File size: 4,485 Bytes
bcc0f94 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 |
import os.path
from typing import Any, Callable, List, Optional, Tuple
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
from torchvision.datasets import VisionDataset
class CocoDetection(VisionDataset):
def __init__(
self,
root: str,
annFile: str,
class_names: Optional[List] = None,
transform: Optional[Callable] = None,
target_transform: Optional[Callable] = None,
transforms: Optional[Callable] = None,
) -> None:
super().__init__(root, transforms, transform, target_transform)
from pycocotools.coco import COCO
self.coco = COCO(annFile)
if class_names is not None:
cat_ids = self._get_category_ids_from_name(category_names=class_names)
self.ids = list(
sorted((self._get_img_ids_for_category_ids(category_ids=cat_ids)))
)
else:
cat_ids = self.coco.getCatIds()
self.ids = list(sorted(self.coco.imgs.keys()))
self.cat2idx = {cat_id: idx + 1 for idx, cat_id in enumerate(cat_ids)}
self.cat2idx[0] = 0
def _load_image(self, id: int) -> Image.Image:
path = self.coco.loadImgs(id)[0]["file_name"]
return Image.open(os.path.join(self.root, path)).convert("RGB")
def _load_target(self, id: int) -> List[Any]:
return self.coco.loadAnns(self.coco.getAnnIds(id))
def __getitem__(self, index: int) -> Tuple[Any, Any]:
id = self.ids[index]
image = self._load_image(id)
mask = self._load_target(id)
mask = self._get_mask_in_channels(image, mask)
if self.transform is not None:
image = self.transform(image=np.array(image))["image"]
if self.target_transform is not None:
mask = self.target_transform(image=mask)["image"]
return image, (mask != 0).int()
def __len__(self) -> int:
return len(self.ids)
def _get_all_classes(self):
catIDs = self.coco.getCatIds()
return self.coco.loadCats(catIDs)
def _get_category_info_from_ids(self, ids: list):
all_cat = self._get_all_classes()
return [category for category in all_cat if category["id"] in ids]
def _get_category_ids_from_name(self, category_names: list):
return self.coco.getCatIds(catNms=category_names)
def _get_img_ids_for_category_ids(self, category_ids: list):
img_ids = []
for catIds in category_ids:
img_ids.extend(self.coco.getImgIds(catIds=catIds))
return img_ids
def _get_img_ids_for_category_names(self, category_names: list):
img_ids = []
category_ids = self._get_category_ids_from_name(category_names=class_names)
for catIds in category_ids:
img_ids.extend(self.coco.getImgIds(catIds=catIds))
return img_ids
def _get_all_category_ids_in_img_id(self, img_id: int) -> List:
target = self._load_target(img_id)
return list({annotation["category_id"] for annotation in target})
def _get_mask_aggregated(self, image: Image, annotations: List) -> np.array:
w, h = image.size
mask = np.zeros((h, w))
for annotation in annotations:
category_id = annotation["category_id"]
if category_id in self.cat2idx:
pixel_value = self.cat2idx[category_id]
mask = np.maximum(self.coco.annToMask(annotation) * pixel_value, mask)
return mask
def _get_mask_in_channels(self, image: Image, annotations: List) -> np.array:
w, h = image.size
mask = np.zeros((len(self.cat2idx), h, w))
for annotation in annotations:
category_id = annotation["category_id"]
if category_id in self.cat2idx:
pixel_value = self.cat2idx[category_id]
mask[pixel_value] = np.maximum(
self.coco.annToMask(annotation), mask[pixel_value]
)
# [h, w, channels]
mask = np.transpose(mask, (1, 2, 0))
return mask
def _plot_image_and_mask(self, index):
image, mask = self.__getitem__(index)
# Create a figure with two subplots side by side
fig, axs = plt.subplots(1, 2, figsize=(7, 3))
axs[0].imshow(image.permute(1, 2, 0))
axs[0].set_title("Image")
axs[1].imshow(mask.sum(0, keepdim=True).permute(1, 2, 0))
axs[1].set_title("Mask")
plt.show()
|