predict.py

#copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
#
#Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
#Unless required by applicable law or agreed to in writing, software
#distributed under the License is distributed on an "AS IS" BASIS,
#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#See the License for the specific language governing permissions and
#limitations under the License.

import argparse
import numpy as np
import cv2
import os
import logging

from paddle import fluid
from paddle.fluid.core import PaddleTensor
from paddle.fluid.core import AnalysisConfig
from paddle.fluid.core import create_paddle_predictor

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)


def resize_short(img, target_size, interpolation=None):
    """resize image
    
    Args:
        img: image data
        target_size: resize short target size
        interpolation: interpolation mode

    Returns:
        resized image data
    """
    percent = float(target_size) / min(img.shape[0], img.shape[1])
    resized_width = int(round(img.shape[1] * percent))
    resized_height = int(round(img.shape[0] * percent))
    if interpolation:
        resized = cv2.resize(
            img, (resized_width, resized_height), interpolation=interpolation)
    else:
        resized = cv2.resize(img, (resized_width, resized_height))
    return resized


def crop_image(img, target_size, center):
    """crop image 
    
    Args:
        img: images data
        target_size: crop target size
        center: crop mode
    
    Returns:
        img: cropped image data
    """
    height, width = img.shape[:2]
    size = target_size
    if center == True:
        w_start = (width - size) // 2
        h_start = (height - size) // 2
    else:
        w_start = np.random.randint(0, width - size + 1)
        h_start = np.random.randint(0, height - size + 1)
    w_end = w_start + size
    h_end = h_start + size
    img = img[h_start:h_end, w_start:w_end, :]
    return img


def preprocess_image(img_path):
    """ preprocess_image """

    mean = [0.485, 0.456, 0.406]
    std = [0.229, 0.224, 0.225]
    crop_size = 224
    target_size = 256

    img = cv2.imread(img_path)
    img = resize_short(img, target_size, interpolation=None)
    img = crop_image(img, target_size=crop_size, center=True)
    img = img[:, :, ::-1]

    img = img.astype('float32').transpose((2, 0, 1)) / 255
    img_mean = np.array(mean).reshape((3, 1, 1))
    img_std = np.array(std).reshape((3, 1, 1))
    img -= img_mean
    img /= img_std
    img = np.expand_dims(img, axis=0).copy()
    return img


def predict(args):
    # config AnalysisConfig
    config = AnalysisConfig(args.model_file, args.params_file)
    if args.gpu_id < 0:
        config.disable_gpu()
    else:
        config.enable_use_gpu(args.gpu_mem, args.gpu_id)

    # you can enable tensorrt engine if paddle is installed with tensorrt
    # config.enable_tensorrt_engine() 

    predictor = create_paddle_predictor(config)

    # input
    inputs = preprocess_image(args.image_path)
    inputs = PaddleTensor(inputs)

    # predict
    outputs = predictor.run([inputs])

    # get output
    output = outputs[0]
    output = output.as_ndarray().flatten()

    cls = np.argmax(output)
    score = output[cls]
    logger.info("class: {0}".format(cls))
    logger.info("score: {0}".format(score))
    return


def check_args(args):
    assert os.path.exists(args.model_file), "model_file({}) not exist!".format(
        args.model_file)
    assert os.path.exists(
        args.params_file), "params_file({}) not exist!".format(args.params_file)
    assert os.path.exists(args.image_path), "image_path({}) not exist!".format(
        args.image_path)
    assert isinstance(args.gpu_id, int)
    assert isinstance(args.gpu_mem, int)


def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--model_file", type=str, default="", help="model filename")
    parser.add_argument(
        "--params_file", type=str, default="", help="parameter filename")
    parser.add_argument("--image_path", type=str, default="", help="image path")
    parser.add_argument(
        "--gpu_id",
        type=int,
        default=0,
        help="gpu id, if less than 0, gpu is disabled")
    parser.add_argument(
        "--gpu_mem", type=int, default=2000, help="gpu memory, unit: MB")
    return parser.parse_args()


def main():
    args = parse_args()
    check_args(args)
    predict(args)


if __name__ == "__main__":
    main()