utils.py

import sys
import csv
import base64
import time
import numpy as np

maxInt = sys.maxsize

while True:
    # decrease the maxInt value by factor 10
    # as long as the OverflowError occurs.

    try:
        csv.field_size_limit(maxInt)
        break
    except OverflowError:
        maxInt = int(maxInt/10)

FIELDNAMES = ["img_id", "img_h", "img_w", "objects_id", "objects_conf",
              "attrs_id", "attrs_conf", "num_boxes", "boxes", "features"]

def load_obj_tsv(fname, npz=True, topk=None):
    """Load object features from tsv file.
    :param fname: The path to the tsv file.
    :param topk: Only load features for top K images (lines) in the tsv file.
        Will load all the features if topk is either -1 or None.
    :return: A list of image object features where each feature is a dict.
        See FILENAMES above for the keys in the feature dict.
    """
    data = []
    start_time = time.time()
    print("Start to load Faster-RCNN detected objects from %s" % fname)
    with open(fname) as f:
        reader = csv.DictReader(f, FIELDNAMES, delimiter="\t")
        for i, item in enumerate(reader):

            for key in ['img_h', 'img_w', 'num_boxes']:
                item[key] = int(item[key])

            boxes = item['num_boxes']
            decode_config = [
                ('objects_id', (boxes, ), np.int64),
                ('objects_conf', (boxes, ), np.float32),
                ('attrs_id', (boxes, ), np.int64),
                ('attrs_conf', (boxes, ), np.float32),
                ('boxes', (boxes, 4), np.float32),
                ('features', (boxes, -1), np.float32),
            ]
            for key, shape, dtype in decode_config:
                item[key] = np.frombuffer(base64.b64decode(item[key]), dtype=dtype)
                item[key] = item[key].reshape(shape)
                item[key].setflags(write=False)

            data.append(item)
            
            if topk is not None and len(data) == topk:
                break
            
            if i % 100 == 0:
                print('Processed {} images in {} seconds \r'.format(i, time.time()-start_time), end='', flush=True)
    
    elapsed_time = time.time() - start_time
    print("Loaded %d images in file %s in %d seconds." % (len(data), fname, elapsed_time))
    return data