diff --git a/README.md b/README.md index 32897cd3e640101ba184f8c4ccd896981de3804a..1caded782dcc7a58a30f1a8607ba8e8dfc19ee12 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,32 @@ ---- -license: mit ---- +

+ English | 中文 +

+ +# Introduction +The original images of this dataset are from [DocLayNet-v1.2](https://huggingface.co/datasets/docling-project/DocLayNet-v1.2). You can run `src/1_save_img_from_parquet.py` to convert the parquet files into images. + +# Page Number Cropping +This dataset uses the `yolo11-medium-page-best-quant-ir9.onnx` weights from [Here](https://huggingface.co/wqzh/DocLayout-PageNumDet/tree/main/models) to detect page number bounding boxes from the images obtained above, and then crop out the page number regions (detection errors may exist, as no post-processing was applied by the author). + +The cropped images are saved in the `images-crops` directory, and the detection results are saved in the `images-labels` directory. The author added a constraint: the YOLO-format label file is only saved when exactly one page number box is detected, while crops of all detected boxes are retained. Therefore, the number of files in `images-crops` is greater than that in `images-labels`. + +You can run `src/2_det_onnx_wo_opencv_cuda.py` to detect page numbers, crop images, and save label files. + +```shell +find images-labels -type f -name "*.txt" | wc -l +find images-crops -type f -name "*.jpg" | wc -l +``` +- Number of `.txt` files in `images-labels`: 15902 +- Number of `.jpg` files in `images-crops`: 17203 + +# Page Number Concatenation +`src/3_concat_crops_by_pages.py` concatenates all cropped images from `images-crops` and saves them in the `images-crops-concat` directory, so you can preview the entire dataset. + +Example + + +Note: If you intend to use the label files from this dataset, they need to be cleaned separately. + +# Page Number Coordinate Distribution +`src/4_visualize_yolo_txt_distribution.py` visualizes the distribution of bounding box centers from all label files in `images-labels`. + diff --git a/README_ZH.md b/README_ZH.md new file mode 100644 index 0000000000000000000000000000000000000000..c503d9a8d73de1965a2447760c6d9c0e72684ec8 --- /dev/null +++ b/README_ZH.md @@ -0,0 +1,34 @@ +

+ 中文 | English +

+ +# 简介 +本数据集的原始图片来源于[DocLayNet-v1.2](https://huggingface.co/datasets/docling-project/DocLayNet-v1.2)。 可以运行`src/1_save_img_from_parquet.py` 把parquet文件转换成图片。 + +# 裁剪页码 +本数据集使用[Here](https://huggingface.co/wqzh/DocLayout-PageNumDet/tree/main/models)的`yolo11-medium-page-best-quant-ir9.onnx`权重，从上一步得到的图像中提取出页码的坐标，并裁剪出页码图片（检测可能存在错误，作者没有做任何后处理）。 + +裁剪图片保存在`images-crops`目录下,检测的结果保存在`images-labels`目录下。作者做了一个限制：只有检测的页码框数量为1的时候，才会保存txt格式的标注文件，而检测的所有页码框的截图会全部保留。因此，`images-crops`目录下的文件数量 会大于 `images-labels`目录下的文件数量。 + +可以运行`src/2_det_onnx_wo_opencv_cuda.py` 检测图片,裁剪图片，保存标注文件。 + +```shell +find images-labels -type f -name "*.txt" | wc -l +find images-crops -type f -name "*.jpg" | wc -l +``` +- images-labels 目录下 .txt 文件数: 15902 +- images-crops 目录下 .jpg 文件数: 17203 + +# 页码合并 +`src/3_concat_crops_by_pages.py`将`images-crops`下所有的图片进行合并，并保存在`images-crops-concat`目录下。因此你可以预览整个数据集。 + +示例 + + +注意：如果要使用这个数据集的label文件，需要单独清洗。 + +# 页码坐标分布 +`src/4_visualize_yolo_txt_distribution.py`将`images-labels`下所有的标注文件的坐标分布可视化。 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--git a/src/1_save_img_from_parquet.py b/src/1_save_img_from_parquet.py new file mode 100644 index 0000000000000000000000000000000000000000..b1926d49d7f0cf253135cb50058f965cd005e66a --- /dev/null +++ b/src/1_save_img_from_parquet.py @@ -0,0 +1,79 @@ +import pyarrow.parquet as pq +import json +import numpy as np +import os + +class NpEncoder(json.JSONEncoder): + def default(self, obj): + if isinstance(obj, np.ndarray): + return obj.tolist() + if isinstance(obj, (np.integer, np.floating)): + return obj.item() + if isinstance(obj, bytes): + return obj.decode("utf-8", errors="replace") + return super().default(obj) + +def process_all_rows(parquet_path, base_output_dir): + table = pq.read_table(parquet_path) + df = table.to_pandas() + + parquet_name = os.path.splitext(os.path.basename(parquet_path))[0] + sub_dir = "-".join(parquet_name.split("-")[:2]) + output_dir = os.path.join(base_output_dir, sub_dir) + os.makedirs(output_dir, exist_ok=True) + par_filenum = df.shape[0] + cat4_cnt = 0 + cat5_cnt = 0 + gt1_cnt = 0 + zero_cnt = 0 + + for idx, row in df.iterrows(): + metadata = row["metadata"] + original_filename = metadata["original_filename"].replace(".pdf", "") + + mask = (row["category_id"] == 4) | (row["category_id"] == 5) + + base_name = f"{original_filename}_{metadata['page_no']}" + + image_bytes = row["image"]["bytes"] + with open(os.path.join(output_dir, f"{base_name}.png"), "wb") as f: + f.write(image_bytes) + + data = { + "bboxes": row["bboxes"][mask], + "category_id": row["category_id"][mask], + "pdf_cells": row["pdf_cells"][mask], + "metadata": { + "original_filename": metadata["original_filename"], + "page_no": metadata["page_no"], + "coco_width": metadata["coco_width"], + "coco_height": metadata["coco_height"], + }, + } + # with open(os.path.join(output_dir, f"{base_name}.json"), "w", encoding="utf-8") as f: + # json.dump(data, f, ensure_ascii=False, indent=2, cls=NpEncoder) + + cat5_row_cnt = (row["category_id"][mask] == 5).sum() + cat4_row_cnt = (row["category_id"][mask] == 4).sum() + cat5_cnt += cat5_row_cnt + cat4_cnt += cat4_row_cnt + + if mask.sum() > 1: + gt1_cnt += 1 + elif mask.sum() == 0: + zero_cnt += 1 + + print(f"{parquet_name}: shape:{par_filenum}, cat4:{cat4_cnt}, cat5:{cat5_cnt}, gt1:{gt1_cnt}, zero:{zero_cnt}", base_output_dir) + + +if __name__ == "__main__": + src_root = "DocLayNet-v1.2/parquets" # contains parquet files downloaded from `https://huggingface.co/datasets/docling-project/DocLayNet-v1.2/tree/main/data` + out_root = "DocLayNet-v1.2/images" + + lists = os.listdir(src_root) + lists = sorted(lists) + + for pi, par_path in enumerate(lists, 1): + if pi == 3: break + + process_all_rows(os.path.join(src_root, par_path), out_root) diff --git a/src/2_det_onnx_wo_opencv_cuda.py b/src/2_det_onnx_wo_opencv_cuda.py new file mode 100644 index 0000000000000000000000000000000000000000..1c38fda1e211a38f319cd17b66216caa3583304e --- /dev/null +++ b/src/2_det_onnx_wo_opencv_cuda.py @@ -0,0 +1,155 @@ +import os +import numpy as np +import onnxruntime as ort +from PIL import Image, ImageOps + +model_size = "nano" +model_size = "small" +model_size = "medium" + +DET_MODEL_PATH = f"DocLayout-PageNumDet/models/yolo11-{model_size}-page-best-quant-ir9.onnx" +DET_INPUT_SIZE = (736, 736) + + +def letterbox(img, new_size): + h, w = img.size + new_h, new_w = new_size + + ratio = min(new_w / w, new_h / h) + new_w_i = int(w * ratio) + new_h_i = int(h * ratio) + + img = img.resize((new_w_i, new_h_i), Image.BILINEAR) + + top = (new_h - new_h_i) // 2 + bottom = new_h - new_h_i - top + left = (new_w - new_w_i) // 2 + right = new_w - new_w_i - left + + img = ImageOps.expand(img, (left, top, right, bottom), fill=114) + return img, ratio, (left, top) + + +def load_det_model(model_path=DET_MODEL_PATH): + sess_options = ort.SessionOptions() + sess_options.log_severity_level = 3 + providers = ["CUDAExecutionProvider", "CPUExecutionProvider"] + sess = ort.InferenceSession(model_path, sess_options, providers=providers) + input_name = sess.get_inputs()[0].name + return {"sess": sess, "input_name": input_name} + + +def nms(boxes, iou_thresh=0.5): + if len(boxes) == 0: + return [] + x1, y1, x2, y2 = boxes[:, 0], boxes[:, 1], boxes[:, 2], boxes[:, 3] + confs = boxes[:, 4] + areas = (x2 - x1 + 1e-6) * (y2 - y1 + 1e-6) + order = np.argsort(confs)[::-1] + keep = [] + while len(order) > 0: + i = order[0] + keep.append(i) + if len(order) == 1: + break + xx1 = np.maximum(x1[i], x1[order[1:]]) + yy1 = np.maximum(y1[i], y1[order[1:]]) + xx2 = np.minimum(x2[i], x2[order[1:]]) + yy2 = np.minimum(y2[i], y2[order[1:]]) + w = np.maximum(0.0, xx2 - xx1) + h = np.maximum(0.0, yy2 - yy1) + inter = w * h + iou = inter / (areas[i] + areas[order[1:]] - inter + 1e-6) + order = order[1:][iou <= iou_thresh] + return boxes[keep] + + +def det_infer(model, img_pil, conf_thresh=0.25, iou_thresh=0.5): + sess = model["sess"] + input_name = model["input_name"] + + r, g, b = img_pil.split() + img_bgr = Image.merge("RGB", (b, g, r)) + + img_lb, ratio, (pad_w, pad_h) = letterbox(img_bgr, DET_INPUT_SIZE) + inp = np.array(img_lb, dtype=np.float32) / 255.0 + inp = inp.transpose(2, 0, 1)[np.newaxis, ...] + + out = sess.run(None, {input_name: inp})[0][0] + + cx, cy, bw, bh, conf = out[0], out[1], out[2], out[3], out[4] + + mask = conf > conf_thresh + cx, cy, bw, bh, conf = cx[mask], cy[mask], bw[mask], bh[mask], conf[mask] + + x1 = (cx - bw / 2 - pad_w) / ratio + y1 = (cy - bh / 2 - pad_h) / ratio + x2 = (cx + bw / 2 - pad_w) / ratio + y2 = (cy + bh / 2 - pad_h) / ratio + + boxes = np.stack([x1, y1, x2, y2, conf], axis=1) + + if len(boxes) > 0: + boxes = nms(boxes, iou_thresh) + + return boxes.tolist() + + +def crop_and_save(image_path, output_dir, det_model=None, txt_dir=None): + if det_model is None: + det_model = load_det_model() + + os.makedirs(output_dir, exist_ok=True) + if txt_dir is not None: + os.makedirs(txt_dir, exist_ok=True) + + exts = (".png", ".jpg", ".jpeg", ".bmp") + img_files = sorted([f for f in os.listdir(image_path) if f.lower().endswith(exts)]) + + for fi, fname in enumerate(img_files): + if fi == 400: break + + img_path = os.path.join(image_path, fname) + img_pil = Image.open(img_path).convert("RGB") + img_w, img_h = img_pil.size + boxes = det_infer(det_model, img_pil) + + base_name = os.path.splitext(fname)[0] + for idx, (x1, y1, x2, y2, conf) in enumerate(boxes): + x1_i, y1_i, x2_i, y2_i = int(x1), int(y1), int(x2), int(y2) + crop = img_pil.crop((x1_i, y1_i, x2_i, y2_i)) + out_name = f"{base_name}_c{idx}.jpg" + crop.save(os.path.join(output_dir, out_name), "JPEG") + # print(f"[{fname}] crop {idx}: ({x1_i}, {y1_i}, {x2_i}, {y2_i}) conf={conf:.3f} -> {out_name}") + + if len(boxes) == 1 and txt_dir is not None: + x1, y1, x2, y2, _ = boxes[0] + cx = (x1 + x2) / 2 / img_w + cy = (y1 + y2) / 2 / img_h + bw = (x2 - x1) / img_w + bh = (y2 - y1) / img_h + txt_name = f"{base_name}.txt" + with open(os.path.join(txt_dir, txt_name), "w") as f: + f.write(f"0 {cx:.6f} {cy:.6f} {bw:.6f} {bh:.6f}\n") + # print(f"[{fname}] saved txt -> {txt_name}") + + +if __name__ == "__main__": + from time import time + + root_dir = "DocLayNet-v1.2/images" + subdir = os.listdir(root_dir) + + det_model = load_det_model() + for si, sub in enumerate(subdir, 1): + print(f"[{si}/{len(subdir)}] {sub}", flush=True) + start_time = time() + # if si == 3: break + + input_dir = os.path.join(root_dir, sub) + output_dir = f"{root_dir}-crop-{model_size}/{sub}" + txt_dir = f"{root_dir}-txt-{model_size}/{sub}" + + crop_and_save(input_dir, output_dir, det_model, txt_dir=txt_dir) + + print(f"耗时: {time() - start_time:.2f}s", flush=True) \ No newline at end of file diff --git a/src/3_concat_crops_by_pages.py b/src/3_concat_crops_by_pages.py new file mode 100644 index 0000000000000000000000000000000000000000..9853552a716fb7139ef0fc42cfd53cae9be0a75c --- /dev/null +++ b/src/3_concat_crops_by_pages.py @@ -0,0 +1,93 @@ +import os +from PIL import Image + + +CROPS_DIR = "DocLayout-PageDataset/images-crops" +OUT_DIR = "DocLayout-PageDataset/images-crops-concat" + +MAX_HEIGHT = 700 +TARGET_WIDTH = 600 + + +def concat_crops_across_pages(crops_dir=CROPS_DIR, out_dir=OUT_DIR, save_prefix="", max_height=MAX_HEIGHT, target_width=TARGET_WIDTH): + os.makedirs(out_dir, exist_ok=True) + + crops = [] + for fname in sorted(os.listdir(crops_dir)): + if not fname.endswith(".jpg"): + continue + path = os.path.join(crops_dir, fname) + img = Image.open(path) + crops.append((fname, img)) + + crops.sort(key=lambda x: (x[0].rsplit("_c", 1)[0], int(x[0].rsplit("_c", 1)[1].rsplit(".", 1)[0]))) + + rows = [] + for fname, img in crops: + w, h = img.size + placed = False + for row in rows: + row_w = sum(box["img"].size[0] for box in row) + if row_w + w <= target_width: + row.append({"fname": fname, "img": img}) + placed = True + break + if not placed: + rows.append([{"fname": fname, "img": img}]) + + row_heights = [max(box["img"].size[1] for box in row) for row in rows] + + def render_page(page_rows): + page_total_h = sum(max(box["img"].size[1] for box in r) for r in page_rows) + result = Image.new("RGB", (target_width, page_total_h), (255, 255, 255)) + y = 0 + for r in page_rows: + r_h = max(box["img"].size[1] for box in r) + x = 0 + for box in r: + result.paste(box["img"], (x, y)) + x += box["img"].size[0] + y += r_h + return result + + current_page = [] + current_height = 0 + page_idx = 1 + all_outputs = [] + + for i, row in enumerate(rows): + row_h = row_heights[i] + if current_height > 0 and current_height + row_h > max_height: + result = render_page(current_page) + out_file = os.path.join(out_dir, f"{save_prefix}_p{page_idx}.png") + result.save(out_file) + all_outputs.append(out_file) + page_idx += 1 + current_page = [row] + current_height = row_h + else: + current_page.append(row) + current_height += row_h + + if current_page: + result = render_page(current_page) + out_file = os.path.join(out_dir, f"{save_prefix}_p{page_idx}.png") + result.save(out_file) + all_outputs.append(out_file) + + return all_outputs + + +if __name__ == "__main__": + src_subs = os.listdir(CROPS_DIR) + for si, src_sub in enumerate(src_subs, 1): + # if si == 3: break + + src_sub_path = os.path.join(CROPS_DIR, src_sub) + + if os.path.isdir(src_sub_path): + outputs = concat_crops_across_pages(src_sub_path, OUT_DIR, src_sub) + + # for o in outputs: + # print(f"Saved: {o}") + print(f"[{si}/{len(src_subs)}], {src_sub}, Total: {len(outputs)}") diff --git a/src/4_visualize_yolo_txt_distribution.py b/src/4_visualize_yolo_txt_distribution.py new file mode 100644 index 0000000000000000000000000000000000000000..4eabb6041da297fdc88a71c24d6e8c851336b00c --- /dev/null +++ b/src/4_visualize_yolo_txt_distribution.py @@ -0,0 +1,60 @@ +import os +import matplotlib.pyplot as plt + +labels_dir = "images-labels" +img_size = 600 + +line_counts = [] +centers_x = [] +centers_y = [] + +for root, dirs, files in os.walk(labels_dir): + for fname in files: + if fname == "classes.txt" or not fname.endswith(".txt"): + continue + + filepath = os.path.join(root, fname) + with open(filepath, "r") as f: + lines = f.readlines() + + count = len([l for l in lines if l.strip()]) + line_counts.append(count) + + for line in lines: + line = line.strip() + if not line: + continue + parts = line.split() + if len(parts) >= 5: + cx = float(parts[1]) + cy = float(parts[2]) + centers_x.append(cx * img_size) + centers_y.append((1 - cy) * img_size) + +plt.figure(figsize=(8, 8)) +plt.scatter(centers_x, centers_y, s=1, alpha=0.5, c='blue') +plt.xlim(0, img_size) +plt.ylim(img_size, 0) +plt.gca().set_aspect('equal') +plt.title(f"YOLO Centers Distribution ({len(centers_x)} points from {len(line_counts)} files)") +plt.xlabel("X (pixels)") +plt.ylabel("Y (pixels)") +plt.grid(True, alpha=0.3) +plt.tight_layout() +plt.savefig("./yolo_centers_distribution.png", dpi=150) +print("Saved: ./yolo_centers_distribution.png") + +# plt.figure(figsize=(10, 4)) +# plt.hist(line_counts, bins=range(max(line_counts) + 2), edgecolor='black', alpha=0.7) +# plt.title("Annotations Per File Distribution") +# plt.xlabel("Number of Annotations") +# plt.ylabel("Number of Files") +# plt.grid(True, alpha=0.3) +# plt.tight_layout() +# plt.savefig("./annotations_per_file.png", dpi=150) +# print("Saved: ./annotations_per_file.png") + +print(f"\nTotal files: {len(line_counts)}") +print(f"Total annotations: {len(centers_x)}") +print(f"Max annotations/file: {max(line_counts)}") +print(f"Avg annotations/file: {sum(line_counts) / len(line_counts):.1f}") diff --git a/yolo_centers_distribution.png b/yolo_centers_distribution.png new file mode 100644 index 0000000000000000000000000000000000000000..d1e506bb4b47242906ed34515a7241fc73c40cde --- /dev/null +++ b/yolo_centers_distribution.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bf5a15d701d45f3e803431aa57abf07ac7b3366127651f6a5d6774e15eb10eb0 +size 97253