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test_widerface.py
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test_widerface.py
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from __future__ import print_function
import argparse
import os
import sys
import cv2
import numpy as np
from rcnn.logger import logger
# from rcnn.config import config, default, generate_config
# from rcnn.tools.test_rcnn import test_rcnn
# from rcnn.tools.test_rpn import test_rpn
from rcnn.processing.bbox_transform import bbox_overlaps
from retinaface import RetinaFace
def parse_args():
parser = argparse.ArgumentParser(description='Test widerface by retinaface detector')
# general
parser.add_argument('--network', help='network name', default='net3', type=str)
parser.add_argument('--dataset', help='dataset name', default='retinaface', type=str)
parser.add_argument('--image-set', help='image_set name', default='val', type=str)
parser.add_argument('--root-path', help='output data folder', default='./data', type=str)
parser.add_argument('--dataset-path', help='dataset path', default='./data/retinaface', type=str)
parser.add_argument('--gpu', help='GPU device to test with', default=0, type=int)
# testing
parser.add_argument('--prefix', help='model to test with', default='', type=str)
parser.add_argument('--epoch', help='model to test with', default=0, type=int)
parser.add_argument('--output', help='output folder', default='./wout', type=str)
parser.add_argument('--nocrop', help='', action='store_true')
parser.add_argument('--thresh', help='valid detection threshold', default=0.02, type=float)
parser.add_argument('--mode', help='test mode, 0 for fast, 1 for accurate', default=1, type=int)
#parser.add_argument('--pyramid', help='enable pyramid test', action='store_true')
#parser.add_argument('--bbox-vote', help='', action='store_true')
parser.add_argument('--part', help='', default=0, type=int)
parser.add_argument('--parts', help='', default=1, type=int)
args = parser.parse_args()
return args
detector = None
args = None
imgid = -1
def get_boxes(roi, pyramid):
global imgid
im = cv2.imread(roi['image'])
do_flip = False
if not pyramid:
target_size = 1200
max_size = 1600
#do_flip = True
target_size = 1504
max_size = 2000
target_size = 1600
max_size = 2150
im_shape = im.shape
im_size_min = np.min(im_shape[0:2])
im_size_max = np.max(im_shape[0:2])
im_scale = float(target_size) / float(im_size_min)
# prevent bigger axis from being more than max_size:
if np.round(im_scale * im_size_max) > max_size:
im_scale = float(max_size) / float(im_size_max)
scales = [im_scale]
else:
do_flip = True
#TEST_SCALES = [500, 800, 1200, 1600]
TEST_SCALES = [500, 800, 1100, 1400, 1700]
target_size = 800
max_size = 1200
im_shape = im.shape
im_size_min = np.min(im_shape[0:2])
im_size_max = np.max(im_shape[0:2])
im_scale = float(target_size) / float(im_size_min)
# prevent bigger axis from being more than max_size:
if np.round(im_scale * im_size_max) > max_size:
im_scale = float(max_size) / float(im_size_max)
scales = [float(scale)/target_size*im_scale for scale in TEST_SCALES]
boxes, landmarks = detector.detect(im, threshold=args.thresh, scales = scales, do_flip=do_flip)
#print(boxes.shape, landmarks.shape)
if imgid>=0 and imgid<100:
font = cv2.FONT_HERSHEY_SIMPLEX
for i in range(boxes.shape[0]):
box = boxes[i]
ibox = box[0:4].copy().astype(np.int)
cv2.rectangle(im, (ibox[0], ibox[1]), (ibox[2], ibox[3]), (255, 0, 0), 2)
#print('box', ibox)
#if len(ibox)>5:
# for l in range(5):
# pp = (ibox[5+l*2], ibox[6+l*2])
# cv2.circle(im, (pp[0], pp[1]), 1, (0, 0, 255), 1)
blur = box[5]
k = "%.3f"%blur
cv2.putText(im,k,(ibox[0]+2,ibox[1]+14), font, 0.6, (0,255,0), 2)
#landmarks = box[6:21].reshape( (5,3) )
if landmarks is not None:
for l in range(5):
color = (0,255,0)
landmark = landmarks[i][l]
pp = (int(landmark[0]), int(landmark[1]))
if landmark[2]-0.5<0.0:
color = (0,0,255)
cv2.circle(im, (pp[0], pp[1]), 1, color, 2)
filename = './testimages/%d.jpg'%imgid
cv2.imwrite(filename, im)
print(filename, 'wrote')
imgid+=1
return boxes
def test(args):
print('test with', args)
global detector
output_folder = args.output
if not os.path.exists(output_folder):
os.mkdir(output_folder)
detector = RetinaFace(args.prefix, args.epoch, args.gpu, network=args.network, nocrop=args.nocrop, vote=args.bbox_vote)
imdb = eval(args.dataset)(args.image_set, args.root_path, args.dataset_path)
roidb = imdb.gt_roidb()
gt_overlaps = np.zeros(0)
overall = [0.0, 0.0]
gt_max = np.array( (0.0, 0.0) )
num_pos = 0
print('roidb size', len(roidb))
for i in range(len(roidb)):
if i%args.parts!=args.part:
continue
#if i%10==0:
# print('processing', i, file=sys.stderr)
roi = roidb[i]
boxes = get_boxes(roi, args.pyramid)
if 'boxes' in roi:
gt_boxes = roi['boxes'].copy()
gt_areas = (gt_boxes[:, 2] - gt_boxes[:, 0] + 1) * (gt_boxes[:, 3] - gt_boxes[:, 1] + 1)
num_pos += gt_boxes.shape[0]
overlaps = bbox_overlaps(boxes.astype(np.float), gt_boxes.astype(np.float))
#print(im_info, gt_boxes.shape, boxes.shape, overlaps.shape, file=sys.stderr)
_gt_overlaps = np.zeros((gt_boxes.shape[0]))
if boxes.shape[0]>0:
_gt_overlaps = overlaps.max(axis=0)
#print('max_overlaps', _gt_overlaps, file=sys.stderr)
for j in range(len(_gt_overlaps)):
if _gt_overlaps[j]>0.5:
continue
#print(j, 'failed', gt_boxes[j], 'max_overlap:', _gt_overlaps[j], file=sys.stderr)
# append recorded IoU coverage level
found = (_gt_overlaps > 0.5).sum()
recall = found / float(gt_boxes.shape[0])
#print('recall', _recall, gt_boxes.shape[0], boxes.shape[0], gt_areas, 'num:', i, file=sys.stderr)
overall[0]+=found
overall[1]+=gt_boxes.shape[0]
#gt_overlaps = np.hstack((gt_overlaps, _gt_overlaps))
#_recall = (gt_overlaps >= threshold).sum() / float(num_pos)
recall_all = float(overall[0])/overall[1]
#print('recall_all', _recall, file=sys.stderr)
print('[%d]'%i, 'recall', recall, (gt_boxes.shape[0], boxes.shape[0]), 'all:', recall_all, file=sys.stderr)
else:
print('[%d]'%i, 'detect %d faces'%boxes.shape[0])
_vec = roidb[i]['image'].split('/')
out_dir = os.path.join(output_folder, _vec[-2])
if not os.path.exists(out_dir):
os.mkdir(out_dir)
out_file = os.path.join(out_dir, _vec[-1].replace('jpg', 'txt'))
with open(out_file, 'w') as f:
name = '/'.join(roidb[i]['image'].split('/')[-2:])
f.write("%s\n"%(name))
f.write("%d\n"%(boxes.shape[0]))
for b in range(boxes.shape[0]):
box = boxes[b]
f.write("%d %d %d %d %g \n"%(box[0], box[1], box[2]-box[0], box[3]-box[1], box[4]))
def main():
global args
args = parse_args()
args.pyramid = False
args.bbox_vote = False
if args.mode==1:
args.pyramid = True
args.bbox_vote = True
elif args.mode==2:
args.pyramid = True
args.bbox_vote = False
logger.info('Called with argument: %s' % args)
test(args)
if __name__ == '__main__':
main()