Upload utils.py

utils.py

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+import sys
+import os
+import logging
+import math
+import random
+import numpy as np
+import tensorflow as tf
+import scipy
+import skimage.color
+import skimage.io
+import skimage.transform
+import urllib.request
+import shutil
+import warnings
+from distutils.version import LooseVersion
+
+
+
+############################################################
+#  Bounding Boxes
+############################################################
+
+def extract_bboxes(mask):
+    """Compute bounding boxes from masks.
+    mask: [height, width, num_instances]. Mask pixels are either 1 or 0.
+
+    Returns: bbox array [num_instances, (y1, x1, y2, x2)].
+    """
+    boxes = np.zeros([mask.shape[-1], 4], dtype=np.int32)
+    for i in range(mask.shape[-1]):
+        m = mask[:, :, i]
+        # Bounding box.
+        horizontal_indicies = np.where(np.any(m, axis=0))[0]
+        vertical_indicies = np.where(np.any(m, axis=1))[0]
+        if horizontal_indicies.shape[0]:
+            x1, x2 = horizontal_indicies[[0, -1]]
+            y1, y2 = vertical_indicies[[0, -1]]
+            # x2 and y2 should not be part of the box. Increment by 1.
+            x2 += 1
+            y2 += 1
+        else:
+            # No mask for this instance. Might happen due to
+            # resizing or cropping. Set bbox to zeros
+            x1, x2, y1, y2 = 0, 0, 0, 0
+        boxes[i] = np.array([y1, x1, y2, x2])
+    return boxes.astype(np.int32)
+
+
+def compute_iou(box, boxes, box_area, boxes_area):
+    """Calculates IoU of the given box with the array of the given boxes.
+    box: 1D vector [y1, x1, y2, x2]
+    boxes: [boxes_count, (y1, x1, y2, x2)]
+    box_area: float. the area of 'box'
+    boxes_area: array of length boxes_count.
+
+    Note: the areas are passed in rather than calculated here for
+    efficiency. Calculate once in the caller to avoid duplicate work.
+    """
+    # Calculate intersection areas
+    y1 = np.maximum(box[0], boxes[:, 0])
+    y2 = np.minimum(box[2], boxes[:, 2])
+    x1 = np.maximum(box[1], boxes[:, 1])
+    x2 = np.minimum(box[3], boxes[:, 3])
+    intersection = np.maximum(x2 - x1, 0) * np.maximum(y2 - y1, 0)
+    union = box_area + boxes_area[:] - intersection[:]
+    iou = intersection / union
+    return iou
+
+
+def compute_overlaps(boxes1, boxes2):
+    """Computes IoU overlaps between two sets of boxes.
+    boxes1, boxes2: [N, (y1, x1, y2, x2)].
+
+    For better performance, pass the largest set first and the smaller second.
+    """
+    # Areas of anchors and GT boxes
+    area1 = (boxes1[:, 2] - boxes1[:, 0]) * (boxes1[:, 3] - boxes1[:, 1])
+    area2 = (boxes2[:, 2] - boxes2[:, 0]) * (boxes2[:, 3] - boxes2[:, 1])
+
+    # Compute overlaps to generate matrix [boxes1 count, boxes2 count]
+    # Each cell contains the IoU value.
+    overlaps = np.zeros((boxes1.shape[0], boxes2.shape[0]))
+    for i in range(overlaps.shape[1]):
+        box2 = boxes2[i]
+        overlaps[:, i] = compute_iou(box2, boxes1, area2[i], area1)
+    return overlaps
+
+
+def compute_overlaps_masks(masks1, masks2):
+    """Computes IoU overlaps between two sets of masks.
+    masks1, masks2: [Height, Width, instances]
+    """
+    
+    # If either set of masks is empty return empty result
+    if masks1.shape[-1] == 0 or masks2.shape[-1] == 0:
+        return np.zeros((masks1.shape[-1], masks2.shape[-1]))
+    # flatten masks and compute their areas
+    masks1 = np.reshape(masks1 > .5, (-1, masks1.shape[-1])).astype(np.float32)
+    masks2 = np.reshape(masks2 > .5, (-1, masks2.shape[-1])).astype(np.float32)
+    area1 = np.sum(masks1, axis=0)
+    area2 = np.sum(masks2, axis=0)
+
+    # intersections and union
+    intersections = np.dot(masks1.T, masks2)
+    union = area1[:, None] + area2[None, :] - intersections
+    overlaps = intersections / union
+
+    return overlaps
+
+
+def non_max_suppression(boxes, scores, threshold):
+    """Performs non-maximum suppression and returns indices of kept boxes.
+    boxes: [N, (y1, x1, y2, x2)]. Notice that (y2, x2) lays outside the box.
+    scores: 1-D array of box scores.
+    threshold: Float. IoU threshold to use for filtering.
+    """
+    assert boxes.shape[0] > 0
+    if boxes.dtype.kind != "f":
+        boxes = boxes.astype(np.float32)
+
+    # Compute box areas
+    y1 = boxes[:, 0]
+    x1 = boxes[:, 1]
+    y2 = boxes[:, 2]
+    x2 = boxes[:, 3]
+    area = (y2 - y1) * (x2 - x1)
+
+    # Get indicies of boxes sorted by scores (highest first)
+    ixs = scores.argsort()[::-1]
+
+    pick = []
+    while len(ixs) > 0:
+        # Pick top box and add its index to the list
+        i = ixs[0]
+        pick.append(i)
+        # Compute IoU of the picked box with the rest
+        iou = compute_iou(boxes[i], boxes[ixs[1:]], area[i], area[ixs[1:]])
+        # Identify boxes with IoU over the threshold. This
+        # returns indices into ixs[1:], so add 1 to get
+        # indices into ixs.
+        remove_ixs = np.where(iou > threshold)[0] + 1
+        # Remove indices of the picked and overlapped boxes.
+        ixs = np.delete(ixs, remove_ixs)
+        ixs = np.delete(ixs, 0)
+    return np.array(pick, dtype=np.int32)
+
+
+def apply_box_deltas(boxes, deltas):
+    """Applies the given deltas to the given boxes.
+    boxes: [N, (y1, x1, y2, x2)]. Note that (y2, x2) is outside the box.
+    deltas: [N, (dy, dx, log(dh), log(dw))]
+    """
+    boxes = boxes.astype(np.float32)
+    # Convert to y, x, h, w
+    height = boxes[:, 2] - boxes[:, 0]
+    width = boxes[:, 3] - boxes[:, 1]
+    center_y = boxes[:, 0] + 0.5 * height
+    center_x = boxes[:, 1] + 0.5 * width
+    # Apply deltas
+    center_y += deltas[:, 0] * height
+    center_x += deltas[:, 1] * width
+    height *= np.exp(deltas[:, 2])
+    width *= np.exp(deltas[:, 3])
+    # Convert back to y1, x1, y2, x2
+    y1 = center_y - 0.5 * height
+    x1 = center_x - 0.5 * width
+    y2 = y1 + height
+    x2 = x1 + width
+    return np.stack([y1, x1, y2, x2], axis=1)
+
+
+def box_refinement_graph(box, gt_box):
+    """Compute refinement needed to transform box to gt_box.
+    box and gt_box are [N, (y1, x1, y2, x2)]
+    """
+    box = tf.cast(box, tf.float32)
+    gt_box = tf.cast(gt_box, tf.float32)
+
+    height = box[:, 2] - box[:, 0]
+    width = box[:, 3] - box[:, 1]
+    center_y = box[:, 0] + 0.5 * height
+    center_x = box[:, 1] + 0.5 * width
+
+    gt_height = gt_box[:, 2] - gt_box[:, 0]
+    gt_width = gt_box[:, 3] - gt_box[:, 1]
+    gt_center_y = gt_box[:, 0] + 0.5 * gt_height
+    gt_center_x = gt_box[:, 1] + 0.5 * gt_width
+
+    dy = (gt_center_y - center_y) / height
+    dx = (gt_center_x - center_x) / width
+    dh = tf.log(gt_height / height)
+    dw = tf.log(gt_width / width)
+
+    result = tf.stack([dy, dx, dh, dw], axis=1)
+    return result
+
+
+def box_refinement(box, gt_box):
+    """Compute refinement needed to transform box to gt_box.
+    box and gt_box are [N, (y1, x1, y2, x2)]. (y2, x2) is
+    assumed to be outside the box.
+    """
+    box = box.astype(np.float32)
+    gt_box = gt_box.astype(np.float32)
+
+    height = box[:, 2] - box[:, 0]
+    width = box[:, 3] - box[:, 1]
+    center_y = box[:, 0] + 0.5 * height
+    center_x = box[:, 1] + 0.5 * width
+
+    gt_height = gt_box[:, 2] - gt_box[:, 0]
+    gt_width = gt_box[:, 3] - gt_box[:, 1]
+    gt_center_y = gt_box[:, 0] + 0.5 * gt_height
+    gt_center_x = gt_box[:, 1] + 0.5 * gt_width
+
+    dy = (gt_center_y - center_y) / height
+    dx = (gt_center_x - center_x) / width
+    dh = np.log(gt_height / height)
+    dw = np.log(gt_width / width)
+
+    return np.stack([dy, dx, dh, dw], axis=1)
+
+
+############################################################
+#  Dataset
+############################################################
+
+class Dataset(object):
+    """The base class for dataset classes.
+    To use it, create a new class that adds functions specific to the dataset
+    you want to use. For example:
+
+    class CatsAndDogsDataset(Dataset):
+        def load_cats_and_dogs(self):
+            ...
+        def load_mask(self, image_id):
+            ...
+        def image_reference(self, image_id):
+            ...
+
+    See COCODataset and ShapesDataset as examples.
+    """
+
+    def __init__(self, class_map=None):
+        self._image_ids = []
+        self.image_info = []
+        # Background is always the first class
+        self.class_info = [{"source": "", "id": 0, "name": "BG"}]
+        self.source_class_ids = {}
+
+    def add_class(self, source, class_id, class_name):
+        assert "." not in source, "Source name cannot contain a dot"
+        # Does the class exist already?
+        for info in self.class_info:
+            if info['source'] == source and info["id"] == class_id:
+                # source.class_id combination already available, skip
+                return
+        # Add the class
+        self.class_info.append({
+            "source": source,
+            "id": class_id,
+            "name": class_name,
+        })
+
+    def add_image(self, source, image_id, path, **kwargs):
+        image_info = {
+            "id": image_id,
+            "source": source,
+            "path": path,
+        }
+        image_info.update(kwargs)
+        self.image_info.append(image_info)
+
+    def image_reference(self, image_id):
+        """Return a link to the image in its source Website or details about
+        the image that help looking it up or debugging it.
+
+        Override for your dataset, but pass to this function
+        if you encounter images not in your dataset.
+        """
+        return ""
+
+    def prepare(self, class_map=None):
+        """Prepares the Dataset class for use.
+
+        TODO: class map is not supported yet. When done, it should handle mapping
+              classes from different datasets to the same class ID.
+        """
+
+        def clean_name(name):
+            """Returns a shorter version of object names for cleaner display."""
+            return ",".join(name.split(",")[:1])
+
+        # Build (or rebuild) everything else from the info dicts.
+        self.num_classes = len(self.class_info)
+        self.class_ids = np.arange(self.num_classes)
+        self.class_names = [clean_name(c["name"]) for c in self.class_info]
+        self.num_images = len(self.image_info)
+        self._image_ids = np.arange(self.num_images)
+
+        # Mapping from source class and image IDs to internal IDs
+        self.class_from_source_map = {"{}.{}".format(info['source'], info['id']): id
+                                      for info, id in zip(self.class_info, self.class_ids)}
+        self.image_from_source_map = {"{}.{}".format(info['source'], info['id']): id
+                                      for info, id in zip(self.image_info, self.image_ids)}
+
+        # Map sources to class_ids they support
+        self.sources = list(set([i['source'] for i in self.class_info]))
+        self.source_class_ids = {}
+        # Loop over datasets
+        for source in self.sources:
+            self.source_class_ids[source] = []
+            # Find classes that belong to this dataset
+            for i, info in enumerate(self.class_info):
+                # Include BG class in all datasets
+                if i == 0 or source == info['source']:
+                    self.source_class_ids[source].append(i)
+
+    def map_source_class_id(self, source_class_id):
+        """Takes a source class ID and returns the int class ID assigned to it.
+
+        For example:
+        dataset.map_source_class_id("coco.12") -> 23
+        """
+        return self.class_from_source_map[source_class_id]
+
+    def get_source_class_id(self, class_id, source):
+        """Map an internal class ID to the corresponding class ID in the source dataset."""
+        info = self.class_info[class_id]
+        assert info['source'] == source
+        return info['id']
+
+    @property
+    def image_ids(self):
+        return self._image_ids
+
+    def source_image_link(self, image_id):
+        """Returns the path or URL to the image.
+        Override this to return a URL to the image if it's available online for easy
+        debugging.
+        """
+        return self.image_info[image_id]["path"]
+
+    def load_image(self, image_id):
+        """Load the specified image and return a [H,W,3] Numpy array.
+        """
+        # Load image
+        image = skimage.io.imread(self.image_info[image_id]['path'])
+        # If grayscale. Convert to RGB for consistency.
+        if image.ndim != 3:
+            image = skimage.color.gray2rgb(image)
+        # If has an alpha channel, remove it for consistency
+        if image.shape[-1] == 4:
+            image = image[..., :3]
+        return image
+
+    def load_mask(self, image_id):
+        """Load instance masks for the given image.
+
+        Different datasets use different ways to store masks. Override this
+        method to load instance masks and return them in the form of am
+        array of binary masks of shape [height, width, instances].
+
+        Returns:
+            masks: A bool array of shape [height, width, instance count] with
+                a binary mask per instance.
+            class_ids: a 1D array of class IDs of the instance masks.
+        """
+        # Override this function to load a mask from your dataset.
+        # Otherwise, it returns an empty mask.
+        logging.warning("You are using the default load_mask(), maybe you need to define your own one.")
+        mask = np.empty([0, 0, 0])
+        class_ids = np.empty([0], np.int32)
+        return mask, class_ids
+
+
+def resize_image(image, min_dim=None, max_dim=None, min_scale=None, mode="square"):
+    """Resizes an image keeping the aspect ratio unchanged.
+
+    min_dim: if provided, resizes the image such that it's smaller
+        dimension == min_dim
+    max_dim: if provided, ensures that the image longest side doesn't
+        exceed this value.
+    min_scale: if provided, ensure that the image is scaled up by at least
+        this percent even if min_dim doesn't require it.
+    mode: Resizing mode.
+        none: No resizing. Return the image unchanged.
+        square: Resize and pad with zeros to get a square image
+            of size [max_dim, max_dim].
+        pad64: Pads width and height with zeros to make them multiples of 64.
+               If min_dim or min_scale are provided, it scales the image up
+               before padding. max_dim is ignored in this mode.
+               The multiple of 64 is needed to ensure smooth scaling of feature
+               maps up and down the 6 levels of the FPN pyramid (2**6=64).
+        crop: Picks random crops from the image. First, scales the image based
+              on min_dim and min_scale, then picks a random crop of
+              size min_dim x min_dim. Can be used in training only.
+              max_dim is not used in this mode.
+
+    Returns:
+    image: the resized image
+    window: (y1, x1, y2, x2). If max_dim is provided, padding might
+        be inserted in the returned image. If so, this window is the
+        coordinates of the image part of the full image (excluding
+        the padding). The x2, y2 pixels are not included.
+    scale: The scale factor used to resize the image
+    padding: Padding added to the image [(top, bottom), (left, right), (0, 0)]
+    """
+    # Keep track of image dtype and return results in the same dtype
+    image_dtype = image.dtype
+    # Default window (y1, x1, y2, x2) and default scale == 1.
+    h, w = image.shape[:2]
+    window = (0, 0, h, w)
+    scale = 1
+    padding = [(0, 0), (0, 0), (0, 0)]
+    crop = None
+
+    if mode == "none":
+        return image, window, scale, padding, crop
+
+    # Scale?
+    if min_dim:
+        # Scale up but not down
+        scale = max(1, min_dim / min(h, w))
+    if min_scale and scale < min_scale:
+        scale = min_scale
+
+    # Does it exceed max dim?
+    if max_dim and mode == "square":
+        image_max = max(h, w)
+        if round(image_max * scale) > max_dim:
+            scale = max_dim / image_max
+
+    # Resize image using bilinear interpolation
+    if scale != 1:
+        image = resize(image, (round(h * scale), round(w * scale)),
+                       preserve_range=True)
+
+    # Need padding or cropping?
+    if mode == "square":
+        # Get new height and width
+        h, w = image.shape[:2]
+        top_pad = (max_dim - h) // 2
+        bottom_pad = max_dim - h - top_pad
+        left_pad = (max_dim - w) // 2
+        right_pad = max_dim - w - left_pad
+        padding = [(top_pad, bottom_pad), (left_pad, right_pad), (0, 0)]
+        image = np.pad(image, padding, mode='constant', constant_values=0)
+        window = (top_pad, left_pad, h + top_pad, w + left_pad)
+    elif mode == "pad64":
+        h, w = image.shape[:2]
+        # Both sides must be divisible by 64
+        assert min_dim % 64 == 0, "Minimum dimension must be a multiple of 64"
+        # Height
+        if h % 64 > 0:
+            max_h = h - (h % 64) + 64
+            top_pad = (max_h - h) // 2
+            bottom_pad = max_h - h - top_pad
+        else:
+            top_pad = bottom_pad = 0
+        # Width
+        if w % 64 > 0:
+            max_w = w - (w % 64) + 64
+            left_pad = (max_w - w) // 2
+            right_pad = max_w - w - left_pad
+        else:
+            left_pad = right_pad = 0
+        padding = [(top_pad, bottom_pad), (left_pad, right_pad), (0, 0)]
+        image = np.pad(image, padding, mode='constant', constant_values=0)
+        window = (top_pad, left_pad, h + top_pad, w + left_pad)
+    elif mode == "crop":
+        # Pick a random crop
+        h, w = image.shape[:2]
+        y = random.randint(0, (h - min_dim))
+        x = random.randint(0, (w - min_dim))
+        crop = (y, x, min_dim, min_dim)
+        image = image[y:y + min_dim, x:x + min_dim]
+        window = (0, 0, min_dim, min_dim)
+    else:
+        raise Exception("Mode {} not supported".format(mode))
+    return image.astype(image_dtype), window, scale, padding, crop
+
+
+def resize_mask(mask, scale, padding, crop=None):
+    """Resizes a mask using the given scale and padding.
+    Typically, you get the scale and padding from resize_image() to
+    ensure both, the image and the mask, are resized consistently.
+
+    scale: mask scaling factor
+    padding: Padding to add to the mask in the form
+            [(top, bottom), (left, right), (0, 0)]
+    """
+    # Suppress warning from scipy 0.13.0, the output shape of zoom() is
+    # calculated with round() instead of int()
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        mask = scipy.ndimage.zoom(mask, zoom=[scale, scale, 1], order=0)
+    if crop is not None:
+        y, x, h, w = crop
+        mask = mask[y:y + h, x:x + w]
+    else:
+        mask = np.pad(mask, padding, mode='constant', constant_values=0)
+    return mask
+
+
+def minimize_mask(bbox, mask, mini_shape):
+    """Resize masks to a smaller version to reduce memory load.
+    Mini-masks can be resized back to image scale using expand_masks()
+
+    See inspect_data.ipynb notebook for more details.
+    """
+    mini_mask = np.zeros(mini_shape + (mask.shape[-1],), dtype=bool)
+    for i in range(mask.shape[-1]):
+        # Pick slice and cast to bool in case load_mask() returned wrong dtype
+        m = mask[:, :, i].astype(bool)
+        y1, x1, y2, x2 = bbox[i][:4]
+        m = m[y1:y2, x1:x2]
+        if m.size == 0:
+            raise Exception("Invalid bounding box with area of zero")
+        # Resize with bilinear interpolation
+        m = resize(m, mini_shape)
+        mini_mask[:, :, i] = np.around(m).astype(np.bool)
+    return mini_mask
+
+
+def expand_mask(bbox, mini_mask, image_shape):
+    """Resizes mini masks back to image size. Reverses the change
+    of minimize_mask().
+
+    See inspect_data.ipynb notebook for more details.
+    """
+    mask = np.zeros(image_shape[:2] + (mini_mask.shape[-1],), dtype=bool)
+    for i in range(mask.shape[-1]):
+        m = mini_mask[:, :, i]
+        y1, x1, y2, x2 = bbox[i][:4]
+        h = y2 - y1
+        w = x2 - x1
+        # Resize with bilinear interpolation
+        m = resize(m, (h, w))
+        mask[y1:y2, x1:x2, i] = np.around(m).astype(np.bool)
+    return mask
+
+
+# TODO: Build and use this function to reduce code duplication
+def mold_mask(mask, config):
+    pass
+
+
+def unmold_mask(mask, bbox, image_shape):
+    """Converts a mask generated by the neural network to a format similar
+    to its original shape.
+    mask: [height, width] of type float. A small, typically 28x28 mask.
+    bbox: [y1, x1, y2, x2]. The box to fit the mask in.
+
+    Returns a binary mask with the same size as the original image.
+    """
+    threshold = 0.5
+    y1, x1, y2, x2 = bbox
+    mask = resize(mask, (y2 - y1, x2 - x1))
+    mask = np.where(mask >= threshold, 1, 0).astype(np.bool)
+
+    # Put the mask in the right location.
+    full_mask = np.zeros(image_shape[:2], dtype=np.bool)
+    full_mask[y1:y2, x1:x2] = mask
+    return full_mask
+
+
+############################################################
+#  Anchors
+############################################################
+
+def generate_anchors(scales, ratios, shape, feature_stride, anchor_stride):
+    """
+    scales: 1D array of anchor sizes in pixels. Example: [32, 64, 128]
+    ratios: 1D array of anchor ratios of width/height. Example: [0.5, 1, 2]
+    shape: [height, width] spatial shape of the feature map over which
+            to generate anchors.
+    feature_stride: Stride of the feature map relative to the image in pixels.
+    anchor_stride: Stride of anchors on the feature map. For example, if the
+        value is 2 then generate anchors for every other feature map pixel.
+    """
+    # Get all combinations of scales and ratios
+    scales, ratios = np.meshgrid(np.array(scales), np.array(ratios))
+    scales = scales.flatten()
+    ratios = ratios.flatten()
+
+    # Enumerate heights and widths from scales and ratios
+    heights = scales / np.sqrt(ratios)
+    widths = scales * np.sqrt(ratios)
+
+    # Enumerate shifts in feature space
+    shifts_y = np.arange(0, shape[0], anchor_stride) * feature_stride
+    shifts_x = np.arange(0, shape[1], anchor_stride) * feature_stride
+    shifts_x, shifts_y = np.meshgrid(shifts_x, shifts_y)
+
+    # Enumerate combinations of shifts, widths, and heights
+    box_widths, box_centers_x = np.meshgrid(widths, shifts_x)
+    box_heights, box_centers_y = np.meshgrid(heights, shifts_y)
+
+    # Reshape to get a list of (y, x) and a list of (h, w)
+    box_centers = np.stack(
+        [box_centers_y, box_centers_x], axis=2).reshape([-1, 2])
+    box_sizes = np.stack([box_heights, box_widths], axis=2).reshape([-1, 2])
+
+    # Convert to corner coordinates (y1, x1, y2, x2)
+    boxes = np.concatenate([box_centers - 0.5 * box_sizes,
+                            box_centers + 0.5 * box_sizes], axis=1)
+    return boxes
+
+
+def generate_pyramid_anchors(scales, ratios, feature_shapes, feature_strides,
+                             anchor_stride):
+    """Generate anchors at different levels of a feature pyramid. Each scale
+    is associated with a level of the pyramid, but each ratio is used in
+    all levels of the pyramid.
+
+    Returns:
+    anchors: [N, (y1, x1, y2, x2)]. All generated anchors in one array. Sorted
+        with the same order of the given scales. So, anchors of scale[0] come
+        first, then anchors of scale[1], and so on.
+    """
+    # Anchors
+    # [anchor_count, (y1, x1, y2, x2)]
+    anchors = []
+    for i in range(len(scales)):
+        anchors.append(generate_anchors(scales[i], ratios, feature_shapes[i],
+                                        feature_strides[i], anchor_stride))
+    return np.concatenate(anchors, axis=0)
+
+
+############################################################
+#  Miscellaneous
+############################################################
+
+def trim_zeros(x):
+    """It's common to have tensors larger than the available data and
+    pad with zeros. This function removes rows that are all zeros.
+
+    x: [rows, columns].
+    """
+    assert len(x.shape) == 2
+    return x[~np.all(x == 0, axis=1)]
+
+
+def compute_matches(gt_boxes, gt_class_ids, gt_masks,
+                    pred_boxes, pred_class_ids, pred_scores, pred_masks,
+                    iou_threshold=0.5, score_threshold=0.0):
+    """Finds matches between prediction and ground truth instances.
+
+    Returns:
+        gt_match: 1-D array. For each GT box it has the index of the matched
+                  predicted box.
+        pred_match: 1-D array. For each predicted box, it has the index of
+                    the matched ground truth box.
+        overlaps: [pred_boxes, gt_boxes] IoU overlaps.
+    """
+    # Trim zero padding
+    # TODO: cleaner to do zero unpadding upstream
+    gt_boxes = trim_zeros(gt_boxes)
+    gt_masks = gt_masks[..., :gt_boxes.shape[0]]
+    pred_boxes = trim_zeros(pred_boxes)
+    pred_scores = pred_scores[:pred_boxes.shape[0]]
+    # Sort predictions by score from high to low
+    indices = np.argsort(pred_scores)[::-1]
+    pred_boxes = pred_boxes[indices]
+    pred_class_ids = pred_class_ids[indices]
+    pred_scores = pred_scores[indices]
+    pred_masks = pred_masks[..., indices]
+
+    # Compute IoU overlaps [pred_masks, gt_masks]
+    overlaps = compute_overlaps_masks(pred_masks, gt_masks)
+
+    # Loop through predictions and find matching ground truth boxes
+    match_count = 0
+    pred_match = -1 * np.ones([pred_boxes.shape[0]])
+    gt_match = -1 * np.ones([gt_boxes.shape[0]])
+    for i in range(len(pred_boxes)):
+        # Find best matching ground truth box
+        # 1. Sort matches by score
+        sorted_ixs = np.argsort(overlaps[i])[::-1]
+        # 2. Remove low scores
+        low_score_idx = np.where(overlaps[i, sorted_ixs] < score_threshold)[0]
+        if low_score_idx.size > 0:
+            sorted_ixs = sorted_ixs[:low_score_idx[0]]
+        # 3. Find the match
+        for j in sorted_ixs:
+            # If ground truth box is already matched, go to next one
+            if gt_match[j] > -1:
+                continue
+            # If we reach IoU smaller than the threshold, end the loop
+            iou = overlaps[i, j]
+            if iou < iou_threshold:
+                break
+            # Do we have a match?
+            if pred_class_ids[i] == gt_class_ids[j]:
+                match_count += 1
+                gt_match[j] = i
+                pred_match[i] = j
+                break
+
+    return gt_match, pred_match, overlaps
+
+
+def compute_ap(gt_boxes, gt_class_ids, gt_masks,
+               pred_boxes, pred_class_ids, pred_scores, pred_masks,
+               iou_threshold=0.5):
+    """Compute Average Precision at a set IoU threshold (default 0.5).
+
+    Returns:
+    mAP: Mean Average Precision
+    precisions: List of precisions at different class score thresholds.
+    recalls: List of recall values at different class score thresholds.
+    overlaps: [pred_boxes, gt_boxes] IoU overlaps.
+    """
+    # Get matches and overlaps
+    gt_match, pred_match, overlaps = compute_matches(
+        gt_boxes, gt_class_ids, gt_masks,
+        pred_boxes, pred_class_ids, pred_scores, pred_masks,
+        iou_threshold)
+
+    # Compute precision and recall at each prediction box step
+    precisions = np.cumsum(pred_match > -1) / (np.arange(len(pred_match)) + 1)
+    recalls = np.cumsum(pred_match > -1).astype(np.float32) / len(gt_match)
+
+    # Pad with start and end values to simplify the math
+    precisions = np.concatenate([[0], precisions, [0]])
+    recalls = np.concatenate([[0], recalls, [1]])
+
+    # Ensure precision values decrease but don't increase. This way, the
+    # precision value at each recall threshold is the maximum it can be
+    # for all following recall thresholds, as specified by the VOC paper.
+    for i in range(len(precisions) - 2, -1, -1):
+        precisions[i] = np.maximum(precisions[i], precisions[i + 1])
+
+    # Compute mean AP over recall range
+    indices = np.where(recalls[:-1] != recalls[1:])[0] + 1
+    mAP = np.sum((recalls[indices] - recalls[indices - 1]) *
+                 precisions[indices])
+
+    return mAP, precisions, recalls, overlaps
+
+
+def compute_ap_range(gt_box, gt_class_id, gt_mask,
+                     pred_box, pred_class_id, pred_score, pred_mask,
+                     iou_thresholds=None, verbose=1):
+    """Compute AP over a range or IoU thresholds. Default range is 0.5-0.95."""
+    # Default is 0.5 to 0.95 with increments of 0.05
+    iou_thresholds = iou_thresholds or np.arange(0.5, 1.0, 0.05)
+    
+    # Compute AP over range of IoU thresholds
+    AP = []
+    for iou_threshold in iou_thresholds:
+        ap, precisions, recalls, overlaps =\
+            compute_ap(gt_box, gt_class_id, gt_mask,
+                        pred_box, pred_class_id, pred_score, pred_mask,
+                        iou_threshold=iou_threshold)
+        if verbose:
+            print("AP @{:.2f}:\t {:.3f}".format(iou_threshold, ap))
+        AP.append(ap)
+    AP = np.array(AP).mean()
+    if verbose:
+        print("AP @{:.2f}-{:.2f}:\t {:.3f}".format(
+            iou_thresholds[0], iou_thresholds[-1], AP))
+    return AP
+
+
+def compute_recall(pred_boxes, gt_boxes, iou):
+    """Compute the recall at the given IoU threshold. It's an indication
+    of how many GT boxes were found by the given prediction boxes.
+
+    pred_boxes: [N, (y1, x1, y2, x2)] in image coordinates
+    gt_boxes: [N, (y1, x1, y2, x2)] in image coordinates
+    """
+    # Measure overlaps
+    overlaps = compute_overlaps(pred_boxes, gt_boxes)
+    iou_max = np.max(overlaps, axis=1)
+    iou_argmax = np.argmax(overlaps, axis=1)
+    positive_ids = np.where(iou_max >= iou)[0]
+    matched_gt_boxes = iou_argmax[positive_ids]
+
+    recall = len(set(matched_gt_boxes)) / gt_boxes.shape[0]
+    return recall, positive_ids
+
+
+# ## Batch Slicing
+# Some custom layers support a batch size of 1 only, and require a lot of work
+# to support batches greater than 1. This function slices an input tensor
+# across the batch dimension and feeds batches of size 1. Effectively,
+# an easy way to support batches > 1 quickly with little code modification.
+# In the long run, it's more efficient to modify the code to support large
+# batches and getting rid of this function. Consider this a temporary solution
+def batch_slice(inputs, graph_fn, batch_size, names=None):
+    """Splits inputs into slices and feeds each slice to a copy of the given
+    computation graph and then combines the results. It allows you to run a
+    graph on a batch of inputs even if the graph is written to support one
+    instance only.
+
+    inputs: list of tensors. All must have the same first dimension length
+    graph_fn: A function that returns a TF tensor that's part of a graph.
+    batch_size: number of slices to divide the data into.
+    names: If provided, assigns names to the resulting tensors.
+    """
+    if not isinstance(inputs, list):
+        inputs = [inputs]
+
+    outputs = []
+    for i in range(batch_size):
+        inputs_slice = [x[i] for x in inputs]
+        output_slice = graph_fn(*inputs_slice)
+        if not isinstance(output_slice, (tuple, list)):
+            output_slice = [output_slice]
+        outputs.append(output_slice)
+    # Change outputs from a list of slices where each is
+    # a list of outputs to a list of outputs and each has
+    # a list of slices
+    outputs = list(zip(*outputs))
+
+    if names is None:
+        names = [None] * len(outputs)
+
+    result = [tf.stack(o, axis=0, name=n)
+              for o, n in zip(outputs, names)]
+    if len(result) == 1:
+        result = result[0]
+
+    return result
+
+
+def download_trained_weights(coco_model_path, verbose=1):
+    """Download COCO trained weights from Releases.
+
+    coco_model_path: local path of COCO trained weights
+    """
+    if verbose > 0:
+        print("Downloading pretrained model to " + coco_model_path + " ...")
+    with urllib.request.urlopen(COCO_MODEL_URL) as resp, open(coco_model_path, 'wb') as out:
+        shutil.copyfileobj(resp, out)
+    if verbose > 0:
+        print("... done downloading pretrained model!")
+
+
+def norm_boxes(boxes, shape):
+    """Converts boxes from pixel coordinates to normalized coordinates.
+    boxes: [N, (y1, x1, y2, x2)] in pixel coordinates
+    shape: [..., (height, width)] in pixels
+
+    Note: In pixel coordinates (y2, x2) is outside the box. But in normalized
+    coordinates it's inside the box.
+
+    Returns:
+        [N, (y1, x1, y2, x2)] in normalized coordinates
+    """
+    h, w = shape
+    scale = np.array([h - 1, w - 1, h - 1, w - 1])
+    shift = np.array([0, 0, 1, 1])
+    return np.divide((boxes - shift), scale).astype(np.float32)
+
+
+def denorm_boxes(boxes, shape):
+    """Converts boxes from normalized coordinates to pixel coordinates.
+    boxes: [N, (y1, x1, y2, x2)] in normalized coordinates
+    shape: [..., (height, width)] in pixels
+
+    Note: In pixel coordinates (y2, x2) is outside the box. But in normalized
+    coordinates it's inside the box.
+
+    Returns:
+        [N, (y1, x1, y2, x2)] in pixel coordinates
+    """
+    h, w = shape
+    scale = np.array([h - 1, w - 1, h - 1, w - 1])
+    shift = np.array([0, 0, 1, 1])
+    return np.around(np.multiply(boxes, scale) + shift).astype(np.int32)
+
+
+def resize(image, output_shape, order=1, mode='constant', cval=0, clip=True,
+           preserve_range=False, anti_aliasing=False, anti_aliasing_sigma=None):
+    """A wrapper for Scikit-Image resize().
+
+    Scikit-Image generates warnings on every call to resize() if it doesn't
+    receive the right parameters. The right parameters depend on the version
+    of skimage. This solves the problem by using different parameters per
+    version. And it provides a central place to control resizing defaults.
+    """
+    if LooseVersion(skimage.__version__) >= LooseVersion("0.14"):
+        # New in 0.14: anti_aliasing. Default it to False for backward
+        # compatibility with skimage 0.13.
+        return skimage.transform.resize(
+            image, output_shape,
+            order=order, mode=mode, cval=cval, clip=clip,
+            preserve_range=preserve_range, anti_aliasing=anti_aliasing,
+            anti_aliasing_sigma=anti_aliasing_sigma)
+    else:
+        return skimage.transform.resize(
+            image, output_shape,
+            order=order, mode=mode, cval=cval, clip=clip,
+            preserve_range=preserve_range)