_normalize | ( | * | inputs, |
max_size = [], |
|||
resize_longer = 0, |
|||
resize_shorter = 0, |
|||
resize_width = 0, |
|||
resize_height = 0, |
|||
scaling_mode = types.SCALING_MODE_DEFAULT, |
|||
interpolation_type = types.LINEAR_INTERPOLATION, |
|||
mean = [0.0], |
|||
std = [1.0], |
|||
mirror = 1, |
|||
device = None, |
|||
output_layout = types.NHWC, |
|||
output_dtype = types.UINT8 |
|||
| ) |
Fused function which performs resize, Normalize and flip on images.
@param inputs the input image passed to the augmentation
@param max_size (int or list of int, optional, default = []) Maximum size of the longer dimension when resizing with resize_shorter. When set with resize_shorter, the shortest dimension will be resized to resize_shorter if the longest dimension is smaller or equal to max_size. If not, the shortest dimension is resized to satisfy the constraint longest_dim == max_size. Can be also an array of size 2, where the two elements are maximum size per dimension (H, W).
Example:
Original image = 400x1200.
Resized with:
resize_shorter = 200 (max_size not set) => 200x600
resize_shorter = 200, max_size = 400 => 132x400
resize_shorter = 200, max_size = 1000 => 200x600
@param resize_longer (int, optional, default = 0) The length of the longer dimension of the resized image. This option is mutually exclusive with resize_shorter,`resize_x` and resize_y. The op will keep the aspect ratio of the original image.
@param resize_shorter (int, optional, default = 0) The length of the shorter dimension of the resized image. This option is mutually exclusive with resize_longer, resize_x and resize_y. The op will keep the aspect ratio of the original image. The longer dimension can be bounded by setting the max_size argument. See max_size argument doc for more info.
@param resize_width (int, optional, default = 0) The length of the X dimension of the resized image. This option is mutually exclusive with resize_shorter. If the resize_y is left at 0, then the op will keep the aspect ratio of the original image.
@param resize_height (int, optional, default = 0) The length of the Y dimension of the resized image. This option is mutually exclusive with resize_shorter. If the resize_x is left at 0, then the op will keep the aspect ratio of the original image.
@param scaling_mode (int, optional, default = types.SCALING_MODE_DEFAULT) resize scaling mode.
@param interpolation_type (int, optional, default = types.LINEAR_INTERPOLATION) Type of interpolation to be used.
@param mean (list of floats, optional, default = [0.0]) mean used for normalization
@param std (list of floats, optional, default = [1.0]) standard deviation used for normalization
@param mirror (int, optional, default = 1) flag for the horizontal flip.
@param device (string, optional, default = None) Parameter unused for augmentation
@param output_layout (int, optional, default = types.NHWC) tensor layout for the augmentation output
@param output_dtype (int, optional, default = types.UINT8) tensor dtype for the augmentation output
@return Transformed Image
◆ rotate()
| def rocAL_pybind.amd.rocal.fn.rotate | ( | * | inputs, |
angle = None, |
|||
dest_width = 0, |
|||
dest_height = 0, |
|||
interpolation_type = types.LINEAR_INTERPOLATION, |
|||
device = None, |
|||
fill_value = None, |
|||
output_layout = types.NHWC, |
|||
output_dtype = types.UINT8 |
|||
| ) |
Rotates images.
@param inputs the input image passed to the augmentation
@param angle (float, optional, default = None) angle used for rotating the image
@param dest_width (int, optional, default = 0) The length of the X dimension of the rotated image
@param dest_height (int, optional, default = 0) The length of the Y dimension of the rotated image
@param interpolation_type (int, optional, default = types.LINEAR_INTERPOLATION) Type of interpolation to be used.
@param device (string, optional, default = None) Parameter unused for augmentation
@param fill_value (float, optional, default = 0.0) Parameter unused for augmentation
@param output_layout (int, optional, default = types.NHWC) tensor layout for the augmentation output
@param output_dtype (int, optional, default = types.UINT8) tensor dtype for the augmentation output
@return Roatated Image
◆ saturation()
| def rocAL_pybind.amd.rocal.fn.saturation | ( | * | inputs, |
saturation = 1.0, |
|||
device = None, |
|||
output_layout = types.NHWC, |
|||
output_dtype = types.UINT8 |
|||
| ) |
Adjusts the saturation in images.
@param inputs the input image passed to the augmentation
@param saturation (float, default = 1.0) The saturation change factor. Values must be non-negative. Example values: 0 - Completely desaturated image, 1 - No change to image's saturation.
@param device (string, optional, default = None) Parameter unused for augmentation
@param output_layout (int, optional, default = types.NHWC) tensor layout for the augmentation output
@param output_dtype (int, optional, default = types.UINT8) tensor dtype for the augmentation output
@return Image with Saturation effect
◆ snow()
| def rocAL_pybind.amd.rocal.fn.snow | ( | * | inputs, |
snow = 0.5, |
|||
device = None, |
|||
output_layout = types.NHWC, |
|||
output_dtype = types.UINT8 |
|||
| ) |
Applies snow effect on images.
@param inputs the input image passed to the augmentation
@param snow (float, default = 0.5) snow fill value used for the augmentation
@param device (string, optional, default = None) Parameter unused for augmentation
@param output_layout (int, optional, default = types.NHWC) tensor layout for the augmentation output
@param output_dtype (int, optional, default = types.UINT8) tensor dtype for the augmentation output
@return Image with snow effect
◆ snp_noise()
| def rocAL_pybind.amd.rocal.fn.snp_noise | ( | * | inputs, |
p_noise = 0.0, |
|||
p_salt = 0.0, |
|||
noise_val = 0.0, |
|||
salt_val = 0.0, |
|||
seed = 0, |
|||
device = None, |
|||
output_layout = types.NHWC, |
|||
output_dtype = types.UINT8 |
|||
| ) |
Applies salt-and-pepper noise to the input image.
@param inputs (list) The input image to which salt-and-pepper noise is applied.
@param p_noise (float, optional, default = 0.0) Noise probability. Default is 0.0.
@param p_salt (float, optional, default = 0.0) Salt probability. Default is 0.0.
@param noise_val (float, optional, default = 0.0) Noise value to be added to the image. Default is 0.0.
@param salt_val (float, optional, default = 0.0) Salt value to be added to the image. Default is 0.0.
@param seed (int, optional, default = 0) Random seed. Default is 0.
@param device (string, optional, default = None) Parameter unused for augmentation
@param output_layout (int, optional, default = types.NHWC) Tensor layout for the augmentation output. Default is types.NHWC.
@param output_dtype (int, optional, default = types.UINT*) Tensor dtype for the augmentation output. Default is types.UINT8.
@return images with salt-and-pepper noise added.
◆ ssd_random_crop()
| def rocAL_pybind.amd.rocal.fn.ssd_random_crop | ( | * | inputs, |
p_threshold = None, |
|||
crop_area_factor = None, |
|||
crop_aspect_ratio = None, |
|||
crop_pos_x = None, |
|||
crop_pos_y = None, |
|||
num_attempts = 20, |
|||
device = None, |
|||
all_boxes_above_threshold = True, |
|||
allow_no_crop = True, |
|||
ltrb = True, |
|||
output_layout = types.NHWC, |
|||
output_dtype = types.UINT8 |
|||
| ) |
Crops images randomly used for SSD training.
@param inputs the input image passed to the augmentation
@param p_threshold (float, optional, default = None) threshold value used for selecting bboxes during crop generation
@param crop_area_factor (float, optional, default = None) area factor used for crop generation
@param crop_aspect_ratio (float, optional, default = None) aspect ratio of the cropping windows
@param crop_pox_x (float, optional, default = None) crop_x position used for crop generation
@param crop_pox_y (float, optional, default = None) crop_y position used for crop generation
@param num_attempts (int, optional, default = 20) number of attempts to get a crop window that matches the area factor and aspect ratio conditions
@param device (string, optional, default = None) Parameter unused for augmentation
@param all_boxes_above_threshold (bool, optional, default = True) Parameter unused for augmentation
@param allow_no_crop (bool, optional, default = True) Parameter unused for augmentation
@param ltrb (bool, optional, default = True) Parameter unused for augmentation
@param output_layout (int, optional, default = types.NHWC) tensor layout for the augmentation output
@param output_dtype (int, optional, default = types.UINT8) tensor dtype for the augmentation output
@return Randomly Cropped images for SSD training
◆ uniform()
| def rocAL_pybind.amd.rocal.fn.uniform | ( | * | inputs, |
range = [-1, 1], |
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device = None |
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| ) |
Applies uniform random number generation to the input images.
@param inputs the input image passed to the augmentation
@param range (list of ints, optional, default = [-1, 1]) uniform distribution used for random number generation
@param device (string, optional, default = None) Parameter unused for augmentation
@return uniform random numbers
◆ vignette()
| def rocAL_pybind.amd.rocal.fn.vignette | ( | * | inputs, |
vignette = 0.5, |
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device = None, |
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output_layout = types.NHWC, |
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output_dtype = types.UINT8 |
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| ) |
Applies Vignette effect.
@param inputs the input image passed to the augmentation
@param vignette (float, default = 0.5) vignette value used for the augmentation output
@param device (string, optional, default = None) Parameter unused for augmentation
@param output_layout (int, optional, default = types.NHWC) tensor layout for the augmentation output
@param output_dtype (int, optional, default = types.UINT8) tensor dtype for the augmentation output
@return Images with Vignette effect
◆ warp_affine()
| def rocAL_pybind.amd.rocal.fn.warp_affine | ( | * | inputs, |
dest_width = 0, |
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dest_height = 0, |
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matrix = [0, 0, 0, 0, 0, 0], |
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interpolation_type = types.LINEAR_INTERPOLATION, |
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device = None, |
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output_layout = types.NHWC, |
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output_dtype = types.UINT8 |
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| ) |
Applies affine transformation to images.
@param inputs the input image passed to the augmentation
@param dest_width (int, optional, default = 0) The length of the X dimension of the transformed image
@param matrix (list of ints, optional, default = [0, 0, 0, 0, 0, 0]) Transformation matrix used to produce a new image
@param dest_height (int, optional, default = 0) The length of the Y dimension of the transformed image
@param interpolation_type (int, optional, default = types.LINEAR_INTERPOLATION) Type of interpolation to be used.
@param device (string, optional, default = None) Parameter unused for augmentation
@param output_layout (int, optional, default = types.NHWC) tensor layout for the augmentation output
@param output_dtype (int, optional, default = types.UINT8) tensor dtype for the augmentation output
@return Affine Transformed Images