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PaddleRS/paddlers/models/ppdet/metrics/widerface_utils.py

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# Copyright (c) 2022 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 cv2
import numpy as np
from collections import OrderedDict
import paddle
from paddlers.models.ppdet.utils.logger import setup_logger
logger = setup_logger(__name__)
__all__ = ['face_eval_run', 'lmk2out']
def face_eval_run(model,
image_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(image_dir, im_path)
if eval_mode == 'fddb':
image_path += '.jpg'
assert os.path.exists(image_path)
image = cv2.imread(image_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
if multi_scale:
shrink, max_shrink = get_shrink(image.shape[0], image.shape[1])
det0 = detect_face(model, image, shrink)
det1 = flip_test(model, image, shrink)
[det2, det3] = multi_scale_test(model, image, max_shrink)
det4 = multi_scale_test_pyramid(model, image, max_shrink)
det = np.row_stack((det0, det1, det2, det3, det4))
dets = bbox_vote(det)
else:
dets = detect_face(model, 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(model, image, shrink):
image_shape = [image.shape[0], image.shape[1]]
if shrink != 1:
h, w = int(image_shape[0] * shrink), int(image_shape[1] * shrink)
image = cv2.resize(image, (w, h))
image_shape = [h, w]
img = face_img_process(image)
image_shape = np.asarray([image_shape])
scale_factor = np.asarray([[shrink, shrink]])
data = {
"image": paddle.to_tensor(
img, dtype='float32'),
"im_shape": paddle.to_tensor(
image_shape, dtype='float32'),
"scale_factor": paddle.to_tensor(
scale_factor, dtype='float32')
}
model.eval()
detection = model(data)
detection = detection['bbox'].numpy()
# 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 = detection[:, 2]
det_ymin = detection[:, 3]
det_xmax = detection[:, 4]
det_ymax = detection[:, 5]
det = np.column_stack((det_xmin, det_ymin, det_xmax, det_ymax, det_conf))
return det
def flip_test(model, image, shrink):
img = cv2.flip(image, 1)
det_f = detect_face(model, img, shrink)
det_t = np.zeros(det_f.shape)
img_width = image.shape[1]
det_t[:, 0] = img_width - det_f[:, 2]
det_t[:, 1] = det_f[:, 1]
det_t[:, 2] = img_width - det_f[:, 0]
det_t[:, 3] = det_f[:, 3]
det_t[:, 4] = det_f[:, 4]
return det_t
def multi_scale_test(model, 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(model, 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(model, 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(model, image, bt)))
bt *= 2
det_b = np.row_stack((det_b, detect_face(model, 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(model, image, max_shrink):
# Use image pyramids to detect faces
det_b = detect_face(model, 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(model, 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 to_chw(image):
"""
Transpose image from HWC to CHW.
Args:
image (np.array): an image with HWC layout.
"""
# HWC to CHW
if len(image.shape) == 3:
image = np.swapaxes(image, 1, 2)
image = np.swapaxes(image, 1, 0)
return image
def face_img_process(image,
mean=[104., 117., 123.],
std=[127.502231, 127.502231, 127.502231]):
img = np.array(image)
img = to_chw(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 get_shrink(height, width):
"""
Args:
height (int): image height.
width (int): image width.
"""
# avoid out of memory
max_shrink_v1 = (0x7fffffff / 577.0 / (height * width))**0.5
max_shrink_v2 = ((678 * 1024 * 2.0 * 2.0) / (height * width))**0.5
def get_round(x, loc):
str_x = str(x)
if '.' in str_x:
str_before, str_after = str_x.split('.')
len_after = len(str_after)
if len_after >= 3:
str_final = str_before + '.' + str_after[0:loc]
return float(str_final)
else:
return x
max_shrink = get_round(min(max_shrink_v1, max_shrink_v2), 2) - 0.3
if max_shrink >= 1.5 and max_shrink < 2:
max_shrink = max_shrink - 0.1
elif max_shrink >= 2 and max_shrink < 3:
max_shrink = max_shrink - 0.2
elif max_shrink >= 3 and max_shrink < 4:
max_shrink = max_shrink - 0.3
elif max_shrink >= 4 and max_shrink < 5:
max_shrink = max_shrink - 0.4
elif max_shrink >= 5:
max_shrink = max_shrink - 0.5
elif max_shrink <= 0.1:
max_shrink = 0.1
shrink = max_shrink if max_shrink < 1 else 1
return shrink, max_shrink
def bbox_vote(det):
order = det[:, 4].ravel().argsort()[::-1]
det = det[order, :]
if det.shape[0] == 0:
dets = np.array([[10, 10, 20, 20, 0.002]])
det = np.empty(shape=[0, 5])
while det.shape[0] > 0:
# IOU
area = (det[:, 2] - det[:, 0] + 1) * (det[:, 3] - det[:, 1] + 1)
xx1 = np.maximum(det[0, 0], det[:, 0])
yy1 = np.maximum(det[0, 1], det[:, 1])
xx2 = np.minimum(det[0, 2], det[:, 2])
yy2 = np.minimum(det[0, 3], det[:, 3])
w = np.maximum(0.0, xx2 - xx1 + 1)
h = np.maximum(0.0, yy2 - yy1 + 1)
inter = w * h
o = inter / (area[0] + area[:] - inter)
# nms
merge_index = np.where(o >= 0.3)[0]
det_accu = det[merge_index, :]
det = np.delete(det, merge_index, 0)
if merge_index.shape[0] <= 1:
if det.shape[0] == 0:
try:
dets = np.row_stack((dets, det_accu))
except:
dets = det_accu
continue
det_accu[:, 0:4] = det_accu[:, 0:4] * np.tile(det_accu[:, -1:], (1, 4))
max_score = np.max(det_accu[:, 4])
det_accu_sum = np.zeros((1, 5))
det_accu_sum[:, 0:4] = np.sum(det_accu[:, 0:4],
axis=0) / np.sum(det_accu[:, -1:])
det_accu_sum[:, 4] = max_score
try:
dets = np.row_stack((dets, det_accu_sum))
except:
dets = det_accu_sum
dets = dets[0:750, :]
keep_index = np.where(dets[:, 4] >= 0.01)[0]
dets = dets[keep_index, :]
return dets
def save_widerface_bboxes(image_path, bboxes_scores, output_dir):
image_name = image_path.split('/')[-1]
image_class = image_path.split('/')[-2]
odir = os.path.join(output_dir, image_class)
if not os.path.exists(odir):
os.makedirs(odir)
ofname = os.path.join(odir, '%s.txt' % (image_name[:-4]))
f = open(ofname, 'w')
f.write('{:s}\n'.format(image_class + '/' + image_name))
f.write('{:d}\n'.format(bboxes_scores.shape[0]))
for box_score in bboxes_scores:
xmin, ymin, xmax, ymax, score = box_score
f.write('{:.1f} {:.1f} {:.1f} {:.1f} {:.3f}\n'.format(xmin, ymin, (
xmax - xmin + 1), (ymax - ymin + 1), score))
f.close()
logger.info("The predicted result is saved as {}".format(ofname))
def save_fddb_bboxes(bboxes_scores,
output_dir,
output_fname='pred_fddb_res.txt'):
if not os.path.exists(output_dir):
os.makedirs(output_dir)
predict_file = os.path.join(output_dir, output_fname)
f = open(predict_file, 'w')
for image_path, dets in bboxes_scores.iteritems():
f.write('{:s}\n'.format(image_path))
f.write('{:d}\n'.format(dets.shape[0]))
for box_score in dets:
xmin, ymin, xmax, ymax, score = box_score
width, height = xmax - xmin, ymax - ymin
f.write('{:.1f} {:.1f} {:.1f} {:.1f} {:.3f}\n'
.format(xmin, ymin, width, height, score))
logger.info("The predicted result is saved as {}".format(predict_file))
return predict_file
def lmk2out(results, is_bbox_normalized=False):
"""
Args:
results: request a dict, should include: `landmark`, `im_id`,
if is_bbox_normalized=True, also need `im_shape`.
is_bbox_normalized: whether or not landmark is normalized.
"""
xywh_res = []
for t in results:
bboxes = t['bbox'][0]
lengths = t['bbox'][1][0]
im_ids = np.array(t['im_id'][0]).flatten()
if bboxes.shape == (1, 1) or bboxes is None:
continue
face_index = t['face_index'][0]
prior_box = t['prior_boxes'][0]
predict_lmk = t['landmark'][0]
prior = np.reshape(prior_box, (-1, 4))
predictlmk = np.reshape(predict_lmk, (-1, 10))
k = 0
for a in range(len(lengths)):
num = lengths[a]
im_id = int(im_ids[a])
for i in range(num):
score = bboxes[k][1]
theindex = face_index[i][0]
me_prior = prior[theindex, :]
lmk_pred = predictlmk[theindex, :]
prior_w = me_prior[2] - me_prior[0]
prior_h = me_prior[3] - me_prior[1]
prior_w_center = (me_prior[2] + me_prior[0]) / 2
prior_h_center = (me_prior[3] + me_prior[1]) / 2
lmk_decode = np.zeros((10))
for j in [0, 2, 4, 6, 8]:
lmk_decode[j] = lmk_pred[
j] * 0.1 * prior_w + prior_w_center
for j in [1, 3, 5, 7, 9]:
lmk_decode[j] = lmk_pred[
j] * 0.1 * prior_h + prior_h_center
im_shape = t['im_shape'][0][a].tolist()
image_h, image_w = int(im_shape[0]), int(im_shape[1])
if is_bbox_normalized:
lmk_decode = lmk_decode * np.array([
image_w, image_h, image_w, image_h, image_w, image_h,
image_w, image_h, image_w, image_h
])
lmk_res = {
'image_id': im_id,
'landmark': lmk_decode,
'score': score,
}
xywh_res.append(lmk_res)
k += 1
return xywh_res