(IMAGE_SHAPE[0], IMAGE_SHAPE[1])).generate()
gen_val = Generator(lines[num_train:],
(IMAGE_SHAPE[0], IMAGE_SHAPE[1])).generate()
epoch_size = num_train
epoch_size_val = num_val
# ------------------------------------#
# 冻结一定部分训练
# ------------------------------------#
for param in model.extractor.parameters():
param.requires_grad = False
# ------------------------------------#
# 由于batch==1所以冻结bn层
# ------------------------------------#
model = model.eval()
for epoch in range(Init_Epoch, Freeze_Epoch):
fit_ont_epoch(model, epoch, epoch_size, epoch_size_val, gen,
gen_val, Freeze_Epoch)
lr_scheduler.step()
if True:
lr = 1e-5
Freeze_Epoch = 25
Unfreeze_Epoch = 50
optimizer = optim.Adam(model.parameters(), lr, weight_decay=5e-4)
# optimizer = optim.SGD(model.parameters(),lr,weight_decay=5e-4,momentum=0.9)
lr_scheduler = optim.lr_scheduler.StepLR(optimizer,
step_size=1,
gamma=0.95)
class FRCNN(object):
_defaults = {
"model_path": 'model_data/voc_weights_resnet.pth',
"classes_path": 'model_data/voc_classes.txt',
"confidence": 0.5,
"iou": 0.3,
"backbone": "resnet50",
"cuda": True,
}
@classmethod
def get_defaults(cls, n):
if n in cls._defaults:
return cls._defaults[n]
else:
return "Unrecognized attribute name '" + n + "'"
#初始化程序
def __init__(self, **kwargs):
self.__dict__.update(self._defaults)
self.class_names = self._get_class()
self.generate()
self.mean = torch.Tensor([0, 0, 0,
0]).repeat(self.num_classes + 1)[None]
self.std = torch.Tensor([0.1, 0.1, 0.2,
0.2]).repeat(self.num_classes + 1)[None]
if self.cuda:
self.mean = self.mean.cuda()
self.std = self.std.cuda()
def _get_class(self):
classes_path = os.path.expanduser(self.classes_path)
with open(classes_path) as f:
class_names = f.readlines()
class_names = [c.strip() for c in class_names]
return class_names
def generate(self):
self.num_classes = len(self.class_names)
#载入模型
self.model = FasterRCNN(self.num_classes,
"predict",
backbone=self.backbone)
print('Loading weights into state dict...')
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
state_dict = torch.load(self.model_path, map_location=device)
self.model.load_state_dict(state_dict)
self.model = self.model.eval()
if self.cuda:
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
self.model = nn.DataParallel(self.model)
self.model = self.model.cuda()
print('{} model, anchors, and classes loaded.'.format(self.model_path))
#目标定位并画框
hsv_tuples = [(x / len(self.class_names), 1., 1.)
for x in range(len(self.class_names))]
self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
self.colors = list(
map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),
self.colors))
#检测图片
def detect_image(self, image):
with torch.no_grad():
start_time = time.time()
image_shape = np.array(np.shape(image)[0:2])
old_width = image_shape[1]
old_height = image_shape[0]
old_image = copy.deepcopy(image)
width, height = get_new_img_size(old_width, old_height)
image = image.resize([width, height], Image.BICUBIC)
photo = np.array(image, dtype=np.float32) / 255
photo = np.transpose(photo, (2, 0, 1))
images = []
images.append(photo)
images = np.asarray(images)
images = torch.from_numpy(images)
if self.cuda:
images = images.cuda()
#非凸性优化，边界框矫正
roi_cls_locs, roi_scores, rois, roi_indices = self.model(images)
decodebox = DecodeBox(self.std, self.mean, self.num_classes)
outputs = decodebox.forward(roi_cls_locs,
roi_scores,
rois,
height=height,
width=width,
nms_iou=self.iou,
score_thresh=self.confidence)
if len(outputs) == 0:
return old_image
bbox = outputs[:, :4]
conf = outputs[:, 4]
label = outputs[:, 5]
bbox[:, 0::2] = (bbox[:, 0::2]) / width * old_width
bbox[:, 1::2] = (bbox[:, 1::2]) / height * old_height
bbox = np.array(bbox, np.int32)
image = old_image
thickness = (np.shape(old_image)[0] +
np.shape(old_image)[1]) // old_width * 2
font = ImageFont.truetype(font='model_data/simhei.ttf',
size=np.floor(3e-2 * np.shape(image)[1] +
0.5).astype('int32'))
for i, c in enumerate(label):
predicted_class = self.class_names[int(c)]
score = conf[i]
left, top, right, bottom = bbox[i]
top = top - 5
left = left - 5
bottom = bottom + 5
right = right + 5
top = max(0, np.floor(top + 0.5).astype('int32'))
left = max(0, np.floor(left + 0.5).astype('int32'))
bottom = min(
np.shape(image)[0],
np.floor(bottom + 0.5).astype('int32'))
right = min(
np.shape(image)[1],
np.floor(right + 0.5).astype('int32'))
#图片输出
label = '{}'.format(predicted_class)
draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
label = label.encode('utf-8')
print(label)
if top - label_size[1] >= 0:
text_origin = np.array([left, top - label_size[1]])
else:
text_origin = np.array([left, top + 1])
for i in range(thickness):
draw.rectangle([left + i, top + i, right - i, bottom - i],
outline=self.colors[int(c)])
draw.rectangle(
[tuple(text_origin),
tuple(text_origin + label_size)],
fill=self.colors[int(c)])
draw.text(text_origin,
str(label, 'UTF-8'),
fill=(0, 0, 0),
font=font)
del draw
print("time:", time.time() - start_time)
return image
class FRCNN(object):
_defaults = {
"model_path": 'model_data/voc_weights_resnet.pth',
"classes_path": 'model_data/voc_classes.txt',
"confidence": 0.5,
"backbone": "resnet50"
}
@classmethod
def get_defaults(cls, n):
if n in cls._defaults:
return cls._defaults[n]
else:
return "Unrecognized attribute name '" + n + "'"
#---------------------------------------------------#
# 初始化faster RCNN
#---------------------------------------------------#
def __init__(self, **kwargs):
self.__dict__.update(self._defaults)
self.class_names = self._get_class()
self.generate()
self.mean = torch.Tensor([0,0,0,0]).cuda().repeat(self.num_classes+1)[None]
self.std = torch.Tensor([0.1, 0.1, 0.2, 0.2]).cuda().repeat(self.num_classes+1)[None]
#---------------------------------------------------#
# 获得所有的分类
#---------------------------------------------------#
def _get_class(self):
classes_path = os.path.expanduser(self.classes_path)
with open(classes_path) as f:
class_names = f.readlines()
class_names = [c.strip() for c in class_names]
return class_names
#---------------------------------------------------#
# 获得所有的分类
#---------------------------------------------------#
def generate(self):
# 计算总的种类
self.num_classes = len(self.class_names)
# 载入模型，如果原来的模型里已经包括了模型结构则直接载入。
# 否则先构建模型再载入
self.model = FasterRCNN(self.num_classes,"predict",backbone=self.backbone).cuda()
self.model = self.model.eval()
self.model.load_state_dict(torch.load(self.model_path))
cudnn.benchmark = True
print('{} model, anchors, and classes loaded.'.format(self.model_path))
# 画框设置不同的颜色
hsv_tuples = [(x / len(self.class_names), 1., 1.)
for x in range(len(self.class_names))]
self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
self.colors = list(
map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),
self.colors))
#---------------------------------------------------#
# 检测图片
#---------------------------------------------------#
def detect_image(self, image):
start_time = time.time()
image_shape = np.array(np.shape(image)[0:2])
old_width = image_shape[1]
old_height = image_shape[0]
old_image = copy.deepcopy(image)
width,height = get_new_img_size(old_width,old_height)
image = image.resize([width,height])
photo = np.array(image,dtype = np.float32)/255
photo = np.transpose(photo, (2, 0, 1))
with torch.no_grad():
images = []
images.append(photo)
images = np.asarray(images)
images = torch.from_numpy(images).cuda()
roi_cls_locs, roi_scores, rois, roi_indices = self.model(images)
decodebox = DecodeBox(self.std, self.mean, self.num_classes)
outputs = decodebox.forward(roi_cls_locs, roi_scores, rois, height=height, width=width, score_thresh = self.confidence)
if len(outputs)==0:
return old_image
bbox = outputs[:,:4]
conf = outputs[:, 4]
label = outputs[:, 5]
bbox[:, 0::2] = (bbox[:, 0::2])/width*old_width
bbox[:, 1::2] = (bbox[:, 1::2])/height*old_height
bbox = np.array(bbox,np.int32)
image = old_image
thickness = (np.shape(old_image)[0] + np.shape(old_image)[1]) // old_width*2
font = ImageFont.truetype(font='model_data/simhei.ttf',size=np.floor(3e-2 * np.shape(image)[1] + 0.5).astype('int32'))
for i, c in enumerate(label):
predicted_class = self.class_names[int(c)]
score = conf[i]
left, top, right, bottom = bbox[i]
top = top - 5
left = left - 5
bottom = bottom + 5
right = right + 5
top = max(0, np.floor(top + 0.5).astype('int32'))
left = max(0, np.floor(left + 0.5).astype('int32'))
bottom = min(np.shape(image)[0], np.floor(bottom + 0.5).astype('int32'))
right = min(np.shape(image)[1], np.floor(right + 0.5).astype('int32'))
# 画框框
label = '{} {:.2f}'.format(predicted_class, score)
draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
label = label.encode('utf-8')
print(label)
if top - label_size[1] >= 0:
text_origin = np.array([left, top - label_size[1]])
else:
text_origin = np.array([left, top + 1])
for i in range(thickness):
draw.rectangle(
[left + i, top + i, right - i, bottom - i],
outline=self.colors[int(c)])
draw.rectangle(
[tuple(text_origin), tuple(text_origin + label_size)],
fill=self.colors[int(c)])
draw.text(text_origin, str(label,'UTF-8'), fill=(0, 0, 0), font=font)
del draw
print("time:",time.time()-start_time)
return image