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提交 8b7c5d20 编写于 作者: _白鹭先生_'s avatar _白鹭先生_
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2007_train.txt
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2007_val.txt
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D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000300.jpg 120,67,156,42,165,56,130,80,0
D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000310.jpg 439,203,459,198,486,299,465,305,0
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D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000513.jpg 411,146,423,142,452,234,440,238,0
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D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000532.jpg 50,109,52,40,78,41,76,110,0
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D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000553.jpg 414,249,425,192,444,196,433,253,0
D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000561.jpg 263,253,280,143,316,148,299,259,0
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D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000656.jpg 88,124,96,122,103,154,95,156,0
D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000662.jpg 27,55,34,52,54,108,46,111,0
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D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000719.jpg 444,272,455,217,474,221,464,275,0
D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000745.jpg 106,163,112,135,264,169,257,197,0 9,139,15,121,81,141,76,159,0
D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000749.jpg 72,152,76,133,178,154,174,173,0 65,163,71,144,173,176,167,195,0 60,180,67,160,167,195,160,215,0 204,191,210,163,435,209,429,237,0 275,225,279,205,430,235,426,255,0
D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000752.jpg 88,40,122,17,200,134,166,157,0 175,169,228,144,315,336,261,360,0
D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000753.jpg 125,143,237,126,241,154,129,171,0 355,288,395,280,398,293,357,301,0
D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000770.jpg 306,170,318,162,335,188,323,195,0
D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000795.jpg 196,80,196,51,208,51,208,80,0
D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000838.jpg 331,101,362,94,374,148,343,155,0
D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000855.jpg 239,139,252,138,260,215,248,216,0
D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000917.jpg 341,149,346,83,360,84,355,150,0
D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000918.jpg 315,152,330,132,385,172,370,192,0
D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000922.jpg 378,125,393,115,417,148,403,158,0
D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000927.jpg 231,146,272,115,287,135,246,166,0
D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000932.jpg 185,106,207,85,265,148,243,168,0
D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000941.jpg 325,160,325,104,347,104,347,160,0
D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000946.jpg 213,152,225,132,278,166,265,186,0
D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000951.jpg 416,138,447,136,448,146,417,148,0
D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000954.jpg 156,260,206,243,212,261,162,278,0 210,77,230,70,241,99,222,107,0
D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000966.jpg 117,135,123,101,132,102,126,136,0
D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000967.jpg 385,182,389,147,398,148,393,183,0
D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000971.jpg 187,70,207,54,215,64,194,80,0
D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000993.jpg 69,161,96,133,155,192,128,220,0
D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/000996.jpg 122,170,139,154,172,189,155,205,0
D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/001019.jpg 179,129,208,101,216,111,188,138,0 124,224,124,211,162,211,162,224,0 33,143,37,131,66,140,62,152,0
D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/001022.jpg 241,222,250,212,269,231,259,240,0 422,133,468,123,473,144,427,155,0
D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/001044.jpg 294,109,326,107,327,124,295,125,0
D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/001054.jpg 295,112,316,112,316,119,295,119,0 409,231,430,229,431,235,410,236,0 319,267,338,264,339,270,320,274,0 209,208,223,206,224,212,210,214,0
D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/001058.jpg 184,66,184,59,208,59,208,66,0 301,178,322,178,322,183,301,183,0 211,214,231,214,231,220,211,220,0 101,155,116,155,116,160,101,160,0
D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/001074.jpg 146,129,162,126,164,136,147,138,0
D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/001078.jpg 153,77,171,68,190,106,172,115,0 147,172,147,150,159,150,159,172,0
D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/001099.jpg 58,177,58,149,70,149,70,177,0 159,65,159,45,168,45,168,65,0 136,214,136,186,148,186,148,214,0 188,152,188,132,198,132,198,152,0 236,120,245,118,248,135,239,137,0 298,144,298,127,308,127,308,144,0 335,68,346,64,353,84,342,88,0 251,236,280,219,288,232,259,250,0 259,265,275,243,286,251,271,273,0
D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/001102.jpg 52,33,54,14,62,15,60,34,0 28,158,41,158,41,184,28,184,0 82,124,82,104,92,104,92,124,0 129,89,141,89,141,107,129,107,0 143,209,170,188,181,202,153,223,0 150,231,172,213,183,226,161,244,0 192,116,192,99,201,99,201,116,0 228,41,238,36,247,54,237,58,0
D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/001128.jpg 54,78,57,68,84,75,81,86,0 69,164,72,154,103,163,100,173,0 193,80,197,69,236,85,231,96,0 186,221,189,211,217,221,214,230,0 310,159,313,149,342,158,339,168,0 377,82,381,71,407,82,403,92,0 412,158,415,147,440,155,437,165,0
D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/001133.jpg 240,81,241,68,282,70,281,84,0 144,173,146,164,168,167,166,177,0
D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/001145.jpg 51,80,51,72,74,72,74,80,0 387,122,388,115,407,119,405,126,0
D:\Notebook\yolov7-pytorch\VOCdevkit/VOC2007/JPEGImages/001158.jpg 91,29,95,22,117,34,113,41,0 26,74,27,66,46,71,45,78,0 25,129,26,122,42,124,41,132,0 79,159,80,153,96,155,95,161,0 215,140,234,134,236,143,218,148,0
train.py
浏览文件 @ 8b7c5d20
......@@ -41,7 +41,7 @@ if __name__ == "__main__":
# Cuda 是否使用Cuda
# 没有GPU可以设置成False
#---------------------------------#
Cuda = True
Cuda = False
#---------------------------------------------------------------------#
# distributed 用于指定是否使用单机多卡分布式运行
# 终端指令仅支持Ubuntu。CUDA_VISIBLE_DEVICES用于在Ubuntu下指定显卡。
......
utils/dataloader.py
浏览文件 @ 8b7c5d20
......@@ -76,7 +76,6 @@ class YoloDataset(Dataset):
#---------------------------------------------------#
# box[:, 2:4] = box[:, 2:4] - box[:, 0:2]
# box[:, 0:2] = box[:, 0:2] + box[:, 2:4] / 2
#---------------------------------------------------#
# 调整顺序，符合训练的格式
# labels_out中序号为0的部分在collect时处理
......@@ -105,6 +104,12 @@ class YoloDataset(Dataset):
# 获得预测框
#------------------------------#
box = np.array([np.array(list(map(int,box.split(',')))) for box in line[1:]])
#------------------------------#
# 将polygon转换为rbox
#------------------------------#
rbox = np.zeros((box.shape[0], 6))
rbox[..., :5] = poly2rbox(box[..., :8], (h, w), use_pi=True)
rbox[..., 5] = box[..., 8]
image = image.resize((w,h), Image.BICUBIC)
image_data = np.array(image, np.float32)
......
utils/utils_bbox.py
浏览文件 @ 8b7c5d20
import numpy as np
import torch
import math
from torchvision.ops import nms
# from utils.nms_rotated import obb_nms
from utils.nms_rotated import obb_nms
class DecodeBox():
def __init__(self, anchors, num_classes, input_shape, anchors_mask = [[6,7,8], [3,4,5], [0,1,2]]):
super(DecodeBox, self).__init__()
self.anchors = anchors
self.num_classes = num_classes
self.bbox_attrs = 5 + 1 + num_classes
self.bbox_attrs = 6 + num_classes
self.input_shape = input_shape
#-----------------------------------------------------------#
# 13x13的特征层对应的anchor是[142, 110],[192, 243],[459, 401]
......@@ -62,6 +63,10 @@ class DecodeBox():
w = torch.sigmoid(prediction[..., 2])
h = torch.sigmoid(prediction[..., 3])
#-----------------------------------------------#
# 获取旋转角度
#-----------------------------------------------#
angle = torch.sigmoid(prediction[..., 4])
#-----------------------------------------------#
# 获得置信度，是否有物体
#-----------------------------------------------#
conf = torch.sigmoid(prediction[..., 5])
......@@ -105,17 +110,17 @@ class DecodeBox():
pred_boxes[..., 1] = y.data * 2. - 0.5 + grid_y
pred_boxes[..., 2] = (w.data * 2) ** 2 * anchor_w
pred_boxes[..., 3] = (h.data * 2) ** 2 * anchor_h
pred_theta = (angle.data - 0.5) * math.pi
#----------------------------------------------------------#
# 将输出结果归一化成小数的形式
#----------------------------------------------------------#
_scale = torch.Tensor([input_width, input_height, input_width, input_height]).type(FloatTensor)
output = torch.cat((pred_boxes.view(batch_size, -1, 4) / _scale,
output = torch.cat((pred_boxes.view(batch_size, -1, 4) / _scale, pred_theta.view(batch_size, -1, 1),
conf.view(batch_size, -1, 1), pred_cls.view(batch_size, -1, self.num_classes)), -1)
outputs.append(output.data)
return outputs
def yolo_correct_boxes(self, box_xy, box_wh, input_shape, image_shape, letterbox_image):
def yolo_correct_boxes(self, box_xy, box_wh, angle, input_shape, image_shape, letterbox_image):
#-----------------------------------------------------------------#
# 把y轴放前面是因为方便预测框和图像的宽高进行相乘
#-----------------------------------------------------------------#
......@@ -136,23 +141,16 @@ class DecodeBox():
box_yx = (box_yx - offset) * scale
box_hw *= scale
box_mins = box_yx - (box_hw / 2.)
box_maxes = box_yx + (box_hw / 2.)
boxes = np.concatenate([box_mins[..., 0:1], box_mins[..., 1:2], box_maxes[..., 0:1], box_maxes[..., 1:2]], axis=-1)
boxes *= np.concatenate([image_shape, image_shape], axis=-1)
box_xy = box_yx[..., ::-1] * image_shape
box_wh = box_hw[..., ::-1] * image_shape
boxes = np.concatenate([box_xy[..., 0:1], box_xy[..., 1:2], box_wh[..., 0:1], box_wh[..., 1:2], angle[..., 0:1] ], axis=-1)
return boxes
def non_max_suppression(self, prediction, num_classes, input_shape, image_shape, letterbox_image, conf_thres=0.5, nms_thres=0.4):
#----------------------------------------------------------#
# 将预测结果的格式转换成左上角右下角的格式。
# prediction [batch_size, num_anchors, 85]
#----------------------------------------------------------#
box_corner = prediction.new(prediction.shape)
box_corner[:, :, 0] = prediction[:, :, 0] - prediction[:, :, 2] / 2
box_corner[:, :, 1] = prediction[:, :, 1] - prediction[:, :, 3] / 2
box_corner[:, :, 2] = prediction[:, :, 0] + prediction[:, :, 2] / 2
box_corner[:, :, 3] = prediction[:, :, 1] + prediction[:, :, 3] / 2
prediction[:, :, :4] = box_corner[:, :, :4]
output = [None for _ in range(len(prediction))]
for i, image_pred in enumerate(prediction):
......@@ -161,13 +159,12 @@ class DecodeBox():
# class_conf [num_anchors, 1] 种类置信度
# class_pred [num_anchors, 1] 种类
#----------------------------------------------------------#
class_conf, class_pred = torch.max(image_pred[:, 5:5 + num_classes], 1, keepdim=True)
class_conf, class_pred = torch.max(image_pred[:, 6:6 + num_classes], 1, keepdim=True)
#----------------------------------------------------------#
# 利用置信度进行第一轮筛选
#----------------------------------------------------------#
conf_mask = (image_pred[:, 4] * class_conf[:, 0] >= conf_thres).squeeze()
conf_mask = (image_pred[:, 5] * class_conf[:, 0] >= conf_thres).squeeze()
#----------------------------------------------------------#
# 根据置信度进行预测结果的筛选
#----------------------------------------------------------#
......@@ -177,10 +174,10 @@ class DecodeBox():
if not image_pred.size(0):
continue
#-------------------------------------------------------------------------#
# detections [num_anchors, 7]
# 7的内容为：x1, y1, x2, y2, obj_conf, class_conf, class_pred
# detections [num_anchors, 8]
# 8的内容为：x, y, w, h, angle, obj_conf, class_conf, class_pred
#-------------------------------------------------------------------------#
detections = torch.cat((image_pred[:, :5], class_conf.float(), class_pred.float()), 1)
detections = torch.cat((image_pred[:, :6], class_conf.float(), class_pred.float()), 1)
#------------------------------------------#
# 获得预测结果中包含的所有种类
......@@ -201,9 +198,9 @@ class DecodeBox():
# 使用官方自带的非极大抑制会速度更快一些！
# 筛选出一定区域内，属于同一种类得分最大的框
#------------------------------------------#
keep = nms(
detections_class[:, :4],
detections_class[:, 4] * detections_class[:, 5],
_, keep = obb_nms(
detections_class[:, :5],
detections_class[:, 5] * detections_class[:, 6],
nms_thres
)
max_detections = detections_class[keep]
......@@ -227,9 +224,9 @@ class DecodeBox():
output[i] = max_detections if output[i] is None else torch.cat((output[i], max_detections))
if output[i] is not None:
output[i] = output[i].cpu().numpy()
box_xy, box_wh = (output[i][:, 0:2] + output[i][:, 2:4])/2, output[i][:, 2:4] - output[i][:, 0:2]
output[i][:, :4] = self.yolo_correct_boxes(box_xy, box_wh, input_shape, image_shape, letterbox_image)
output[i] = output[i].cpu().numpy()
box_xy, box_wh, angle = output[i][:, 0:2], output[i][:, 2:4], output[i][:, 4:5]
output[i][:, :5] = self.yolo_correct_boxes(box_xy, box_wh, angle, input_shape, image_shape, letterbox_image)
return output
def non_max_suppression_obb(self, prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=False,
......
voc_annotation.py
浏览文件 @ 8b7c5d20
......@@ -56,7 +56,10 @@ def convert_annotation(year, image_id, list_file):
continue
cls_id = classes.index(cls)
xmlbox = obj.find('rotated_bndbox')
b = (int(float(xmlbox.find('rotated_bbox_cx').text)), int(float(xmlbox.find('rotated_bbox_cy').text)), int(float(xmlbox.find('rotated_bbox_w').text)), int(float(xmlbox.find('rotated_bbox_h').text)), int(float(xmlbox.find('rotated_bbox_theta').text)))
b = (int(float(xmlbox.find('x1').text)), int(float(xmlbox.find('y1').text)), \
int(float(xmlbox.find('x2').text)), int(float(xmlbox.find('y2').text)), \
int(float(xmlbox.find('x3').text)), int(float(xmlbox.find('y3').text)), \
int(float(xmlbox.find('x4').text)), int(float(xmlbox.find('y4').text)))
list_file.write(" " + ",".join([str(a) for a in b]) + ',' + str(cls_id))
nums[classes.index(cls)] = nums[classes.index(cls)] + 1
......
yolo.py
浏览文件 @ 8b7c5d20
......@@ -25,8 +25,8 @@ class YOLO(object):
# 验证集损失较低不代表mAP较高，仅代表该权值在验证集上泛化性能较好。
# 如果出现shape不匹配，同时要注意训练时的model_path和classes_path参数的修改
#--------------------------------------------------------------------------#
"model_path" : 'model_data/yolov7_weights.pth',
"classes_path" : 'model_data/coco_classes.txt',
"model_path" : 'logs/best_epoch_weights.pth',
"classes_path" : 'model_data/ssdd_classes.txt',
#---------------------------------------------------------------------#
# anchors_path代表先验框对应的txt文件，一般不修改。
# anchors_mask用于帮助代码找到对应的先验框，一般不修改。
......@@ -46,7 +46,7 @@ class YOLO(object):
#---------------------------------------------------------------------#
# 只有得分大于置信度的预测框会被保留下来
#---------------------------------------------------------------------#
"confidence" : 0.5,
"confidence" : 0.05,
#---------------------------------------------------------------------#
# 非极大抑制所用到的nms_iou大小
#---------------------------------------------------------------------#
......@@ -60,7 +60,7 @@ class YOLO(object):
# 是否使用Cuda
# 没有GPU可以设置成False
#-------------------------------#
"cuda" : True,
"cuda" : False,
}
@classmethod
......@@ -148,7 +148,8 @@ class YOLO(object):
#---------------------------------------------------------#
# 将预测框进行堆叠，然后进行非极大抑制
#---------------------------------------------------------#
results = self.bbox_util.non_max_suppression_obb(torch.cat(outputs, 1), self.confidence, self.nms_iou, classes=self.num_classes)
results = self.bbox_util.non_max_suppression(torch.cat(outputs, 1), self.num_classes, self.input_shape,
image_shape, self.letterbox_image, conf_thres = self.confidence, nms_thres = self.nms_iou)
if results[0] is None:
return image
......@@ -179,12 +180,10 @@ class YOLO(object):
#---------------------------------------------------------#
for i, c in list(enumerate(top_label)):
predicted_class = self.class_names[int(c)]
poly = top_polys[i]
poly = top_polys[i].astype(np.int32)
score = top_conf[i]
polygon_list = [(poly[0], poly[1]), (poly[2], poly[3]), \
(poly[4], poly[5]), (poly[6], poly[7])]
polygon_list = list(poly)
label = '{} {:.2f}'.format(predicted_class, score)
draw = ImageDraw.Draw(image)
label_size = draw.textsize(label, font)
......@@ -193,7 +192,7 @@ class YOLO(object):
text_origin = np.array([poly[0], poly[1]], np.int32)
draw.polygon(xy=polygon_list, fill=(0, 0, 0), outline=self.colors[i], width=label_size)
draw.polygon(xy=polygon_list, outline=self.colors[c])
draw.text(text_origin, str(label,'UTF-8'), fill=(0, 0, 0), font=font)
del draw
......
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