predict_messi.py

# load model and apply quantization
model = Model.loadModel("bigdl_messi.model").quantize()

# load dataset
myclip = VideoFileClip("messi_clip.mp4")
video_rdd = sc.parallelize(myclip.iter_frames(fps=5))
image_frame = DistributedImageFrame(video_rdd)

# Define Predictor Configure
preprocess = Pipeline([Resize(300, 300), ChannelNormalize(123.0, 117.0, 104.0), MatToTensor(), ImageFrameToSample()])
postprocess = ScaleDetection()
label_map = {0: '__background__', 1: 'messi'}
configure = Configure(preprocess, postprocess, 2, label_map)

# Predict and visualize detection back to clips
predictor = Predictor(model, configure)
output = predictor.predict(image_frame)
visualizer = Visualizer(predictor.configure.label_map())
results = visualizer(output).get_image(to_chw=False).collect()

# save clip to video
clip = ImageSequenceClip(results, fps=5)
%time clip.write_videofile('/tmp/out.mp4', audio=False)