change find_face model to cnn

.gitignore

@@ -127,3 +127,9 @@ dmypy.json
 
 # Pyre type checker
 .pyre/
+
+# myrules
+#all
+__pycache__/
+tmp/
+output/
\ No newline at end of file

CV/del_noface_image.py

+import face_recognition
+import os
+import shutil
+import datetime
+import concurrent.futures
+import cv2
+import numpy as np
+import random
+import string
+
+def random_str(length=12):
+    """
+    Generate secret key from alpha and digit.
+    :param length: length of secret key.
+    :return: [length] long secret key.
+    """
+    key = ''
+    while length:
+        key += random.choice(string.ascii_letters + string.digits)
+        length -= 1
+    return key
+
+def get_bar(percent,num = 25):
+    # graphs = ' ▏▎▍▋▊▉'
+    percent = round(percent)
+    bar = '['
+    for i in range(num):
+        if i < round(percent/int((100/num))):
+            bar += '#'
+        else:
+            bar += '-'
+    bar += ']'
+    return bar
+
+def lapulase(resImg):
+    img2gray = cv2.cvtColor(resImg, cv2.COLOR_BGR2GRAY)  # 将图片压缩为单通道的灰度图
+    res = cv2.Laplacian(img2gray, cv2.CV_64F)
+    score = res.var()
+    return score
+
+def resize(img,size):
+    h, w = img.shape[:2]
+    if min(h, w) ==size:
+        return img
+    if w >= h:
+        res = cv2.resize(img,(int(size*w/h), size),interpolation=cv2.INTER_LANCZOS4)
+    else:
+        res = cv2.resize(img,(size, int(size*h/w)),interpolation=cv2.INTER_LANCZOS4)
+    return res
+
+def Traversal(filedir):
+    file_list=[]
+    for root,dirs,files in os.walk(filedir): 
+        for file in files:
+            file_list.append(os.path.join(root,file)) 
+        for dir in dirs:
+            Traversal(dir)
+    return file_list
+
+def is_img(ext):
+    ext = ext.lower()
+    if ext in ['.jpg','.png','.jpeg','.bmp']:
+        return True
+    else:
+        return False
+
+def picture_select(file_list):
+    imgpath_list=[]
+    for pic in file_list:
+        if is_img(os.path.splitext(pic)[1]):
+            imgpath_list.append(pic)
+    return imgpath_list
+
+def del_noface_image(input_path):
+    try:
+        image = cv2.imread(input_path)
+        h,w = image.shape[:2]
+        face_locations = face_recognition.face_locations(image)
+        if len(face_locations)>0 and lapulase(image)>Del_Blur_Score:       
+            return 0
+        else:
+            try:
+                os.remove(input_path)
+                return 1
+            except Exception as e:
+                return 0
+    except Exception as e:
+        try:
+            os.remove(input_path)
+            return 1
+        except Exception as e:
+            return 0
+
+
+filedir = (input("filedir:").strip()).replace("'","")
+WORKERS = int((input("cpu_workers(defult=4):").strip()).replace("'",""))
+
+Del_Blur_Score = 50
+
+
+file_list = Traversal(filedir)
+imgpath_list = picture_select(file_list)
+random.shuffle(imgpath_list)
+all_length = len(imgpath_list)
+
+print("Find picture:"+" "+str(all_length))
+print('Begining......')
+print('Finished/del images  % Bar  Usedtime/Totaltime')
+
+starttime = datetime.datetime.now()
+del_cnt=0
+with concurrent.futures.ProcessPoolExecutor(max_workers=WORKERS) as executor:
+    for i,imgpath,count in zip(range(1,len(imgpath_list)+1),
+                               imgpath_list,
+                               executor.map(del_noface_image,imgpath_list)):
+
+        del_cnt+=count
+        if i%100==0:
+            endtime = datetime.datetime.now()
+            used_time = (endtime-starttime).seconds
+            percent = round(100*i/all_length,1)
+
+            print('\r','',str(i)+'/'+str(del_cnt)+' ',
+                str(percent)+'%'+get_bar(percent,30),
+                ' '+str(int(used_time))+'s/'+str(int(used_time/i*all_length))+'s',end= " ")
+
+    print('\nFinished!','Finall del image:',del_cnt,' Cost time:',(datetime.datetime.now()-starttime).seconds,'s')
\ No newline at end of file

CV/find_face.py

@@ -40,14 +40,14 @@ def lapulase(resImg):
     score = res.var()
     return score
 
-def resize(img,size):
+def resize(img,size,interpolation=cv2.INTER_LINEAR):
     h, w = img.shape[:2]
-    if min(h, w) ==size:
+    if np.min((w,h)) ==size:
         return img
     if w >= h:
-        res = cv2.resize(img,(int(size*w/h), size),interpolation=cv2.INTER_LANCZOS4)
+        res = cv2.resize(img,(int(size*w/h), size),interpolation=interpolation)
     else:
-        res = cv2.resize(img,(size, int(size*h/w)),interpolation=cv2.INTER_LANCZOS4)
+        res = cv2.resize(img,(size, int(size*h/w)),interpolation=interpolation)
     return res
 
 def Traversal(filedir):
@@ -77,44 +77,56 @@ def find_save_resize_face(input_path):
     try:
 
         filename,extension = os.path.splitext(os.path.split(input_path)[1])
-        # image = face_recognition.load_image_file(input_path)
-        image = cv2.imread(input_path)
-        h,w = image.shape[:2]
-        #print(image.dtype)
-        # Find all the faces in the image and print
-        face_locations = face_recognition.face_locations(image)
-        # print("found {} face(s) in this photograph.".format(len(face_locations)))
         
         origin_image = cv2.imread(input_path)
+        h,w = origin_image.shape[:2]
         mask = np.zeros(origin_image.shape[:2],dtype = "uint8")
+        rat = min(origin_image.shape[:2])/LOADSIZE
+        image = resize(origin_image, LOADSIZE,interpolation = cv2.INTER_AREA)
 
+        face_locations = face_recognition.face_locations(image,number_of_times_to_upsample=1,model=MODEL)
+               
         count=0
+        mask_count = 0
         for face_location in face_locations:
-            # Print the location of each face in this image
+            
             top, right, bottom, left = face_location
-            #print("A face is located at pixel location Top: {}, Left: {}, Bottom: {}, Right: {}".format(top, left, bottom, right))
-            # You can access the actual face itself like this:
+            top, right, bottom, left = int(top*rat), int(right*rat), int(bottom*rat), int(left*rat)
+            # EX
             if IS_random_EXTEND:
                 ex=int(((EXTEND-1+0.2*random.random())*(bottom-top))/2)
             else:
                 ex=int(((EXTEND-1)*(bottom-top))/2)
-            # ex_face=int((1*(bottom-top))/2)
+
             if ((bottom-top)>MINSIZE) and 0.95<abs((bottom-top)/(left-right))<1.05 and (top-ex)>0 and (bottom+ex)<h and (left-ex)>0 and (right+ex)<w:
                 face = origin_image[top-ex:bottom+ex, left-ex:right+ex]
                 face = cv2.resize(face, (512,512),interpolation=cv2.INTER_LANCZOS4)
-                # cv2.imwrite(os.path.join(outdir_face,outname+filename+'_'+str(count)+'.jpg'),face)
                 if lapulase(face)>Del_Blur_Score:
-                    #print(os.path.join(outdir_face,random_str()+'.jpg'))
                     cv2.imwrite(os.path.join(outdir_face,random_str()+'.jpg'),face)
                     count = count+1
-            #mask = cv2.rectangle(mask,(left-ex,top-ex),(right+ex,bottom+ex),255,-1)  
-        #mask         
-        # if count > 0:
-        #     mask = resize(mask,256)
-        #     origin_image = resize(origin_image,1024)
-        #     cv2.imwrite(os.path.join(outdir_ori,outname+filename+'.jpg'),origin_image)
-        #     cv2.imwrite(os.path.join(outdir_mask,outname+filename+'.png'),mask)
-        # print(output_path)
+            if SAVE_MASK:
+                try:
+                    if MASK_TYPE=='contour':
+                        _ex = int((bottom - top)*0.5)
+                        face_get_landmark = origin_image[top-_ex:bottom+_ex, left-_ex:right+_ex]
+                        face_landmark = face_recognition.face_landmarks(face_get_landmark)[0]
+                        face_point = []
+                        face_point = face_point+face_landmark['left_eyebrow']+face_landmark['right_eyebrow']+face_landmark['chin'][::-1]
+                        face_point = np.array(face_point) + np.array([left-_ex,top-_ex])
+                        face_point[:10] = face_point[:10] - np.array([0,int((bottom-top)*HIGH_MASK)])
+                        mask = cv2.fillPoly(mask,[face_point],(255))
+                    elif MASK_TYPE=='rect':
+                        _ex = int((bottom - top)*0.05)
+                        mask = cv2.rectangle(mask,(int(left-_ex*0.5),top-_ex),(int(right+_ex*0.5),bottom+_ex),(255),-1)
+                    mask_count += 1
+                except Exception as e:
+                    pass
+        if SAVE_MASK:     
+            if count == mask_count and count > 0:
+                mask = resize(mask,512,interpolation = cv2.INTER_AREA)
+                origin_image = resize(origin_image,512,interpolation = cv2.INTER_AREA)
+                cv2.imwrite(os.path.join(outdir_ori,outname+filename+'.jpg'),origin_image)
+                cv2.imwrite(os.path.join(outdir_mask,outname+filename+'.png'),mask)
         return count
 
     except Exception as e:
@@ -129,10 +141,13 @@ WORKERS = int((input("cpu_workers(defult=4):").strip()).replace("'",""))
 
 # EXTEND=1.4
 EXTEND = 1.6
-Del_Blur_Score = 50
+Del_Blur_Score = 20  # normal-> 20 | clear -> recommed 50
 IS_random_EXTEND = False
-# WORKERS = 4
-# MINSIZE = 256
+MODEL = 'cnn' # 'hog' | 'cnn'
+SAVE_MASK = True
+MASK_TYPE = 'rect' # rect | contour
+HIGH_MASK = 0.2 # more vertical mask
+LOADSIZE = 512 # load to this size and process
 
 outdir='./output/'+outname
 

CV/threshold_photo.py

+import numpy
+import cv2
+import os
+
+img_dir = '/media/hypo/Hypo_U/桂林一调/理综'
+out_dir = os.path.join(img_dir,'threshold')
+THR = 12
+
+img_names = os.listdir(img_dir)
+if not os.path.isdir(out_dir):
+    os.makedirs(out_dir)
+
+
+for img_name in img_names:
+    img = cv2.imread(os.path.join(img_dir,img_name), 0)
+    img = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,21,THR)
+    cv2.imwrite(os.path.join(out_dir,img_name), img)

Notes/linux.md

-[toc]
+[TOC]
+### 安装显卡驱动
+```bash
+#卸载旧的驱动
+sudo apt-get purge nvidia*
+sudo apt-get autoremove #这个命令有时候不用也可以
+#
+sudo add-apt-repository ppa:graphics-drivers/ppa
+sudo apt update
+ubuntu-drivers devices
+sudo apt install nvidia-430
+```
 ### 挂载U盘
 ```bash
 root@lthpc:/home/hypo# fdisk -l
@@ -8,12 +19,29 @@ Device     Start        End    Sectors  Size Type
 root@lthpc:/home/hypo# cd /media  
 root@lthpc:/media# mkdir usb
 root@lthpc:/media# mount -t ntfs-3g /dev/sdc1 /media/usb 
+
+#卸载挂载点
+$ umount /dev/hda2
+$ umount /usr
+#参数可以是设备文件或安装点
 ```
 ### shell 中运行基本应用
 ```bash
 nautilus #文件管理器
 firefox #火狐浏览器
 ```
+### 批量杀死应用
+```bash
+ps aux|grep python|grep -v grep|cut -c 9-15|xargs kill -15
+# 管道符“|”用来隔开两个命令，管道符左边命令的输出会作为管道符右边命令的输入。下面说说用管道符联接起来的 
+#几个命令： 
+#“ps aux”是linux 里查看所有进程的命令。这时检索出的进程将作为下一条命令“grep python”的输入。 
+#“grep python”的输出结果是，所有含有关键字“python”的进程，这是python程序
+#“grep -v grep”是在列出的进程中去除含有关键字“grep”的进程。 
+#“cut -c 9-15”是截取输入行的第9个字符到第15个字符，而这正好是进程号PID。 
+#“xargs kill -15”中的xargs命令是用来把前面命令的输出结果（PID）作为“kill -15”命令的参数，并执行该令。 
+#“kill -15”会正常退出指定进程，-9强行杀掉
+```
 ### 常用系统命令
 * 删除文件
 ```bash
@@ -51,7 +79,7 @@ dpkg -L <package> #列出 <package> 安装的所有文件清单。同时请看 d
 dpkg -s <package> #显示已安装包裹的信息。同时请看 apt-cache 显示 Debian 存档中的包裹信息，以及 dpkg -I 来显示从一个 .deb 文件中提取的包裹信息。
 dpkg-reconfigure <package> #重新配制一个已经安装的包裹，如果它使用的是 debconf (debconf 为包裹安装提供了一个统一的配制界面)。
 ```
-* 调整CPU性能模式
+### 调整CPU性能模式
 ```bash
 sudo apt-get install cpufrequtils
 cpufreq-info
@@ -60,6 +88,16 @@ sudo cpufreq-set -g performance
 sudo apt-get install sysfsutils
 sudo gedit  /sys/devices/system/cpu/cpu0/cpufreq/scaling_governor
 ```
+### 添加或禁用开机启动项
+```bash
+#禁用开机启动项
+$ systemctl list-unit-files --type=service|grep enabled #查看开机启动的服务
+$ sudo systemctl disable apache2.service #禁用掉该服务
+#添加开机启动项
+$ systemctl list-unit-files --type=service|grep team
+teamviewerd.service                        enabled    
+$ sudo systemctl enable teamviewerd.service
+```
 ### 压缩解压
 ```bash
 tar xvf filename.tar #.tar 解包 
@@ -128,6 +166,7 @@ firefox &
 ```bash
 #压缩+解压流传输 把本地的文件复制到远程主机上
 tar -c './dir' |pigz |ssh hypo@172.31.73.116 "gzip -d|tar -xC /home/hypo/MyProject"
+tar -c './DeepMosaics' |pigz |ssh hypo@172.30.194.156 "gzip -d|tar -xC /media/hypo/Project/MyProject/DeepMosaics"
 
 #把远程的文件复制到本地
 scp root@www.test.com:/val/test/test.tar.gz /val/test/test.tar.gz

Notes/python.md

-[toc]
+[TOC]
 # Python
 ## base
 ### main
@@ -265,7 +265,23 @@ cv2.IMREAD_UNCHANGED：加载图像，包括alpha通道
 您可以简单地分别传递整数1,0或-1，而不是这三个标志。
 '''
 ```
+### resize
+```python
+#CV2.INTER_NEAREST      最邻近插值点法
+#CV2.NTER_LINEAR         双线性插值法
+#CV2.INTER_AREA             邻域像素再取样插补
+#CV2.INTER_CUBIC           双立方插补，4*4大小的补点
+#CV2.INTER_LANCZOS4         8x8像素邻域的Lanczos插值
+#當我們縮小影像時，使用CV_INTER_AREA會有比較好的效果，當我們放大影像，CV_INTER_CUBIC會有最好的效果，但是計算花費時間較多，CV_INTER_LINEAR能在影像品質和花費時間上取得不錯的平衡。 CV_INTER_LANCZOS4    Lanczos插补，8*8大小的补点
+
+```
+
+
+
+
+
 ### imshow
+
 ```python
 cv2.imshow('image',img)
 '''

System/monitor_linux.py

@@ -38,7 +38,12 @@ def get_cpu_freq():
 
 # Cpu freq
 def get_cpu_temp():
-    temp_str = os.popen('cat /sys/class/hwmon/hwmon0/device/hwmon/hwmon0/temp1_input').read()
+    if os.path.isfile('/sys/class/hwmon/hwmon0/device/hwmon/hwmon0/temp1_input'):
+        temp_str = os.popen('cat /sys/class/hwmon/hwmon0/device/hwmon/hwmon0/temp1_input').read()
+    elif os.path.isfile('/sys/class/hwmon/hwmon0/device/hwmon0/temp1_input'):
+        temp_str = os.popen('cat /sys/class/hwmon/hwmon0/device/hwmon0/temp1_input').read()
+    else:
+        return -1
     return (float(temp_str)/1000)
 
 # Mem
@@ -71,9 +76,10 @@ def get_swap_use():
 ###############################################################
 gpus_str = os.popen('nvidia-smi -L').read()
 gpus =[]
-while gpus_str.find('\n') != -1:
-    gpus.append(gpus_str[gpus_str.find(':')+2:gpus_str.find('(')-1])
-    gpus_str=gpus_str[gpus_str.find('\n')+1:]
+if 'communicate with the NVIDIA driver' not in gpus_str:
+    while gpus_str.find('\n') != -1:
+        gpus.append(gpus_str[gpus_str.find(':')+2:gpus_str.find('(')-1])
+        gpus_str=gpus_str[gpus_str.find('\n')+1:]
 
 def get_gpu_use():
     
@@ -85,7 +91,10 @@ def get_gpu_use():
         infos_str = infos_str[:infos_str.find('\n')+1]
         infos_str = infos_str.split()
         #['|', '50%', '42C', 'P0', '19W', '/', '75W', '|', '929MiB', '/', '5050MiB', '|', '14%', 'Default', '|']
-        fan = int(infos_str[1].replace('%',''))        # %
+        if infos_str[1].replace('%','') == 'N/A':
+            fan = -1
+        else:
+            fan = int(infos_str[1].replace('%',''))        # %
         temp = int(infos_str[2].replace('C',''))       # C
         if infos_str[4] == 'N/A':
             power_used = -1