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	"""
	Mask R-CNN
	Configurations and data loading code for MS COCO.

	Copyright (c) 2017 Matterport, Inc.
	Licensed under the MIT License (see LICENSE for details)
	Written by Waleed Abdulla

	------------------------------------------------------------

	Usage: import the module (see Jupyter notebooks for examples), or run from
	the command line as such:

	# Train a new model starting from pre-trained COCO weights
	python3 coco.py train --dataset=/path/to/coco/ --model=coco

	# Train a new model starting from ImageNet weights
	python3 coco.py train --dataset=/path/to/coco/ --model=imagenet

	# Continue training a model that you had trained earlier
	python3 coco.py train --dataset=/path/to/coco/ --model=/path/to/weights.h5

	# Continue training the last model you trained
	python3 coco.py train --dataset=/path/to/coco/ --model=last

	# Run COCO evaluatoin on the last model you trained
	python3 coco.py evaluate --dataset=/path/to/coco/ --model=last
	"""

	import os
	import time
	import numpy as np

	# Download and install the Python COCO tools from https://github.com/waleedka/coco
	# That's a fork from the original https://github.com/pdollar/coco with a bug
	# fix for Python 3.
	# I submitted a pull request https://github.com/cocodataset/cocoapi/pull/50
	# If the PR is merged then use the original repo.
	# Note: Edit PythonAPI/Makefile and replace "python" with "python3".
	from pycocotools.coco import COCO
	from pycocotools.cocoeval import COCOeval
	from pycocotools import mask as maskUtils

	import zipfile
	import urllib.request
	import shutil

	from config import Config
	import utils
	import model as modellib

	# Root directory of the project
	ROOT_DIR = os.getcwd()

	# Path to trained weights file
	COCO_MODEL_PATH = os.path.join(ROOT_DIR, "mask_rcnn_coco.h5")

	# Directory to save logs and model checkpoints, if not provided
	# through the command line argument --logs
	DEFAULT_LOGS_DIR = os.path.join(ROOT_DIR, "logs")
	DEFAULT_DATASET_YEAR = "2014"

	############################################################
	# Configurations
	############################################################


	class CocoConfig(Config):
	"""Configuration for training on MS COCO.
	Derives from the base Config class and overrides values specific
	to the COCO dataset.
	"""
	# Give the configuration a recognizable name
	NAME = "coco"

	# We use a GPU with 12GB memory, which can fit two images.
	# Adjust down if you use a smaller GPU.
	IMAGES_PER_GPU = 2

	# Uncomment to train on 8 GPUs (default is 1)
	# GPU_COUNT = 8

	# Number of classes (including background)
	NUM_CLASSES = 1 + 80 # COCO has 80 classes


	############################################################
	# Dataset
	############################################################

	class CocoDataset(utils.Dataset):
	def load_coco(self, dataset_dir, subset, year=DEFAULT_DATASET_YEAR, class_ids=None,
	class_map=None, return_coco=False, auto_download=False):
	"""Load a subset of the COCO dataset.
	dataset_dir: The root directory of the COCO dataset.
	subset: What to load (train, val, minival, valminusminival)
	year: What dataset year to load (2014, 2017) as a string, not an integer
	class_ids: If provided, only loads images that have the given classes.
	class_map: TODO: Not implemented yet. Supports maping classes from
	different datasets to the same class ID.
	return_coco: If True, returns the COCO object.
	auto_download: Automatically download and unzip MS-COCO images and annotations
	"""

	if auto_download is True:
	self.auto_download(dataset_dir, subset, year)

	coco = COCO("{}/annotations/instances_{}{}.json".format(dataset_dir, subset, year))
	if subset == "minival" or subset == "valminusminival":
	subset = "val"
	image_dir = "{}/{}{}".format(dataset_dir, subset, year)

	# Load all classes or a subset?
	if not class_ids:
	# All classes
	class_ids = sorted(coco.getCatIds())

	# All images or a subset?
	if class_ids:
	image_ids = []
	for id in class_ids:
	image_ids.extend(list(coco.getImgIds(catIds=[id])))
	# Remove duplicates
	image_ids = list(set(image_ids))
	else:
	# All images
	image_ids = list(coco.imgs.keys())

	# Add classes
	for i in class_ids:
	self.add_class("coco", i, coco.loadCats(i)[0]["name"])

	# Add images
	for i in image_ids:
	self.add_image(
	"coco", image_id=i,
	path=os.path.join(image_dir, coco.imgs[i]['file_name']),
	width=coco.imgs[i]["width"],
	height=coco.imgs[i]["height"],
	annotations=coco.loadAnns(coco.getAnnIds(
	imgIds=[i], catIds=class_ids, iscrowd=None)))
	if return_coco:
	return coco

	def auto_download(self, dataDir, dataType, dataYear):
	"""Download the COCO dataset/annotations if requested.
	dataDir: The root directory of the COCO dataset.
	dataType: What to load (train, val, minival, valminusminival)
	dataYear: What dataset year to load (2014, 2017) as a string, not an integer
	Note:
	For 2014, use "train", "val", "minival", or "valminusminival"
	For 2017, only "train" and "val" annotations are available
	"""

	# Setup paths and file names
	if dataType == "minival" or dataType == "valminusminival":
	imgDir = "{}/{}{}".format(dataDir, "val", dataYear)
	imgZipFile = "{}/{}{}.zip".format(dataDir, "val", dataYear)
	imgURL = "http://images.cocodataset.org/zips/{}{}.zip".format("val", dataYear)
	else:
	imgDir = "{}/{}{}".format(dataDir, dataType, dataYear)
	imgZipFile = "{}/{}{}.zip".format(dataDir, dataType, dataYear)
	imgURL = "http://images.cocodataset.org/zips/{}{}.zip".format(dataType, dataYear)
	# print("Image paths:"); print(imgDir); print(imgZipFile); print(imgURL)

	# Create main folder if it doesn't exist yet
	if not os.path.exists(dataDir):
	os.makedirs(dataDir)

	# Download images if not available locally
	if not os.path.exists(imgDir):
	os.makedirs(imgDir)
	print("Downloading images to " + imgZipFile + " ...")
	with urllib.request.urlopen(imgURL) as resp, open(imgZipFile, 'wb') as out:
	shutil.copyfileobj(resp, out)
	print("... done downloading.")
	print("Unzipping " + imgZipFile)
	with zipfile.ZipFile(imgZipFile, "r") as zip_ref:
	zip_ref.extractall(dataDir)
	print("... done unzipping")
	print("Will use images in " + imgDir)

	# Setup annotations data paths
	annDir = "{}/annotations".format(dataDir)
	if dataType == "minival":
	annZipFile = "{}/instances_minival2014.json.zip".format(dataDir)
	annFile = "{}/instances_minival2014.json".format(annDir)
	annURL = "https://dl.dropboxusercontent.com/s/o43o90bna78omob/instances_minival2014.json.zip?dl=0"
	unZipDir = annDir
	elif dataType == "valminusminival":
	annZipFile = "{}/instances_valminusminival2014.json.zip".format(dataDir)
	annFile = "{}/instances_valminusminival2014.json".format(annDir)
	annURL = "https://dl.dropboxusercontent.com/s/s3tw5zcg7395368/instances_valminusminival2014.json.zip?dl=0"
	unZipDir = annDir
	else:
	annZipFile = "{}/annotations_trainval{}.zip".format(dataDir, dataYear)
	annFile = "{}/instances_{}{}.json".format(annDir, dataType, dataYear)
	annURL = "http://images.cocodataset.org/annotations/annotations_trainval{}.zip".format(dataYear)
	unZipDir = dataDir
	# print("Annotations paths:"); print(annDir); print(annFile); print(annZipFile); print(annURL)

	# Download annotations if not available locally
	if not os.path.exists(annDir):
	os.makedirs(annDir)
	if not os.path.exists(annFile):
	if not os.path.exists(annZipFile):
	print("Downloading zipped annotations to " + annZipFile + " ...")
	with urllib.request.urlopen(annURL) as resp, open(annZipFile, 'wb') as out:
	shutil.copyfileobj(resp, out)
	print("... done downloading.")
	print("Unzipping " + annZipFile)
	with zipfile.ZipFile(annZipFile, "r") as zip_ref:
	zip_ref.extractall(unZipDir)
	print("... done unzipping")
	print("Will use annotations in " + annFile)

	def load_mask(self, image_id):
	"""Load instance masks for the given image.

	Different datasets use different ways to store masks. This
	function converts the different mask format to one format
	in the form of a bitmap [height, width, instances].

	Returns:
	masks: A bool array of shape [height, width, instance count] with
	one mask per instance.
	class_ids: a 1D array of class IDs of the instance masks.
	"""
	# If not a COCO image, delegate to parent class.
	image_info = self.image_info[image_id]
	if image_info["source"] != "coco":
	return super(CocoDataset, self).load_mask(image_id)

	instance_masks = []
	class_ids = []
	annotations = self.image_info[image_id]["annotations"]
	# Build mask of shape [height, width, instance_count] and list
	# of class IDs that correspond to each channel of the mask.
	for annotation in annotations:
	class_id = self.map_source_class_id(
	"coco.{}".format(annotation['category_id']))
	if class_id:
	m = self.annToMask(annotation, image_info["height"],
	image_info["width"])
	# Some objects are so small that they're less than 1 pixel area
	# and end up rounded out. Skip those objects.
	if m.max() < 1:
	continue
	# Is it a crowd? If so, use a negative class ID.
	if annotation['iscrowd']:
	# Use negative class ID for crowds
	class_id *= -1
	# For crowd masks, annToMask() sometimes returns a mask
	# smaller than the given dimensions. If so, resize it.
	if m.shape[0] != image_info["height"] or m.shape[1] != image_info["width"]:
	m = np.ones([image_info["height"], image_info["width"]], dtype=bool)
	instance_masks.append(m)
	class_ids.append(class_id)

	# Pack instance masks into an array
	if class_ids:
	mask = np.stack(instance_masks, axis=2)
	class_ids = np.array(class_ids, dtype=np.int32)
	return mask, class_ids
	else:
	# Call super class to return an empty mask
	return super(CocoDataset, self).load_mask(image_id)

	def image_reference(self, image_id):
	"""Return a link to the image in the COCO Website."""
	info = self.image_info[image_id]
	if info["source"] == "coco":
	return "http://cocodataset.org/#explore?id={}".format(info["id"])
	else:
	super(CocoDataset, self).image_reference(image_id)

	# The following two functions are from pycocotools with a few changes.

	def annToRLE(self, ann, height, width):
	"""
	Convert annotation which can be polygons, uncompressed RLE to RLE.
	:return: binary mask (numpy 2D array)
	"""
	segm = ann['segmentation']
	if isinstance(segm, list):
	# polygon -- a single object might consist of multiple parts
	# we merge all parts into one mask rle code
	rles = maskUtils.frPyObjects(segm, height, width)
	rle = maskUtils.merge(rles)
	elif isinstance(segm['counts'], list):
	# uncompressed RLE
	rle = maskUtils.frPyObjects(segm, height, width)
	else:
	# rle
	rle = ann['segmentation']
	return rle

	def annToMask(self, ann, height, width):
	"""
	Convert annotation which can be polygons, uncompressed RLE, or RLE to binary mask.
	:return: binary mask (numpy 2D array)
	"""
	rle = self.annToRLE(ann, height, width)
	m = maskUtils.decode(rle)
	return m


	############################################################
	# COCO Evaluation
	############################################################

	def build_coco_results(dataset, image_ids, rois, class_ids, scores, masks):
	"""Arrange resutls to match COCO specs in http://cocodataset.org/#format
	"""
	# If no results, return an empty list
	if rois is None:
	return []

	results = []
	for image_id in image_ids:
	# Loop through detections
	for i in range(rois.shape[0]):
	class_id = class_ids[i]
	score = scores[i]
	bbox = np.around(rois[i], 1)
	mask = masks[:, :, i]

	result = {
	"image_id": image_id,
	"category_id": dataset.get_source_class_id(class_id, "coco"),
	"bbox": [bbox[1], bbox[0], bbox[3] - bbox[1], bbox[2] - bbox[0]],
	"score": score,
	"segmentation": maskUtils.encode(np.asfortranarray(mask))
	}
	results.append(result)
	return results


	def evaluate_coco(model, dataset, coco, eval_type="bbox", limit=0, image_ids=None):
	"""Runs official COCO evaluation.
	dataset: A Dataset object with valiadtion data
	eval_type: "bbox" or "segm" for bounding box or segmentation evaluation
	limit: if not 0, it's the number of images to use for evaluation
	"""
	# Pick COCO images from the dataset
	image_ids = image_ids or dataset.image_ids

	# Limit to a subset
	if limit:
	image_ids = image_ids[:limit]

	# Get corresponding COCO image IDs.
	coco_image_ids = [dataset.image_info[id]["id"] for id in image_ids]

	t_prediction = 0
	t_start = time.time()

	results = []
	for i, image_id in enumerate(image_ids):
	# Load image
	image = dataset.load_image(image_id)

	# Run detection
	t = time.time()
	r = model.detect([image], verbose=0)[0]
	t_prediction += (time.time() - t)

	# Convert results to COCO format
	image_results = build_coco_results(dataset, coco_image_ids[i:i + 1],
	r["rois"], r["class_ids"],
	r["scores"], r["masks"])
	results.extend(image_results)

	# Load results. This modifies results with additional attributes.
	coco_results = coco.loadRes(results)

	# Evaluate
	cocoEval = COCOeval(coco, coco_results, eval_type)
	cocoEval.params.imgIds = coco_image_ids
	cocoEval.evaluate()
	cocoEval.accumulate()
	cocoEval.summarize()

	print("Prediction time: {}. Average {}/image".format(
	t_prediction, t_prediction / len(image_ids)))
	print("Total time: ", time.time() - t_start)


	############################################################
	# Training
	############################################################


	if __name__ == '__main__':
	import argparse

	# Parse command line arguments
	parser = argparse.ArgumentParser(
	description='Train Mask R-CNN on MS COCO.')
	parser.add_argument("command",
	metavar="<command>",
	help="'train' or 'evaluate' on MS COCO")
	parser.add_argument('--dataset', required=True,
	metavar="/path/to/coco/",
	help='Directory of the MS-COCO dataset')
	parser.add_argument('--year', required=False,
	default=DEFAULT_DATASET_YEAR,
	metavar="<year>",
	help='Year of the MS-COCO dataset (2014 or 2017) (default=2014)')
	parser.add_argument('--model', required=True,
	metavar="/path/to/weights.h5",
	help="Path to weights .h5 file or 'coco'")
	parser.add_argument('--logs', required=False,
	default=DEFAULT_LOGS_DIR,
	metavar="/path/to/logs/",
	help='Logs and checkpoints directory (default=logs/)')
	parser.add_argument('--limit', required=False,
	default=500,
	metavar="<image count>",
	help='Images to use for evaluation (default=500)')
	parser.add_argument('--download', required=False,
	default=False,
	metavar="<True\|False>",
	help='Automatically download and unzip MS-COCO files (default=False)',
	type=bool)
	args = parser.parse_args()
	print("Command: ", args.command)
	print("Model: ", args.model)
	print("Dataset: ", args.dataset)
	print("Year: ", args.year)
	print("Logs: ", args.logs)
	print("Auto Download: ", args.download)

	# Configurations
	if args.command == "train":
	config = CocoConfig()
	else:
	class InferenceConfig(CocoConfig):
	# Set batch size to 1 since we'll be running inference on
	# one image at a time. Batch size = GPU_COUNT * IMAGES_PER_GPU
	GPU_COUNT = 1
	IMAGES_PER_GPU = 1
	DETECTION_MIN_CONFIDENCE = 0
	config = InferenceConfig()
	config.display()

	# Create model
	if args.command == "train":
	model = modellib.MaskRCNN(mode="training", config=config,
	model_dir=args.logs)
	else:
	model = modellib.MaskRCNN(mode="inference", config=config,
	model_dir=args.logs)

	# Select weights file to load
	if args.model.lower() == "coco":
	model_path = COCO_MODEL_PATH
	elif args.model.lower() == "last":
	# Find last trained weights
	model_path = model.find_last()[1]
	elif args.model.lower() == "imagenet":
	# Start from ImageNet trained weights
	model_path = model.get_imagenet_weights()
	else:
	model_path = args.model

	# Load weights
	print("Loading weights ", model_path)
	model.load_weights(model_path, by_name=True)

	# Train or evaluate
	if args.command == "train":
	# Training dataset. Use the training set and 35K from the
	# validation set, as as in the Mask RCNN paper.
	dataset_train = CocoDataset()
	dataset_train.load_coco(args.dataset, "train", year=args.year, auto_download=args.download)
	dataset_train.load_coco(args.dataset, "valminusminival", year=args.year, auto_download=args.download)
	dataset_train.prepare()

	# Validation dataset
	dataset_val = CocoDataset()
	dataset_val.load_coco(args.dataset, "minival", year=args.year, auto_download=args.download)
	dataset_val.prepare()

	# * This training schedule is an example. Update to your needs *

	# Training - Stage 1
	print("Training network heads")
	model.train(dataset_train, dataset_val,
	learning_rate=config.LEARNING_RATE,
	epochs=40,
	layers='heads')

	# Training - Stage 2
	# Finetune layers from ResNet stage 4 and up
	print("Fine tune Resnet stage 4 and up")
	model.train(dataset_train, dataset_val,
	learning_rate=config.LEARNING_RATE,
	epochs=120,
	layers='4+')

	# Training - Stage 3
	# Fine tune all layers
	print("Fine tune all layers")
	model.train(dataset_train, dataset_val,
	learning_rate=config.LEARNING_RATE / 10,
	epochs=160,
	layers='all')

	elif args.command == "evaluate":
	# Validation dataset
	dataset_val = CocoDataset()
	coco = dataset_val.load_coco(args.dataset, "minival", year=args.year, return_coco=True, auto_download=args.download)
	dataset_val.prepare()
	print("Running COCO evaluation on {} images.".format(args.limit))
	evaluate_coco(model, dataset_val, coco, "bbox", limit=int(args.limit))
	else:
	print("'{}' is not recognized. "
	"Use 'train' or 'evaluate'".format(args.command))