#!/usr/bin/env python3
# -*- coding: utf-8 -*-
__author__ = "Christian"
__doc__ = r"""
Created on {date}
"""
import gzip
import pathlib
import pickle
from urllib import request
import cv2
import numpy
import tqdm
from matplotlib import pyplot
from neodroidvision import PROJECT_APP_PATH
filename = [
["training_images", "train-images-idx3-ubyte.gz"],
["test_images", "t10k-images-idx3-ubyte.gz"],
["training_labels", "train-labels-idx1-ubyte.gz"],
["test_labels", "t10k-labels-idx1-ubyte.gz"],
]
SAVE_PATH = pathlib.Path(PROJECT_APP_PATH.user_data / "Data" / "mnist_original")
__all__ = [
"download_mnist",
"extract_mnist",
"calculate_iou",
"compute_iou_all",
"tight_bbox",
]
[docs]def download_mnist():
"""description"""
SAVE_PATH.mkdir(exist_ok=True, parents=True)
base_url = "http://yann.lecun.com/exdb/mnist/"
for name in filename:
filepath = SAVE_PATH / name[1]
if filepath.is_file():
continue
print(f"Downloading {name[1]}...")
request.urlretrieve(base_url + name[1], filepath)
def load():
"""
:return:
:rtype:
"""
download_mnist()
extract_mnist()
dataset_path = SAVE_PATH / "mnist.pkl"
with open(dataset_path, "rb") as f:
mnist = pickle.load(f)
X_train, Y_train, X_test, Y_test = (
mnist["training_images"],
mnist["training_labels"],
mnist["test_images"],
mnist["test_labels"],
)
return X_train.reshape(-1, 28, 28), Y_train, X_test.reshape(-1, 28, 28), Y_test
[docs]def calculate_iou(prediction_box, gt_box):
"""Calculate intersection over union of single predicted and ground truth box.
Args:
prediction_box (numpy.array of floats): location of predicted object as
[xmin, ymin, xmax, ymax]
gt_box (numpy.array of floats): location of ground truth object as
[xmin, ymin, xmax, ymax]
returns:
float: value of the intersection of union for the two boxes.
"""
# YOUR CODE HERE
x1_t, y1_t, x2_t, y2_t = gt_box
x1_p, y1_p, x2_p, y2_p = prediction_box
if x2_t < x1_p or x2_p < x1_t or y2_t < y1_p or y2_p < y1_t:
return 0.0
# Compute intersection
x1i = max(x1_t, x1_p)
x2i = min(x2_t, x2_p)
y1i = max(y1_t, y1_p)
y2i = min(y2_t, y2_p)
intersection = (x2i - x1i) * (y2i - y1i)
# Compute union
pred_area = (x2_p - x1_p) * (y2_p - y1_p)
gt_area = (x2_t - x1_t) * (y2_t - y1_t)
union = pred_area + gt_area - intersection
iou = intersection / union
assert 0 <= iou <= 1, "iou must be in [0, 1]"
return iou
[docs]def compute_iou_all(bbox, all_bboxes):
"""
:param bbox:
:type bbox:
:param all_bboxes:
:type all_bboxes:
:return:
:rtype:
"""
ious = [0]
for other_bbox in all_bboxes:
ious.append(calculate_iou(bbox, other_bbox))
return ious
[docs]def tight_bbox(digit, orig_bbox):
"""
:param digit:
:type digit:
:param orig_bbox:
:type orig_bbox:
:return:
:rtype:
"""
xmin, ymin, xmax, ymax = orig_bbox
# xmin
shift = 0
for i in range(digit.shape[1]):
if digit[:, i].sum() != 0:
break
shift += 1
xmin += shift
# xmax
shift = 0
for i in range(-1, -digit.shape[1], -1):
if digit[:, i].sum() != 0:
break
shift += 1
xmax -= shift
shift = 0
for i in range(digit.shape[0]):
if digit[i, :].sum() != 0:
break
shift += 1
ymin += shift
shift = 0
for i in range(-1, -digit.shape[0], -1):
if digit[i, :].sum() != 0:
break
shift += 1
ymax -= shift
return [xmin, ymin, xmax, ymax]
def generate_dataset(
dirpath: pathlib.Path,
num_images: int,
max_digit_size: int,
min_digit_size: int,
imsize: int,
max_digits_per_image: int,
mnist_images: numpy.ndarray,
mnist_labels: numpy.ndarray,
):
"""
:param dirpath:
:type dirpath:
:param num_images:
:type num_images:
:param max_digit_size:
:type max_digit_size:
:param min_digit_size:
:type min_digit_size:
:param imsize:
:type imsize:
:param max_digits_per_image:
:type max_digits_per_image:
:param mnist_images:
:type mnist_images:
:param mnist_labels:
:type mnist_labels:
"""
max_image_value = 255
assert mnist_images.dtype == numpy.uint8
image_dir = dirpath / "images"
label_dir = dirpath / "annotations"
image_dir.mkdir(exist_ok=True, parents=True)
label_dir.mkdir(exist_ok=True, parents=True)
for image_id in tqdm.trange(
num_images, description=f"Generating dataset, saving to: {dirpath}"
):
im = numpy.zeros((imsize, imsize), dtype=numpy.float32)
labels = []
bboxes = []
masks = []
num_images = numpy.random.randint(0, max_digits_per_image)
for ith in range(num_images + 1):
while True:
width = numpy.random.randint(min_digit_size, max_digit_size)
x0 = numpy.random.randint(0, imsize - width)
y0 = numpy.random.randint(0, imsize - width)
ious = compute_iou_all([x0, y0, x0 + width, y0 + width], bboxes)
if max(ious) < 0.25:
break
digit_idx = numpy.random.randint(0, len(mnist_images))
digit = mnist_images[digit_idx].astype(numpy.float32)
digit = cv2.resize(digit, (width, width))
label = mnist_labels[digit_idx]
labels.append(label)
assert (
im[y0 : y0 + width, x0 : x0 + width].shape == digit.shape
), f"imshape: {im[y0:y0 + width, x0:x0 + width].shape}, digit shape: {digit.shape}"
bbox = tight_bbox(digit, [x0, y0, x0 + width, y0 + width])
bboxes.append(bbox)
mask = digit > 0.5
if ith > 3:
break
else:
pyplot.imshow(mask)
pyplot.show()
# masks.append(mask)
im[y0 : y0 + width, x0 : x0 + width] += digit
im[im > max_image_value] = max_image_value
image_target_path = (image_dir / f"{image_id}").with_suffix(".png")
label_target_path = (label_dir / f"{image_id}").with_suffix(".csv")
im = im.astype(numpy.uint8)
cv2.imwrite(str(image_target_path), im)
with open(label_target_path, "w") as fp:
fp.write("label,xmin,ymin,xmax,ymax\n")
for l, bbox in zip(labels, bboxes):
bbox = [str(_) for _ in bbox]
to_write = f"{l}," + ",".join(bbox) + "\n"
fp.write(to_write)
if __name__ == "__main__":
def main(
base_path=PROJECT_APP_PATH.user_data / "Data" / "mnist_segmentation",
imsize=300,
max_digit_size=100,
min_digit_size=15,
num_train_images=10000,
num_test_images=1000,
max_digits_per_image=20,
):
"""
:param base_path:
:type base_path:
:param imsize:
:type imsize:
:param max_digit_size:
:type max_digit_size:
:param min_digit_size:
:type min_digit_size:
:param num_train_images:
:type num_train_images:
:param num_test_images:
:type num_test_images:
:param max_digits_per_image:
:type max_digits_per_image:
"""
X_train, Y_train, X_test, Y_test = load()
for dataset, (X, Y) in zip(
["train", "test"], [[X_train, Y_train], [X_test, Y_test]]
):
num_images = num_train_images if dataset == "train" else num_test_images
generate_dataset(
pathlib.Path(base_path, dataset),
num_images,
max_digit_size,
min_digit_size,
imsize,
max_digits_per_image,
X,
Y,
)
main()
# TODO: FINISH
