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cocoextra.py
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import os
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
import skimage.io as io
from pycocotools.coco import COCO
import json
# Extra utility COCO functions, not included in the default API
class COCOExtra:
def __init__(self, coco_ann, train_images_path):
self.coco_ann = coco_ann
self.train_images_path = train_images_path
# Overlay annotated category text directly on images
def ann_on_image(self, img_id, modified_ann_ids=None, save_fig=False, window_title=""):
coco_ann = self.coco_ann
img_info = coco_ann.loadImgs([img_id])[0]
img_file_name = img_info["file_name"]
image_path = os.path.join(self.train_images_path, img_file_name)
im = io.imread(image_path)
fig, ax = plt.subplots()
fig.canvas.set_window_title(window_title)
ax.axis("off")
ax.imshow(np.asarray(im))
ann_ids = coco_ann.getAnnIds(imgIds=[img_id], iscrowd=None)
anns = coco_ann.loadAnns(ann_ids)
coco_ann.showAnns(anns, draw_bbox=True)
cat_ids = coco_ann.getCatIds()
for i, ann in enumerate(anns):
entity_id = anns[i]["category_id"]
if entity_id not in cat_ids:
continue
entity = coco_ann.loadCats(entity_id)[0]["name"]
if modified_ann_ids and anns[i]["id"] in modified_ann_ids:
facecolor = 'red'
else:
facecolor = 'white'
ax.text(anns[i]['bbox'][0], anns[i]['bbox'][1], entity, style='italic',
bbox={'facecolor': facecolor, 'alpha': 0.7, 'pad': 5})
if save_fig:
if modified_ann_ids:
plt.savefig(f"{img_id}_annotated_injected.jpg", bbox_inches="tight", pad_inches=0)
else:
plt.savefig(f"{img_id}_annotated.jpg", bbox_inches="tight", pad_inches=0)
else:
plt.show()
# Overlay faulty annotations directly on the image (right),
# side by side with the original annotated version of the image (left)
def ann_on_image_sidebyside(self, img_id, coco_ann_orig, modified_ann_ids=None, save_fig=False, window_title=""):
coco_ann = self.coco_ann
img_info = coco_ann.loadImgs([img_id])[0]
img_file_name = img_info["file_name"]
image_path = os.path.join(self.train_images_path, img_file_name)
im_orig = io.imread(image_path)
im = io.imread(image_path)
fig, ax = plt.subplots(1, 2)
fig.canvas.set_window_title(window_title)
ax[0].axis("off")
ax[1].axis("off")
ax[0].set_title("Before")
ax[1].set_title("After")
ax[0].imshow(np.asarray(im_orig))
ax[1].imshow(np.asarray(im))
# Orig annotations
orig_ann_ids = coco_ann_orig.getAnnIds(imgIds=[img_id], iscrowd=None)
orig_anns = coco_ann_orig.loadAnns(orig_ann_ids)
coco_ann_orig.showAnns(orig_anns, draw_bbox=True, ax_select=ax[0])
cat_ids = coco_ann_orig.getCatIds()
for i, ann in enumerate(orig_anns):
entity_id = orig_anns[i]["category_id"]
if entity_id not in cat_ids:
continue
entity = coco_ann.loadCats(entity_id)[0]["name"]
ax[0].text(orig_anns[i]['bbox'][0], orig_anns[i]['bbox'][1], entity, style='italic',
bbox={'facecolor': 'white', 'alpha': 0.7, 'pad': 5})
# Faulty annotations
ann_ids = coco_ann.getAnnIds(imgIds=[img_id], iscrowd=None)
anns = coco_ann.loadAnns(ann_ids)
coco_ann.showAnns(anns, draw_bbox=True)
cat_ids = coco_ann.getCatIds()
for i, ann in enumerate(anns):
entity_id = anns[i]["category_id"]
if entity_id not in cat_ids:
continue
entity = coco_ann.loadCats(entity_id)[0]["name"]
if modified_ann_ids and anns[i]["id"] in modified_ann_ids:
facecolor = 'red'
else:
facecolor = 'white'
ax[1].text(anns[i]['bbox'][0], anns[i]['bbox'][1], entity, style='italic',
bbox={'facecolor': facecolor, 'alpha': 0.7, 'pad': 5})
if save_fig:
if modified_ann_ids:
plt.savefig(f"{img_id}_annotated_injected_sidebyside.jpg", bbox_inches="tight", pad_inches=0)
else:
plt.savefig(f"{img_id}_annotated.jpg", bbox_inches="tight", pad_inches=0)
else:
plt.show()
# Display image by image id
def show_img(self, img_id, save_fig=False):
coco_ann = self.coco_ann
img_info = coco_ann.loadImgs([img_id])[0]
img_file_name = img_info["file_name"]
image_path = os.path.join(self.train_images_path, img_file_name)
im = io.imread(image_path)
plt.axis("off")
plt.imshow(np.asarray(im))
if save_fig:
plt.savefig(f"{img_id}.jpg", bbox_inches="tight", pad_inches=0)
else:
plt.show()
# Save modified annotation file
def save_ann(self, filename):
with open(filename, "w") as outfile:
json.dump(self.coco_ann.dataset, outfile, indent=4)
# Get dict: {supercategory: [category_ids]}
def get_supercat_dict(self):
coco_ann = self.coco_ann
allcats = coco_ann.cats
result = {}
for catId, cat in allcats.items():
supercat = cat["supercategory"]
if not supercat:
raise ValueError("Dataset has no supercategories!")
if supercat not in result:
result[supercat] = []
result[supercat].append(catId)
return result