Python numpy.flip() Examples

The following are 30 code examples for showing how to use numpy.flip(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.

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Example 1
Project: scanorama   Author: brianhie   File: time_align.py    License: MIT License 7 votes vote down vote up
def time_align_visualize(alignments, time, y, namespace='time_align'):
    plt.figure()
    heat = np.flip(alignments + alignments.T +
                   np.eye(alignments.shape[0]), axis=0)
    sns.heatmap(heat, cmap="YlGnBu", vmin=0, vmax=1)
    plt.savefig(namespace + '_heatmap.svg')

    G = nx.from_numpy_matrix(alignments)
    G = nx.maximum_spanning_tree(G)

    pos = {}
    for i in range(len(G.nodes)):
        pos[i] = np.array([time[i], y[i]])

    mst_edges = set(nx.maximum_spanning_tree(G).edges())
    
    weights = [ G[u][v]['weight'] if (not (u, v) in mst_edges) else 8
                for u, v in G.edges() ]
    
    plt.figure()
    nx.draw(G, pos, edges=G.edges(), width=10)
    plt.ylim([-1, 1])
    plt.savefig(namespace + '.svg') 
Example 2
Project: tf2-yolo3   Author: akkaze   File: utils.py    License: Apache License 2.0 7 votes vote down vote up
def draw_outputs(img, outputs, class_names=None):
    boxes, objectness, classes = outputs
    #boxes, objectness, classes = boxes[0], objectness[0], classes[0]
    wh = np.flip(img.shape[0:2])
    if img.ndim == 2 or img.shape[2] == 1:
        img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
    min_wh = np.amin(wh)
    if min_wh <= 100:
        font_size = 0.5
    else:
        font_size = 1
    for i in range(classes.shape[0]):
        x1y1 = tuple((np.array(boxes[i][0:2]) * wh).astype(np.int32))
        x2y2 = tuple((np.array(boxes[i][2:4]) * wh).astype(np.int32))
        img = cv2.rectangle(img, x1y1, x2y2, (255, 0, 0), 1)
        img = cv2.putText(img, '{}'.format(int(classes[i])), x1y1, cv2.FONT_HERSHEY_COMPLEX_SMALL, font_size,
                          (0, 0, 255), 1)
    return img 
Example 3
Project: tf2-yolo3   Author: akkaze   File: utils.py    License: Apache License 2.0 7 votes vote down vote up
def draw_labels(x, y, class_names=None):
    img = x.numpy()
    if img.ndim == 2 or img.shape[2] == 1:
        img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
    boxes, classes = tf.split(y, (4, 1), axis=-1)
    classes = classes[..., 0]
    wh = np.flip(img.shape[0:2])
    min_wh = np.amin(wh)
    if min_wh <= 100:
        font_size = 0.5
    else:
        font_size = 1
    for i in range(len(boxes)):
        x1y1 = tuple((np.array(boxes[i][0:2]) * wh).astype(np.int32))
        x2y2 = tuple((np.array(boxes[i][2:4]) * wh).astype(np.int32))
        img = cv2.rectangle(img, x1y1, x2y2, (255, 0, 0), 1)
        if class_names:
            img = cv2.putText(img, class_names[classes[i]], x1y1, cv2.FONT_HERSHEY_COMPLEX_SMALL, font_size,
                              (0, 0, 255), 1)
        else:
            img = cv2.putText(img, str(classes[i]), x1y1, cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 1)
    return img 
Example 4
Project: sparse-subspace-clustering-python   Author: abhinav4192   File: BuildAdjacency.py    License: MIT License 6 votes vote down vote up
def BuildAdjacency(CMat, K):
    CMat = CMat.astype(float)
    CKSym = None
    N, _ = CMat.shape
    CAbs = np.absolute(CMat).astype(float)
    for i in range(0, N):
        c = CAbs[:, i]
        PInd = np.flip(np.argsort(c), 0)
        CAbs[:, i] = CAbs[:, i] / float(np.absolute(c[PInd[0]]))
    CSym = np.add(CAbs, CAbs.T).astype(float)
    if K != 0:
        Ind = np.flip(np.argsort(CSym, axis=0), 0)
        CK = np.zeros([N, N]).astype(float)
        for i in range(0, N):
            for j in range(0, K):
                CK[Ind[j, i], i] = CSym[Ind[j, i], i] / float(np.absolute(CSym[Ind[0, i], i]))
        CKSym = np.add(CK, CK.T)
    else:
        CKSym = CSym
    return CKSym 
Example 5
Project: py360convert   Author: sunset1995   File: utils.py    License: MIT License 6 votes vote down vote up
def equirect_facetype(h, w):
    '''
    0F 1R 2B 3L 4U 5D
    '''
    tp = np.roll(np.arange(4).repeat(w // 4)[None, :].repeat(h, 0), 3 * w // 8, 1)

    # Prepare ceil mask
    mask = np.zeros((h, w // 4), np.bool)
    idx = np.linspace(-np.pi, np.pi, w // 4) / 4
    idx = h // 2 - np.round(np.arctan(np.cos(idx)) * h / np.pi).astype(int)
    for i, j in enumerate(idx):
        mask[:j, i] = 1
    mask = np.roll(np.concatenate([mask] * 4, 1), 3 * w // 8, 1)

    tp[mask] = 4
    tp[np.flip(mask, 0)] = 5

    return tp.astype(np.int32) 
Example 6
Project: pymoo   Author: msu-coinlab   File: randomized_argsort.py    License: Apache License 2.0 6 votes vote down vote up
def randomized_argsort(A, method="numpy", order='ascending'):
    if method == "numpy":
        P = np.random.permutation(len(A))
        I = np.argsort(A[P], kind='quicksort')
        I = P[I]

    elif method == "quicksort":
        I = quicksort(A)

    else:
        raise Exception("Randomized sort method not known.")

    if order == 'ascending':
        return I
    elif order == 'descending':
        return np.flip(I, axis=0)
    else:
        raise Exception("Unknown sorting order: ascending or descending.") 
Example 7
Project: HorizonNet   Author: sunset1995   File: inference.py    License: MIT License 6 votes vote down vote up
def augment_undo(x_imgs_augmented, aug_type):
    x_imgs_augmented = x_imgs_augmented.cpu().numpy()
    sz = x_imgs_augmented.shape[0] // len(aug_type)
    x_imgs = []
    for i, aug in enumerate(aug_type):
        x_img = x_imgs_augmented[i*sz : (i+1)*sz]
        if aug == 'flip':
            x_imgs.append(np.flip(x_img, axis=-1))
        elif aug.startswith('rotate'):
            shift = int(aug.split()[-1])
            x_imgs.append(np.roll(x_img, -shift, axis=-1))
        elif aug == '':
            x_imgs.append(x_img)
        else:
            raise NotImplementedError()

    return np.array(x_imgs) 
Example 8
Project: HorizonNet   Author: sunset1995   File: dataset.py    License: MIT License 6 votes vote down vote up
def __init__(self, root_dir,
                 flip=False, rotate=False, gamma=False, stretch=False,
                 p_base=0.96, max_stretch=2.0,
                 normcor=False, return_cor=False, return_path=False):
        self.img_dir = os.path.join(root_dir, 'img')
        self.cor_dir = os.path.join(root_dir, 'label_cor')
        self.img_fnames = sorted([
            fname for fname in os.listdir(self.img_dir)
            if fname.endswith('.jpg') or fname.endswith('.png')
        ])
        self.txt_fnames = ['%s.txt' % fname[:-4] for fname in self.img_fnames]
        self.flip = flip
        self.rotate = rotate
        self.gamma = gamma
        self.stretch = stretch
        self.p_base = p_base
        self.max_stretch = max_stretch
        self.normcor = normcor
        self.return_cor = return_cor
        self.return_path = return_path

        self._check_dataset() 
Example 9
Project: Keras-BiGAN   Author: manicman1999   File: bigan.py    License: MIT License 6 votes vote down vote up
def __init__(self, steps = 1, lr = 0.0001, decay = 0.00001, silent = True):

        self.GAN = GAN(steps = steps, lr = lr, decay = decay)
        self.DisModel = self.GAN.DisModel()
        self.AdModel = self.GAN.AdModel()

        self.lastblip = time.clock()

        self.noise_level = 0

        self.im = dataGenerator(directory, suffix = suff, flip = True)

        self.silent = silent

        #Train Generator to be in the middle, not all the way at real. Apparently works better??
        self.ones = np.ones((BATCH_SIZE, 1), dtype=np.float32)
        self.zeros = np.zeros((BATCH_SIZE, 1), dtype=np.float32)
        self.nones = -self.ones 
Example 10
Project: recruit   Author: Frank-qlu   File: test_function_base.py    License: Apache License 2.0 6 votes vote down vote up
def test_multiple_axes(self):
        a = np.array([[[0, 1],
                       [2, 3]],
                      [[4, 5],
                       [6, 7]]])

        assert_equal(np.flip(a, axis=()), a)

        b = np.array([[[5, 4],
                       [7, 6]],
                      [[1, 0],
                       [3, 2]]])

        assert_equal(np.flip(a, axis=(0, 2)), b)

        c = np.array([[[3, 2],
                       [1, 0]],
                      [[7, 6],
                       [5, 4]]])

        assert_equal(np.flip(a, axis=(1, 2)), c) 
Example 11
Project: tensornets   Author: taehoonlee   File: utils.py    License: MIT License 6 votes vote down vote up
def crop(img, crop_size, crop_loc=4, crop_grid=(3, 3)):
    if isinstance(crop_loc, list):
        imgs = np.zeros((img.shape[0], len(crop_loc), crop_size, crop_size, 3),
                        np.float32)
        for (i, loc) in enumerate(crop_loc):
            r, c = crop_idx(img.shape[1:3], crop_size, loc, crop_grid)
            imgs[:, i] = img[:, r:r+crop_size, c:c+crop_size, :]
        return imgs
    elif crop_loc == np.prod(crop_grid) + 1:
        imgs = np.zeros((img.shape[0], crop_loc, crop_size, crop_size, 3),
                        np.float32)
        r, c = crop_idx(img.shape[1:3], crop_size, 4, crop_grid)
        imgs[:, 0] = img[:, r:r+crop_size, c:c+crop_size, :]
        imgs[:, 1] = img[:, 0:crop_size, 0:crop_size, :]
        imgs[:, 2] = img[:, 0:crop_size, -crop_size:, :]
        imgs[:, 3] = img[:, -crop_size:, 0:crop_size, :]
        imgs[:, 4] = img[:, -crop_size:, -crop_size:, :]
        imgs[:, 5:] = np.flip(imgs[:, :5], axis=3)
        return imgs
    else:
        r, c = crop_idx(img.shape[1:3], crop_size, crop_loc, crop_grid)
        return img[:, r:r+crop_size, c:c+crop_size, :] 
Example 12
Project: mabalgs   Author: alison-carrera   File: algs.py    License: Apache License 2.0 6 votes vote down vote up
def select(self):
        """
            This method selects the best arm chosen by Thompsom Sampling.

            :return: Return selected arm number.
                    Arm number returned is (n_arm - 1).

                    Returns a list of arms by importance.
                    The chosen arm is the index 0 of this list.
        """
        rewards_0 = self.n_impressions - self.n_rewards
        rewards_0[rewards_0 <= 0] = 1
        theta_value = np.random.beta(self.n_rewards, rewards_0)
        ranked_arms = np.flip(np.argsort(theta_value), axis=0)
        chosen_arm = ranked_arms[0]
        self.n_impressions[chosen_arm] += 1

        return chosen_arm, ranked_arms 
Example 13
Project: StyleGAN2-Tensorflow-2.0   Author: manicman1999   File: datagen.py    License: MIT License 6 votes vote down vote up
def __init__(self, folder, im_size, mss = (1024 ** 3), flip = True, verbose = True):
        self.folder = folder
        self.im_size = im_size
        self.segment_length = mss // (im_size * im_size * 3)
        self.flip = flip
        self.verbose = verbose

        self.segments = []
        self.images = []
        self.update = 0

        if self.verbose:
            print("Importing images...")
            print("Maximum Segment Size: ", self.segment_length)

        try:
            os.mkdir("data/" + self.folder + "-npy-" + str(self.im_size))
        except:
            self.load_from_npy(folder)
            return

        self.folder_to_npy(self.folder)
        self.load_from_npy(self.folder) 
Example 14
Project: StyleGAN2-Tensorflow-2.0   Author: manicman1999   File: datagen.py    License: MIT License 6 votes vote down vote up
def get_batch(self, num):

        if self.update > self.images.shape[0]:
            self.load_from_npy(self.folder)

        self.update = self.update + num

        idx = np.random.randint(0, self.images.shape[0] - 1, num)
        out = []

        for i in idx:
            out.append(self.images[i])
            if self.flip and random.random() < 0.5:
                out[-1] = np.flip(out[-1], 1)

        return np.array(out).astype('float32') / 255.0 
Example 15
Project: signaltrain   Author: drscotthawley   File: viz.py    License: GNU General Public License v3.0 6 votes vote down vote up
def show_2d(array_2d, title="weights_layer", colormap=rainbow, flip=True):

    #print("weights_layer.shape = ",weights_layer.shape)
    if len(array_2d.shape) < 2:
        return
    img = np.clip(array_2d*255 ,-255,255).astype(np.uint8)   # scale
    if flip:
        img = np.flip(np.transpose(img))
    img = np.repeat(img[:,:,np.newaxis],3,axis=2)            # add color channels
    img = cv2.applyColorMap(img, colormap)                   # rainbow: blue=low, red=high
    # see if it exists
    new_window = not check_window_exists(title)
    window = cv2.namedWindow(title,cv2.WINDOW_NORMAL)
    cv2.imshow(title, img)
    if new_window:
        cv2.resizeWindow(title, img.shape[1], img.shape[0])                      # show what we've got
    #aspect = img.shape[0] / img.shape[1]
    #if aspect > 3:
    #    cv2.resizeWindow(title, 200, 1024)   # zoom in/out (can use two-finger-scroll to zoom in)
        #print(f"size for {title} = 1024, {int(1024/aspect*img.shape[0])}")
    #else:
    #    cv2.resizeWindow(title, int(imWidth/2),int(imWidth/2))   # zoom in/out (can use two-finger-scroll to zoom in)


# draw weights for all layers using model state dict 
Example 16
Project: neuropythy   Author: noahbenson   File: core.py    License: GNU Affero General Public License v3.0 5 votes vote down vote up
def reverse(self):
        '''
        curve.reverse() yields the inverted spline-curve equivalent to curve.
        '''
        return CurveSpline(
            np.flip(self.coordinates, axis=1),
            distances=(None if self.distances is None else np.flip(self.distances, axis=0)),
            order=self.order, weights=self.weights, smoothing=self.smoothing,
            periodic=self.periodic, meta_data=self.meta_data) 
Example 17
Project: neural-combinatorial-optimization-rl-tensorflow   Author: MichelDeudon   File: dataset.py    License: MIT License 5 votes vote down vote up
def swap2opt(tsptw_sequence,i,j):
    new_tsptw_sequence = np.copy(tsptw_sequence)
    new_tsptw_sequence[i:j+1] = np.flip(tsptw_sequence[i:j+1], axis=0) # flip or swap ?
    return new_tsptw_sequence

# One step of 2opt  = one double loop and return first improved sequence 
Example 18
Project: graph-neural-networks   Author: alelab-upenn   File: graphTools.py    License: GNU General Public License v3.0 5 votes vote down vote up
def permDegree(S):
    """
    permDegree: determines the permutation by degree (nodes ordered from highest
        degree to lowest)

    Input:
        S (np.array): matrix

    Output:
        permS (np.array): matrix permuted
        order (list): list of indices to permute S to turn into permS.
    """
    assert len(S.shape) == 2 or len(S.shape) == 3
    if len(S.shape) == 2:
        assert S.shape[0] == S.shape[1]
        S = S.reshape([1, S.shape[0], S.shape[1]])
        scalarWeights = True
    else:
        assert S.shape[1] == S.shape[2]
        scalarWeights = False
    # Compute the degree
    d = np.sum(np.sum(S, axis = 1), axis = 0)
    # Sort ascending order (from min degree to max degree)
    order = np.argsort(d)
    # Reverse sorting
    order = np.flip(order,0)
    # And update S
    S = S[:,order,:][:,:,order]
    # If the original GSO assumed scalar weights, get rid of the extra dimension
    if scalarWeights:
        S = S.reshape([S.shape[1], S.shape[2]])

    return S, order.tolist() 
Example 19
Project: pytorch-mri-segmentation-3D   Author: Achilleas   File: augmentations.py    License: MIT License 5 votes vote down vote up
def applyFLIPS(images, flip_lvl = 0):
	if flip_lvl == 0:
		p = np.random.rand(2) > 0.5
	else:
		p = np.random.rand(3) > 0.5
	locations = np.where(p == 1)[0] + 2

	new_imgs = []
	for img in images:
		for i in locations:
			img = np.flip(img, axis=i)
		new_imgs.append(img)
	return new_imgs 
Example 20
Project: pytorch-mri-segmentation-3D   Author: Achilleas   File: augmentations.py    License: MIT License 5 votes vote down vote up
def applyFLIPS2(images, locations):
	new_imgs = []
	for img in images:
		for i in locations:
			img = np.flip(img, axis=i)
		new_imgs.append(img)
	return new_imgs 
Example 21
Project: py360convert   Author: sunset1995   File: utils.py    License: MIT License 5 votes vote down vote up
def sample_cubefaces(cube_faces, tp, coor_y, coor_x, order):
    cube_faces = cube_faces.copy()
    cube_faces[1] = np.flip(cube_faces[1], 1)
    cube_faces[2] = np.flip(cube_faces[2], 1)
    cube_faces[4] = np.flip(cube_faces[4], 0)

    # Pad up down
    pad_ud = np.zeros((6, 2, cube_faces.shape[2]))
    pad_ud[0, 0] = cube_faces[5, 0, :]
    pad_ud[0, 1] = cube_faces[4, -1, :]
    pad_ud[1, 0] = cube_faces[5, :, -1]
    pad_ud[1, 1] = cube_faces[4, ::-1, -1]
    pad_ud[2, 0] = cube_faces[5, -1, ::-1]
    pad_ud[2, 1] = cube_faces[4, 0, ::-1]
    pad_ud[3, 0] = cube_faces[5, ::-1, 0]
    pad_ud[3, 1] = cube_faces[4, :, 0]
    pad_ud[4, 0] = cube_faces[0, 0, :]
    pad_ud[4, 1] = cube_faces[2, 0, ::-1]
    pad_ud[5, 0] = cube_faces[2, -1, ::-1]
    pad_ud[5, 1] = cube_faces[0, -1, :]
    cube_faces = np.concatenate([cube_faces, pad_ud], 1)

    # Pad left right
    pad_lr = np.zeros((6, cube_faces.shape[1], 2))
    pad_lr[0, :, 0] = cube_faces[1, :, 0]
    pad_lr[0, :, 1] = cube_faces[3, :, -1]
    pad_lr[1, :, 0] = cube_faces[2, :, 0]
    pad_lr[1, :, 1] = cube_faces[0, :, -1]
    pad_lr[2, :, 0] = cube_faces[3, :, 0]
    pad_lr[2, :, 1] = cube_faces[1, :, -1]
    pad_lr[3, :, 0] = cube_faces[0, :, 0]
    pad_lr[3, :, 1] = cube_faces[2, :, -1]
    pad_lr[4, 1:-1, 0] = cube_faces[1, 0, ::-1]
    pad_lr[4, 1:-1, 1] = cube_faces[3, 0, :]
    pad_lr[5, 1:-1, 0] = cube_faces[1, -2, :]
    pad_lr[5, 1:-1, 1] = cube_faces[3, -2, ::-1]
    cube_faces = np.concatenate([cube_faces, pad_lr], 2)

    return map_coordinates(cube_faces, [tp, coor_y, coor_x], order=order, mode='wrap') 
Example 22
Project: py360convert   Author: sunset1995   File: utils.py    License: MIT License 5 votes vote down vote up
def cube_h2dice(cube_h):
    assert cube_h.shape[0] * 6 == cube_h.shape[1]
    w = cube_h.shape[0]
    cube_dice = np.zeros((w * 3, w * 4, cube_h.shape[2]), dtype=cube_h.dtype)
    cube_list = cube_h2list(cube_h)
    # Order: F R B L U D
    sxy = [(1, 1), (2, 1), (3, 1), (0, 1), (1, 0), (1, 2)]
    for i, (sx, sy) in enumerate(sxy):
        face = cube_list[i]
        if i in [1, 2]:
            face = np.flip(face, axis=1)
        if i == 4:
            face = np.flip(face, axis=0)
        cube_dice[sy*w:(sy+1)*w, sx*w:(sx+1)*w] = face
    return cube_dice 
Example 23
Project: py360convert   Author: sunset1995   File: utils.py    License: MIT License 5 votes vote down vote up
def cube_dice2h(cube_dice):
    w = cube_dice.shape[0] // 3
    assert cube_dice.shape[0] == w * 3 and cube_dice.shape[1] == w * 4
    cube_h = np.zeros((w, w * 6, cube_dice.shape[2]), dtype=cube_dice.dtype)
    # Order: F R B L U D
    sxy = [(1, 1), (2, 1), (3, 1), (0, 1), (1, 0), (1, 2)]
    for i, (sx, sy) in enumerate(sxy):
        face = cube_dice[sy*w:(sy+1)*w, sx*w:(sx+1)*w]
        if i in [1, 2]:
            face = np.flip(face, axis=1)
        if i == 4:
            face = np.flip(face, axis=0)
        cube_h[:, i*w:(i+1)*w] = face
    return cube_h 
Example 24
Project: pymoo   Author: msu-coinlab   File: flowshop_scheduling.py    License: Apache License 2.0 5 votes vote down vote up
def visualize(problem, x, path=None, label=True):
    with plt.style.context('ggplot'):
        n_machines, n_jobs = problem.data.shape
        machine_times = problem.get_machine_times(x)

        fig = plt.figure()
        ax = fig.add_subplot(111)

        Y = np.flip(np.arange(n_machines))

        for i in range(n_machines):
            for j in range(n_jobs):
                width = problem.data[i][x[j]]
                left = machine_times[i][j]
                ax.barh(Y[i], width, left=left,
                        align='center', color='gray',
                        edgecolor='black', linewidth=0.8
                        )
                if label:
                    ax.text((left + width / 2), Y[i], str(x[j] + 1), ha='center', va='center', color='white',
                            fontsize=15)
        ax.set_xlabel("Time")
        ax.set_yticks(np.arange(n_machines))
        ax.set_yticklabels(["M%d" % (i + 1) for i in Y])
        ax.set_title("Makespan: %s" % np.round(problem.makespan(x), 3))
        if path is not None:
            plt.savefig(path)
        plt.show() 
Example 25
Project: pymoo   Author: msu-coinlab   File: mw.py    License: Apache License 2.0 5 votes vote down vote up
def _evaluate(self, X, out, *args, **kwargs):
        g = self.g1(X)
        f = g.reshape((-1, 1)) * np.ones((X.shape[0], self.n_obj))
        f[:, 1:] *= X[:, (self.n_obj - 2)::-1]
        f[:, 0:-1] *= np.flip(np.cumprod(1 - X[:, :(self.n_obj - 1)], axis=1), axis=1)

        g0 = f.sum(axis=1) - 1 - self.LA1(0.4, 2.5, 1.0, 8.0, f[:, -1] - f[:, :-1].sum(axis=1))
        out["F"] = f
        out["G"] = g0.reshape((-1, 1)) 
Example 26
Project: pymoo   Author: msu-coinlab   File: mw.py    License: Apache License 2.0 5 votes vote down vote up
def _evaluate(self, X, out, *args, **kwargs):
        g = self.g2(X)
        f = g.reshape((-1, 1)) * np.ones((X.shape[0], self.n_obj))
        f[:, 1:] *= np.sin(0.5 * np.pi * X[:, (self.n_obj - 2)::-1])
        cos = np.cos(0.5 * np.pi * X[:, :(self.n_obj - 1)])
        f[:, 0:-1] *= np.flip(np.cumprod(cos, axis=1), axis=1)

        f_squared = (f ** 2).sum(axis=1)
        g0 = f_squared - (1.25 - self.LA2(0.5, 6.0, 1.0, 2.0, np.arcsin(f[:, -1] / np.sqrt(f_squared)))) * (
                1.25 - self.LA2(0.5, 6.0, 1.0, 2.0, np.arcsin(f[:, -1] / np.sqrt(f_squared))))
        out["F"] = f
        out["G"] = g0.reshape((-1, 1)) 
Example 27
Project: pymoo   Author: msu-coinlab   File: inversion_mutation.py    License: Apache License 2.0 5 votes vote down vote up
def inversion_mutation(y, seq, inplace=True):
    y = y if inplace else np.copy(y)

    seq = seq if not None else random_sequence(len(y))
    start, end = seq

    y[start:end + 1] = np.flip(y[start:end + 1])
    return y 
Example 28
Project: HorizonNet   Author: sunset1995   File: inference.py    License: MIT License 5 votes vote down vote up
def augment(x_img, flip, rotate):
    x_img = x_img.numpy()
    aug_type = ['']
    x_imgs_augmented = [x_img]
    if flip:
        aug_type.append('flip')
        x_imgs_augmented.append(np.flip(x_img, axis=-1))
    for shift_p in rotate:
        shift = int(round(shift_p * x_img.shape[-1]))
        aug_type.append('rotate %d' % shift)
        x_imgs_augmented.append(np.roll(x_img, shift, axis=-1))
    return torch.FloatTensor(np.concatenate(x_imgs_augmented, 0)), aug_type 
Example 29
Project: Keras-BiGAN   Author: manicman1999   File: bigan.py    License: MIT License 5 votes vote down vote up
def __init__(self, loc, flip = True, suffix = 'png'):
        self.flip = flip
        self.suffix = suffix
        self.files = []
        self.n = 1e10

        print("Importing Images...")

        try:
            os.mkdir("data/" + loc + "-npy-" + str(im_size))
        except:
            self.load_from_npy(loc)
            return

        for dirpath, dirnames, filenames in os.walk("data/" + loc):
            for filename in [f for f in filenames if f.endswith("."+str(self.suffix))]:
                print('\r' + str(len(self.files)), end = '\r')
                fname = os.path.join(dirpath, filename)
                temp = Image.open(fname).convert(cmode)
                if not size_adjusted:
                    temp = temp.resize((im_size, im_size), Image.BILINEAR)
                temp = np.array(temp, dtype='uint8')
                self.files.append(temp)
                if self.flip:
                    self.files.append(np.flip(temp, 1))

        self.files = np.array(self.files)
        np.save("data/" + loc + "-npy-" + str(im_size) + "/data.npy", self.files)

        self.n = self.files.shape[0]

        print("Found " + str(self.n) + " images in " + loc + ".") 
Example 30
Project: Keras-BiGAN   Author: manicman1999   File: guess.py    License: MIT License 5 votes vote down vote up
def __init__(self, loc, flip = True, suffix = 'png'):
        self.flip = flip
        self.suffix = suffix
        self.files = []
        self.n = 1e10

        print("Importing Images...")

        try:
            os.mkdir("data/" + loc + "-npy-" + str(im_size))
        except:
            self.load_from_npy(loc)
            return

        for dirpath, dirnames, filenames in os.walk("data/" + loc):
            for filename in [f for f in filenames if f.endswith("."+str(self.suffix))]:
                print('\r' + str(len(self.files)), end = '\r')
                fname = os.path.join(dirpath, filename)
                temp = Image.open(fname).convert(cmode)
                if not size_adjusted:
                    temp = temp.resize((im_size, im_size), Image.BILINEAR)
                temp = np.array(temp, dtype='uint8')
                self.files.append(temp)
                if self.flip:
                    self.files.append(np.flip(temp, 1))

        self.files = np.array(self.files)
        np.save("data/" + loc + "-npy-" + str(im_size) + "/data.npy", self.files)

        self.n = self.files.shape[0]

        print("Found " + str(self.n) + " images in " + loc + ".")