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import argparse |
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import collections |
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import datetime |
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import glob |
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import json |
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import os |
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import os.path as osp |
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import sys |
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import uuid |
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import imgviz |
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import numpy as np |
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import labelme |
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try: |
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import pycocotools.mask |
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except ImportError: |
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print("Please install pycocotools:\n\n pip install pycocotools\n") |
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sys.exit(1) |
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def main(): |
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parser = argparse.ArgumentParser( |
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formatter_class=argparse.ArgumentDefaultsHelpFormatter |
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) |
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parser.add_argument("input_dir", help="input annotated directory") |
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parser.add_argument("output_dir", help="output dataset directory") |
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parser.add_argument("--labels", help="labels file", required=True) |
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parser.add_argument( |
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"--noviz", help="no visualization", action="store_true" |
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) |
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args = parser.parse_args() |
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if osp.exists(args.output_dir): |
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print("Output directory already exists:", args.output_dir) |
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sys.exit(1) |
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os.makedirs(args.output_dir) |
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os.makedirs(osp.join(args.output_dir, "JPEGImages")) |
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if not args.noviz: |
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os.makedirs(osp.join(args.output_dir, "Visualization")) |
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print("Creating dataset:", args.output_dir) |
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now = datetime.datetime.now() |
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data = dict( |
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info=dict( |
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description=None, |
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url=None, |
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version=None, |
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year=now.year, |
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contributor=None, |
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date_created=now.strftime("%Y-%m-%d %H:%M:%S.%f"), |
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), |
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licenses=[dict(url=None, id=0, name=None,)], |
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images=[ |
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], |
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type="instances", |
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annotations=[ |
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], |
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categories=[ |
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], |
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) |
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class_name_to_id = {} |
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for i, line in enumerate(open(args.labels).readlines()): |
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class_id = i - 1 |
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class_name = line.strip() |
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if class_id == -1: |
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assert class_name == "__ignore__" |
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continue |
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class_name_to_id[class_name] = class_id |
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data["categories"].append( |
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dict(supercategory=None, id=class_id, name=class_name,) |
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) |
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out_ann_file = osp.join(args.output_dir, "annotations.json") |
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label_files = glob.glob(osp.join(args.input_dir, "*.json")) |
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for image_id, filename in enumerate(label_files): |
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print("Generating dataset from:", filename) |
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label_file = labelme.LabelFile(filename=filename) |
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base = osp.splitext(osp.basename(filename))[0] |
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out_img_file = osp.join(args.output_dir, "JPEGImages", base + ".jpg") |
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img = labelme.utils.img_data_to_arr(label_file.imageData) |
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imgviz.io.imsave(out_img_file, img) |
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data["images"].append( |
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dict( |
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license=0, |
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url=None, |
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file_name=osp.relpath(out_img_file, osp.dirname(out_ann_file)), |
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height=img.shape[0], |
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width=img.shape[1], |
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date_captured=None, |
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id=image_id, |
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) |
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) |
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masks = {} |
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segmentations = collections.defaultdict(list) |
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for shape in label_file.shapes: |
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points = shape["points"] |
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label = shape["label"] |
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group_id = shape.get("group_id") |
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shape_type = shape.get("shape_type", "polygon") |
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mask = labelme.utils.shape_to_mask( |
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img.shape[:2], points, shape_type |
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) |
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if group_id is None: |
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group_id = uuid.uuid1() |
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instance = (label, group_id) |
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if instance in masks: |
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masks[instance] = masks[instance] | mask |
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else: |
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masks[instance] = mask |
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if shape_type == "rectangle": |
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(x1, y1), (x2, y2) = points |
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x1, x2 = sorted([x1, x2]) |
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y1, y2 = sorted([y1, y2]) |
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points = [x1, y1, x2, y1, x2, y2, x1, y2] |
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else: |
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points = np.asarray(points).flatten().tolist() |
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segmentations[instance].append(points) |
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segmentations = dict(segmentations) |
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for instance, mask in masks.items(): |
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cls_name, group_id = instance |
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if cls_name not in class_name_to_id: |
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continue |
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cls_id = class_name_to_id[cls_name] |
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mask = np.asfortranarray(mask.astype(np.uint8)) |
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mask = pycocotools.mask.encode(mask) |
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area = float(pycocotools.mask.area(mask)) |
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bbox = pycocotools.mask.toBbox(mask).flatten().tolist() |
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data["annotations"].append( |
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dict( |
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id=len(data["annotations"]), |
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image_id=image_id, |
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category_id=cls_id, |
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segmentation=segmentations[instance], |
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area=area, |
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bbox=bbox, |
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iscrowd=0, |
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) |
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) |
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if not args.noviz: |
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labels, captions, masks = zip( |
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*[ |
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(class_name_to_id[cnm], cnm, msk) |
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for (cnm, gid), msk in masks.items() |
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if cnm in class_name_to_id |
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] |
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) |
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viz = imgviz.instances2rgb( |
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image=img, |
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labels=labels, |
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masks=masks, |
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captions=captions, |
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font_size=15, |
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line_width=2, |
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) |
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out_viz_file = osp.join( |
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args.output_dir, "Visualization", base + ".jpg" |
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) |
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imgviz.io.imsave(out_viz_file, viz) |
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with open(out_ann_file, "w") as f: |
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json.dump(data, f) |
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if __name__ == "__main__": |
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main() |
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