新增公式检测模块
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TonyLee1256
197
src/infer_det.py
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197
src/infer_det.py
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import os
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import yaml
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import argparse
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import numpy as np
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import glob
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from onnxruntime import InferenceSession
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from tqdm import tqdm
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from models.det_model.preprocess import Compose
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import cv2
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# 注意：文件名要标准，最好都用下划线
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# Global dictionary
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SUPPORT_MODELS = {
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'YOLO', 'PPYOLOE', 'RCNN', 'SSD', 'Face', 'FCOS', 'SOLOv2', 'TTFNet',
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'S2ANet', 'JDE', 'FairMOT', 'DeepSORT', 'GFL', 'PicoDet', 'CenterNet',
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'TOOD', 'RetinaNet', 'StrongBaseline', 'STGCN', 'YOLOX', 'HRNet',
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'DETR'
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}
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument("--infer_cfg", type=str, help="infer_cfg.yml",
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default="./models/det_model/model/infer_cfg.yml"
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)
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parser.add_argument('--onnx_file', type=str, help="onnx model file path",
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default="./models/det_model/model/rtdetr_r50vd_6x_coco.onnx"
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)
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parser.add_argument("--image_dir", type=str)
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parser.add_argument("--image_file", type=str, default='/data/ljm/TexTeller/src/Tr00_0001015-page02.jpg')
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parser.add_argument("--imgsave_dir", type=str,
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default="."
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)
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def get_test_images(infer_dir, infer_img):
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"""
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Get image path list in TEST mode
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"""
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assert infer_img is not None or infer_dir is not None, \
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"--image_file or --image_dir should be set"
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assert infer_img is None or os.path.isfile(infer_img), \
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"{} is not a file".format(infer_img)
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assert infer_dir is None or os.path.isdir(infer_dir), \
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"{} is not a directory".format(infer_dir)
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# infer_img has a higher priority
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if infer_img and os.path.isfile(infer_img):
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return [infer_img]
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images = set()
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infer_dir = os.path.abspath(infer_dir)
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assert os.path.isdir(infer_dir), \
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"infer_dir {} is not a directory".format(infer_dir)
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exts = ['jpg', 'jpeg', 'png', 'bmp']
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exts += [ext.upper() for ext in exts]
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for ext in exts:
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images.update(glob.glob('{}/*.{}'.format(infer_dir, ext)))
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images = list(images)
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assert len(images) > 0, "no image found in {}".format(infer_dir)
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print("Found {} inference images in total.".format(len(images)))
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return images
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class PredictConfig(object):
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"""set config of preprocess, postprocess and visualize
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Args:
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infer_config (str): path of infer_cfg.yml
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"""
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def __init__(self, infer_config):
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# parsing Yaml config for Preprocess
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with open(infer_config) as f:
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yml_conf = yaml.safe_load(f)
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self.check_model(yml_conf)
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self.arch = yml_conf['arch']
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self.preprocess_infos = yml_conf['Preprocess']
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self.min_subgraph_size = yml_conf['min_subgraph_size']
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self.label_list = yml_conf['label_list']
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self.use_dynamic_shape = yml_conf['use_dynamic_shape']
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self.draw_threshold = yml_conf.get("draw_threshold", 0.5)
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self.mask = yml_conf.get("mask", False)
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self.tracker = yml_conf.get("tracker", None)
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self.nms = yml_conf.get("NMS", None)
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self.fpn_stride = yml_conf.get("fpn_stride", None)
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# 预定义颜色池
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color_pool = [(0, 255, 0), (255, 0, 0), (0, 0, 255), (255, 255, 0), (0, 255, 255)]
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# 根据label_list动态生成颜色映射
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self.colors = {label: color_pool[i % len(color_pool)] for i, label in enumerate(self.label_list)}
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if self.arch == 'RCNN' and yml_conf.get('export_onnx', False):
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print(
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'The RCNN export model is used for ONNX and it only supports batch_size = 1'
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)
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self.print_config()
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def check_model(self, yml_conf):
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"""
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Raises:
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ValueError: loaded model not in supported model type
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"""
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for support_model in SUPPORT_MODELS:
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if support_model in yml_conf['arch']:
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return True
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raise ValueError("Unsupported arch: {}, expect {}".format(yml_conf[
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'arch'], SUPPORT_MODELS))
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def print_config(self):
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print('----------- Model Configuration -----------')
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print('%s: %s' % ('Model Arch', self.arch))
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print('%s: ' % ('Transform Order'))
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for op_info in self.preprocess_infos:
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print('--%s: %s' % ('transform op', op_info['type']))
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print('--------------------------------------------')
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def draw_bbox(image, outputs, infer_config):
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for output in outputs:
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cls_id, score, xmin, ymin, xmax, ymax = output
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if score > infer_config.draw_threshold:
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# 获取类别名
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label = infer_config.label_list[int(cls_id)]
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# 根据类别名获取颜色
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color = infer_config.colors[label]
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cv2.rectangle(image, (int(xmin), int(ymin)), (int(xmax), int(ymax)), color, 2)
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cv2.putText(image, "{}: {:.2f}".format(label, score),
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(int(xmin), int(ymin - 5)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
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return image
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def predict_image(infer_config, predictor, img_list):
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# load preprocess transforms
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transforms = Compose(infer_config.preprocess_infos)
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errImgList = []
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# Check and create subimg_save_dir if not exist
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subimg_save_dir = os.path.join(FLAGS.imgsave_dir, 'subimages')
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os.makedirs(subimg_save_dir, exist_ok=True)
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# predict image
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for img_path in tqdm(img_list):
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img = cv2.imread(img_path)
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if img is None:
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print(f"Warning: Could not read image {img_path}. Skipping...")
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errImgList.append(img_path)
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continue
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inputs = transforms(img_path)
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inputs_name = [var.name for var in predictor.get_inputs()]
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inputs = {k: inputs[k][None, ] for k in inputs_name}
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outputs = predictor.run(output_names=None, input_feed=inputs)
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print("ONNXRuntime predict: ")
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if infer_config.arch in ["HRNet"]:
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print(np.array(outputs[0]))
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else:
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bboxes = np.array(outputs[0])
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for bbox in bboxes:
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if bbox[0] > -1 and bbox[1] > infer_config.draw_threshold:
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print(f"{int(bbox[0])} {bbox[1]} "
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f"{bbox[2]} {bbox[3]} {bbox[4]} {bbox[5]}")
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# Save the subimages (crop from the original image)
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subimg_counter = 1
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for output in np.array(outputs[0]):
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cls_id, score, xmin, ymin, xmax, ymax = output
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if score > infer_config.draw_threshold:
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label = infer_config.label_list[int(cls_id)]
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subimg = img[int(ymin):int(ymax), int(xmin):int(xmax)]
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subimg_filename = f"{os.path.splitext(os.path.basename(img_path))[0]}_{label}_{xmin:.2f}_{ymin:.2f}_{xmax:.2f}_{ymax:.2f}.jpg"
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subimg_path = os.path.join(subimg_save_dir, subimg_filename)
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cv2.imwrite(subimg_path, subimg)
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subimg_counter += 1
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# Draw bounding boxes and save the image with bounding boxes
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img_with_bbox = draw_bbox(img, np.array(outputs[0]), infer_config)
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output_dir = FLAGS.imgsave_dir
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os.makedirs(output_dir, exist_ok=True)
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output_file = os.path.join(output_dir, "output_" + os.path.basename(img_path))
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cv2.imwrite(output_file, img_with_bbox)
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print("ErrorImgs:")
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print(errImgList)
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if __name__ == '__main__':
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FLAGS = parser.parse_args()
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# load image list
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img_list = get_test_images(FLAGS.image_dir, FLAGS.image_file)
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# load predictor
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predictor = InferenceSession(FLAGS.onnx_file)
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# load infer config
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infer_config = PredictConfig(FLAGS.infer_cfg)
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predict_image(infer_config, predictor, img_list)
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