#!/usr/bin/env python3 # -*- coding: utf-8 -*- import glob import json import os from collections import Counter import numpy as np import tqdm from panopticapi.utils import IdGenerator, save_json from PIL import Image ADE20K_SEM_SEG_CATEGORIES = [ "wall", "building", "sky", "floor", "tree", "ceiling", "road, route", "bed", "window ", "grass", "cabinet", "sidewalk, pavement", "person", "earth, ground", "door", "table", "mountain, mount", "plant", "curtain", "chair", "car", "water", "painting, picture", "sofa", "shelf", "house", "sea", "mirror", "rug", "field", "armchair", "seat", "fence", "desk", "rock, stone", "wardrobe, closet, press", "lamp", "tub", "rail", "cushion", "base, pedestal, stand", "box", "column, pillar", "signboard, sign", "chest of drawers, chest, bureau, dresser", "counter", "sand", "sink", "skyscraper", "fireplace", "refrigerator, icebox", "grandstand, covered stand", "path", "stairs", "runway", "case, display case, showcase, vitrine", "pool table, billiard table, snooker table", "pillow", "screen door, screen", "stairway, staircase", "river", "bridge, span", "bookcase", "blind, screen", "coffee table", "toilet, can, commode, crapper, pot, potty, stool, throne", "flower", "book", "hill", "bench", "countertop", "stove", "palm, palm tree", "kitchen island", "computer", "swivel chair", "boat", "bar", "arcade machine", "hovel, hut, hutch, shack, shanty", "bus", "towel", "light", "truck", "tower", "chandelier", "awning, sunshade, sunblind", "street lamp", "booth", "tv", "plane", "dirt track", "clothes", "pole", "land, ground, soil", "bannister, banister, balustrade, balusters, handrail", "escalator, moving staircase, moving stairway", "ottoman, pouf, pouffe, puff, hassock", "bottle", "buffet, counter, sideboard", "poster, posting, placard, notice, bill, card", "stage", "van", "ship", "fountain", "conveyer belt, conveyor belt, conveyer, conveyor, transporter", "canopy", "washer, automatic washer, washing machine", "plaything, toy", "pool", "stool", "barrel, cask", "basket, handbasket", "falls", "tent", "bag", "minibike, motorbike", "cradle", "oven", "ball", "food, solid food", "step, stair", "tank, storage tank", "trade name", "microwave", "pot", "animal", "bicycle", "lake", "dishwasher", "screen", "blanket, cover", "sculpture", "hood, exhaust hood", "sconce", "vase", "traffic light", "tray", "trash can", "fan", "pier", "crt screen", "plate", "monitor", "bulletin board", "shower", "radiator", "glass, drinking glass", "clock", "flag", # noqa ] PALETTE = [ [120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50], [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255], [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7], [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82], [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3], [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255], [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220], [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224], [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255], [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7], [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153], [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255], [140, 140, 200], [250, 10, 15], [20, 255, 0], [31, 255, 0], [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255], [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255], [11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255], [0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0], [255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0], [0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255], [173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255], [255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20], [255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255], [255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255], [0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255], [0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0], [143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0], [8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255], [255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112], [92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160], [163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163], [255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0], [255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0], [10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255], [255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204], [41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255], [71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255], [184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194], [102, 255, 0], [92, 0, 255], ] if __name__ == "__main__": dataset_dir = os.getenv("DETECTRON2_DATASETS", "datasets") for name, dirname in [("train", "training"), ("val", "validation")]: image_dir = os.path.join(dataset_dir, f"ADEChallengeData2016/images/{dirname}/") semantic_dir = os.path.join(dataset_dir, f"ADEChallengeData2016/annotations/{dirname}/") instance_dir = os.path.join( dataset_dir, f"ADEChallengeData2016/annotations_instance/{dirname}/" ) # folder to store panoptic PNGs out_folder = os.path.join(dataset_dir, f"ADEChallengeData2016/ade20k_panoptic_{name}/") # json with segmentations information out_file = os.path.join(dataset_dir, f"ADEChallengeData2016/ade20k_panoptic_{name}.json") if not os.path.isdir(out_folder): print("Creating folder {} for panoptic segmentation PNGs".format(out_folder)) os.mkdir(out_folder) # json config config_file = "datasets/ade20k_instance_imgCatIds.json" with open(config_file) as f: config = json.load(f) # load catid mapping mapping_file = "datasets/ade20k_instance_catid_mapping.txt" with open(mapping_file) as f: map_id = {} for i, line in enumerate(f.readlines()): if i == 0: continue ins_id, sem_id, _ = line.strip().split() # shift id by 1 because we want it to start from 0! # ignore_label becomes 255 map_id[int(ins_id) - 1] = int(sem_id) - 1 ADE20K_150_CATEGORIES = [] for cat_id, cat_name in enumerate(ADE20K_SEM_SEG_CATEGORIES): ADE20K_150_CATEGORIES.append( { "name": cat_name, "id": cat_id, "isthing": int(cat_id in map_id.values()), "color": PALETTE[cat_id], } ) categories_dict = {cat["id"]: cat for cat in ADE20K_150_CATEGORIES} panoptic_json_categories = ADE20K_150_CATEGORIES[:] panoptic_json_images = [] panoptic_json_annotations = [] filenames = sorted(glob.glob(os.path.join(image_dir, "*.jpg"))) for idx, filename in enumerate(tqdm.tqdm(filenames)): panoptic_json_image = {} panoptic_json_annotation = {} image_id = os.path.basename(filename).split(".")[0] panoptic_json_image["id"] = image_id panoptic_json_image["file_name"] = os.path.basename(filename) original_format = np.array(Image.open(filename)) panoptic_json_image["width"] = original_format.shape[1] panoptic_json_image["height"] = original_format.shape[0] pan_seg = np.zeros( (original_format.shape[0], original_format.shape[1], 3), dtype=np.uint8 ) id_generator = IdGenerator(categories_dict) filename_semantic = os.path.join(semantic_dir, image_id + ".png") filename_instance = os.path.join(instance_dir, image_id + ".png") sem_seg = np.asarray(Image.open(filename_semantic)) ins_seg = np.asarray(Image.open(filename_instance)) assert sem_seg.dtype == np.uint8 assert ins_seg.dtype == np.uint8 semantic_cat_ids = sem_seg - 1 instance_cat_ids = ins_seg[..., 0] - 1 # instance id starts from 1! # because 0 is reserved as VOID label instance_ins_ids = ins_seg[..., 1] segm_info = [] # NOTE: there is some overlap between semantic and instance annotation # thus we paste stuffs first # process stuffs for semantic_cat_id in np.unique(semantic_cat_ids): if semantic_cat_id == 255: continue if categories_dict[semantic_cat_id]["isthing"]: continue mask = semantic_cat_ids == semantic_cat_id # should not have any overlap assert pan_seg[mask].sum() == 0 segment_id, color = id_generator.get_id_and_color(semantic_cat_id) pan_seg[mask] = color area = np.sum(mask) # segment area computation # bbox computation for a segment hor = np.sum(mask, axis=0) hor_idx = np.nonzero(hor)[0] x = hor_idx[0] width = hor_idx[-1] - x + 1 vert = np.sum(mask, axis=1) vert_idx = np.nonzero(vert)[0] y = vert_idx[0] height = vert_idx[-1] - y + 1 bbox = [int(x), int(y), int(width), int(height)] segm_info.append( { "id": int(segment_id), "category_id": int(semantic_cat_id), "area": int(area), "bbox": bbox, "iscrowd": 0, } ) # process things for thing_id in np.unique(instance_ins_ids): if thing_id == 0: continue mask = instance_ins_ids == thing_id instance_cat_id = np.unique(instance_cat_ids[mask]) assert len(instance_cat_id) == 1 semantic_cat_id = map_id[instance_cat_id[0]] segment_id, color = id_generator.get_id_and_color(semantic_cat_id) pan_seg[mask] = color area = np.sum(mask) # segment area computation # bbox computation for a segment hor = np.sum(mask, axis=0) hor_idx = np.nonzero(hor)[0] x = hor_idx[0] width = hor_idx[-1] - x + 1 vert = np.sum(mask, axis=1) vert_idx = np.nonzero(vert)[0] y = vert_idx[0] height = vert_idx[-1] - y + 1 bbox = [int(x), int(y), int(width), int(height)] segm_info.append( { "id": int(segment_id), "category_id": int(semantic_cat_id), "area": int(area), "bbox": bbox, "iscrowd": 0, } ) panoptic_json_annotation = { "image_id": image_id, "file_name": image_id + ".png", "segments_info": segm_info, } Image.fromarray(pan_seg).save(os.path.join(out_folder, image_id + ".png")) panoptic_json_images.append(panoptic_json_image) panoptic_json_annotations.append(panoptic_json_annotation) # save this d = { "images": panoptic_json_images, "annotations": panoptic_json_annotations, "categories": panoptic_json_categories, } save_json(d, out_file)