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union_area = box1_area + box2_area - intersection_area

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def iou(box1, box2):
    """Implement the intersection over union (IoU) between box1 and box2
    
    Arguments:
    box1 -- first box, list object with coordinates (x1, y1, x2, y2)
    box2 -- second box, list object with coordinates (x1, y1, x2, y2)
    """

    # Calculate the (y1, x1, y2, x2) coordinates of the intersection of box1 and box2. Calculate its Area.
    xi1 = max(box1[0], box2[0])

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from keras import backend as K

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def yolo_non_max_suppression(scores, boxes, classes, max_boxes = 10, iou_threshold = 0.5):
    """
    Applies Non-max suppression (NMS) to set of boxes
    
    Arguments:
    scores -- tensor of shape (None,), output of yolo_filter_boxes()
    boxes -- tensor of shape (None, 4), output of yolo_filter_boxes() that have been scaled to the image size (see later)
    classes -- tensor of shape (None,), output of yolo_filter_boxes()
    max_boxes -- integer, maximum number of predicted boxes you'd like
    iou_threshold -- real value, "intersection over union" threshold used for NMS filtering

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yolo_model = load_model("model_data/yolo.h5")
# Convert final layer features to bounding box parameters
yolo_outputs = yolo_head(yolo_model.output, anchors, len(class_names))
image_shape = K.placeholder(shape=(2, ))

# Retrieve outputs of the YOLO model
box_confidence, box_xy, box_wh, box_class_probs = yolo_outputs

# Convert boxes to be ready for filtering functions 
boxes = yolo_boxes_to_corners(box_xy, box_wh)

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# Preprocess your image
image, image_data = preprocess_image("images/" + image_file, model_image_size = (608, 608))

sess = K.get_session()

# Run the session with the correct tensors and choose the correct placeholders in the feed_dict.
out_scores, out_boxes, out_classes = sess.run([scores, boxes, classes], feed_dict={yolo_model.input: image_data,
                                                                                   input_image_shape: [image.size[1], image.size[0]],
                                                                                   K.learning_phase(): 0})

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import tensorflow as tf
device_name = tf.test.gpu_device_name()
if device_name != '/device:GPU:0':
  raise SystemError('GPU device not found')
print('Found GPU at: {}'.format(device_name))

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from tensorflow.python.client import device_lib
device_lib.list_local_devices()

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!pip install Cython
!git clone https://github.com/waleedka/coco
!pip install -U setuptools
!pip install -U wheel
!make install -C coco/PythonAPI

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!git clone https://github.com/matterport/Mask_RCNN
# cd to the code directory and optionally download the weights file
import os
os.chdir('./Mask_RCNN')
!wget https://github.com/matterport/Mask_RCNN/releases/download/v2.0/mask_rcnn_coco.h5