# -*- coding:utf-8 -*- import base64 import os import time import cv2 import numpy as np __all__ = ['cv2_to_base64', 'base64_to_cv2', 'postprocess'] def cv2_to_base64(image): data = cv2.imencode('.jpg', image)[1] return base64.b64encode(data.tostring()).decode('utf8') def base64_to_cv2(b64str): data = base64.b64decode(b64str.encode('utf8')) data = np.fromstring(data, np.uint8) data = cv2.imdecode(data, cv2.IMREAD_COLOR) return data def postprocess(data_out, org_im, org_im_shape, org_im_path, output_dir, visualization): """ Postprocess output of network. one image at a time. Args: data_out (numpy.ndarray): output of network. org_im (numpy.ndarray): original image. org_im_shape (list): shape pf original image. org_im_path (list): path of riginal image. output_dir (str): output directory to store image. visualization (bool): whether to save image or not. Returns: result (dict): The data of processed image. """ result = dict() for logit in data_out: logit = (logit * 255).astype(np.uint8) logit = cv2.resize(logit, (org_im_shape[1], org_im_shape[0])) rgba = np.concatenate((org_im, np.expand_dims(logit, axis=2)), axis=2) if visualization: check_dir(output_dir) save_im_path = get_save_image_name(org_im, org_im_path, output_dir) cv2.imwrite(save_im_path, rgba) result['save_path'] = save_im_path result['data'] = logit else: result['data'] = logit return result def check_dir(dir_path): if not os.path.exists(dir_path): os.makedirs(dir_path) elif os.path.isfile(dir_path): os.remove(dir_path) os.makedirs(dir_path) def get_save_image_name(org_im, org_im_path, output_dir): """ Get save image name from source image path. """ # name prefix of orginal image org_im_name = os.path.split(org_im_path)[-1] im_prefix = os.path.splitext(org_im_name)[0] ext = '.png' # save image path save_im_path = os.path.join(output_dir, im_prefix + ext) if os.path.exists(save_im_path): save_im_path = os.path.join(output_dir, im_prefix + 'time={}'.format(int(time.time())) + ext) return save_im_path