| 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. |