記錄下第一次正式參加線上演算法比賽的解題流程，雖然錯過了B榜時間，但識訓匪淺！

專案介紹

大賽鏈接：廣東電網智慧現場作業挑戰賽 賽道三：識別高空作業及安全帶佩戴，

資料處理

標簽資料提取

從csv中提取出標簽資料轉存成json檔案，再將json檔案轉為單個的coco資料集格式標簽，其中box坐標為歸一化后的x,y,w,h，

（1）將csv資料標簽存為json檔案，（data_deal.py）根據具體文本格式改寫自己的資料處理的代碼，

'''
官方給出的csv中的
{
 "meta":{},
 "id":"88eb919f-6f12-486d-9223-cd0c4b581dbf",
 "items":
[
     {"meta":{"rectStartPointerXY":[622,2728],"pointRatio":0.5,"geometry":[622,2728,745,3368],"type":"BBOX"},"id":"e520a291-bbf7-4032-92c6-dc84a1fc864e","properties":{"create_time":1620610883573,"accept_meta":{},"mark_by":"LABEL","is_system_map":false},"labels":{"鏍囩":"ground"}}
     {"meta":{"pointRatio":0.5,"geometry":[402.87,621.81,909,1472.01],"type":"BBOX"},"id":"2c097366-fbb3-4f9d-b5bb-286e70970eba","properties":{"create_time":1620610907831,"accept_meta":{},"mark_by":"LABEL","is_system_map":false},"labels":{"鏍囩":"safebelt"}}
     {"meta":{"rectStartPointerXY":[692,1063],"pointRatio":0.5,"geometry":[697.02,1063,1224,1761],"type":"BBOX"},"id":"8981c722-79e8-4ae8-a3a3-ae451300d625","properties":{"create_time":1620610943766,"accept_meta":{},"mark_by":"LABEL","is_system_map":false},"labels":{"鏍囩":"offground"}}
 ],
 "properties":{"seq":"1714"},"labels":{"invalid":"false"},"timestamp":1620644812068
 }
'''

import pandas as pd
import json
import os
from PIL import Image

df = pd.read_csv("3train_rname.csv",header=None)
df_img_path = df[4]
df_img_mark = df[5]
# print(df_img_mark)
# 統計一下類別,并且重新生成原資料集標注檔案，保存到json檔案中
dict_class = {
    "badge": 0,
    "offground": 0,
    "ground": 0,
    "safebelt": 0
}
dict_lable = {
    "badge": 1,
    "offground": 2,
    "ground": 3,
    "safebelt": 4
}
data_dict_json = []
image_width, image_height = 0, 0
ids = 0
false = False  # 將其中false欄位轉化為布林值False
true = True  # 將其中true欄位轉化為布林值True
for img_id, one_img in enumerate(df_img_mark):
    # print('img_id',img_id)
    one_img = eval(one_img)["items"]
    # print('one_img',one_img)
    one_img_name = df_img_path[img_id]
    img = Image.open(os.path.join("./", one_img_name))
    # print(os.path.join("./", one_img_name))
    ids = ids + 1
    w, h = img.size
    image_width += w
    # print(image_width)
    image_height += h
    # print(one_img_name)
    i=1
    for one_mark in one_img:
        # print('%d      '%i,one_mark)

        one_label = one_mark["labels"]['標簽']
        # print('%d      '%i,one_label)
        try:
            dict_class[str(one_label)] += 1
            # category = str(one_label)
            category = dict_lable[str(one_label)]
            bbox = one_mark["meta"]["geometry"]
        except:
            dict_class["badge"] += 1  # 標簽為"監護袖章(紅only)"表示類別"badge"
            # category = "badge"
            category = 1
            bbox = one_mark["meta"]["geometry"]
        i+=1

        one_dict = {}
        one_dict["name"] = str(one_img_name)
        one_dict["category"] = category
        one_dict["bbox"] = bbox
        data_dict_json.append(one_dict)
print(image_height / ids, image_width / ids)
print(dict_class)
print(len(data_dict_json))
print(data_dict_json[0])
with open("./data.json2", 'w') as fp:
    json.dump(data_dict_json, fp, indent=1, separators=(',', ': '))  # 縮進設定為1，元素之間用逗號隔開 ， key和內容之間 用冒號隔開
    fp.close()

生成data.json檔案：

標簽資料集制作

（2）將data.json檔案按照coco資料的標簽格式準備資料（將json檔案按照圖片的名稱保存labels資訊）json_to_txt.py 這里將所有的標簽都減了一，可以不改，自己對的上就可以，當前標簽：“badge”: 0,“offground”: 1,“ground”: 2,“safebelt”:3 bbox做了歸一化（這個分資料集，有的資料集格式不一樣，具體情況具體改）

import json
import os
import cv2

file_name_list = {}

with open("./data.json", 'r', encoding='utf-8') as fr:
        data_list = json.load(fr)
file_name = ''
label = 0
[x1, y1, x2, y2] = [0, 0, 0, 0]

for data_dict in data_list:
    for k,v in data_dict.items():
        if k == "category":
            label = v
        if k == "bbox":
            [x1, y1, x2, y2] = v
        if k == "name":
            file_name = v[9:-4]

    if not os.path.exists('./data1/'):
        os.mkdir('./data1/')
    print('./3_images/' + file_name + '.jpg')
    img = cv2.imread('./3_images/' + file_name + '.jpg')    
    size = img.shape # (h, w, channel)
    dh = 1. / size[0]
    dw = 1. / size[1]
    x = (x1 + x2) / 2.0
    y = (y1 + y2) / 2.0
    w = x2 - x1
    h = y2 - y1
    x = x * dw
    w = w * dw
    y = y * dh
    h = h * dh

    # print(size)
    # cv2.imshow('image', img)
    # cv2.waitKey(0)

    content = str(label-1) + " " + str(x) + " " + str(y) + " " + str(w) + " " + str(h) + "\n"
    if not content:
        print(file_name)
    with open('./data1/' + file_name + '.txt', 'a+', encoding='utf-8') as fw:
        fw.write(content)

模型訓練

參考：yolov5訓練自己的資料集（一文搞定訓練）

資料集劃分（這里之前有一個步驟！ 因為劃分資料集的時候的腳本是按照檔案名索引的，但是這次的圖片的格式不止一種，所以在此之前先將所有的圖片都改為統一的后綴：remane.py）

import os
class BatchRename():
    # 批量重命名檔案夾中的圖片檔案
    def __init__(self):
        self.path = './3_images' #表示需要命名處理的檔案夾
    def rename(self):
        filelist = os.listdir(self.path)      #獲取檔案路徑
        total_num = len(filelist)             #獲取檔案長度（個數）
        print(total_num)
        i = 1                                 #表示檔案的命名是從1開始的
        for item in filelist:
            # print(item)
            file_name=item.split('.',-1)[0]
            # print(file_name)
            src = os.path.join(os.path.abspath(self.path), item)
            # print(src)
            dst = os.path.join(os.path.abspath(self.path), file_name + '.jpg')
            # print(dst)

            try:
                os.rename(src, dst)
                print ('converting %s to %s ...' % (src, dst))
                i = i + 1
            except:
                continue
        print ('total %d to rename & converted %d jpgs' % (total_num, i))
if __name__ == '__main__':
    demo = BatchRename()
    demo.rename()

修改訓練引數（路徑及自己的類別）

訓練

撰寫自己的detect.py檔案(這里其實不用改，只需要將所需要的引數都存下來就行，都在檢測結果中，detect.py檔案里傳入下面引數)

資料整合

檢測出的結果（圖片和所有的標簽檔案）：

每個txt中存了當前圖片檢測出的cls bbox score：

我們要做的是按照主辦方提供的測驗資料的csv中的圖片順序，去到結果檔案夾中索引對應的檢測結果，并將所有的結果按照主辦方給出的資料格式存到json檔案中，result_imerge_2.py檔案（這里由于訓練資料標簽與提交的標簽并不完全相同，提交的結果必須是所屬類的對應的人的標簽，所以這里需要對結果整合，提取有用資料，目前我們的邏輯關系還需要進一步改善）

import pandas as pd
import json
import os
import copy

global data_dict_json
data_dict_json = []


def check_equip(id, equip_list, people_list, cls_result, cls_result2=-1):
    for people in people_list:
        dict4 = {}
        dict_cls = {'image_id': id, 'category_id': -1, 'bbox': [], 'score': 0}
        x1, y1, x2, y2, score2 = people

        if equip_list:
            for equip in equip_list:
                dict1, dict2, dict3 = {}, {}, {}
                equip_x1, equip_y1, equip_x2, equip_y2, score = equip
                center_x = (int(equip_x1) + int(equip_x2)) / 2
                center_y = (int(equip_y1) + int(equip_y2)) / 2

                if center_x > int(x1) and center_x < int(x2) and center_y < int(y2) and center_y > int(y1):
                    dict1 = copy.deepcopy(dict_cls)
                    dict1['image_id'] = id
                    dict1['category_id'] = cls_result
                    dict1['bbox'] = list(map(int, people[:-1]))
                    dict1['score'] = float(score2)

                    if dict1['category_id'] != -1:
                        if not dict1 in data_dict_json:
                            data_dict_json.append(dict1)

                    dict2 = copy.deepcopy(dict_cls)
                    dict2['image_id'] = id
                    dict2['category_id'] = cls_result2
                    dict2['bbox'] = list(map(int, people[:-1]))
                    dict2['score'] = float(score2)

                    if dict2['category_id'] != -1:
                        if not dict2 in data_dict_json:
                            data_dict_json.append(dict2)

                else:
                    dict3 = copy.deepcopy(dict3)
                    dict3['image_id'] = id
                    dict3['category_id'] = cls_result2
                    dict3['bbox'] = list(map(int, people[:-1]))
                    dict3['score'] = float(score2)

                    if dict3['category_id'] != -1:
                        if not dict3 in data_dict_json:
                            data_dict_json.append(dict3)

        else:
            dict4 = copy.deepcopy(dict_cls)
            dict4['image_id'] = id
            dict4['category_id'] = cls_result2
            dict4['bbox'] = list(map(int, people[:-1]))
            dict4['score'] = float(score2)

            if dict4['category_id'] != -1:
                if not dict4 in data_dict_json:
                    data_dict_json.append(dict4)


def save_json(file_lines):
    badge_list = []
    off_list = []
    ground_list = []
    safebelt_list = []
    person_list=[]

    for line in file_lines:
        line2 = str(line.strip('\n'))
        content = line2.split(' ', -1)

        if int(content[0]) == 0:
            badge_list.append(content[:])
        elif int(content[0]) == 1:
            off_list.append(content[:])
            person_list.append(content[:-1])
        elif int(content[0]) == 2:
            ground_list.append(content[:])
            person_list.append(content[:-1])
        elif int(content[0]) == 3:
            safebelt_list.append(content[:])
    # print('+++++++',person_list)
    return person_list


df = pd.read_csv("3_testa_user.csv", header=None)
df_img_path = df[0]
for id, one_img in enumerate(df_img_path):
    # dict_data={}
    file_name_img = (str(one_img)).split('/', -1)[1]
    # print(file_name_img)
    file_name_label = file_name_img.split('.', -1)[0] + '.txt'
    # print(file_name_label)
    path = os.path.join("./exp_epo50_089/labels/", file_name_label)  # +file_name_label
    file = open(path, 'r')
    file_lines = file.readlines()
    # print(id, file_lines)
    person_list=save_json(file_lines)
    dict1, dict2, dict3 = {}, {}, {}

    for line in file_lines:
        # dict1, dict2, dict3 = {}, {}, {}
        # print('___+++___')
        line2 = str(line.strip('\n'))
        content = line2.split(' ', -1)
        cls, equip_x1, equip_y1, equip_x2, equip_y2, score = content[:]
        center_x = (int(equip_x1) + int(equip_x2)) / 2
        center_y = (int(equip_y1) + int(equip_y2)) / 2

        # print(content)
        if int(content[0])==1:
            dict3['image_id'] = int(id)
            dict3['category_id'] = 3
            dict3['bbox'] = list(map(int, content[1:-1]))
            dict3['score'] = float(content[-1])
            if dict3 not in data_dict_json:
                data_dict_json.append(dict3)
        elif int(content[0])==0:
            for i in person_list:
                print(i)
                cls,x1,y1,x2,y2=i
                if int(center_x)<int(x2) and int(x1)<int(center_x) and int(y1)<int(center_y) and int(center_y)<int(y2):
                    dict1['image_id'] = int(id)
                    dict1['category_id'] = 1
                    dict1['bbox'] = list(map(int, i[1:]))
                    # print('       ',list(map(int, i_list[1:-1])))
                    dict1['score'] = float(content[-1])
            if dict1 not in data_dict_json:
                data_dict_json.append(dict1)
        elif int(content[0])==3:
            for i in person_list:
                cls,x1,y1,x2,y2=i
                if int(center_x) < int(x2) and int(x1) < int(center_x) and int(y1) < int(center_y) and int(
                        center_y) < int(y2):
                    dict2['image_id'] = int(id)
                    dict2['category_id'] = 2
                    dict2['bbox'] = list(map(int, i[1:]))
                    dict2['score'] = float(content[-1])
            if dict2 not in data_dict_json:
                data_dict_json.append(dict2)


with open("./data_result2.json", 'w') as fp:
    json.dump(data_dict_json, fp, indent=1, separators=(',', ': '))  # 縮進設定為1，元素之間用逗號隔開 ， key和內容之間 用冒號隔開
    fp.close()

生成結果：data_result.json檔案

可視化顯示

將最后的結果在原圖上畫出來，可以方便我們查看結果的正確程度，result_show.py

import cv2
import json
import os
import pandas as pd


file_name_list= {}
df = pd.read_csv("3_testa_user.csv",header=None)
# print(df[0][0])

dict_cls={1:'guarder',2:'safebeltperson',3:'offgroundperson'}

with open("data_resultcopy2.json",'r',encoding='utf-8')as fr:
        data_list = json.load(fr)

# file_name = ''
# label = 0
# [x, y, w, h] = [0, 0, 0, 0]
i=0
for data_dict in data_list:
    print(data_dict)
    img_id = data_dict['image_id']
    print(img_id)
    file_path=df[0][img_id]
    save_path='test_view_data_resultcopy2/'
    if not os.path.exists(save_path):
        os.mkdir(save_path)
    save_name=save_path+str(i)+'_'+(str(df[0][img_id])).split('/',-1)[1]
    print(save_name)
    img = cv2.imread(file_path)

    # cv2.imshow('a',img)
    # cv2.waitKey(0)
    cls=dict_cls[data_dict['category_id']]
    score=data_dict['score']
    x1,y1,x2,y2=data_dict['bbox']
    # print(x1,y1,x2,y2)
    cv2.rectangle(img, (x1, y1), (x2, y2), (0, 0, 255), 2)
    cv2.putText(img,str(cls)+' '+str(score),(x1,y1),cv2.FONT_HERSHEY_SIMPLEX,2,(0,0,255),3)
    cv2.imwrite(save_name,img)
    i+=1

繼續改進思路

資料增強

觀察得到offground與ground都是人，所以為了最后提交的人的框的準確度提高，將所有的offground與ground還有賽道一和二中的person類組成一個大的person資料集作為第4個標簽，最后索引person類的bbox會更準確點，然后對于小目標袖標，我們將賽道一和二中的資料進行提取，

賽道一二資料提取

根據所給的csv標簽，單獨提取出袖標和person的標簽資料，存入json檔案，利用data_deal.py檔案，如下：

對提出來的資料進行可視化：

將json標簽轉為歸一化后的coco資料集格式json_to_txt.py

將原始資料集中的圖片統一成jpg格式(方便劃分資料集)

將所需的標簽對應的圖片copy出來，然后加到賽道三的資料中copy_file.py (繼續將賽道二，賽道一都用該方法將袖標資料提出來，所要注意的是每個賽道的label要改的與官方提示一致)

最終結果