156 lines
5.0 KiB
Python
156 lines
5.0 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# 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 cv2
|
|
import numpy as np
|
|
|
|
|
|
def parse_args():
|
|
def str2bool(v):
|
|
return v.lower() in ("true", "t", "1")
|
|
|
|
# general params
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument("-i", "--image_file", type=str)
|
|
parser.add_argument("--use_gpu", type=str2bool, default=True)
|
|
|
|
# params for preprocess
|
|
parser.add_argument("--resize_short", type=int, default=256)
|
|
parser.add_argument("--resize", type=int, default=224)
|
|
parser.add_argument("--normalize", type=str2bool, default=True)
|
|
|
|
# params for predict
|
|
parser.add_argument("--model_file", type=str)
|
|
parser.add_argument("--params_file", type=str)
|
|
parser.add_argument("-b", "--batch_size", type=int, default=1)
|
|
parser.add_argument("--use_fp16", type=str2bool, default=False)
|
|
parser.add_argument("--ir_optim", type=str2bool, default=True)
|
|
parser.add_argument("--use_tensorrt", type=str2bool, default=False)
|
|
parser.add_argument("--gpu_mem", type=int, default=8000)
|
|
parser.add_argument("--enable_benchmark", type=str2bool, default=False)
|
|
parser.add_argument("--model_name", type=str)
|
|
parser.add_argument("--top_k", type=int, default=1)
|
|
parser.add_argument("--hubserving", type=str2bool, default=False)
|
|
|
|
# params for infer
|
|
parser.add_argument("--model", type=str)
|
|
parser.add_argument("--pretrained_model", type=str)
|
|
parser.add_argument("--class_num", type=int, default=1000)
|
|
parser.add_argument(
|
|
"--load_static_weights",
|
|
type=str2bool,
|
|
default=False,
|
|
help='Whether to load the pretrained weights saved in static mode')
|
|
|
|
# parameters for pre-label the images
|
|
parser.add_argument(
|
|
"--pre_label_image",
|
|
type=str2bool,
|
|
default=False,
|
|
help="Whether to pre-label the images using the loaded weights")
|
|
parser.add_argument("--pre_label_out_idr", type=str, default=None)
|
|
|
|
return parser.parse_args()
|
|
|
|
|
|
def preprocess(img, args):
|
|
resize_op = ResizeImage(resize_short=args.resize_short)
|
|
img = resize_op(img)
|
|
crop_op = CropImage(size=(args.resize, args.resize))
|
|
img = crop_op(img)
|
|
if args.normalize:
|
|
img_mean = [0.485, 0.456, 0.406]
|
|
img_std = [0.229, 0.224, 0.225]
|
|
img_scale = 1.0 / 255.0
|
|
normalize_op = NormalizeImage(
|
|
scale=img_scale, mean=img_mean, std=img_std)
|
|
img = normalize_op(img)
|
|
tensor_op = ToTensor()
|
|
img = tensor_op(img)
|
|
return img
|
|
|
|
|
|
def postprocess(output, args):
|
|
output = output.flatten()
|
|
classes = np.argpartition(output, -args.top_k)[-args.top_k:]
|
|
classes = classes[np.argsort(-output[classes])]
|
|
scores = output[classes]
|
|
return classes, scores
|
|
|
|
|
|
class ResizeImage(object):
|
|
def __init__(self, resize_short=None):
|
|
self.resize_short = resize_short
|
|
|
|
def __call__(self, img):
|
|
img_h, img_w = img.shape[:2]
|
|
percent = float(self.resize_short) / min(img_w, img_h)
|
|
w = int(round(img_w * percent))
|
|
h = int(round(img_h * percent))
|
|
return cv2.resize(img, (w, h))
|
|
|
|
|
|
class CropImage(object):
|
|
def __init__(self, size):
|
|
if type(size) is int:
|
|
self.size = (size, size)
|
|
else:
|
|
self.size = size
|
|
|
|
def __call__(self, img):
|
|
w, h = self.size
|
|
img_h, img_w = img.shape[:2]
|
|
w_start = (img_w - w) // 2
|
|
h_start = (img_h - h) // 2
|
|
|
|
w_end = w_start + w
|
|
h_end = h_start + h
|
|
return img[h_start:h_end, w_start:w_end, :]
|
|
|
|
|
|
class NormalizeImage(object):
|
|
def __init__(self, scale=None, mean=None, std=None):
|
|
self.scale = np.float32(scale if scale is not None else 1.0 / 255.0)
|
|
mean = mean if mean is not None else [0.485, 0.456, 0.406]
|
|
std = std if std is not None else [0.229, 0.224, 0.225]
|
|
|
|
shape = (1, 1, 3)
|
|
self.mean = np.array(mean).reshape(shape).astype('float32')
|
|
self.std = np.array(std).reshape(shape).astype('float32')
|
|
|
|
def __call__(self, img):
|
|
return (img.astype('float32') * self.scale - self.mean) / self.std
|
|
|
|
|
|
class ToTensor(object):
|
|
def __init__(self):
|
|
pass
|
|
|
|
def __call__(self, img):
|
|
img = img.transpose((2, 0, 1))
|
|
return img
|
|
|
|
|
|
class Base64ToCV2(object):
|
|
def __init__(self):
|
|
pass
|
|
|
|
def __call__(self, b64str):
|
|
import base64
|
|
data = base64.b64decode(b64str.encode('utf8'))
|
|
data = np.fromstring(data, np.uint8)
|
|
data = cv2.imdecode(data, cv2.IMREAD_COLOR)[:, :, ::-1]
|
|
return data
|