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LICENSE

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+MIT License
+
+Copyright (c) 2020 JiaQi Xu
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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-# mobilenet-yolov4-lite-pytorch
-这是一个mobilenet-yolov4-lite的库，把yolov4主干网络修改成了mobilenet，修改了Panet的卷积组成，使参数量大幅度缩小。
+## YOLOV4：You Only Look Once目标检测模型在pytorch当中的实现
+---
+
+### 目录
+1. [所需环境 Environment](#所需环境)
+2. [注意事项 Attention](#注意事项)
+3. [小技巧的设置 TricksSet](#小技巧的设置)
+4. [文件下载 Download](#文件下载))
+5. [训练步骤 How2train](#训练步骤)
+6. [参考资料 Reference](#Reference)
+
+### YOLOV4的改进
+- [x] 主干特征提取网络：DarkNet53 => CSPDarkNet53
+- [x] 特征金字塔：SPP，PAN
+- [x] 训练用到的小技巧：Mosaic数据增强、Label Smoothing平滑、CIOU、学习率余弦退火衰减
+- [x] 激活函数：使用Mish激活函数
+- [ ] ……balabla
+
+### 所需环境
+torch==1.2.0
+
+### 注意事项
+代码中的yolo4_weights.pth是基于608x608的图片训练的，但是由于显存原因。我将代码中的图片大小修改成了416x416。有需要的可以修改回来。 代码中的默认anchors是基于608x608的图片的。   
+
+### 小技巧的设置
+在train.py文件下：   
+1、mosaic参数可用于控制是否实现Mosaic数据增强。   
+2、Cosine_scheduler可用于控制是否使用学习率余弦退火衰减。   
+3、label_smoothing可用于控制是否Label Smoothing平滑。
+
+### 文件下载
+训练所需的yolo4_weights.pth可在百度网盘中下载。  
+链接: https://pan.baidu.com/s/1VNSYi39AaqjHVbdNpW_7sw 提取码: q2iv  
+yolo4_weights.pth是coco数据集的权重。  
+yolo4_voc_weights.pth是voc数据集的权重。
+
+### 训练步骤
+1、本文使用VOC格式进行训练。  
+2、训练前将标签文件放在VOCdevkit文件夹下的VOC2007文件夹下的Annotation中。  
+3、训练前将图片文件放在VOCdevkit文件夹下的VOC2007文件夹下的JPEGImages中。  
+4、在训练前利用voc2yolo4.py文件生成对应的txt。  
+5、再运行根目录下的voc_annotation.py，运行前需要将classes改成你自己的classes。  
+```python
+classes = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]
+```
+6、就会生成对应的2007_train.txt，每一行对应其图片位置及其真实框的位置。  
+7、在训练前需要修改model_data里面的voc_classes.txt文件，需要将classes改成你自己的classes。  
+8、运行train.py即可开始训练。
+
+### mAP目标检测精度计算更新
+更新了get_gt_txt.py、get_dr_txt.py和get_map.py文件。  
+get_map文件克隆自https://github.com/Cartucho/mAP  
+具体mAP计算过程可参考：https://www.bilibili.com/video/BV1zE411u7Vw
+
+### Reference
+https://github.com/qqwweee/keras-yolo3/  
+https://github.com/Cartucho/mAP  
+https://github.com/Ma-Dan/keras-yolo4  

VOCdevkit/VOC2007/Annotations/README.md

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+存放标签文件

VOCdevkit/VOC2007/ImageSets/Main/README.md

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+存放训练索引文件

VOCdevkit/VOC2007/JPEGImages/README.md

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+存放图片文件

VOCdevkit/VOC2007/voc2yolo4.py

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+import os
+import random 
+
+xmlfilepath=r'./VOCdevkit/VOC2007/Annotations'
+saveBasePath=r"./VOCdevkit/VOC2007/ImageSets/Main/"
+
+trainval_percent=1
+train_percent=1
+
+temp_xml = os.listdir(xmlfilepath)
+total_xml = []
+for xml in temp_xml:
+    if xml.endswith(".xml"):
+        total_xml.append(xml)
+
+num=len(total_xml)  
+list=range(num)  
+tv=int(num*trainval_percent)  
+tr=int(tv*train_percent)  
+trainval= random.sample(list,tv)  
+train=random.sample(trainval,tr)  
+
+print("train and val size",tv)
+print("traub suze",tr)
+ftrainval = open(os.path.join(saveBasePath,'trainval.txt'), 'w')  
+ftest = open(os.path.join(saveBasePath,'test.txt'), 'w')  
+ftrain = open(os.path.join(saveBasePath,'train.txt'), 'w')  
+fval = open(os.path.join(saveBasePath,'val.txt'), 'w')  
+
+for i  in list:  
+    name=total_xml[i][:-4]+'\n'  
+    if i in trainval:  
+        ftrainval.write(name)  
+        if i in train:  
+            ftrain.write(name)  
+        else:  
+            fval.write(name)  
+    else:  
+        ftest.write(name)  
+
+ftrainval.close()  
+ftrain.close()  
+fval.close()  
+ftest .close()

ciou_test.py

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+import torch
+import math
+import numpy as np
+def box_ciou(b1, b2):
+    """
+    输入为：
+    ----------
+    b1: tensor, shape=(batch, feat_w, feat_h, anchor_num, 4), xywh
+    b2: tensor, shape=(batch, feat_w, feat_h, anchor_num, 4), xywh
+
+    返回为：
+    -------
+    ciou: tensor, shape=(batch, feat_w, feat_h, anchor_num, 1)
+    """
+    # 求出预测框左上角右下角
+    b1_xy = b1[..., :2]
+    b1_wh = b1[..., 2:4]
+    b1_wh_half = b1_wh/2.
+    b1_mins = b1_xy - b1_wh_half
+    b1_maxes = b1_xy + b1_wh_half
+    # 求出真实框左上角右下角
+    b2_xy = b2[..., :2]
+    b2_wh = b2[..., 2:4]
+    b2_wh_half = b2_wh/2.
+    b2_mins = b2_xy - b2_wh_half
+    b2_maxes = b2_xy + b2_wh_half
+
+    # 求真实框和预测框所有的iou
+    intersect_mins = torch.max(b1_mins, b2_mins)
+    intersect_maxes = torch.min(b1_maxes, b2_maxes)
+    intersect_wh = torch.max(intersect_maxes - intersect_mins, torch.zeros_like(intersect_maxes))
+    intersect_area = intersect_wh[..., 0] * intersect_wh[..., 1]
+    b1_area = b1_wh[..., 0] * b1_wh[..., 1]
+    b2_area = b2_wh[..., 0] * b2_wh[..., 1]
+    union_area = b1_area + b2_area - intersect_area
+    iou = intersect_area / (union_area + 1e-7)
+
+    # 计算中心的差距
+    center_distance = torch.sum(torch.pow((b1_xy - b2_xy), 2), axis=-1)
+    # 找到包裹两个框的最小框的左上角和右下角
+    enclose_mins = torch.min(b1_mins, b2_mins)
+    enclose_maxes = torch.max(b1_maxes, b2_maxes)
+    enclose_wh = torch.max(enclose_maxes - enclose_mins, torch.zeros_like(intersect_maxes))
+    # 计算对角线距离
+    enclose_diagonal = torch.sum(torch.pow(enclose_wh,2), axis=-1)
+    ciou = iou - 1.0 * (center_distance) / (enclose_diagonal + 1e-7)
+
+    v = (4 / (math.pi ** 2)) * torch.pow((torch.atan(b1_wh[..., 0]/b1_wh[..., 1]) - torch.atan(b2_wh[..., 0]/b2_wh[..., 1])), 2)
+    alpha = v / (1.0 - iou + v)
+    ciou = ciou - alpha * v
+    return ciou
+
+box1 = torch.from_numpy(np.array([[25,25,40,40]])).type(torch.FloatTensor)
+box2 = torch.from_numpy(np.array([[25,25,30,40]])).type(torch.FloatTensor)
+
+print(box_ciou(box1,box2))

get_dr_txt.py

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+#-------------------------------------#
+#       mAP所需文件计算代码
+#       具体教程请查看Bilibili
+#       Bubbliiiing
+#-------------------------------------#
+import colorsys
+import os
+
+import cv2
+import numpy as np
+import torch
+import torch.backends.cudnn as cudnn
+import torch.nn as nn
+from PIL import Image, ImageDraw, ImageFont
+from torch.autograd import Variable
+from tqdm import tqdm
+
+from nets.yolo4 import YoloBody
+from utils.utils import (DecodeBox, bbox_iou, letterbox_image,
+                         non_max_suppression, yolo_correct_boxes)
+from yolo import YOLO
+
+
+class mAP_Yolo(YOLO):
+    #---------------------------------------------------#
+    #   检测图片
+    #---------------------------------------------------#
+    def detect_image(self,image_id,image):
+        self.confidence = 0.01
+        self.iou = 0.5  
+        f = open("./input/detection-results/"+image_id+".txt","w") 
+        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(3):
+            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)
+
+        for i, c in enumerate(top_label):
+            predicted_class = self.class_names[c]
+            score = str(top_conf[i])
+
+            top, left, bottom, right = boxes[i]
+            f.write("%s %s %s %s %s %s\n" % (predicted_class, score[:6], str(int(left)), str(int(top)), str(int(right)),str(int(bottom))))
+
+        f.close()
+        return 
+
+yolo = mAP_Yolo()
+image_ids = open('VOCdevkit/VOC2007/ImageSets/Main/test.txt').read().strip().split()
+
+if not os.path.exists("./input"):
+    os.makedirs("./input")
+if not os.path.exists("./input/detection-results"):
+    os.makedirs("./input/detection-results")
+if not os.path.exists("./input/images-optional"):
+    os.makedirs("./input/images-optional")
+
+for image_id in tqdm(image_ids):
+    image_path = "./VOCdevkit/VOC2007/JPEGImages/"+image_id+".jpg"
+    image = Image.open(image_path)
+    # 开启后在之后计算mAP可以可视化
+    # image.save("./input/images-optional/"+image_id+".jpg")
+    yolo.detect_image(image_id,image)
+
+print("Conversion completed!")

get_gt_txt.py

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+#----------------------------------------------------#
+#   获取测试集的ground-truth
+#   具体视频教程可查看
+#   https://www.bilibili.com/video/BV1zE411u7Vw
+#----------------------------------------------------#
+import glob
+import os
+import sys
+import xml.etree.ElementTree as ET
+
+image_ids = open('VOCdevkit/VOC2007/ImageSets/Main/test.txt').read().strip().split()
+
+if not os.path.exists("./input"):
+    os.makedirs("./input")
+if not os.path.exists("./input/ground-truth"):
+    os.makedirs("./input/ground-truth")
+
+for image_id in image_ids:
+    with open("./input/ground-truth/"+image_id+".txt", "w") as new_f:
+        root = ET.parse("VOCdevkit/VOC2007/Annotations/"+image_id+".xml").getroot()
+        for obj in root.findall('object'):
+            difficult_flag = False
+            if obj.find('difficult')!=None:
+                difficult = obj.find('difficult').text
+                if int(difficult)==1:
+                    difficult_flag = True
+            obj_name = obj.find('name').text
+            bndbox = obj.find('bndbox')
+            left = bndbox.find('xmin').text
+            top = bndbox.find('ymin').text
+            right = bndbox.find('xmax').text
+            bottom = bndbox.find('ymax').text
+            if difficult_flag:
+                new_f.write("%s %s %s %s %s difficult\n" % (obj_name, left, top, right, bottom))
+            else:
+                new_f.write("%s %s %s %s %s\n" % (obj_name, left, top, right, bottom))
+
+print("Conversion completed!")