Merge pull request '增加扫描件分类' (#8) from zzc into main

Reviewed-on: #8

helper/content_recognition/main.py

@@ -87,8 +87,10 @@ def rec(page_detection_results: List[PageDetectionResult]) -> List[List[LayoutRe
         outputs = [_ for _ in outputs if _.clsid != E.TABLE_CAPTION.value]
         # 将表格转为数据库中的枚举 1-表格
         for o in outputs:
-            if o.clsid == E.TABLE.value:
+            if o.clsid == E.TABLE.value or o.clsid == E.SCANNED_DOCUMENT.value:
                 o.clsid = 1
+            else:
+                o.clsid = 0
         page_recognition_results.append(outputs)
         
     return page_recognition_results

helper/image_helper.py

@@ -2,12 +2,12 @@ from typing import List
 from typing_extensions import deprecated
 import numpy as np
 from pdf2image import convert_from_path
-import os
 import cv2
 from .page_detection.utils import PageDetectionResult
 from paddleocr import PaddleOCR
 from .constants import PageDetectionEnum as E
 from paddlex import create_model
+from tqdm import tqdm
 
 
 ocr = PaddleOCR(use_angle_cls=False, lang='ch', use_gpu=True, show_log=False)
@@ -82,7 +82,7 @@ def image_orient_cls(images):
         images = [images]
 
     angles = []
-    for img in images:
+    for img in tqdm(images, '文本方向分类'):
         h, w = img.shape[:2]
         det_res = ocr.ocr(img, det=True, rec=False, cls=False)[0]
         boxes = []

helper/page_detection/main.py

@@ -89,6 +89,8 @@ def layout_analysis(images) -> List[PageDetectionResult]:
         # box外扩，便于后续的ocr
         h, w = image.shape[:2]
         for layout in page_detecion_outputs.boxes:
+            if layout.clsid != E.TEXT.value:
+                continue
             layout.pos[0] -= expand_pixel
             layout.pos[1] -= expand_pixel
             layout.pos[2] += expand_pixel

helper/page_detection/pdf_detection.py

@@ -14,9 +14,6 @@
 
 import os
 import yaml
-import glob
-import json
-from pathlib import Path
 
 import cv2
 import numpy as np
@@ -34,7 +31,7 @@ from preprocess import preprocess, Resize, NormalizeImage, Permute, PadStride, L
 from .picodet_postprocess import PicoDetPostProcess
 from clrnet_postprocess import CLRNetPostProcess
 from visualize import visualize_box_mask, imshow_lanes
-from utils import argsparser, Timer, multiclass_nms, coco_clsid2catid
+from utils import Timer
 
 # Global dictionary
 SUPPORT_MODELS = {
@@ -45,7 +42,6 @@ SUPPORT_MODELS = {
 }
 
 
-
 class Detector(object):
     """
     Args:
@@ -770,13 +766,10 @@ def load_predictor(model_dir,
             precision_mode=precision_map[run_mode],
             use_static=False,
             use_calib_mode=trt_calib_mode)
-        if FLAGS.collect_trt_shape_info:
-            config.collect_shape_range_info(FLAGS.tuned_trt_shape_file)
-        elif os.path.exists(FLAGS.tuned_trt_shape_file):
-            print(f'Use dynamic shape file: '
-                  f'{FLAGS.tuned_trt_shape_file} for TRT...')
+    if os.path.exists('shape_range_info.pbtxt'):
+        print('Use dynamic shape file: shape_range_info.pbtxt for TRT...')
         config.enable_tuned_tensorrt_dynamic_shape(
-                FLAGS.tuned_trt_shape_file, True)
+            'shape_range_info.pbtxt', True)
 
         if use_dynamic_shape:
             min_input_shape = {
@@ -849,19 +842,9 @@ def visualize(image_list, result, labels, output_dir='output/', threshold=0.5):
         print("save result to: " + out_path)
 
 
-def print_arguments(args):
-    print('-----------  Running Arguments -----------')
-    for arg, value in sorted(vars(args).items()):
-        print('%s: %s' % (arg, value))
-    print('------------------------------------------')
-
-
 class Pipeline(object):
 
     def __init__(self, model_dir):
-        if FLAGS.use_fd_format:
-            deploy_file = os.path.join(model_dir, 'inference.yml')
-        else:
         deploy_file = os.path.join(model_dir, 'infer_cfg.yml')
         with open(deploy_file) as f:
             yml_conf = yaml.safe_load(f)
@@ -876,40 +859,27 @@ class Pipeline(object):
 
         self.detector = eval(detector_func)(
             model_dir,
-            device=FLAGS.device,
-            run_mode=FLAGS.run_mode,
-            batch_size=FLAGS.batch_size,
-            trt_min_shape=FLAGS.trt_min_shape,
-            trt_max_shape=FLAGS.trt_max_shape,
-            trt_opt_shape=FLAGS.trt_opt_shape,
-            trt_calib_mode=FLAGS.trt_calib_mode,
-            cpu_threads=FLAGS.cpu_threads,
-            enable_mkldnn=FLAGS.enable_mkldnn,
-            enable_mkldnn_bfloat16=FLAGS.enable_mkldnn_bfloat16,
-            threshold=FLAGS.threshold,
-            output_dir=FLAGS.output_dir,
-            use_fd_format=FLAGS.use_fd_format)
+            device='GPU',
+            run_mode='paddle',
+            batch_size=1,
+            trt_min_shape=1,
+            trt_max_shape=1280,
+            trt_opt_shape=640,
+            trt_calib_mode=False,
+            cpu_threads=1,
+            enable_mkldnn=False,
+            enable_mkldnn_bfloat16=False,
+            threshold=0.5,
+            output_dir='output',
+            use_fd_format=False)
 
     def __call__(self, image):
         if isinstance(image, np.ndarray):
             image = [image]
         results = self.detector.predict_image(
             image,
-            visual=FLAGS.save_images)
+            visual=False)
         return results
             
 
 paddle.enable_static()
-parser = argsparser()
-FLAGS = parser.parse_args()
-# print_arguments(FLAGS)
-FLAGS.device = 'GPU'
-FLAGS.save_images = False
-FLAGS.device = FLAGS.device.upper()
-assert FLAGS.device in ['CPU', 'GPU', 'XPU', 'NPU', 'MLU', 'GCU'
-                        ], "device should be CPU, GPU, XPU, MLU, NPU or GCU"
-assert not FLAGS.use_gpu, "use_gpu has been deprecated, please use --device"
-
-assert not (
-    FLAGS.enable_mkldnn == False and FLAGS.enable_mkldnn_bfloat16 == True
-), 'To enable mkldnn bfloat, please turn on both enable_mkldnn and enable_mkldnn_bfloat16'

helper/page_detection/utils.py

@@ -33,199 +33,6 @@ class PageDetectionResult(object):
         self.image = image
 
 
-def argsparser():
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument(
-        "--image_dir",
-        type=str,
-        default=None,
-        help="Dir of image file, `image_file` has a higher priority.")
-    parser.add_argument(
-        "--batch_size", type=int, default=1, help="batch_size for inference.")
-    parser.add_argument(
-        "--video_file",
-        type=str,
-        default=None,
-        help="Path of video file, `video_file` or `camera_id` has a highest priority."
-    )
-    parser.add_argument(
-        "--camera_id",
-        type=int,
-        default=-1,
-        help="device id of camera to predict.")
-    parser.add_argument(
-        "--threshold", type=float, default=0.5, help="Threshold of score.")
-    parser.add_argument(
-        "--output_dir",
-        type=str,
-        default="output",
-        help="Directory of output visualization files.")
-    parser.add_argument(
-        "--run_mode",
-        type=str,
-        default='paddle',
-        help="mode of running(paddle/trt_fp32/trt_fp16/trt_int8)")
-    parser.add_argument(
-        "--device",
-        type=str,
-        default='cpu',
-        help="Choose the device you want to run, it can be: CPU/GPU/XPU/NPU, default is CPU."
-    )
-    parser.add_argument(
-        "--use_gpu",
-        type=ast.literal_eval,
-        default=False,
-        help="Deprecated, please use `--device`.")
-    parser.add_argument(
-        "--run_benchmark",
-        type=ast.literal_eval,
-        default=False,
-        help="Whether to predict a image_file repeatedly for benchmark")
-    parser.add_argument(
-        "--enable_mkldnn",
-        type=ast.literal_eval,
-        default=False,
-        help="Whether use mkldnn with CPU.")
-    parser.add_argument(
-        "--enable_mkldnn_bfloat16",
-        type=ast.literal_eval,
-        default=False,
-        help="Whether use mkldnn bfloat16 inference with CPU.")
-    parser.add_argument(
-        "--cpu_threads", type=int, default=1, help="Num of threads with CPU.")
-    parser.add_argument(
-        "--trt_min_shape", type=int, default=1, help="min_shape for TensorRT.")
-    parser.add_argument(
-        "--trt_max_shape",
-        type=int,
-        default=1280,
-        help="max_shape for TensorRT.")
-    parser.add_argument(
-        "--trt_opt_shape",
-        type=int,
-        default=640,
-        help="opt_shape for TensorRT.")
-    parser.add_argument(
-        "--trt_calib_mode",
-        type=bool,
-        default=False,
-        help="If the model is produced by TRT offline quantitative "
-        "calibration, trt_calib_mode need to set True.")
-    parser.add_argument(
-        '--save_images',
-        type=ast.literal_eval,
-        default=True,
-        help='Save visualization image results.')
-    parser.add_argument(
-        '--save_mot_txts',
-        action='store_true',
-        help='Save tracking results (txt).')
-    parser.add_argument(
-        '--save_mot_txt_per_img',
-        action='store_true',
-        help='Save tracking results (txt) for each image.')
-    parser.add_argument(
-        '--scaled',
-        type=bool,
-        default=False,
-        help="Whether coords after detector outputs are scaled, False in JDE YOLOv3 "
-        "True in general detector.")
-    parser.add_argument(
-        "--tracker_config", type=str, default=None, help=("tracker donfig"))
-    parser.add_argument(
-        "--reid_model_dir",
-        type=str,
-        default=None,
-        help=("Directory include:'model.pdiparams', 'model.pdmodel', "
-              "'infer_cfg.yml', created by tools/export_model.py."))
-    parser.add_argument(
-        "--reid_batch_size",
-        type=int,
-        default=50,
-        help="max batch_size for reid model inference.")
-    parser.add_argument(
-        '--use_dark',
-        type=ast.literal_eval,
-        default=True,
-        help='whether to use darkpose to get better keypoint position predict ')
-    parser.add_argument(
-        "--action_file",
-        type=str,
-        default=None,
-        help="Path of input file for action recognition.")
-    parser.add_argument(
-        "--window_size",
-        type=int,
-        default=50,
-        help="Temporal size of skeleton feature for action recognition.")
-    parser.add_argument(
-        "--random_pad",
-        type=ast.literal_eval,
-        default=False,
-        help="Whether do random padding for action recognition.")
-    parser.add_argument(
-        "--save_results",
-        action='store_true',
-        default=False,
-        help="Whether save detection result to file using coco format")
-    parser.add_argument(
-        '--use_coco_category',
-        action='store_true',
-        default=False,
-        help='Whether to use the coco format dictionary `clsid2catid`')
-    parser.add_argument(
-        "--slice_infer",
-        action='store_true',
-        help="Whether to slice the image and merge the inference results for small object detection."
-    )
-    parser.add_argument(
-        '--slice_size',
-        nargs='+',
-        type=int,
-        default=[640, 640],
-        help="Height of the sliced image.")
-    parser.add_argument(
-        "--overlap_ratio",
-        nargs='+',
-        type=float,
-        default=[0.25, 0.25],
-        help="Overlap height ratio of the sliced image.")
-    parser.add_argument(
-        "--combine_method",
-        type=str,
-        default='nms',
-        help="Combine method of the sliced images' detection results, choose in ['nms', 'nmm', 'concat']."
-    )
-    parser.add_argument(
-        "--match_threshold",
-        type=float,
-        default=0.6,
-        help="Combine method matching threshold.")
-    parser.add_argument(
-        "--match_metric",
-        type=str,
-        default='ios',
-        help="Combine method matching metric, choose in ['iou', 'ios'].")
-    parser.add_argument(
-        "--collect_trt_shape_info",
-        action='store_true',
-        default=False,
-        help="Whether to collect dynamic shape before using tensorrt.")
-    parser.add_argument(
-        "--tuned_trt_shape_file",
-        type=str,
-        default="shape_range_info.pbtxt",
-        help="Path of a dynamic shape file for tensorrt.")
-    parser.add_argument("--use_fd_format", action="store_true")
-    parser.add_argument(
-        "--task_type",
-        type=str,
-        default='Detection',
-        help="How to save the coco result, it only work with save_results==True.  Optional inputs are Rotate or Detection, default is Detection."
-    )
-    return parser
-
-
 class Times(object):
     def __init__(self):
         self.time = 0.
@@ -325,212 +132,6 @@ class Timer(Times):
         return dic
 
 
-
-def multiclass_nms(bboxs, num_classes, match_threshold=0.6, match_metric='iou'):
-    final_boxes = []
-    for c in range(num_classes):
-        idxs = bboxs[:, 0] == c
-        if np.count_nonzero(idxs) == 0: continue
-        r = nms(bboxs[idxs, 1:], match_threshold, match_metric)
-        final_boxes.append(np.concatenate([np.full((r.shape[0], 1), c), r], 1))
-    return final_boxes
-
-
-def nms(dets, match_threshold=0.6, match_metric='iou'):
-    """ Apply NMS to avoid detecting too many overlapping bounding boxes.
-        Args:
-            dets: shape [N, 5], [score, x1, y1, x2, y2]
-            match_metric: 'iou' or 'ios'
-            match_threshold: overlap thresh for match metric.
-    """
-    if dets.shape[0] == 0:
-        return dets[[], :]
-    scores = dets[:, 0]
-    x1 = dets[:, 1]
-    y1 = dets[:, 2]
-    x2 = dets[:, 3]
-    y2 = dets[:, 4]
-    areas = (x2 - x1 + 1) * (y2 - y1 + 1)
-    order = scores.argsort()[::-1]
-
-    ndets = dets.shape[0]
-    suppressed = np.zeros((ndets), dtype=np.int32)
-
-    for _i in range(ndets):
-        i = order[_i]
-        if suppressed[i] == 1:
-            continue
-        ix1 = x1[i]
-        iy1 = y1[i]
-        ix2 = x2[i]
-        iy2 = y2[i]
-        iarea = areas[i]
-        for _j in range(_i + 1, ndets):
-            j = order[_j]
-            if suppressed[j] == 1:
-                continue
-            xx1 = max(ix1, x1[j])
-            yy1 = max(iy1, y1[j])
-            xx2 = min(ix2, x2[j])
-            yy2 = min(iy2, y2[j])
-            w = max(0.0, xx2 - xx1 + 1)
-            h = max(0.0, yy2 - yy1 + 1)
-            inter = w * h
-            if match_metric == 'iou':
-                union = iarea + areas[j] - inter
-                match_value = inter / union
-            elif match_metric == 'ios':
-                smaller = min(iarea, areas[j])
-                match_value = inter / smaller
-            else:
-                raise ValueError()
-            if match_value >= match_threshold:
-                suppressed[j] = 1
-    keep = np.where(suppressed == 0)[0]
-    dets = dets[keep, :]
-    return dets
-
-
-coco_clsid2catid = {
-    0: 1,
-    1: 2,
-    2: 3,
-    3: 4,
-    4: 5,
-    5: 6,
-    6: 7,
-    7: 8,
-    8: 9,
-    9: 10,
-    10: 11,
-    11: 13,
-    12: 14,
-    13: 15,
-    14: 16,
-    15: 17,
-    16: 18,
-    17: 19,
-    18: 20,
-    19: 21,
-    20: 22,
-    21: 23,
-    22: 24,
-    23: 25,
-    24: 27,
-    25: 28,
-    26: 31,
-    27: 32,
-    28: 33,
-    29: 34,
-    30: 35,
-    31: 36,
-    32: 37,
-    33: 38,
-    34: 39,
-    35: 40,
-    36: 41,
-    37: 42,
-    38: 43,
-    39: 44,
-    40: 46,
-    41: 47,
-    42: 48,
-    43: 49,
-    44: 50,
-    45: 51,
-    46: 52,
-    47: 53,
-    48: 54,
-    49: 55,
-    50: 56,
-    51: 57,
-    52: 58,
-    53: 59,
-    54: 60,
-    55: 61,
-    56: 62,
-    57: 63,
-    58: 64,
-    59: 65,
-    60: 67,
-    61: 70,
-    62: 72,
-    63: 73,
-    64: 74,
-    65: 75,
-    66: 76,
-    67: 77,
-    68: 78,
-    69: 79,
-    70: 80,
-    71: 81,
-    72: 82,
-    73: 84,
-    74: 85,
-    75: 86,
-    76: 87,
-    77: 88,
-    78: 89,
-    79: 90
-}
-
-
-def gaussian_radius(bbox_size, min_overlap):
-    height, width = bbox_size
-
-    a1 = 1
-    b1 = (height + width)
-    c1 = width * height * (1 - min_overlap) / (1 + min_overlap)
-    sq1 = np.sqrt(b1**2 - 4 * a1 * c1)
-    radius1 = (b1 + sq1) / (2 * a1)
-
-    a2 = 4
-    b2 = 2 * (height + width)
-    c2 = (1 - min_overlap) * width * height
-    sq2 = np.sqrt(b2**2 - 4 * a2 * c2)
-    radius2 = (b2 + sq2) / 2
-
-    a3 = 4 * min_overlap
-    b3 = -2 * min_overlap * (height + width)
-    c3 = (min_overlap - 1) * width * height
-    sq3 = np.sqrt(b3**2 - 4 * a3 * c3)
-    radius3 = (b3 + sq3) / 2
-    return min(radius1, radius2, radius3)
-
-
-def gaussian2D(shape, sigma_x=1, sigma_y=1):
-    m, n = [(ss - 1.) / 2. for ss in shape]
-    y, x = np.ogrid[-m:m + 1, -n:n + 1]
-
-    h = np.exp(-(x * x / (2 * sigma_x * sigma_x) + y * y / (2 * sigma_y *
-                                                            sigma_y)))
-    h[h < np.finfo(h.dtype).eps * h.max()] = 0
-    return h
-
-
-def draw_umich_gaussian(heatmap, center, radius, k=1):
-    """
-    draw_umich_gaussian, refer to https://github.com/xingyizhou/CenterNet/blob/master/src/lib/utils/image.py#L126
-    """
-    diameter = 2 * radius + 1
-    gaussian = gaussian2D(
-        (diameter, diameter), sigma_x=diameter / 6, sigma_y=diameter / 6)
-
-    x, y = int(center[0]), int(center[1])
-
-    height, width = heatmap.shape[0:2]
-
-    left, right = min(x, radius), min(width - x, radius + 1)
-    top, bottom = min(y, radius), min(height - y, radius + 1)
-
-    masked_heatmap = heatmap[y - top:y + bottom, x - left:x + right]
-    masked_gaussian = gaussian[radius - top:radius + bottom, radius - left:
-                               radius + right]
-    if min(masked_gaussian.shape) > 0 and min(masked_heatmap.shape) > 0:
-        np.maximum(masked_heatmap, masked_gaussian * k, out=masked_heatmap)
-    return heatmap
-
-
 def iou(box1, box2):
     """计算两个框的 IoU（交并比）"""
     x1 = max(box1[0], box2[0])

pipeline.py

@@ -3,8 +3,9 @@ import os
 from loguru import logger
 
 env = os.environ.get('env', 'dev')
-logger.info(f'Configure using this environment: {env}')
-load_dotenv(dotenv_path='.env.dev' if env == 'dev' else '.env', override=True)
+dotenv_path = '.env.dev' if env == 'dev' else '.env'
+logger.info(f'Configure using this dotenv path: {dotenv_path}')
+load_dotenv(dotenv_path=dotenv_path, override=True)
 
 import time
 import traceback
@@ -42,7 +43,7 @@ def _pdf2markdown_pipeline(pdf_path, visual):
             img = cv2.rotate(img, cv2.ROTATE_90_CLOCKWISE)
         images[i] = img
 
-    # images = images[90: 123]
+    # images = images[:2]
 
     # 3. 版面分析
     t5 = time.time()
@@ -76,6 +77,7 @@ def pdf2markdown_pipeline(pdf_path: str, visual=False, insert_db=True):
     pdf_name = pdf_path.split('/')[-1]
     start_time = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S.%f')
     process_status = 0
+    pdf_id = None
     try:
         results = _pdf2markdown_pipeline(pdf_path, visual)
     except Exception:
@@ -84,7 +86,6 @@ def pdf2markdown_pipeline(pdf_path: str, visual=False, insert_db=True):
         end_time = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S.%f')
         if insert_db:
             insert_pdf2md_table(pdf_path, pdf_name, process_status, start_time, end_time, None)
-        pdf_id = None
     else:
         process_status = PDFAnalysisStatus.SUCCESS.value
         end_time = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S.%f')
@@ -94,6 +95,7 @@ def pdf2markdown_pipeline(pdf_path: str, visual=False, insert_db=True):
 
 
 if __name__ == '__main__':
-    pdf2markdown_pipeline('/mnt/pdf2markdown/龙源电力：2021年年度报告.PDF', visual=True, insert_db=True)
-    # pdf2markdown_pipeline('/mnt/pdf2markdown/龙源电力：2022年年度报告.PDF', visual=True, insert_db=True)
-    # pdf2markdown_pipeline('/mnt/pdf2markdown/龙源电力：2023年年度报告.PDF', visual=True, insert_db=True)
+    insert_db = False
+    pdf2markdown_pipeline('/mnt/pdf2markdown/龙源电力：2021年年度报告.PDF', visual=True, insert_db=insert_db)
+    # pdf2markdown_pipeline('/mnt/pdf2markdown/龙源电力：2022年年度报告.PDF', visual=True, insert_db=insert_db)
+    # pdf2markdown_pipeline('/mnt/pdf2markdown/龙源电力：2023年年度报告.PDF', visual=True, insert_db=insert_db)

server.py

@@ -1,19 +1,27 @@
 from flask import Flask, request, jsonify
 import requests
 from pipeline import pdf2markdown_pipeline
+import concurrent.futures
+from loguru import logger
 
 
 app = Flask(__name__)
+executor = concurrent.futures.ThreadPoolExecutor(max_workers=1)
+
+
+def pdf2markdown_task(pdf_paths, callback_url):
+    for pdf_path in pdf_paths:
+        process_status, pdf_id = pdf2markdown_pipeline(pdf_path)
+        requests.post(callback_url, json={'pdfId': pdf_id, 'processStatus': process_status})
 
 
 @app.route('/pdf-qa-server/pdf-to-md', methods=['POST'])
 def pdf2markdown():
     data = request.json
+    logger.info(f'request params: {data}')
     pdf_paths = data['pathList']
     callback_url = data['webhookUrl']
-    for pdf_path in pdf_paths:
-        process_status, pdf_id = pdf2markdown_pipeline(pdf_path)
-        requests.post(callback_url, json={'pdfId': pdf_id, 'processStatus': process_status})
+    executor.submit(pdf2markdown_task, pdf_paths, callback_url)
     return jsonify({})
 
 

server.sh

@@ -0,0 +1,3 @@
+#! /bin/bash
+
+gunicorn -w 1 -b 0.0.0.0:8000 server:app