Python matplotlib.cm.binary() Examples

The following are 3 code examples for showing how to use matplotlib.cm.binary(). 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: GLRM   Author: powerscorinne   File: util.py    License: MIT License 5 votes vote down vote up
def pplot(As, titles):
    # setup
    try: vmin = min([A.min() for A, t in zip(As[:-1], titles) if "missing" not in t]) # for pixel color reference
    except: vmin = As[0].min()
    try: vmax = max([A.max() for A, t in zip(As[:-1], titles) if "missing" not in t])
    except: vmax = As[0].max()
    my_dpi = 96
    plt.figure(figsize=(1.4*(250*len(As))/my_dpi, 250/my_dpi), dpi = my_dpi)
    for i, (A, title) in enumerate(zip(As, titles)):
        plt.subplot(1, len(As), i+1)
        if i == len(As)-1: vmin, vmax = A.min(), A.max()
        if "missing" in title:
            missing = A
            masked_data = ones(As[i-1].shape)
            for j,k in missing:  masked_data[j,k] = 0
            masked_data = masked_where(masked_data > 0.5, masked_data)
            plt.imshow(As[i-1], interpolation = 'nearest', vmin = vmin, vmax = vmax)
            plt.colorbar()
            plt.imshow(masked_data, cmap = cm.binary, interpolation = "nearest")
        else:
            plt.imshow(A, interpolation = 'nearest', vmin = vmin, vmax = vmax)
            plt.colorbar()
        plt.title(title)
        plt.axis("off")
   
    plt.show()
# 
# def unroll_missing(missing, ns):
#     missing_unrolled = []
#     for i, (MM, n) in enumerate(zip(missing, ns)):
#         for m in MM:
#             n2 = m[1] + sum([ns[j] for j in range(i)])
#             missing_unrolled.append((m[0], n2))
#     return missing_unrolled
# 
Example 2
Project: aiexamples   Author: mogoweb   File: ssd_viz.py    License: Apache License 2.0 5 votes vote down vote up
def plot_activation(model, input_image, layer_name):
    """Plots a mosaic of feature activation.
    
    # Arguments
        model: Keras model
        input_image: Test image which is feed into the network
        layer_name: Layer name of feature map
    """
    from keras import backend as K
    from IPython.display import display
    
    f = K.function(model.inputs, [model.get_layer(layer_name).output])
    output = f([[input_image]])
    output = np.moveaxis(output[0][0], [0,1,2], [1,2,0])
    print('%-20s input_shape: %-16s output_shape: %-16s' % (layer_name, str(input_image.shape), str(output.shape)))
    
    num_y = num_x = int(np.ceil(np.sqrt(output.shape[0])))
    data = mosaic(output, (num_x, num_y), '5%')
    
    #plt.figure(figsize=(12, 12))
    ax = plt.gca()
    divider = make_axes_locatable(ax)
    cax = divider.append_axes('right', size=0.1, pad=0.05)
    im = ax.imshow(data, vmin=data.min(), vmax=data.max(), interpolation='nearest', cmap=cm.binary)
    plt.colorbar(im, cax=cax)
    
    display(plt.gcf())
    plt.close() 
Example 3
Project: spinalcordtoolbox   Author: neuropoly   File: concatenate_WM_and_GM_tracts.py    License: MIT License 4 votes vote down vote up
def sorting_value_of_zone(zone):
    group_value=[[[]],[[]]]  # liste(liste_value, liste_nb_of_this-value)
    for i in range(len(zone)):
        if zone[i] not in group_value[0][0]:
            group_value[0][0].append(zone[i])
            group_value[1][0].append(1)
        else:
            index = group_value[0][0].index(zone[i])
            group_value[1][0][index] += 1
    return group_value




# #
# #
# # plt.imshow(arr_bin, cmap=cm.binary)
# # plt.show()
# plt.imshow(arr_bin, cmap=cm.binary)
# plt.show()


# from scipy.ndimage import gaussian_filter, median_filter
#
# #kernel = np.ones((5,5),np.float32)/25
# #img_smooth_1 = gaussian_filter(img, sigma=(20, 20), order=0)
# img_smooth_2 = median_filter(image, size=(30,30))
# img_smooth_2.astype(dtype='uint8')
#
# im = Image.fromarray(img_smooth_2)
# #im_1 = Image.fromarray(img_1)
# if im.mode != 'RGB':
#     im2 = im.convert('RGB')
# im2.save('gm_white_inv_smooth.png')
#
# plt.subplot(2,1,1)
# plt.imshow(image, cmap=cm.binary)
# # plt.subplot(2,2,2)
# # plt.imshow(img_smooth_1, cmap=cm.binary)
# plt.subplot(2,1,2)
# plt.imshow(img_smooth_2, cmap=cm.binary)
# plt.show()



#=======================================================================================================================
# Start program
#=======================================================================================================================