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datasets.py
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datasets.py
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import os
import shutil
import sys
import urllib.request
import zipfile
import numpy as np
import skimage.io
from mrcnn import utils as mrcnn_utils
try:
from coco.pycocotools.coco import COCO
from coco.pycocotools import mask as maskUtils
except Exception:
try:
from pycocotools.coco import COCO
from pycocotools import mask as maskUtils
except Exception:
print("Run ./coco/install_locally.sh to install pycocotools")
raise Exception
def get_source_id_string(source: str, id):
return "{}.{}".format(source, id)
class SegmentationDataset(mrcnn_utils.Dataset):
@staticmethod
def get_model_datasets(own_datasets_configs: list, dataset_dir, required_classes: list, eval_fraction=0.1,
# Since on images of own dataset we have 1-3 labels but coco mostly only 1
# we increase this value from 0.55 to 0.9
coco_own_fraction=0.9):
own_train_dataset, own_eval_dataset = OwnDataset.get_model_datasets(own_datasets_configs, dataset_dir,
required_classes, eval_fraction)
own_eval_dataset.prepare()
own_train_dataset.prepare()
train_coco_images = int(len(own_train_dataset.image_info) * coco_own_fraction)
eval_coco_images = int(len(own_eval_dataset.image_info) * coco_own_fraction)
# 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(dataset_dir, "train", auto_download=True, class_names=required_classes,
total_images=train_coco_images)
dataset_train.load_coco(dataset_dir, "valminusminival", auto_download=True, class_names=required_classes,
total_images=train_coco_images)
dataset_train.prepare()
# Validation dataset
dataset_val = CocoDataset()
dataset_val.load_coco(dataset_dir, "minival", auto_download=True, class_names=required_classes,
total_images=eval_coco_images)
dataset_val.prepare()
return SegmentationDataset(dataset_train, own_train_dataset, required_classes, coco_own_fraction), \
SegmentationDataset(dataset_train, own_train_dataset, required_classes, coco_own_fraction)
def __init__(self, coco_dataset: mrcnn_utils.Dataset, own_dataset: mrcnn_utils.Dataset, classes, coco_own_fraction):
super().__init__(class_map=None)
self.own_dataset = own_dataset
self.coco_dataset = coco_dataset
self.coco_len = len(coco_dataset.image_info)
self.own_len = len(own_dataset.image_info)
# {
# "source": source,
# "id": class_id,
# "name": class_name,
# }
for class_i in coco_dataset.class_info:
self.add_class(class_i["source"],
coco_dataset.map_source_class_id(get_source_id_string(class_i["source"], class_i["id"])),
class_i["name"])
for class_i in own_dataset.class_info:
self.add_class(class_i["source"],
own_dataset.map_source_class_id(get_source_id_string(class_i["source"], class_i["id"])),
class_i["name"])
# image_info = {
# "id": image_id,
# "source": source,
# "path": path,
# }
id = 0
for image_i in coco_dataset.image_info:
self.add_image(image_i["source"], id, image_i["path"])
id += 1
for image_i in own_dataset.image_info:
self.add_image(image_i["source"], id, image_i["path"])
id += 1
def load_mask(self, image_id):
image = self.image_info[image_id]
if image_id >= self.coco_len:
dataset = self.own_dataset
image_id = image_id - self.coco_len
else:
dataset = self.coco_dataset
masks, class_ids = dataset.load_mask(image_id)
for i in range(len(class_ids)):
id = class_ids[i]
# Handle COCO crowds
# A crowd box in COCO is a bounding box around several instances
if id < 0:
id *= -1
id = self.map_source_class_id(get_source_id_string(image["source"], id))
id *= -1
else:
id = self.map_source_class_id(get_source_id_string(image["source"], id))
class_ids[i] = id
return masks, class_ids
class OwnDataset(mrcnn_utils.Dataset):
@staticmethod
def get_model_datasets(datasets: list, dataset_dir, classes, eval_fraction):
if not os.path.exists(os.path.abspath(dataset_dir)):
os.makedirs(os.path.abspath(dataset_dir))
train_items = []
eval_items = []
classes_tuples = []
dataset_dirs = []
class_names = set()
source_names = set()
for dataset in datasets:
if dataset.source in source_names:
raise RuntimeError("Multiple json files has same file names " + dataset.source)
dir = os.path.join(dataset_dir, dataset.source)
dataset_dirs.append(dir)
if not os.path.exists(dir):
os.mkdir(dir)
amount = len(dataset.images)
eval = np.random.choice(amount, int(amount * eval_fraction), replace=False)
eval_items.append(eval)
train_items.append(list(filter(lambda x: x not in eval, range(len(dataset.images)))))
for class_id in range(len(dataset.classes)):
class_name = dataset.classes[class_id]
if class_name in class_names:
raise RuntimeError("Different json has same class names " + class_name)
classes_tuples.append({"source": dataset.source, "id": class_id, "class_name": class_name})
class_names.add(class_name)
return OwnDataset(classes_tuples, datasets, train_items, dataset_dirs), \
OwnDataset(classes_tuples, datasets, eval_items, dataset_dirs)
def __init__(self, classes: list, datasets: list, datasets_items: list, dataset_dirs: list):
super().__init__(class_map=None)
self.pr_datasets = datasets
self.dataset_dirs = dataset_dirs
self.dataset_items = datasets_items
for item in classes:
self.add_class(item.get("source"), item.get("id"), item.get("class_name"))
id = 0
for i in range(len(datasets_items)):
dataset = datasets[i]
dataset_items = datasets_items[i]
dataset_dir = dataset_dirs[i]
for item in dataset_items:
self.add_image(dataset.source,
id,
dataset.get_image_file(dataset_dir, dataset.images[item], auto_load=True))
id += 1
def bmp_to_binary(self, path):
# img = Image.open(path)
# h, w = img.size
# pixels = list(img.getdata())
# aux = []
# for x in range(h):
# aux.append(pixels[x * w: x * w + w])
# for y in range(w):
# aux[x].append(pixels[x*h + y])
# return aux
# return np.array(img.getdata(),
# np.uint8).reshape(img.size[1], img.size[0], 3)
return skimage.io.imread(path)
def load_mask_path(self, image_id):
dataset_position = 0
dataset_item = 0
for i in range(len(self.dataset_items)):
d = self.dataset_items[i]
if image_id >= len(d):
image_id -= len(d)
else:
dataset_position = i
dataset_item = d[image_id]
break
dataset = self.pr_datasets[dataset_position]
dataset_dir = self.dataset_dirs[dataset_position]
mask_files, class_ids = dataset.get_mask_files(dataset_dir, dataset.images[dataset_item], auto_load=True)
# Map to current dataset ids
for i in range(len(class_ids)):
class_ids[i] = self.map_source_class_id(get_source_id_string(dataset.source, class_ids[i]))
return mask_files, class_ids
def load_mask(self, image_id):
mask_files, class_ids = self.load_mask_path(image_id)
masks_bytes = []
for file in mask_files:
masks_bytes.append(self.bmp_to_binary(file))
masks = np.stack(masks_bytes, axis=2).astype(np.bool)
class_ids = np.array(class_ids, dtype=np.int32)
return masks, class_ids
DEFAULT_DATASET_YEAR = "2014"
class CocoDataset(mrcnn_utils.Dataset):
def load_coco(self, dataset_dir, subset, year=DEFAULT_DATASET_YEAR, class_names=None,
class_map=None, return_coco=False, auto_download=False, total_images=None):
"""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
"""
dataset_dir = os.path.join(dataset_dir, "coco")
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)
# All classes
class_ids = sorted(coco.getCatIds())
# Only subset of classes
if class_names:
class_ids = list(filter(lambda id: coco.loadCats(id)[0]["name"] in class_names, class_ids))
images_per_class = sys.maxsize if total_images is None or total_images <= 0 else int(
total_images // len(class_ids))
# All images or a subset?
if class_ids:
image_ids = []
for id in class_ids:
images = list(coco.getImgIds(catIds=[id]))
image_ids.extend(images if len(images) < images_per_class else images[:images_per_class])
# 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(
get_source_id_string("coco", 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).astype(np.bool)
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