Initial commit

.gitignore

+*.avi
+camera
+face
+.idea

.ropeproject/config.py

+# The default ``config.py``
+
+
+def set_prefs(prefs):
+    """This function is called before opening the project"""
+
+    # Specify which files and folders to ignore in the project.
+    # Changes to ignored resources are not added to the history and
+    # VCSs.  Also they are not returned in `Project.get_files()`.
+    # Note that ``?`` and ``*`` match all characters but slashes.
+    # '*.pyc': matches 'test.pyc' and 'pkg/test.pyc'
+    # 'mod*.pyc': matches 'test/mod1.pyc' but not 'mod/1.pyc'
+    # '.svn': matches 'pkg/.svn' and all of its children
+    # 'build/*.o': matches 'build/lib.o' but not 'build/sub/lib.o'
+    # 'build//*.o': matches 'build/lib.o' and 'build/sub/lib.o'
+    prefs['ignored_resources'] = ['*.pyc', '*~', '.ropeproject',
+                                  '.hg', '.svn', '_svn', '.git']
+
+    # Specifies which files should be considered python files.  It is
+    # useful when you have scripts inside your project.  Only files
+    # ending with ``.py`` are considered to be python files by
+    # default.
+    #prefs['python_files'] = ['*.py']
+
+    # Custom source folders:  By default rope searches the project
+    # for finding source folders (folders that should be searched
+    # for finding modules).  You can add paths to that list.  Note
+    # that rope guesses project source folders correctly most of the
+    # time; use this if you have any problems.
+    # The folders should be relative to project root and use '/' for
+    # separating folders regardless of the platform rope is running on.
+    # 'src/my_source_folder' for instance.
+    #prefs.add('source_folders', 'src')
+
+    # You can extend python path for looking up modules
+    #prefs.add('python_path', '~/python/')
+
+    # Should rope save object information or not.
+    prefs['save_objectdb'] = True
+    prefs['compress_objectdb'] = False
+
+    # If `True`, rope analyzes each module when it is being saved.
+    prefs['automatic_soa'] = True
+    # The depth of calls to follow in static object analysis
+    prefs['soa_followed_calls'] = 0
+
+    # If `False` when running modules or unit tests "dynamic object
+    # analysis" is turned off.  This makes them much faster.
+    prefs['perform_doa'] = True
+
+    # Rope can check the validity of its object DB when running.
+    prefs['validate_objectdb'] = True
+
+    # How many undos to hold?
+    prefs['max_history_items'] = 32
+
+    # Shows whether to save history across sessions.
+    prefs['save_history'] = True
+    prefs['compress_history'] = False
+
+    # Set the number spaces used for indenting.  According to
+    # :PEP:`8`, it is best to use 4 spaces.  Since most of rope's
+    # unit-tests use 4 spaces it is more reliable, too.
+    prefs['indent_size'] = 4
+
+    # Builtin and c-extension modules that are allowed to be imported
+    # and inspected by rope.
+    prefs['extension_modules'] = []
+
+    # Add all standard c-extensions to extension_modules list.
+    prefs['import_dynload_stdmods'] = True
+
+    # If `True` modules with syntax errors are considered to be empty.
+    # The default value is `False`; When `False` syntax errors raise
+    # `rope.base.exceptions.ModuleSyntaxError` exception.
+    prefs['ignore_syntax_errors'] = False
+
+    # If `True`, rope ignores unresolvable imports.  Otherwise, they
+    # appear in the importing namespace.
+    prefs['ignore_bad_imports'] = False
+
+
+def project_opened(project):
+    """This function is called after opening the project"""
+    # Do whatever you like here!

.ropeproject/globalnames

+}q
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Uunregister_archive_formatqUrmtreeqUErrorqUcopyqU	ExecErrorqUcopy2qUSpecialFileErrorqUcopystatqUWindowsErrorqeUos]q(Upopen4qUexecleqUpopen3qUSEEK_CURqUspawnlpqUexeclpqUenvironqUspawnvpqUspawnlq Uexecvpq!Umakedirsq"Ulinesepq#UP_WAITq$Uspawnvq%Uspawnveq&Uexecvpeq'Uunsetenvq(USEEK_SETq)Ugetenvq*Upopen2q+Uwalkq,Uspawnleq-Uexeclq.Unameq/USEEK_ENDq0Uspawnlpeq1U	P_NOWAITOq2UP_NOWAITq3U
+removedirsq4Urenamesq5Uspawnvpeq6Uexeclpeq7eUdatetime]q8(U	timedeltaq9UMAXYEARq:Udateq;U
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\ No newline at end of file

.ropeproject/history

+]q
(]q]qe.
\ No newline at end of file

.ropeproject/objectdb

+}q
.
\ No newline at end of file

README.md

+# opencv-face
+基于python-opencv调用摄像头的实时人脸检测和识别

face.py

+#coding=utf-8
+import cv2
+import cv2.cv as cv
+import numpy as np
+import math
+ 
+def detect(img, cascade):
+    '''detectMultiScale函数中smallImg表示的是要检测的输入图像为smallImg，
+faces表示检测到的人脸目标序列，1.3表示每次图像尺寸减小的比例为1.3，
+ 4表示每一个目标至少要被检测到3次才算是真的目标(因为周围的像素和不同的窗口大小都可以检测到人脸),
+ CV_HAAR_SCALE_IMAGE表示不是缩放分类器来检测，而是缩放图像，Size(20, 20)为目标的最小最大尺寸'''
+    rects = cascade.detectMultiScale(img, scaleFactor=1.3,
+                                    minNeighbors=5, minSize=(30, 30), flags = cv.CV_HAAR_SCALE_IMAGE)
+    if len(rects) == 0:
+        return []
+    rects[:,2:] += rects[:,:2]
+    print rects
+    return rects
+ 
+#在img上绘制矩形
+def draw_rects(img, rects, color):
+    for x1, y1, x2, y2 in rects:
+        cv2.rectangle(img, (x1, y1), (x2, y2), color, 2)
+        
+def rotate_about_center(src, angle, scale=1.):
+    w = src.shape[1]
+    h = src.shape[0]
+    rangle = np.deg2rad(angle)  # angle in radians
+      # now calculate new image width and height
+    nw = (abs(np.sin(rangle) * h) + abs(np.cos(rangle) * w)) * scale
+    nh = (abs(np.cos(rangle) * h) + abs(np.sin(rangle) * w)) * scale
+      # ask OpenCV for the rotation matrix
+    rot_mat = cv2.getRotationMatrix2D((nw * 0.5, nh * 0.5), angle, scale)
+      # calculate the move from the old center to the new center combined
+      # with the rotation
+    rot_move = np.dot(rot_mat, np.array([(nw - w) * 0.5, (nh - h) * 0.5, 0]))
+      # the move only affects the translation, so update the translation
+      # part of the transform
+    rot_mat[0, 2] += rot_move[0]
+    rot_mat[1, 2] += rot_move[1]
+    return cv2.warpAffine(src, rot_mat, (int(math.ceil(nw)), int(math.ceil(nh))), flags=cv2.INTER_LANCZOS4)
+
+class FaceDetect(object):
+    def __init__(self):
+        #正脸
+        self.front_fn = 'haarcascades/haarcascade_frontalface_alt.xml'
+        # cascade_fn = 'lbpcascades/lbpcascade_frontalface.xml'
+        #侧脸
+        self.profile_fn = 'haarcascades/haarcascade_profileface.xml'
+        # cascade_fn = 'lbpcascades/lbpcascade_profileface.xml'
+
+        #读取分类器,CascadeClassifier下面有一个detectMultiScale方法来得到矩形
+        self.frontCascade = cv2.CascadeClassifier(self.front_fn)
+        self.profileCascade = cv2.CascadeClassifier(self.profile_fn)
+        return
+
+    def detect(self, img):
+        """
+        :param img:{numpy}
+        :return:
+        """
+        #vis为img副本
+        vis = img.copy()
+
+        #转换为灰度图
+        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        #直方图均衡处理
+        gray = cv2.equalizeHist(gray)
+
+        #通过分类器得到rects
+        rects = detect(gray, self.frontCascade)
+        if len(rects) == 0:
+            #侧脸检测
+            rects = detect(gray, self.profileCascade)
+            if len(rects) == 0:
+                #镜像 在侧脸检测
+                gray = cv2.flip(gray, 1)
+                rects = detect(gray, self.profileCascade)
+                vis = cv2.flip(vis, 1)
+
+        result = []
+        #画矩形
+        draw_rects(vis, rects, (0, 255, 0))
+        if len(rects) != 0:
+            for x1, y1, x2, y2 in rects:
+                result.append(vis[y1:y2, x1:x2])
+        else:
+            result.append(vis)
+        return result
+
+if __name__ == '__main__':
+    img = cv2.imread("face/1.jpg")
+    model = FaceDetect()
+    vis = model.detect(img)
+
+    cv2.imshow('facedetect', vis[0])
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
\ No newline at end of file

haarcascades/haarcascade_licence_plate_rus_16stages.xml

+<?xml version="1.0"?>
+<opencv_storage>
+<!-- Automatically converted from haarcascade2, window size = 64x16 -->
+<haarcascade_pltzzz64x16_16STG type_id="opencv-haar-classifier">
+  <size>
+    64 16</size>
+  <stages>
+    <_>
+      <!-- stage 0 -->
+      <trees>
+        <_>
+          <!-- tree 0 -->
+          <_>
+            <!-- root node -->
+            <feature>
+              <rects>
+                <_>
+                  32 2 8 6 -1.</_>
+                <_>
+                  32 4 8 2 3.</_></rects>
+              <tilted>0</tilted></feature>
+            <threshold>1.6915600746870041e-002</threshold>
+            <left_val>-9.5547717809677124e-001</left_val>
+            <right_val>8.9129137992858887e-001</right_val></_></_>
+        <_>
+          <!-- tree 1 -->
+          <_>
+            <!-- root node -->
+            <feature>
+              <rects>
+                <_>
+                  0 4 6 10 -1.</_>
+                <_>
+                  3 4 3 10 2.</_></rects>
+              <tilted>0</tilted></feature>
+            <threshold>2.4228349328041077e-002</threshold>
+            <left_val>-9.2089319229125977e-001</left_val>
+            <right_val>8.8723921775817871e-001</right_val></_></_>
+        <_>
+          <!-- tree 2 -->
+          <_>
+            <!-- root node -->
+            <feature>
+              <rects>
+                <_>
+                  55 0 8 6 -1.</_>
+                <_>
+                  55 0 4 3 2.</_>
+                <_>
+                  59 3 4 3 2.</_></rects>
+              <tilted>0</tilted></feature>
+            <threshold>-1.0168660432100296e-002</threshold>
+            <left_val>8.8940089941024780e-001</left_val>
+            <right_val>-7.7847331762313843e-001</right_val></_></_>
+        <_>
+          <!-- tree 3 -->
+          <_>
+            <!-- root node -->
+            <feature>
+              <rects>
+                <_>
+                  44 7 4 9 -1.</_>
+                <_>
+                  44 10 4 3 3.</_></rects>
+              <tilted>0</tilted></feature>
+            <threshold>2.0863260142505169e-003</threshold>
+            <left_val>-8.7998157739639282e-001</left_val>
+            <right_val>5.8651781082153320e-001</right_val></_></_></trees>
+      <stage_threshold>-2.0683259963989258e+000</stage_threshold>
+      <parent>-1</parent>
+      <next>-1</next></_>
+    <_>
+      <!-- stage 1 -->
+      <trees>
+        <_>
+          <!-- tree 0 -->
+          <_>
+            <!-- root node -->
+            <feature>
+              <rects>
+                <_>
+                  29 1 16 4 -1.</_>
+                <_>
+                  29 3 16 2 2.</_></rects>
+              <tilted>0</tilted></feature>
+            <threshold>2.9062159359455109e-002</threshold>
+            <left_val>-8.7765061855316162e-001</left_val>
+            <right_val>8.5373121500015259e-001</right_val></_></_>
+        <_>
+          <!-- tree 1 -->
+          <_>
+            <!-- root node -->
+            <feature>
+              <rects>
+                <_>
+                  0 5 9 8 -1.</_>
+                <_>
+                  3 5 3 8 3.</_></rects>
+              <tilted>0</tilted></feature>
+            <threshold>2.3903399705886841e-002</threshold>
+            <left_val>-9.2079448699951172e-001</left_val>
+            <right_val>7.5155001878738403e-001</right_val></_></_>
+        <_>
+          <!-- tree 2 -->
+          <_>
+            <!-- root node -->
+            <feature>
+              <rects>
+                <_>
+                  44 0 20 14 -1.</_>
+                <_>
+                  44 0 10 7 2.</_>
+                <_>
+                  54 7 10 7 2.</_></rects>
+              <tilted>0</tilted></feature>
+            <threshold>-3.5404648631811142e-002</threshold>
+            <left_val>6.7834627628326416e-001</left_val>
+            <right_val>-9.0937072038650513e-001</right_val></_></_>
+        <_>
+          <!-- tree 3 -->
+          <_>
+            <!-- root node -->
+            <feature>
+              <rects>
+                <_>
+                  41 7 6 9 -1.</_>
+                <_>
+                  43 7 2 9 3.</_></rects>
+              <tilted>0</tilted></feature>
+            <threshold>6.2988721765577793e-003</threshold>
+            <left_val>-8.1054258346557617e-001</left_val>
+            <right_val>5.8985030651092529e-001</right_val></_></_>
+        <_>
+          <!-- tree 4 -->
+          <_>
+            <!-- root node -->
+            <feature>
+              <rects>
+                <_>
+                  0 4 21 4 -1.</_>
+                <_>
+                  7 4 7 4 3.</_></rects>
+              <tilted>0</tilted></feature>
+            <threshold>3.4959490876644850e-003</threshold>
+            <left_val>-9.7632282972335815e-001</left_val>
+            <right_val>4.5473039150238037e-001</right_val></_></_></trees>
+      <stage_threshold>-1.6632349491119385e+000</stage_threshold>
+      <parent>0</parent>
+      <next>-1</next></_>
+    <_>
+      <!-- stage 2 -->
+      <trees>
+        <_>
+          <!-- tree 0 -->
+          <_>
+            <!-- root node -->
+            <feature>
+              <rects>
+                <_>
+                  31 2 11 6 -1.</_>
+                <_>
+                  31 4 11 2 3.</_></rects>
+              <tilted>0</tilted></feature>
+            <threshold>2.3864099755883217e-002</threshold>
+            <left_val>-9.3137168884277344e-001</left_val>
+            <right_val>8.2478952407836914e-001</right_val></_></_>
+        <_>
+          <!-- tree 1 -->
+          <_>
+            <!-- root node -->
+            <feature>
+              <rects>
+                <_>
+                  56 3 6 11 -1.</_>
+                <_>
+                  59 3 3 11 2.</_></rects>
+              <tilted>0</tilted></feature>
+            <threshold>-2.5775209069252014e-002</threshold>
+            <left_val>8.5526448488235474e-001</left_val>
+            <right_val>-8.7574672698974609e-001</right_val></_></_>
+        <_>
+          <!-- tree 2 -->
+          <_>
+            <!-- root node -->
+            <feature>
+              <rects>
+                <_>
+                  32 14 32 2 -1.</_>
+                <_>
+                  32 15 32 1 2.</_></rects>
+              <tilted>0</tilted></feature>
+            <threshold>-1.0646049864590168e-002</threshold>
+            <left_val>8.5167151689529419e-001</left_val>
+            <right_val>-6.7789041996002197e-001</right_val></_></_>
+        <_>
+          <!-- tree 3 -->
+          <_>
+            <!-- root node -->
+            <feature>
+              <rects>
+                <_>
+                  0 2 8 14 -1.</_>
+                <_>
+                  4 2 4 14 2.</_></rects>
+              <tilted>0</tilted></feature>
+            <threshold>2.7000989764928818e-002</threshold>
+            <left_val>-8.0041092634201050e-001</left_val>
+            <right_val>6.4893317222595215e-001</right_val></_></_>
+        <_>
+          <!-- tree 4 -->
+          <_>
+            <!-- root node -->
+            <feature>
+              <rects>
+                <_>
+                  19 0 22 6 -1.</_>
+                <_>
+                  19 0 11 3 2.</_>
+                <_>
+                  30 3 11 3 2.</_></rects>
+              <tilted>0</tilted></feature>
+            <threshold>5.2989721298217773e-003</threshold>
+            <left_val>-9.5342522859573364e-001</left_val>
+            <right_val>5.0140267610549927e-001</right_val></_></_></trees>
+      <stage_threshold>-1.3346730470657349e+000</stage_threshold>
+      <parent>1</parent>
+      <next>-1</next></_>
+    <_>
+      <!-- stage 3 -->
+      <trees>
+        <_>
+          <!-- tree 0 -->
+          <_>
+            <!-- root node -->
+            <feature>
+              <rects>
+                <_>
+                  56 0 6 6 -1.</_>
+                <_>
+                  56 0 3 3 2.</_>
+                <_>
+                  59 3 3 3 2.</_></rects>
+              <tilted>0</tilted></feature>
+            <threshold>-6.9233630783855915e-003</threshold>
+            <left_val>8.2654470205307007e-001</left_val>
+            <right_val>-8.5396027565002441e-001</right_val></_></_>
+        <_>
+          <!-- tree 1 -->
+          <_>
+            <!-- root node -->
+            <feature>
+              <rects>
+                <_>
+                  32 0 14 12 -1.</_>
+                <_>
+                  32 0 7 6 2.</_>
+                <_>
+                  39 6 7 6 2.</_></rects>
+              <tilted>0</tilted></feature>
+            <threshold>1.2539249658584595e-001</threshold>
+            <left_val>-1.2996139936149120e-002</left_val>
+            <right_val>-3.2377028808593750e+003</right_val></_></_>
+        <_>
+          <!-- tree 2 -->
+          <_>
+            <!-- root node -->
+            <feature>
+              <rects>
+                <_>
+                  2 1 43 4 -1.</_>
+                <_>
+                  2 3 43 2 2.</_></rects>
+              <tilted>0</tilted></feature>
+            <threshold>6.3474893569946289e-002</threshold>
+            <left_val>-6.4648061990737915e-001</left_val>
+            <right_val>8.2302427291870117e-001</right_val></_></_>
+        <_>
+          <!-- tree 3 -->
+          <_>
+            <!-- root node -->
+            <feature>
+              <rects>
+                <_>
+                  34 10 30 5 -1.</_>
+                <_>
+                  44 10 10 5 3.</_></rects>
+              <tilted>0</tilted></feature>
+            <threshold>4.2217150330543518e-002</threshold>
+            <left_val>-7.5190877914428711e-001</left_val>
+            <right_val>6.3705182075500488e-001</right_val></_></_>
+        <_>
+          <!-- tree 4 -->
+          <_>
+            <!-- root node -->
+            <feature>
+              <rects>
+                <_>
+                  0 9 9 5 -1.</_>
+                <_>
+                  3 9 3 5 3.</_></rects>
+              <tilted>0</tilted></feature>
+            <threshold>2.0000640302896500e-002</threshold>
+            <left_val>-6.2077498435974121e-001</left_val>
+            <right_val>6.1317932605743408e-001</right_val></_></_></trees>
+      <stage_threshold>-1.6521669626235962e+000</stage_threshold>
+      <parent>2</parent>
+      <next>-1</next></_>
+    <_>
+      <!-- stage 4 -->
+      <trees>
+        <_>
+          <!-- tree 0 -->
+          <_>
+            <!-- root node -->
+            <feature>
+              <rects>
+                <_>
+                  2 1 43 6 -1.</_>
+                <_>
+                  2 3 43 2 3.</_></rects>
+              <tilted>0</tilted></feature>
+            <threshold>9.2297486960887909e-002</threshold>
+            <left_val>-7.2764229774475098e-001</left_val>
+            <right_val>8.0554759502410889e-001</right_val></_></_>
+        <_>
+          <!-- tree 1 -->
+          <_>
+            <!-- root node -->
+            <feature>
+              <rects>
+                <_>
+                  53 4 9 8 -1.</_>
+                <_>
+                  56 4 3 8 3.</_></rects>
+              <tilted>0</tilted></feature>
+            <threshold>2.7613969519734383e-002</threshold>
+            <left_val>-7.0769268274307251e-001</left_val>
+            <right_val>7.3315787315368652e-001</right_val></_></_>
+        <_>
+          <!-- tree 2 -->
+          <_>
+            <!-- root node -->
+            <feature>
+              <rects>
+                <_>
+                  36 4 14 8 -1.</_>
+                <_>
+                  36 4 7 4 2.</_>
+                <_>
+                  43 8 7 4 2.</_></rects>
+              <tilted>0</tilted></feature>
+            <threshold>1.2465449981391430e-002</threshold>
+            <left_val>-8.4359270334243774e-001</left_val>
+            <right_val>5.7046437263488770e-001</right_val></_></_>
+        <_>
+          <!-- tree 3 -->
+          <_>
+            <!-- root node -->
+            <feature>
+              <rects>
+                <_>
+                  14 14 49 2 -1.</_>
+                <_>
+                  14 15 49 1 2.</_></rects>
+              <tilted>0</tilted></feature>
+            <threshold>-2.3886829614639282e-002</threshold>
+            <left_val>8.2656508684158325e-001</left_val>
+            <right_val>-5.2783298492431641e-001</right_val></_></_></trees>
+      <stage_threshold>-1.4523630142211914e+000</stage_threshold>
+      <parent>3</parent>
+      <next>-1</next></_>
+    <_>
+      <!-- stage 5 -->
+      <trees>
+        <_>
+          <!-- tree 0 -->
+          <_>
+            <!-- root node -->
+            <feature>
+              <rects>
+                <_>
+                  0 5 4 9 -1.</_>
+                <_>
+                  2 5 2 9 2.</_></rects>
+              <tilted>0</tilted></feature>
+            <threshold>1.8821349367499352e-002</threshold>
+            <left_val>-8.1122857332229614e-001</left_val>
+            <right_val>6.9127470254898071e-001</right_val></_></_>
+        <_>
+          <!-- tree 1 -->
+          <_>
+            <!-- root node -->
+            <feature>
+              <rects>
+                <_>
+                  21 1 38 4 -1.</_>
+                <_>
+                  21 3 38 2 2.</_></rects>
+              <tilted>0</tilted></feature>
+            <threshold>6.1703320592641830e-002</threshold>
+            <left_val>-7.6482647657394409e-001</left_val>
+            <right_val>6.4212161302566528e-001</right_val></_></_>
+        <_>
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+              <rects>
+                <_>
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+                <_>
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+              <tilted>0</tilted></feature>
+            <threshold>-8.6617246270179749e-003</threshold>
+            <left_val>8.4029090404510498e-001</left_val>
+            <right_val>-7.1949690580368042e-001</right_val></_></_>
+        <_>
+          <!-- tree 2 -->
+          <_>
+            <!-- root node -->
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+                <_>
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+              <tilted>0</tilted></feature>
+            <threshold>1.6897840425372124e-002</threshold>
+            <left_val>-5.3601992130279541e-001</left_val>
+            <right_val>9.5484441518783569e-001</right_val></_></_>
+        <_>
+          <!-- tree 3 -->
+          <_>
+            <!-- root node -->
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+              <rects>
+                <_>
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+              <tilted>0</tilted></feature>
+            <threshold>4.7526158596156165e-005</threshold>
+            <left_val>-7.6412862539291382e-001</left_val>
+            <right_val>7.5398761034011841e-001</right_val></_></_>
+        <_>
+          <!-- tree 4 -->
+          <_>
+            <!-- root node -->
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+              <tilted>0</tilted></feature>
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+            <left_val>-9.9346441030502319e-001</left_val>
+            <right_val>6.4864277839660645e-001</right_val></_></_></trees>
+      <stage_threshold>-7.3307347297668457e-001</stage_threshold>
+      <parent>12</parent>
+      <next>-1</next></_>
+    <_>
+      <!-- stage 14 -->
+      <trees>
+        <_>
+          <!-- tree 0 -->
+          <_>
+            <!-- root node -->
+            <feature>
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+            <left_val>-7.3275578022003174e-001</left_val>
+            <right_val>8.4619927406311035e-001</right_val></_></_>
+        <_>
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+            <!-- root node -->
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+              <tilted>0</tilted></feature>
+            <threshold>-2.8920811018906534e-004</threshold>
+            <left_val>7.1564781665802002e-001</left_val>
+            <right_val>-8.8221758604049683e-001</right_val></_></_>
+        <_>
+          <!-- tree 2 -->
+          <_>
+            <!-- root node -->
+            <feature>
+              <rects>
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+              <tilted>0</tilted></feature>
+            <threshold>1.0838840156793594e-002</threshold>
+            <left_val>-8.7420248985290527e-001</left_val>
+            <right_val>6.0648679733276367e-001</right_val></_></_>
+        <_>
+          <!-- tree 3 -->
+          <_>
+            <!-- root node -->
+            <feature>
+              <rects>
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+              <tilted>0</tilted></feature>
+            <threshold>5.0803890917450190e-004</threshold>
+            <left_val>-9.0554022789001465e-001</left_val>
+            <right_val>6.4213967323303223e-001</right_val></_></_>
+        <_>
+          <!-- tree 4 -->
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+            <!-- root node -->
+            <feature>
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+                <_>
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+              <tilted>0</tilted></feature>
+            <threshold>2.3357039317488670e-003</threshold>
+            <left_val>-9.2574918270111084e-001</left_val>
+            <right_val>8.6384928226470947e-001</right_val></_></_>
+        <_>
+          <!-- tree 5 -->
+          <_>
+            <!-- root node -->
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+            <left_val>-9.9618428945541382e-001</left_val>
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+        <_>
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+            <left_val>-1.</left_val>
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+        <_>
+          <!-- tree 7 -->
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+            <!-- root node -->
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+            <left_val>0.</left_val>
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+        <_>
+          <!-- tree 8 -->
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+            <!-- root node -->
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+            <left_val>-1.</left_val>
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+        <_>
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+            <!-- root node -->
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+</opencv_storage>

haarcascades/haarcascade_russian_plate_number.xml

+<?xml version="1.0"?>
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+  <featureType>HAAR</featureType>
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+    <maxWeakCount>100</maxWeakCount></stageParams>
+  <featureParams>
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+    <featSize>1</featSize>
+    <mode>ALL</mode></featureParams>
+  <stageNum>20</stageNum>
+  <stages>
+    <!-- stage 0 -->
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+    <!-- stage 1 -->
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+    <!-- stage 2 -->
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+    <!-- stage 3 -->
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+    <!-- stage 4 -->
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+    <!-- stage 5 -->
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+        <_>
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+    <!-- stage 6 -->
+    <_>
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+      <stageThreshold>-9.0274518728256226e-001</stageThreshold>
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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>
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+        <_>
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+        <_>
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+        <_>
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+          <leafValues>
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+        <_>
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+    <!-- stage 7 -->
+    <_>
+      <maxWeakCount>10</maxWeakCount>
+      <stageThreshold>-1.4518526792526245e+000</stageThreshold>
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+        <_>
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+        <_>
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+        <_>
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+        <_>
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+        <_>
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+        <_>
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+          <leafValues>
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+        <_>
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+        <_>
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+        <_>
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+    <!-- stage 8 -->
+    <_>
+      <maxWeakCount>11</maxWeakCount>
+      <stageThreshold>-1.3153649568557739e+000</stageThreshold>
+      <weakClassifiers>
+        <_>
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+        <_>
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+          <leafValues>
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+        <_>
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+        <_>
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+        <_>
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+        <_>
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+        <_>
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+        <_>
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+        <_>
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+    <!-- stage 9 -->
+    <_>
+      <maxWeakCount>13</maxWeakCount>
+      <stageThreshold>-1.4625015258789063e+000</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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+        <_>
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+        <_>
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+        <_>
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+        <_>
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+        <_>
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+          <leafValues>
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+        <_>
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+    <!-- stage 10 -->
+    <_>
+      <maxWeakCount>14</maxWeakCount>
+      <stageThreshold>-1.4959813356399536e+000</stageThreshold>
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+        <_>
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+        <_>
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+        <_>
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+        <_>
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+        <_>
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+        <_>
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+        <_>
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+        <_>
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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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+        <_>
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+    <!-- stage 11 -->
+    <_>
+      <maxWeakCount>9</maxWeakCount>
+      <stageThreshold>-1.1183819770812988e+000</stageThreshold>
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+        <_>
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+        <_>
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+        <_>
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+        <_>
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+        <_>
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+        <_>
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+        <_>
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+    <!-- stage 12 -->
+    <_>
+      <maxWeakCount>12</maxWeakCount>
+      <stageThreshold>-1.5434337854385376e+000</stageThreshold>
+      <weakClassifiers>
+        <_>
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+        <_>
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+        <_>
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+        <_>
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+    <!-- stage 13 -->
+    <_>
+      <maxWeakCount>12</maxWeakCount>
+      <stageThreshold>-1.4440233707427979e+000</stageThreshold>
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+        <_>
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+        <_>
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+        <_>
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+        <_>
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+    <!-- stage 14 -->
+    <_>
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+      <stageThreshold>-1.2532578706741333e+000</stageThreshold>
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lbpcascades/lbpcascade_frontalface.xml

+<?xml version="1.0"?>
+<!--
+number of positive samples 3000
+number of negative samples 1500
+-->
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+    <!-- stage 7 -->
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+        <!-- tree 5 -->
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+    <!-- stage 8 -->
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+    <!-- stage 9 -->
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+    <!-- stage 10 -->
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+    <!-- stage 11 -->
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+    <!-- stage 12 -->
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+    <!-- stage 13 -->
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lbpcascades/lbpcascade_profileface.xml

+<?xml version="1.0"?>
+<!--
+This is 20x34 detector of profile faces using LBP features.
+It was created by Attila Novak during GSoC 2012.
+Note that the detector only detects faces rotated to the right,
+so you may want to run it on the original and on
+the flipped image to detect different profile faces.
+-->
+<opencv_storage>
+<cascade>
+  <stageType>BOOST</stageType>
+  <featureType>LBP</featureType>
+  <height>34</height>
+  <width>20</width>
+  <stageParams>
+    <boostType>GAB</boostType>
+    <minHitRate>9.9500000476837158e-001</minHitRate>
+    <maxFalseAlarm>3.0000001192092896e-001</maxFalseAlarm>
+    <weightTrimRate>9.4999999999999996e-001</weightTrimRate>
+    <maxDepth>1</maxDepth>
+    <maxWeakCount>100</maxWeakCount></stageParams>
+  <featureParams>
+    <maxCatCount>256</maxCatCount>
+    <featSize>1</featSize></featureParams>
+  <stageNum>16</stageNum>
+  <stages>
+    <!-- stage 0 -->
+    <_>
+      <maxWeakCount>4</maxWeakCount>
+      <stageThreshold>-5.9480339288711548e-001</stageThreshold>
+      <weakClassifiers>
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+          <leafValues>
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+    <!-- stage 1 -->
+    <_>
+      <maxWeakCount>6</maxWeakCount>
+      <stageThreshold>-5.4879629611968994e-001</stageThreshold>
+      <weakClassifiers>
+        <_>
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+        <_>
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+        <_>
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+          <leafValues>
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+    <!-- stage 2 -->
+    <_>
+      <maxWeakCount>4</maxWeakCount>
+      <stageThreshold>-4.3886357545852661e-001</stageThreshold>
+      <weakClassifiers>
+        <_>
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+        <_>
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+    <!-- stage 3 -->
+    <_>
+      <maxWeakCount>6</maxWeakCount>
+      <stageThreshold>-4.6629825234413147e-001</stageThreshold>
+      <weakClassifiers>
+        <_>
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+          <leafValues>
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+    <!-- stage 4 -->
+    <_>
+      <maxWeakCount>8</maxWeakCount>
+      <stageThreshold>-6.0948312282562256e-001</stageThreshold>
+      <weakClassifiers>
+        <_>
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+    <!-- stage 5 -->
+    <_>
+      <maxWeakCount>8</maxWeakCount>
+      <stageThreshold>-5.4387503862380981e-001</stageThreshold>
+      <weakClassifiers>
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+    <!-- stage 6 -->
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+      <maxWeakCount>8</maxWeakCount>
+      <stageThreshold>-6.3195121288299561e-001</stageThreshold>
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+    <!-- stage 7 -->
+    <_>
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+      <stageThreshold>-5.9874147176742554e-001</stageThreshold>
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+    <!-- stage 8 -->
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+    <!-- stage 9 -->
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+</opencv_storage>

lbpcascades/lbpcascade_silverware.xml

+<?xml version="1.0"?>
+<!--
+    This is 12x80 detector of the silverware (forks, spoons, knives) using LBP features.
+    It was created by Attila Novak during GSoC 2012.
+    Note that the detector only detects vertically oriented silverware,
+    so you should care of the proper image orientation
+    (probably should run detector several times).
+    It also assumes the "top view" when the camera optical axis is orthogonal to the table plane.
+-->
+<opencv_storage>
+<cascade>
+  <stageType>BOOST</stageType>
+  <featureType>LBP</featureType>
+  <height>80</height>
+  <width>12</width>
+  <stageParams>
+    <boostType>GAB</boostType>
+    <minHitRate>9.9500000476837158e-001</minHitRate>
+    <maxFalseAlarm>3.0000001192092896e-001</maxFalseAlarm>
+    <weightTrimRate>9.4999999999999996e-001</weightTrimRate>
+    <maxDepth>1</maxDepth>
+    <maxWeakCount>100</maxWeakCount></stageParams>
+  <featureParams>
+    <maxCatCount>256</maxCatCount>
+    <featSize>1</featSize></featureParams>
+  <stageNum>16</stageNum>
+  <stages>
+    <!-- stage 0 -->
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+    <!-- stage 1 -->
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+    <!-- stage 2 -->
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+    <!-- stage 3 -->
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+    <!-- stage 4 -->
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test.py

+import os
+import os.path
+import numpy as np
+import cv2
+import time
+
+
+cap = cv2.VideoCapture(0)
+# cap.set(3, 1080);
+cap.set(4, 720);
+face_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_default.xml')
+eye_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_eye.xml')
+
+
+while(1):
+    # get a frame
+    ret, img = cap.read()
+    # show a frame
+    # gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    # ret2, binary = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
+    # contours, hierarchy = cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
+    # cv2.drawContours(frame, contours, -1, (0, 0, 255), 3)
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
+    # img = cv2.rectangle(img, (0, 0), (10, 10), (255, 0, 0), 2)
+    imgOrigin = cv2.rectangle(img, (0, 0), (10, 10), (255, 0, 0), 2)
+    for (x, y, w, h) in faces:
+        cv2.imwrite("cap_1.jpg", img);
+        img = cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
+        # roi_gray = gray[y:y + h, x:x + w]
+        # roi_color = img[y:y + h, x:x + w]
+        # eyes = eye_cascade.detectMultiScale(roi_gray)
+
+
+    cv2.imshow("capture", img)
+    time.sleep(0.1)
+    k = cv2.waitKey(1) & 0xFF;
+    if k == ord('a'):
+        print( cap.get(3));
+        print( cap.get(4));
+        # print(cap.get(4));
+    if k == ord('s'):
+
+        print( cap.get(3));
+        print( cap.get(4));
+        # print(cap.get(4));
+    elif k == ord('q'):
+        break
+
+
+cap.release()
+cv2.destroyAllWindows()
+
+
+
+
+
+
+# for (x, y, w, h) in faces:
+#     img = cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
+#     roi_gray = gray[y:y + h, x:x + w]
+#     roi_color = img[y:y + h, x:x + w]
+#     eyes = eye_cascade.detectMultiScale(roi_gray)
+#
+#     for (ex, ey, ew, eh) in eyes:
+#         cv2.rectangle(roi_color, (ex, ey), (ex + ew, ey + eh), (0, 255, 0), 2)
+
+# cv2.imshow('img', img)
+# cv2.waitKey(0)
+# cv2.destroyAllWindows()
+

testcamera.py

+__author__ = 'yinchuandong'
+
+import cv2
+from face import FaceDetect
+
+model = FaceDetect()
+
+capture = cv2.VideoCapture(0)
+size = (int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH)),
+        int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT)))
+# video = cv2.VideoWriter("VideoTest.avi", cv2.cv.CV_FOURCC('I', '4', '2', '0'), 30, size)
+# print capture.isOpened()
+num = 0
+while True:
+    ret, img = capture.read()
+    imgArr = model.detect(img)
+    for img in imgArr:
+        # video.write(img)
+        cv2.imshow('Video', img)
+        key = cv2.waitKey(1)
+        cv2.imwrite('camera/%s.jpg' % (str(num)), img)
+        num = num + 1
+        if key == ord('q'):
+            break
+
+# video.release()
+capture.release()
+cv2.destroyAllWindows()
\ No newline at end of file