update,

vinniesniper-54816/task1/_lab/007-test/.gitignore

@@ -0,0 +1,2 @@
+*.txt
+*.vec

vinniesniper-54816/task1/_lab/007-test/README.md

@@ -0,0 +1,66 @@
+# OPENCV级联分类器训练
+
+## 1.收集样本
+
+### 正样本
+  - 转化成灰度（可用_RGBtoGray.py脚本）
+  - 转化成jpg格式（可用_BMP2JPG.py脚本）
+  - 分辨率20X20
+  - 从0开始按序号命名（可用_ReName.py脚本）
+### 负样本
+  - 转化成灰度（可用_RGBtoGray.py脚本）
+  - 转化成jpg格式（可用_BMP2JPG.py脚本）
+  - 分辨率随意
+  - 需要明显多于正样本
+  - 从0开始按序号命名（可用_ReName.py脚本）
+
+## 2.生成txt数据集路径表
+
+  - 生成文件（可用_GenTXT.py脚本）
+  - 拷贝到项目顶层目录（可用_copy.py脚本）
+
+## 3.生成正样本数据集（负样本数据在下一步用于训练）
+  - 编辑step1.py，根据自己的样本情况修改参数
+  - 执行step1.py脚本
+
+## 4.训练
+  - 编辑step2.py，根据自己的样本情况修改参数
+  - 执行step2.py脚本
+
+## 3.得到模型
+  - 进入xml文件夹，里面就有训练各个层数的模型
+
+## 5.示范
+
+ ```python
+import cv2
+
+# 读取待检测的图像
+image = cv2.imread('12.jpg')
+
+# 获取 XML 文件，加载人脸检测器
+faceCascade = cv2.CascadeClassifier('cascade.xml')
+
+# 色彩转换，转换为灰度图像
+gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
+
+# 调用函数 detectMultiScale
+faces = faceCascade.detectMultiScale(gray,scaleFactor = 1.15,minNeighbors = 5,minSize = (5,5))
+
+#print(faces)
+# 打印输出的测试结果
+print("发现{0}个人脸!".format(len(faces)))
+
+# 逐个标注人脸
+for(x,y,w,h) in faces:
+    cv2.rectangle(image,(x,y),(x+w,y+w),(0,255,0),2) #矩形标注
+    #cv2.circle(image,(int((x+x+w)/2),int((y+y+h)/2)),int(w/2),(0,255,0),2)
+    
+# 显示结果
+cv2.imshow("dect",image)
+# 保存检测结果
+cv2.imwrite("re.jpg",image)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
+
+ ```

vinniesniper-54816/task1/_lab/007-test/clear.bat

@@ -0,0 +1,6 @@
+del neg.txt
+del pos.txt
+del pos.vec
+del .\\xml\\*
+del .\\negdata\\neg.txt
+del .\\posdata\\pos.txt

vinniesniper-54816/task1/_lab/007-test/negdata.1/.gitignore

@@ -0,0 +1,5 @@
+*.jpg
+*.png
+*.bmp
+*.gif
+*.webp

vinniesniper-54816/task1/_lab/007-test/negdata.1/_GenTXT.py

@@ -0,0 +1,9 @@
+# -*- coding:utf8 -*-
+import os
+
+
+# 生成路径列表文件
+os.system("dir /b/s/p/w *.jpg > neg.txt")
+
+
+os.system("pause")

vinniesniper-54816/task1/_lab/007-test/negdata.1/_PNG2JPG.py

@@ -0,0 +1,26 @@
+import os
+from PIL import Image
+ 
+ 
+# 获取指定目录下的所有png图片
+def get_all_png_files(dir):
+    files_list = []
+    for root, dirs, files in os.walk(dir):
+        for file in files:
+            if os.path.splitext(file)[1] == '.png':
+                files_list.append(os.path.join(root, file))
+    return files_list
+ 
+ 
+# 批量转换png图片为jpg格式
+def png2jpg(files_list):
+    for file in files_list:
+        img = Image.open(file)
+        new_file = os.path.splitext(file)[0] + '.jpg'
+        img.convert('RGB').save(new_file)
+ 
+ 
+if __name__ == '__main__':
+    dir = './' #png图片目录
+    files_list = get_all_png_files(dir)
+    png2jpg(files_list)

vinniesniper-54816/task1/_lab/007-test/negdata.1/_RGBtoGray.py

@@ -0,0 +1,26 @@
+# -*- coding:utf8 -*-
+import cv2
+import numpy as np 
+import os
+import re
+import shutil
+from pathlib2 import Path
+ 
+ 
+# 批量灰度化图片
+def GrayPic(srcImgDir):
+    for item in srcImgDir.rglob("*.jpg"):
+        # 获取图片名
+        imgName = item.name
+        img=cv2.imread(imgName)
+        gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
+        cv2.imwrite("gray"+imgName,gray)
+        
+        
+if __name__ == '__main__':
+
+    # 文件路径--跟代码同目录
+    srcImgPath = Path("./")
+    GrayPic(srcImgPath)
+    
+    os.system("pause")

vinniesniper-54816/task1/_lab/007-test/negdata.1/_ReName.py

@@ -0,0 +1,27 @@
+# -*- coding:utf8 -*-
+ 
+import os
+import re
+import shutil
+from pathlib2 import Path
+ 
+ 
+# 批量命名图片
+def renamePic(srcImgDir):
+    i=0
+    for item in srcImgDir.rglob("*.jpg"):
+        # 获取图片名
+        imgName = item.name
+        newName = str(i)+".jpg"
+        i=i+1
+        # 重命名
+        print(f"prepare to rename {imgName}")
+        item.rename(newName)
+ 
+if __name__ == '__main__':
+
+    # 文件路径--跟代码同目录
+    srcImgPath = Path("./")
+    renamePic(srcImgPath)
+    
+    os.system("pause")

vinniesniper-54816/task1/_lab/007-test/negdata.1/_copy.py

@@ -0,0 +1,8 @@
+# -*- coding:utf8 -*-
+import os
+
+
+os.system("copy .\\neg.txt ..\\")
+
+
+os.system("pause")

vinniesniper-54816/task1/_lab/007-test/negdata/_GenTXT.py

@@ -0,0 +1,9 @@
+# -*- coding:utf8 -*-
+import os
+
+
+# 生成路径列表文件
+# os.system("dir /b/s/p/w *.jpg > neg.txt")
+os.system("find " + os.path.abspath(os.curdir) + " -name '*.jpg' > neg.txt")
+
+# os.system("pause")

vinniesniper-54816/task1/_lab/007-test/negdata/_ReName.py

@@ -0,0 +1,46 @@
+# -*- coding:utf8 -*-
+ 
+import os
+import re
+import shutil
+from pathlib2 import Path
+ 
+import string
+import random
+
+ 
+# 批量命名图片
+def renamePic(srcImgDir):
+    i=0
+    for item in srcImgDir.rglob("*.jpg"):
+        # 获取图片名
+        imgName = item.name
+        newName = str(i)+".jpg"
+        i=i+1
+        # 重命名
+        print(f"prepare to rename {imgName}")
+        item.rename(newName)
+ 
+# 批量命名图片
+def renamePicWithRandomName(srcImgDir):
+    for item in srcImgDir.rglob("*.jpg"):
+        # 获取图片名
+        imgName = item.name
+        # 生成随机字符串
+        random_str = ''.join(random.sample(string.ascii_letters + string.digits, 32))
+        # 生成随机数字
+        random_int = str(random.randint(0, 10000))
+        # 生成新的图片名
+        newName = random_str + random_int + ".jpg"
+        # 重命名
+        print(f"prepare to rename {imgName} to {newName}")
+        item.rename(newName)
+
+
+if __name__ == '__main__':
+
+    # 文件路径--跟代码同目录
+    srcImgPath = Path("./")
+    renamePicWithRandomName(srcImgPath)
+    renamePic(srcImgPath)
+    

vinniesniper-54816/task1/_lab/007-test/negdata/_copy.py

@@ -0,0 +1,8 @@
+# -*- coding:utf8 -*-
+import os
+
+
+# os.system("copy .\\neg.txt ..\\")
+os.system("cp ./neg.txt ../")
+
+# os.system("pause")

vinniesniper-54816/task1/_lab/007-test/notes.md

@@ -0,0 +1,8 @@
+### vnc 
+host 192.168.10.21
+
+http://192.168.10.21:6199/
+
+
+work path 
+/home/logic/_workspace/task-list/servers/logic-NUC8i5BEH/opencv-workdesk/001_monitor/src/007-test

vinniesniper-54816/task1/_lab/007-test/posdata.1/.gitignore

@@ -0,0 +1,5 @@
+*.jpg
+*.png
+*.bmp
+*.gif
+*.webp

vinniesniper-54816/task1/_lab/007-test/posdata.1/_BMP2JPG.py

@@ -0,0 +1,16 @@
+import os
+from PIL import Image
+
+json_dir = "./"
+label_names = os.listdir(json_dir)
+label_dir = []
+
+for filename in label_names:
+    label_dir.append(os.path.join(json_dir,filename))
+
+for i,filename in enumerate(label_dir):
+
+    im = Image.open(filename)  # open ppm file
+
+    newname = label_names[i].split('.')[0] + '.jpg'  # new name for png file
+    im.save(os.path.join(json_dir,newname))

vinniesniper-54816/task1/_lab/007-test/posdata.1/_GenTXT.py

@@ -0,0 +1,9 @@
+# -*- coding:utf8 -*-
+import os
+
+
+# 生成路径列表文件
+os.system("dir /b/s/p/w *.jpg > pos.txt")
+
+
+os.system("pause")

vinniesniper-54816/task1/_lab/007-test/posdata.1/_RGBtoGray.py

@@ -0,0 +1,26 @@
+# -*- coding:utf8 -*-
+import cv2
+import numpy as np 
+import os
+import re
+import shutil
+from pathlib2 import Path
+ 
+ 
+# 批量灰度化图片
+def GrayPic(srcImgDir):
+    for item in srcImgDir.rglob("*.jpg"):
+        # 获取图片名
+        imgName = item.name
+        img=cv2.imread(imgName)
+        gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
+        cv2.imwrite("gray"+imgName,gray)
+        
+        
+if __name__ == '__main__':
+
+    # 文件路径--跟代码同目录
+    srcImgPath = Path("./")
+    GrayPic(srcImgPath)
+    
+    os.system("pause")

vinniesniper-54816/task1/_lab/007-test/posdata.1/_ReName.py

@@ -0,0 +1,27 @@
+# -*- coding:utf8 -*-
+ 
+import os
+import re
+import shutil
+from pathlib2 import Path
+ 
+ 
+# 批量命名图片
+def renamePic(srcImgDir):
+    i=0
+    for item in srcImgDir.rglob("*.jpg"):
+        # 获取图片名
+        imgName = item.name
+        newName = str(i)+".jpg"
+        i=i+1
+        # 重命名
+        print(f"prepare to rename {imgName}")
+        item.rename(newName)
+ 
+if __name__ == '__main__':
+
+    # 文件路径--跟代码同目录
+    srcImgPath = Path("./")
+    renamePic(srcImgPath)
+    
+    os.system("pause")

vinniesniper-54816/task1/_lab/007-test/posdata.1/_copy.py

@@ -0,0 +1,8 @@
+# -*- coding:utf8 -*-
+import os
+
+
+os.system("copy .\\pos.txt ..\\")
+
+
+os.system("pause")

vinniesniper-54816/task1/_lab/007-test/posdata/_GenTXT.py

@@ -0,0 +1,10 @@
+# -*- coding:utf8 -*-
+import os
+
+
+# 生成路径列表文件
+#
+# os.system("dir /b/s/p/w *.jpg > pos.txt")
+os.system("find " + os.path.abspath(os.curdir) + " -name '*.jpg' > pos.txt")
+
+# os.system("pause")

vinniesniper-54816/task1/_lab/007-test/posdata/_ReName.py

@@ -0,0 +1,46 @@
+# -*- coding:utf8 -*-
+ 
+import os
+import re
+import shutil
+from pathlib2 import Path
+ 
+import string
+import random
+
+# 批量命名图片
+def renamePicWithRandomName(srcImgDir):
+    for item in srcImgDir.rglob("*.jpg"):
+        # 获取图片名
+        imgName = item.name
+        # 生成随机字符串
+        random_str = ''.join(random.sample(string.ascii_letters + string.digits, 32))
+        # 生成随机数字
+        random_int = str(random.randint(0, 10000))
+        # 生成新的图片名
+        newName = random_str + random_int + ".jpg"
+        # 重命名
+        print(f"prepare to rename {imgName} to {newName}")
+        item.rename(newName)
+
+
+ 
+# 批量命名图片
+def renamePic(srcImgDir):
+    i=0
+    for item in srcImgDir.rglob("*.jpg"):
+        # 获取图片名
+        imgName = item.name
+        newName = str(i)+".jpg"
+        i=i+1
+        # 重命名
+        print(f"prepare to rename {imgName}")
+        item.rename(newName)
+ 
+if __name__ == '__main__':
+
+    # 文件路径--跟代码同目录
+    srcImgPath = Path("./")
+    renamePicWithRandomName(srcImgPath)
+    renamePic(srcImgPath)
+    

vinniesniper-54816/task1/_lab/007-test/posdata/_copy.py

@@ -0,0 +1,11 @@
+# -*- coding:utf8 -*-
+import os
+
+import platform
+
+# if platform.system() == "Windows":
+# os.system("copy .\\pos.txt ..\\")
+# else:
+os.system("cp ./pos.txt ../")
+
+# os.system("pause")

vinniesniper-54816/task1/_lab/007-test/process_txt_files.py

@@ -0,0 +1,20 @@
+import os
+
+def trim_last_newline(file_path):
+    with open(file_path, 'r+') as file:
+        file.seek(0, os.SEEK_END)
+        file.seek(file.tell() - 1, os.SEEK_SET)
+        file.truncate()
+
+def process_pos_file():
+    with open('pos.txt', 'r') as file:
+        data = file.read()
+    data = data.replace('.jpg', '.jpg 1 0 0 20 20')
+    with open('pos.txt', 'w') as file:
+        file.write(data)
+
+
+if __name__ == "__main__":
+    trim_last_newline('neg.txt')
+    trim_last_newline('pos.txt')
+    process_pos_file()

vinniesniper-54816/task1/_lab/007-test/step1.py

@@ -0,0 +1,9 @@
+import os
+
+# 处理前要将pos.txt里面每行按"*.jpg 1 0 0 20 20"的格式修改，neg.txt不需要改格式
+# neg.txt和pos.txt里面末尾的空行要删去
+# neg.txt里面图片大小和数量可以不用特别指定，也不需要生成vec文件
+# os.system("opencv_createsamples.exe -vec pos.vec -info pos.txt -num 100 -w 20 -h 20")
+os.system("opencv_createsamples -info pos.txt -vec pos.vec  -num 100 -w 20 -h 20")
+
+# os.system("pause")

vinniesniper-54816/task1/_lab/007-test/step1.sh

@@ -0,0 +1,7 @@
+#!/usr/bin/env bash
+
+set -x
+
+rm -rf pos.vec
+
+opencv_createsamples -info pos.txt -vec pos.vec  -num 100 -w 20 -h 20

vinniesniper-54816/task1/_lab/007-test/step2.py

@@ -0,0 +1,15 @@
+import os
+
+# numPos和numNeg是训练时每一层的样本数，numPos可以小于vec中的数目（推荐），numNeg可以大于实际数目
+# maxFalseAlarmRate是训练时的FA，只有小于它才会进入下一层
+# 网上推荐numPos：numNeg=1：3
+# 正样本用vec描述，负样本用txt指明路径即可
+# xml目录需要自己提前创建
+# 训练时出现的列表中：N训练层数，HR命中率，FA警告（FA<maxFalseAlarmRate进入下一层）
+# 负样本不用指定图中:目标个数，Xmin,Ymin,Xmax,Ymax。训练时会自动resize
+# os.system("opencv_traincascade.exe -data xml -vec pos.vec -bg neg.txt -numPos 90 -numNeg 198 -numStages 20 -w 20 -h 20 -maxFalseAlarmRate 0.5 -mode ALL")
+os.system(
+    "opencv_traincascade -data xml -vec pos.vec -bg neg.txt -numPos 90 -numNeg 198 -numStages 20 -w 20 -h 20 -maxFalseAlarmRate 0.5 -mode ALL"
+)
+
+os.system("pause")

vinniesniper-54816/task1/_lab/007-test/step2_t1.sh

@@ -0,0 +1,22 @@
+#!/usr/bin/env bash
+
+set -x
+
+rm -rf xml_step2_t1/*.xml
+
+echo "clean done"
+
+opencv_traincascade \
+    -data xml_step2_t1 \
+    -vec pos.vec \
+    -bg neg.txt \
+    -numPos 50 \
+    -numNeg 198 \
+    -numStages 15 \
+    -maxFalseAlarmRate 0.5 \
+    -w 20 \
+    -h 20 \
+    -mode ALL
+
+
+# i want to check the maxFalseAlarmRate larger, i want to complete the 20 steps

vinniesniper-54816/task1/_lab/007-test/test_case/beach_test/cascade.xml

@@ -0,0 +1,594 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<cascade>
+  <stageType>BOOST</stageType>
+  <featureType>HAAR</featureType>
+  <height>20</height>
+  <width>20</width>
+  <stageParams>
+    <boostType>GAB</boostType>
+    <minHitRate>9.9500000476837158e-01</minHitRate>
+    <maxFalseAlarm>5.0000000000000000e-01</maxFalseAlarm>
+    <weightTrimRate>9.4999999999999996e-01</weightTrimRate>
+    <maxDepth>1</maxDepth>
+    <maxWeakCount>100</maxWeakCount></stageParams>
+  <featureParams>
+    <maxCatCount>0</maxCatCount>
+    <featSize>1</featSize>
+    <mode>ALL</mode></featureParams>
+  <stageNum>12</stageNum>
+  <stages>
+    <!-- stage 0 -->
+    <_>
+      <maxWeakCount>2</maxWeakCount>
+      <stageThreshold>-2.5608992576599121e-01</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 19 7.4999824166297913e-02</internalNodes>
+          <leafValues>
+            -9.1044777631759644e-01 7.4468082189559937e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 6 9.9113196134567261e-02</internalNodes>
+          <leafValues>
+            -9.2193228006362915e-01 6.5435785055160522e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 1 -->
+    <_>
+      <maxWeakCount>3</maxWeakCount>
+      <stageThreshold>-1.0775502920150757e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 7 6.4352616667747498e-02</internalNodes>
+          <leafValues>
+            -7.8280544281005859e-01 9.2592591047286987e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 5 8.6798444390296936e-02</internalNodes>
+          <leafValues>
+            -8.2423853874206543e-01 5.4696905612945557e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 35 4.9258150160312653e-02</internalNodes>
+          <leafValues>
+            -9.4359773397445679e-01 5.2949368953704834e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 2 -->
+    <_>
+      <maxWeakCount>2</maxWeakCount>
+      <stageThreshold>-5.0766927003860474e-01</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 4 8.1167593598365784e-02</internalNodes>
+          <leafValues>
+            -9.2307692766189575e-01 3.0303031206130981e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 8 -9.1085352003574371e-02</internalNodes>
+          <leafValues>
+            4.3652963638305664e-01 -8.1069958209991455e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 3 -->
+    <_>
+      <maxWeakCount>3</maxWeakCount>
+      <stageThreshold>-1.0677227973937988e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 24 3.9891637861728668e-02</internalNodes>
+          <leafValues>
+            -8.2857143878936768e-01 6.8421053886413574e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 27 -8.9151952415704727e-03</internalNodes>
+          <leafValues>
+            4.6722087264060974e-01 -9.0501767396926880e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 14 -8.2798525691032410e-02</internalNodes>
+          <leafValues>
+            6.7119181156158447e-01 -7.0637220144271851e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 4 -->
+    <_>
+      <maxWeakCount>3</maxWeakCount>
+      <stageThreshold>-1.2176305055618286e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 11 -2.1015079692006111e-02</internalNodes>
+          <leafValues>
+            2.8000000119209290e-01 -8.1818181276321411e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 23 5.0599854439496994e-02</internalNodes>
+          <leafValues>
+            -7.0883417129516602e-01 4.5477023720741272e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 13 1.9625321030616760e-02</internalNodes>
+          <leafValues>
+            -8.5421890020370483e-01 4.3564090132713318e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 5 -->
+    <_>
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+    <!-- stage 8 -->
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+    <!-- stage 9 -->
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+      <stageThreshold>-1.0123676061630249e+00</stageThreshold>
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+    <!-- stage 10 -->
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+        <_>
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+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/test_case/beach_test/cascade_working_example.xml

@@ -0,0 +1,544 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<cascade>
+  <stageType>BOOST</stageType>
+  <featureType>HAAR</featureType>
+  <height>20</height>
+  <width>20</width>
+  <stageParams>
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+    <minHitRate>9.9500000476837158e-01</minHitRate>
+    <maxFalseAlarm>5.0000000000000000e-01</maxFalseAlarm>
+    <weightTrimRate>9.4999999999999996e-01</weightTrimRate>
+    <maxDepth>1</maxDepth>
+    <maxWeakCount>100</maxWeakCount></stageParams>
+  <featureParams>
+    <maxCatCount>0</maxCatCount>
+    <featSize>1</featSize>
+    <mode>ALL</mode></featureParams>
+  <stageNum>12</stageNum>
+  <stages>
+    <!-- stage 0 -->
+    <_>
+      <maxWeakCount>2</maxWeakCount>
+      <stageThreshold>-2.0792216528207064e-03</stageThreshold>
+      <weakClassifiers>
+        <_>
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+    <!-- stage 1 -->
+    <_>
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+      <stageThreshold>-1.2283716201782227e+00</stageThreshold>
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+    <!-- stage 2 -->
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+      <stageThreshold>-5.2522403001785278e-01</stageThreshold>
+      <weakClassifiers>
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+    <!-- stage 3 -->
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+      <stageThreshold>-1.2258527278900146e+00</stageThreshold>
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+    <!-- stage 4 -->
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+      <stageThreshold>-1.1094360351562500e+00</stageThreshold>
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+    <!-- stage 5 -->
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+    <!-- stage 6 -->
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+        <_>
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+          <leafValues>
+            3.2428205013275146e-01 -8.6097669601440430e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 8 -->
+    <_>
+      <maxWeakCount>3</maxWeakCount>
+      <stageThreshold>-9.8578220605850220e-01</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
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+          <leafValues>
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+        <_>
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+          <leafValues>
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+        <_>
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+          <leafValues>
+            -6.1228525638580322e-01 5.7147806882858276e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 9 -->
+    <_>
+      <maxWeakCount>3</maxWeakCount>
+      <stageThreshold>-1.2791478633880615e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 11 -1.5193784609436989e-02</internalNodes>
+          <leafValues>
+            -6.0240965336561203e-02 -8.6666667461395264e-01</leafValues></_>
+        <_>
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+          <leafValues>
+            -7.7955096960067749e-01 2.7296727895736694e-01</leafValues></_>
+        <_>
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+          <leafValues>
+            -8.2253754138946533e-01 3.6706984043121338e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 10 -->
+    <_>
+      <maxWeakCount>5</maxWeakCount>
+      <stageThreshold>-9.7741711139678955e-01</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 36 -7.1106562390923500e-03</internalNodes>
+          <leafValues>
+            7.4074074625968933e-02 -7.8350514173507690e-01</leafValues></_>
+        <_>
+          <internalNodes>
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+          <leafValues>
+            -9.6380352973937988e-01 1.2861841917037964e-01</leafValues></_>
+        <_>
+          <internalNodes>
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+          <leafValues>
+            3.1283980607986450e-01 -7.8981834650039673e-01</leafValues></_>
+        <_>
+          <internalNodes>
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+          <leafValues>
+            -9.2314887046813965e-01 3.5634875297546387e-01</leafValues></_>
+        <_>
+          <internalNodes>
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+          <leafValues>
+            3.7375229597091675e-01 -7.4664002656936646e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 11 -->
+    <_>
+      <maxWeakCount>5</maxWeakCount>
+      <stageThreshold>-1.3949387073516846e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 19 4.9728322774171829e-03</internalNodes>
+          <leafValues>
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+        <_>
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+          <leafValues>
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+        <_>
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+          <leafValues>
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+        <_>
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+          <leafValues>
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+        <_>
+          <internalNodes>
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+          <leafValues>
+            -8.7441539764404297e-01 4.0108311176300049e-01</leafValues></_></weakClassifiers></_></stages>
+  <features>
+    <_>
+      <rects>
+        <_>
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+      <tilted>0</tilted></_>
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+      <rects>
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+    <_>
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+        <_>
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+      <tilted>0</tilted></_>
+    <_>
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+        <_>
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+      <tilted>0</tilted></_>
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+    <_>
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+        <_>
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+    <_>
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+        <_>
+          8 0 4 6 -1.</_>
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+          6 2 4 2 3.</_></rects>
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+        <_>
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+    <_>
+      <rects>
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+    <_>
+      <rects>
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+        <_>
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+      <tilted>1</tilted></_>
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+        <_>
+          14 2 6 3 -1.</_>
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+          14 3 6 1 3.</_></rects>
+      <tilted>0</tilted></_>
+    <_>
+      <rects>
+        <_>
+          16 3 2 8 -1.</_>
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+          16 7 2 4 2.</_></rects>
+      <tilted>0</tilted></_>
+    <_>
+      <rects>
+        <_>
+          16 18 4 2 -1.</_>
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+          18 18 2 2 2.</_></rects>
+      <tilted>0</tilted></_>
+    <_>
+      <rects>
+        <_>
+          18 0 1 20 -1.</_>
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+          18 5 1 10 2.</_></rects>
+      <tilted>0</tilted></_></features></cascade>
+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/test_case/beach_test/test.py

@@ -0,0 +1,45 @@
+import cv2
+import os,sys
+
+# 获取 XML 文件，加载人脸检测器
+faceCascade = cv2.CascadeClassifier("cascade.xml")
+
+# remove all jpg files in iut_re folder
+for f in os.listdir("iut_re"):
+    if f.endswith(".jpg"):
+        os.remove(os.path.join("iut_re", f))
+
+image_set = []
+for f in os.listdir("iut"):
+    if f.endswith(".jpg"):
+        file_name = os.path.join("iut", f)
+        image_set.append([cv2.imread(file_name), file_name])
+
+for i in range(len(image_set)):
+    image = image_set[i][0]
+    file_name = image_set[i][1]
+    re_file_name = file_name.replace(".jpg","_re.jpg").replace('iut/', 'iut_re/')
+    # 色彩转换，转换为灰度图像
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+    # 调用函数 detectMultiScale
+    faces = faceCascade.detectMultiScale(gray, scaleFactor=1.15, minNeighbors=5, minSize=(5, 5))
+
+    # print(faces)
+    # 打印输出的测试结果
+    # print("found {0} face in {1}".format(len(faces), os.path.basename(file_name)))
+    print((re_file_name, len(faces)))
+
+    # 逐个标注人脸
+    for x, y, w, h in faces:
+        cv2.rectangle(image, (x, y), (x + w, y + w), (0, 255, 0), 2)  # 矩形标注
+        # cv2.circle(image,(int((x+x+w)/2),int((y+y+h)/2)),int(w/2),(0,255,0),2)
+
+    # 显示结果
+    cv2.imshow("dect", image)
+    # 保存检测结果
+    cv2.imwrite(re_file_name, image)
+    # cv2.waitKey(0)
+    cv2.destroyAllWindows()
+
+    

vinniesniper-54816/task1/_lab/007-test/test_case/beach_test/test.sh

@@ -0,0 +1,5 @@
+#!/usr/bin/env bash
+
+set -ex
+
+npx nodemon --ext xml --exec "python ./test.py"

vinniesniper-54816/task1/_lab/007-test/test_case/beach_test/test_w.sh

@@ -0,0 +1,5 @@
+#!/usr/bin/env bash
+
+set -ex
+
+python ./test.py

vinniesniper-54816/task1/_lab/007-test/test_case/face_test/cascade.xml

@@ -0,0 +1,544 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<cascade>
+  <stageType>BOOST</stageType>
+  <featureType>HAAR</featureType>
+  <height>20</height>
+  <width>20</width>
+  <stageParams>
+    <boostType>GAB</boostType>
+    <minHitRate>9.9500000476837158e-01</minHitRate>
+    <maxFalseAlarm>5.0000000000000000e-01</maxFalseAlarm>
+    <weightTrimRate>9.4999999999999996e-01</weightTrimRate>
+    <maxDepth>1</maxDepth>
+    <maxWeakCount>100</maxWeakCount></stageParams>
+  <featureParams>
+    <maxCatCount>0</maxCatCount>
+    <featSize>1</featSize>
+    <mode>ALL</mode></featureParams>
+  <stageNum>12</stageNum>
+  <stages>
+    <!-- stage 0 -->
+    <_>
+      <maxWeakCount>2</maxWeakCount>
+      <stageThreshold>-2.0792216528207064e-03</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 23 3.5195697098970413e-02</internalNodes>
+          <leafValues>
+            -9.7000002861022949e-01 9.5833331346511841e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 32 4.9420498311519623e-02</internalNodes>
+          <leafValues>
+            -9.3253821134567261e-01 9.6792078018188477e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 1 -->
+    <_>
+      <maxWeakCount>3</maxWeakCount>
+      <stageThreshold>-1.2283716201782227e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 6 2.1136376261711121e-01</internalNodes>
+          <leafValues>
+            -8.8292682170867920e-01 7.6744186878204346e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 27 -3.6786209791898727e-02</internalNodes>
+          <leafValues>
+            6.4631646871566772e-01 -8.5865759849548340e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 2 8.2553006708621979e-02</internalNodes>
+          <leafValues>
+            -9.6414572000503540e-01 5.1321285963058472e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 2 -->
+    <_>
+      <maxWeakCount>2</maxWeakCount>
+      <stageThreshold>-5.2522403001785278e-01</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 34 -7.0488560013473034e-03</internalNodes>
+          <leafValues>
+            4.6428570151329041e-01 -9.0625000000000000e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 1 2.0252991467714310e-02</internalNodes>
+          <leafValues>
+            -9.2222303152084351e-01 3.8102594017982483e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 3 -->
+    <_>
+      <maxWeakCount>3</maxWeakCount>
+      <stageThreshold>-1.2258527278900146e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 35 2.7621425688266754e-02</internalNodes>
+          <leafValues>
+            -9.3296086788177490e-01 2.7536231279373169e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 18 2.4644609540700912e-02</internalNodes>
+          <leafValues>
+            -7.8042775392532349e-01 5.6246817111968994e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 28 4.4026337563991547e-02</internalNodes>
+          <leafValues>
+            -7.2078734636306763e-01 5.9029269218444824e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 4 -->
+    <_>
+      <maxWeakCount>3</maxWeakCount>
+      <stageThreshold>-1.1094360351562500e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 13 5.6299664080142975e-02</internalNodes>
+          <leafValues>
+            -8.5567009449005127e-01 3.3333334326744080e-01</leafValues></_>
+        <_>
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+          <leafValues>
+            -6.9705963134765625e-01 6.2128168344497681e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 26 3.3632911741733551e-02</internalNodes>
+          <leafValues>
+            -7.4570971727371216e-01 6.2287092208862305e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 5 -->
+    <_>
+      <maxWeakCount>4</maxWeakCount>
+      <stageThreshold>-1.0068651437759399e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 14 2.7963671088218689e-01</internalNodes>
+          <leafValues>
+            -8.6885243654251099e-01 1.6923077404499054e-01</leafValues></_>
+        <_>
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+            0 -1 25 1.2323195114731789e-02</internalNodes>
+          <leafValues>
+            -7.9252362251281738e-01 3.5135197639465332e-01</leafValues></_>
+        <_>
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+            0 -1 29 1.1619093269109726e-01</internalNodes>
+          <leafValues>
+            -8.5458588600158691e-01 4.3786722421646118e-01</leafValues></_>
+        <_>
+          <internalNodes>
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+          <leafValues>
+            -8.9688044786453247e-01 3.6522126197814941e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 6 -->
+    <_>
+      <maxWeakCount>3</maxWeakCount>
+      <stageThreshold>-9.4377774000167847e-01</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 17 2.8343785554170609e-02</internalNodes>
+          <leafValues>
+            -7.5813955068588257e-01 4.5454546809196472e-01</leafValues></_>
+        <_>
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+          <leafValues>
+            6.8800747394561768e-01 -5.7961523532867432e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 9 -9.6869543194770813e-03</internalNodes>
+          <leafValues>
+            5.2493661642074585e-01 -8.1870794296264648e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 7 -->
+    <_>
+      <maxWeakCount>2</maxWeakCount>
+      <stageThreshold>-5.8824944496154785e-01</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 8 2.2776421904563904e-01</internalNodes>
+          <leafValues>
+            -7.8431373834609985e-01 2.7272728085517883e-01</leafValues></_>
+        <_>
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+          <leafValues>
+            3.2428205013275146e-01 -8.6097669601440430e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 8 -->
+    <_>
+      <maxWeakCount>3</maxWeakCount>
+      <stageThreshold>-9.8578220605850220e-01</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 31 3.1056167557835579e-02</internalNodes>
+          <leafValues>
+            -7.7464789152145386e-01 4.8571428656578064e-01</leafValues></_>
+        <_>
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+          <leafValues>
+            -7.8261238336563110e-01 4.7452750802040100e-01</leafValues></_>
+        <_>
+          <internalNodes>
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+          <leafValues>
+            -6.1228525638580322e-01 5.7147806882858276e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 9 -->
+    <_>
+      <maxWeakCount>3</maxWeakCount>
+      <stageThreshold>-1.2791478633880615e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 11 -1.5193784609436989e-02</internalNodes>
+          <leafValues>
+            -6.0240965336561203e-02 -8.6666667461395264e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 3 1.2929022312164307e-01</internalNodes>
+          <leafValues>
+            -7.7955096960067749e-01 2.7296727895736694e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 30 2.4830256006680429e-04</internalNodes>
+          <leafValues>
+            -8.2253754138946533e-01 3.6706984043121338e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 10 -->
+    <_>
+      <maxWeakCount>5</maxWeakCount>
+      <stageThreshold>-9.7741711139678955e-01</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 36 -7.1106562390923500e-03</internalNodes>
+          <leafValues>
+            7.4074074625968933e-02 -7.8350514173507690e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 37 1.0002901777625084e-02</internalNodes>
+          <leafValues>
+            -9.6380352973937988e-01 1.2861841917037964e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 12 -1.1527233757078648e-02</internalNodes>
+          <leafValues>
+            3.1283980607986450e-01 -7.8981834650039673e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 0 7.7132084406912327e-03</internalNodes>
+          <leafValues>
+            -9.2314887046813965e-01 3.5634875297546387e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 33 -2.8069035615772009e-03</internalNodes>
+          <leafValues>
+            3.7375229597091675e-01 -7.4664002656936646e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 11 -->
+    <_>
+      <maxWeakCount>5</maxWeakCount>
+      <stageThreshold>-1.3949387073516846e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 19 4.9728322774171829e-03</internalNodes>
+          <leafValues>
+            -7.8894472122192383e-01 1.8367347121238708e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 21 8.1018730998039246e-03</internalNodes>
+          <leafValues>
+            -7.9892826080322266e-01 2.1818427741527557e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 5 9.7033903002738953e-03</internalNodes>
+          <leafValues>
+            -5.7855868339538574e-01 4.4443655014038086e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 20 -6.7393705248832703e-02</internalNodes>
+          <leafValues>
+            6.7329019308090210e-01 -4.4816955924034119e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 7 3.2395221292972565e-02</internalNodes>
+          <leafValues>
+            -8.7441539764404297e-01 4.0108311176300049e-01</leafValues></_></weakClassifiers></_></stages>
+  <features>
+    <_>
+      <rects>
+        <_>
+          0 0 10 1 -1.</_>
+        <_>
+          5 0 5 1 2.</_></rects>
+      <tilted>0</tilted></_>
+    <_>
+      <rects>
+        <_>
+          0 0 20 1 -1.</_>
+        <_>
+          5 0 10 1 2.</_></rects>
+      <tilted>0</tilted></_>
+    <_>
+      <rects>
+        <_>
+          0 0 12 6 -1.</_>
+        <_>
+          6 0 6 6 2.</_></rects>
+      <tilted>0</tilted></_>
+    <_>
+      <rects>
+        <_>
+          0 0 7 20 -1.</_>
+        <_>
+          0 5 7 10 2.</_></rects>
+      <tilted>0</tilted></_>
+    <_>
+      <rects>
+        <_>
+          0 0 20 9 -1.</_>
+        <_>
+          0 3 20 3 3.</_></rects>
+      <tilted>0</tilted></_>
+    <_>
+      <rects>
+        <_>
+          0 4 2 16 -1.</_>
+        <_>
+          1 4 1 16 2.</_></rects>
+      <tilted>0</tilted></_>
+    <_>
+      <rects>
+        <_>
+          0 4 20 12 -1.</_>
+        <_>
+          0 7 20 6 2.</_></rects>
+      <tilted>0</tilted></_>
+    <_>
+      <rects>
+        <_>
+          0 5 6 9 -1.</_>
+        <_>
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+      <tilted>0</tilted></_>
+    <_>
+      <rects>
+        <_>
+          0 6 20 12 -1.</_>
+        <_>
+          10 6 10 12 2.</_></rects>
+      <tilted>0</tilted></_>
+    <_>
+      <rects>
+        <_>
+          0 12 3 8 -1.</_>
+        <_>
+          0 16 3 4 2.</_></rects>
+      <tilted>0</tilted></_>
+    <_>
+      <rects>
+        <_>
+          0 14 8 6 -1.</_>
+        <_>
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+      <tilted>0</tilted></_>
+    <_>
+      <rects>
+        <_>
+          1 2 15 3 -1.</_>
+        <_>
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+      <tilted>0</tilted></_>
+    <_>
+      <rects>
+        <_>
+          1 2 16 3 -1.</_>
+        <_>
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+      <tilted>0</tilted></_>
+    <_>
+      <rects>
+        <_>
+          1 3 5 6 -1.</_>
+        <_>
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+      <tilted>0</tilted></_>
+    <_>
+      <rects>
+        <_>
+          1 3 18 16 -1.</_>
+        <_>
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+      <tilted>0</tilted></_>
+    <_>
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+        <_>
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+      <tilted>0</tilted></_>
+    <_>
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+        <_>
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+        <_>
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+      <tilted>1</tilted></_>
+    <_>
+      <rects>
+        <_>
+          2 9 6 9 -1.</_>
+        <_>
+          4 9 2 9 3.</_></rects>
+      <tilted>0</tilted></_>
+    <_>
+      <rects>
+        <_>
+          3 0 1 12 -1.</_>
+        <_>
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+      <tilted>0</tilted></_>
+    <_>
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+        <_>
+          3 2 6 2 -1.</_>
+        <_>
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+        <_>
+          6 3 3 1 2.</_></rects>
+      <tilted>0</tilted></_>
+    <_>
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+        <_>
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+        <_>
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+      <tilted>0</tilted></_>
+    <_>
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+      <tilted>0</tilted></_>
+    <_>
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+        <_>
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+      <tilted>0</tilted></_>
+    <_>
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+      <tilted>0</tilted></_>
+    <_>
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+        <_>
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+      <tilted>0</tilted></_>
+    <_>
+      <rects>
+        <_>
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+          9 3 2 2 3.</_></rects>
+      <tilted>0</tilted></_>
+    <_>
+      <rects>
+        <_>
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+      <tilted>0</tilted></_>
+    <_>
+      <rects>
+        <_>
+          8 0 4 6 -1.</_>
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+          6 2 4 2 3.</_></rects>
+      <tilted>1</tilted></_>
+    <_>
+      <rects>
+        <_>
+          9 1 6 14 -1.</_>
+        <_>
+          9 1 3 7 2.</_>
+        <_>
+          12 8 3 7 2.</_></rects>
+      <tilted>0</tilted></_>
+    <_>
+      <rects>
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+      <tilted>0</tilted></_>
+    <_>
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+      <tilted>0</tilted></_>
+    <_>
+      <rects>
+        <_>
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+      <tilted>0</tilted></_>
+    <_>
+      <rects>
+        <_>
+          14 1 6 8 -1.</_>
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+          12 3 6 4 2.</_></rects>
+      <tilted>1</tilted></_>
+    <_>
+      <rects>
+        <_>
+          14 2 4 1 -1.</_>
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+      <tilted>1</tilted></_>
+    <_>
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+        <_>
+          14 2 6 3 -1.</_>
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+          14 3 6 1 3.</_></rects>
+      <tilted>0</tilted></_>
+    <_>
+      <rects>
+        <_>
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+      <tilted>0</tilted></_>
+    <_>
+      <rects>
+        <_>
+          16 18 4 2 -1.</_>
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+      <tilted>0</tilted></_>
+    <_>
+      <rects>
+        <_>
+          18 0 1 20 -1.</_>
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+          18 5 1 10 2.</_></rects>
+      <tilted>0</tilted></_></features></cascade>
+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/test_case/face_test/cascade_my.xml

@@ -0,0 +1,39 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<cascade>
+  <stageType>BOOST</stageType>
+  <featureType>HAAR</featureType>
+  <height>20</height>
+  <width>20</width>
+  <stageParams>
+    <boostType>GAB</boostType>
+    <minHitRate>9.9500000476837158e-01</minHitRate>
+    <maxFalseAlarm>5.0000000000000000e-01</maxFalseAlarm>
+    <weightTrimRate>9.4999999999999996e-01</weightTrimRate>
+    <maxDepth>1</maxDepth>
+    <maxWeakCount>100</maxWeakCount></stageParams>
+  <featureParams>
+    <maxCatCount>0</maxCatCount>
+    <featSize>1</featSize>
+    <mode>ALL</mode></featureParams>
+  <stageNum>1</stageNum>
+  <stages>
+    <!-- stage 0 -->
+    <_>
+      <maxWeakCount>1</maxWeakCount>
+      <stageThreshold>1.</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 0 1.1519325524568558e-01</internalNodes>
+          <leafValues>
+            -1. 1.</leafValues></_></weakClassifiers></_></stages>
+  <features>
+    <_>
+      <rects>
+        <_>
+          16 2 3 12 -1.</_>
+        <_>
+          12 6 3 4 3.</_></rects>
+      <tilted>1</tilted></_></features></cascade>
+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/test_case/face_test/cascade_working_example.xml

@@ -0,0 +1,887 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<cascade>
+  <stageType>BOOST</stageType>
+  <featureType>HAAR</featureType>
+  <height>20</height>
+  <width>20</width>
+  <stageParams>
+    <boostType>GAB</boostType>
+    <minHitRate>9.9500000476837158e-001</minHitRate>
+    <maxFalseAlarm>5.0000000000000000e-001</maxFalseAlarm>
+    <weightTrimRate>9.4999999999999996e-001</weightTrimRate>
+    <maxDepth>1</maxDepth>
+    <maxWeakCount>100</maxWeakCount></stageParams>
+  <featureParams>
+    <maxCatCount>0</maxCatCount>
+    <featSize>1</featSize>
+    <mode>ALL</mode></featureParams>
+  <stageNum>15</stageNum>
+  <stages>
+    <!-- stage 0 -->
+    <_>
+      <maxWeakCount>2</maxWeakCount>
+      <stageThreshold>-1.1904125660657883e-001</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 27 3.4366466104984283e-002</internalNodes>
+          <leafValues>
+            -9.5959597826004028e-001 9.1111111640930176e-001</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 17 3.5351995378732681e-002</internalNodes>
+          <leafValues>
+            -9.8020136356353760e-001 8.4055471420288086e-001</leafValues></_></weakClassifiers></_>
+    <!-- stage 1 -->
+    <_>
+      <maxWeakCount>2</maxWeakCount>
+      <stageThreshold>-1.6065055131912231e-001</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 5 2.9048809409141541e-001</internalNodes>
+          <leafValues>
+            -9.4708997011184692e-001 7.1717172861099243e-001</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 50 5.0262402743101120e-002</internalNodes>
+          <leafValues>
+            -7.7234727144241333e-001 7.8643941879272461e-001</leafValues></_></weakClassifiers></_>
+    <!-- stage 2 -->
+    <_>
+      <maxWeakCount>3</maxWeakCount>
+      <stageThreshold>-9.6334284543991089e-001</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 51 5.3355745971202850e-002</internalNodes>
+          <leafValues>
+            -8.4158414602279663e-001 7.2093021869659424e-001</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 32 1.9017890095710754e-002</internalNodes>
+          <leafValues>
+            -7.9365950822830200e-001 5.7417571544647217e-001</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 9 8.4164272993803024e-003</internalNodes>
+          <leafValues>
+            -6.9593441486358643e-001 6.7576801776885986e-001</leafValues></_></weakClassifiers></_>
+    <!-- stage 3 -->
+    <_>
+      <maxWeakCount>5</maxWeakCount>
+      <stageThreshold>-1.0980907678604126e+000</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 49 1.3862924277782440e-001</internalNodes>
+          <leafValues>
+            -7.8835976123809814e-001 4.1414141654968262e-001</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 7 1.2018929421901703e-001</internalNodes>
+          <leafValues>
+            -8.2995903491973877e-001 5.1739817857742310e-001</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 29 6.9296143949031830e-002</internalNodes>
+          <leafValues>
+            -7.7216577529907227e-001 5.2250796556472778e-001</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 55 -6.9693401455879211e-003</internalNodes>
+          <leafValues>
+            6.8117576837539673e-001 -5.3906446695327759e-001</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 23 -4.6523470431566238e-002</internalNodes>
+          <leafValues>
+            5.3678452968597412e-001 -7.2046381235122681e-001</leafValues></_></weakClassifiers></_>
+    <!-- stage 4 -->
+    <_>
+      <maxWeakCount>4</maxWeakCount>
+      <stageThreshold>-7.5287377834320068e-001</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 30 5.9669867157936096e-002</internalNodes>
+          <leafValues>
+            -7.4489796161651611e-001 4.1304346919059753e-001</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 2 1.3445684686303139e-002</internalNodes>
+          <leafValues>
+            -7.7788120508193970e-001 4.6229514479637146e-001</leafValues></_>
+        <_>
+          <internalNodes>
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+          <leafValues>
+            3.7505811452865601e-001 -8.3357995748519897e-001</leafValues></_>
+        <_>
+          <internalNodes>
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+          <leafValues>
+            4.6328634023666382e-001 -7.6309412717819214e-001</leafValues></_></weakClassifiers></_>
+    <!-- stage 5 -->
+    <_>
+      <maxWeakCount>4</maxWeakCount>
+      <stageThreshold>-7.4342429637908936e-001</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 6 -5.8313719928264618e-003</internalNodes>
+          <leafValues>
+            3.4653463959693909e-001 -7.6470589637756348e-001</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 25 3.2979757525026798e-003</internalNodes>
+          <leafValues>
+            -7.3521333932876587e-001 4.0015175938606262e-001</leafValues></_>
+        <_>
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+          <leafValues>
+            -7.7022463083267212e-001 3.5349735617637634e-001</leafValues></_>
+        <_>
+          <internalNodes>
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+          <leafValues>
+            4.2861956357955933e-001 -7.0824295282363892e-001</leafValues></_></weakClassifiers></_>
+    <!-- stage 6 -->
+    <_>
+      <maxWeakCount>4</maxWeakCount>
+      <stageThreshold>-6.4555394649505615e-001</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 4 5.7939004898071289e-001</internalNodes>
+          <leafValues>
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+        <_>
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+            0 -1 21 -1.6687558963894844e-002</internalNodes>
+          <leafValues>
+            5.4647672176361084e-001 -5.3975820541381836e-001</leafValues></_>
+        <_>
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+          <leafValues>
+            -6.2752234935760498e-001 3.9351144433021545e-001</leafValues></_>
+        <_>
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+          <leafValues>
+            -9.2253303527832031e-001 2.4533702433109283e-001</leafValues></_></weakClassifiers></_>
+    <!-- stage 7 -->
+    <_>
+      <maxWeakCount>5</maxWeakCount>
+      <stageThreshold>-8.6999106407165527e-001</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
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+          <leafValues>
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+        <_>
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+          <leafValues>
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+        <_>
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+          <leafValues>
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+        <_>
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+          <leafValues>
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+        <_>
+          <internalNodes>
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+          <leafValues>
+            7.4588859081268311e-001 -3.7923958897590637e-001</leafValues></_></weakClassifiers></_>
+    <!-- stage 8 -->
+    <_>
+      <maxWeakCount>4</maxWeakCount>
+      <stageThreshold>-7.2486633062362671e-001</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
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+          <leafValues>
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+        <_>
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+          <leafValues>
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+          <leafValues>
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+        <_>
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+          <leafValues>
+            -8.4023118019104004e-001 2.9721990227699280e-001</leafValues></_></weakClassifiers></_>
+    <!-- stage 9 -->
+    <_>
+      <maxWeakCount>4</maxWeakCount>
+      <stageThreshold>-6.0818475484848022e-001</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
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+          <leafValues>
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+        <_>
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+          <leafValues>
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+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/test_case/face_test/test.py

@@ -0,0 +1,41 @@
+import cv2
+import os,sys
+
+# 获取 XML 文件，加载人脸检测器
+faceCascade = cv2.CascadeClassifier("cascade.xml")
+
+
+# 读取待检测的图像
+image_set = []
+for i in range(1,12+1):
+    file_name = "iut/{}.jpg".format(i)
+    image_set.append([cv2.imread(file_name), file_name])
+
+for i in range(len(image_set)):
+    image = image_set[i][0]
+    file_name = image_set[i][1]
+    re_file_name = file_name.replace(".jpg","_re.jpg").replace('iut/', 'iut_re/')
+    # 色彩转换，转换为灰度图像
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+    # 调用函数 detectMultiScale
+    faces = faceCascade.detectMultiScale(gray, scaleFactor=1.15, minNeighbors=5, minSize=(5, 5))
+
+    # print(faces)
+    # 打印输出的测试结果
+    # print("found {0} face in {1}".format(len(faces), os.path.basename(file_name)))
+    print((re_file_name, len(faces)))
+
+    # 逐个标注人脸
+    for x, y, w, h in faces:
+        cv2.rectangle(image, (x, y), (x + w, y + w), (0, 255, 0), 2)  # 矩形标注
+        # cv2.circle(image,(int((x+x+w)/2),int((y+y+h)/2)),int(w/2),(0,255,0),2)
+
+    # 显示结果
+    cv2.imshow("dect", image)
+    # 保存检测结果
+    cv2.imwrite(re_file_name, image)
+    # cv2.waitKey(0)
+    cv2.destroyAllWindows()
+
+    

vinniesniper-54816/task1/_lab/007-test/test_case/face_test/test.sh

@@ -0,0 +1,5 @@
+#!/usr/bin/env bash
+
+set -ex
+
+npx nodemon --ext xml --exec "python ./test.py"

vinniesniper-54816/task1/_lab/007-test/test_case/face_test_original/cascade.xml

@@ -0,0 +1,544 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<cascade>
+  <stageType>BOOST</stageType>
+  <featureType>HAAR</featureType>
+  <height>20</height>
+  <width>20</width>
+  <stageParams>
+    <boostType>GAB</boostType>
+    <minHitRate>9.9500000476837158e-01</minHitRate>
+    <maxFalseAlarm>5.0000000000000000e-01</maxFalseAlarm>
+    <weightTrimRate>9.4999999999999996e-01</weightTrimRate>
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+    <maxWeakCount>100</maxWeakCount></stageParams>
+  <featureParams>
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+    <featSize>1</featSize>
+    <mode>ALL</mode></featureParams>
+  <stageNum>12</stageNum>
+  <stages>
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+    <_>
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+      <stageThreshold>-2.0792216528207064e-03</stageThreshold>
+      <weakClassifiers>
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+    <!-- stage 1 -->
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+    <!-- stage 2 -->
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+      <stageThreshold>-5.2522403001785278e-01</stageThreshold>
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+        <_>
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+      <weakClassifiers>
+        <_>
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+    <!-- stage 4 -->
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+      <stageThreshold>-1.1094360351562500e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
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+    <!-- stage 5 -->
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+    <!-- stage 6 -->
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+      <stageThreshold>-9.4377774000167847e-01</stageThreshold>
+      <weakClassifiers>
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+    <!-- stage 7 -->
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+      <stageThreshold>-5.8824944496154785e-01</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
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+    <!-- stage 8 -->
+    <_>
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+      <stageThreshold>-9.8578220605850220e-01</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
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+        <_>
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+    <!-- stage 9 -->
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+      <stageThreshold>-1.2791478633880615e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
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+    <!-- stage 10 -->
+    <_>
+      <maxWeakCount>5</maxWeakCount>
+      <stageThreshold>-9.7741711139678955e-01</stageThreshold>
+      <weakClassifiers>
+        <_>
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+    <!-- stage 11 -->
+    <_>
+      <maxWeakCount>5</maxWeakCount>
+      <stageThreshold>-1.3949387073516846e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
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+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/test_case/face_test_original/cascade_my.xml

@@ -0,0 +1,39 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<cascade>
+  <stageType>BOOST</stageType>
+  <featureType>HAAR</featureType>
+  <height>20</height>
+  <width>20</width>
+  <stageParams>
+    <boostType>GAB</boostType>
+    <minHitRate>9.9500000476837158e-01</minHitRate>
+    <maxFalseAlarm>5.0000000000000000e-01</maxFalseAlarm>
+    <weightTrimRate>9.4999999999999996e-01</weightTrimRate>
+    <maxDepth>1</maxDepth>
+    <maxWeakCount>100</maxWeakCount></stageParams>
+  <featureParams>
+    <maxCatCount>0</maxCatCount>
+    <featSize>1</featSize>
+    <mode>ALL</mode></featureParams>
+  <stageNum>1</stageNum>
+  <stages>
+    <!-- stage 0 -->
+    <_>
+      <maxWeakCount>1</maxWeakCount>
+      <stageThreshold>1.</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 0 1.1519325524568558e-01</internalNodes>
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+  <features>
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+          12 6 3 4 3.</_></rects>
+      <tilted>1</tilted></_></features></cascade>
+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/test_case/face_test_original/cascade_working_example.xml

@@ -0,0 +1,887 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<cascade>
+  <stageType>BOOST</stageType>
+  <featureType>HAAR</featureType>
+  <height>20</height>
+  <width>20</width>
+  <stageParams>
+    <boostType>GAB</boostType>
+    <minHitRate>9.9500000476837158e-001</minHitRate>
+    <maxFalseAlarm>5.0000000000000000e-001</maxFalseAlarm>
+    <weightTrimRate>9.4999999999999996e-001</weightTrimRate>
+    <maxDepth>1</maxDepth>
+    <maxWeakCount>100</maxWeakCount></stageParams>
+  <featureParams>
+    <maxCatCount>0</maxCatCount>
+    <featSize>1</featSize>
+    <mode>ALL</mode></featureParams>
+  <stageNum>15</stageNum>
+  <stages>
+    <!-- stage 0 -->
+    <_>
+      <maxWeakCount>2</maxWeakCount>
+      <stageThreshold>-1.1904125660657883e-001</stageThreshold>
+      <weakClassifiers>
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+          <leafValues>
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+    <!-- stage 1 -->
+    <_>
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+      <stageThreshold>-1.6065055131912231e-001</stageThreshold>
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+    <!-- stage 2 -->
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+    <!-- stage 4 -->
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+    <!-- stage 5 -->
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+    <!-- stage 6 -->
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+    <!-- stage 7 -->
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+      <tilted>0</tilted></_></features></cascade>
+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/test_case/face_test_original/face_test.py

@@ -0,0 +1,29 @@
+import cv2
+
+# 读取待检测的图像
+image = cv2.imread("12.jpg")
+
+# 获取 XML 文件，加载人脸检测器
+faceCascade = cv2.CascadeClassifier("cascade.xml")
+
+# 色彩转换，转换为灰度图像
+gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+# 调用函数 detectMultiScale
+faces = faceCascade.detectMultiScale(gray, scaleFactor=1.15, minNeighbors=5, minSize=(5, 5))
+
+# print(faces)
+# 打印输出的测试结果
+print("发现{0}个人脸!".format(len(faces)))
+
+# 逐个标注人脸
+for x, y, w, h in faces:
+    cv2.rectangle(image, (x, y), (x + w, y + w), (0, 255, 0), 2)  # 矩形标注
+    # cv2.circle(image,(int((x+x+w)/2),int((y+y+h)/2)),int(w/2),(0,255,0),2)
+
+# 显示结果
+cv2.imshow("dect", image)
+# 保存检测结果
+cv2.imwrite("re.jpg", image)
+# cv2.waitKey(0)
+cv2.destroyAllWindows()

vinniesniper-54816/task1/_lab/007-test/test_case/face_test_original/test.sh

@@ -0,0 +1,5 @@
+#!/usr/bin/env bash
+
+set -ex
+
+npx nodemon --ext xml --exec "python ./face_test.py"

vinniesniper-54816/task1/_lab/007-test/train.sh

@@ -0,0 +1,29 @@
+#!/usr/bin/env bash
+set -ex
+
+cd negdata
+    python _ReName.py
+    python ./_GenTXT.py
+    python ./_copy.py
+cd ..
+
+cd posdata
+    python _ReName.py
+    python ./_GenTXT.py
+    python ./_copy.py
+cd ..
+
+python ./process_txt_files.py
+
+./step1.sh
+./step2_t1.sh
+
+
+cp xml_step2_t1/cascade.xml test_case/beach_test/cascade.xml
+cd test_case/beach_test
+    ./test.sh
+cd ..
+
+
+# echo "done"
+# exit 0

vinniesniper-54816/task1/_lab/007-test/xml_step2_t1/cascade.xml

@@ -0,0 +1,594 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<cascade>
+  <stageType>BOOST</stageType>
+  <featureType>HAAR</featureType>
+  <height>20</height>
+  <width>20</width>
+  <stageParams>
+    <boostType>GAB</boostType>
+    <minHitRate>9.9500000476837158e-01</minHitRate>
+    <maxFalseAlarm>5.0000000000000000e-01</maxFalseAlarm>
+    <weightTrimRate>9.4999999999999996e-01</weightTrimRate>
+    <maxDepth>1</maxDepth>
+    <maxWeakCount>100</maxWeakCount></stageParams>
+  <featureParams>
+    <maxCatCount>0</maxCatCount>
+    <featSize>1</featSize>
+    <mode>ALL</mode></featureParams>
+  <stageNum>12</stageNum>
+  <stages>
+    <!-- stage 0 -->
+    <_>
+      <maxWeakCount>2</maxWeakCount>
+      <stageThreshold>-2.5608992576599121e-01</stageThreshold>
+      <weakClassifiers>
+        <_>
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+          <leafValues>
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+    <!-- stage 1 -->
+    <_>
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+      <stageThreshold>-1.0775502920150757e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
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+          <leafValues>
+            -9.4359773397445679e-01 5.2949368953704834e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 2 -->
+    <_>
+      <maxWeakCount>2</maxWeakCount>
+      <stageThreshold>-5.0766927003860474e-01</stageThreshold>
+      <weakClassifiers>
+        <_>
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+          <leafValues>
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+    <!-- stage 3 -->
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+      <stageThreshold>-1.0677227973937988e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
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+            6.7119181156158447e-01 -7.0637220144271851e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 4 -->
+    <_>
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+      <stageThreshold>-1.2176305055618286e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
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+    <!-- stage 5 -->
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+      <weakClassifiers>
+        <_>
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+    <!-- stage 6 -->
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+      <stageThreshold>-1.5175080299377441e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
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+        <_>
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+    <!-- stage 7 -->
+    <_>
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+      <stageThreshold>-1.1100143194198608e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
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+    <!-- stage 8 -->
+    <_>
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+      <stageThreshold>-9.2488884925842285e-01</stageThreshold>
+      <weakClassifiers>
+        <_>
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+    <!-- stage 9 -->
+    <_>
+      <maxWeakCount>5</maxWeakCount>
+      <stageThreshold>-1.0123676061630249e+00</stageThreshold>
+      <weakClassifiers>
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+      <tilted>0</tilted></_></features></cascade>
+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/xml_step2_t1/params.xml

@@ -0,0 +1,19 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<params>
+  <stageType>BOOST</stageType>
+  <featureType>HAAR</featureType>
+  <height>20</height>
+  <width>20</width>
+  <stageParams>
+    <boostType>GAB</boostType>
+    <minHitRate>9.9500000476837158e-01</minHitRate>
+    <maxFalseAlarm>5.0000000000000000e-01</maxFalseAlarm>
+    <weightTrimRate>9.4999999999999996e-01</weightTrimRate>
+    <maxDepth>1</maxDepth>
+    <maxWeakCount>100</maxWeakCount></stageParams>
+  <featureParams>
+    <maxCatCount>0</maxCatCount>
+    <featSize>1</featSize>
+    <mode>ALL</mode></featureParams></params>
+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/xml_step2_t1/stage0.xml

@@ -0,0 +1,17 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage0>
+  <maxWeakCount>2</maxWeakCount>
+  <stageThreshold>-2.5608992576599121e-01</stageThreshold>
+  <weakClassifiers>
+    <_>
+      <internalNodes>
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+      <leafValues>
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+    <_>
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+      <leafValues>
+        -9.2193228006362915e-01 6.5435785055160522e-01</leafValues></_></weakClassifiers></stage0>
+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/xml_step2_t1/stage1.xml

@@ -0,0 +1,22 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage1>
+  <maxWeakCount>3</maxWeakCount>
+  <stageThreshold>-1.0775502920150757e+00</stageThreshold>
+  <weakClassifiers>
+    <_>
+      <internalNodes>
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+      <leafValues>
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+    <_>
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+      <leafValues>
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+    <_>
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+      <leafValues>
+        -9.4359773397445679e-01 5.2949368953704834e-01</leafValues></_></weakClassifiers></stage1>
+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/xml_step2_t1/stage10.xml

@@ -0,0 +1,32 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage10>
+  <maxWeakCount>5</maxWeakCount>
+  <stageThreshold>-1.0622880458831787e+00</stageThreshold>
+  <weakClassifiers>
+    <_>
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+      <leafValues>
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+    <_>
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+      <leafValues>
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+    <_>
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+      <leafValues>
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+    <_>
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+      <leafValues>
+        6.8716096878051758e-01 -4.0297099947929382e-01</leafValues></_>
+    <_>
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+      <leafValues>
+        -7.4560004472732544e-01 3.7435856461524963e-01</leafValues></_></weakClassifiers></stage10>
+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/xml_step2_t1/stage11.xml

@@ -0,0 +1,32 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage11>
+  <maxWeakCount>5</maxWeakCount>
+  <stageThreshold>-9.6727126836776733e-01</stageThreshold>
+  <weakClassifiers>
+    <_>
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+      <leafValues>
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+    <_>
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+      <leafValues>
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+    <_>
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+      <leafValues>
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+    <_>
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+      <leafValues>
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+    <_>
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+      <leafValues>
+        2.8650224208831787e-01 -9.0300935506820679e-01</leafValues></_></weakClassifiers></stage11>
+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/xml_step2_t1/stage2.xml

@@ -0,0 +1,17 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage2>
+  <maxWeakCount>2</maxWeakCount>
+  <stageThreshold>-5.0766927003860474e-01</stageThreshold>
+  <weakClassifiers>
+    <_>
+      <internalNodes>
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+      <leafValues>
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+    <_>
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+      <leafValues>
+        4.3652963638305664e-01 -8.1069958209991455e-01</leafValues></_></weakClassifiers></stage2>
+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/xml_step2_t1/stage3.xml

@@ -0,0 +1,22 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage3>
+  <maxWeakCount>3</maxWeakCount>
+  <stageThreshold>-1.0677227973937988e+00</stageThreshold>
+  <weakClassifiers>
+    <_>
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+    <_>
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+    <_>
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+        6.7119181156158447e-01 -7.0637220144271851e-01</leafValues></_></weakClassifiers></stage3>
+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/xml_step2_t1/stage4.xml

@@ -0,0 +1,22 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage4>
+  <maxWeakCount>3</maxWeakCount>
+  <stageThreshold>-1.2176305055618286e+00</stageThreshold>
+  <weakClassifiers>
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+    <_>
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+      <leafValues>
+        -8.5421890020370483e-01 4.3564090132713318e-01</leafValues></_></weakClassifiers></stage4>
+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/xml_step2_t1/stage5.xml

@@ -0,0 +1,22 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage5>
+  <maxWeakCount>3</maxWeakCount>
+  <stageThreshold>-1.1659464836120605e+00</stageThreshold>
+  <weakClassifiers>
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+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/xml_step2_t1/stage6.xml

@@ -0,0 +1,27 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage6>
+  <maxWeakCount>4</maxWeakCount>
+  <stageThreshold>-1.5175080299377441e+00</stageThreshold>
+  <weakClassifiers>
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+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/xml_step2_t1/stage7.xml

@@ -0,0 +1,22 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage7>
+  <maxWeakCount>3</maxWeakCount>
+  <stageThreshold>-1.1100143194198608e+00</stageThreshold>
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+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/xml_step2_t1/stage8.xml

@@ -0,0 +1,27 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage8>
+  <maxWeakCount>4</maxWeakCount>
+  <stageThreshold>-9.2488884925842285e-01</stageThreshold>
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+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/xml_step2_t1/stage9.xml

@@ -0,0 +1,32 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage9>
+  <maxWeakCount>5</maxWeakCount>
+  <stageThreshold>-1.0123676061630249e+00</stageThreshold>
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+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/xml_step2_t2/cascade.xml

@@ -0,0 +1,674 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<cascade>
+  <stageType>BOOST</stageType>
+  <featureType>HAAR</featureType>
+  <height>20</height>
+  <width>20</width>
+  <stageParams>
+    <boostType>GAB</boostType>
+    <minHitRate>9.9500000476837158e-01</minHitRate>
+    <maxFalseAlarm>5.0000000000000000e-01</maxFalseAlarm>
+    <weightTrimRate>9.4999999999999996e-01</weightTrimRate>
+    <maxDepth>1</maxDepth>
+    <maxWeakCount>100</maxWeakCount></stageParams>
+  <featureParams>
+    <maxCatCount>0</maxCatCount>
+    <featSize>1</featSize>
+    <mode>ALL</mode></featureParams>
+  <stageNum>12</stageNum>
+  <stages>
+    <!-- stage 0 -->
+    <_>
+      <maxWeakCount>2</maxWeakCount>
+      <stageThreshold>-6.9857284426689148e-02</stageThreshold>
+      <weakClassifiers>
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+    <!-- stage 1 -->
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+      <stageThreshold>-1.0008283853530884e+00</stageThreshold>
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+    <!-- stage 2 -->
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+      <stageThreshold>-1.0523968935012817e+00</stageThreshold>
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+    <!-- stage 9 -->
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vinniesniper-54816/task1/_lab/007-test/xml_step2_t2/params.xml

@@ -0,0 +1,19 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<params>
+  <stageType>BOOST</stageType>
+  <featureType>HAAR</featureType>
+  <height>20</height>
+  <width>20</width>
+  <stageParams>
+    <boostType>GAB</boostType>
+    <minHitRate>9.9500000476837158e-01</minHitRate>
+    <maxFalseAlarm>5.0000000000000000e-01</maxFalseAlarm>
+    <weightTrimRate>9.4999999999999996e-01</weightTrimRate>
+    <maxDepth>1</maxDepth>
+    <maxWeakCount>100</maxWeakCount></stageParams>
+  <featureParams>
+    <maxCatCount>0</maxCatCount>
+    <featSize>1</featSize>
+    <mode>ALL</mode></featureParams></params>
+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/xml_step2_t2/stage0.xml

@@ -0,0 +1,17 @@
+<?xml version="1.0"?>
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+<stage0>
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+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/xml_step2_t2/stage1.xml

@@ -0,0 +1,22 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage1>
+  <maxWeakCount>3</maxWeakCount>
+  <stageThreshold>-1.0008283853530884e+00</stageThreshold>
+  <weakClassifiers>
+    <_>
+      <internalNodes>
+        0 -1 962 -1.1832243949174881e-01</internalNodes>
+      <leafValues>
+        6.5957444906234741e-01 -8.7628865242004395e-01</leafValues></_>
+    <_>
+      <internalNodes>
+        0 -1 66537 1.2765679508447647e-02</internalNodes>
+      <leafValues>
+        -7.8486323356628418e-01 6.1405700445175171e-01</leafValues></_>
+    <_>
+      <internalNodes>
+        0 -1 111620 6.1872858554124832e-02</internalNodes>
+      <leafValues>
+        -7.3859679698944092e-01 6.7582374811172485e-01</leafValues></_></weakClassifiers></stage1>
+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/xml_step2_t2/stage10.xml

@@ -0,0 +1,32 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage10>
+  <maxWeakCount>5</maxWeakCount>
+  <stageThreshold>-8.8048666715621948e-01</stageThreshold>
+  <weakClassifiers>
+    <_>
+      <internalNodes>
+        0 -1 6445 2.7959531173110008e-02</internalNodes>
+      <leafValues>
+        -6.3888889551162720e-01 4.1666665673255920e-01</leafValues></_>
+    <_>
+      <internalNodes>
+        0 -1 31369 -1.4312742277979851e-02</internalNodes>
+      <leafValues>
+        4.8493552207946777e-01 -5.4626697301864624e-01</leafValues></_>
+    <_>
+      <internalNodes>
+        0 -1 80232 -1.9462900236248970e-02</internalNodes>
+      <leafValues>
+        4.8587536811828613e-01 -5.7627987861633301e-01</leafValues></_>
+    <_>
+      <internalNodes>
+        0 -1 9562 4.8883416457101703e-04</internalNodes>
+      <leafValues>
+        -6.8754994869232178e-01 3.4999880194664001e-01</leafValues></_>
+    <_>
+      <internalNodes>
+        0 -1 91971 -6.8803662434220314e-03</internalNodes>
+      <leafValues>
+        5.3095024824142456e-01 -4.6870502829551697e-01</leafValues></_></weakClassifiers></stage10>
+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/xml_step2_t2/stage11.xml

@@ -0,0 +1,32 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage11>
+  <maxWeakCount>5</maxWeakCount>
+  <stageThreshold>-1.0835331678390503e+00</stageThreshold>
+  <weakClassifiers>
+    <_>
+      <internalNodes>
+        0 -1 12823 2.0331215113401413e-02</internalNodes>
+      <leafValues>
+        -7.6623374223709106e-01 7.4626863002777100e-02</leafValues></_>
+    <_>
+      <internalNodes>
+        0 -1 125038 3.1020954251289368e-02</internalNodes>
+      <leafValues>
+        -5.4749816656112671e-01 4.0196087956428528e-01</leafValues></_>
+    <_>
+      <internalNodes>
+        0 -1 65314 -1.4530843123793602e-02</internalNodes>
+      <leafValues>
+        4.0155622363090515e-01 -5.7374048233032227e-01</leafValues></_>
+    <_>
+      <internalNodes>
+        0 -1 79830 -1.1550894007086754e-02</internalNodes>
+      <leafValues>
+        7.9867005348205566e-01 -2.6203200221061707e-01</leafValues></_>
+    <_>
+      <internalNodes>
+        0 -1 9930 2.0659237634390593e-05</internalNodes>
+      <leafValues>
+        -7.5018608570098877e-01 3.3275553584098816e-01</leafValues></_></weakClassifiers></stage11>
+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/xml_step2_t2/stage2.xml

@@ -0,0 +1,22 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage2>
+  <maxWeakCount>3</maxWeakCount>
+  <stageThreshold>-1.0523968935012817e+00</stageThreshold>
+  <weakClassifiers>
+    <_>
+      <internalNodes>
+        0 -1 19725 -5.7852640748023987e-02</internalNodes>
+      <leafValues>
+        4.2056074738502502e-01 -8.4530389308929443e-01</leafValues></_>
+    <_>
+      <internalNodes>
+        0 -1 106510 -4.0381100028753281e-02</internalNodes>
+      <leafValues>
+        6.6613984107971191e-01 -6.6914182901382446e-01</leafValues></_>
+    <_>
+      <internalNodes>
+        0 -1 55982 4.3017961084842682e-02</internalNodes>
+      <leafValues>
+        -8.4719657897949219e-01 4.6204876899719238e-01</leafValues></_></weakClassifiers></stage2>
+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/xml_step2_t2/stage3.xml

@@ -0,0 +1,27 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage3>
+  <maxWeakCount>4</maxWeakCount>
+  <stageThreshold>-7.1327447891235352e-01</stageThreshold>
+  <weakClassifiers>
+    <_>
+      <internalNodes>
+        0 -1 21398 3.5173662006855011e-02</internalNodes>
+      <leafValues>
+        -9.1812866926193237e-01 4.1880342364311218e-01</leafValues></_>
+    <_>
+      <internalNodes>
+        0 -1 291 2.9779970645904541e-03</internalNodes>
+      <leafValues>
+        -8.9671385288238525e-01 4.9135905504226685e-01</leafValues></_>
+    <_>
+      <internalNodes>
+        0 -1 112262 1.0506974160671234e-01</internalNodes>
+      <leafValues>
+        -7.1667867898941040e-01 6.5290194749832153e-01</leafValues></_>
+    <_>
+      <internalNodes>
+        0 -1 16772 -1.7827928066253662e-02</internalNodes>
+      <leafValues>
+        4.8131462931632996e-01 -8.8741576671600342e-01</leafValues></_></weakClassifiers></stage3>
+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/xml_step2_t2/stage4.xml

@@ -0,0 +1,22 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage4>
+  <maxWeakCount>3</maxWeakCount>
+  <stageThreshold>-1.0446274280548096e+00</stageThreshold>
+  <weakClassifiers>
+    <_>
+      <internalNodes>
+        0 -1 3177 2.7196373790502548e-02</internalNodes>
+      <leafValues>
+        -8.6826348304748535e-01 3.0578511953353882e-01</leafValues></_>
+    <_>
+      <internalNodes>
+        0 -1 62842 6.4487643539905548e-02</internalNodes>
+      <leafValues>
+        -5.9504520893096924e-01 6.7478001117706299e-01</leafValues></_>
+    <_>
+      <internalNodes>
+        0 -1 5800 2.4889567866921425e-03</internalNodes>
+      <leafValues>
+        -7.5536733865737915e-01 4.8187285661697388e-01</leafValues></_></weakClassifiers></stage4>
+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/xml_step2_t2/stage5.xml

@@ -0,0 +1,27 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage5>
+  <maxWeakCount>4</maxWeakCount>
+  <stageThreshold>-5.6323349475860596e-01</stageThreshold>
+  <weakClassifiers>
+    <_>
+      <internalNodes>
+        0 -1 33773 -1.0600250959396362e-01</internalNodes>
+      <leafValues>
+        8.4313726425170898e-01 -6.3713079690933228e-01</leafValues></_>
+    <_>
+      <internalNodes>
+        0 -1 3564 1.9594368338584900e-01</internalNodes>
+      <leafValues>
+        -6.8043452501296997e-01 5.4470825195312500e-01</leafValues></_>
+    <_>
+      <internalNodes>
+        0 -1 81656 -2.2170819342136383e-02</internalNodes>
+      <leafValues>
+        4.5749467611312866e-01 -7.6138621568679810e-01</leafValues></_>
+    <_>
+      <internalNodes>
+        0 -1 392 8.3648981526494026e-03</internalNodes>
+      <leafValues>
+        -9.2820900678634644e-01 2.9683715105056763e-01</leafValues></_></weakClassifiers></stage5>
+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/xml_step2_t2/stage6.xml

@@ -0,0 +1,32 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage6>
+  <maxWeakCount>5</maxWeakCount>
+  <stageThreshold>-1.1174263954162598e+00</stageThreshold>
+  <weakClassifiers>
+    <_>
+      <internalNodes>
+        0 -1 3548 2.6116007566452026e-01</internalNodes>
+      <leafValues>
+        -7.5789475440979004e-01 3.6734694242477417e-01</leafValues></_>
+    <_>
+      <internalNodes>
+        0 -1 12006 -5.3467126563191414e-03</internalNodes>
+      <leafValues>
+        5.5447220802307129e-01 -6.3109600543975830e-01</leafValues></_>
+    <_>
+      <internalNodes>
+        0 -1 69940 1.2671231292188168e-02</internalNodes>
+      <leafValues>
+        -6.8555009365081787e-01 4.3052643537521362e-01</leafValues></_>
+    <_>
+      <internalNodes>
+        0 -1 115596 -6.4184088259935379e-03</internalNodes>
+      <leafValues>
+        5.7122170925140381e-01 -5.9808850288391113e-01</leafValues></_>
+    <_>
+      <internalNodes>
+        0 -1 81164 2.2593162953853607e-02</internalNodes>
+      <leafValues>
+        -8.5607987642288208e-01 4.3912649154663086e-01</leafValues></_></weakClassifiers></stage6>
+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/xml_step2_t2/stage7.xml

@@ -0,0 +1,32 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage7>
+  <maxWeakCount>5</maxWeakCount>
+  <stageThreshold>-9.2381137609481812e-01</stageThreshold>
+  <weakClassifiers>
+    <_>
+      <internalNodes>
+        0 -1 12692 1.8414466083049774e-01</internalNodes>
+      <leafValues>
+        -7.3404252529144287e-01 3.0000001192092896e-01</leafValues></_>
+    <_>
+      <internalNodes>
+        0 -1 96486 1.4040502905845642e-01</internalNodes>
+      <leafValues>
+        -5.2232235670089722e-01 5.7304370403289795e-01</leafValues></_>
+    <_>
+      <internalNodes>
+        0 -1 94 1.0363029316067696e-02</internalNodes>
+      <leafValues>
+        -5.7177013158798218e-01 5.4220241308212280e-01</leafValues></_>
+    <_>
+      <internalNodes>
+        0 -1 57644 -5.4269127547740936e-02</internalNodes>
+      <leafValues>
+        5.9223252534866333e-01 -5.5288833379745483e-01</leafValues></_>
+    <_>
+      <internalNodes>
+        0 -1 231 5.2523061633110046e-02</internalNodes>
+      <leafValues>
+        -7.8327494859695435e-01 4.2789927124977112e-01</leafValues></_></weakClassifiers></stage7>
+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/xml_step2_t2/stage8.xml

@@ -0,0 +1,27 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage8>
+  <maxWeakCount>4</maxWeakCount>
+  <stageThreshold>-5.2678936719894409e-01</stageThreshold>
+  <weakClassifiers>
+    <_>
+      <internalNodes>
+        0 -1 51133 -2.7845047414302826e-02</internalNodes>
+      <leafValues>
+        7.1428573131561279e-01 -5.9832638502120972e-01</leafValues></_>
+    <_>
+      <internalNodes>
+        0 -1 1 -3.9416522486135364e-04</internalNodes>
+      <leafValues>
+        -9.2486000061035156e-01 2.7400460839271545e-01</leafValues></_>
+    <_>
+      <internalNodes>
+        0 -1 4330 1.0721801221370697e-01</internalNodes>
+      <leafValues>
+        -7.2711890935897827e-01 3.0229723453521729e-01</leafValues></_>
+    <_>
+      <internalNodes>
+        0 -1 46957 -1.0302538052201271e-02</internalNodes>
+      <leafValues>
+        6.9409972429275513e-01 -4.2416870594024658e-01</leafValues></_></weakClassifiers></stage8>
+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/xml_step2_t2/stage9.xml

@@ -0,0 +1,37 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage9>
+  <maxWeakCount>6</maxWeakCount>
+  <stageThreshold>-1.4493153095245361e+00</stageThreshold>
+  <weakClassifiers>
+    <_>
+      <internalNodes>
+        0 -1 4146 2.4479243159294128e-01</internalNodes>
+      <leafValues>
+        -6.5740740299224854e-01 4.7222220897674561e-01</leafValues></_>
+    <_>
+      <internalNodes>
+        0 -1 69935 3.7433374673128128e-03</internalNodes>
+      <leafValues>
+        -5.6935876607894897e-01 4.9817276000976562e-01</leafValues></_>
+    <_>
+      <internalNodes>
+        0 -1 115281 -2.8475340455770493e-02</internalNodes>
+      <leafValues>
+        5.4433584213256836e-01 -4.8710265755653381e-01</leafValues></_>
+    <_>
+      <internalNodes>
+        0 -1 124429 -3.1239273957908154e-03</internalNodes>
+      <leafValues>
+        4.6561488509178162e-01 -5.2121520042419434e-01</leafValues></_>
+    <_>
+      <internalNodes>
+        0 -1 72193 9.1940350830554962e-03</internalNodes>
+      <leafValues>
+        -4.9664518237113953e-01 5.2496069669723511e-01</leafValues></_>
+    <_>
+      <internalNodes>
+        0 -1 388 1.1519105173647404e-02</internalNodes>
+      <leafValues>
+        -7.3585474491119385e-01 3.6338412761688232e-01</leafValues></_></weakClassifiers></stage9>
+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/xml_step2_t3/cascade.xml

@@ -0,0 +1,658 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<cascade>
+  <stageType>BOOST</stageType>
+  <featureType>HAAR</featureType>
+  <height>20</height>
+  <width>20</width>
+  <stageParams>
+    <boostType>GAB</boostType>
+    <minHitRate>9.9500000476837158e-01</minHitRate>
+    <maxFalseAlarm>5.0000000000000000e-01</maxFalseAlarm>
+    <weightTrimRate>9.4999999999999996e-01</weightTrimRate>
+    <maxDepth>1</maxDepth>
+    <maxWeakCount>100</maxWeakCount></stageParams>
+  <featureParams>
+    <maxCatCount>0</maxCatCount>
+    <featSize>1</featSize>
+    <mode>ALL</mode></featureParams>
+  <stageNum>14</stageNum>
+  <stages>
+    <!-- stage 0 -->
+    <_>
+      <maxWeakCount>2</maxWeakCount>
+      <stageThreshold>-1.5131105482578278e-01</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 15 7.3132544755935669e-02</internalNodes>
+          <leafValues>
+            -9.6969699859619141e-01 8.7999999523162842e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 22 3.7634912878274918e-02</internalNodes>
+          <leafValues>
+            -9.7909134626388550e-01 8.1838589906692505e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 1 -->
+    <_>
+      <maxWeakCount>3</maxWeakCount>
+      <stageThreshold>-9.9654263257980347e-01</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 3 2.1198132634162903e-01</internalNodes>
+          <leafValues>
+            -9.3999999761581421e-01 8.3333331346511841e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 27 -5.1079854369163513e-02</internalNodes>
+          <leafValues>
+            7.5422900915145874e-01 -8.4309655427932739e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 42 3.2548047602176666e-02</internalNodes>
+          <leafValues>
+            -8.1077164411544800e-01 8.0424255132675171e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 2 -->
+    <_>
+      <maxWeakCount>3</maxWeakCount>
+      <stageThreshold>-8.4922987222671509e-01</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 36 6.5322615206241608e-02</internalNodes>
+          <leafValues>
+            -8.7939697504043579e-01 5.5102038383483887e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 4 1.9205823540687561e-01</internalNodes>
+          <leafValues>
+            -7.1354663372039795e-01 7.1951913833618164e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 34 -1.0098288208246231e-01</internalNodes>
+          <leafValues>
+            7.4371367692947388e-01 -6.5768426656723022e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 3 -->
+    <_>
+      <maxWeakCount>2</maxWeakCount>
+      <stageThreshold>-5.2841639518737793e-01</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 2 2.3883845657110214e-02</internalNodes>
+          <leafValues>
+            -8.3756345510482788e-01 3.3333334326744080e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 41 1.0589134693145752e-01</internalNodes>
+          <leafValues>
+            -8.6174976825714111e-01 3.8202732801437378e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 4 -->
+    <_>
+      <maxWeakCount>3</maxWeakCount>
+      <stageThreshold>-9.0350991487503052e-01</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 14 -9.1196425259113312e-02</internalNodes>
+          <leafValues>
+            6.2162160873413086e-01 -8.1042653322219849e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 9 8.7607488036155701e-02</internalNodes>
+          <leafValues>
+            -7.8688693046569824e-01 6.2912172079086304e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 24 5.6541308760643005e-02</internalNodes>
+          <leafValues>
+            -6.6161835193634033e-01 6.9380360841751099e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 5 -->
+    <_>
+      <maxWeakCount>3</maxWeakCount>
+      <stageThreshold>-9.5506101846694946e-01</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 11 8.9198779314756393e-03</internalNodes>
+          <leafValues>
+            -7.6958525180816650e-01 6.1290323734283447e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 45 -3.1269099563360214e-03</internalNodes>
+          <leafValues>
+            5.1104712486267090e-01 -7.0055544376373291e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 7 -9.2323711141943932e-03</internalNodes>
+          <leafValues>
+            5.3979915380477905e-01 -6.9652289152145386e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 6 -->
+    <_>
+      <maxWeakCount>3</maxWeakCount>
+      <stageThreshold>-1.2330348491668701e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 0 1.1109821498394012e-02</internalNodes>
+          <leafValues>
+            -8.7356323003768921e-01 5.4054055362939835e-02</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 40 -7.7119180932641029e-03</internalNodes>
+          <leafValues>
+            5.3126132488250732e-01 -6.4888054132461548e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 1 1.6299208626151085e-02</internalNodes>
+          <leafValues>
+            -8.9073300361633301e-01 3.8267943263053894e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 7 -->
+    <_>
+      <maxWeakCount>3</maxWeakCount>
+      <stageThreshold>-6.9402027130126953e-01</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 25 6.2881767749786377e-02</internalNodes>
+          <leafValues>
+            -7.6255708932876587e-01 6.5517240762710571e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 29 -1.2179381214082241e-02</internalNodes>
+          <leafValues>
+            8.1599736213684082e-01 -5.0710964202880859e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 23 1.5992985665798187e-01</internalNodes>
+          <leafValues>
+            -7.4746054410934448e-01 6.2096637487411499e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 8 -->
+    <_>
+      <maxWeakCount>5</maxWeakCount>
+      <stageThreshold>-1.2014210224151611e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 39 1.9785316661000252e-02</internalNodes>
+          <leafValues>
+            -7.2687226533889771e-01 8.0952382087707520e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 5 5.5234711617231369e-03</internalNodes>
+          <leafValues>
+            -8.0144739151000977e-01 3.9590144157409668e-01</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 6 -1.4743935316801071e-02</internalNodes>
+          <leafValues>
+            4.0145263075828552e-01 -7.5309175252914429e-01</leafValues></_>
+        <_>
+          <internalNodes>
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+        <_>
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+          <leafValues>
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+    <!-- stage 9 -->
+    <_>
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+      <stageThreshold>-1.2738654613494873e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
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+        <_>
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+          <leafValues>
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+        <_>
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+          <leafValues>
+            3.2863748073577881e-01 -6.9046407938003540e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 10 -->
+    <_>
+      <maxWeakCount>4</maxWeakCount>
+      <stageThreshold>-6.8541038036346436e-01</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
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+          <leafValues>
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+        <_>
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+          <leafValues>
+            -6.4717310667037964e-01 5.1321762800216675e-01</leafValues></_>
+        <_>
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+          <leafValues>
+            3.8076385855674744e-01 -7.0071691274642944e-01</leafValues></_>
+        <_>
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+          <leafValues>
+            -4.7960722446441650e-01 6.6265738010406494e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 11 -->
+    <_>
+      <maxWeakCount>5</maxWeakCount>
+      <stageThreshold>-1.3034328222274780e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 13 -6.2260786071419716e-03</internalNodes>
+          <leafValues>
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+        <_>
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+          <leafValues>
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+        <_>
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+          <leafValues>
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+        <_>
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+          <leafValues>
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+        <_>
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+          <leafValues>
+            3.0445036292076111e-01 -8.4970545768737793e-01</leafValues></_></weakClassifiers></_>
+    <!-- stage 12 -->
+    <_>
+      <maxWeakCount>4</maxWeakCount>
+      <stageThreshold>-6.6577988862991333e-01</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
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+          <leafValues>
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+        <_>
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+          <leafValues>
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+        <_>
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+          <leafValues>
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+        <_>
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+          <leafValues>
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+    <!-- stage 13 -->
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+      <maxWeakCount>5</maxWeakCount>
+      <stageThreshold>-7.6405721902847290e-01</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
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+        <_>
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+          <leafValues>
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+        <_>
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+          <leafValues>
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+        <_>
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+          <leafValues>
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+      <rects>
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+    <_>
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+      <tilted>1</tilted></_>
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+      <tilted>0</tilted></_></features></cascade>
+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/xml_step2_t3/params.xml

@@ -0,0 +1,19 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<params>
+  <stageType>BOOST</stageType>
+  <featureType>HAAR</featureType>
+  <height>20</height>
+  <width>20</width>
+  <stageParams>
+    <boostType>GAB</boostType>
+    <minHitRate>9.9500000476837158e-01</minHitRate>
+    <maxFalseAlarm>5.0000000000000000e-01</maxFalseAlarm>
+    <weightTrimRate>9.4999999999999996e-01</weightTrimRate>
+    <maxDepth>1</maxDepth>
+    <maxWeakCount>100</maxWeakCount></stageParams>
+  <featureParams>
+    <maxCatCount>0</maxCatCount>
+    <featSize>1</featSize>
+    <mode>ALL</mode></featureParams></params>
+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/xml_step2_t3/stage0.xml

@@ -0,0 +1,17 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage0>
+  <maxWeakCount>2</maxWeakCount>
+  <stageThreshold>-1.5131105482578278e-01</stageThreshold>
+  <weakClassifiers>
+    <_>
+      <internalNodes>
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+      <leafValues>
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+    <_>
+      <internalNodes>
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+      <leafValues>
+        -9.7909134626388550e-01 8.1838589906692505e-01</leafValues></_></weakClassifiers></stage0>
+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/xml_step2_t3/stage1.xml

@@ -0,0 +1,22 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage1>
+  <maxWeakCount>3</maxWeakCount>
+  <stageThreshold>-9.9654263257980347e-01</stageThreshold>
+  <weakClassifiers>
+    <_>
+      <internalNodes>
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+      <leafValues>
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+    <_>
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+      <leafValues>
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+    <_>
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+      <leafValues>
+        -8.1077164411544800e-01 8.0424255132675171e-01</leafValues></_></weakClassifiers></stage1>
+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/xml_step2_t3/stage10.xml

@@ -0,0 +1,27 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage10>
+  <maxWeakCount>4</maxWeakCount>
+  <stageThreshold>-6.8541038036346436e-01</stageThreshold>
+  <weakClassifiers>
+    <_>
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+      <leafValues>
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+    <_>
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+    <_>
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+      <leafValues>
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+    <_>
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+        -4.7960722446441650e-01 6.6265738010406494e-01</leafValues></_></weakClassifiers></stage10>
+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/xml_step2_t3/stage11.xml

@@ -0,0 +1,32 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage11>
+  <maxWeakCount>5</maxWeakCount>
+  <stageThreshold>-1.3034328222274780e+00</stageThreshold>
+  <weakClassifiers>
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+        3.0445036292076111e-01 -8.4970545768737793e-01</leafValues></_></weakClassifiers></stage11>
+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/xml_step2_t3/stage12.xml

@@ -0,0 +1,27 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage12>
+  <maxWeakCount>4</maxWeakCount>
+  <stageThreshold>-6.6577988862991333e-01</stageThreshold>
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+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/xml_step2_t3/stage13.xml

@@ -0,0 +1,32 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage13>
+  <maxWeakCount>5</maxWeakCount>
+  <stageThreshold>-7.6405721902847290e-01</stageThreshold>
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+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/xml_step2_t3/stage2.xml

@@ -0,0 +1,22 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage2>
+  <maxWeakCount>3</maxWeakCount>
+  <stageThreshold>-8.4922987222671509e-01</stageThreshold>
+  <weakClassifiers>
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+    <_>
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+        7.4371367692947388e-01 -6.5768426656723022e-01</leafValues></_></weakClassifiers></stage2>
+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/xml_step2_t3/stage3.xml

@@ -0,0 +1,17 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage3>
+  <maxWeakCount>2</maxWeakCount>
+  <stageThreshold>-5.2841639518737793e-01</stageThreshold>
+  <weakClassifiers>
+    <_>
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+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/xml_step2_t3/stage4.xml

@@ -0,0 +1,22 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage4>
+  <maxWeakCount>3</maxWeakCount>
+  <stageThreshold>-9.0350991487503052e-01</stageThreshold>
+  <weakClassifiers>
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+      <leafValues>
+        6.2162160873413086e-01 -8.1042653322219849e-01</leafValues></_>
+    <_>
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+      <leafValues>
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+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/xml_step2_t3/stage5.xml

@@ -0,0 +1,22 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage5>
+  <maxWeakCount>3</maxWeakCount>
+  <stageThreshold>-9.5506101846694946e-01</stageThreshold>
+  <weakClassifiers>
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+    <_>
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+      <leafValues>
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+    <_>
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+      <leafValues>
+        5.3979915380477905e-01 -6.9652289152145386e-01</leafValues></_></weakClassifiers></stage5>
+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/xml_step2_t3/stage6.xml

@@ -0,0 +1,22 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage6>
+  <maxWeakCount>3</maxWeakCount>
+  <stageThreshold>-1.2330348491668701e+00</stageThreshold>
+  <weakClassifiers>
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+    <_>
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+        -8.9073300361633301e-01 3.8267943263053894e-01</leafValues></_></weakClassifiers></stage6>
+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/xml_step2_t3/stage7.xml

@@ -0,0 +1,22 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage7>
+  <maxWeakCount>3</maxWeakCount>
+  <stageThreshold>-6.9402027130126953e-01</stageThreshold>
+  <weakClassifiers>
+    <_>
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+    <_>
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+    <_>
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+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/xml_step2_t3/stage8.xml

@@ -0,0 +1,32 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage8>
+  <maxWeakCount>5</maxWeakCount>
+  <stageThreshold>-1.2014210224151611e+00</stageThreshold>
+  <weakClassifiers>
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+</opencv_storage>

vinniesniper-54816/task1/_lab/007-test/xml_step2_t3/stage9.xml

@@ -0,0 +1,22 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<stage9>
+  <maxWeakCount>3</maxWeakCount>
+  <stageThreshold>-1.2738654613494873e+00</stageThreshold>
+  <weakClassifiers>
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+</opencv_storage>