class YOLO(object):
_defaults = {
#"model_path" : 'model_data/yolov4_tiny_weights_voc.pth',
#"model_path": 'model_data/voc_self_20.pth',
"model_path": 'model_data/bimac_3.pth',
"anchors_path": 'model_data/yolo_anchors.txt',
#"classes_path" : 'model_data/voc_classes.txt',
"classes_path": 'model_data/bimac_classes.txt',
"model_image_size": (416, 416, 3),
"confidence": 0.5,
"iou": 0.3,
"cuda": True,
#---------------------------------------------------------------------#
# 该变量用于控制是否使用letterbox_image对输入图像进行不失真的resize，
# 在多次测试后，发现关闭letterbox_image直接resize的效果更好
#---------------------------------------------------------------------#
"letterbox_image": False,
}
@classmethod
def get_defaults(cls, n):
if n in cls._defaults:
return cls._defaults[n]
else:
return "Unrecognized attribute name '" + n + "'"
#---------------------------------------------------#
# 初始化YOLO
#---------------------------------------------------#
def __init__(self, **kwargs):
self.__dict__.update(self._defaults)
self.class_names = self._get_class()
self.anchors = self._get_anchors()
self.generate()
#---------------------------------------------------#
# 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
#---------------------------------------------------#
# 获得所有的先验框
#---------------------------------------------------#
def _get_anchors(self):
anchors_path = os.path.expanduser(self.anchors_path)
with open(anchors_path) as f:
anchors = f.readline()
anchors = [float(x) for x in anchors.split(',')]
return np.array(anchors).reshape([-1, 3, 2])
#---------------------------------------------------#
# 生成模型
#---------------------------------------------------#
def generate(self):
#---------------------------------------------------#
# 建立yolov4_tiny模型
#---------------------------------------------------#
self.net = YoloBody(len(self.anchors[0]), len(self.class_names)).eval()
#---------------------------------------------------#
# 载入yolov4_tiny模型的权重
#---------------------------------------------------#
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.net.load_state_dict(state_dict)
print('Finished!')
if self.cuda:
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
self.net = nn.DataParallel(self.net)
self.net = self.net.cuda()
#---------------------------------------------------#
# 建立特征层解码用的工具
#---------------------------------------------------#
self.yolo_decodes = []
self.anchors_mask = [[3, 4, 5], [1, 2, 3]]
for i in range(2):
self.yolo_decodes.append(
DecodeBox(
np.reshape(self.anchors, [-1, 2])[self.anchors_mask[i]],
len(self.class_names),
(self.model_image_size[1], self.model_image_size[0])))
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):
image_shape = np.array(np.shape(image)[0:2])
#---------------------------------------------------------#
# 给图像增加灰条，实现不失真的resize
# 也可以直接resize进行识别
#---------------------------------------------------------#
if self.letterbox_image:
crop_img = np.array(
letterbox_image(
image,
(self.model_image_size[1], self.model_image_size[0])))
else:
crop_img = image.convert('RGB')
crop_img = crop_img.resize(
(self.model_image_size[1], self.model_image_size[0]),
Image.BICUBIC)
photo = np.array(crop_img, dtype=np.float32) / 255.0
photo = np.transpose(photo, (2, 0, 1))
#---------------------------------------------------------#
# 添加上batch_size维度
#---------------------------------------------------------#
images = [photo]
with torch.no_grad():
images = torch.from_numpy(np.asarray(images))
if self.cuda:
images = images.cuda()
#---------------------------------------------------------#
# 将图像输入网络当中进行预测！
#---------------------------------------------------------#
outputs = self.net(images)
output_list = []
for i in range(2):
output_list.append(self.yolo_decodes[i](outputs[i]))
#---------------------------------------------------------#
# 将预测框进行堆叠，然后进行非极大抑制
#---------------------------------------------------------#
output = torch.cat(output_list, 1)
batch_detections = non_max_suppression(output,
len(self.class_names),
conf_thres=self.confidence,
nms_thres=self.iou)
#---------------------------------------------------------#
# 如果没有检测出物体，返回原图
#---------------------------------------------------------#
try:
batch_detections = batch_detections[0].cpu().numpy()
except:
return image
#---------------------------------------------------------#
# 对预测框进行得分筛选
#---------------------------------------------------------#
top_index = batch_detections[:,
4] * batch_detections[:,
5] > self.confidence
top_conf = batch_detections[top_index,
4] * batch_detections[top_index, 5]
top_label = np.array(batch_detections[top_index, -1], np.int32)
top_bboxes = np.array(batch_detections[top_index, :4])
top_xmin, top_ymin, top_xmax, top_ymax = np.expand_dims(
top_bboxes[:, 0],
-1), np.expand_dims(top_bboxes[:, 1], -1), np.expand_dims(
top_bboxes[:, 2],
-1), np.expand_dims(top_bboxes[:, 3], -1)
#-----------------------------------------------------------------#
# 在图像传入网络预测前会进行letterbox_image给图像周围添加灰条
# 因此生成的top_bboxes是相对于有灰条的图像的
# 我们需要对其进行修改，去除灰条的部分。
#-----------------------------------------------------------------#
if self.letterbox_image:
boxes = yolo_correct_boxes(
top_ymin, top_xmin, top_ymax, top_xmax,
np.array(
[self.model_image_size[0], self.model_image_size[1]]),
image_shape)
else:
top_xmin = top_xmin / self.model_image_size[1] * image_shape[1]
top_ymin = top_ymin / self.model_image_size[0] * image_shape[0]
top_xmax = top_xmax / self.model_image_size[1] * image_shape[1]
top_ymax = top_ymax / self.model_image_size[0] * image_shape[0]
boxes = np.concatenate(
[top_ymin, top_xmin, top_ymax, top_xmax], axis=-1)
font = ImageFont.truetype(font='model_data/simhei.ttf',
size=np.floor(3e-2 * np.shape(image)[1] +
0.5).astype('int32'))
thickness = max((np.shape(image)[0] + np.shape(image)[1]) //
self.model_image_size[0], 1)
for i, c in enumerate(top_label):
predicted_class = self.class_names[c]
score = top_conf[i]
top, left, bottom, right = boxes[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, top, left, bottom, right)
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[self.class_names.index(
predicted_class)])
draw.rectangle(
[tuple(text_origin),
tuple(text_origin + label_size)],
fill=self.colors[self.class_names.index(predicted_class)])
draw.text(text_origin,
str(label, 'UTF-8'),
fill=(0, 0, 0),
font=font)
del draw
return image
#-------------------------------------------#
# 权值文件的下载请看README
#-------------------------------------------#
model_path = "model_data/yolov4_tiny_weights_coco.pth"
# 加快模型训练的效率
print('Loading weights into state dict...')
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model_dict = model.state_dict()
pretrained_dict = torch.load(model_path, map_location=device)
pretrained_dict = {
k: v
for k, v in pretrained_dict.items()
if np.shape(model_dict[k]) == np.shape(v)
}
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
print('Finished!')
net = model.train()
if Cuda:
net = torch.nn.DataParallel(model)
cudnn.benchmark = True
net = net.cuda()
# 建立loss函数
yolo_losses = []
for i in range(2):
yolo_losses.append(YOLOLoss(np.reshape(anchors,[-1,2]),num_classes, \
(input_shape[1], input_shape[0]), smoooth_label, Cuda))
class YOLO(object):
_defaults = {
"model_path": 'model_data/yolov4_tiny_weights_coco.pth',
"anchors_path": 'model_data/yolo_anchors.txt',
"classes_path": 'model_data/coco_classes.txt',
"model_image_size": (416, 416, 3),
"confidence": 0.5,
"iou": 0.3,
"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YOLO
#---------------------------------------------------#
def __init__(self, **kwargs):
self.__dict__.update(self._defaults)
self.class_names = self._get_class()
self.anchors = self._get_anchors()
self.generate()
#---------------------------------------------------#
# 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
#---------------------------------------------------#
# 获得所有的先验框
#---------------------------------------------------#
def _get_anchors(self):
anchors_path = os.path.expanduser(self.anchors_path)
with open(anchors_path) as f:
anchors = f.readline()
anchors = [float(x) for x in anchors.split(',')]
return np.array(anchors).reshape([-1, 3, 2])
#---------------------------------------------------#
# 获得所有的分类
#---------------------------------------------------#
def generate(self):
self.net = YoloBody(len(self.anchors[0]), len(self.class_names)).eval()
# 加快模型训练的效率
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.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!')
self.yolo_decodes = []
self.anchors_mask = [[3, 4, 5], [1, 2, 3]]
for i in range(2):
self.yolo_decodes.append(
DecodeBox(
np.reshape(self.anchors, [-1, 2])[self.anchors_mask[i]],
len(self.class_names),
(self.model_image_size[1], self.model_image_size[0])))
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):
image_shape = np.array(np.shape(image)[0:2])
crop_img = np.array(
letterbox_image(
image, (self.model_image_size[1], self.model_image_size[0])))
photo = np.array(crop_img, dtype=np.float32)
photo /= 255.0
photo = np.transpose(photo, (2, 0, 1))
photo = photo.astype(np.float32)
images = []
images.append(photo)
images = np.asarray(images)
with torch.no_grad():
images = torch.from_numpy(images)
if self.cuda:
images = images.cuda()
outputs = self.net(images)
output_list = []
for i in range(2):
output_list.append(self.yolo_decodes[i](outputs[i]))
output = torch.cat(output_list, 1)
batch_detections = non_max_suppression(output,
len(self.class_names),
conf_thres=self.confidence,
nms_thres=self.iou)
try:
batch_detections = batch_detections[0].cpu().numpy()
except:
return image
top_index = batch_detections[:,
4] * batch_detections[:,
5] > self.confidence
top_conf = batch_detections[top_index, 4] * batch_detections[top_index,
5]
top_label = np.array(batch_detections[top_index, -1], np.int32)
top_bboxes = np.array(batch_detections[top_index, :4])
top_xmin, top_ymin, top_xmax, top_ymax = np.expand_dims(
top_bboxes[:, 0],
-1), np.expand_dims(top_bboxes[:, 1], -1), np.expand_dims(
top_bboxes[:, 2], -1), np.expand_dims(top_bboxes[:, 3], -1)
# 去掉灰条
boxes = yolo_correct_boxes(
top_ymin, top_xmin, top_ymax, top_xmax,
np.array([self.model_image_size[0], self.model_image_size[1]]),
image_shape)
font = ImageFont.truetype(font='model_data/simhei.ttf',
size=np.floor(3e-2 * np.shape(image)[1] +
0.5).astype('int32'))
thickness = (np.shape(image)[0] +
np.shape(image)[1]) // self.model_image_size[0]
for i, c in enumerate(top_label):
predicted_class = self.class_names[c]
score = top_conf[i]
top, left, bottom, right = boxes[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[self.class_names.index(
predicted_class)])
draw.rectangle(
[tuple(text_origin),
tuple(text_origin + label_size)],
fill=self.colors[self.class_names.index(predicted_class)])
draw.text(text_origin,
str(label, 'UTF-8'),
fill=(0, 0, 0),
font=font)
del draw
return image
