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face_eval.py
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face_eval.py
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# Copyright (c) 2019 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import paddle.fluid as fluid
import numpy as np
from PIL import Image
from collections import OrderedDict
import ppdet.utils.checkpoint as checkpoint
from ppdet.utils.cli import ArgsParser
from ppdet.utils.check import check_gpu
from ppdet.utils.widerface_eval_utils import get_shrink, bbox_vote, \
save_widerface_bboxes, save_fddb_bboxes, to_chw_bgr
from ppdet.core.workspace import load_config, merge_config, create
from ppdet.modeling.model_input import create_feed
import logging
FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
logging.basicConfig(level=logging.INFO, format=FORMAT)
logger = logging.getLogger(__name__)
def face_img_process(image,
mean=[104., 117., 123.],
std=[127.502231, 127.502231, 127.502231]):
img = np.array(image)
img = to_chw_bgr(img)
img = img.astype('float32')
img -= np.array(mean)[:, np.newaxis, np.newaxis].astype('float32')
img /= np.array(std)[:, np.newaxis, np.newaxis].astype('float32')
img = [img]
img = np.array(img)
return img
def face_eval_run(exe,
compile_program,
fetches,
img_root_dir,
gt_file,
pred_dir='output/pred',
eval_mode='widerface',
multi_scale=False):
# load ground truth files
with open(gt_file, 'r') as f:
gt_lines = f.readlines()
imid2path = []
pos_gt = 0
while pos_gt < len(gt_lines):
name_gt = gt_lines[pos_gt].strip('\n\t').split()[0]
imid2path.append(name_gt)
pos_gt += 1
n_gt = int(gt_lines[pos_gt].strip('\n\t').split()[0])
pos_gt += 1 + n_gt
logger.info('The ground truth file load {} images'.format(len(imid2path)))
dets_dist = OrderedDict()
for iter_id, im_path in enumerate(imid2path):
image_path = os.path.join(img_root_dir, im_path)
if eval_mode == 'fddb':
image_path += '.jpg'
image = Image.open(image_path).convert('RGB')
if multi_scale:
shrink, max_shrink = get_shrink(image.size[1], image.size[0])
det0 = detect_face(exe, compile_program, fetches, image, shrink)
det1 = flip_test(exe, compile_program, fetches, image, shrink)
[det2, det3] = multi_scale_test(exe, compile_program, fetches,
image, max_shrink)
det4 = multi_scale_test_pyramid(exe, compile_program, fetches,
image, max_shrink)
det = np.row_stack((det0, det1, det2, det3, det4))
dets = bbox_vote(det)
else:
dets = detect_face(exe, compile_program, fetches, image, 1)
if eval_mode == 'widerface':
save_widerface_bboxes(image_path, dets, pred_dir)
else:
dets_dist[im_path] = dets
if iter_id % 100 == 0:
logger.info('Test iter {}'.format(iter_id))
if eval_mode == 'fddb':
save_fddb_bboxes(dets_dist, pred_dir)
logger.info("Finish evaluation.")
def detect_face(exe, compile_program, fetches, image, shrink):
image_shape = [3, image.size[1], image.size[0]]
if shrink != 1:
h, w = int(image_shape[1] * shrink), int(image_shape[2] * shrink)
image = image.resize((w, h), Image.ANTIALIAS)
image_shape = [3, h, w]
img = face_img_process(image)
detection, = exe.run(compile_program,
feed={'image': img},
fetch_list=[fetches['bbox']],
return_numpy=False)
detection = np.array(detection)
# layout: xmin, ymin, xmax. ymax, score
if np.prod(detection.shape) == 1:
logger.info("No face detected")
return np.array([[0, 0, 0, 0, 0]])
det_conf = detection[:, 1]
det_xmin = image_shape[2] * detection[:, 2] / shrink
det_ymin = image_shape[1] * detection[:, 3] / shrink
det_xmax = image_shape[2] * detection[:, 4] / shrink
det_ymax = image_shape[1] * detection[:, 5] / shrink
det = np.column_stack((det_xmin, det_ymin, det_xmax, det_ymax, det_conf))
return det
def flip_test(exe, compile_program, fetches, image, shrink):
img = image.transpose(Image.FLIP_LEFT_RIGHT)
det_f = detect_face(exe, compile_program, fetches, img, shrink)
det_t = np.zeros(det_f.shape)
# image.size: [width, height]
det_t[:, 0] = image.size[0] - det_f[:, 2]
det_t[:, 1] = det_f[:, 1]
det_t[:, 2] = image.size[0] - det_f[:, 0]
det_t[:, 3] = det_f[:, 3]
det_t[:, 4] = det_f[:, 4]
return det_t
def multi_scale_test(exe, compile_program, fetches, image, max_shrink):
# Shrink detecting is only used to detect big faces
st = 0.5 if max_shrink >= 0.75 else 0.5 * max_shrink
det_s = detect_face(exe, compile_program, fetches, image, st)
index = np.where(
np.maximum(det_s[:, 2] - det_s[:, 0] + 1, det_s[:, 3] - det_s[:, 1] + 1)
> 30)[0]
det_s = det_s[index, :]
# Enlarge one times
bt = min(2, max_shrink) if max_shrink > 1 else (st + max_shrink) / 2
det_b = detect_face(exe, compile_program, fetches, image, bt)
# Enlarge small image x times for small faces
if max_shrink > 2:
bt *= 2
while bt < max_shrink:
det_b = np.row_stack((det_b, detect_face(exe, compile_program,
fetches, image, bt)))
bt *= 2
det_b = np.row_stack((det_b, detect_face(exe, compile_program, fetches,
image, max_shrink)))
# Enlarged images are only used to detect small faces.
if bt > 1:
index = np.where(
np.minimum(det_b[:, 2] - det_b[:, 0] + 1,
det_b[:, 3] - det_b[:, 1] + 1) < 100)[0]
det_b = det_b[index, :]
# Shrinked images are only used to detect big faces.
else:
index = np.where(
np.maximum(det_b[:, 2] - det_b[:, 0] + 1,
det_b[:, 3] - det_b[:, 1] + 1) > 30)[0]
det_b = det_b[index, :]
return det_s, det_b
def multi_scale_test_pyramid(exe, compile_program, fetches, image, max_shrink):
# Use image pyramids to detect faces
det_b = detect_face(exe, compile_program, fetches, image, 0.25)
index = np.where(
np.maximum(det_b[:, 2] - det_b[:, 0] + 1, det_b[:, 3] - det_b[:, 1] + 1)
> 30)[0]
det_b = det_b[index, :]
st = [0.75, 1.25, 1.5, 1.75]
for i in range(len(st)):
if st[i] <= max_shrink:
det_temp = detect_face(exe, compile_program, fetches, image, st[i])
# Enlarged images are only used to detect small faces.
if st[i] > 1:
index = np.where(
np.minimum(det_temp[:, 2] - det_temp[:, 0] + 1,
det_temp[:, 3] - det_temp[:, 1] + 1) < 100)[0]
det_temp = det_temp[index, :]
# Shrinked images are only used to detect big faces.
else:
index = np.where(
np.maximum(det_temp[:, 2] - det_temp[:, 0] + 1,
det_temp[:, 3] - det_temp[:, 1] + 1) > 30)[0]
det_temp = det_temp[index, :]
det_b = np.row_stack((det_b, det_temp))
return det_b
def main():
"""
Main evaluate function
"""
cfg = load_config(FLAGS.config)
if 'architecture' in cfg:
main_arch = cfg.architecture
else:
raise ValueError("'architecture' not specified in config file.")
merge_config(FLAGS.opt)
# check if set use_gpu=True in paddlepaddle cpu version
check_gpu(cfg.use_gpu)
if 'eval_feed' not in cfg:
eval_feed = create(main_arch + 'EvalFeed')
else:
eval_feed = create(cfg.eval_feed)
# define executor
place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
# build program
model = create(main_arch)
startup_prog = fluid.Program()
eval_prog = fluid.Program()
with fluid.program_guard(eval_prog, startup_prog):
with fluid.unique_name.guard():
_, feed_vars = create_feed(eval_feed, iterable=True)
fetches = model.eval(feed_vars)
eval_prog = eval_prog.clone(True)
# load model
exe.run(startup_prog)
if 'weights' in cfg:
checkpoint.load_params(exe, eval_prog, cfg.weights)
assert cfg.metric in ['WIDERFACE'], \
"unknown metric type {}".format(cfg.metric)
annotation_file = getattr(eval_feed.dataset, 'annotation', None)
dataset_dir = FLAGS.dataset_dir if FLAGS.dataset_dir else \
getattr(eval_feed.dataset, 'dataset_dir', None)
img_root_dir = dataset_dir
if FLAGS.eval_mode == "widerface":
image_dir = getattr(eval_feed.dataset, 'image_dir', None)
img_root_dir = os.path.join(dataset_dir, image_dir)
gt_file = os.path.join(dataset_dir, annotation_file)
pred_dir = FLAGS.output_eval if FLAGS.output_eval else 'output/pred'
face_eval_run(
exe,
eval_prog,
fetches,
img_root_dir,
gt_file,
pred_dir=pred_dir,
eval_mode=FLAGS.eval_mode,
multi_scale=FLAGS.multi_scale)
if __name__ == '__main__':
parser = ArgsParser()
parser.add_argument(
"-d",
"--dataset_dir",
default=None,
type=str,
help="Dataset path, same as DataFeed.dataset.dataset_dir")
parser.add_argument(
"-f",
"--output_eval",
default=None,
type=str,
help="Evaluation file directory, default is current directory.")
parser.add_argument(
"-e",
"--eval_mode",
default="widerface",
type=str,
help="Evaluation mode, include `widerface` and `fddb`, default is `widerface`."
)
parser.add_argument(
"--multi_scale",
action='store_true',
default=False,
help="If True it will select `multi_scale` evaluation. Default is `False`, it will select `single-scale` evaluation."
)
FLAGS = parser.parse_args()
main()