import numpy as np from typing import List, Union, Tuple, Dict import random from PIL import Image import cv2 import imageio, os import os.path as osp from tqdm import tqdm from panopticapi.utils import rgb2id import traceback from utils.io_utils import mask2rle, dict2json, fgbg_hist_matching from utils.logger import LOGGER from utils.constants import CATEGORIES, IMAGE_ID_ZFILL from .transforms import get_fg_transforms, get_bg_transforms, quantize_image, resize2height, rotate_image from .sampler import random_load_valid_bg, random_load_valid_fg, NameSampler, NormalSampler, PossionSampler, PersonBBoxSampler from .paste_methods import regular_paste, partition_paste def syn_animecoco_dataset( bg_list: List, fg_info_list: List[Dict], dataset_save_dir: str, policy: str='train', tgt_size=640, syn_num_multiplier=2.5, regular_paste_prob=0.4, person_paste_prob=0.4, max_syn_num=-1, image_id_start=0, obj_id_start=0, hist_match_prob=0.2, quantize_prob=0.25): LOGGER.info(f'syn data policy: {policy}') LOGGER.info(f'background: {len(bg_list)} foreground: {len(fg_info_list)}') numfg_sampler = PossionSampler(min_val=1, max_val=9, lam=2.5) numfg_regpaste_sampler = PossionSampler(min_val=2, max_val=9, lam=3.5) regpaste_size_sampler = NormalSampler(scalar=tgt_size, to_int=True, max_scale=0.75) color_correction_sampler = NameSampler({'hist_match': hist_match_prob, 'quantize': quantize_prob}, ) paste_method_sampler = NameSampler({'regular': regular_paste_prob, 'personbbox': person_paste_prob, 'partition': 1-regular_paste_prob-person_paste_prob}) fg_transform = get_fg_transforms(tgt_size, transform_variant=policy) fg_distort_transform = get_fg_transforms(tgt_size, transform_variant='distort_only') bg_transform = get_bg_transforms('train', tgt_size) image_id = image_id_start + 1 obj_id = obj_id_start + 1 det_annotations, image_meta = [], [] syn_num = int(syn_num_multiplier * len(fg_info_list)) if max_syn_num > 0: syn_num = max_syn_num ann_save_dir = osp.join(dataset_save_dir, 'annotations') image_save_dir = osp.join(dataset_save_dir, policy) if not osp.exists(image_save_dir): os.makedirs(image_save_dir) if not osp.exists(ann_save_dir): os.makedirs(ann_save_dir) is_train = policy == 'train' if is_train: jpg_save_quality = [75, 85, 95] else: jpg_save_quality = [95] if isinstance(fg_info_list[0], str): for ii, fgp in enumerate(fg_info_list): if isinstance(fgp, str): fg_info_list[ii] = {'file_path': fgp, 'tag_string': [], 'danbooru': False, 'category_id': 0} if person_paste_prob > 0: personbbox_sampler = PersonBBoxSampler( 'data/cocoperson_bbox_samples.json', fg_info_list, fg_transform=fg_distort_transform if is_train else None, is_train=is_train) total = tqdm(range(syn_num)) for fin in total: try: paste_method = paste_method_sampler.sample() fgs = [] if paste_method == 'regular': num_fg = numfg_regpaste_sampler.sample() size = regpaste_size_sampler.sample() while len(fgs) < num_fg: tgt_height = int(random.uniform(0.7, 1.2) * size) fg, fginfo = random_load_valid_fg(fg_info_list) fg = resize2height(fg, tgt_height) if is_train: fg = fg_distort_transform(image=fg)['image'] rotate_deg = random.randint(-40, 40) else: rotate_deg = random.randint(-30, 30) if random.random() < 0.3: fg = rotate_image(fg, rotate_deg, alpha_crop=True) fgs.append({'image': fg, 'fginfo': fginfo}) while len(fgs) < num_fg and random.random() < 0.15: fgs.append({'image': fg, 'fginfo': fginfo}) elif paste_method == 'personbbox': fgs = personbbox_sampler.sample_matchfg(tgt_size) else: num_fg = numfg_sampler.sample() fgs = [] for ii in range(num_fg): fg, fginfo = random_load_valid_fg(fg_info_list) fg = fg_transform(image=fg)['image'] h, w = fg.shape[:2] if num_fg > 6: downscale = min(tgt_size / 2.5 / w, tgt_size / 2.5 / h) if downscale < 1: fg = cv2.resize(fg, (int(w * downscale), int(h * downscale)), interpolation=cv2.INTER_AREA) fgs.append({'image': fg, 'fginfo': fginfo}) bg = random_load_valid_bg(bg_list) bg = bg_transform(image=bg)['image'] color_correct = color_correction_sampler.sample() if color_correct == 'hist_match': fgbg_hist_matching(fgs, bg) bg: Image = Image.fromarray(bg) if paste_method == 'regular': segments_info, segments = regular_paste(fgs, bg, regen_bboxes=True) elif paste_method == 'personbbox': segments_info, segments = regular_paste(fgs, bg, regen_bboxes=False) elif paste_method == 'partition': segments_info, segments = partition_paste(fgs, bg, ) else: print(f'invalid paste method: {paste_method}') raise NotImplementedError image = np.array(bg) if color_correct == 'quantize': mask = cv2.inRange(segments, np.array([0,0,0]), np.array([0,0,0])) # cv2.imshow("mask", mask) image = quantize_image(image, random.choice([12, 16, 32]), 'kmeans', mask=mask)[0] # postprocess & check if instance is valid for ii, segi in enumerate(segments_info): if segi['area'] == 0: continue x, y, w, h = segi['bbox'] x2, y2 = x+w, y+h c = segments[y: y2, x: x2] pan_png = rgb2id(c) cmask = (pan_png == segi['id']) area = cmask.sum() if paste_method != 'partition' and \ area / (fgs[ii]['image'][..., 3] > 30).sum() < 0.25: # cv2.imshow('im', fgs[ii]['image']) # cv2.imshow('mask', fgs[ii]['image'][..., 3]) # cv2.imshow('seg', segments) # cv2.waitKey(0) cmask_ids = np.where(cmask) segments[y: y2, x: x2][cmask_ids] = 0 image[y: y2, x: x2][cmask_ids] = (127, 127, 127) continue cmask = cmask.astype(np.uint8) * 255 dx, dy, w, h = cv2.boundingRect(cv2.findNonZero(cmask)) _bbox = [dx + x, dy + y, w, h] seg = cv2.copyMakeBorder(cmask, y, tgt_size-y2, x, tgt_size-x2, cv2.BORDER_CONSTANT) > 0 assert seg.shape[0] == tgt_size and seg.shape[1] == tgt_size segmentation = mask2rle(seg) det_annotations.append({ 'id': obj_id, 'category_id': fgs[ii]['fginfo']['category_id'], 'iscrowd': 0, 'segmentation': segmentation, 'image_id': image_id, 'area': area, 'tag_string': fgs[ii]['fginfo']['tag_string'], 'tag_string_character': fgs[ii]['fginfo']['tag_string_character'], 'bbox': [float(c) for c in _bbox] }) obj_id += 1 # cv2.imshow('c', cv2.cvtColor(c, cv2.COLOR_RGB2BGR)) # cv2.imshow('cmask', cmask) # cv2.waitKey(0) image_id_str = str(image_id).zfill(IMAGE_ID_ZFILL) image_file_name = image_id_str + '.jpg' image_meta.append({ "id": image_id,"height": tgt_size,"width": tgt_size, "file_name": image_file_name, "id": image_id }) # LOGGER.info(f'paste method: {paste_method} color correct: {color_correct}') # cv2.imshow('image', cv2.cvtColor(image, cv2.COLOR_RGB2BGR)) # cv2.imshow('segments', cv2.cvtColor(segments, cv2.COLOR_RGB2BGR)) # cv2.waitKey(0) imageio.imwrite(osp.join(image_save_dir, image_file_name), image, quality=random.choice(jpg_save_quality)) image_id += 1 except: LOGGER.error(traceback.format_exc()) continue det_meta = { "info": {}, "licenses": [], "images": image_meta, "annotations": det_annotations, "categories": CATEGORIES } detp = osp.join(ann_save_dir, f'det_{policy}.json') dict2json(det_meta, detp) LOGGER.info(f'annotations saved to {detp}') return image_id, obj_id