def gen_landmark48_data(data_dir,
anno_file,
pnet_model_file,
rnet_model_file,
prefix_path='',
use_cuda=True,
vis=False):
pnet, rnet, _ = create_mtcnn_net(p_model_path=pnet_model_file,
r_model_path=rnet_model_file,
use_cuda=use_cuda)
mtcnn_detector = MtcnnDetector(pnet=pnet, rnet=rnet, min_face_size=12)
imagedb = ImageDB(anno_file, mode="test", prefix_path=prefix_path)
imdb = imagedb.load_imdb()
image_reader = TestImageLoader(imdb, 1, False)
all_boxes = list()
batch_idx = 0
for databatch in image_reader:
if batch_idx % 100 == 0:
print("%d images done" % batch_idx)
im = databatch
if im.shape[0] >= 1200 or im.shape[1] >= 1200:
all_boxes.append(np.array([]))
batch_idx += 1
continue
t = time.time()
p_boxes, p_boxes_align = mtcnn_detector.detect_pnet(im=im)
boxes, boxes_align = mtcnn_detector.detect_rnet(im=im,
dets=p_boxes_align)
if boxes_align is None:
all_boxes.append(np.array([]))
batch_idx += 1
continue
if vis:
rgb_im = cv2.cvtColor(np.asarray(im), cv2.COLOR_BGR2RGB)
vision.vis_two(rgb_im, boxes, boxes_align)
t1 = time.time() - t
t = time.time()
all_boxes.append(boxes_align)
batch_idx += 1
save_path = config.MODEL_STORE_DIR
if not os.path.exists(save_path):
os.mkdir(save_path)
save_file = os.path.join(save_path, "detections_%d.pkl" % int(time.time()))
with open(save_file, 'wb') as f:
cPickle.dump(all_boxes, f, cPickle.HIGHEST_PROTOCOL)
gen_sample_data(data_dir, anno_file, save_file, prefix_path)
import cv2
from core.detect import create_mtcnn_net, MtcnnDetector
import core.vision as vision
if __name__ == '__main__':
# refer to your local model path
p_model = "./model_store/pnet_epoch.pt"
r_model = "./model_store/rnet_epoch.pt"
o_model = "./model_store/onet_epoch.pt"
pnet, rnet, onet = create_mtcnn_net(p_model_path=p_model, r_model_path=r_model, o_model_path=o_model, use_cuda=True)
mtcnn_detector = MtcnnDetector(pnet=pnet, rnet=rnet, onet=onet, min_face_size=24)
img = cv2.imread("./test.jpg")
b, g, r = cv2.split(img)
img2 = cv2.merge([r, g, b])
bboxs, landmarks = mtcnn_detector.detect_face(img)
# print box_align
vision.vis_face(img2,bboxs,landmarks)
import cv2
from core.detect import create_mtcnn_net, MtcnnDetector
import core.vision as vision
if __name__ == '__main__':
pnet, rnet, onet = create_mtcnn_net(
p_model_path="./model_store/pnet_epoch.pt",
r_model_path="./model_store/rnet_epoch.pt",
o_model_path="./model_store/onet_epoch.pt",
use_cuda=True)
mtcnn_detector = MtcnnDetector(pnet=pnet,
rnet=rnet,
onet=onet,
min_face_size=24)
img = cv2.imread(
"/home/oljike/PycharmProjects/GlassDataset/Dataset/positive/with_glasses/17.jpg"
)
img_bg = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
bboxs, landmarks = mtcnn_detector.detect_face(img)
# print box_align
vision.vis_face(img_bg, bboxs, landmarks)
import cv2
from setuptools.command.saveopts import saveopts
from core.detect import create_mtcnn_net, MtcnnDetector
from core.vision import vis_face
if __name__ == '__main__':
pnet, rnet, onet = create_mtcnn_net(
p_model_path="./original_model/pnet_epoch.pt",
r_model_path="./original_model/rnet_epoch.pt",
o_model_path="./original_model/onet_epoch.pt",
use_cuda=False)
mtcnn_detector = MtcnnDetector(pnet=pnet,
rnet=rnet,
onet=onet,
min_face_size=24)
img = cv2.imread("./test.jpg")
img_bg = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
#b, g, r = cv2.split(img)
#img2 = cv2.merge([r, g, b])
bboxs, landmarks = mtcnn_detector.detect_face(img)
print(bboxs)
# print box_align
save_name = 'r_5.jpg'
# for i in bboxs:
# print(bboxs.shape)
vis_face(img_bg, bboxs, landmarks, save_name)
img = cv2.imread(save_name)
cv2.imshow("img", img)
cv2.waitKey(0)
def gen_rnet_data(data_dir,
anno_file,
pnet_model_file,
prefix_path='',
use_cuda=True,
vis=False):
"""
:param data_dir: train data
:param anno_file:
:param pnet_model_file:
:param prefix_path:
:param use_cuda:
:param vis:
:return:
"""
# load trained pnet model
pnet, _, _ = create_mtcnn_net(p_model_path=pnet_model_file,
use_cuda=use_cuda)
mtcnn_detector = MtcnnDetector(pnet=pnet, min_face_size=12)
# load original_anno_file, length = 12880
imagedb = ImageDB(anno_file, mode="test", prefix_path=prefix_path)
imdb = imagedb.load_imdb()
image_reader = TestImageLoader(imdb, 1, False)
all_boxes = list()
batch_idx = 0
print('size:%d' % image_reader.size)
for databatch in image_reader:
if batch_idx % 100 == 0:
print("%d images done" % batch_idx)
im = databatch
t = time.time()
# obtain boxes and aligned boxes
boxes, boxes_align = mtcnn_detector.detect_pnet(im=im)
if boxes_align is None:
all_boxes.append(np.array([]))
batch_idx += 1
continue
if vis:
rgb_im = cv2.cvtColor(np.asarray(im), cv2.COLOR_BGR2RGB)
vision.vis_two(rgb_im, boxes, boxes_align)
t1 = time.time() - t
t = time.time()
all_boxes.append(boxes_align)
batch_idx += 1
# if batch_idx == 100:
# break
# print("shape of all boxes {0}".format(all_boxes))
# time.sleep(5)
# save_path = model_store_path()
# './model_store'
save_path = '/home/quang/Downloads/mtcnn-pytorch/model_store'
if not os.path.exists(save_path):
os.mkdir(save_path)
save_file = os.path.join(save_path, "detections_%d.pkl" % int(time.time()))
with open(save_file, 'wb') as f:
cPickle.dump(all_boxes, f, cPickle.HIGHEST_PROTOCOL)
gen_rnet_sample_data(data_dir, anno_file, save_file, prefix_path)
