From cb57576576a35949a375625474ecb83259739183 Mon Sep 17 00:00:00 2001 From: Changlin Li Date: Tue, 14 Sep 2021 00:10:38 +0800 Subject: [PATCH 1/3] Add files via upload --- MOT_labeling/annotations/train.json | 2370 ++++++++++++++++++++++++ MOT_labeling/annotations/val.json | 646 +++++++ MOT_labeling/train/carema_0001_001.jpg | Bin 0 -> 78854 bytes MOT_labeling/train/carema_0001_002.jpg | Bin 0 -> 58299 bytes MOT_labeling/train/carema_0001_005.jpg | Bin 0 -> 73048 bytes MOT_labeling/train/carema_0001_006.jpg | Bin 0 -> 60035 bytes MOT_labeling/train/carema_0001_007.jpg | Bin 0 -> 61674 bytes MOT_labeling/train/carema_0001_008.jpg | Bin 0 -> 66937 bytes MOT_labeling/train/carema_0001_011.jpg | Bin 0 -> 84221 bytes MOT_labeling/train/carema_0001_012.jpg | Bin 0 -> 29943 bytes MOT_labeling/train/carema_0002_001.jpg | Bin 0 -> 78854 bytes MOT_labeling/train/carema_0002_002.jpg | Bin 0 -> 58299 bytes MOT_labeling/train/carema_0002_003.jpg | Bin 0 -> 47099 bytes MOT_labeling/train/carema_0002_005.jpg | Bin 0 -> 73048 bytes MOT_labeling/train/carema_0002_006.jpg | Bin 0 -> 60035 bytes MOT_labeling/train/carema_0002_007.jpg | Bin 0 -> 61674 bytes MOT_labeling/train/carema_0002_008.jpg | Bin 0 -> 66937 bytes MOT_labeling/train/carema_0002_009.jpg | Bin 0 -> 77795 bytes MOT_labeling/train/carema_0002_010.jpg | Bin 0 -> 64822 bytes MOT_labeling/train/carema_0002_011.jpg | Bin 0 -> 84221 bytes MOT_labeling/train/carema_0002_012.jpg | Bin 0 -> 29943 bytes MOT_labeling/val/carema_0001_003.jpg | Bin 0 -> 47099 bytes MOT_labeling/val/carema_0001_004.jpg | Bin 0 -> 59323 bytes MOT_labeling/val/carema_0001_009.jpg | Bin 0 -> 77795 bytes MOT_labeling/val/carema_0001_010.jpg | Bin 0 -> 64822 bytes MOT_labeling/val/carema_0002_004.jpg | Bin 0 -> 59323 bytes 26 files changed, 3016 insertions(+) create mode 100644 MOT_labeling/annotations/train.json create mode 100644 MOT_labeling/annotations/val.json create mode 100644 MOT_labeling/train/carema_0001_001.jpg 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zA%0cR{dpU%C0OLUL8rNldqw zjB^^#A0KO4{HA%sjViU&O3o~HeXS~9#5$dqHU?UXrrfPb)XX9M*$_)fI|VY&A_l09 zmmav3(rMBJ^+2n84}IZZ=R5ztUeU2i!7}CBtfd|duhnm%s?Z%sCb$L>>ifybBdo#y zq=U`%k2>@RS)+3y%4mZS7RC_FlgvuB%zh4>fWJtRwJ{bH>=MwzHc+Fy6}2NNk0`g| z=g{-}i8WBVb>oq>eVDhLx_Qg&UC>C2N`bn5OtSpDXFW#`->r{$)C=w8{H7_J_)$;V z>=c~=U9oK4^{{f&;&O4=>*`8onU?PI;RqCrD7U$q;GDAWvQnf`D<~1|EpkePygSpxxNqS`9ue<)QRP22l>>UVurv=wWPb}%U>y%$&CzA;yp9Z zeC8i7EU>X5AYw&ZR`PDT>1l@+pvM+IvEd{K<@NUldSjaE41H5ND;pNB+HS{2$=9w| zpi(6E#aEoC$UKE274_Qyf;N(dSc}7K4=&5Vt|*E}x((#;dSognzEeRYkzY8o+p(Al zw?qnMdzrO=sx4z_>EK^4ynSGCrcv;~WT+;!|BoJQ)tO=Hio~YZV=w%|_61peyC)ll z_&YyV_#as7&|b-NdX5;PlH{ zYKZ=^Jgxv4?9Hw}MpmQ#FjbG&#z$d5eCOtdmWo%ZEv|NFjw)NudzgZ)nn!+-Cg|Brj<_w@e&2z^wE literal 0 HcmV?d00001 From f35d5b0add85b29b1b46a6fe9140018cc2d6a866 Mon Sep 17 00:00:00 2001 From: Changlin Li Date: Tue, 14 Sep 2021 00:16:06 +0800 Subject: [PATCH 3/3] Add files via upload --- tools/eiseg2coco_MOT.py | 213 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 213 insertions(+) create mode 100644 tools/eiseg2coco_MOT.py diff --git a/tools/eiseg2coco_MOT.py b/tools/eiseg2coco_MOT.py new file mode 100644 index 0000000..8ec1105 --- /dev/null +++ b/tools/eiseg2coco_MOT.py @@ -0,0 +1,213 @@ +import argparse +import glob +import json +import os +import shutil +# follow openMMDet path processing patterns +from pathlib import Path +import numpy as np +import PIL.ImageDraw + +from sklearn.model_selection import train_test_split + +from mtmc.config import settings + +useQtBottomLeft = True # should be Qt TopLeft + +""" +Add MOT configuration, with following fields: + +By now the given data has no occlusion issues. And all labeling are visible. +Considering that we may need inference result of detection, set conf=1 to match inference +set ignore = 0 and visibility_ratio = 1 to use all annotations +carema_id and frame_id are extracted automatically from folder and filename + +In MTMC we may need to consider how multiple caremas associate together. +So we split dataset after merging all annotations and image. +This requires a refactor of the current annotation structures. +""" + +class MyEncoder(json.JSONEncoder): + def default(self, val): + if isinstance(val, np.integer): + return int(val) + elif isinstance(val, np.floating): + return float(val) + elif isinstance(val, np.ndarray): + return val.tolist() + else: + return super(MyEncoder, self).default(val) + + +def merge_coco_annotation(coco_annotation_paths): + data_coco = { + "images": [], + "annotations": [] + } + coco_image_id = 0 + imgs_info_indexed_by_img_name = {} + imgs_info_indexed_by_img_idx = {} + + # print('Generating dataset from:', coco_annotation_path) + for coco_annotation_path in coco_annotation_paths: + with open(coco_annotation_path) as f: + data = json.load(f) + # fetch categroies, info and licenses fields + # data_coco["categories"] = data["categories"] + # data_coco["info"] = data["info"] + # data_coco["licenses"] = data["licenses"] + # fetch images and annotations + camera_id = coco_annotation_path.split("/")[-3] + for idx, img_info in enumerate(data['images']): + # For image, make sure you update id and filename + img_name = img_info["file_name"] + img_info["old_id"] = img_info["id"] + img_name = camera_id + "_" + img_name + img_info["file_name"] = img_name + img_info["id"] = coco_image_id + if imgs_info_indexed_by_img_name.get(img_name, None) is None: + imgs_info_indexed_by_img_name[img_name] = {} + imgs_info_indexed_by_img_name[img_name]["img_info"] = img_info + imgs_info_indexed_by_img_idx[img_info["id"]] = img_info + for record in data["annotations"]: + if "img_id_update" not in record and record["image_id"] == img_info["old_id"]: + record["image_id"] = img_info["id"] + record["img_id_update"] = True + coco_image_id += 1 + + for annotation in data['annotations']: + # if you update image_id, then reflect in annotations + # update image_id and file_name and camera_id + # rest is fine. Do whatever you want + # need post visualization on this + img_idx = annotation["image_id"] + img_info = imgs_info_indexed_by_img_idx[img_idx] + img_name = img_info["file_name"] + print("img:",img_name) + # annotation polygon + epsilon = 1e-5 + if 'iscrowd' not in annotation: + annotation['iscrowd'] = 0 + if annotation['area'] < epsilon: + annotation['area'] = annotation['bbox'][2] * annotation['bbox'][3] + if useQtBottomLeft: + annotation['bbox'][1] = annotation['bbox'][1] - annotation['bbox'][3] + + annotation["carema_id"] = camera_id + annotation["frame_id"] = img_name.split("_")[-1].split(".")[0] + annotation["conf"] = 1 + annotation["ignore"] = 0 + annotation["visibility_ratio"] = 1 + imgs_info_indexed_by_img_name[img_name]["img_annotation"] = annotation + return imgs_info_indexed_by_img_name + +def save_coco_annotations(dataset, imgs_info_indexed_by_img_name): + data_coco = { + "images": [], + "annotations": [] + } + for img_path in dataset: + img_file = Path(img_path) + img_name = img_path.split("/")[-3] + "_" + img_file.name + img_info_with_annotation = imgs_info_indexed_by_img_name[img_name] + print(img_name) + data_coco["images"].append(img_info_with_annotation["img_info"]) + data_coco["annotations"].append(img_info_with_annotation["img_annotation"]) + return data_coco + + +def main(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter) + parser.add_argument('--dataset_type', default='eiseg', help='type of dataset is supported is EISeg') + parser.add_argument('--json_input_dir', default='%s/data/%s' % (settings.PROJECT_ROOT, settings.TEST_DATASET), + help='input annotation directory') + parser.add_argument('--image_input_dir', default='%s/data/%s' % (settings.PROJECT_ROOT, settings.TEST_DATASET), + help='image directory') + parser.add_argument('--output_dir', + default='%s/data/MOT_labeling' % (settings.PROJECT_ROOT), + help='output dataset directory') + + args = parser.parse_args() + try: + assert args.dataset_type in ['coco', 'eiseg', 'labelme'] + except AssertionError as e: + print('Only coco and EISeg dataset is supported for the moment!') + return -1 + + coco_annotation_dir = args.json_input_dir + camera_available = [] + for root, dirs, files in os.walk(coco_annotation_dir, topdown=True): + camera_available.append(dirs) + break + camera_available = camera_available[0] + + + assert (settings.TRAIN_PROPORTION > 0 and settings.TRAIN_PROPORTION < 1) + assert (settings.TRAIN_PROPORTION + settings.VALIDATION_PROPORTION == 1) + + setattr(settings, 'coco_dataset_path', args.output_dir) + train_path = os.path.join(settings.coco_dataset_path, "train") + val_path = os.path.join(settings.coco_dataset_path, "val") + annotation_path = os.path.join(settings.coco_dataset_path, "annotations") + if not os.path.exists(settings.coco_dataset_path): + os.makedirs(train_path) + os.makedirs(val_path) + os.makedirs(annotation_path) + + setattr(settings, 'DATA_DIR', args.image_input_dir) + comb_images = [] + for camera in camera_available: + raw_images = list(glob.iglob(os.path.join(settings.DATA_DIR, camera, "img", "*.jpg"))) + raw_images = sorted(raw_images, key=lambda x: int(os.path.split(x)[1].split('.')[0])) + comb_images += raw_images + + train_dataset, val_dataset = train_test_split(comb_images, train_size=settings.TRAIN_PROPORTION) + # Disk task : move images and annotation files to coco files hierarchy + # TODO: new_name: add carema id to current filename update image_id and filename accordingly + for img_path in train_dataset: + img_name = Path(img_path).name # similar to boot api + img_name_to = img_path.split("/")[-3] + "_" + img_name + shutil.copyfile( + img_path, + os.path.join(train_path, img_name_to) + ) + + for img_path in val_dataset: + img_name = Path(img_path).name # similar to boot api + img_name_to = img_path.split("/")[-3] + "_" + img_name + shutil.copyfile( + img_path, + os.path.join(val_path, img_name_to) + ) + + coco_annotation_files = [] + for camera in camera_available: + # carema -> carema_0001 + coco_annotation_file = os.path.join(coco_annotation_dir, camera, "gt", "coco.json") + coco_annotation_files.append(coco_annotation_file) + + dataset = merge_coco_annotation(coco_annotation_files) + train_coco_col = save_coco_annotations(train_dataset, dataset) + val_coco_col = save_coco_annotations(val_dataset, dataset) + + train_json_path = os.path.join(annotation_path, "train.json") + json.dump( + train_coco_col, + open(train_json_path, 'w'), + indent=4, + cls=MyEncoder + ) + + val_json_path = os.path.join(annotation_path, "val.json") + json.dump( + val_coco_col, + open(val_json_path, 'w'), + indent=4, + cls=MyEncoder + ) + return 0 + +if __name__ == '__main__': + import sys + sys.exit(main())

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