Python matplotlib.cm.Greys_r() Examples

The following are 15 code examples for showing how to use matplotlib.cm.Greys_r(). 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: Dropout_BBalpha   Author: YingzhenLi   File: loading_utils.py    License: MIT License 6 votes vote down vote up
def plot_images(ax, images, shape, color = False):
     # finally save to file
    import matplotlib
    matplotlib.use('Agg')
    import matplotlib.pyplot as plt

    # flip 0 to 1
    images = 1.0 - images

    images = reshape_and_tile_images(images, shape, n_cols=len(images))
    if color:
        from matplotlib import cm
        plt.imshow(images, cmap=cm.Greys_r, interpolation='nearest')
    else:
        plt.imshow(images, cmap='Greys')
    ax.axis('off') 
Example 2
Project: med2image   Author: FNNDSC   File: med2image.py    License: MIT License 6 votes vote down vote up
def slice_save(self, astr_outputFile):
        '''
        Saves a single slice.

        ARGS

        o astr_output
        The output filename to save the slice to.
        '''
        self.dp.qprint('Outputfile = %s' % astr_outputFile)
        fformat = astr_outputFile.split('.')[-1]
        if fformat == 'dcm':
            if self._dcm:
                self._dcm.pixel_array.flat = self._Mnp_2Dslice.flat
                self._dcm.PixelData = self._dcm.pixel_array.tostring()
                self._dcm.save_as(astr_outputFile)
            else:
                raise ValueError('dcm output format only available for DICOM files')
        else:
            pylab.imsave(astr_outputFile, self._Mnp_2Dslice, format=fformat, cmap = cm.Greys_r) 
Example 3
Project: variational-continual-learning   Author: nvcuong   File: visualisation.py    License: Apache License 2.0 6 votes vote down vote up
def plot_images(images, shape, path, filename, n_rows = 10, color = True):
     # finally save to file
    import matplotlib
    matplotlib.use('Agg')
    import matplotlib.pyplot as plt
    images = reshape_and_tile_images(images, shape, n_rows)
    if color:
        from matplotlib import cm
        plt.imsave(fname=path+filename+".png", arr=images, cmap=cm.Greys_r)
    else:
        plt.imsave(fname=path+filename+".png", arr=images, cmap='Greys')
    #plt.axis('off')
    #plt.tight_layout()
    #plt.savefig(path + filename + ".png", format="png")
    print "saving image to " + path + filename + ".png"
    plt.close() 
Example 4
Project: aitom   Author: xulabs   File: util.py    License: GNU General Public License v3.0 6 votes vote down vote up
def dsp_img(v, new_figure=True):

    import matplotlib.pyplot as plt

    if new_figure:
        fig = plt.figure()
        ax = fig.add_subplot(111)
    else:
        ax = plt


    import matplotlib.cm as cm
    
    ax_u = ax.imshow(  v, cmap = cm.Greys_r )
    ax.axis('off') # clear x- and y-axes

    plt.pause(0.001)        # calling pause will display the figure without blocking the program, see segmentation.active_contour.morphsnakes.evolve_visual 
Example 5
Project: Python-Deep-Learning-SE   Author: ivan-vasilev   File: chapter_04_001.py    License: MIT License 5 votes vote down vote up
def conv(image, im_filter):
    """
    :param image: grayscale image as a 2-dimensional numpy array
    :param im_filter: 2-dimensional numpy array
    """

    # input dimensions
    height = image.shape[0]
    width = image.shape[1]

    # output image with reduced dimensions
    im_c = np.zeros((height - len(im_filter) + 1,
                     width - len(im_filter) + 1))

    # iterate over all rows and columns
    for row in range(len(im_c)):
        for col in range(len(im_c[0])):
            # apply the filter
            for i in range(len(im_filter)):
                for j in range(len(im_filter[0])):
                    im_c[row, col] += image[row + i, col + j] * im_filter[i][j]

    # fix out-of-bounds values
    im_c[im_c > 255] = 255
    im_c[im_c < 0] = 0

    # plot images for comparison
    import matplotlib.pyplot as plt
    import matplotlib.cm as cm

    plt.figure()
    plt.imshow(image, cmap=cm.Greys_r)
    plt.show()

    plt.imshow(im_c, cmap=cm.Greys_r)
    plt.show() 
Example 6
Project: a-PyTorch-Tutorial-to-Image-Captioning   Author: sgrvinod   File: caption.py    License: MIT License 5 votes vote down vote up
def visualize_att(image_path, seq, alphas, rev_word_map, smooth=True):
    """
    Visualizes caption with weights at every word.

    Adapted from paper authors' repo: https://github.com/kelvinxu/arctic-captions/blob/master/alpha_visualization.ipynb

    :param image_path: path to image that has been captioned
    :param seq: caption
    :param alphas: weights
    :param rev_word_map: reverse word mapping, i.e. ix2word
    :param smooth: smooth weights?
    """
    image = Image.open(image_path)
    image = image.resize([14 * 24, 14 * 24], Image.LANCZOS)

    words = [rev_word_map[ind] for ind in seq]

    for t in range(len(words)):
        if t > 50:
            break
        plt.subplot(np.ceil(len(words) / 5.), 5, t + 1)

        plt.text(0, 1, '%s' % (words[t]), color='black', backgroundcolor='white', fontsize=12)
        plt.imshow(image)
        current_alpha = alphas[t, :]
        if smooth:
            alpha = skimage.transform.pyramid_expand(current_alpha.numpy(), upscale=24, sigma=8)
        else:
            alpha = skimage.transform.resize(current_alpha.numpy(), [14 * 24, 14 * 24])
        if t == 0:
            plt.imshow(alpha, alpha=0)
        else:
            plt.imshow(alpha, alpha=0.8)
        plt.set_cmap(cm.Greys_r)
        plt.axis('off')
    plt.show() 
Example 7
Project: Diffusion-Probabilistic-Models   Author: Sohl-Dickstein   File: viz.py    License: MIT License 5 votes vote down vote up
def plot_parameter(theta_in, base_fname_part1, base_fname_part2="", title = '', n_colors=None):
    """
    Save both a raw and receptive field style plot of the contents of theta_in.
    base_fname_part1 provides the mandatory root of the filename.
    """

    theta = np.array(theta_in.copy()) # in case it was a scalar
    print "%s min %g median %g mean %g max %g shape"%(
        title, np.min(theta), np.median(theta), np.mean(theta), np.max(theta)), theta.shape
    theta = np.squeeze(theta)
    if len(theta.shape) == 0:
        # it's a scalar -- make it a 1d array
        theta = np.array([theta])
    shp = theta.shape
    if len(shp) > 2:
        theta = theta.reshape((theta.shape[0], -1))
        shp = theta.shape

    ## display basic figure
    plt.figure(figsize=[8,8])
    if len(shp) == 1:
        plt.plot(theta, '.', alpha=0.5)
    elif len(shp) == 2:
        plt.imshow(theta, interpolation='nearest', aspect='auto', cmap=cm.Greys_r)
        plt.colorbar()

    plt.title(title)
    plt.savefig(base_fname_part1 + '_raw_' + base_fname_part2 + '.pdf')
    plt.close()

    ## also display it in basis function view if it's a matrix, or
    ## if it's a bias with a square number of entries
    if len(shp) >= 2 or is_square(shp[0]):
        if len(shp) == 1:
            theta = theta.reshape((-1,1))
        plt.figure(figsize=[8,8])
        if show_receptive_fields(theta, n_colors=n_colors):
            plt.suptitle(title + "receptive fields")
            plt.savefig(base_fname_part1 + '_rf_' + base_fname_part2 + '.pdf')
        plt.close() 
Example 8
Project: aitom   Author: xulabs   File: io.py    License: GNU General Public License v3.0 5 votes vote down vote up
def save_image_matplotlib(m, out_file, vmin=None, vmax=None):
    import matplotlib.pyplot as PLT
    import matplotlib.cm as CM

    if vmin is None:        vmin = m.min()
    if vmax is None:        vmax = m.max()

    ax_u = PLT.imshow(  m, cmap = CM.Greys_r, vmin=vmin, vmax=vmax)
    PLT.axis('off')
    PLT.draw()

    PLT.savefig(out_file, bbox_inches='tight')
    PLT.close("all") 
Example 9
Project: Python-Deep-Learning-Second-Edition   Author: PacktPublishing   File: chapter_04_001.py    License: MIT License 5 votes vote down vote up
def conv(image, im_filter):
    """
    :param image: grayscale image as a 2-dimensional numpy array
    :param im_filter: 2-dimensional numpy array
    """

    # input dimensions
    height = image.shape[0]
    width = image.shape[1]

    # output image with reduced dimensions
    im_c = np.zeros((height - len(im_filter) + 1,
                     width - len(im_filter) + 1))

    # iterate over all rows and columns
    for row in range(len(im_c)):
        for col in range(len(im_c[0])):
            # apply the filter
            for i in range(len(im_filter)):
                for j in range(len(im_filter[0])):
                    im_c[row, col] += image[row + i, col + j] * im_filter[i][j]

    # fix out-of-bounds values
    im_c[im_c > 255] = 255
    im_c[im_c < 0] = 0

    # plot images for comparison
    import matplotlib.pyplot as plt
    import matplotlib.cm as cm

    plt.figure()
    plt.imshow(image, cmap=cm.Greys_r)
    plt.show()

    plt.imshow(im_c, cmap=cm.Greys_r)
    plt.show() 
Example 10
Project: aggregation   Author: zooniverse   File: ouroboros_api.py    License: Apache License 2.0 5 votes vote down vote up
def __display_image__(self,subject_id,args_l,kwargs_l,block=True,title=None):
        """
        return the file names for all the images associated with a given subject_id
        also download them if necessary
        :param subject_id:
        :return:
        """
        subject = self.subject_collection.find_one({"zooniverse_id": subject_id})
        url = subject["location"]["standard"]

        slash_index = url.rfind("/")
        object_id = url[slash_index+1:]

        if not(os.path.isfile(self.base_directory+"/Databases/"+self.project+"/images/"+object_id)):
            urllib.urlretrieve(url, self.base_directory+"/Databases/"+self.project+"/images/"+object_id)

        fname = self.base_directory+"/Databases/"+self.project+"/images/"+object_id

        image_file = cbook.get_sample_data(fname)
        image = plt.imread(image_file)

        fig, ax = plt.subplots()
        im = ax.imshow(image,cmap = cm.Greys_r)

        for args,kwargs in zip(args_l,kwargs_l):
            print args,kwargs
            ax.plot(*args,**kwargs)

        if title is not None:
            ax.set_title(title)
        plt.show(block=block) 
Example 11
Project: grammar-activity-prediction   Author: SiyuanQi   File: rrt.py    License: MIT License 5 votes vote down vote up
def plan_trajectory_with_ui(img):
    fig = ppl.gcf()
    fig.clf()
    ax = fig.add_subplot(1, 1, 1)
    ax.imshow(img, cmap=cm.Greys_r)
    ax.axis('image')
    ppl.draw()
    print 'Map is', len(img[0]), 'x', len(img)
    start, goal = select_start_goal_points(ax, img)
    path = rrt(img, start, goal, ax)
    return path 
Example 12
Project: LSTM_morse   Author: ag1le   File: MorseDecoder.py    License: MIT License 5 votes vote down vote up
def infer(model, fnImg):
    "recognize text in image provided by file path"
    img = create_image2(fnImg, model.imgSize)
    plt.imshow(img,cmap = cm.Greys_r)
    batch = Batch(None, [img])
    (recognized, probability) = model.inferBatch(batch, True)
    print('Recognized:', '"' + recognized[0] + '"')
    print('Probability:', probability[0])
    print(recognized)


#from pyAudioAnalysis.audioSegmentation import silence_removal 
Example 13
Project: Image-Captioning-PyTorch   Author: foamliu   File: demo.py    License: Apache License 2.0 5 votes vote down vote up
def visualize_att(image_path, seq, alphas, rev_word_map, i, smooth=True):
    """
    Visualizes caption with weights at every word.
    Adapted from paper authors' repo: https://github.com/kelvinxu/arctic-captions/blob/master/alpha_visualization.ipynb
    :param image_path: path to image that has been captioned
    :param seq: caption
    :param alphas: weights
    :param rev_word_map: reverse word mapping, i.e. ix2word
    :param smooth: smooth weights?
    """
    image = Image.open(image_path)
    image = image.resize([14 * 24, 14 * 24], Image.LANCZOS)

    words = [rev_word_map[ind] for ind in seq]
    print(words)

    for t in range(len(words)):
        if t > 50:
            break
        plt.subplot(np.ceil(len(words) / 5.), 5, t + 1)

        plt.text(0, 1, '%s' % (words[t]), color='black', backgroundcolor='white', fontsize=12)
        plt.imshow(image)
        current_alpha = alphas[t, :]
        if smooth:
            alpha = skimage.transform.pyramid_expand(current_alpha.numpy(), upscale=24, sigma=8)
        else:
            alpha = skimage.transform.resize(current_alpha.numpy(), [14 * 24, 14 * 24])
        if t == 0:
            plt.imshow(alpha, alpha=0)
        else:
            plt.imshow(alpha, alpha=0.8)
        plt.set_cmap(cm.Greys_r)
        plt.axis('off')

    plt.savefig('images/out_{}.jpg'.format(i))
    plt.close() 
Example 14
Project: DeepVis-PredDiff   Author: lmzintgraf   File: utils_visualise.py    License: MIT License 4 votes vote down vote up
def plot_results(x_test, x_test_im, sensMap, predDiff, tarFunc, classnames, testIdx, save_path):
    '''
    Plot the results of the relevance estimation
    '''
    imsize = x_test.shape  
    
    tarIdx = np.argmax(tarFunc(x_test)[-1])
    tarClass = classnames[tarIdx]
    #tarIdx = 287
    
    plt.figure()
    plt.subplot(2,2,1)
    plt.imshow(x_test_im, interpolation='nearest')
    plt.title('original')
    frame = pylab.gca()
    frame.axes.get_xaxis().set_ticks([])
    frame.axes.get_yaxis().set_ticks([]) 
    plt.subplot(2,2,2)
    plt.imshow(sensMap, cmap=cm.Greys_r, interpolation='nearest')
    plt.title('sensitivity map')
    frame = pylab.gca()
    frame.axes.get_xaxis().set_ticks([])
    frame.axes.get_yaxis().set_ticks([]) 
    plt.subplot(2,2,3)
    p = predDiff.reshape((imsize[1],imsize[2],-1))[:,:,tarIdx]
    plt.imshow(p, cmap=cm.seismic, vmin=-np.max(np.abs(p)), vmax=np.max(np.abs(p)), interpolation='nearest')
    plt.colorbar()
    #plt.imshow(np.abs(p), cmap=cm.Greys_r)
    plt.title('weight of evidence')
    frame = pylab.gca()
    frame.axes.get_xaxis().set_ticks([])
    frame.axes.get_yaxis().set_ticks([]) 
    plt.subplot(2,2,4)
    plt.title('class: {}'.format(tarClass))
    p = get_overlayed_image(x_test_im, p)
    #p = predDiff[0,:,:,np.argmax(netPred(net, x_test)[0]),1].reshape((224,224))
    plt.imshow(p, cmap=cm.seismic, vmin=-np.max(np.abs(p)), vmax=np.max(np.abs(p)), interpolation='nearest')
    #plt.title('class entropy')
    frame = pylab.gca()
    frame.axes.get_xaxis().set_ticks([])
    frame.axes.get_yaxis().set_ticks([]) 
    
    fig = plt.gcf()
    fig.set_size_inches(np.array([12,12]), forward=True)
    plt.tight_layout()
    plt.tight_layout()
    plt.tight_layout()
    plt.savefig(save_path)
    plt.close() 
Example 15
Project: MJHMC   Author: rueberger   File: poe_fig.py    License: GNU General Public License v2.0 4 votes vote down vote up
def plot_imgs(imgs, samp_names, step_nums, vmin = -2, vmax = 2):
    plt.figure(figsize=(5.5,3.6))

    nsamplers = len(samp_names)
    nsteps = len(step_nums)

    plt.subplot(nsamplers+1, nsteps+1, 1)
    plt.axis('off')
    plt.text(0.9, -0.1, "# grads",
        horizontalalignment='right',
        verticalalignment='bottom')

    for step_i in range(nsteps):
        plt.subplot(nsamplers+1, nsteps+1, 2 + step_i)
        plt.axis('off')
        plt.text(0.5, -0.1, "%d"%step_nums[step_i],
            horizontalalignment='center',
            verticalalignment='bottom')
    for samp_i in range(nsamplers):
        plt.subplot(nsamplers+1, nsteps+1, 1 + (samp_i+1)*(nsteps+1))
        plt.axis('off')
        plt.text(0.9, 0.5, samp_names[samp_i],
            horizontalalignment='right',
            verticalalignment='center')


    for samp_i in range(nsamplers):
        for step_i in range(nsteps):
            plt.subplot(nsamplers+1, nsteps+1, 2 + step_i + (samp_i+1)*(nsteps+1))

            ptch = imgs[samp_i][step_i].copy()
            img_w = np.sqrt(np.prod(ptch.shape))
            ptch = ptch.reshape((img_w, img_w))

            ptch -= vmin
            ptch /= vmax-vmin
            plt.imshow(ptch, interpolation='nearest', cmap=cm.Greys_r )
            plt.axis('off')

    # plt.tight_layout()
    plt.savefig('poe_samples.pdf')
    plt.close()