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

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from imageai.Detection import ObjectDetection


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import os


# ### Set up detector

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detector = ObjectDetection()
detector.setModelTypeAsRetinaNet()


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# Load model
model_path = 'resnet50_coco_best_v2.0.1.h5'

detector.setModelPath(model_path)
detector.loadModel()


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custom_objects = detector.CustomObjects(dog=True, cat=True)


# ### Detect on image

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input_path = 'header_cats_and_dogs.jpg'


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output_path = 'result.png'


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detections = detector.detectCustomObjectsFromImage(input_image=input_path, output_image_path=output_path, custom_objects=custom_objects, minimum_percentage_probability=45)


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for obj in detections:
    print(obj['name'], obj['percentage_probability'])


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from IPython.display import Image


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Image('result.png')


# ### Detect a Folder of Images

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import glob


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input_images_path = 'dog-pictures/*'


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output_images_path = 'results/'


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custom_objects = detector.CustomObjects(dog=True, cat=True, bird=True, cow=True, elephant=True, bear=True)


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detector.detectCustomObjectsFromImage(input_image='dog-pictures/dog-cat.jpg', output_image_path='results/result_0.jpg', custom_objects=custom_objects, minimum_percentage_probability=40)


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# Iterate through folder of images
count = 0
for f in glob.glob(input_images_path):
    # Set output file path
    output_path = output_images_path + 'result_' + str(count) + '.jpg'
    
    # Detect
    print('Detecting...', output_path)
    detector.detectCustomObjectsFromImage(input_image=f, output_image_path=output_path, custom_objects=custom_objects, minimum_percentage_probability=40)
    
    count += 1
    


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# Display results in notebook
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
get_ipython().run_line_magic('matplotlib', 'inline')

images = []
output_images = output_images_path + '*'

for f in glob.glob(output_images):
    images.append(mpimg.imread(f))
    
plt.figure(figsize=(200,200))
columns = 1
for i, image in enumerate(images):
    plt.subplot(len(images) / columns + 1, columns, i + 1)
    plt.imshow(image)
    


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