#! /usr/bin/python
# -*- coding: utf-8 -*-


import matplotlib
## use this, if you got the following error:
#  _tkinter.TclError: no display name and no $DISPLAY environment variable
# matplotlib.use('Agg')

import numpy as np
import os
from . import prepro

# save/read image(s)
import scipy.misc

def read_image(image, path=''):
    """ Read one image.

    Parameters
    -----------
    images : string, file name.
    path : string, path.
    """
    return scipy.misc.imread(os.path.join(path, image))

def read_images(img_list, path='', n_threads=10, printable=True):
    """ Returns all images in list by given path and name of each image file.

    Parameters
    -------------
    img_list : list of string, the image file names.
    path : string, image folder path.
    n_threads : int, number of thread to read image.
    printable : bool, print infomation when reading images, default is True.
    """
    imgs = []
    for idx in range(0, len(img_list), n_threads):
        b_imgs_list = img_list[idx : idx + n_threads]
        b_imgs = prepro.threading_data(b_imgs_list, fn=read_image, path=path)
        # print(b_imgs.shape)
        imgs.extend(b_imgs)
        if printable:
            print('read %d from %s' % (len(imgs), path))
    return imgs

def save_image(image, image_path=''):
    """Save one image.

    Parameters
    -----------
    images : numpy array [w, h, c]
    image_path : string.
    """
    try: # RGB
        scipy.misc.imsave(image_path, image)
    except: # Greyscale
        scipy.misc.imsave(image_path, image[:,:,0])

def save_images(images, size, image_path=''):
    """Save mutiple images into one single image.

    Parameters
    -----------
    images : numpy array [batch, w, h, c]
    size : list of two int, row and column number.
        number of images should be equal or less than size[0] * size[1]
    image_path : string.

    Examples
    ---------
    >>> images = np.random.rand(64, 100, 100, 3)
    >>> tl.visualize.save_images(images, [8, 8], 'temp.png')
    """
    def merge(images, size):
        h, w = images.shape[1], images.shape[2]
        img = np.zeros((h * size[0], w * size[1], 3))
        for idx, image in enumerate(images):
            i = idx % size[1]
            j = idx // size[1]
            img[j*h:j*h+h, i*w:i*w+w, :] = image
        return img

    def imsave(images, size, path):
        return scipy.misc.imsave(path, merge(images, size))

    assert len(images) <= size[0] * size[1], "number of images should be equal or less than size[0] * size[1] {}".format(len(images))
    return imsave(images, size, image_path)

# for object detection
def draw_boxes_and_labels_to_image(image, classes=[], coords=[],
                scores=[], classes_list=[],
                is_center=True, is_rescale=True, save_name=None):
    """ Draw bboxes and class labels on image. Return or save the image with bboxes, example in the docs of ``tl.prepro``.

    Parameters
    -----------
    image : RGB image in numpy.array, [height, width, channel].
    classes : a list of class ID (int).
    coords : a list of list for coordinates.
        - Should be [x, y, x2, y2] (up-left and botton-right format)
        - If [x_center, y_center, w, h] (set is_center to True).
    scores : a list of score (float). (Optional)
    classes_list : list of string, for converting ID to string on image.
    is_center : boolean, defalt is True.
        If coords is [x_center, y_center, w, h], set it to True for converting [x_center, y_center, w, h] to [x, y, x2, y2] (up-left and botton-right).
        If coords is [x1, x2, y1, y2], set it to False.
    is_rescale : boolean, defalt is True.
        If True, the input coordinates are the portion of width and high, this API will scale the coordinates to pixel unit internally.
        If False, feed the coordinates with pixel unit format.
    save_name : None or string
        The name of image file (i.e. image.png), if None, not to save image.

    References
    -----------
    - OpenCV rectangle and putText.
    - `scikit-image <http://scikit-image.org/docs/dev/api/skimage.draw.html#skimage.draw.rectangle>`_.
    """
    assert len(coords) == len(classes), "number of coordinates and classes are equal"
    if len(scores) > 0:
        assert len(scores) == len(classes), "number of scores and classes are equal"

    import cv2

        # image = copy.copy(image)    # don't change the original image
    image = image.copy()    # don't change the original image, and avoid error https://stackoverflow.com/questions/30249053/python-opencv-drawing-errors-after-manipulating-array-with-numpy

    imh, imw = image.shape[0:2]
    thick = int((imh + imw) // 430)

    for i in range(len(coords)):
        if is_center:
            x, y, x2, y2 = prepro.obj_box_coord_centroid_to_upleft_butright(coords[i])
        else:
            x, y, x2, y2 = coords[i]

        if is_rescale: # scale back to pixel unit if the coords are the portion of width and high
            x, y, x2, y2 = prepro.obj_box_coord_scale_to_pixelunit([x, y, x2, y2], (imh, imw))

        cv2.rectangle(image,
            (int(x), int(y)), (int(x2), int(y2)),   # up-left and botton-right
            [0,255,0],
            thick)

        cv2.putText(
            image,
            classes_list[classes[i]] + ((" %.2f" % (scores[i])) if (len(scores) != 0) else " "),
            (int(x), int(y)),   # button left
            0,
            1.5e-3 * imh,       # bigger = larger font
            [0,0,256],          # self.meta['colors'][max_indx],
            int(thick/2)+1)     # bold

    if save_name is not None:
        # cv2.imwrite('_my.png', image)
        save_image(image, save_name)
    # if len(coords) == 0:
    #     print("draw_boxes_and_labels_to_image: no bboxes exist, cannot draw !")
    return image

# old APIs
def W(W=None, second=10, saveable=True, shape=[28,28], name='mnist', fig_idx=2396512):
    """Visualize every columns of the weight matrix to a group of Greyscale img.

    Parameters
    ----------
    W : numpy.array
        The weight matrix
    second : int
        The display second(s) for the image(s), if saveable is False.
    saveable : boolean
        Save or plot the figure.
    shape : a list with 2 int
        The shape of feature image, MNIST is [28, 80].
    name : a string
        A name to save the image, if saveable is True.
    fig_idx : int
        matplotlib figure index.

    Examples
    --------
    >>> tl.visualize.W(network.all_params[0].eval(), second=10, saveable=True, name='weight_of_1st_layer', fig_idx=2012)
    """
    import matplotlib.pyplot as plt
    if saveable is False:
        plt.ion()
    fig = plt.figure(fig_idx)      # show all feature images
    size = W.shape[0]
    n_units = W.shape[1]

    num_r = int(np.sqrt(n_units))  # 每行显示的个数   若25个hidden unit -> 每行显示5个
    num_c = int(np.ceil(n_units/num_r))
    count = int(1)
    for row in range(1, num_r+1):
        for col in range(1, num_c+1):
            if count > n_units:
                break
            a = fig.add_subplot(num_r, num_c, count)
            # ------------------------------------------------------------
            # plt.imshow(np.reshape(W[:,count-1],(28,28)), cmap='gray')
            # ------------------------------------------------------------
            feature = W[:,count-1] / np.sqrt( (W[:,count-1]**2).sum())
            # feature[feature<0.0001] = 0   # value threshold
            # if count == 1 or count == 2:
            #     print(np.mean(feature))
            # if np.std(feature) < 0.03:      # condition threshold
            #     feature = np.zeros_like(feature)
            # if np.mean(feature) < -0.015:      # condition threshold
            #     feature = np.zeros_like(feature)
            plt.imshow(np.reshape(feature ,(shape[0],shape[1])),
                    cmap='gray', interpolation="nearest")#, vmin=np.min(feature), vmax=np.max(feature))
            # plt.title(name)
            # ------------------------------------------------------------
            # plt.imshow(np.reshape(W[:,count-1] ,(np.sqrt(size),np.sqrt(size))), cmap='gray', interpolation="nearest")
            plt.gca().xaxis.set_major_locator(plt.NullLocator())    # distable tick
            plt.gca().yaxis.set_major_locator(plt.NullLocator())
            count = count + 1
    if saveable:
        plt.savefig(name+'.pdf',format='pdf')
    else:
        plt.draw()
        plt.pause(second)

def frame(I=None, second=5, saveable=True, name='frame', cmap=None, fig_idx=12836):
    """Display a frame(image). Make sure OpenAI Gym render() is disable before using it.

    Parameters
    ----------
    I : numpy.array
        The image
    second : int
        The display second(s) for the image(s), if saveable is False.
    saveable : boolean
        Save or plot the figure.
    name : a string
        A name to save the image, if saveable is True.
    cmap : None or string
        'gray' for greyscale, None for default, etc.
    fig_idx : int
        matplotlib figure index.

    Examples
    --------
    >>> env = gym.make("Pong-v0")
    >>> observation = env.reset()
    >>> tl.visualize.frame(observation)
    """
    import matplotlib.pyplot as plt
    if saveable is False:
        plt.ion()
    fig = plt.figure(fig_idx)      # show all feature images

    if len(I.shape) and I.shape[-1]==1:     # (10,10,1) --> (10,10)
        I = I[:,:,0]

    plt.imshow(I, cmap)
    plt.title(name)
    # plt.gca().xaxis.set_major_locator(plt.NullLocator())    # distable tick
    # plt.gca().yaxis.set_major_locator(plt.NullLocator())

    if saveable:
        plt.savefig(name+'.pdf',format='pdf')
    else:
        plt.draw()
        plt.pause(second)

def CNN2d(CNN=None, second=10, saveable=True, name='cnn', fig_idx=3119362):
    """Display a group of RGB or Greyscale CNN masks.

    Parameters
    ----------
    CNN : numpy.array
        The image. e.g: 64 5x5 RGB images can be (5, 5, 3, 64).
    second : int
        The display second(s) for the image(s), if saveable is False.
    saveable : boolean
        Save or plot the figure.
    name : a string
        A name to save the image, if saveable is True.
    fig_idx : int
        matplotlib figure index.

    Examples
    --------
    >>> tl.visualize.CNN2d(network.all_params[0].eval(), second=10, saveable=True, name='cnn1_mnist', fig_idx=2012)
    """
    import matplotlib.pyplot as plt
    # print(CNN.shape)    # (5, 5, 3, 64)
    # exit()
    n_mask = CNN.shape[3]
    n_row = CNN.shape[0]
    n_col = CNN.shape[1]
    n_color = CNN.shape[2]
    row = int(np.sqrt(n_mask))
    col = int(np.ceil(n_mask/row))
    plt.ion()   # active mode
    fig = plt.figure(fig_idx)
    count = 1
    for ir in range(1, row+1):
        for ic in range(1, col+1):
            if count > n_mask:
                break
            a = fig.add_subplot(col, row, count)
            # print(CNN[:,:,:,count-1].shape, n_row, n_col)   # (5, 1, 32) 5 5
            # exit()
            # plt.imshow(
            #         np.reshape(CNN[count-1,:,:,:], (n_row, n_col)),
            #         cmap='gray', interpolation="nearest")     # theano
            if n_color == 1:
                plt.imshow(
                        np.reshape(CNN[:,:,:,count-1], (n_row, n_col)),
                        cmap='gray', interpolation="nearest")
            elif n_color == 3:
                plt.imshow(
                        np.reshape(CNN[:,:,:,count-1], (n_row, n_col, n_color)),
                        cmap='gray', interpolation="nearest")
            else:
                raise Exception("Unknown n_color")
            plt.gca().xaxis.set_major_locator(plt.NullLocator())    # distable tick
            plt.gca().yaxis.set_major_locator(plt.NullLocator())
            count = count + 1
    if saveable:
        plt.savefig(name+'.pdf',format='pdf')
    else:
        plt.draw()
        plt.pause(second)

def images2d(images=None, second=10, saveable=True, name='images', dtype=None,
                                                            fig_idx=3119362):
    """Display a group of RGB or Greyscale images.

    Parameters
    ----------
    images : numpy.array
        The images.
    second : int
        The display second(s) for the image(s), if saveable is False.
    saveable : boolean
        Save or plot the figure.
    name : a string
        A name to save the image, if saveable is True.
    dtype : None or numpy data type
        The data type for displaying the images.
    fig_idx : int
        matplotlib figure index.

    Examples
    --------
    >>> X_train, y_train, X_test, y_test = tl.files.load_cifar10_dataset(shape=(-1, 32, 32, 3), plotable=False)
    >>> tl.visualize.images2d(X_train[0:100,:,:,:], second=10, saveable=False, name='cifar10', dtype=np.uint8, fig_idx=20212)
    """
    import matplotlib.pyplot as plt
    # print(images.shape)    # (50000, 32, 32, 3)
    # exit()
    if dtype:
        images = np.asarray(images, dtype=dtype)
    n_mask = images.shape[0]
    n_row = images.shape[1]
    n_col = images.shape[2]
    n_color = images.shape[3]
    row = int(np.sqrt(n_mask))
    col = int(np.ceil(n_mask/row))
    plt.ion()   # active mode
    fig = plt.figure(fig_idx)
    count = 1
    for ir in range(1, row+1):
        for ic in range(1, col+1):
            if count > n_mask:
                break
            a = fig.add_subplot(col, row, count)
            # print(images[:,:,:,count-1].shape, n_row, n_col)   # (5, 1, 32) 5 5
            # plt.imshow(
            #         np.reshape(images[count-1,:,:,:], (n_row, n_col)),
            #         cmap='gray', interpolation="nearest")     # theano
            if n_color == 1:
                plt.imshow(
                        np.reshape(images[count-1,:,:], (n_row, n_col)),
                        cmap='gray', interpolation="nearest")
                # plt.title(name)
            elif n_color == 3:
                plt.imshow(images[count-1,:,:],
                        cmap='gray', interpolation="nearest")
                # plt.title(name)
            else:
                raise Exception("Unknown n_color")
            plt.gca().xaxis.set_major_locator(plt.NullLocator())    # distable tick
            plt.gca().yaxis.set_major_locator(plt.NullLocator())
            count = count + 1
    if saveable:
        plt.savefig(name+'.pdf',format='pdf')
    else:
        plt.draw()
        plt.pause(second)

def tsne_embedding(embeddings, reverse_dictionary, plot_only=500,
                        second=5, saveable=False, name='tsne', fig_idx=9862):
    """Visualize the embeddings by using t-SNE.

    Parameters
    ----------
    embeddings : a matrix
        The images.
    reverse_dictionary : a dictionary
        id_to_word, mapping id to unique word.
    plot_only : int
        The number of examples to plot, choice the most common words.
    second : int
        The display second(s) for the image(s), if saveable is False.
    saveable : boolean
        Save or plot the figure.
    name : a string
        A name to save the image, if saveable is True.
    fig_idx : int
        matplotlib figure index.

    Examples
    --------
    >>> see 'tutorial_word2vec_basic.py'
    >>> final_embeddings = normalized_embeddings.eval()
    >>> tl.visualize.tsne_embedding(final_embeddings, labels, reverse_dictionary,
    ...                   plot_only=500, second=5, saveable=False, name='tsne')
    """
    import matplotlib.pyplot as plt
    def plot_with_labels(low_dim_embs, labels, figsize=(18, 18), second=5,
                                    saveable=True, name='tsne', fig_idx=9862):
        assert low_dim_embs.shape[0] >= len(labels), "More labels than embeddings"
        if saveable is False:
            plt.ion()
            plt.figure(fig_idx)
        plt.figure(figsize=figsize)  #in inches
        for i, label in enumerate(labels):
            x, y = low_dim_embs[i,:]
            plt.scatter(x, y)
            plt.annotate(label,
                     xy=(x, y),
                     xytext=(5, 2),
                     textcoords='offset points',
                     ha='right',
                     va='bottom')
        if saveable:
            plt.savefig(name+'.pdf',format='pdf')
        else:
            plt.draw()
            plt.pause(second)

    try:
        from sklearn.manifold import TSNE
        import matplotlib.pyplot as plt
        from six.moves import xrange

        tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)
        # plot_only = 500
        low_dim_embs = tsne.fit_transform(embeddings[:plot_only,:])
        labels = [reverse_dictionary[i] for i in xrange(plot_only)]
        plot_with_labels(low_dim_embs, labels, second=second, saveable=saveable, \
                                                    name=name, fig_idx=fig_idx)
    except ImportError:
        print("Please install sklearn and matplotlib to visualize embeddings.")


#
