Ejemplo n.º 1
0
def main():
     try:
         instanceSize = 10
         step = 1
	 numOfClasses = 100
         filename = 'detector_7_no_5_angle_2.jpg'
         mode = 'PRO'
         image_files, bubble_num, bubble_regions = getinfo()
         data_set = ds.read_data_sets(
                   instanceSize = instanceSize,
                   stride = step,
                   instanceMode = 'test', 
                   labelMode = mode,
                   imageName = filename,
                   dtype = tf.float32,
                   plot_show = 0,
                   label_mutiplier = 1.0)
         print data_set.labels.shape[-1]
         print 'Sum of labels:  ' + str(np.sum(data_set.labels))
         print 'Number bubbles: ' + str(bubble_num[image_files.index(filename)])

     except KeyboardInterrupt:
         print "Shutdown requested... exiting"
     except Exception:
         traceback.print_exc(file=sys.stdout)
     sys.exit(0)
def main():
     try:
         instanceSize = 20
         step = 20
	 numOfClasses = 100
         
         print 'Testing data generation ... '
         data_set = ds.read_data_sets(instanceSize, step, numOfClasses,
           'train', 'PRO', plot_show = 0)
         print 'Instances shape :'
         print data_set.images.shape
         print 'label shape :'
         print data_set.labels.shape
         print 'Testing batch data generation :'
        
         batch_size = 1000
         batch_num  = 1000
         for i in range(batch_num):
            print ('batch num: ' + str(i) +
                  str(data_set.next_batch(batch_size)[0].shape) +
                  str(data_set.next_batch(batch_size)[1].shape))
         ds.read_data_sets(instanceSize, step, numOfClasses,
           'test', 'PRO', 'detector_1_no_5_angle_3.jpg', plot_show = 1)
         ds.read_data_sets(instanceSize, step, numOfClasses, 'train', 'NUM',
           plot_show = 1)
         ds.read_data_sets(instanceSize, step, numOfClasses,
           'test', 'NUM', 'detector_1_no_5_angle_3.jpg', plot_show = 1)

         
     except KeyboardInterrupt:
         print "Shutdown requested... exiting"
     except Exception:
         traceback.print_exc(file=sys.stdout)
     sys.exit(0)
Ejemplo n.º 3
0
def labellinearity(patch_size,
                   stride,
                   labelMode = 'PRO',
                   label_mutiplier = 1,
                   progress_show = 1,
                   plot_show = 1):
    gv.ds_show_filename = False 
    image_files, bubble_num, bubble_regions = getinfo()
    bubble_num_afterlabel  =  np.zeros(len(image_files))
    probar = progress.progress(0, len(image_files))

    for i, image in enumerate(image_files):
        probar.setCurrentIteration(i+1)
        probar.setInfo(prefix_info = 'dataset linearity ...',
                       suffix_info = image)
        probar.printProgress() 

        image_ds = ds.read_data_sets(
             instanceSize = patch_size,
             stride = stride,
             instanceMode = 'test',
             labelMode = labelMode,
             imageName = image,
             label_mutiplier = label_mutiplier)

        labels = image_ds.labels
        bubble_num_afterlabel[i] = np.sum(labels)
 
    slope, intercept, r_value, p_value, std_err = (linregress(bubble_num,
                                                      bubble_num_afterlabel))

    if(plot_show == 1):    
        yfit = slope * bubble_num + intercept    
        fig, ax = plt.subplots(1,1)
        ax.set_title('Linearity of Labeling Methods')
        ax.set_xlabel('Bubble number counted manually')
        ax.set_ylabel('Bubble number after labeling')
        ax.scatter(bubble_num, bubble_num_afterlabel,
                   color = 'blue', label = 'bubble number')
        ax.plot(bubble_num, yfit, color = 'red', label = 'linear regression')
        handles, labels = ax.get_legend_handles_labels()
        ax.legend(handles, labels, loc="upper left")    
        text_x = np.amax(bubble_num)*0.6
        text_y = np.amax(bubble_num_afterlabel)*0.1
        text   = "r_squared: %.5f\nstd_err: %.5f" % (r_value**2, std_err)
        ax.annotate(text, xy = (text_x, text_y), xytext = (text_x, text_y))
        plt.show()
    
    return ([[slope, intercept, r_value, p_value, std_err],
             bubble_num, bubble_num_afterlabel])