demo.py

import glob, time, dlib, matplotlib.pyplot as plt, numpy as np
import AUmaps

AUdetector = AUmaps.AUdetector('shape_predictor_68_face_landmarks.dat', enable_cuda=True)

path_imgs = 'example_video'
files = sorted(glob.glob(path_imgs + '/*.png'))

fig = plt.figure(figsize=plt.figaspect(.5))
axs = fig.subplots(5, 2)

# Init subplots and images within
implots = []
for ax in axs.reshape(-1):
    ax.axis('off')
    implots.append(ax.imshow(np.zeros((256, 256))))

tstart_time = time.time()
nframes = len(files)
for names in files:
    start_time = time.time()
    img = dlib.load_rgb_image(names)

    pred,map,img = AUdetector.detectAU(img)
    for j in range(0,5):
        resized_map = dlib.resize_image(map[j,:,:].cpu().data.numpy(),rows=256,cols=256)

        # Update face image subplot
        implots[2*j].set_data(img)

        # Update heatmap subplot
        implots[2*j+1].set_data(resized_map)

        # Set correct heatmap limits
        resized_map_flat = resized_map.flatten()
        implots[2*j+1].set_clim(min(resized_map_flat), max(resized_map_flat))

        # To plot heatmaps the original way - looks identical to the new way!
        # ax = fig.add_subplot(5,2,2*j+1)
        # ax.imshow(resized_map)
        # ax.axis('off')

    plt.pause(0.001)
    plt.draw()

    elapsed_time = time.time() - start_time
    print(' ** Frame: {0:s}; FPS Elapsed: {1:.3f}'.format(names, 1.0 / elapsed_time))

    # To move upon key press
    # input("Press Enter to continue...")

telapsed_time = time.time() - tstart_time
print('\n ** Mean FPS Elapsed: {0:.3f} \n'.format(1.0 / (telapsed_time / nframes)))