#!/usr/bin/env python
# coding: utf-8

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get_ipython().run_line_magic('load_ext', 'watermark')
get_ipython().run_line_magic('watermark', '-a "Romell D.Z." -u -d -p numpy,pandas,matplotlib,keras,imageai')


# # 5. Objects Detection & Extraction

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get_ipython().run_line_magic('matplotlib', 'inline')
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from IPython.display import Image,display


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pathA = 'snapshot/teamA.jpg'
pathB = 'snapshot/teamB.jpg'


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from imageai.Detection import ObjectDetection
import os

detector = ObjectDetection()
detector.setModelTypeAsRetinaNet()
detector.setModelPath( "../../../Python Samples/_TensorFlow/models/resnet50_coco_best_v2.0.1.h5")
detector.loadModel()


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detections = detector.detectObjectsFromImage(input_image= "snapshot/teamA.jpg",
                                            output_image_path= "snapshot/objectDetectionTeamA.jpg",
                                             minimum_percentage_probability = 80)


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for eachObject in detections:
    print(eachObject["name"] + " : " + str(eachObject["percentage_probability"]) )


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display(Image(filename="snapshot/teamB.jpg"))


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detections, extracted_images = detector.detectObjectsFromImage(input_image= "snapshot/teamB.jpg",
                                                               output_image_path= "snapshot/objectDetectionTeamB.jpg",
                                                               minimum_percentage_probability = 65,
                                                               extract_detected_objects=True)


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display(Image(filename="snapshot/objectDetectionTeamB.jpg"))


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for eachObject,image in zip(detections,extracted_images):
    print(eachObject["name"] + " : " + str(eachObject["percentage_probability"]) )
    display(Image(filename=image))


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