Python numpy.flip方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import flip [as 別名]
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.") 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import flip [as 別名]
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 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import flip [as 別名]
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) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import flip [as 別名]
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') 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import flip [as 別名]
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 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import flip [as 別名]
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 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import flip [as 別名]
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) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import flip [as 別名]
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() 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import flip [as 別名]
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 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import flip [as 別名]
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) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import flip [as 別名]
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, :] 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import flip [as 別名]
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 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import flip [as 別名]
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) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import flip [as 別名]
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 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import flip [as 別名]
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