Python matplotlib.cm.binary() Examples

The following are 3 code examples of matplotlib.cm.binary(). 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. You may also want to check out all available functions/classes of the module matplotlib.cm , or try the search function .
Example #1
Source File: util.py    From GLRM with 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
Source File: ssd_viz.py    From aiexamples with 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
Source File: concatenate_WM_and_GM_tracts.py    From spinalcordtoolbox with 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
#=======================================================================================================================