Python pylab.imshow方法代码示例

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import imshow [as 别名]
def generate_png_chess_dp_vertex(self):
    """Produces pictures of the dominant product vertex a chessboard convention"""
    import matplotlib.pylab as plt
    plt.ioff()
    dab2v = self.get_dp_vertex_doubly_sparse()
    for i, ab in enumerate(dab2v): 
        fname = "chess-v-{:06d}.png".format(i)
        print('Matrix No.#{}, Size: {}, Type: {}'.format(i+1, ab.shape, type(ab)), fname)
        if type(ab) != 'numpy.ndarray': ab = ab.toarray()
        fig = plt.figure()
        ax = fig.add_subplot(1,1,1)
        ax.set_aspect('equal')
        plt.imshow(ab, interpolation='nearest', cmap=plt.cm.ocean)
        plt.colorbar()
        plt.savefig(fname)
        plt.close(fig) 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import imshow [as 别名]
def plotallfuncs(allfuncs=allfuncs):
    from matplotlib import pylab as pl
    pl.ioff()
    nnt = NNTester(npoints=1000)
    lpt = LinearTester(npoints=1000)
    for func in allfuncs:
        print(func.title)
        nnt.plot(func, interp=False, plotter='imshow')
        pl.savefig('%s-ref-img.png' % func.func_name)
        nnt.plot(func, interp=True, plotter='imshow')
        pl.savefig('%s-nn-img.png' % func.func_name)
        lpt.plot(func, interp=True, plotter='imshow')
        pl.savefig('%s-lin-img.png' % func.func_name)
        nnt.plot(func, interp=False, plotter='contour')
        pl.savefig('%s-ref-con.png' % func.func_name)
        nnt.plot(func, interp=True, plotter='contour')
        pl.savefig('%s-nn-con.png' % func.func_name)
        lpt.plot(func, interp=True, plotter='contour')
        pl.savefig('%s-lin-con.png' % func.func_name)
    pl.ion() 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import imshow [as 别名]
def __init__(self, name = "unknown", data = -1, is_show = False):
        self.name   = name
        self.data   = data
        self.size   = np.shape(self.data)
        self.is_show = is_show
        self.color_space = "unknown"
        self.bayer_pattern = "unknown"
        self.channel_gain = (1.0, 1.0, 1.0, 1.0)
        self.bit_depth = 0
        self.black_level = (0, 0, 0, 0)
        self.white_level = (1, 1, 1, 1)
        self.color_matrix = [[1., .0, .0],\
                             [.0, 1., .0],\
                             [.0, .0, 1.]] # xyz2cam
        self.min_value = np.min(self.data)
        self.max_value = np.max(self.data)
        self.data_type = self.data.dtype

        # Display image only isShow = True
        if (self.is_show):
            plt.imshow(self.data)
            plt.show() 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import imshow [as 别名]
def plotallfuncs(allfuncs=allfuncs):
    from matplotlib import pylab as pl
    pl.ioff()
    nnt = NNTester(npoints=1000)
    lpt = LinearTester(npoints=1000)
    for func in allfuncs:
        print(func.title)
        nnt.plot(func, interp=False, plotter='imshow')
        pl.savefig('%s-ref-img.png' % func.__name__)
        nnt.plot(func, interp=True, plotter='imshow')
        pl.savefig('%s-nn-img.png' % func.__name__)
        lpt.plot(func, interp=True, plotter='imshow')
        pl.savefig('%s-lin-img.png' % func.__name__)
        nnt.plot(func, interp=False, plotter='contour')
        pl.savefig('%s-ref-con.png' % func.__name__)
        nnt.plot(func, interp=True, plotter='contour')
        pl.savefig('%s-nn-con.png' % func.__name__)
        lpt.plot(func, interp=True, plotter='contour')
        pl.savefig('%s-lin-con.png' % func.__name__)
    pl.ion() 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import imshow [as 别名]
def _show_video(video, fps=10):
    # Import matplotlib/pylab only if needed
    import matplotlib
    matplotlib.use('TkAgg')
    import matplotlib.pylab as pl
    pl.style.use('ggplot')
    pl.axis('off')

    if fps < 0:
        fps = 25
    video /= 255.  # Pylab works in [0, 1] range
    img = None
    pause_length = 1. / fps
    try:
        for f in range(video.shape[0]):
            im = video[f, :, :, :]
            if img is None:
                img = pl.imshow(im)
            else:
                img.set_data(im)
            pl.pause(pause_length)
            pl.draw()
    except:
        pass 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import imshow [as 别名]
def show_pred(images, predictions, ground_truth):
    # choose 10 indice from images and visualize them
    indice = [np.random.randint(0, len(images)) for i in range(40)]
    for i in range(0, 40):
        plt.figure()
        plt.subplot(1, 3, 1)
        plt.tight_layout()
        plt.title('deformed image')
        plt.imshow(images[indice[i]])
        plt.subplot(1, 3, 2)
        plt.tight_layout()
        plt.title('predicted mask')
        plt.imshow(predictions[indice[i]])
        plt.subplot(1, 3, 3)
        plt.tight_layout()
        plt.title('ground truth label')
        plt.imshow(ground_truth[indice[i]])
    plt.show()

# Load Data Science Bowl 2018 training dataset 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import imshow [as 别名]
def matrix(msg, mobj):
    """
    Interpret a user string, convert it to a list and graph it as a matrix
    Uses ast.literal_eval to parse input into a list
    """
    fname = bot_data("{}.png".format(mobj.author.id))
    try:
        list_input = literal_eval(msg)
        if not isinstance(list_input, list):
            raise ValueError("Not a list")
        m = np_matrix(list_input)
        fig = plt.figure()
        ax = fig.add_subplot(1,1,1)
        ax.set_aspect('equal')
        plt.imshow(m, interpolation='nearest', cmap=plt.cm.ocean)
        plt.colorbar()
        plt.savefig(fname)
        await client.send_file(mobj.channel, fname)
        f_remove(fname)
        return
    except Exception as ex:
        logger("!matrix: {}".format(ex))
    return await client.send_message(mobj.channel, "Failed to render graph") 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import imshow [as 别名]
def _repr_html_(self):
        from . import variable_describe
        def _plot(fn):
            from PIL import Image
            try:
                import matplotlib.pylab as plt
                t = np.array(Image.open(fn))
                plt.figure()
                plt.imshow(self._crop(t))
                plt.axis('off')
                return "<img src='data:image/png;base64," + variable_describe._plot_to_string() + "'>"
            except:
                return "<br/>Failed to open."
        s = "<b>ImageSequence</b> size="+str(self.size)
        s += ", offset = "+str(self.offset)
        s += ", repeat = "+str(self.repeat)
        s += ", is_color = "+str(self.is_color)
        s += ", [frame "+str(self.i)+"/"+str(len(self))+"]"
        s += "<div style='background:#ff;padding:10px'><b>Input Images:</b>"
        for t in np.unique(self.file_list)[:10]:
            s += "<div style='background:#fff; margin:10px;padding:10px; border-left: 4px solid #eee;'>"+str(t)+": "+_plot(t)+"</div>"
        s += "</div>"
        return s 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import imshow [as 别名]
def calibrate_division_model_test():
    img = rgb2gray(plt.imread('test/kamera2.png'))
    y0 = np.array(img.shape)[::-1][np.newaxis].T / 2.
    z_n = np.linalg.norm(np.array(img.shape) / 2.)
    points = pilab_annotate_load('test/kamera2_lines.xml')
    points_per_line = 5
    num_lines = points.shape[0] / points_per_line
    lines_coords = np.array([points[i * points_per_line:i * points_per_line + points_per_line] for i in xrange(num_lines)])
    c = camera.calibrate_division_model(lines_coords, y0, z_n)

    import matplotlib.cm as cm
    plt.figure()
    plt.imshow(img, cmap=cm.gray)
    for line in xrange(num_lines):
        x = lines_coords[line, :, 0]
        plt.plot(x, lines_coords[line, :, 1], 'g')
        mc = camera.fit_line(lines_coords[line].T)
        plt.plot(x, mc[0] * x + mc[1], 'y')
        xy = c.undistort(lines_coords[line].T)
        plt.plot(xy[0, :], xy[1, :], 'r')
    plt.show()
    plt.close() 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import imshow [as 别名]
def check_HDF5(size=64):
    """
    Plot images with landmarks to check the processing
    """

    # Get hdf5 file
    hdf5_file = os.path.join(data_dir, "CelebA_%s_data.h5" % size)

    with h5py.File(hdf5_file, "r") as hf:
        data_color = hf["data"]
        for i in range(data_color.shape[0]):
            plt.figure()
            img = data_color[i, :, :, :].transpose(1,2,0)
            plt.imshow(img)
            plt.show()
            plt.clf()
            plt.close() 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import imshow [as 别名]
def check_HDF5(jpeg_dir, nb_channels):
    """
    Plot images with landmarks to check the processing
    """

    # Get hdf5 file
    file_name = os.path.basename(jpeg_dir.rstrip("/"))
    hdf5_file = os.path.join(data_dir, "%s_data.h5" % file_name)

    with h5py.File(hdf5_file, "r") as hf:
        data_full = hf["train_data_full"]
        data_sketch = hf["train_data_sketch"]
        for i in range(data_full.shape[0]):
            plt.figure()
            img = data_full[i, :, :, :].transpose(1,2,0)
            img2 = data_sketch[i, :, :, :].transpose(1,2,0)
            img = np.concatenate((img, img2), axis=1)
            if nb_channels == 1:
                plt.imshow(img[:, :, 0], cmap="gray")
            else:
                plt.imshow(img)
            plt.show()
            plt.clf()
            plt.close() 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import imshow [as 别名]
def check_HDF5(size):
    """
    Plot images with landmarks to check the processing
    """

    # Get hdf5 file
    hdf5_file = os.path.join(data_dir, "lfw_%s_data.h5" % size)

    with h5py.File(hdf5_file, "r") as hf:
        data_color = hf["data"]
        label = hf["labels"]
        attrs = label.attrs["label_names"]
        for i in range(data_color.shape[0]):
            plt.figure(figsize=(20, 10))
            img = data_color[i, :, :, :].transpose(1,2,0)[:, :, ::-1]
            # Get the 10 labels with highest values
            idx = label[i].argsort()[-10:]
            plt.xlabel(",  ".join(attrs[idx]), fontsize=12)
            plt.imshow(img)
            plt.show()
            plt.clf()
            plt.close() 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import imshow [as 别名]
def format_plot(X, epoch=None, title=None, figsize=(15, 10)):

    plt.figure(figsize=figsize)

    if X.shape[-1] == 1:
        plt.imshow(X[:, :, 0], cmap="gray")
    else:
        plt.imshow(X)

    plt.axis("off")
    plt.gca().xaxis.set_major_locator(mp.ticker.NullLocator())
    plt.gca().yaxis.set_major_locator(mp.ticker.NullLocator())

    if epoch is not None and title is None:
        save_path = os.path.join(FLAGS.fig_dir, "current_batch_%s.png" % epoch)
    elif epoch is not None and title is not None:
        save_path = os.path.join(FLAGS.fig_dir, "%s_%s.png" % (title, epoch))
    elif title is not None:
        save_path = os.path.join(FLAGS.fig_dir, "%s.png" % title)
    plt.savefig(save_path, bbox_inches='tight', pad_inches=0)
    plt.clf()
    plt.close() 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import imshow [as 别名]
def plot_spectrogram_to_numpy(spectrogram):
    spectrogram = spectrogram.transpose(1, 0)
    fig, ax = plt.subplots(figsize=(12, 3))
    im = ax.imshow(spectrogram, aspect="auto", origin="lower",
                   interpolation='none')
    plt.colorbar(im, ax=ax)
    plt.xlabel("Frames")
    plt.ylabel("Channels")
    plt.tight_layout()

    fig.canvas.draw()
    data = _save_figure_to_numpy(fig)
    plt.close()
    return data


####################
# PLOT SPECTROGRAM #
#################### 

# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import imshow [as 别名]
def plot_spectrograms(model, mag_val, mag_val_hat):
	'''
	Routine for plotting magnitude and phase spectorgrams
	'''
	plt.figure(1)
	plt.imshow(mag_val.data.cpu().numpy()[0, :, :].T, aspect='auto', origin='lower')
	plt.title('Initial magnitude')
	savefig('mag.png')
	plt.figure(2)  # <---- Check this out! Some "sub-harmonic" content is generated for the compressor if the analysis weights make only small perturbations
	plt.imshow(mag_val_hat.data.cpu().numpy()[0, :, :].T, aspect='auto', origin='lower')
	plt.title('Processed magnitude')
	savefig('mag_hat.png')

	#if isinstance(model, nn_proc.AsymMPAEC):     # Plot the spectrograms
	plt.matshow(model.mpaec.dft_analysis.conv_analysis_real.weight.data.cpu().numpy().astype(float)[:, 0, :] + 1)
	plt.title('Conv-Analysis Real')
	savefig('conv_anal_real.png')
	plt.matshow(model.mpaec.dft_analysis.conv_analysis_imag.weight.data.cpu().numpy().astype(float)[:, 0, :])
	plt.title('Conv-Analysis Imag')
	savefig('conv_anal_imag.png')
	plt.matshow(model.mpaec.dft_synthesis.conv_synthesis_real.weight.data.cpu().numpy().astype(float)[:, 0, :])
	plt.title('Conv-Synthesis Real')
	savefig('conv_synth_real.png')
	plt.matshow(model.mpaec.dft_synthesis.conv_synthesis_imag.weight.data.cpu().numpy().astype(float)[:, 0, :])
	plt.title('Conv-Synthesis Imag')
	savefig('conv_synth_imag.png')

	return