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@@ -216,6 +216,39 @@ class MinerUWiredTableRecognizer:
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preprocessed_w = int(w_up_ * scale_factor + 0.5)
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preprocessed_h = int(h_up_ * scale_factor + 0.5)
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+ # 关键：手动调用 resize_img 获取实际的 w_scale 和 h_scale
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+ # 因为 keep_ratio=True 时，w_scale 和 h_scale 可能略有不同
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+ try:
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+ from mineru.model.table.rec.unet_table.utils import resize_img
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+ img_preprocessed_test, w_scale_actual, h_scale_actual = resize_img(
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+ img_up_, (inp_height, inp_width), keep_ratio=True
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+ )
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+ # 类型检查：确保是numpy数组
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+ if isinstance(img_preprocessed_test, np.ndarray):
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+ preprocessed_h_actual, preprocessed_w_actual = img_preprocessed_test.shape[:2]
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+ else:
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+ # 如果不是numpy数组，使用计算值
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+ preprocessed_h_actual = preprocessed_h
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+ preprocessed_w_actual = preprocessed_w
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+ scale_diff = abs(w_scale_actual - h_scale_actual)
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+ logger.info(
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+ f"🔍 UNet预处理缩放因子验证: "
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+ f"w_scale={w_scale_actual:.6f}, h_scale={h_scale_actual:.6f}, "
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+ f"差异={scale_diff:.6f}, "
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+ f"预处理后实际尺寸=[{preprocessed_h_actual}, {preprocessed_w_actual}]"
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+ )
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+ if scale_diff > 1e-6:
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+ logger.warning(
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+ f"⚠️ w_scale 和 h_scale 不相等！这可能导致坐标偏移。"
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+ f"w_scale={w_scale_actual:.6f}, h_scale={h_scale_actual:.6f}"
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+ )
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+ except Exception as e:
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+ logger.warning(f"无法获取实际缩放因子: {e}")
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+ w_scale_actual = scale_factor
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+ h_scale_actual = scale_factor
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+ preprocessed_h_actual = preprocessed_h
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+ preprocessed_w_actual = preprocessed_w
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+
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img_info = wired_rec.table_structure.preprocess(img_obj)
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pred_ = wired_rec.table_structure.infer(img_info)
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@@ -224,11 +257,15 @@ class MinerUWiredTableRecognizer:
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# 调试：记录尺寸信息
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pred_h, pred_w = pred_.shape[:2]
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- logger.debug(
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- f"UNet 推理: 上采样图像尺寸=[{h_up_}, {w_up_}], "
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- f"预处理后尺寸=[{preprocessed_h}, {preprocessed_w}], "
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- f"预测结果尺寸=[{pred_h}, {pred_w}], "
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- f"缩放因子={scale_factor:.6f}, upscale={upscale:.3f}"
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+ logger.info(
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+ f"🔍 UNet 推理详细日志:\n"
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+ f" - 上采样图像尺寸: [{h_up_}, {w_up_}]\n"
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+ f" - 计算预处理后尺寸: [{preprocessed_h}, {preprocessed_w}]\n"
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+ f" - 实际预处理后尺寸: [{preprocessed_h_actual}, {preprocessed_w_actual}]\n"
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+ f" - 预测结果尺寸: [{pred_h}, {pred_w}]\n"
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+ f" - 计算缩放因子: {scale_factor:.6f}\n"
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+ f" - 实际缩放因子: w_scale={w_scale_actual:.6f}, h_scale={h_scale_actual:.6f}\n"
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+ f" - upscale: {upscale:.3f}"
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)
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# 关键修复：正确地将预测结果 resize 回上采样尺寸
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@@ -237,28 +274,76 @@ class MinerUWiredTableRecognizer:
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# 所以我们应该使用 img_up_.shape 来 resize 预测结果
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# 但是，由于预处理时改变了图像尺寸（保持长宽比），我们需要确保 resize 是正确的
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- # 验证：检查预测结果尺寸是否与预处理后的尺寸一致
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- if pred_h != preprocessed_h or pred_w != preprocessed_w:
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+ # 验证：检查预测结果尺寸是否与预处理后的尺寸一致（仅用于警告）
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+ if pred_h != preprocessed_h_actual or pred_w != preprocessed_w_actual:
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logger.warning(
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- f"⚠️ 预测结果尺寸 [{pred_h}, {pred_w}] 与预处理后尺寸 [{preprocessed_h}, {preprocessed_w}] 不一致！"
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- f"这可能导致坐标偏移。使用预处理后尺寸进行 resize。"
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+ f"⚠️ 预测结果尺寸 [{pred_h}, {pred_w}] 与预处理后实际尺寸 "
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+ f"[{preprocessed_h_actual}, {preprocessed_w_actual}] 不一致！"
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+ f"这可能导致坐标偏移。"
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+ )
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+
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+ # 修复：统一将预测结果resize回上采样尺寸，避免舍入误差
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+ # 理论上：target_size = pred_size / unet_scale ≈ upsampled_size
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+ # 但为了确保完全一致，直接使用上采样尺寸作为目标，避免任何舍入误差
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+ # 这样可以保证：mask坐标系 = 上采样坐标系，坐标转换链路清晰
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+ hpred_up_ = cv2.resize(hpred_, (w_up_, h_up_), interpolation=cv2.INTER_NEAREST)
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+ vpred_up_ = cv2.resize(vpred_, (w_up_, h_up_), interpolation=cv2.INTER_NEAREST)
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+
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+ # 记录验证信息：检查理论target尺寸与实际上采样尺寸的差异
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+ # 这些差异应该非常小（<2像素），如果差异较大说明UNet预处理有问题
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+ if abs(w_scale_actual - h_scale_actual) > 1e-6:
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+ target_w_theoretical = int(pred_w / w_scale_actual + 0.5)
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+ target_h_theoretical = int(pred_h / h_scale_actual + 0.5)
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+ diff_w = abs(target_w_theoretical - w_up_)
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+ diff_h = abs(target_h_theoretical - h_up_)
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+ if diff_w > 2 or diff_h > 2:
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+ logger.warning(
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+ f"⚠️ 理论resize尺寸 [{target_h_theoretical}, {target_w_theoretical}] "
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+ f"与上采样尺寸 [{h_up_}, {w_up_}] 差异较大 (diff=[{diff_h}, {diff_w}])！"
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+ f"w_scale={w_scale_actual:.6f}, h_scale={h_scale_actual:.6f}"
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+ )
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+ else:
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+ logger.debug(
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+ f"✓ 理论resize尺寸 [{target_h_theoretical}, {target_w_theoretical}] "
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+ f"与上采样尺寸 [{h_up_}, {w_up_}] 一致 (diff=[{diff_h}, {diff_w}])"
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+ )
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+
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+ # 记录resize后的mask尺寸
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+ hpred_up_h, hpred_up_w = hpred_up_.shape[:2]
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+ vpred_up_h, vpred_up_w = vpred_up_.shape[:2]
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+ logger.info(
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+ f"🔍 Resize后mask尺寸: "
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+ f"hpred_up=[{hpred_up_h}, {hpred_up_w}], "
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+ f"vpred_up=[{vpred_up_h}, {vpred_up_w}], "
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+ f"img_up=[{h_up_}, {w_up_}]"
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+ )
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+
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+ # 详细的坐标转换链路日志
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+ logger.info(
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+ f"🔍 UNet推理完成 - 坐标转换链路验证:\n"
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+ f" [1] 原图尺寸: [{h}, {w}]\n"
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+ f" [2] 上采样尺寸: [{h_up_}, {w_up_}] (upscale={upscale:.3f})\n"
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+ f" [3] UNet输入尺寸: [{pred_h}, {pred_w}] (h_scale={h_scale_actual:.6f}, w_scale={w_scale_actual:.6f})\n"
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+ f" [4] Mask尺寸: [{hpred_up_h}, {hpred_up_w}] (已resize回上采样尺寸)\n"
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+ f" 验证: 理论upscale = {h_up_ / h:.3f} (h), {w_up_ / w:.3f} (w)"
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+ )
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+
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+ # 验证mask尺寸是否与上采样图像一致
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+ if hpred_up_h != h_up_ or hpred_up_w != w_up_:
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+ logger.error(
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+ f"❌ hpred_up 尺寸 [{hpred_up_h}, {hpred_up_w}] 与上采样图像尺寸 "
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+ f"[{h_up_}, {w_up_}] 不一致！"
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+ )
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+ if vpred_up_h != h_up_ or vpred_up_w != w_up_:
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+ logger.error(
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+ f"❌ vpred_up 尺寸 [{vpred_up_h}, {vpred_up_w}] 与上采样图像尺寸 "
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+ f"[{h_up_}, {w_up_}] 不一致！"
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)
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- # 如果尺寸不一致，先 resize 到预处理后尺寸，再 resize 到上采样尺寸
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- # 但实际上，预测结果应该就是预处理后的尺寸，所以这个警告不应该出现
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- hpred_temp = cv2.resize(hpred_, (preprocessed_w, preprocessed_h), interpolation=cv2.INTER_NEAREST)
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- vpred_temp = cv2.resize(vpred_, (preprocessed_w, preprocessed_h), interpolation=cv2.INTER_NEAREST)
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- hpred_up_ = cv2.resize(hpred_temp, (w_up_, h_up_), interpolation=cv2.INTER_NEAREST)
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- vpred_up_ = cv2.resize(vpred_temp, (w_up_, h_up_), interpolation=cv2.INTER_NEAREST)
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- else:
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- # 正常情况：预测结果就是预处理后的尺寸，直接 resize 到上采样尺寸
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- # 这相当于 UNet postprocess 中的逻辑：cv2.resize(pred, (ori_shape[1], ori_shape[0]))
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- hpred_up_ = cv2.resize(hpred_, (w_up_, h_up_), interpolation=cv2.INTER_NEAREST)
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- vpred_up_ = cv2.resize(vpred_, (w_up_, h_up_), interpolation=cv2.INTER_NEAREST)
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- return hpred_up_, vpred_up_, img_up_
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+ return hpred_up_, vpred_up_, img_up_, w_scale_actual, h_scale_actual
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# Step 1: 首次运行 UNet 获取初步 mask
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- hpred_up, vpred_up, img_up = run_unet(table_image)
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+ hpred_up, vpred_up, img_up, w_scale_actual, h_scale_actual = run_unet(table_image)
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# Step 1.1: 基于 Mask 的高精度倾斜检测与矫正
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if self.skew_detector.enable_deskew:
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@@ -275,7 +360,7 @@ class MinerUWiredTableRecognizer:
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h, w = table_image.shape[:2]
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# 重新运行 UNet (确保 mask 与矫正后的图完全对齐)
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- hpred_up, vpred_up, img_up = run_unet(table_image)
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+ hpred_up, vpred_up, img_up, w_scale_actual, h_scale_actual = run_unet(table_image)
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else:
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logger.debug(f"表格倾斜 {skew_angle:.3f}° 小于阈值，无需矫正")
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@@ -302,7 +387,7 @@ class MinerUWiredTableRecognizer:
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debug_dir = dbg.output_dir
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debug_prefix = f"{dbg.prefix}_grid" if dbg.prefix else "grid"
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- # 传入原图的实际尺寸，用于计算真实的缩放比例
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+ # 传入原图的实际尺寸和UNet预处理时的缩放因子，用于计算真实的缩放比例
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# 这样可以正确处理 UNet 预处理改变图像尺寸的情况
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bboxes = self.grid_recovery.compute_cells_from_lines(
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hpred_up,
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@@ -310,6 +395,8 @@ class MinerUWiredTableRecognizer:
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upscale,
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orig_h=h,
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orig_w=w,
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+ unet_w_scale=w_scale_actual,
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+ unet_h_scale=h_scale_actual,
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debug_dir=debug_dir,
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debug_prefix=debug_prefix
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)
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@@ -350,10 +437,13 @@ class MinerUWiredTableRecognizer:
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# 策略调整：默认对所有单元格进行 Cropped OCR，以解决 Header 误合并和文本分配错误问题。
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# Full-page OCR 结果仅作为 Fallback（在 text_filling.py 中逻辑是: 如果 Cropped OCR 结果为空或低分，才保留原值）
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if hasattr(self, 'ocr_engine') and self.ocr_engine:
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+ # 从 debug_options 中获取输出目录
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+ output_dir = dbg.output_dir if dbg and dbg.enabled else None
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texts = self.text_filler.second_pass_ocr_fill(
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table_image, bboxes_merged, texts, scores,
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need_reocr_indices=need_reocr_indices,
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- force_all=False # Force Per-Cell OCR
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+ force_all=False, # Force Per-Cell OCR
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+ output_dir=output_dir
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)
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for i, cell in enumerate(merged_cells):
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