1) 实现了文本-公式混排识别; 2) 重构了项目结构

src/infer_det.py

@@ -1,35 +1,21 @@
 import os
-import yaml
 import argparse
-import numpy as np
 import glob
+
 from onnxruntime import InferenceSession
-from tqdm import tqdm
+from pathlib import Path
+from models.det_model.inference import PredictConfig, predict_image
 
-from models.det_model.preprocess import Compose
-import cv2
-
-
-# Global dictionary
-SUPPORT_MODELS = {
-    'YOLO', 'PPYOLOE', 'RCNN', 'SSD', 'Face', 'FCOS', 'SOLOv2', 'TTFNet',
-    'S2ANet', 'JDE', 'FairMOT', 'DeepSORT', 'GFL', 'PicoDet', 'CenterNet',
-    'TOOD', 'RetinaNet', 'StrongBaseline', 'STGCN', 'YOLOX', 'HRNet', 
-    'DETR'
-}
 
 parser = argparse.ArgumentParser(description=__doc__)
 parser.add_argument("--infer_cfg", type=str, help="infer_cfg.yml",
-                    default="./models/det_model/model/infer_cfg.yml"
-                    )
+                    default="./models/det_model/model/infer_cfg.yml")
 parser.add_argument('--onnx_file', type=str, help="onnx model file path",
-                    default="./models/det_model/model/rtdetr_r50vd_6x_coco.onnx"
-                    )
+                    default="./models/det_model/model/rtdetr_r50vd_6x_coco.onnx")
 parser.add_argument("--image_dir", type=str)
-parser.add_argument("--image_file", type=str, default='/data/ljm/TexTeller/src/Tr00_0001015-page02.jpg')
-parser.add_argument("--imgsave_dir", type=str, 
-                    default="."
-                    )
+parser.add_argument("--image_file", type=str, required=True)
+parser.add_argument("--imgsave_dir", type=str, default="./detect_results")
+
 
 def get_test_images(infer_dir, infer_img):
     """
@@ -62,125 +48,11 @@ def get_test_images(infer_dir, infer_img):
     return images
 
 
-class PredictConfig(object):
-    """set config of preprocess, postprocess and visualize
-    Args:
-        infer_config (str): path of infer_cfg.yml
-    """
-
-    def __init__(self, infer_config):
-        # parsing Yaml config for Preprocess
-        with open(infer_config) as f:
-            yml_conf = yaml.safe_load(f)
-        self.check_model(yml_conf)
-        self.arch = yml_conf['arch']
-        self.preprocess_infos = yml_conf['Preprocess']
-        self.min_subgraph_size = yml_conf['min_subgraph_size']
-        self.label_list = yml_conf['label_list']
-        self.use_dynamic_shape = yml_conf['use_dynamic_shape']
-        self.draw_threshold = yml_conf.get("draw_threshold", 0.5)
-        self.mask = yml_conf.get("mask", False)
-        self.tracker = yml_conf.get("tracker", None)
-        self.nms = yml_conf.get("NMS", None)
-        self.fpn_stride = yml_conf.get("fpn_stride", None)
-
-        color_pool = [(0, 255, 0), (255, 0, 0), (0, 0, 255), (255, 255, 0), (0, 255, 255)]
-        self.colors = {label: color_pool[i % len(color_pool)] for i, label in enumerate(self.label_list)}
-
-        if self.arch == 'RCNN' and yml_conf.get('export_onnx', False):
-            print(
-                'The RCNN export model is used for ONNX and it only supports batch_size = 1'
-            )
-        self.print_config()
-
-    def check_model(self, yml_conf):
-        """
-        Raises:
-            ValueError: loaded model not in supported model type
-        """
-        for support_model in SUPPORT_MODELS:
-            if support_model in yml_conf['arch']:
-                return True
-        raise ValueError("Unsupported arch: {}, expect {}".format(yml_conf[
-            'arch'], SUPPORT_MODELS))
-
-    def print_config(self):
-        print('-----------  Model Configuration -----------')
-        print('%s: %s' % ('Model Arch', self.arch))
-        print('%s: ' % ('Transform Order'))
-        for op_info in self.preprocess_infos:
-            print('--%s: %s' % ('transform op', op_info['type']))
-        print('--------------------------------------------')
-
-
-def draw_bbox(image, outputs, infer_config):
-    for output in outputs:
-        cls_id, score, xmin, ymin, xmax, ymax = output
-        if score > infer_config.draw_threshold:
-            label = infer_config.label_list[int(cls_id)]
-            color = infer_config.colors[label]
-            cv2.rectangle(image, (int(xmin), int(ymin)), (int(xmax), int(ymax)), color, 2)
-            cv2.putText(image, "{}: {:.2f}".format(label, score),
-                        (int(xmin), int(ymin - 5)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
-    return image
-
-
-def predict_image(infer_config, predictor, img_list):
-    # load preprocess transforms
-    transforms = Compose(infer_config.preprocess_infos)
-    errImgList = []
-
-    # Check and create subimg_save_dir if not exist
-    subimg_save_dir = os.path.join(FLAGS.imgsave_dir, 'subimages')
-    os.makedirs(subimg_save_dir, exist_ok=True)
-
-    # predict image
-    for img_path in tqdm(img_list):
-        img = cv2.imread(img_path)
-        if img is None:
-            print(f"Warning: Could not read image {img_path}. Skipping...")
-            errImgList.append(img_path)
-            continue
-
-        inputs = transforms(img_path)
-        inputs_name = [var.name for var in predictor.get_inputs()]
-        inputs = {k: inputs[k][None, ] for k in inputs_name}
-
-        outputs = predictor.run(output_names=None, input_feed=inputs)
-
-        print("ONNXRuntime predict: ")
-        if infer_config.arch in ["HRNet"]:
-            print(np.array(outputs[0]))
-        else:
-            bboxes = np.array(outputs[0])
-            for bbox in bboxes:
-                if bbox[0] > -1 and bbox[1] > infer_config.draw_threshold:
-                    print(f"{int(bbox[0])} {bbox[1]} "
-                          f"{bbox[2]} {bbox[3]} {bbox[4]} {bbox[5]}")
-
-        # Save the subimages (crop from the original image)
-        subimg_counter = 1
-        for output in np.array(outputs[0]):
-            cls_id, score, xmin, ymin, xmax, ymax = output
-            if score > infer_config.draw_threshold:
-                label = infer_config.label_list[int(cls_id)]
-                subimg = img[int(ymin):int(ymax), int(xmin):int(xmax)]
-                subimg_filename = f"{os.path.splitext(os.path.basename(img_path))[0]}_{label}_{xmin:.2f}_{ymin:.2f}_{xmax:.2f}_{ymax:.2f}.jpg"
-                subimg_path = os.path.join(subimg_save_dir, subimg_filename)
-                cv2.imwrite(subimg_path, subimg)
-                subimg_counter += 1
-
-        # Draw bounding boxes and save the image with bounding boxes
-        img_with_bbox = draw_bbox(img, np.array(outputs[0]), infer_config)
-        output_dir = FLAGS.imgsave_dir
-        os.makedirs(output_dir, exist_ok=True)
-        output_file = os.path.join(output_dir, "output_" + os.path.basename(img_path))
-        cv2.imwrite(output_file, img_with_bbox)
-
-    print("ErrorImgs:")
-    print(errImgList)
-
 if __name__ == '__main__':
+    cur_path = os.getcwd()
+    script_dirpath = Path(__file__).resolve().parent
+    os.chdir(script_dirpath)
+
     FLAGS = parser.parse_args()
     # load image list
     img_list = get_test_images(FLAGS.image_dir, FLAGS.image_file)
@@ -189,4 +61,6 @@ if __name__ == '__main__':
     # load infer config
     infer_config = PredictConfig(FLAGS.infer_cfg)
 
-    predict_image(infer_config, predictor, img_list)
+    predict_image(FLAGS.imgsave_dir, infer_config, predictor, img_list)
+
+    os.chdir(cur_path)