def Run(target):
assert target in ['rnet', 'onet']
model_path = GetModelPaths()
batch_size = [2048, 256, 16]
thresh = [0.4, 0.05, 0.7]
min_face_size = 24
detectors = [None, None, None]
detectors[0] = FcnDetector(P_Net, model_path[0])
if target == 'onet':
detectors[1] = Detector(R_Net, 24, batch_size[1], model_path[1])
data = read_annotation()
if cfg.debug:
data['bboxes'] = data['bboxes'][:20]
data['images'] = data['images'][:20]
test_data = TestLoader(data['images'])
mtcnn_detector = MtcnnDetector(detectors=detectors,
min_face_size=min_face_size,
stride=2,
threshold=thresh)
detections, _ = mtcnn_detector.detect_face(test_data)
save_file = join(cfg.path_output_files, 'detections_%s.pkl' % target)
with open(save_file, 'wb') as f:
pickle.dump(detections, f, 1)
save_hard_example(target, data, save_file)
def t_net(prefix, epoch,
batch_size, test_mode="PNet",
thresh=[0.6, 0.6, 0.7], min_face_size=25,
stride=2, slide_window=False, shuffle=False, vis=False):
detectors = [None, None, None]
print("Test model: ", test_mode)
#PNet-echo
model_path = ['%s-%s' % (x, y) for x, y in zip(prefix, epoch)]
print(model_path[0])
# load pnet model
if slide_window:
PNet = Detector(P_Net, 12, batch_size[0], model_path[0])
else:
PNet = FcnDetector(P_Net, model_path[0])
detectors[0] = PNet
# load rnet model
if test_mode in ["RNet", "ONet"]:
print("==================================", test_mode)
RNet = Detector(R_Net, 24, batch_size[1], model_path[1])
detectors[1] = RNet
# load onet model
if test_mode == "ONet":
print("==================================", test_mode)
ONet = Detector(O_Net, 48, batch_size[2], model_path[2])
detectors[2] = ONet
basedir = '.'
#anno_file
filename = './wider_face_train_bbx_gt.txt'
#read annatation(type:dict)
data = read_annotation(basedir,filename)
mtcnn_detector = MtcnnDetector(detectors=detectors, min_face_size=min_face_size,
stride=stride, threshold=thresh, slide_window=slide_window)
print("==================================")
# 注意是在“test”模式下
# imdb = IMDB("wider", image_set, root_path, dataset_path, 'test')
# gt_imdb = imdb.gt_imdb()
test_data = TestLoader(data['images'][:100])
#list
detections,_ = mtcnn_detector.detect_face(test_data)
save_net = 'RNet'
if test_mode == "PNet":
save_net = "RNet"
elif test_mode == "RNet":
save_net = "ONet"
#save detect result
save_path = os.path.join(data_dir, save_net)
print save_path
if not os.path.exists(save_path):
os.mkdir(save_path)
save_file = os.path.join(save_path, "detections.pkl")
with open(save_file, 'wb') as f:
pickle.dump(detections, f,1)
print("%s测试完成开始OHEM" % image_size)
save_hard_example(image_size, data, save_path)
def t_net(prefix, epoch,
batch_size, test_mode="PNet",
thresh=[0.6, 0.6, 0.7], min_face_size=25,
stride=2, slide_window=False, shuffle=False, vis=False):
detectors = [None, None, None]
print("Test model: ", test_mode)
#PNet-echo
model_path = ['%s-%s' % (x, y) for x, y in zip(prefix, epoch)]
print(model_path[0])
# load pnet model
if slide_window:
PNet = Detector(P_Net, 12, batch_size[0], model_path[0])
else:
PNet = FcnDetector(P_Net, model_path[0])
detectors[0] = PNet
# load rnet model
if test_mode in ["RNet", "ONet"]:
print("==================================", test_mode)
RNet = Detector(R_Net, 24, batch_size[1], model_path[1])
detectors[1] = RNet
# load onet model
if test_mode == "ONet":
print("==================================", test_mode)
ONet = Detector(O_Net, 48, batch_size[2], model_path[2])
detectors[2] = ONet
basedir = '.'
#anno_file
filename = './wider_face_train_bbx_gt.txt'
#read annatation(type:dict)
data = read_annotation(basedir,filename)
mtcnn_detector = MtcnnDetector(detectors=detectors, min_face_size=min_face_size,
stride=stride, threshold=thresh, slide_window=slide_window)
print("==================================")
# 注意是在“test”模式下
# imdb = IMDB("wider", image_set, root_path, dataset_path, 'test')
# gt_imdb = imdb.gt_imdb()
test_data = TestLoader(data['images'])
#list
detections,_ = mtcnn_detector.detect_face(test_data)
save_net = 'RNet'
if test_mode == "PNet":
save_net = "RNet"
elif test_mode == "RNet":
save_net = "ONet"
#save detect result
save_path = os.path.join(data_dir, save_net)
print save_path
if not os.path.exists(save_path):
os.mkdir(save_path)
save_file = os.path.join(save_path, "detections.pkl")
with open(save_file, 'wb') as f:
pickle.dump(detections, f,1)
print("%s测试完成开始OHEM" % image_size)
save_hard_example(image_size, data, save_path)
def t_net(prefix,
epoch,
data_dir,
batch_size,
PNet_factory,
RNet_factory,
test_mode="PNet",
thresh=[0.6, 0.6, 0.7],
min_face_size=25,
stride=2,
slide_window=False,
shuffle=False,
vis=False):
# load detectors
detectors = [None, None, None]
model_path = ['%s-%s' % (x, y) for x, y in zip(prefix, epoch)]
# load pnet model
if slide_window:
PNet = Detector(PNet_factory, 12, batch_size[0], model_path[0])
else:
PNet = FcnDetector(PNet_factory, model_path[0])
detectors[0] = PNet
# load rnet model
if test_mode == "RNet":
RNet = Detector(RNet_factory, 24, batch_size[1], model_path[1])
detectors[1] = RNet
# Get detections
basedir = '.'
filename = './wider_face_train_bbx_gt.txt'
data = read_annotation(basedir, filename)
mtcnn_detector = MtcnnDetector(detectors=detectors,
min_face_size=min_face_size,
stride=stride,
threshold=thresh,
slide_window=slide_window)
test_data = TestLoader(data['images'])
print("Getting detections for {} images.".format(test_data.size))
detections, _ = mtcnn_detector.detect_face(test_data)
# Save detections
if test_mode == "PNet":
save_net = "RNet"
elif test_mode == "RNet":
save_net = "ONet"
save_path = os.path.join(data_dir, save_net)
if not os.path.exists(save_path):
os.mkdir(save_path)
save_file = os.path.join(save_path, "detections.pkl")
with open(save_file, 'wb') as f:
pickle.dump(detections, f, 1)
save_hard_example(image_size, data, save_path)
imdb = get_imdb_fddb(data_dir)
nfold = len(imdb)
for i in xrange(nfold):
image_names = imdb[i]
print image_names
dets_file_name = os.path.join(out_dir,
'FDDB-det-fold-%02d.txt' % (i + 1))
fid = open(dets_file_name, 'w')
sys.stdout.write('%s ' % (i + 1))
image_names_abs = [
os.path.join(data_dir, image_name + '.jpg')
for image_name in image_names
]
print(image_names_abs)
test_data = TestLoader(image_names_abs)
all_boxes, _ = mtcnn_detector.detect_face(test_data)
for idx, im_name in enumerate(image_names):
img_path = os.path.join(data_dir, im_name + '.jpg')
image = cv2.imread(img_path)
boxes = all_boxes[idx]
if boxes is None:
fid.write(im_name + '\n')
fid.write(str(1) + '\n')
fid.write('%f %f %f %f %f\n' % (0, 0, 0, 0, 0.99))
continue
fid.write(im_name + '\n')
fid.write(str(len(boxes)) + '\n')
for box in boxes:
fid.write(
mtcnn_detector = MtcnnDetector(detectors=detectors,
min_face_size=min_face_size,
stride=stride,
threshold=thresh,
slide_window=slide_window)
gt_imdb = []
#gt_imdb.append("35_Basketball_Basketball_35_515.jpg")
#imdb_ = dict()"
#imdb_['image'] = im_path
#imdb_['label'] = 5
path = "../../DATA/test/lfpw_testImage"
for item in os.listdir(path):
gt_imdb.append(os.path.join(path, item))
test_data = TestLoader(gt_imdb)
all_boxes, landmarks = mtcnn_detector.detect_face(test_data)
count = 0
for imagepath in gt_imdb:
print(imagepath)
image = cv2.imread(imagepath)
for bbox in all_boxes[count]:
cv2.putText(image,
str(np.round(bbox[4], 2)), (int(bbox[0]), int(bbox[1])),
cv2.FONT_HERSHEY_TRIPLEX,
1,
color=(255, 0, 255))
cv2.rectangle(image, (int(bbox[0]), int(bbox[1])),
(int(bbox[2]), int(bbox[3])), (0, 0, 255))
'''
for landmark in landmarks[count]:
def t_net(prefix,
epoch,
batch_size,
test_mode="PNet",
thresh=(0.6, 0.6, 0.7),
min_face_size=25,
stride=2,
slide_window=False,
shuffle=False,
vis=False):
"""
:param prefix:
:param epoch:
:param batch_size:
:param test_mode:
:param thresh:
:param min_face_size:
:param stride:
:param slide_window:
:param shuffle:
:param vis:
:return:
"""
detectors = [None, None, None]
print("Test model: ", test_mode)
# PNet-echo
model_path = []
for x, y in zip(prefix, epoch):
model_path.append(os.path.join(x, test_mode.lower() + '.ckpt-%s' % y))
# model_path = ['%s/pnet.ckpt-%s' % (x, y) for x, y in zip(prefix, epoch)]
# load pnet model
if slide_window:
PNet = Detector(P_Net, 12, batch_size[0], model_path[0])
else:
PNet = FcnDetector(P_Net, model_path[0])
detectors[0] = PNet
# load rnet model
if test_mode in ["RNet", "ONet"]:
print("==================================", test_mode)
RNet = Detector(R_Net, 24, batch_size[1], model_path[1])
detectors[1] = RNet
# load onet model
if test_mode == "ONet":
print("==================================", test_mode)
ONet = Detector(O_Net, 48, batch_size[2], model_path[2])
detectors[2] = ONet
basedir = '../../DATA/'
# anno_file
filename = './wider_face_train_bbx_gt.txt'
# read anotation(type:dict), include 'images' and 'bboxes'
data = read_annotation(basedir, filename) # 12880张图片
mtcnn_detector = MtcnnDetector(detectors=detectors,
min_face_size=min_face_size,
stride=stride,
threshold=thresh,
slide_window=slide_window)
# 注意是在“test”模式下
# imdb = IMDB("wider", image_set, root_path, dataset_path, 'test')
# gt_imdb = imdb.gt_imdb()
print('load test data')
test_data = TestLoader(data['images'])
print('finish loading')
# list
print('start detecting....')
detections, _ = mtcnn_detector.detect_face(test_data)
print('finish detecting ')
save_net = 'RNet'
if test_mode == "PNet":
save_net = "RNet"
elif test_mode == "RNet":
save_net = "ONet"
# save detect result
save_path = os.path.join(data_dir, save_net)
print('save_path is :')
print(save_path)
if not os.path.exists(save_path):
os.mkdir(save_path)
save_file = os.path.join(save_path, "detections.pkl")
with open(save_file, 'wb') as f:
pickle.dump(detections, f, 1)
print("%s测试完成开始OHEM" % image_size)
save_hard_example(image_size, data, save_path)
mtcnn_detector = MtcnnDetector(detectors=detectors, min_face_size=min_face_size,
stride=stride, threshold=thresh, slide_window=slide_window)
imdb = get_imdb_fddb(data_dir)
nfold = len(imdb)
for i in xrange(nfold):
image_names = imdb[i]
print image_names
dets_file_name = os.path.join(out_dir, 'FDDB-det-fold-%02d.txt' % (i + 1))
fid = open(dets_file_name,'w')
sys.stdout.write('%s ' % (i + 1))
image_names_abs = [os.path.join(data_dir,'originalPics',image_name+'.jpg') for image_name in image_names]
test_data = TestLoader(image_names_abs)
all_boxes,_ = mtcnn_detector.detect_face(test_data, vis=vis)
for idx,im_name in enumerate(image_names):
img_path = os.path.join(data_dir,'originalPics',im_name+'.jpg')
image = cv2.imread(img_path)
boxes = all_boxes[idx]
if boxes is None:
fid.write(im_name+'\n')
fid.write(str(1) + '\n')
fid.write('%f %f %f %f %f\n' % (0, 0, 0, 0, 0.99))
continue
fid.write(im_name+'\n')
fid.write(str(len(boxes)) + '\n')
for box in boxes:
fid.write('%f %f %f %f %f\n' % (float(box[0]), float(box[1]), float(box[2]-box[0]+1), float(box[3]-box[1]+1),box[4]))
def t_net(
prefix,
epoch,
batch_size,
test_mode="PNet",
thresh=[0.6, 0.6, 0.7],
min_face_size=25, # prefix保存模型文件路径
stride=2,
slide_window=False,
shuffle=False,
vis=False):
detectors = [None, None, None]
print("Test model: ", test_mode)
#PNet-echo
model_path = [
'%s-%s' % (x, y) for x, y in zip(prefix, epoch)
] # model_path == <class 'list'>: ['../data/MTCNN_model/PNet_landmark/PNet-16', '../data/MTCNN_model/RNet_landmark/RNet-6', '../data/MTCNN_model/ONet/ONet-22']
if slide_window:
PNet = Detector(P_Net, 12, batch_size[0], model_path[0])
else:
PNet = FcnDetector(
P_Net, model_path[0]
) # '../data/MTCNN_model/PNet_landmark/PNet-16'， 生成全连接detection的检测对象P_Net
detectors[0] = PNet # 将PNet对象放入检测器的第1个位
# load rnet model
if test_mode in ["RNet", "ONet"]:
print("==================================", test_mode)
RNet = Detector(R_Net, 24, batch_size[1], model_path[1])
detectors[1] = RNet
# load onet model
if test_mode == "ONet":
print("==================================", test_mode)
ONet = Detector(O_Net, 48, batch_size[2], model_path[2])
detectors[2] = ONet
basedir = '.'
filename = './wider_face_train_bbx_gt.txt' # 获取检测框的ground truth值
data = read_annotation(
basedir, filename
) # 读pic的文件名，和box的ground truth值，data['images']==all image pathes
# data['bboxes'] =all image bboxes
mtcnn_detector = MtcnnDetector(detectors=detectors,
min_face_size=min_face_size,
stride=stride,
threshold=thresh,
slide_window=slide_window)
print("==================================")
test_data = TestLoader(
data['images']
) # 生成输入图片的管理对象test_data， # 注意是在“test”模式下， imdb = IMDB("wider", image_set, root_path, dataset_path, 'test')， gt_imdb = imdb.gt_imdb()
detections, _ = mtcnn_detector.detect_face(
test_data) # 从test_data输入人脸pixel,返回detection和cls
save_net = 'RNet'
if test_mode == "PNet":
save_net = "RNet"
elif test_mode == "RNet":
save_net = "ONet"
#save detect result
save_path = os.path.join(data_dir, save_net) # save_path == “24 / Rnet”
print(save_path)
if not os.path.exists(save_path):
os.mkdir(save_path)
save_file = os.path.join(save_path, "detections.pkl")
with open(save_file, 'wb') as f: # save_file == detections.pkl
pickle.dump(detections, f, 1) # 将detection结果写入文件
print("%s测试完成开始OHEM" % image_size)
save_hard_example(image_size, data,
save_path) # data，读pic的文件名，和box的ground truth值，
def t_net(prefix,
epoch,
batch_size,
test_mode='PNet',
thresh=[0.6, 0.6, 0.6],
min_face_size=25,
stride=2,
slide_window=False,
shuffle=False,
vis=False):
"""
:param prefix: 模型名字的前缀
:param epoch:上一个模型的训练次数
:param batch_size:用在预测中的批的尺寸
:param test_mode:测试模式
:param thresh:阈值
:param min_face_size:最小检测人脸大小
:param stride:
:param slide_window:是否在pnet中应用了sliding window
:param shuffle:
:param vis:
:return:
"""
detectors = [None, None, None]
model_path = ['%s-%s' % (x, y) for x, y in zip(prefix, epoch)]
if slide_window:
PNet = Detector(P_Net, 12, batch_size[0], model_path[0])
else:
PNet = FcnDetector(P_Net, model_path[0])
detectors[0] = PNet
if test_mode in ["RNet", "ONet"]:
RNet = Detector(R_Net, 24, batch_size[1], model_path[1])
detectors[1] = RNet
basedir = '.'
# 注解文件
filename = './wider_face_train_bbx_gt.txt'
data = read_annotation(basedir, filename)
mtcnn_detector = MtcnnDetector(detectors=detectors,
min_face_size=min_face_size,
stride=stride,
threshold=thresh,
slide_window=slide_window)
test_data = TestLoader(data['images'])
detections, _ = mtcnn_detector.detect_face(test_data)
save_net = 'RNet'
if test_mode == "PNet":
save_net = "RNet"
elif test_mode == "RNet":
save_net = "ONet"
# 保存探测结果
save_path = os.path.join(data_dir, save_net)
if not os.path.exists(save_path):
os.mkdir(save_path)
save_file = os.path.join(save_path, "detections.pkl")
with open(save_file, 'wb') as f:
pickle.dump(detections, f, 1)
print("%s测试完成开始OHEM" % image_size)
save_hard_example(image_size, data, save_path)
if test_mode == "ONet":
ONet = Detector(O_Net, 48, batch_size[2], model_path[2])
detectors[2] = ONet
mtcnn_detector = MtcnnDetector(detectors=detectors, min_face_size=min_face_size,
stride=stride, threshold=thresh, slide_window=slide_window)
gt_imdb = []
#gt_imdb.append("35_Basketball_Basketball_35_515.jpg")
#imdb_ = dict()"
#imdb_['image'] = im_path
#imdb_['label'] = 5
path = "lala"
for item in os.listdir(path):
gt_imdb.append(os.path.join(path,item))
test_data = TestLoader(gt_imdb)
all_boxes,landmarks = mtcnn_detector.detect_face(test_data)
count = 0
for imagepath in gt_imdb:
print imagepath
image = cv2.imread(imagepath)
for bbox in all_boxes[count]:
cv2.putText(image,str(np.round(bbox[4],2)),(int(bbox[0]),int(bbox[1])),cv2.FONT_HERSHEY_TRIPLEX,1,color=(255,0,255))
cv2.rectangle(image, (int(bbox[0]),int(bbox[1])),(int(bbox[2]),int(bbox[3])),(0,0,255))
for landmark in landmarks[count]:
for i in range(len(landmark)/2):
cv2.circle(image, (int(landmark[2*i]),int(int(landmark[2*i+1]))), 3, (0,0,255))
count = count + 1
#cv2.imwrite("result_landmark/%d.png" %(count),image)
cv2.imshow("lala",image)
def t_net(data_dir,
prefix=[
'./modelfiles/PNet/PNet', './modelfiles/RNet/RNet',
'./modelfiles/ONet/ONet'
],
epoch=[18, 14, 16],
batch_size=[2048, 256, 16],
test_mode="PNet",
thresh=[0.6, 0.6, 0.7],
min_face_size=25,
stride=2,
slide_window=False,
shuffle=False,
vis=False):
detectors = [None, None, None]
print("Test model: ", test_mode)
#PNet-echo
model_path = ['%s-%s' % (x, y) for x, y in zip(prefix, epoch)]
print(model_path[0])
# load pnet model
if slide_window:
PNet = Detector(P_Net, 12, batch_size[0], model_path[0])
else:
PNet = FcnDetector(P_Net, model_path[0])
detectors[0] = PNet
# load rnet model
if test_mode in ["RNet", "ONet"]:
print("==================================", test_mode)
RNet = Detector(R_Net, 24, batch_size[1], model_path[1])
detectors[1] = RNet
# load onet model
if test_mode == "ONet":
print("==================================", test_mode)
ONet = Detector(O_Net, 48, batch_size[2], model_path[2])
detectors[2] = ONet
# anno_file
# read anotation(type:dict), include 'images' and 'bboxes'
data = read_annotation(data_dir,
'./prepare_data/wider_face_train_bbx_gt.txt')
mtcnn_detector = MtcnnDetector(detectors=detectors,
min_face_size=min_face_size,
stride=stride,
threshold=thresh,
slide_window=slide_window)
print("==================================")
print('load test data')
test_data = TestLoader(data['images'])
print('finish loading')
save_net = 'RNet'
if test_mode == "PNet":
save_net = "RNet"
image_size = 24
elif test_mode == "RNet":
save_net = "ONet"
image_size = 48
# save detect result
save_path = os.path.join(data_dir, save_net)
print('save_path is :')
print(save_path)
if not os.path.exists(save_path):
os.mkdir(save_path)
save_file = os.path.join(save_path, "detections.pkl")
print('start detecting....')
if not os.path.exists(save_file):
detections, _ = mtcnn_detector.detect_face(test_data)
with open(save_file, 'wb') as f:
pickle.dump(detections, f, 1)
print('finish detecting ')
print("%s测试完成开始OHEM" % image_size)
def t_net(prefix, epoch,batch_size, img_saved_dir,anno_file,gen_anno_file,gen_imgs_dir,data_set_name,ignore_det=False,test_mode="PNet",thresh=[0.6, 0.6, 0.7], min_face_size=25,\
stride=2):
slide_window = False
detectors = [None, None, None]
print("Test model: ", test_mode)
#PNet-echo
print("epoch num ", epoch[0])
''' #for Pnet test
epoch_num = epoch[0]
epoch_c = np.arange(2,epoch_num,2)
prefix_c = []
prefix = prefix[0]
[prefix_c.append(prefix) for i in range(len(epoch_c))]
'''
print("prefixs is ", prefix)
model_path = ['%s-%s' % (x, y) for x, y in zip(prefix, epoch)]
#print("after zip model_path is ",model_path)
#model_path[0] = prefix + '-'+str(epoch_num) #for Pnet test
print("model_path 0 is ", model_path[0])
# load pnet model
if slide_window:
PNet = Detector(P_Net, 12, batch_size[0], model_path[0])
else:
PNet = FcnDetector(P_Net, model_path[0])
detectors[0] = PNet
# load rnet model
if test_mode in ["RNet", "ONet"]:
print("==================================", test_mode)
RNet = Detector(R_Net, 24, batch_size[1], model_path[1])
detectors[1] = RNet
# load onet model
if test_mode == "ONet":
print("==================================", test_mode)
ONet = Detector(O_Net, 48, batch_size[2], model_path[2])
detectors[2] = ONet
#read annatation(type:dict)
#img_box_dic = read_annotation(img_saved_dir,anno_file)
img_box_dic = rd_anotation(img_saved_dir, anno_file, data_set_name)
print("gen_hardexample threshold ", thresh)
if not ignore_det:
mtcnn_detector = MtcnnDetector(detectors=detectors,
min_face_size=min_face_size,
stride=stride,
threshold=thresh)
print("==================================")
# 注意是在“test”模式下
# imdb = IMDB("wider", image_set, root_path, dataset_path, 'test')
# gt_imdb = imdb.gt_imdb()
test_data = TestLoader(img_box_dic['images'])
#list
if not ignore_det:
detections, _ = mtcnn_detector.detect_face(test_data)
if test_mode == "PNet":
save_net = "RNet"
save_path = '24/RNet'
elif test_mode == "RNet":
save_net = "ONet"
save_path = "48/ONet"
#save detect result
#save_path = os.path.join(data_dir, save_net)
print("save path is", save_path)
if not os.path.exists(save_path):
os.mkdir(save_path)
save_file = os.path.join(save_path, "detections.pkl")
if not ignore_det:
with open(save_file, 'wb') as f:
pickle.dump(detections, f, 1)
f.close()
print("%s Test is Over and begin OHEM" % image_size)
save_hard_example(gen_anno_file, gen_imgs_dir, img_box_dic, save_path,
test_mode)
def t_net(prefix,
epoch,
batch_size,
test_mode="PNet",
thresh=[0.6, 0.6, 0.7],
min_face_size=25,
stride=2,
slide_window=False,
shuffle=False,
vis=False):
"""
Parameter
----------------
"""
detectors = [None, None, None]
print("Test model: ", test_mode)
#PNet-echo
model_path = ['%s-%s' % (x, y) for x, y in zip(prefix, epoch)]
print(model_path[0])
# load pnet model
if slide_window:
PNet = Detector(P_Net, 12, batch_size[0], model_path[0])
else:
PNet = FcnDetector(P_Net, model_path[0])
detectors[0] = PNet
# load rnet model
pnet_detections = None
if test_mode in ["RNet", "ONet"]:
print("==================================", test_mode)
RNet = Detector(R_Net, 24, batch_size[1], model_path[1])
detectors[1] = RNet
print("load pnet predictions...")
pnet_detections = pickle.load(
open(os.path.join("../data/24/RNet", 'detections.pkl'), 'rb'))
print("load pnet predictions done.")
# load onet model
rnet_detections = None
if test_mode == "ONet":
print("==================================", test_mode)
ONet = Detector(O_Net, 48, batch_size[2], model_path[2])
detectors[2] = ONet
print("load rnet predictions...")
rnet_detections = pickle.load(
open(os.path.join("../data/48/ONet", 'detections.pkl'), 'rb'))
print("load rnet predictions done.")
# detec的数据源始终是一样的
basedir = 'E:/Document/Datasets/Wider Face'
#anno_file
filename = 'wider_face_train_bbx_gt.txt'
#data是一个dict，data["images"]为所有照片路径list，data["bboxes"]是所有照片bboxes的值，一一对应
data = read_annotation(basedir, filename)
# 初始化mtcnn_detector，就是训练好的模型
mtcnn_detector = MtcnnDetector(detectors=detectors,
min_face_size=min_face_size,
stride=stride,
threshold=thresh,
slide_window=slide_window)
print("==================================")
# 注意是在“test”模式下
# imdb = IMDB("wider", image_set, root_path, dataset_path, 'test')
# gt_imdb = imdb.gt_imdb()
print('load test data')
#所有照片，训练时为12880张
test_data = TestLoader(data['images'])
print('finish loading')
#list
print('start detecting....')
detections, _ = mtcnn_detector.detect_face(
test_data, pnet_detections, rnet_detections) #调用训练好的pnet，返回的是candidate
print('finish detecting ')
save_net = 'RNet'
if test_mode == "PNet":
save_net = "RNet"
elif test_mode == "RNet":
save_net = "ONet"
# #save detect result
save_path = os.path.join(data_dir, save_net)
print('save_path is :')
print(save_path)
if not os.path.exists(save_path):
os.mkdir(save_path)
# 把所有candidate写到文件中序列化
save_file = os.path.join(save_path, "detections.pkl")
with open(save_file, 'wb') as f:
pickle.dump(detections, f, 1)
print("save done.")
print("%s测试完成开始OHEM" % image_size)
# 然后根据candidate和gt的Iou把每个candidate分类并保存到文本和相应的照片
save_hard_example(image_size, data, save_path)
def t_net(prefix,
epoch,
batch_size,
test_mode="PNet",
thresh=[0.6, 0.6, 0.7],
min_face_size=25,
stride=2,
slide_window=False,
shuffle=False,
vis=False):
detectors = [None, None, None]
print("Test model: ", test_mode)
model_path = ['%s-%s' % (x, y) for x, y in zip(prefix, epoch)]
print(model_path[0])
if slide_window:
PNet = Detector(P_Net, 12, batch_size[0], model_path[0])
else:
PNet = FcnDetector(P_Net, model_path[0])
detectors[0] = PNet
if test_mode in ["RNet", "ONet"]:
print("==================================", test_mode)
RNet = Detector(R_Net, 24, batch_size[1], model_path[1])
detectors[1] = RNet
if test_mode == "ONet":
print("==================================", test_mode)
ONet = Detector(O_Net, 48, batch_size[2], model_path[2])
detectors[2] = ONet
basedir = '../../DATA/'
filename = './wider_face_train_bbx_gt.txt'
data = read_annotation(basedir, filename)
mtcnn_detector = MtcnnDetector(detectors=detectors,
min_face_size=min_face_size,
stride=stride,
threshold=thresh,
slide_window=slide_window)
print("==================================")
print('load test data')
test_data = TestLoader(data['images'])
print('finish loading')
print('start detecting....')
detections, _ = mtcnn_detector.detect_face(test_data)
print('finish detecting ')
save_net = 'RNet'
if test_mode == "PNet":
save_net = "RNet"
elif test_mode == "RNet":
save_net = "ONet"
save_path = os.path.join(data_dir, save_net)
print('save_path is :')
print(save_path)
if not os.path.exists(save_path):
os.mkdir(save_path)
save_file = os.path.join(save_path, "detections.pkl")
with open(save_file, 'wb') as f:
pickle.dump(detections, f, 1)
save_hard_example(image_size, data, save_path)
