# create new OrderedDict that does not contain `module.`
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in state_dict.items():
head = k[:7]
if head == 'module.':
name = k[7:] # remove `module.`
else:
name = k
new_state_dict[name] = v
net.load_state_dict(new_state_dict)
if cfg.TRAIN.num_gpus > 1 and cfg.TRAIN.use_gpu:
net = torch.nn.DataParallel(net).cuda()
else:
net = net.cuda()
cudnn.benchmark = True
optimizer = optim.SGD(net.parameters(),
lr=cfg.TRAIN.LR.initial_lr,
momentum=cfg.TRAIN.LR.momentum,
weight_decay=cfg.TRAIN.LR.weight_decay)
criterion = MultiBoxLoss(cfg)
anchor_generator = AnchorGenerator(cfg)
with torch.no_grad():
anchors = anchor_generator.generate_anchors()
anchors = anchors.cuda()
class Retinaface(object):
_defaults = {
"model_path": 'model_data/Retinaface_mobilenet0.25.pth',
"confidence": 0.5,
"backbone": "mobilenet",
"cuda": True
}
@classmethod
def get_defaults(cls, n):
if n in cls._defaults:
return cls._defaults[n]
else:
return "Unrecognized attribute name '" + n + "'"
#---------------------------------------------------#
# 初始化Retinaface
#---------------------------------------------------#
def __init__(self, **kwargs):
self.__dict__.update(self._defaults)
if self.backbone == "mobilenet":
self.cfg = cfg_mnet
else:
self.cfg = cfg_re50
self.generate()
#---------------------------------------------------#
# 获得所有的分类
#---------------------------------------------------#
def generate(self):
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
self.net = RetinaFace(cfg=self.cfg, phase='eval').eval()
# 加快模型训练的效率
print('Loading weights into state dict...')
state_dict = torch.load(self.model_path)
self.net.load_state_dict(state_dict)
if self.cuda:
self.net = nn.DataParallel(self.net)
self.net = self.net.cuda()
print('Finished!')
#---------------------------------------------------#
# 检测图片
#---------------------------------------------------#
def detect_image(self, image):
# 绘制人脸框
old_image = image.copy()
image = np.array(image,np.float32)
im_height, im_width, _ = np.shape(image)
# 它的作用是将归一化后的框坐标转换成原图的大小
scale = torch.Tensor([np.shape(image)[1], np.shape(image)[0], np.shape(image)[1], np.shape(image)[0]])
scale_for_landmarks = torch.Tensor([np.shape(image)[1], np.shape(image)[0], np.shape(image)[1], np.shape(image)[0],
np.shape(image)[1], np.shape(image)[0], np.shape(image)[1], np.shape(image)[0],
np.shape(image)[1], np.shape(image)[0]])
# pytorch
image = preprocess_input(image).transpose(2, 0, 1)
# 增加batch_size维度
image = torch.from_numpy(image).unsqueeze(0)
# 计算先验框
anchors = Anchors(self.cfg, image_size=(im_height, im_width)).get_anchors()
with torch.no_grad():
if self.cuda:
scale = scale.cuda()
scale_for_landmarks = scale_for_landmarks.cuda()
image = image.cuda()
anchors = anchors.cuda()
loc, conf, landms = self.net(image) # forward pass
boxes = decode(loc.data.squeeze(0), anchors, self.cfg['variance'])
boxes = boxes * scale
boxes = boxes.cpu().numpy()
conf = conf.data.squeeze(0)[:,1:2].cpu().numpy()
landms = decode_landm(landms.data.squeeze(0), anchors, self.cfg['variance'])
landms = landms * scale_for_landmarks
landms = landms.cpu().numpy()
boxes_conf_landms = np.concatenate([boxes,conf,landms],-1)
boxes_conf_landms = non_max_suppression(boxes_conf_landms, self.confidence)
for b in boxes_conf_landms:
text = "{:.4f}".format(b[4])
b = list(map(int, b))
cv2.rectangle(old_image, (b[0], b[1]), (b[2], b[3]), (0, 0, 255), 2)
cx = b[0]
cy = b[1] + 12
cv2.putText(old_image, text, (cx, cy),
cv2.FONT_HERSHEY_DUPLEX, 0.5, (255, 255, 255))
# landms
cv2.circle(old_image, (b[5], b[6]), 1, (0, 0, 255), 4)
cv2.circle(old_image, (b[7], b[8]), 1, (0, 255, 255), 4)
cv2.circle(old_image, (b[9], b[10]), 1, (255, 0, 255), 4)
cv2.circle(old_image, (b[11], b[12]), 1, (0, 255, 0), 4)
cv2.circle(old_image, (b[13], b[14]), 1, (255, 0, 0), 4)
return old_image
class Retinaface(object):
_defaults = {
"model_path":
'model_data/Epoch114-Total_Loss0.3237-Conf_Loss.pth0.0999-Regression_Loss0.2241.pth',
"backbone": 'resnet50',
"confidence": 0.9,
"nms_iou": 0.45,
"cuda": True,
#----------------------------------------------------------------------#
# 是否需要进行图像大小限制。
# 开启后，会将输入图像的大小限制为input_shape。否则使用原图进行预测。
# 可根据输入图像的大小自行调整input_shape，注意为32的倍数，如[640, 640, 3]
#----------------------------------------------------------------------#
"input_shape": [768, 768, 3],
"letterbox_image": True
}
@classmethod
def get_defaults(cls, n):
if n in cls._defaults:
return cls._defaults[n]
else:
return "Unrecognized attribute name '" + n + "'"
#---------------------------------------------------#
# 初始化Retinaface
#---------------------------------------------------#
def __init__(self, **kwargs):
self.confidence = None
self.__dict__.update(self._defaults)
if self.backbone == "mobilenet":
self.cfg = cfg_mnet
else:
self.cfg = cfg_re50
self.generate()
if self.letterbox_image:
self.anchors = Anchors(
self.cfg,
image_size=[self.input_shape[0],
self.input_shape[1]]).get_anchors()
#---------------------------------------------------#
# 载入模型
#---------------------------------------------------#
def generate(self):
self.net = RetinaFace(cfg=self.cfg, mode='eval').eval()
#-------------------------------#
# 载入模型与权值
#-------------------------------#
print('Loading weights into state dict...')
state_dict = torch.load(self.model_path)
self.net.load_state_dict(state_dict)
if self.cuda:
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
self.net = nn.DataParallel(self.net)
self.net = self.net.cuda()
print('Finished!')
#---------------------------------------------------#
# 检测图片
#---------------------------------------------------#
def detect_image(self, image):
#---------------------------------------------------#
# 对输入图像进行一个备份，后面用于绘图
#---------------------------------------------------#
old_image = image.copy()
image = np.array(image, np.float32)
#---------------------------------------------------#
# 计算scale，用于将获得的预测框转换成原图的高宽
#---------------------------------------------------#
scale = [
np.shape(image)[1],
np.shape(image)[0],
np.shape(image)[1],
np.shape(image)[0]
]
scale_for_landmarks = [
np.shape(image)[1],
np.shape(image)[0],
np.shape(image)[1],
np.shape(image)[0],
np.shape(image)[1],
np.shape(image)[0],
np.shape(image)[1],
np.shape(image)[0],
np.shape(image)[1],
np.shape(image)[0]
]
im_height, im_width, _ = np.shape(image)
#---------------------------------------------------------#
# letterbox_image可以给图像增加灰条，实现不失真的resize
#---------------------------------------------------------#
if self.letterbox_image:
image = np.array(
letterbox_image(image,
[self.input_shape[1], self.input_shape[0]]),
np.float32)
else:
self.anchors = Anchors(self.cfg,
image_size=(im_height,
im_width)).get_anchors()
with torch.no_grad():
#-----------------------------------------------------------#
# 图片预处理，归一化。
#-----------------------------------------------------------#
image = torch.from_numpy(
preprocess_input(image).transpose(2, 0, 1)).unsqueeze(0)
if self.cuda:
self.anchors = self.anchors.cuda()
image = image.cuda()
loc, conf = self.net(image)
#-----------------------------------------------------------#
# 将预测结果进行解码
#-----------------------------------------------------------#
boxes = decode(loc.data.squeeze(0), self.anchors,
self.cfg['variance'])
boxes = boxes.cpu().numpy()
conf = conf.data.squeeze(0)[:, 1:2].cpu().numpy()
boxes_conf_landms = np.concatenate([boxes, conf], -1)
print(boxes_conf_landms.shape)
# boxes_conf_landms = non_max_suppression(boxes_conf_landms, 0.9)
mask = boxes_conf_landms[:, 4] >= 0.15
boxes_conf_landms = boxes_conf_landms[mask]
print(boxes_conf_landms.shape)
if len(boxes_conf_landms) <= 0:
return old_image
#---------------------------------------------------------#
# 如果使用了letterbox_image的话，要把灰条的部分去除掉。
#---------------------------------------------------------#
if self.letterbox_image:
boxes_conf_landms = retinaface_correct_boxes(boxes_conf_landms, \
np.array([self.input_shape[0], self.input_shape[1]]), np.array([im_height, im_width]))
boxes_conf_landms[:, :4] = boxes_conf_landms[:, :4] * scale
for b in boxes_conf_landms:
text = "{:.4f}".format(b[4])
b = list(map(int, b))
# b[0]-b[3]为人脸框的坐标，b[4]为得分
cv2.rectangle(old_image, (b[0], b[1]), (b[2], b[3]), (0, 0, 255),
2)
cx = b[0]
cy = b[1] + 12
cv2.putText(old_image, text, (cx, cy), cv2.FONT_HERSHEY_DUPLEX,
0.5, (255, 255, 255))
print(b[0], b[1], b[2], b[3], b[4])
# b[5]-b[14]为人脸关键点的坐标
return old_image
class Retinaface(object):
_defaults = {
"model_path" : 'model_data/Retinaface_mobilenet0.25.pth',
"backbone" : 'mobilenet',
"confidence" : 0.5,
"nms_iou" : 0.45,
"cuda" : True,
#----------------------------------------------------------------------#
# 是否需要进行图像大小限制。
# 开启后，会将输入图像的大小限制为input_shape。否则使用原图进行预测。
# 可根据输入图像的大小自行调整input_shape，注意为32的倍数，如[640, 640, 3]
#----------------------------------------------------------------------#
"input_shape" : [1280, 1280, 3],
"letterbox_image" : True
}
@classmethod
def get_defaults(cls, n):
if n in cls._defaults:
return cls._defaults[n]
else:
return "Unrecognized attribute name '" + n + "'"
#---------------------------------------------------#
# 初始化Retinaface
#---------------------------------------------------#
def __init__(self, **kwargs):
self.__dict__.update(self._defaults)
if self.backbone == "mobilenet":
self.cfg = cfg_mnet
else:
self.cfg = cfg_re50
self.generate()
if self.letterbox_image:
self.anchors = Anchors(self.cfg, image_size=[self.input_shape[0], self.input_shape[1]]).get_anchors()
#---------------------------------------------------#
# 载入模型
#---------------------------------------------------#
def generate(self):
self.net = RetinaFace(cfg=self.cfg, mode='eval').eval()
#-------------------------------#
# 载入模型与权值
#-------------------------------#
print('Loading weights into state dict...')
state_dict = torch.load(self.model_path)
self.net.load_state_dict(state_dict)
if self.cuda:
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
self.net = nn.DataParallel(self.net)
self.net = self.net.cuda()
print('Finished!')
#---------------------------------------------------#
# 检测图片
#---------------------------------------------------#
def detect_image(self, image, picpath, frameNo):
#---------------------------------------------------#
# 对输入图像进行一个备份，后面用于绘图
#---------------------------------------------------#
old_image = image.copy()
image = np.array(image,np.float32)
#---------------------------------------------------#
# 计算scale，用于将获得的预测框转换成原图的高宽
#---------------------------------------------------#
scale = [np.shape(image)[1], np.shape(image)[0], np.shape(image)[1], np.shape(image)[0]]
scale_for_landmarks = [np.shape(image)[1], np.shape(image)[0], np.shape(image)[1], np.shape(image)[0],
np.shape(image)[1], np.shape(image)[0], np.shape(image)[1], np.shape(image)[0],
np.shape(image)[1], np.shape(image)[0]]
im_height, im_width, _ = np.shape(image)
#---------------------------------------------------------#
# letterbox_image可以给图像增加灰条，实现不失真的resize
#---------------------------------------------------------#
if self.letterbox_image:
image = np.array(letterbox_image(image, [self.input_shape[1], self.input_shape[0]]), np.float32)
else:
self.anchors = Anchors(self.cfg, image_size=(im_height, im_width)).get_anchors()
with torch.no_grad():
#-----------------------------------------------------------#
# 图片预处理，归一化。
#-----------------------------------------------------------#
image = torch.from_numpy(preprocess_input(image).transpose(2, 0, 1)).unsqueeze(0)
if self.cuda:
self.anchors = self.anchors.cuda()
image = image.cuda()
loc, conf, landms = self.net(image)
#-----------------------------------------------------------#
# 将预测结果进行解码
#-----------------------------------------------------------#
boxes = decode(loc.data.squeeze(0), self.anchors, self.cfg['variance'])
boxes = boxes.cpu().numpy()
conf = conf.data.squeeze(0)[:,1:2].cpu().numpy()
landms = decode_landm(landms.data.squeeze(0), self.anchors, self.cfg['variance'])
landms = landms.cpu().numpy()
boxes_conf_landms = np.concatenate([boxes, conf, landms],-1)
#到这里为止我们已经利用Retinaface_pytorch的预训练模型检测完了人脸，并获得了人脸框和人脸五个特征点的坐标信息，全保存在dets中，接下来为人脸剪切部分
# 用来储存生成的单张人脸的路径
path_save = "./curve/faces/" #你可以将这里的路径换成你自己的路径
'''
#剪切图片
#if args.show_cutting_image：
for num, b in enumerate(boxes_conf_landms): # dets中包含了人脸框和五个特征点的坐标
#if b[4] < 0.6:
# continue
b = list(map(int, b))
# landms，在人脸上画出特征点，要是你想保存不显示特征点的人脸图，你可以把这里注释掉
cv2.circle(old_image, (b[5], b[6]), 1, (0, 0, 255), 4)
cv2.circle(old_image, (b[7], b[8]), 1, (0, 255, 255), 4)
cv2.circle(old_image, (b[9], b[10]), 1, (255, 0, 255), 4)
cv2.circle(old_image, (b[11], b[12]), 1, (0, 255, 0), 4)
cv2.circle(old_image, (b[13], b[14]), 1, (255, 0, 0), 4)
#计算人脸框矩形大小
Height = b[3] - b[1]
Width = b[2] - b[0]
# 显示人脸矩阵大小
print("人脸数 / faces in all:", str(num+1), "\n")
print("窗口大小 / The size of window:"
, '\n', "高度 / height:", Height
, '\n', "宽度 / width: ", Width)
#根据人脸框大小，生成空白的图片
img_blank = np.zeros((Height, Width, 3), np.uint8)
# 将人脸填充到空白图片
for h in range(Height):
for w in range(Width):
img_blank[h][w] = old_image[b[1] + h][b[0] + w]
cv2.namedWindow("img_faces") # , 2)
#cv2.imshow("img_faces", img_blank) #显示图片
cv2.imwrite(path_save + "img_face_4" + str(num + 1) + ".jpg", img_blank) #将图片保存至你指定的文件夹
print("Save into:", path_save + "img_face_4" + str(num + 1) + ".jpg")
cv2.waitKey(0)
'''
boxes_conf_landms = non_max_suppression(boxes_conf_landms, self.confidence)
if len(boxes_conf_landms)<=0:
return False, old_image
#---------------------------------------------------------#
# 如果使用了letterbox_image的话，要把灰条的部分去除掉。
#---------------------------------------------------------#
if self.letterbox_image:
boxes_conf_landms = retinaface_correct_boxes(boxes_conf_landms, \
np.array([self.input_shape[0], self.input_shape[1]]), np.array([im_height, im_width]))
boxes_conf_landms[:,:4] = boxes_conf_landms[:,:4]*scale
boxes_conf_landms[:,5:] = boxes_conf_landms[:,5:]*scale_for_landmarks
num = 0
for b in boxes_conf_landms:
text = "{:.4f}".format(b[4])
b = list(map(int, b))
# b[0]-b[3]为人脸框的坐标，b[4]为得分
cv2.rectangle(old_image, (b[0], b[1]), (b[2], b[3]), (0, 0, 255), 2)
cx = b[0]
cy = b[1] + 12
cv2.putText(old_image, text, (cx, cy),
cv2.FONT_HERSHEY_DUPLEX, 0.5, (255, 255, 255))
print(b[0], b[1], b[2], b[3], b[4])
c = [-50, -50,50, 50, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
#b += c
c = np.array(b) + np.array(c)
if (c > 0).all() :
b = c
else:
for i, ind in enumerate(b[:4]):
if ind < 0:
print(i, b[i])
b[i] = 0
print(b[0], b[1], b[2], b[3], b[4])
#计算人脸框矩形大小
Height = b[3] - b[1]
Width = b[2] - b[0]
print("窗口大小 / The size of window:"
, '\n', "高度 / height:", Height
, '\n', "宽度 / width: ", Width)
img_blank = old_image[int(b[1]):int(b[3]), int(b[0]):int(b[2])] # height, width
savepath = picpath + "/" + str(frameNo) + "_" + str(num)+".jpg"
#cv2.namedWindow("img_faces") # , 2)
#cv2.imshow("img_faces", img_blank) #显示图片
#img_blank = cv2.cvtColor(img_blank,cv2.COLOR_RGB2BGR)
cv2.imwrite(savepath, img_blank) #将图片保存至你指定的文件夹
print("Save into:", savepath)
#cv2.waitKey(0)
'''
# b[5]-b[14]为人脸关键点的坐标
cv2.circle(old_image, (b[5], b[6]), 1, (0, 0, 255), 4)
cv2.circle(old_image, (b[7], b[8]), 1, (0, 255, 255), 4)
cv2.circle(old_image, (b[9], b[10]), 1, (255, 0, 255), 4)
cv2.circle(old_image, (b[11], b[12]), 1, (0, 255, 0), 4)
cv2.circle(old_image, (b[13], b[14]), 1, (255, 0, 0), 4)
'''
num += 1
return True, old_image
