Python numpy.invert方法代码示例

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import invert [as 别名]
def eval_batch(table_batch, label_batch, mask_batch):
    # reshap (table_batch * table_size * features)
    for f_g in table_batch:
        table_batch[f_g] = table_batch[f_g].view(batch_size * MAX_COL_COUNT, -1)

    emissions = classifier(table_batch).view(batch_size, MAX_COL_COUNT, -1)
    pred = model.decode(emissions, mask_batch)

    pred = np.concatenate(pred)
    labels = label_batch.view(-1).cpu().numpy()
    masks = mask_batch.view(-1).cpu().numpy()
    invert_masks = np.invert(masks==1)
    
    return pred, ma.array(labels, mask=invert_masks).compressed()

# randomly shuffle the orders of columns in a table batch 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import invert [as 别名]
def test_ufunc_at_manual(self):
        def check(ufunc, a, ind, b=None):
            a0 = a.copy()
            if b is None:
                ufunc.at(a0, ind.copy())
                c1 = a0.copy()
                ufunc.at(a, ind)
                c2 = a.copy()
            else:
                ufunc.at(a0, ind.copy(), b.copy())
                c1 = a0.copy()
                ufunc.at(a, ind, b)
                c2 = a.copy()
            assert_array_equal(c1, c2)

        # Overlap with index
        a = np.arange(10000, dtype=np.int16)
        check(np.invert, a[::-1], a)

        # Overlap with second data array
        a = np.arange(100, dtype=np.int16)
        ind = np.arange(0, 100, 2, dtype=np.int16)
        check(np.add, a, ind, a[25:75]) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import invert [as 别名]
def map_charades(y_true, y_pred):
    """ Returns mAP """
    m_aps = []
    n_classes = y_pred.shape[1]
    for oc_i in range(n_classes):
        pred_row = y_pred[:, oc_i]
        sorted_idxs = np.argsort(-pred_row)
        true_row = y_true[:, oc_i]
        tp = true_row[sorted_idxs] == 1
        fp = np.invert(tp)
        n_pos = tp.sum()
        if n_pos < 0.1:
            m_aps.append(float('nan'))
            continue
        f_pcs = np.cumsum(fp)
        t_pcs = np.cumsum(tp)
        prec = t_pcs / (f_pcs + t_pcs).astype(float)
        avg_prec = 0
        for i in range(y_pred.shape[0]):
            if tp[i]:
                avg_prec += prec[i]
        m_aps.append(avg_prec / n_pos.astype(float))
    m_aps = np.array(m_aps)
    m_ap = np.mean(m_aps)
    return m_ap 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import invert [as 别名]
def invert(data):
    """
    Inverts the byte data it received utilizing an XOR operation.

    :param data: A chunk of byte data
    :return inverted: The same size of chunked data inverted bitwise
    """

    # Convert the bytestring into an integer
    intwave = np.fromstring(data, np.int32)
    # Invert the integer
    intwave = np.invert(intwave)
    # Convert the integer back into a bytestring
    inverted = np.frombuffer(intwave, np.byte)
    # Return the inverted audio data
    return inverted 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import invert [as 别名]
def gen_mask_array(icon_dir: str, invert_mask: bool, size: int):
    """Generates a numpy array of an icon mask."""
    icon = Image.open(os.path.join(icon_dir, "icon.png"))

    if isinstance(size, int):
        size = (size, size)

    # https://stackoverflow.com/a/2563883
    icon_w, icon_h = icon.size
    icon_mask = Image.new("RGBA", icon.size, (255, 255, 255, 255))
    icon_mask.paste(icon, icon)
    mask = Image.new("RGBA", size, (255, 255, 255, 255))
    mask_w, mask_h = mask.size
    offset = ((mask_w - icon_w) // 2, (mask_h - icon_h) // 2)
    mask.paste(icon_mask, offset)
    mask_array = np.array(mask, dtype="uint8")

    if invert_mask:
        mask_array = np.invert(mask_array)

    return mask_array 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import invert [as 别名]
def augment_occlusion_mask(self, masks, verbose=False, min_trans = 0.2, max_trans=0.7, max_occl = 0.25,min_occl = 0.0):


        new_masks = np.zeros_like(masks,dtype=np.bool)
        occl_masks_batch = self.random_syn_masks[np.random.choice(len(self.random_syn_masks),len(masks))]
        for idx,mask in enumerate(masks):
            occl_mask = occl_masks_batch[idx]
            while True:
                trans_x = int(np.random.choice([-1,1])*(np.random.rand()*(max_trans-min_trans) + min_trans)*occl_mask.shape[0])
                trans_y = int(np.random.choice([-1,1])*(np.random.rand()*(max_trans-min_trans) + min_trans)*occl_mask.shape[1])
                M = np.float32([[1,0,trans_x],[0,1,trans_y]])

                transl_occl_mask = cv2.warpAffine(occl_mask,M,(occl_mask.shape[0],occl_mask.shape[1]))

                overlap_matrix = np.invert(mask.astype(np.bool)) * transl_occl_mask.astype(np.bool)
                overlap = len(overlap_matrix[overlap_matrix==True])/float(len(mask[mask==0]))

                if overlap < max_occl and overlap > min_occl:
                    new_masks[idx,...] = np.logical_xor(mask.astype(np.bool), overlap_matrix)
                    if verbose:
                        print('overlap is ', overlap)
                    break

        return new_masks 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import invert [as 别名]
def degamma_srgb(self, clip_range=[0, 65535]):

        # bring data in range 0 to 1
        data = np.clip(self.data, clip_range[0], clip_range[1])
        data = np.divide(data, clip_range[1])

        data = np.asarray(data)
        mask = data > 0.04045

        # basically, if data[x, y, c] > 0.04045, data[x, y, c] = ( (data[x, y, c] + 0.055) / 1.055 ) ^ 2.4
        #            else, data[x, y, c] = data[x, y, c] / 12.92
        data[mask] += 0.055
        data[mask] /= 1.055
        data[mask] **= 2.4

        data[np.invert(mask)] /= 12.92

        # rescale
        return np.clip(data * clip_range[1], clip_range[0], clip_range[1]) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import invert [as 别名]
def gamma_srgb(self, clip_range=[0, 65535]):

        # bring data in range 0 to 1
        data = np.clip(self.data, clip_range[0], clip_range[1])
        data = np.divide(data, clip_range[1])

        data = np.asarray(data)
        mask = data > 0.0031308

        # basically, if data[x, y, c] > 0.0031308, data[x, y, c] = 1.055 * ( var_R(i, j) ^ ( 1 / 2.4 ) ) - 0.055
        #            else, data[x, y, c] = data[x, y, c] * 12.92
        data[mask] **= 0.4167
        data[mask] *= 1.055
        data[mask] -= 0.055

        data[np.invert(mask)] *= 12.92

        # rescale
        return np.clip(data * clip_range[1], clip_range[0], clip_range[1]) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import invert [as 别名]
def xyz2lab(self, cie_version="1931", illuminant="d65"):

        xyz_reference = helpers().get_xyz_reference(cie_version, illuminant)

        data = self.data
        data[:, :, 0] = data[:, :, 0] / xyz_reference[0]
        data[:, :, 1] = data[:, :, 1] / xyz_reference[1]
        data[:, :, 2] = data[:, :, 2] / xyz_reference[2]

        data = np.asarray(data)

        # if data[x, y, c] > 0.008856, data[x, y, c] = data[x, y, c] ^ (1/3)
        # else, data[x, y, c] = 7.787 * data[x, y, c] + 16/116
        mask = data > 0.008856
        data[mask] **= 1./3.
        data[np.invert(mask)] *= 7.787
        data[np.invert(mask)] += 16./116.

        data = np.float32(data)
        output = np.empty(np.shape(self.data), dtype=np.float32)
        output[:, :, 0] = 116. * data[:, :, 1] - 16.
        output[:, :, 1] = 500. * (data[:, :, 0] - data[:, :, 1])
        output[:, :, 2] = 200. * (data[:, :, 1] - data[:, :, 2])

        return output 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import invert [as 别名]
def lab2xyz(self, cie_version="1931", illuminant="d65"):

        output = np.empty(np.shape(self.data), dtype=np.float32)

        output[:, :, 1] = (self.data[:, :, 0] + 16.) / 116.
        output[:, :, 0] = (self.data[:, :, 1] / 500.) + output[:, :, 1]
        output[:, :, 2] = output[:, :, 1] - (self.data[:, :, 2] / 200.)

        # if output[x, y, c] > 0.008856, output[x, y, c] ^ 3
        # else, output[x, y, c] = ( output[x, y, c] - 16/116 ) / 7.787
        output = np.asarray(output)
        mask = output > 0.008856
        output[mask] **= 3.
        output[np.invert(mask)] -= 16/116
        output[np.invert(mask)] /= 7.787

        xyz_reference = helpers().get_xyz_reference(cie_version, illuminant)

        output = np.float32(output)
        output[:, :, 0] = output[:, :, 0] * xyz_reference[0]
        output[:, :, 1] = output[:, :, 1] * xyz_reference[1]
        output[:, :, 2] = output[:, :, 2] * xyz_reference[2]

        return output 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import invert [as 别名]
def create_test_set(x_lst):
    n = len(x_lst)
    x_lens = np.array(map(len, x_lst))
    max_len = max(map(len, x_lst)) - 1
    u_out = np.zeros((n, max_len, OUTDIM), dtype='float32')*np.nan
    x_out = np.zeros((n, max_len, OUTDIM), dtype='float32')*np.nan
    for row, vec in enumerate(x_lst):
        l = len(vec) - 1
        u = vec[:-1]  # all but last element
        x = vec[1:]   # all but first element

        x_out[row, :l] = x
        u_out[row, :l] = u

    mask = np.invert(np.isnan(x_out))
    x_out[np.isnan(x_out)] = 0
    u_out[np.isnan(u_out)] = 0
    mask = mask[:, :, 0]
    assert np.all((mask.sum(axis=1)+1) == x_lens)
    return u_out, x_out, mask.astype('float32') 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import invert [as 别名]
def get_map_to_predict(src_locs, src_x_axiss, src_y_axiss, map, map_size,
                       interpolation=cv2.INTER_LINEAR):
  fss = []
  valids = []

  center = (map_size-1.0)/2.0
  dst_theta = np.pi/2.0
  dst_loc = np.array([center, center])
  dst_x_axis = np.array([np.cos(dst_theta), np.sin(dst_theta)])
  dst_y_axis = np.array([np.cos(dst_theta+np.pi/2), np.sin(dst_theta+np.pi/2)])

  def compute_points(center, x_axis, y_axis):
    points = np.zeros((3,2),dtype=np.float32)
    points[0,:] = center
    points[1,:] = center + x_axis
    points[2,:] = center + y_axis
    return points

  dst_points = compute_points(dst_loc, dst_x_axis, dst_y_axis)
  for i in range(src_locs.shape[0]):
    src_loc = src_locs[i,:]
    src_x_axis = src_x_axiss[i,:]
    src_y_axis = src_y_axiss[i,:]
    src_points = compute_points(src_loc, src_x_axis, src_y_axis)
    M = cv2.getAffineTransform(src_points, dst_points)

    fs = cv2.warpAffine(map, M, (map_size, map_size), None, flags=interpolation,
                        borderValue=np.NaN)
    valid = np.invert(np.isnan(fs))
    valids.append(valid)
    fss.append(fs)
  return fss, valids 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import invert [as 别名]
def solve_with_covariance_matrices(self, Z, Y):
        A = np.cov(Z.T)
        B = np.cov(Z.T, Y.T)
        W = np.invert(A) @ B
        self.layers[-1].set_weights([W, np.array([0] * self.layers[-1].neurons)], fold=False) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import invert [as 别名]
def eval_batch(classifier, model, val_dataset, batch_size, device, n_worker, MAX_COL_COUNT):


    validation = datasets.generate_batches(val_dataset,
                                           batch_size=batch_size,
                                           shuffle=False, 
                                           drop_last=True,
                                           device=device,
                                           n_workers=n_worker)
    y_pred, y_true = [], []
    for table_batch, label_batch, mask_batch in tqdm(validation):
        #pred, labels = eval_batch(table_batch, label_batch, mask_batch)
            
        # reshap (table_batch * table_size * features)
        for f_g in table_batch:
            table_batch[f_g] = table_batch[f_g].view(batch_size * MAX_COL_COUNT, -1)

        emissions = classifier(table_batch).view(batch_size, MAX_COL_COUNT, -1)
        pred = model.decode(emissions, mask_batch)

        pred = np.concatenate(pred)
        labels = label_batch.view(-1).cpu().numpy()
        masks = mask_batch.view(-1).cpu().numpy()
        invert_masks = np.invert(masks==1)
        
        y_pred.extend(pred)
        y_true.extend(ma.array(labels, mask=invert_masks).compressed())

    val_acc = classification_report(y_true, y_pred, output_dict=True)
    return val_acc 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import invert [as 别名]
def _in1d_dispatcher(ar1, ar2, assume_unique=None, invert=None):
    return (ar1, ar2)