问题描述
我有一个由焊缝和遮罩组成的数据集（白色代表焊缝，黑色代表背景），虽然我需要使用 Mask R-CNN，所以我必须将它们转换为 COCO 数据集注释。有人对如何做到这一点有任何建议吗？
我试过这个：https://github.com/chrise96/image-to-coco-json-converter
但我收到此错误：
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-3-0ddc235b1528> in <module>
94
95 # Create images and annotations sections
---> 96 coco_format["images"],coco_format["annotations"],annotation_cnt = images_annotations_info(mask_path)
97
98 with open("output/{}.json".format(keyword),"w") as outfile:
<ipython-input-3-0ddc235b1528> in images_annotations_info(maskpath)
57 sub_masks = create_sub_masks(mask_image_open,w,h)
58 for color,sub_mask in sub_masks.items():
---> 59 category_id = category_colors[color]
60
61 # "annotations" info
KeyError: '(1,1,1)'
import glob
from src.create_annotations import *
# Label ids of the dataset
category_ids = {
"outlier": 0,"window": 1,"wall": 2,"balcony": 3,"door": 4,"roof": 5,"sky": 6,"shop": 7,"chimney": 8,"weld": 9,}
# Define which colors match which categories in the images
category_colors = {
"(0,0)": 0,# Outlier
"(255,0)": 1,# Window
"(255,255,0)": 2,# Wall
"(128,255)": 3,# Balcony
"(255,128,0)": 4,# Door
"(0,255)": 5,# Roof
"(128,255)": 6,# Sky
"(0,0)": 7,# Shop
"(128,128)": 8,# Chimney
"(255,255)": 9 # Weld
}
# Define the ids that are a multiplolygon. In our case: wall,roof and sky
multipolygon_ids = [9,2,5,6]
# Get "images" and "annotations" info
def images_annotations_info(maskpath):
# This id will be automatically increased as we go
annotation_id = 0
image_id = 0
annotations = []
images = []
for mask_image in glob.glob(maskpath + "*.png"):
# The mask image is *.png but the original image is *.jpg.
# We make a reference to the original file in the COCO JSON file
original_file_name = os.path.basename(mask_image).split(".")[0] + ".jpg"
# Open the image and (to be sure) we convert it to RGB
mask_image_open = Image.open(mask_image).convert("RGB")
w,h = mask_image_open.size
# "images" info
image = create_image_annotation(original_file_name,h,image_id)
images.append(image)
sub_masks = create_sub_masks(mask_image_open,h)
for color,sub_mask in sub_masks.items():
category_id = category_colors[color]
# "annotations" info
polygons,segmentations = create_sub_mask_annotation(sub_mask)
# Check if we have classes that are a multipolygon
if category_id in multipolygon_ids:
# Combine the polygons to calculate the bounding Box and area
multi_poly = Multipolygon(polygons)
annotation = create_annotation_format(multi_poly,segmentations,image_id,category_id,annotation_id)
annotations.append(annotation)
annotation_id += 1
else:
for i in range(len(polygons)):
# Cleaner to recalculate this variable
segmentation = [np.array(polygons[i].exterior.coords).ravel().tolist()]
annotation = create_annotation_format(polygons[i],segmentation,annotation_id)
annotations.append(annotation)
annotation_id += 1
image_id += 1
return images,annotations,annotation_id
if __name__ == "__main__":
# Get the standard COCO JSON format
coco_format = get_coco_json_format()
for keyword in ["train","val"]:
mask_path = "dataset/{}_mask/".format(keyword)
# Create category section
coco_format["categories"] = create_category_annotation(category_ids)
# Create images and annotations sections
coco_format["images"],annotation_cnt = images_annotations_info(mask_path)
with open("output/{}.json".format(keyword),"w") as outfile:
json.dump(coco_format,outfile)
print("Created %d annotations for images in folder: %s" % (annotation_cnt,mask_path))
解决方法
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