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CocoDetection() can use MS COCO dataset as shown below. *This is for train2017 with stuff_train2017.json, val2017 with stuff_val2017.json, stuff_train2017_pixelmaps with stuff_train2017.json, stuff_val2017_pixelmaps with stuff_val2017.json, panoptic_train2017 with panoptic_train2017.json, panoptic_val2017 with panoptic_val2017.json and unlabeled2017 with image_info_unlabeled2017.json:
from torchvision.datasets import CocoDetection
stf_train2017_data = CocoDetection(
root="data/coco/imgs/train2017",
annFile="data/coco/anns/stuff_trainval2017/stuff_train2017.json"
)
stf_val2017_data = CocoDetection(
root="data/coco/imgs/val2017",
annFile="data/coco/anns/stuff_trainval2017/stuff_val2017.json"
)
len(stf_train2017_data), len(stf_val2017_data)
# (118287, 5000)
# pms_stf_train2017_data = CocoDetection(
# root="data/coco/anns/stuff_trainval2017/stuff_train2017_pixelmaps",
# annFile="data/coco/anns/stuff_trainval2017/stuff_train2017.json"
# ) # Error
# pms_stf_val2017_data = CocoDetection(
# root="data/coco/anns/stuff_trainval2017/stuff_val2017_pixelmaps",
# annFile="data/coco/anns/stuff_trainval2017/stuff_val2017.json"
# ) # Error
# pan_train2017_data = CocoDetection(
# root="data/coco/anns/panoptic_trainval2017/panoptic_train2017",
# annFile="data/coco/anns/panoptic_trainval2017/panoptic_train2017.json"
# ) # Error
# pan_val2017_data = CocoDetection(
# root="data/coco/anns/panoptic_trainval2017/panoptic_val2017",
# annFile="data/coco/anns/panoptic_trainval2017/panoptic_val2017.json"
# ) # Error
unlabeled2017_data = CocoDetection(
root="data/coco/imgs/unlabeled2017",
annFile="data/coco/anns/unlabeled2017/image_info_unlabeled2017.json"
)
len(unlabeled2017_data)
# 123403
stf_train2017_data[2]
# (<PIL.Image.Image image mode=RGB size=640x428>,
# [{'segmentation': {'counts': 'W2a0S2Q1T7mNmHS1R7mN...0100000000',
# 'size': [428, 640]}, 'area': 112666.0, 'iscrowd': 0, 'image_id': 30,
# 'bbox': [0.0, 0.0, 640.0, 321.0], 'category_id': 119, 'id': 10000010},
# {'segmentation': ..., 'category_id': 124, 'id': 10000011},
# ...
# {'segmentation': ..., 'category_id': 183, 'id': 10000014}])
stf_train2017_data[47]
# (<PIL.Image.Image image mode=RGB size=640x427>,
# [{'segmentation': {'counts': '\\j1h0[<a0G2N001O0...00001O0000',
# 'size': [427, 640]}, 'area': 65213.0, 'iscrowd': 0, 'image_id': 294,
# 'bbox': [140.0, 0.0, 500.0, 326.0], 'category_id': 98, 'id': 10000284},
# {'segmentation': ..., 'category_id': 123, 'id': 10000285},
# ...
# {'segmentation': ..., 'category_id': 183, 'id': 10000291}])
stf_train2017_data[64]
# (<PIL.Image.Image image mode=RGB size=480x640>,
# [{'segmentation': {'counts': '0[9e:1O000000O100000...O5mc0F^Zj7',
# 'size': [640, 480]}, 'area': 20503.0, 'iscrowd': 0, 'image_id': 370,
# 'bbox': [0.0, 0.0, 79.0, 316.0], 'category_id': 102, 'id': 10000383},
# {'segmentation': ..., 'category_id': 105, 'id': 10000384},
# ...
# {'segmentation': ..., 'category_id': 183, 'id': 10000389}])
stf_val2017_data[2]
# (<PIL.Image.Image image mode=RGB size=640x483>,
# [{'segmentation': {'counts': '\9g5]9O1O1O;EU1kNU1...VMKQ?NY`d3',
# 'size': [483, 640]}, 'area': 5104.0, 'iscrowd': 0, 'image_id': 632,
# 'bbox': [0.0, 300.0, 392.0, 183.0], 'category_id': 93, 'id': 20000017},
# {'segmentation': ..., 'category_id': 128, 'id': 20000018},
# ...
# {'segmentation': ..., 'category_id': 183, 'id': 20000020}])
stf_val2017_data[47]
# (<PIL.Image.Image image mode=RGB size=640x480>,
# [{'segmentation': {'counts': '[da7T1X>D3M2J5M4M4LoQg1',
# 'size': [480, 640]}, 'area': 122.0, 'iscrowd': 0, 'image_id': 5001,
# 'bbox': [515.0, 235.0, 7.0, 36.0], 'category_id': 104, 'id': 20000247},
# {'segmentation': ..., 'category_id': 105, 'id': 20000248},
# ...
# {'segmentation': ..., 'category_id': 183, 'id': 20000256}])
stf_val2017_data[64]
# (<PIL.Image.Image image mode=RGB size=640x483>,
# [{'segmentation': {'counts': 'U<^1W>N020mN]B2e>N1O...Mb@N^?2hd2',
# 'size': [500, 375]}, 'area': 2404.0, 'iscrowd': 0, 'image_id': 6763,
# 'bbox': [0.0, 235.0, 369.0, 237.0], 'category_id': 105, 'id': 20000356},
# {'segmentation': ..., 'category_id': 123, 'id': 20000357},
# ...
# {'segmentation': ..., 'category_id': 183, 'id': 20000362}])
unlabeled2017_data[2]
# (<PIL.Image.Image image mode=RGB size=640x427>, [])
unlabeled2017_data[47]
# (<PIL.Image.Image image mode=RGB size=428x640>, [])
unlabeled2017_data[64]
# (<PIL.Image.Image image mode=RGB size=640x480>, [])
import matplotlib.pyplot as plt
from matplotlib.patches import Polygon, Rectangle
import numpy as np
from pycocotools import mask
# `show_images1()` doesn't work very well for the images with
# segmentations so for it, use `show_images2()` which
# more uses the original coco functions.
def show_images1(data, ims, main_title=None):
file = data.root.split('/')[-1]
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(14, 8))
fig.suptitle(t=main_title, y=0.9, fontsize=14)
for i, axis in zip(ims, axes.ravel()):
if data[i][1] and "segmentation" in data[i][1][0]:
im, anns = data[i]
axis.imshow(X=im)
axis.set_title(label=anns[0]["image_id"])
ec = ['g', 'r', 'c', 'm', 'y', 'w']
ec_index = 0
for ann in anns:
seg = ann['segmentation']
compressed_rld = mask.decode(rleObjs=seg)
y_plts, x_plts = np.nonzero(a=np.squeeze(a=compressed_rld))
axis.plot(x_plts, y_plts, alpha=0.4)
x, y, w, h = ann['bbox']
rect = Rectangle(xy=(x, y), width=w, height=h,
linewidth=3, edgecolor=ec[ec_index],
facecolor='none', zorder=2)
ec_index += 1
if ec_index == len(ec)-1:
ec_index = 0
axis.add_patch(p=rect)
elif not data[i][1]:
im, _ = data[i]
axis.imshow(X=im)
fig.tight_layout()
plt.show()
ims = (2, 47, 64)
show_images1(data=stf_train2017_data, ims=ims,
main_title="stf_train2017_data")
show_images1(data=stf_val2017_data, ims=ims,
main_title="stf_val2017_data")
show_images1(data=unlabeled2017_data, ims=ims,
main_title="unlabeled2017_data")
def show_images2(data, index, main_title=None):
img_set = data[index]
img, img_anns = img_set
if img_anns and "segmentation" in img_anns[0]:
img_id = img_anns[0]['image_id']
coco = data.coco
def show_image(imgIds, areaRng=[],
iscrowd=None, draw_bbox=False):
plt.figure(figsize=(11, 8))
plt.imshow(X=img)
plt.suptitle(t=main_title, y=1, fontsize=14)
plt.title(label=img_id, fontsize=14)
anns_ids = coco.getAnnIds(imgIds=img_id,
areaRng=areaRng, iscrowd=iscrowd)
anns = coco.loadAnns(ids=anns_ids)
coco.showAnns(anns=anns, draw_bbox=draw_bbox)
plt.show()
show_image(imgIds=img_id, draw_bbox=True)
show_image(imgIds=img_id, draw_bbox=False)
show_image(imgIds=img_id, iscrowd=False, draw_bbox=True)
show_image(imgIds=img_id, areaRng=[0, 5000], draw_bbox=True)
elif not img_anns:
plt.figure(figsize=(11, 8))
plt.imshow(X=img)
plt.suptitle(t=main_title, y=1, fontsize=14)
plt.show()
show_images2(data=stf_val2017_data, index=47,
main_title="stf_train2017_data")
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