#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) # create predictor predictor = create_paddle_predictor(config.to_native_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: ", cls) logger.info("score: ", 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()