import os import yaml import argparse import numpy as np import glob from onnxruntime import InferenceSession from tqdm import tqdm 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" ) parser.add_argument('--onnx_file', type=str, help="onnx model file path", 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="." ) def get_test_images(infer_dir, infer_img): """ Get image path list in TEST mode """ assert infer_img is not None or infer_dir is not None, \ "--image_file or --image_dir should be set" assert infer_img is None or os.path.isfile(infer_img), \ "{} is not a file".format(infer_img) assert infer_dir is None or os.path.isdir(infer_dir), \ "{} is not a directory".format(infer_dir) # infer_img has a higher priority if infer_img and os.path.isfile(infer_img): return [infer_img] images = set() infer_dir = os.path.abspath(infer_dir) assert os.path.isdir(infer_dir), \ "infer_dir {} is not a directory".format(infer_dir) exts = ['jpg', 'jpeg', 'png', 'bmp'] exts += [ext.upper() for ext in exts] for ext in exts: images.update(glob.glob('{}/*.{}'.format(infer_dir, ext))) images = list(images) assert len(images) > 0, "no image found in {}".format(infer_dir) print("Found {} inference images in total.".format(len(images))) 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__': FLAGS = parser.parse_args() # load image list img_list = get_test_images(FLAGS.image_dir, FLAGS.image_file) # load predictor predictor = InferenceSession(FLAGS.onnx_file) # load infer config infer_config = PredictConfig(FLAGS.infer_cfg) predict_image(infer_config, predictor, img_list)