import json import cv2 import numpy as np import os from PIL import Image from .data_utils import * from .base import BaseDataset from util.box_ops import mask_to_bbox_xywh, compute_iou_matrix, draw_bboxes from pathlib import Path import shutil IS_VERIFY = False class MapillaryVistasDataset(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, image_dir, instance_dir, labels, idx): json_list = os.listdir(instance_dir) image_name = json_list[idx][:-4] image_path = os.path.join(image_dir, image_name+'.jpg') image = cv2.imread(image_path) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) instance_path = os.path.join(instance_dir, image_name+'.png') instance_image = Image.open(instance_path) instance_array = np.array(instance_image, dtype=np.uint16) instance_label_array = np.array(instance_array / 256, dtype=np.uint8) instance_ids_array = np.array(instance_array % 256, dtype=np.uint8) img_h, img_w = image.shape[0:2] image_area = img_h*img_w # vehicle_keywords = ['car', 'truck', 'bus'] # excluded_keywords = ['bicycle'] # vehicle_ids = [] # for idx, label in enumerate(labels): # name = label['name'].lower() # if any(k in name for k in vehicle_keywords) and not any(k in name for k in excluded_keywords): # vehicle_ids.append(idx) ''' ids: 107, 'name': 'object--vehicle--bus', 'readable': 'Bus', 'color': [0, 60, 100] ids: 108, 'name': 'object--vehicle--car', 'readable': 'Car', 'color': [0, 0, 142] ids: 109, 'name': 'object--vehicle--caravan', 'readable': 'Caravan', 'color': [0, 0, 90] ids: 114, 'name': 'object--vehicle--truck', 'readable': 'Truck', 'color': [0, 0, 70] ''' target_class_ids = [107, 108, 109, 114] max_instance = np.max(instance_ids_array) obj_ids = [] obj_areas = [] obj_bbox = [] counter = 0 for target_id in target_class_ids: semantic_mask = (instance_label_array == target_id) for idx in range(max_instance): instance_mask = (instance_ids_array == idx) mask = np.logical_and(semantic_mask, instance_mask).astype(np.uint8) area = np.sum(mask) bbox = mask_to_bbox_xywh(mask) if area > image_area * self.area_ratio: obj_ids.append(counter) obj_areas.append(area) obj_bbox.append(bbox) counter += 1 if len(obj_bbox) < 2: print(f"[Info] Skip image index {image_name} 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] os.makedirs(Path(self.construct_dataset_dir) / image_name, exist_ok=True) if IS_VERIFY: image_with_boxes = draw_bboxes(image, bbox_xyxy) cv2.imwrite(str(Path(self.construct_dataset_dir) / image_name / "bboxes_image.png"), cv2.cvtColor(image_with_boxes, cv2.COLOR_RGB2BGR)) 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} due to no overlapping bboxes.") return dst = Path(self.construct_dataset_dir) / image_name / "image.jpg" dst.parent.mkdir(parents=True, exist_ok=True) shutil.copy(image_path, dst) counter = 0 found = False for target_id in target_class_ids: semantic_mask = (instance_label_array == target_id) for idx in range(max_instance): if counter == obj_ids[index0]: instance_mask = (instance_ids_array == idx) mask = np.logical_and(semantic_mask, instance_mask).astype(np.uint8) found = True break counter += 1 if found: break cv2.imwrite(str(Path(self.construct_dataset_dir) / image_name / "object_0_mask.png"), 255*mask) patch = self.get_patch(image, mask) patch = cv2.cvtColor(patch, cv2.COLOR_RGB2BGR) cv2.imwrite(str(Path(self.construct_dataset_dir) / image_name / "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) counter = 0 found = False for target_id in target_class_ids: semantic_mask = (instance_label_array == target_id) for idx in range(max_instance): if counter == obj_ids[index1]: instance_mask = (instance_ids_array == idx) mask = np.logical_and(semantic_mask, instance_mask).astype(np.uint8) found = True break counter += 1 if found: break cv2.imwrite(str(Path(self.construct_dataset_dir) / image_name / "object_1_mask.png"), 255*mask) patch = self.get_patch(image, mask) patch = cv2.cvtColor(patch, cv2.COLOR_RGB2BGR) cv2.imwrite(str(Path(self.construct_dataset_dir) / image_name / "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 / "highlighted_image.png"), cv2.cvtColor(image_with_boxes, cv2.COLOR_RGB2BGR)) 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: 603/190 ''' import argparse parser = argparse.ArgumentParser(description="MapillaryVistasDataset 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='MVD', 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() version = "v2.0" # "v1.2" config_path = Path(args.dataset_dir) / args.dataset_name / f'config_{version}.json' with open(config_path) as config_file: config = json.load(config_file) labels = config['labels'] if args.is_train: image_dir = Path(args.dataset_dir) / args.dataset_name / "training" / "images" instance_dir = Path(args.dataset_dir) / args.dataset_name / "training" / "v2.0" / "instances" max_num = 18000 else: image_dir = Path(args.dataset_dir) / args.dataset_name / "validation" / "images" instance_dir = Path(args.dataset_dir) / args.dataset_name / "validation" / "v2.0" / "instances" max_num = 2000 dataset = MapillaryVistasDataset( 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(max_num): dataset._intersect_2_obj(image_dir, instance_dir, labels, index) print('Done index ', index) else: for index in range(len(os.listdir(args.construct_dataset_dir))): collage = dataset._get_sample(index) ''' 25,000 high-resolution images 124 semantic object categories 100 instance-specifically annotated categories Global reach, covering 6 continents Variety of weather, season, time of day, camera, and viewpoint '''