def test(FAPrefix, SSHPrefix, imgPath):
FAPredictor = FaceAttributesPredictor(FAPrefix, epoch=1, ctxId=0)
faceDetector = SSHDetector(SSHPrefix, epoch=0, ctx_id=0)
images_name = get_images_name_list(imgPath)
for img in images_name:
print('image: %s' % (img))
imgMat = cv2.imread(img)
faces = faceDetector.detect(imgMat, threshold=0.8)
print(faces.shape[0], ' faces detected.\n')
imgMatList = []
for num in range(faces.shape[0]):
kpoint = faces[num, 5:15]
kpoint = np.array(kpoint).reshape(5, 2)
cropImg = norm_crop(imgMat, kpoint)
imgMatList.append(cropImg)
faResult = FAPredictor.predict(imgMatList)
assert len(faResult) == len(imgMatList)
show_face_rec_kpoint_attribute(faces, img, imgMat, faResult)
def main(args):
detector = SSHDetector(args.prefix, args.epoch, args.gpuId)
imgDir = args.imagesDir
savePath = args.savePath
imgNameList = os.listdir(imgDir)
for imgName in imgNameList:
imgPath = imgDir + imgName
if not os.path.isfile(imgPath):
continue
print(imgPath)
img = cv2.imread(imgPath)
im_shape = img.shape
print(im_shape)
target_size = scales[0]
max_size = scales[1]
im_size_min = np.min(im_shape[0:2])
im_size_max = np.max(im_shape[0:2])
if im_size_min > target_size or im_size_max > max_size:
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)
img = cv2.resize(img, None, None, fx=im_scale, fy=im_scale)
print('resize to', img.shape)
# timea = datetime.datetime.now()
faces = detector.detect(img, threshold=0.8)
# timeb = datetime.datetime.now()
for num in range(faces.shape[0]):
bbox = faces[num, 0:4]
cv2.rectangle(img, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (0, 255, 0), 1)
kpoint = faces[num, 5:15]
for knum in range(5):
cv2.circle(img, (kpoint[2 * knum], kpoint[2 * knum + 1]), 2, [0, 0, 255], 2)
cv2.imwrite(savePath + imgName, img)
import cv2
import sys
import numpy as np
import datetime
#sys.path.append('.')
from ssh_detector import SSHDetector
scales = [1200, 1600]
t = 2
detector = SSHDetector('./model/e2ef', 0)
f = '../sample-images/t1.jpg'
if len(sys.argv) > 1:
f = sys.argv[1]
img = cv2.imread(f)
im_shape = img.shape
print(im_shape)
target_size = scales[0]
max_size = scales[1]
im_size_min = np.min(im_shape[0:2])
im_size_max = np.max(im_shape[0:2])
if im_size_min > target_size or im_size_max > max_size:
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)
img = cv2.resize(img, None, None, fx=im_scale, fy=im_scale)
print('resize to', img.shape)
for i in xrange(t - 1): #warmup
faces = detector.detect(img)
timea = datetime.datetime.now()
import cv2
import sys
import numpy as np
import datetime
sys.path.append('../SSH')
sys.path.append('../alignment')
from ssh_detector import SSHDetector
from alignment import Alignment
from embedding import Embedding
#short_max = 800
scales = [1200, 1600]
t = 2
detector = SSHDetector('../SSH/model/e2ef', 0)
alignment = Alignment('../alignment/model/3d_I5', 12)
embedding = Embedding('./model/model', 0)
out_filename = './out.png'
f = '../sample-images/t1.jpg'
if len(sys.argv) > 1:
f = sys.argv[1]
img = cv2.imread(f)
im_shape = img.shape
print(im_shape)
target_size = scales[0]
max_size = scales[1]
im_size_min = np.min(im_shape[0:2])
im_size_max = np.max(im_shape[0:2])
if im_size_min > target_size or im_size_max > max_size:
im_scale = float(target_size) / float(im_size_min)
import cv2
import sys
import numpy as np
import datetime
from alignment import Alignment
sys.path.append('../SSH')
from ssh_detector import SSHDetector
#short_max = 800
scales = [1200, 1600]
t = 2
detector = SSHDetector('../SSH/model/e2ef', 0)
alignment = Alignment('./model/3d_I5', 12)
out_filename = './out.png'
f = '../sample-images/t1.jpg'
if len(sys.argv)>1:
f = sys.argv[1]
img = cv2.imread(f)
im_shape = img.shape
print(im_shape)
target_size = scales[0]
max_size = scales[1]
im_size_min = np.min(im_shape[0:2])
im_size_max = np.max(im_shape[0:2])
if im_size_min>target_size or im_size_max>max_size:
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)
import cv2
import sys
import numpy as np
import datetime
#sys.path.append('.')
from ssh_detector import SSHDetector
scales = [1200, 1600]
#scales = [600, 1200]
t = 2
detector = SSHDetector('./model/e2ef', 0)
f = '../sample-images/t1.jpg'
if len(sys.argv)>1:
f = sys.argv[1]
img = cv2.imread(f)
im_shape = img.shape
print(im_shape)
target_size = scales[0]
max_size = scales[1]
im_size_min = np.min(im_shape[0:2])
im_size_max = np.max(im_shape[0:2])
if im_size_min>target_size or im_size_max>max_size:
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)
img = cv2.resize(img, None, None, fx=im_scale, fy=im_scale)
print('resize to', img.shape)
for i in xrange(t-1): #warmup
import pims
import lz
# v = pims.Video(lz.home_path + 'prj/insightface/datasets/facebank/video.mp4')
# shape = v.frame_shape
# v[0].shape
#
# for frame in v:
# if frame is None:
# break
scales = [1200, 1600]
# scales = [600, 1200]
t = 2
detector = SSHDetector('./model/e2ef', 0)
f = '../sample-images/t1.jpg'
if len(sys.argv) > 1:
f = sys.argv[1]
img = cv2.imread(f)
xrange = range
def det_face(img):
im_shape = img.shape
print(im_shape)
target_size = scales[0]
max_size = scales[1]
im_size_min = np.min(im_shape[0:2])
im_size_max = np.max(im_shape[0:2])