Detect messi demo

This notebook shows how to use a pretrained model in customized dataset to detect Lionel Messi.

We used one of the Messi videos in Youtube for demo, which is a scene of football game. We use a pretrained model (downloadable from link which is finetuned based on a SSD with VGG16 model for detection. It is able to detect Messi in the game. In the video Messi is identified with boxes and class scores are shown on top.

Intialization

import necesary libraries



In [1]:

    
import os
from IPython.display import Image, display
%pylab inline
from moviepy.editor import *









    



Populating the interactive namespace from numpy and matplotlib

import zoo libraries



In [2]:

    
%matplotlib inline

from zoo.common.nncontext import *
from zoo.feature.image import *
from zoo.feature.common import ChainedPreprocessing
from zoo.models.image.common.image_config import ImageConfigure
from zoo.models.image.objectdetection import *

init variables



In [3]:

    
sc = init_nncontext("Detect Messi Example")

Prepare model and data

Download model

Download Video Clip

Move the model and video to the same directory with this notebook

Load a pretrained model



In [4]:

    
model=ObjectDetector.load_model("bigdl_messi_quantize.model")

print 'load model done'









    



load model done

Load data



In [5]:

    
path = "messi_clip.mp4"
myclip = VideoFileClip(path)

video_rdd = sc.parallelize(myclip.iter_frames(fps=5))
image_set = DistributedImageSet(video_rdd)









    



creating: createDistributedImageSet

Define ImageConfigure



In [6]:

    
preprocess = ChainedPreprocessing([ImageResize(300, 300), ImageChannelNormalize(123.0, 117.0, 104.0), ImageMatToTensor(), ImageSetToSample()])
postprocess = ScaleDetection()

label_map = {0: '__background__', 1: 'messi'}
config = ImageConfigure(preprocess, postprocess, 2, label_map)









    



creating: createImageResize
creating: createImageChannelNormalize
creating: createImageMatToTensor
creating: createImageSetToSample
creating: createChainedPreprocessing
creating: createScaleDetection
creating: createImageConfigure

Predict and visualize detection back to clips



In [7]:

    
output = model.predict_image_set(image_set, config)

visualizer = Visualizer(config.label_map())
visualized = visualizer(output).get_image(to_chw=False).collect()









    



creating: createVisualizer

Save clips to file



In [8]:

    
clip = ImageSequenceClip(visualized, fps=5)

output_path = '/tmp/out.mp4'
%time clip.write_videofile(output_path, audio=False)
clip.write_gif("messi.gif")









    



[MoviePy] >>>> Building video /tmp/out.mp4
[MoviePy] Writing video /tmp/out.mp4






    



100%|██████████| 44/44 [00:00<00:00, 708.77it/s]






    



[MoviePy] Done.
[MoviePy] >>>> Video ready: /tmp/out.mp4 

CPU times: user 24.2 ms, sys: 4.93 ms, total: 29.2 ms
Wall time: 237 ms

[MoviePy] Building file messi.gif with imageio






    



100%|██████████| 44/44 [00:02<00:00, 15.18it/s]

Display Messi Detection Video



In [9]:

    
with open("messi.gif",'rb') as f:
    display(Image(f.read()))