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import json
format annotations the same as for training, no need for data augmentation
def val_formatted_anns(image_id, objects):
annotations = []
for i in range(0, len(objects["id"])):
new_ann = {
"id": objects["id"][i],
"category_id": objects["category"][i],
"iscrowd": 0,
"image_id": image_id,
"area": objects["area"][i],
"bbox": objects["bbox"][i],
}
annotations.append(new_ann)
return annotations
Save images and annotations into the files torchvision.datasets.CocoDetection expects
def save_cppe5_annotation_file_images(cppe5):
output_json = {}
path_output_cppe5 = f"{os.getcwd()}/cppe5/"
if not os.path.exists(path_output_cppe5):
os.makedirs(path_output_cppe5)
path_anno = os.path.join(path_output_cppe5, "cppe5_ann.json")
categories_json = [{"supercategory": "none", "id": id, "name": id2label[id]} for id in id2label]
output_json["images"] = []
output_json["annotations"] = []
for example in cppe5:
ann = val_formatted_anns(example["image_id"], example["objects"])
output_json["images"].append(
{
"id": example["image_id"],
"width": example["image"].width,
"height": example["image"].height,
"file_name": f"{example['image_id']}.png",
}
)
output_json["annotations"].extend(ann)
output_json["categories"] = categories_json
with open(path_anno, "w") as file:
json.dump(output_json, file, ensure_ascii=False, indent=4)
for im, img_id in zip(cppe5["image"], cppe5["image_id"]):
path_img = os.path.join(path_output_cppe5, f"{img_id}.png")
im.save(path_img)
return path_output_cppe5, path_anno
Next, prepare an instance of a CocoDetection class that can be used with cocoevaluator. |