Python patches.ConnectionPatch方法代码示例

# 需要导入模块: from matplotlib import patches [as 别名]
# 或者: from matplotlib.patches import ConnectionPatch [as 别名]
def draw(self, img_q, img_t, pt_qt):
        import matplotlib
        matplotlib.use('Agg')
        from matplotlib import pyplot as plt
        from matplotlib.patches import ConnectionPatch

        fig, (ax_q, ax_t) = plt.subplots(1, 2, figsize=(8, 4))
        for pt_q, pt_t in pt_qt:
            con = ConnectionPatch(pt_t, pt_q,
                                  coordsA='data', coordsB='data',
                                  axesA=ax_t, axesB=ax_q,
                                  color='g', linewidth=0.5)
            ax_t.add_artist(con)
            ax_q.plot(pt_q[0], pt_q[1], 'rx')
            ax_t.plot(pt_t[0], pt_t[1], 'rx')
        ax_q.imshow(img_q)
        ax_t.imshow(img_t)
        ax_q.axis('off')
        ax_t.axis('off')
        plt.subplots_adjust(wspace=0, hspace=0)
        plt.show() 

# 需要导入模块: from matplotlib import patches [as 别名]
# 或者: from matplotlib.patches import ConnectionPatch [as 别名]
def test_connection_patch():
    fig, (ax1, ax2) = plt.subplots(1, 2)

    con = mpatches.ConnectionPatch(xyA=(0.1, 0.1), xyB=(0.9, 0.9),
                                   coordsA='data', coordsB='data',
                                   axesA=ax2, axesB=ax1,
                                   arrowstyle="->")
    ax2.add_artist(con) 

# 需要导入模块: from matplotlib import patches [as 别名]
# 或者: from matplotlib.patches import ConnectionPatch [as 别名]
def get_path_saliency(samples, labels, paths, pred, model, tree_idx, name, orientation = 'horizontal'):
    # show the saliency maps for the input samples with their 
    # computational paths 
    plt.figure(figsize=(20,4))
    plt.rcParams.update({'font.size': 18})
    num_samples = len(samples)
    path_length = len(paths[0])
    for sample_idx in range(num_samples):
        sample = samples[sample_idx]
        # plot the sample
        plt.subplot(num_samples, path_length + 1, sample_idx*(path_length + 1) + 1)
        sample_to_plot = revert_preprocessing(sample.unsqueeze(dim=0), name)
        plt.imshow(sample_to_plot.squeeze().cpu().numpy().transpose((1,2,0)))            
        plt.axis('off')        
        plt.title('Pred:{:.2f}, GT:{:.0f}'.format(pred[sample_idx].data.item()*100,
                  labels[sample_idx]*100))
        path = paths[sample_idx]
        for node_idx in range(path_length):
            # compute and plot saliency for each node
            node = path[node_idx][0]
            # probability of arriving at this node
            prob = path[node_idx][1]            
            saliency_map = get_map(model, sample, node, tree_idx, name)
            if orientation == 'horizontal':
                sub_plot_idx = sample_idx*(path_length + 1) + node_idx + 2
                plt.subplot(num_samples, path_length + 1, sub_plot_idx)
            elif orientation == 'vertical':
                raise NotImplementedError             
            else:
                raise NotImplementedError
            plt.imshow(saliency_map,cmap='hot')
            plt.title('(N{:d}, P{:.2f})'.format(node, prob))
            plt.axis('off')
        # draw some arrows 
        for arrow_idx in range(num_samples*(path_length + 1) - 1):
            if (arrow_idx+1) % (path_length+1) == 0 and arrow_idx != 0:
                continue
            ax1 = plt.subplot(num_samples, path_length + 1, arrow_idx + 1)
            ax2 = plt.subplot(num_samples, path_length + 1, arrow_idx + 2)
            arrow = ConnectionPatch(xyA=[1.1,0.5], xyB=[-0.1, 0.5], coordsA='axes fraction', coordsB='axes fraction',
                      axesA=ax1, axesB=ax2, arrowstyle="fancy")
            ax1.add_artist(arrow)
    left  = 0.02  # the left side of the subplots of the figure
    right = 1   # the right side of the subplots of the figure
    bottom = 0.01   # the bottom of the subplots of the figure
    top = 0.90     # the top of the subplots of the figure
    wspace = 0.20 # the amount of width reserved for space between subplots,
                   # expressed as a fraction of the average axis width
    hspace = 0.24   # the amount of height reserved for space between subplots,
                   # expressed as a fraction of the average axis height  
    plt.subplots_adjust(left, bottom, right, top, wspace, hspace)
    plt.show()
    return