郎朗坤
您可以使用以下方法将更宽的数据集转换为 TFRecords。1.您需要创建一个config.py文件。# TrainingTRAIN_WIDER_PATH = "widerface/WIDER_train/"#ValidationVAL_WIDER_PATH = "widerface/WIDER_val/"#TestingTEST_WIDER_PATH = "widerface/WIDER_test/"# Ground TruthGROUND_TRUTH_PATH = "widerface/wider_face_split/"# OutputOUTPUT_PATH = "datasets/widerface/TF_data/" 生成 TFRecords 的代码（create_tf_record.py）。这是代码：import tensorflow as tfimport numpyimport cv2import osimport hashlibimport configfrom utils import dataset_utildef parse_test_example(f, images_path): height = None # Image height width = None # Image width filename = None # Filename of the image. Empty if image is not from file encoded_image_data = None # Encoded image bytes image_format = b'jpeg' # b'jpeg' or b'png' filename = f.readline().rstrip() if not filename: raise IOError() filepath = os.path.join(images_path, filename) image_raw = cv2.imread(filepath) encoded_image_data = open(filepath, "rb").read() key = hashlib.sha256(encoded_image_data).hexdigest() height, width, channel = image_raw.shape tf_example = tf.train.Example(features=tf.train.Features(feature={ 'image/height': dataset_util.int64_feature(int(height)), 'image/width': dataset_util.int64_feature(int(width)), 'image/filename': dataset_util.bytes_feature(filename.encode('utf-8')), 'image/source_id': dataset_util.bytes_feature(filename.encode('utf-8')), 'image/key/sha256': dataset_util.bytes_feature(key.encode('utf8')), 'image/encoded': dataset_util.bytes_feature(encoded_image_data), 'image/format': dataset_util.bytes_feature('jpeg'.encode('utf8')), })) return tf_exampledef parse_example(f, images_path): height = None # Image height width = None # Image width filename = None # Filename of the image. Empty if image is not from file encoded_image_data = None # Encoded image bytes image_format = b'jpeg' # b'jpeg' or b'png' xmins = [] # List of normalized left x coordinates in bounding box (1 per box) xmaxs = [] # List of normalized right x coordinates in bounding box (1 per box) ymins = [] # List of normalized top y coordinates in bounding box (1 per box) ymaxs = [] # List of normalized bottom y coordinates in bounding box (1 per box) classes_text = [] # List of string class name of bounding box (1 per box) classes = [] # List of integer class id of bounding box (1 per box) poses = [] truncated = [] difficult_obj = [] filename = f.readline().rstrip() if not filename: raise IOError() filepath = os.path.join(images_path, filename) image_raw = cv2.imread(filepath) encoded_image_data = open(filepath, "rb").read() key = hashlib.sha256(encoded_image_data).hexdigest() height, width, channel = image_raw.shape face_num = int(f.readline().rstrip()) if not face_num: face_num += 1 # raise Exception() for i in range(face_num): annot = f.readline().rstrip().split() if not annot: raise Exception() # WIDER FACE DATASET CONTAINS SOME ANNOTATIONS WHAT EXCEEDS THE IMAGE BOUNDARY if(float(annot[2]) > 25.0): if(float(annot[3]) > 30.0): xmins.append( max(0.005, (float(annot[0]) / width) ) ) ymins.append( max(0.005, (float(annot[1]) / height) ) ) xmaxs.append( min(0.995, ((float(annot[0]) + float(annot[2])) / width) ) ) ymaxs.append( min(0.995, ((float(annot[1]) + float(annot[3])) / height) ) ) classes_text.append(b'face') classes.append(1) poses.append("front".encode('utf8')) truncated.append(int(0)) tf_example = tf.train.Example(features=tf.train.Features(feature={ 'image/height': dataset_util.int64_feature(int(height)), 'image/width': dataset_util.int64_feature(int(width)), 'image/filename': dataset_util.bytes_feature(filename.encode('utf-8')), 'image/source_id': dataset_util.bytes_feature(filename.encode('utf-8')), 'image/key/sha256': dataset_util.bytes_feature(key.encode('utf8')), 'image/encoded': dataset_util.bytes_feature(encoded_image_data), 'image/format': dataset_util.bytes_feature('jpeg'.encode('utf8')), 'image/object/bbox/xmin': dataset_util.float_list_feature(xmins), 'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs), 'image/object/bbox/ymin': dataset_util.float_list_feature(ymins), 'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs), 'image/object/class/text': dataset_util.bytes_list_feature(classes_text), 'image/object/class/label': dataset_util.int64_list_feature(classes), 'image/object/difficult': dataset_util.int64_list_feature(int(0)), 'image/object/truncated': dataset_util.int64_list_feature(truncated), 'image/object/view': dataset_util.bytes_list_feature(poses), })) return tf_exampledef run(images_path, description_file, output_path, no_bbox=False): f = open(description_file) writer = tf.python_io.TFRecordWriter(output_path) i = 0 print("Processing {}".format(images_path)) while True: try: if no_bbox: tf_example = parse_test_example(f, images_path) else: tf_example = parse_example(f, images_path) writer.write(tf_example.SerializeToString()) i += 1 except IOError: break except Exception: raise writer.close() print("Correctly created record for {} images\n".format(i))def main(unused_argv): # Training if config.TRAIN_WIDER_PATH is not None: images_path = os.path.join(config.TRAIN_WIDER_PATH, "images") description_file = os.path.join(config.GROUND_TRUTH_PATH, "wider_face_train_bbx_gt.txt") output_path = os.path.join(config.OUTPUT_PATH, "train.tfrecord") run(images_path, description_file, output_path) # Validation if config.VAL_WIDER_PATH is not None: images_path = os.path.join(config.VAL_WIDER_PATH, "images") description_file = os.path.join(config.GROUND_TRUTH_PATH, "wider_face_val_bbx_gt.txt") output_path = os.path.join(config.OUTPUT_PATH, "val.tfrecord") run(images_path, description_file, output_path) # Testing. This set does not contain bounding boxes, so the tfrecord will contain images only if config.TEST_WIDER_PATH is not None: images_path = os.path.join(config.TEST_WIDER_PATH, "images") description_file = os.path.join(config.GROUND_TRUTH_PATH, "wider_face_test_filelist.txt") output_path = os.path.join(config.OUTPUT_PATH, "test.tfrecord") run(images_path, description_file, output_path, no_bbox=True)if __name__ == '__main__': tf.app.run() 运行create_tf_record.py生成 TFRecord 文件。python create_tf_record.py 希望这能回答你的问题，快乐学习！