import json import cv2 import numpy as np import os from .data_utils import * from .base import BaseDataset from pycocotools import mask as mask_utils from pathlib import Path from util.box_ops import compute_iou_matrix, draw_bboxes import shutil IS_VERIFY = False IS_BOX = False def save_bboxes(bbox_xyxy, save_path="bboxes.txt"): bbox_xyxy = np.atleast_2d(bbox_xyxy) with open(save_path, "a") as f: np.savetxt(f, bbox_xyxy, fmt="%.2f", delimiter=" ") class Objects365Dataset(BaseDataset): def __init__(self, construct_dataset_dir, obj_thr=20, area_ratio=0.02): self.obj_thr = obj_thr self.construct_dataset_dir = construct_dataset_dir os.makedirs(Path(self.construct_dataset_dir), exist_ok=True) self.area_ratio = area_ratio self.sample_list = os.listdir(self.construct_dataset_dir) def _get_all_file_paths_recursive(self, root_dir): all_files = [] for dirpath, _, filenames in os.walk(root_dir): for f in filenames: abs_path = os.path.abspath(os.path.join(dirpath, f)) all_files.append(abs_path) return all_files def _get_image_path(self, file_name): for img_dir in self.image_dir: path = img_dir / file_name if path.exists(): return str(path) raise FileNotFoundError(f"File {file_name} not found in any of the image_dir.") def _intersect_2_obj(self, image_dir, json_dir, idx): self.image_dir = image_dir self.json_list = self._get_all_file_paths_recursive(json_dir) json_path = self.json_list[idx] image_name = json_path.split('/')[-1] image_subset = json_path.split('/')[-2] image_path = os.path.join(os.path.join(image_dir, image_subset), image_name[:-5]+'.jpg') image = cv2.imread(image_path) with open(json_path) as f: data = json.load(f) image_id = data["image_id"] annotations = data["annotations"] img_h, img_w = image.shape[0:2] image_area = img_h*img_w anno = annotations # filter by area obj_ids = [] obj_areas = [] obj_bbox = [] for i in range(len(anno)): obj = anno[i] area = obj['area'] bbox = obj['bbox'] # xyhw if area > image_area * self.area_ratio: obj_ids.append(i) obj_areas.append(area) obj_bbox.append(bbox) if len(obj_bbox) < 2: print(f"[Info] Skip image index {image_name[:-5]} due to insufficient bbox.") return # filter by IOU bbox_xyxy = [] for box in obj_bbox: x, y, w, h = box bbox_xyxy.append([x, y, x + w, y + h]) bbox_xyxy = np.array(bbox_xyxy) # shape: [N, 4] if IS_VERIFY: os.makedirs(Path(self.construct_dataset_dir) / image_name[:-5], exist_ok=True) image_with_boxes = draw_bboxes(image, bbox_xyxy) cv2.imwrite(str(Path(self.construct_dataset_dir) / image_name[:-5] / "bboxes_image.png"), image_with_boxes) iou_matrix = compute_iou_matrix(bbox_xyxy) np.fill_diagonal(iou_matrix, -1) # Exclude self-comparisons (i.e., each box with itself) max_index = np.unravel_index(np.argmax(iou_matrix), iou_matrix.shape) index0, index1 = max_index[0], max_index[1] max_iou = iou_matrix[index0, index1] if max_iou <= 0: print(f"[Info] Skip image index {image_name[:-5]} due to no overlapping bboxes.") return if IS_BOX: save_bboxes(bbox_xyxy[index0], '/home/hang18/links/projects/rrg-vislearn/hang18/bboxes0.txt') save_bboxes(bbox_xyxy[index1], '/home/hang18/links/projects/rrg-vislearn/hang18/bboxes1.txt') os.makedirs(Path(self.construct_dataset_dir) / image_name[:-5], exist_ok=True) # cv2.imwrite(str(Path(self.construct_dataset_dir) / image_name[:-4] / "image.jpg"), image) # source image dst = Path(self.construct_dataset_dir) / image_name[:-5] / "image.jpg" dst.parent.mkdir(parents=True, exist_ok=True) shutil.copy(image_path, dst) segmentation = anno[obj_ids[index0]]["segmentation"] rles = mask_utils.frPyObjects(segmentation, img_h, img_w) rle = mask_utils.merge(rles) mask = mask_utils.decode(rle) cv2.imwrite(str(Path(self.construct_dataset_dir) / image_name[:-5] / "object_0_mask.png"), 255*mask) patch = self.get_patch(cv2.cvtColor(image, cv2.COLOR_BGR2RGB), mask) patch = cv2.cvtColor(patch, cv2.COLOR_RGB2BGR) cv2.imwrite(str(Path(self.construct_dataset_dir) / image_name[:-5] / "object_0.png"), patch) if IS_VERIFY: mask_color = np.stack([mask * 255]*3, axis=-1).astype(np.uint8) highlight = np.zeros_like(image) highlight[:, :, 2] = 255 # red channel alpha = 0.5 image_with_boxes = np.where(mask_color == 255, cv2.addWeighted(image_with_boxes, 1 - alpha, highlight, alpha, 0), image_with_boxes) segmentation = anno[obj_ids[index1]]["segmentation"] rles = mask_utils.frPyObjects(segmentation, img_h, img_w) rle = mask_utils.merge(rles) mask = mask_utils.decode(rle) cv2.imwrite(str(Path(self.construct_dataset_dir) / image_name[:-5] / "object_1_mask.png"), 255*mask) patch = self.get_patch(cv2.cvtColor(image, cv2.COLOR_BGR2RGB), mask) patch = cv2.cvtColor(patch, cv2.COLOR_RGB2BGR) cv2.imwrite(str(Path(self.construct_dataset_dir) / image_name[:-5] / "object_1.png"), patch) if IS_VERIFY: mask_color = np.stack([mask * 255]*3, axis=-1).astype(np.uint8) highlight = np.zeros_like(image) highlight[:, :, 0] = 255 # blue channel alpha = 0.5 image_with_boxes = np.where(mask_color == 255, cv2.addWeighted(image_with_boxes, 1 - alpha, highlight, alpha, 0), image_with_boxes) cv2.imwrite(str(Path(self.construct_dataset_dir) / image_name[:-5] / "highlighted_image.png"), image_with_boxes) def _get_sample(self, idx): sample_path = os.path.join(self.construct_dataset_dir, self.sample_list[idx]) image = cv2.cvtColor(cv2.imread(os.path.join(sample_path, "image.jpg")), cv2.COLOR_BGR2RGB) object_0 = cv2.cvtColor(cv2.imread(os.path.join(sample_path, "object_0.png")), cv2.COLOR_BGR2RGB) object_1 = cv2.cvtColor(cv2.imread(os.path.join(sample_path, "object_1.png")), cv2.COLOR_BGR2RGB) mask_0 = cv2.imread(os.path.join(sample_path, "object_0_mask.png"), cv2.IMREAD_GRAYSCALE) mask_1 = cv2.imread(os.path.join(sample_path, "object_1_mask.png"), cv2.IMREAD_GRAYSCALE) collage = self._construct_collage(image, object_0, object_1, mask_0, mask_1) return collage def __len__(self): return len(os.listdir(self.construct_dataset_dir)) if __name__ == "__main__": ''' two-object case: train/test: TODO/51791 ''' import argparse parser = argparse.ArgumentParser(description="Objects365Dataset Analysis") parser.add_argument("--dataset_dir", type=str, required=True, help="Path to the dataset directory.") parser.add_argument("--construct_dataset_dir", type=str, default='bin', help="Path to the debug bin directory.") parser.add_argument("--dataset_name", type=str, default='object365', help="Dataset name.") parser.add_argument('--is_train', action='store_true', help="Train/Test") parser.add_argument('--is_build_data', action='store_true', help="Build data") parser.add_argument('--is_multiple', action='store_true', help="Multiple/Two objects") parser.add_argument("--area_ratio", type=float, default=0.01171, help="Area ratio for filtering out small objects.") parser.add_argument("--obj_thr", type=int, default=20, help="Object threshold for filtering.") parser.add_argument("--index", type=int, default=0, help="Index of the sample to test.") args = parser.parse_args() if args.is_train: image_dir = Path(args.dataset_dir) / args.dataset_name / "images" / "train" json_dir = Path(args.dataset_dir) / args.dataset_name / "labels" / "train" max_num = 1742289 else: image_dir = Path(args.dataset_dir) / args.dataset_name / "images" / "val" json_dir = Path(args.dataset_dir) / args.dataset_name / "labels" / "val" max_num = 80000 dataset = Objects365Dataset( # json_dir = json_dir, construct_dataset_dir = args.construct_dataset_dir, obj_thr = args.obj_thr, area_ratio = args.area_ratio, ) if args.is_build_data: if not args.is_multiple: for index in range(0, max_num): dataset._intersect_2_obj(image_dir, json_dir, index) else: for index in range(len(os.listdir(args.construct_dataset_dir))): collage = dataset._get_sample(index)