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How to use as_transform method in Slash

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policy_operations.py

Source:policy_operations.py

...101                value, input_dtype, requires_uint8_scaling=self.requires_uint8_scaling102            )103        else:104            transform_param = None105        return self.as_transform(transform_param, p)106def value_to_transform_param(value, input_dtype="float32", requires_uint8_scaling=False):107    value = value.item()108    if input_dtype == "uint8" and requires_uint8_scaling:109        value = int(value * 255.0)110    return value111def convert_value_range(value, new_range):112    lower, upper = new_range113    total = upper - lower114    return value * total + lower115class ShiftRGB(Operation):116    def __init__(self, temperature, shift_r=False, shift_g=False, shift_b=False):117        super().__init__(temperature, value_range=(-1.0, 1.0), requires_uint8_scaling=True)118        self.shift_r = shift_r119        self.shift_g = shift_g120        self.shift_b = shift_b121    def apply_operation(self, input, value):122        return F.shift_rgb(123            input,124            r_shift=value if self.shift_r else 0.0,125            g_shift=value if self.shift_g else 0.0,126            b_shift=value if self.shift_b else 0.0,127        )128    def as_transform(self, value, p):129        limit = (value, value)130        return A.RGBShift(131            r_shift_limit=limit if self.shift_r else 0.0,132            g_shift_limit=limit if self.shift_g else 0.0,133            b_shift_limit=limit if self.shift_b else 0.0,134            p=p,135        )136class RandomBrightness(Operation):137    def __init__(self, temperature):138        super().__init__(temperature, value_range=(-1.0, 1.0))139    def apply_operation(self, input, value):140        return F.brightness_adjust(input, beta=value)141    def as_transform(self, value, p):142        return A.RandomBrightnessContrast(brightness_limit=(value, value), contrast_limit=0, p=p)143class RandomContrast(Operation):144    def __init__(self, temperature):145        super().__init__(temperature, value_range=(0.0, 10.0))146    def apply_operation(self, input, value):147        return F.contrast_adjust(input, alpha=value)148    def as_transform(self, value, p):149        return A.RandomBrightnessContrast(brightness_limit=0, contrast_limit=(value, value), p=p)150class Solarize(Operation):151    def __init__(self, temperature):152        super().__init__(temperature, requires_uint8_scaling=True)153    def apply_operation(self, input, value):154        return F.solarize(input, threshold=value)155    def as_transform(self, value, p):156        return A.Solarize(threshold=value, p=p)157class HorizontalFlip(Operation):158    def __init__(self, temperature):159        super().__init__(temperature, has_magnitude=False, is_spatial_level=True)160    def apply_operation(self, input, value):161        return F.hflip(input)162    def as_transform(self, value, p):163        return A.HorizontalFlip(p=p)164class VerticalFlip(Operation):165    def __init__(self, temperature):166        super().__init__(temperature, has_magnitude=False, is_spatial_level=True)167    def apply_operation(self, input, value):168        return F.vflip(input)169    def as_transform(self, value, p):170        return A.VerticalFlip(p=p)171class ShiftX(Operation):172    def __init__(self, temperature):173        super().__init__(temperature, value_range=(-1.0, 1.0), is_spatial_level=True)174    def apply_operation(self, input, value):175        return F.shift_x(input, dx=value)176    def as_transform(self, value, p):177        return A.ShiftScaleRotate(178            shift_limit_x=(value, value),179            shift_limit_y=(0, 0),180            rotate_limit=(0, 0),181            scale_limit=(0, 0),182            p=p,183        )184class ShiftY(Operation):185    def __init__(self, temperature):186        super().__init__(temperature, value_range=(-1.0, 1.0), is_spatial_level=True)187    def apply_operation(self, input, value):188        return F.shift_y(input, dy=value)189    def as_transform(self, value, p):190        return A.ShiftScaleRotate(191            shift_limit_x=(0, 0),192            shift_limit_y=(value, value),193            rotate_limit=(0, 0),194            scale_limit=(0, 0),195            p=p,196        )197class Scale(Operation):198    def __init__(self, temperature):199        super().__init__(temperature, value_range=(0 + 1e-8, 10.0), is_spatial_level=True)200    def apply_operation(self, input, value):201        return F.scale(input, scale=value)202    def as_transform(self, value, p):203        return A.ShiftScaleRotate(204            shift_limit_x=(0, 0),205            shift_limit_y=(0, 0),206            rotate_limit=(0, 0),207            scale_limit=(value, value),208            p=p,209        )210class Rotate(Operation):211    def __init__(self, temperature):212        super().__init__(temperature, value_range=(-180, 180), is_spatial_level=True)213    def apply_operation(self, input, value):214        return F.rotate(input, angle=value)215    def as_transform(self, value, p):216        return A.ShiftScaleRotate(217            shift_limit_x=(0, 0),218            shift_limit_y=(0, 0),219            rotate_limit=(value, value),220            scale_limit=(0, 0),221            p=p,222        )223class Cutout(Operation):224    def __init__(self, temperature, value_range=(0.0, 1.0)):225        super().__init__(temperature, value_range=value_range, ste=True)226        self.register_buffer("saved_image_shape", torch.Tensor([0, 0]).type(torch.int64))227        self.is_image_shape_saved = False228    def _save_image_shape(self, image_shape):229        if not torch.equal(self.saved_image_shape, image_shape):230            if self.is_image_shape_saved:231                warnings.warn(232                    f"Shape of images in a batch changed between iterations "233                    f"from {self.saved_image_shape} to {image_shape}. "234                    f"This will affect the created Albumentations transform. "235                    f"The transform will use the shape {image_shape} to initialize its parameters",236                    RuntimeWarning,237                )238            self.is_image_shape_saved = True239            self.saved_image_shape = image_shape240    def apply_operation(self, input, value):241        image_shape = input.shape[-2:]242        self._save_image_shape(torch.tensor(image_shape).to(input.device))243        return self._apply_cutout(input, value, image_shape)244    def as_transform(self, value, p):245        image_shape = self.saved_image_shape246        return self._as_cutout_transform(value, p, image_shape)247    def _apply_cutout(self, input, value, image_shape):248        raise NotImplementedError249    def _as_cutout_transform(self, value, p, image_shape):250        raise NotImplementedError251class CutoutFixedNumberOfHoles(Cutout):252    def __init__(self, temperature, num_holes=16):253        super().__init__(temperature)254        self.num_holes = num_holes255    def _calculate_hole_size(self, value, image_shape):256        height, width = image_shape257        min_size = min(height, width)258        return max(int(min_size * value), 1)...

read.py

Source:read.py

1"""Reading functinons."""2import numpy as np3import pandas as pd4import os5import os.path as op6from seegpy.contacts.utils import detect_seeg_contacts7def read_trm(path, as_transform=True, inverse=False):8    """Read a transformation file.9    Parameters10    ----------11    path : string12        Path to the trm file13    as_transform : bool14        Get either the array as a usable (4, 4) array (True) or just simply15        or just the array contained in the file (False)16    inverse : bool | False17        Whether to inverse the transformation or not18    Returns19    -------20    tr : array_like21        Transformation array22    """23    tr = np.genfromtxt(path)24    if as_transform:25        tr = np.vstack((np.c_[tr[1::, :], tr[0, :]], np.array([0, 0, 0, 1])))26    if inverse:27        tr = np.linalg.inv(tr)28    return tr29def read_contacts_trc(path):30    """Read the channels that are contained inside a TRC file.31    This function uses the neo python package.32    Parameters33    ----------34    path : string35        Path to the trc file36    Returns37    -------38    all_chan : list39        List of contacts40    units : list41        List of units per channels42    """43    import neo44    # -------------------------------------------------------------------------45    # read the channels46    micro = neo.MicromedIO(filename=path)47    seg = micro.read_segment(signal_group_mode='split-all', lazy=True)48    all_chan = [sig.name.replace(' ', '').strip().upper()49                for sig in seg.analogsignals]50    units = [str(sig.units) for sig in seg.analogsignals]51    return all_chan, units52def read_3dslicer_fiducial(path):53    """Read coordinates in a fiducial fcsv file.54    Parameters55    ----------56    path : string57        Path to the fcsv file58    Returns59    -------60    df : DataFrame61        DataFrame with the columns label, x, y and z62    """63    return pd.read_csv(path, skiprows=[0, 1])[['label', 'x', 'y', 'z']]64def read_trc(bloc):65    """Read a TRC file.66    Parameters67    ----------68    bloc : str69        Path to the bloc to read70    Returns71    -------72    sf : float73        The sampling frequency74    raw : array_like75        Array of raw data of shape (n_seeg_chan, n_times)76    seeg_chan : array_like77        Array of sEEG channels names78    trig_event : array_like79        Events in the trigger channel of length (n_events,)80    trig_time : array_like81        Time associated to each event of length (n_events,)82    """83    assert op.isfile(bloc)84    # -------------------------------------------------------------------------85    # SAMPLING FREQUENCY86    # -------------------------------------------------------------------------87    import neo88    micro = neo.MicromedIO(filename=bloc)89    seg = micro.read_segment(signal_group_mode='split-all', lazy=True)90    sf = float(seg.analogsignals[0].sampling_rate)91    # -------------------------------------------------------------------------92    # CHANNELS93    # -------------------------------------------------------------------------94    # read again the channels in first bloc95    ch_names, ch_units = read_contacts_trc(bloc)96    # detect seeg / non-seeg channels97    is_seeg = detect_seeg_contacts(ch_names, ch_units=ch_units, seeg_unit='uV')98    seeg_chan = np.array(ch_names)[is_seeg]99    seeg_nb = np.arange(len(ch_names))[is_seeg]100    # -------------------------------------------------------------------------101    # TRIGGERS AND RAW102    # -------------------------------------------------------------------------103    # load the bloc104    micro = neo.MicromedIO(filename=bloc)105    seg = micro.read_segment(signal_group_mode='split-all', lazy=False)106    # read the trigger107    _event = seg.events[0]108    trig_event = np.array(_event.labels).astype(int)109    trig_time = np.array(_event.times)110    # read the raw data111    raw = []112    for c in seeg_nb:113        raw += [seg.analogsignals[c].squeeze()]114    raw = np.stack(raw)115    return sf, raw, seeg_chan.tolist(), trig_event, trig_time116def read_pramat(mat_root):117    """Read a Pragues file.118    Parameters119    ----------120    mat_root : str121        Path to the root matlab folder122    Returns123    -------124    sf : float125        The sampling frequency126    raw : array_like127        Array of raw data of shape (n_seeg_chan, n_times)128    seeg_chan : array_like129        Array of sEEG channels names130    trig_event : array_like131        Events in the trigger channel of length (n_events,)132    trig_time : array_like133        Time associated to each event of length (n_events,)134    """135    import h5py136    assert op.isdir(mat_root)137    # -------------------------------------------------------------------------138    # BUILD PATH139    # -------------------------------------------------------------------------140    # header file141    path_head = op.join(mat_root, 'alignedData')142    files_head = os.listdir(path_head)143    assert len(files_head) == 1144    path_head = op.join(path_head, files_head[0])145    # raw file146    path_raw = op.join(mat_root, 'rawData', 'amplifierData')147    files_raw = os.listdir(path_raw)148    assert len(files_raw) == 1149    path_raw = op.join(path_raw, files_raw[0])150    # -------------------------------------------------------------------------151    # CHANNELS152    # -------------------------------------------------------------------------153    f = h5py.File(path_head, 'r')['H']154    fc = f['channels']155    # read channel names and types156    cn = [''.join(chr(i) for i in f[k[0]][:]) for k in list(fc['name'])]157    ct = [''.join(chr(i) for i in f[k[0]][:]) for k in list(fc['signalType'])]158    ch_names = np.array([k.upper() for k in cn])159    # get only sEEG channels160    is_seeg = np.array(ct) == 'SEEG'161    seeg_chan = np.array(ch_names)[is_seeg]162    # -------------------------------------------------------------------------163    # TRIGGER164    # -------------------------------------------------------------------------165    # sampling frequency166    f = h5py.File(path_raw, 'r')167    sf = float(np.array(f['srate'])[0][0])168    # load the time vector169    times = np.array(f['time']).squeeze()170    # load trigger data171    trig_event = np.round(f['raw'][-1, :]).astype(int).squeeze()172    # keep only the first trigger changes (rm duplicates)173    first_only = np.where(trig_event[1::] - trig_event[:-1])[0] + 1174    trig_event = trig_event[first_only]175    times = times[first_only]176    # remove inter zeros177    nnz = trig_event != 0178    trig_event = trig_event[nnz]179    trig_time = times[nnz]180    # now extract the raw data181    raw = np.array(f['raw'][is_seeg, :])182    return sf, raw, seeg_chan.tolist(), trig_event, trig_time183if __name__ == '__main__':184    path_mat = '/run/media/etienne/Samsung_T5/BACKUPS/RAW/CausaL/PRAGUES_2019_PR7_day1/'...

kinect_transform.py

Source:kinect_transform.py

...36    i = 037    transforms = []38    while not rospy.is_shutdown() and i < 500:39        if head_pose and hand_pose:40            head_transform = tfx.transform(head_pose, parent='camera_rgb_optical_frame', child='ar_frame')#.as_transform()41            hand_transform = tfx.transform(hand_pose, parent='hand_kinect_optical_frame', child='ar_frame')#.as_transform()42            #head_to_ar = tfx.transform(transformer.lookupTransform('ar_frame', 'camera_rgb_optical_frame', rospy.Time()), parent='camera_rgb_optical_frame', child='ar_frame')43            #ar_to_hand = tfx.transform(transformer.lookupTransform('hand_kinect_optical_frame', 'ar_frame', rospy.Time()), parent='ar_frame', child='hand_kinect_optical_frame')44            head_to_hand = tfx.transform(head_transform.matrix * hand_transform.inverse().matrix, parent='camera_rgb_optical_frame', child='hand_kinect_optical_frame')45            #head_to_hand = tfx.transform(head_to_ar.inverse().matrix * ar_to_hand.matrix, parent='camera_rgb_optical_frame', child='hand_kinect_optical_frame')46            #rospy.loginfo(head_transform)47            #rospy.loginfo(hand_transform.inverse())48            #rospy.loginfo(head_to_ar)49            #rospy.loginfo(ar_to_hand)50            #print head_to_hand51            wrist_to_head = tfx.lookupTransform('r_gripper_tool_frame', 'camera_rgb_optical_frame')52            #wrist_to_head = tfx.transform(transformer.lookupTransform('r_gripper_tool_frame', 'camera_rgb_optical_frame', rospy.Time()), child='camera_rgb_optical_frame', parent='r_gripper_tool_frame')53            wrist_to_hand = tfx.transform(wrist_to_head.matrix * head_to_hand.matrix, parent='r_gripper_tool_frame', child='hand_kinect_optical_frame')54            #print wrist_to_head55            print wrist_to_hand...

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