diff --git a/mrcnn/utils.py b/mrcnn/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..5195ae0852affdd38ae4194e15e48aa1f9adcf14
--- /dev/null
+++ b/mrcnn/utils.py
@@ -0,0 +1,927 @@
+"""
+MRCNN Particle Detection
+Common utility functions and classes.
+
+The source code of "MRCNN Particle Detection" (https://git.rwth-aachen.de/avt-fvt/private/mrcnn-particle-detection)
+is based on the source code of "Mask R-CNN" (https://github.com/matterport/Mask_RCNN).
+
+The source code of "Mask R-CNN" is licensed under the MIT License (MIT).
+Copyright (c) 2017 Matterport, Inc.
+Written by Waleed Abdulla
+
+All source code modifications to the source code of "Mask R-CNN" in "MRCNN Particle Detection"
+are licensed under the Eclipse Public License v2.0 (EPL 2.0).
+Copyright (c) 2022-2023 Fluid Process Engineering (AVT.FVT), RWTH Aachen University
+Edited by Stepan Sibirtsev, Mathias Neufang & Jakob Seiler
+
+The coyprights and license terms are given in LICENSE.
+
+Ideas and a small code snippets were adapted from these sources:
+https://github.com/mat02/Mask_RCNN
+"""
+
+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
+
+# URL from which to download the latest COCO trained weights
+COCO_MODEL_URL = "https://github.com/matterport/Mask_RCNN/releases/download/v2.0/mask_rcnn_coco.h5"
+
+
+############################################################
+# 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]
+
+ if pred_masks:
+ pred_masks = pred_masks[..., indices]
+ # Compute IoU overlaps [pred_masks, gt_masks]
+ overlaps = compute_overlaps_masks(pred_masks, gt_masks)
+ else:
+ # Compute overlaps [pred_boxes, gt_boxes]
+ overlaps = compute_overlaps(pred_boxes, gt_boxes)
+
+
+ # 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])
+ #print(boxes)
+ 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)