import os import json import cv2 import numpy as np from pathlib import Path import random OBJECT_SIZE_TEMPLATES = { "28": [ "There is a <>. Adjust the dimensions of <> to make it 1.2 times its original size.", "There is a <>. Enlarge the <> such that it becomes 1.2 times its starting size.", "There is a <>. Ensure the <> grows to precisely 1.2 times its original size.", "There is a <>. Increase the size of <> to 1.2 times its original size.", "There is a <>. Resize the <> to ensure it is 1.2 times its original dimensions.", "There is a <>. Scale the <> up so that it reaches 1.2 times its initial dimensions.", "There is a <>. Transform the <> by setting its size to 1.2 times its original value.", ], "29": [ "There is a <>. Adjust the height of <> to be 20cm taller.", "There is a <>. Enlarge the <> in height by 20cm.", "There is a <>. Increase the height of <> by 20cm.", "There is a <>. Raise <>'s height by 20cm", ], "30": [ "There is a <>. Adjust the length of <> to be 50cm longer.", "There is a <>. Enlarge the <> in length by 50cm.", "There is a <>. Extend <>'s length by 50cm", "There is a <>. Increase the length of <> by 50cm.", ], "31": [ "There is a <>. Adjust the width of <> to be 40cm wider.", "There is a <>. Enlarge the <> in width by 40cm.", "There is a <>. Increase the width of <> by 40cm.", "There is a <>. Widen <>'s width by 40cm", ], } OPERATIONS = { "28": (1.3, 1.3), "29": (1.0, 1.3), "30": (1.3, 1.0), "31": (1.3, 1.0), } def is_mask_single_component(mask_path): mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE) if mask is None: return False _, binary = cv2.threshold(mask, 127, 255, cv2.THRESH_BINARY) num_labels, _ = cv2.connectedComponents(binary) return (num_labels - 1) == 1 def resize_and_paste_back_with_repair_mask( image_path, mask_path, scale_width=1.0, scale_height=1.0, output_image_path="output.png" ): image = cv2.imread(image_path) mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE) if image is None or mask is None: raise FileNotFoundError("Image or mask not found!") h, w = image.shape[:2] assert mask.shape == (h, w), "Mask and image size mismatch!" coords = cv2.findNonZero(mask) if coords is None: raise ValueError("No object in mask!") x, y, obj_w, obj_h = cv2.boundingRect(coords) center_x = x + obj_w // 2 center_y = y + obj_h // 2 rgba = np.dstack([image, mask]) obj_rgba = rgba[y:y+obj_h, x:x+obj_w] new_w = max(1, int(obj_w * scale_width)) new_h = max(1, int(obj_h * scale_height)) resized_obj = cv2.resize(obj_rgba, (new_w, new_h), interpolation=cv2.INTER_AREA) new_x = center_x - new_w // 2 new_y = center_y - new_h // 2 dst_x_start = max(0, new_x) dst_y_start = max(0, new_y) dst_x_end = min(w, new_x + new_w) dst_y_end = min(h, new_y + new_h) src_x_start = max(0, -new_x) src_y_start = max(0, -new_y) src_x_end = min(new_w, w - dst_x_start) src_y_end = min(new_h, h - dst_y_start) background = image.copy() background[mask > 0] = [255, 255, 255] output_img = background.copy() if src_x_end > src_x_start and src_y_end > src_y_start: obj_part = resized_obj[src_y_start:src_y_end, src_x_start:src_x_end] alpha = obj_part[:, :, 3].astype(np.float32) / 255.0 bg_part = output_img[dst_y_start:dst_y_end, dst_x_start:dst_x_end] fg_part = obj_part[:, :, :3] # 防止任何极端情况下 shape 不一致 hh = min(fg_part.shape[0], bg_part.shape[0], alpha.shape[0]) ww = min(fg_part.shape[1], bg_part.shape[1], alpha.shape[1]) fg_part = fg_part[:hh, :ww] bg_part = bg_part[:hh, :ww] alpha = alpha[:hh, :ww] blended = fg_part * alpha[..., None] + bg_part * (1 - alpha[..., None]) output_img[dst_y_start:dst_y_start+hh, dst_x_start:dst_x_start+ww] = blended.astype(np.uint8) os.makedirs(os.path.dirname(output_image_path), exist_ok=True) cv2.imwrite(output_image_path, output_img) return output_img def load_object_class_mapping(genspace_json_path): with open(genspace_json_path, "r", encoding="utf-8") as f: data = json.load(f) return {s["sample_id"]: s["object_class"] for s in data["samples"]} def main(): MASK_DIR = "/mnt/prev_nas/qhy_1/datasets/flux_gen_images_masks" IMAGE_DIR = "/mnt/prev_nas/qhy_1/datasets/flux_gen_images" OUTPUT_DIR = "/mnt/prev_nas/qhy_1/datasets/flux_gen_images_size_change" GENSPACE_JSON = "/mnt/prev_nas/qhy_1/datasets/unedit_image_prompts/genspace_prompts_vlm.json" OUTPUT_JSONL_PATH = os.path.join(OUTPUT_DIR, "size_edit_annotations.jsonl") os.makedirs(OUTPUT_DIR, exist_ok=True) obj_class_map = load_object_class_mapping(GENSPACE_JSON) mask_files = sorted([f for f in os.listdir(MASK_DIR) if f.endswith(".png")]) print(f"Found {len(mask_files)} mask files.") num_imgs_generated = 0 num_lines_written = 0 num_skipped_multi = 0 num_missing_img = 0 with open(OUTPUT_JSONL_PATH, "w", encoding="utf-8") as f_out: for mask_file in mask_files: mask_path = os.path.join(MASK_DIR, mask_file) if not is_mask_single_component(mask_path): num_skipped_multi += 1 print(f"💥 Skipping multi-component mask: {mask_file}") continue stem = Path(mask_file).stem # e.g. allocentric_002 object_class = obj_class_map.get(stem, None) if object_class is None: # 没有对应 class 的就跳过 print(f"💥 No class for {stem}") continue image_path = os.path.join(IMAGE_DIR, mask_file) # same name if not os.path.exists(image_path): num_missing_img += 1 print(f"💥 Missing image for {stem}") continue for task_id, (scale_w, scale_h) in OPERATIONS.items(): output_name = f"{stem}_{task_id}.png" output_path = os.path.join(OUTPUT_DIR, output_name) # 1) 图片已存在：不再生成 if not os.path.exists(output_path): try: resize_and_paste_back_with_repair_mask( image_path=image_path, mask_path=mask_path, scale_width=scale_w, scale_height=scale_h, output_image_path=output_path, ) num_imgs_generated += 1 except Exception as e: print(f"💥 Error generating {output_name}: {e}") continue else: print(f"✅ Already exists: {output_name}") # 2) 无论是否生成，jsonl 都写一条（只写需要内容） template = random.choice(OBJECT_SIZE_TEMPLATES[task_id]) instruction = template.replace("<>", f"<{object_class}>") item = { "task_type": "edit", "instruction": instruction, "input_images": [image_path], "output_image": output_path, "object_class": object_class, "task_id": int(task_id), } f_out.write(json.dumps(item, ensure_ascii=False) + "\n") num_lines_written += 1 print("\nDone.") print("images generated:", num_imgs_generated) print("jsonl lines written:", num_lines_written) print("skipped (multi-components):", num_skipped_multi) print("missing images:", num_missing_img) print("jsonl saved to:", OUTPUT_JSONL_PATH) if __name__ == "__main__": main()