在Jupyter Notebook 中显示动画

在 jupyter notebook 中显示动画比较麻烦,查了好多方法都不行,整理了两个可以实现功能的函数,一个函数 plot_sequence_images 用于显示有序的图片,达到动画的效果,另一个函数 plot_animation_function 用于显示动态函数:

显示图片序列

例子中,将一个 mp4 视频通过 opencv 读取为一个图片序列,就可以在 Jupyter notebook 中显示视频了:

import matplotlib.pyplot as plt
from matplotlib import animation
from IPython.display import display, HTML
import numpy as np
def plot_sequence_images(image_array):
    ''' Display images sequence as an animation in jupyter notebook
    Args:
        image_array(numpy.ndarray): image_array.shape equal to (num_images, height, width, num_channels)
    dpi = 72.0
    xpixels, ypixels = image_array[0].shape[:2]
    fig = plt.figure(figsize=(ypixels/dpi, xpixels/dpi), dpi=dpi)
    im = plt.figimage(image_array[0])
    def animate(i):
        im.set_array(image_array[i])
        return (im,)
    anim = animation.FuncAnimation(fig, animate, frames=len(image_array), interval=33, repeat_delay=1, repeat=True)
    display(HTML(anim.to_html5_video()))
# Demo of plot_sequence_images
import cv2
video_path = "/home/linux_fhb/data/suzhouc/test/top_camera.mp4"
video = cv2.VideoCapture()
video.open(video_path)
imgs = []
while True:
    is_valid, img = video.read()
    if not is_valid: break
    imgs.append(img)
video.release()
plot_sequence_images(imgs)

显示动态函数

import matplotlib.pyplot as plt from matplotlib import animation from IPython.display import display, HTML import numpy as np def plot_animation_function(xy_generator, frames=100, figsize=(6, 4), xlim=(0,2), ylim=(0, 2),interval=20, blit=True): fig, ax = plt.subplots(figsize=figsize) ax.set_xlim(xlim) ax.set_ylim(ylim) line, = ax.plot([], [], lw=2) def init(): line.set_data([], []) return (line,) def animate(*args, **kwargs): x, y = next(xy_generator) line.set_data(x, y) return (line,) anim = animation.FuncAnimation(fig, animate, init_func=init, frames=frames, interval=interval, blit=blit) display(HTML(anim.to_html5_video())) fig.delaxes(ax) # Demo of plot_animation_function frames = 100 def GeneratorXY(frames): for i in range(frames): x = np.linspace(0, 2, 1000) y = np.sin(2 * np.pi * (x - 0.01 * i))