Python animation.ArtistAnimation方法代码示例

# 需要导入模块: from matplotlib import animation [as 别名]
# 或者: from matplotlib.animation import ArtistAnimation [as 别名]
def test_overall(self, face_img):
        shape = (10, 11)

        delta_x = np.random.random(shape)
        delta_y = np.random.random(shape)

        df = DisplacementField(delta_x, delta_y)
        ani = create_animation(df, face_img, fps=2, n_seconds=1)

        assert isinstance(ani, ArtistAnimation) 

# 需要导入模块: from matplotlib import animation [as 别名]
# 或者: from matplotlib.animation import ArtistAnimation [as 别名]
def animate(self, *args, **kwargs) -> ArtistAnimation:
        """Animate the snapshots taken.

        Uses matplotlib.animation.ArtistAnimation

        Returns
        -------
        ArtistAnimation

        """
        return ArtistAnimation(self._figure, self._photos, *args, **kwargs) 

# 需要导入模块: from matplotlib import animation [as 别名]
# 或者: from matplotlib.animation import ArtistAnimation [as 别名]
def animate_spike_ensembles(pcnn_output_dynamic, image_size):
        """!
        @brief Shows animation of output dynamic (output of each oscillator) during simulation.
        
        @param[in] pcnn_output_dynamic (pcnn_dynamic): Output dynamic of the pulse-coupled neural network.
        @param[in] image_size (tuple): Image size represented as (height, width).
        
        """
        
        figure = plt.figure()
        
        time_signal = pcnn_output_dynamic.allocate_time_signal()
        spike_ensembles = pcnn_output_dynamic.allocate_spike_ensembles()
        
        spike_animation = []
        ensemble_index = 0
        for t in range(len(time_signal)):
            image_color_segments = [(255, 255, 255)] * (image_size[0] * image_size[1])
            
            if time_signal[t] > 0:
                for index_pixel in spike_ensembles[ensemble_index]:
                    image_color_segments[index_pixel] = (0, 0, 0)
                
                ensemble_index += 1

            stage = numpy.array(image_color_segments, numpy.uint8)
            stage = numpy.reshape(stage, image_size + ((3),)) # ((3),) it's size of RGB - third dimension.
            image_cluster = Image.fromarray(stage, 'RGB')
            
            spike_animation.append( [ plt.imshow(image_cluster, interpolation='none') ] )
            
        
        im_ani = animation.ArtistAnimation(figure, spike_animation, interval=75, repeat_delay=3000, blit=True)
        plt.show() 

# 需要导入模块: from matplotlib import animation [as 别名]
# 或者: from matplotlib.animation import ArtistAnimation [as 别名]
def animate_life(
    universe_size,
    seed,
    seed_position,
    quality=200,
    cmap="Purples",
    n_generations=50,
    interval=300,
    save=False,
):
    """
    Animate the Game of Life.

    :param universe_size: dimensions of the universe
    :type universe_size: tuple (int, int)
    :param seed: initial starting array
    :type seed: list of lists, np.ndarray
    :param seed_position: coordinates where the top-left corner of the seed array should
                          be pinned
    :type seed_position: tuple (int, int)
    :param cmap: the matplotlib cmap that should be used
    :type cmap: str
    :param n_generations: number of universe iterations, defaults to 30
    :param n_generations: int, optional
    :param interval: time interval between updates (milliseconds), defaults to 300ms
    :param interval: int, optional
    :param save: whether the animation should be saved, defaults to False
    :param save: bool, optional
    """
    # Initialise the universe and seed
    universe = np.zeros(universe_size)
    x_start, y_start = seed_position[0], seed_position[1]
    seed_array = np.array(seeds[seed])
    x_end, y_end = x_start + seed_array.shape[0], y_start + seed_array.shape[1]
    universe[x_start:x_end, y_start:y_end] = seed_array

    # Animate
    fig = plt.figure(dpi=quality)
    plt.axis("off")
    ims = []
    for i in range(n_generations):
        ims.append((plt.imshow(universe, cmap=cmap),))
        universe = generation(universe)
    im_ani = animation.ArtistAnimation(
        fig, ims, interval=interval, repeat_delay=3000, blit=True
    )
    # Optional: save the animation, with a name based on the seed.
    if save:
        im_ani.save((str(seed) + ".gif"), writer="imagemagick") 

# 需要导入模块: from matplotlib import animation [as 别名]
# 或者: from matplotlib.animation import ArtistAnimation [as 别名]
def collect_data(num_traj,save_dir,animate=False,**kwargs):
    transition_noise = kwargs['transition_noise'] if 'transition_noise' in kwargs.keys() else 0.05
    location_noise = kwargs['location_noise'] if 'location_noise' in kwargs.keys() else 0.2
    env = NavEnv(location_noise,transition_noise)
    dataset = {'states':[],'actions':[],'gt_onsets':[],'tasks':[],'params':kwargs}
    train_sketches = [[0,1,3],[1,2,3],[3,2,1],[2,3,0],[1,2,0],[1,0,2],[1,3,2],[0,3,2],[1,2,3],[3,2,0],[2,1,3],[1,3,0]]

    if animate:
        fig = plt.figure()
        ims = []

    for i in range(num_traj):
        g_state,r_state = env.reset()
        colours = ['b', 'r', 'g', 'y', 'k']
        # randomly sample integets that constitute the sketch
        sketch = train_sketches[np.random.randint(0,len(train_sketches),1)[0]]
        curr_idx = 0
        curr_subtask = sketch[curr_idx]
        sketch_traj = []
        # begin trajectory
        traj_states = []
        traj_actions = []
        while True:
            sketch_traj.append(curr_subtask) # This gives us ground truth about the task executed at each timestep
            #all_pos = np.array([agent_pos,red_pos,green_pos,yel_pos,black_pos])
            #state = np.ravel(get_state(all_pos[0],all_pos[1:],type = 'rel'))
            traj_states.append(r_state)
            action = get_action(g_state,curr_subtask)
            g_state,r_state,done = env.step(action,curr_subtask)
            traj_actions.append(action)
            if animate:
                ims.append((plt.scatter(g_state[:, 0], g_state[:, 1], c=colours),))
            if done:
                if curr_idx<len(sketch)-1:
                    curr_idx+=1

                    curr_subtask = sketch[curr_idx]
                else:
                    dataset['states'].append(traj_states)
                    dataset['actions'].append(traj_actions)
                    dataset['gt_onsets'].append(sketch_traj)
                    dataset['tasks'].append(sketch)
                    break
    save_dir = save_dir if save_dir[-2:] == '.p' else save_dir+'.p'
    pickle.dump(dataset,open(save_dir,'wb'))

    if animate:
        print('WRITING')
        Writer = animation.writers['ffmpeg']
        writer = Writer(fps=15, metadata=dict(artist='Me'), bitrate=1800)
        im_ani = animation.ArtistAnimation(fig, ims, interval=50, repeat_delay=3000,
                                           blit=True)
        im_ani.save(save_dir+'im_mod.mp4', writer=writer) 

# 需要导入模块: from matplotlib import animation [as 别名]
# 或者: from matplotlib.animation import ArtistAnimation [as 别名]
def evaluate_model(nb_traj,policy,save_dir= None,animate=False,**kwargs):
    transition_noise = kwargs['transition_noise'] if 'transition_noise' in kwargs.keys() else 0.02
    location_noise = kwargs['location_noise'] if 'location_noise' in kwargs.keys() else 0.1
    env = NavEnv(location_noise, transition_noise,done_thresh=0.03)
    if kwargs['zero_shot']:
        test_sketches = [[0, 1, 3,0], [3, 2, 3,2], [0, 2, 1,0], [1, 3, 0,2], [1, 3, 0,3], [1, 3, 2,1], [1, 3, 2,3], [2, 3, 2,1], [1, 2, 3,1],
                      [3, 1, 0,1], [3, 1, 2,0], [2, 3, 0,1],[1,3,1,3],[2,3,2,3],[1,2,1,2],[0,1,0,1],[0,2,0,2],[0,3,0,3]]
    else:
        test_sketches = [[0, 1, 3], [1, 2, 3], [3, 2, 1], [2, 3, 0], [1, 2, 0], [1, 0, 2], [1, 3, 2], [0, 3, 2],
                          [1, 2, 3], [3, 2, 0], [2, 1, 3], [1, 3, 0]]

    score = []
    if animate:
        fig = plt.figure()
        ims = []
    for i in range(nb_traj):
        task_score = []
        g_state,r_state = env.reset()
        colours = ['b', 'r', 'g', 'y', 'k']
        # randomly sample integets that constitute the sketch
        sketch = test_sketches[np.random.randint(0,len(test_sketches),1)[0]]
        curr_idx = 0
        curr_subtask = sketch[curr_idx]
        counter=0
        while True:
            action,stop = policy.forward_full([[r_state]], curr_subtask, dropout=1.)
            g_state,r_state,done = env.step(action,curr_subtask)
            if stop == 1 or counter >100:
                if done:
                    task_score.append(1)
                else:
                    task_score.append(0)
                if curr_idx < len(sketch) - 1:
                    curr_idx += 1
                    curr_subtask = sketch[curr_idx]
                    counter = 0
                else:
                    score.append(task_score)
                    break

            if animate:
                ims.append((plt.scatter(g_state[:, 0], g_state[:, 1], c=colours),))
            counter+=1
    if animate:
        print('writing video at:',save_dir+'im_mod.mp4')
        Writer = animation.writers['ffmpeg']
        writer = Writer(fps=15, metadata=dict(artist='Me'), bitrate=1800)
        im_ani = animation.ArtistAnimation(fig, ims, interval=50, repeat_delay=3000,
                                           blit=True)
        im_ani.save(save_dir+'im_mod.mp4', writer=writer)
    acc = 0
    for s in score:
        if np.sum(sum(s)) == len(s):
            acc+=1
    acc /=float(len(score))
    # also returns accuracy so you dont calculate it outside
    return score,acc 

# 需要导入模块: from matplotlib import animation [as 别名]
# 或者: from matplotlib.animation import ArtistAnimation [as 别名]
def create_animation(df, img, include_backwards=True, fps=24, n_seconds=2, figsize=(8, 8), repeat=True):
    """Create animation from a displacement field.

    Parameters
    ----------
    df : DisplacementField
        Instance of the ``DisplacementField`` representing the coordinate transformation.

    img : np.ndarray
        Image.

    include_backwards : bool
        If True, then animation also played backwards after its played forwards.

    fps : int
        Frames per second.

    n_seconds : int
        Number of seconds to play the animation forwards.

    figsize : tuple
        Size of the figure.

    repeat : bool
        If True, then animation always replayed at the end.

    Returns
    -------
    ani : matplotlib.animation.ArtistAnimation
        Animation showing the transformation.

    Notes
    -----
    To enable animation viewing in a jupyter notebook write:
    ```
    from matplotlib import rc
    rc('animation', html='html5')
    ```

    """
    n_frames = int(n_seconds * fps)
    interval = (1 / fps) * 1000
    frames = []

    fig = plt.figure(figsize=figsize)
    plt.axis('off')

    for i in range(n_frames + 1):
        df_new = df * (i / n_frames)
        warped_img = df_new.warp(img)
        frames.append([plt.imshow(warped_img, cmap='gray')])

    if include_backwards:
        frames += frames[::-1]

    ani = ArtistAnimation(fig, frames, interval=interval, repeat=repeat)

    return ani 

# 需要导入模块: from matplotlib import animation [as 别名]
# 或者: from matplotlib.animation import ArtistAnimation [as 别名]
def main():
    # Initialize colors
    color_candidates = []
    for n in range(args.num_colors):
        hue = n / args.num_colors
        saturation = 1
        lightness = 1
        red, green, blue = colorsys.hsv_to_rgb(hue, saturation, lightness)
        color_candidates.append((red, green, blue))

    renderer = OffscreenRenderer(
        viewport_width=args.image_size, viewport_height=args.image_size)

    rad_step = math.pi / 18
    total_frames = int(math.pi * 2 / rad_step)
    camera_distance = 2

    fig = plt.figure(figsize=(3, 3))
    ims = []

    for num_cubes in range(1, 8):
        scene = build_scene(num_cubes, color_candidates)[0]
        camera = OrthographicCamera(xmag=0.9, ymag=0.9)
        camera_node = Node(camera=camera)
        scene.add_node(camera_node)

        current_rad = 0
        
        for _ in range(total_frames):
            camera_position = np.array((math.sin(current_rad),
                                        math.sin(math.pi / 6),
                                        math.cos(current_rad)))
            camera_position = camera_distance * camera_position / np.linalg.norm(
                camera_position)
            # Compute yaw and pitch
            yaw, pitch = compute_yaw_and_pitch(camera_position)

            camera_node.rotation = genearte_camera_quaternion(yaw, pitch)
            camera_node.translation = camera_position

            # Rendering
            flags = RenderFlags.SHADOWS_DIRECTIONAL
            if args.anti_aliasing:
                flags |= RenderFlags.ANTI_ALIASING
            image = renderer.render(scene, flags=flags)[0]
            im = plt.imshow(image, interpolation="none", animated=True)
            ims.append([im])

            current_rad += rad_step

    renderer.delete()

    ani = animation.ArtistAnimation(
        fig, ims, interval=1 / 24, blit=True, repeat_delay=0)
    ani.save("shepard_metzler.gif", writer="imagemagick")