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0103f17 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 | 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)
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