import cv2 import numpy as np import os from PIL import Image from .data_utils import * from .base import BaseDataset from pathlib import Path from util.box_ops import mask_to_bbox_xywh, draw_bboxes, compute_iou_matrix import shutil IS_VERIFY = False class VITONHDDataset(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 _intersect_2_obj(self, asset_dir, idx): image_dir = os.path.join(asset_dir, 'image') image_list = os.listdir(image_dir) image_path = os.path.join(image_dir, image_list[idx]) image_name = os.path.basename(image_path) image = cv2.imread(image_path) mask_dir = os.path.join(asset_dir, 'image-parse-v3') segmentation_path = os.path.join(mask_dir, image_name[:-4]+'.png') segmentation = Image.open(segmentation_path).convert('P') segmentation = np.array(segmentation) h, w = image.shape[0:2] image_area = h*w ids = np.unique(segmentation) ids = [ i for i in ids if i!=0 ] # remove background mask if len(ids) < 2: print(f"[Info] Skip image index {image_name[:-4]} due to insufficient bbox.") return # filter by area obj_ids = [] obj_areas = [] obj_bbox = [] for i in ids: mask_id = (segmentation == int(i)).astype(np.uint8) bbox = mask_to_bbox_xywh(mask_id) # xyhw area = np.sum(mask_id) 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[:-4]} 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[:-4], exist_ok=True) image_with_boxes = draw_bboxes(image, bbox_xyxy) cv2.imwrite(str(Path(self.construct_dataset_dir) / image_name[:-4] / "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) sorted_obj_ids = np.argsort(obj_areas)[::-1] assert len(sorted_obj_ids) > 0 index0 = sorted_obj_ids[0] index1 = sorted_obj_ids[1] os.makedirs(Path(self.construct_dataset_dir) / image_name[:-4], exist_ok=True) dst = Path(self.construct_dataset_dir) / image_name[:-4] / "image.jpg" dst.parent.mkdir(parents=True, exist_ok=True) shutil.copy(image_path, dst) mask = (segmentation == int(obj_ids[index0])).astype(np.uint8) cv2.imwrite(str(Path(self.construct_dataset_dir) / image_name[:-4] / "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[:-4] / "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) mask = (segmentation == int(obj_ids[index1])).astype(np.uint8) cv2.imwrite(str(Path(self.construct_dataset_dir) / image_name[:-4] / "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[:-4] / "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[:-4] / "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: 11626/2028 ''' import argparse parser = argparse.ArgumentParser(description="VITONHDDataset 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='VitonHD', 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: asset_dir = Path(args.dataset_dir) / args.dataset_name / "train" else: asset_dir = Path(args.dataset_dir) / args.dataset_name / "test" dataset = VITONHDDataset( construct_dataset_dir = args.construct_dataset_dir, obj_thr = args.obj_thr, area_ratio = args.area_ratio, ) max_num = 20000 if args.is_build_data: if not args.is_multiple: for index in range(max_num): dataset._intersect_2_obj(asset_dir, index) else: for index in range(len(os.listdir(args.construct_dataset_dir))): collage = dataset._get_sample(index)