data-feat-model framework almost established.

Chunk
1 parent b93fbc48
Showing 26 changed files with 776 additions and 115 deletions Show diff stats
common.py
common.pyc
mdata/CV.py
mdata/CV.pyc
mdata/MSR.py
mdata/MSR.pyc
mdata/__init__.py
mdata/__init__.pyc
mfeat/HOG.pyc
mfeat/IntraBlockDiff.pyc
mfeat/__init__.pyc
mjpeg/__init__.pyc
mjpeg/base.pyc
mjpeg/dct.pyc
mmodel/SVM.py
mmodel/SVM.pyc
mmodel/__init__.py
mmodel/__init__.pyc
msteg/__init__.pyc
msteg/steganalysis/MPB.pyc
@@ -12,6 +12,8 @@ import time
 import StringIO
 import ConfigParser
+import numpy as np
+
 class Timer():
     def __init__(self):
@@ -134,19 +136,18 @@ def bits2bytes(arry_bits):
     """
     str_bits = ''.join(map(str, arry_bits))
     arry_bytes = [int(str_bits[i:i + 8], 2) for i in range(0, len(str_bits), 8)]
-    return np.array(arry_bytes,dtype=np.uint8).ravel()
-
-
+    return np.array(arry_bytes, dtype=np.uint8).ravel()
 def test_grammer():
-        a = 'fsaf'
-        b = ['dasf', 'dff']
-        c = 'dgfsfdg'
-        # print a + b
-        print [a] + b  # ['fsaf', 'dasf', 'dff']
-        print [a] + [b]  # ['fsaf', ['dasf', 'dff']]
-        print [a] + [c]  # ['fsaf', 'dgfsfdg']
+    a = 'fsaf'
+    b = ['dasf', 'dff']
+    c = 'dgfsfdg'
+    # print a + b
+    print [a] + b  # ['fsaf', 'dasf', 'dff']
+    print [a] + [b]  # ['fsaf', ['dasf', 'dff']]
+    print [a] + [c]  # ['fsaf', 'dgfsfdg']
+
 if __name__ == '__main__':
     timer = Timer()
 __author__ = 'chunk'
 from mdata import *
-from mfeat import HOG
+from mfeat import HOG, IntraBlockDiff
 import os, sys
 from PIL import Image
@@ -138,19 +138,40 @@ class DataCV(DataDumperBase):
             pass
-    def get_feat(self, feattype='hog'):
+    def get_feat(self, image, feattype='hog', **kwargs):
+        size = kwargs.get('size', (48, 48))
-        dict_tagbuf = {}
+        if feattype == 'hog':
+            feater = HOG.FeatHOG(size=size)
+        elif feattype == 'ibd':
+            feater = IntraBlockDiff.FeatIntraBlockDiff()
+        else:
+            raise Exception("Unknown feature type!")
+
+        desc = feater.feat(image)
+
+        return desc
+
+    def extract_feat(self, feattype='hog'):
+
+        if feattype == 'hog':
+            feater = HOG.FeatHOG(size=(48, 48))
+        elif feattype == 'ibd':
+            feater = IntraBlockDiff.FeatIntraBlockDiff()
+        else:
+            raise Exception("Unknown feature type!")
+
+        list_image = []
         with open(self.list_file, 'rb') as tsvfile:
             tsvfile = csv.reader(tsvfile, delimiter='\t')
             for line in tsvfile:
-                dict_tagbuf[line[0] + '.jpg'] = line[1]
+                list_image.append(line[0])
         dict_featbuf = {}
-        for imgname, imgtag in dict_tagbuf.items():
+        for imgname in list_image:
             # if imgtag == 'True':
-            image = self.img_dir + imgname[:3] + '/' + imgname[3:]
-            desc = HOG.FeatHOG().feat(image, size=(48, 48))
+            image = self.img_dir + imgname[:3] + '/' + imgname[3:] + '.jpg'
+            desc = feater.feat(image)
             dict_featbuf[imgname] = desc
         for imgname, desc in dict_featbuf.items():
@@ -184,3 +205,33 @@ class DataCV(DataDumperBase):
             raise
             pass
+    def load_data(self, mode='local', feattype='hog'):
+        INDEX = []
+        X = []
+        Y = []
+
+        if mode == "local":
+
+            dict_tagbuf = {}
+            with open(self.list_file, 'rb') as tsvfile:
+                tsvfile = csv.reader(tsvfile, delimiter='\t')
+                for line in tsvfile:
+                    imgname = line[0] + '.jpg'
+                    dict_tagbuf[imgname] = line[1]
+
+            dict_dataset = {}
+            for path, subdirs, files in os.walk(self.feat_dir):
+                for name in files:
+                    featpath = os.path.join(path, name)
+                    with open(featpath, 'rb') as featfile:
+                        imgname = path.split('/')[-1] + name.replace('.' + feattype, '.jpg')
+                        dict_dataset[imgname] = json.loads(featfile.read())
+
+            for imgname, tag in dict_tagbuf.items():
+                tag = 1 if tag == 'True' else 0
+                INDEX.append(imgname)
+                X.append(dict_dataset[imgname])
+                Y.append(tag)
+
+        return X, Y
+
 __author__ = 'chunk'
 from mdata import *
-from mfeat import *
+from mfeat import HOG, IntraBlockDiff
 import os, sys
 import base64
@@ -146,8 +146,73 @@ class DataMSR(DataDumperBase):
             raise
             pass
-    def get_feat(self, feattype):
-        pass
-    def store_feat(self, feattype):
-        pass
+    def get_feat(self, image, feattype='ibd', **kwargs):
+        size = kwargs.get('size', (48, 48))
+
+        if feattype == 'hog':
+            feater = HOG.FeatHOG(size=size)
+        elif feattype == 'ibd':
+            feater = IntraBlockDiff.FeatIntraBlockDiff()
+        else:
+            raise Exception("Unknown feature type!")
+
+        desc = feater.feat(image)
+
+        return desc
+
+    def extract_feat(self, feattype='ibd'):
+
+        if feattype == 'hog':
+            feater = HOG.FeatHOG(size=(48, 48))
+        elif feattype == 'ibd':
+            feater = IntraBlockDiff.FeatIntraBlockDiff()
+        else:
+            raise Exception("Unknown feature type!")
+
+        list_image = []
+        with open(self.list_file, 'rb') as tsvfile:
+            tsvfile = csv.reader(tsvfile, delimiter='\t')
+            for line in tsvfile:
+                list_image.append(line[0])
+
+        dict_featbuf = {}
+        for imgname in list_image:
+            # if imgtag == 'True':
+            image = self.img_dir + imgname
+            desc = feater.feat(image)
+            dict_featbuf[imgname] = desc
+
+        for imgname, desc in dict_featbuf.items():
+            # print imgname, desc
+            dir = self.feat_dir + imgname[:4]
+            if not os.path.exists(dir):
+                os.makedirs(dir)
+            featpath = dir + imgname[4:].split('.')[0] + '.' + feattype
+            with open(featpath, 'wb') as featfile:
+                featfile.write(json.dumps(desc.tolist()))
+
+    def store_feat(self, feattype='ibd'):
+        if self.table == None:
+            self.table = self.get_table()
+
+        dict_featbuf = {}
+        for path, subdirs, files in os.walk(self.feat_dir):
+            for name in files:
+                featpath = os.path.join(path, name)
+                # print featpath
+                with open(featpath, 'rb') as featfile:
+                    imgname = path.split('/')[-1] + name.replace('.' + feattype, '.jpg')
+                    dict_featbuf[imgname] = featfile.read()
+
+        try:
+            # with self.table.batch(batch_size=5000) as b:
+            # for imgname, featdesc in dict_featbuf.items():
+            # b.put(imgname, {'cf_feat:' + feattype: featdesc})
+            with self.table.batch(batch_size=5000) as b:
+                for key, data in self.table.scan():
+                    b.put(key, {'cf_feat:' + feattype: dict_featbuf[key]})
+
+        except ValueError:
+            raise
+            pass
@@ -9,6 +9,7 @@ class DataDumperBase(object):
     Base class for image data dumping & retrieving.
     A regular directory pattern would be like this:
+        dst
         ├── Dev (category)
             ├── file-tag.tsv (list_file)
             │
@@ -50,8 +51,9 @@ class DataDumperBase(object):
             └── ...
-    convention:
-        'img' for image file data while 'image' for file path;
+    Convention:
+
+        'im' or 'img' is for image file data while 'image' or 'image_path' for file path;
     """
@@ -86,10 +88,21 @@ class DataDumperBase(object):
     def store_tag(self, feattype):
         pass
-    def get_feat(self, feattype):
+    def store_feat(self, feattype):
         pass
-    def store_feat(self, feattype):
+
+    def get_feat(self, image, feattype):
+        pass
+
+    def extract_feat(self, feattype):
         pass
+    def load_data(self, mode):
+        pass
+
+
+
+
+
@@ -28,113 +28,106 @@ class ModelSVM(ModelBase):
         ModelBase.__init__(self)
-    def load(self, file, mode='local'):
-        timer = Timer()
-        INDEX = []
-        X = []
-        Y = []
-
-        base_dir = '/home/hadoop/data/HeadShoulder/'
-        dir = base_dir + 'Img/'
-        maplst = dir + 'images_map_Train.tsv'
-        dict_tagbuf = {}
-        with open(maplst, 'rb') as tsvfile:
-            tsvfile = csv.reader(tsvfile, delimiter='\t')
-            for line in tsvfile:
-                imgname = line[0] + '.jpg'
-                dict_tagbuf[imgname] = line[1]
-
-        dir = base_dir + 'Feat/'
-        dict_dataset = {}
+    def _train_sk(self, X, Y):
+        lin_clf = svm.LinearSVC()
+        lin_clf.fit(X, Y)
+        with open('res/svm_sk.model', 'wb') as modelfile:
+            model = pickle.dump(lin_clf, modelfile)
-        timer.mark()
-        for path, subdirs, files in os.walk(dir + 'Train/'):
-            for name in files:
-                featpath = os.path.join(path, name)
-                # print featpath
-                with open(featpath, 'rb') as featfile:
-                    imgname = path.split('/')[-1] + name.replace('.hog', '.jpg')
-                    dict_dataset[imgname] = json.loads(featfile.read())
-        timer.report()  # 5.122354s
+        self.model = lin_clf
-        timer.mark()
-        for imgname, tag in dict_tagbuf.items():
-            tag = 1 if tag == 'True' else 0
-            INDEX.append(imgname)
-            X.append(dict_dataset[imgname])
-            Y.append(tag)
-        timer.report()  # 0.047625s
+        return lin_clf
-        return X, Y
+    def _predict_sk(self, feat, model=None):
+        """N.B. sklearn.svm.base.predict :
+            Perform classification on samples in X.
+                Parameters
+                ----------
+                X : {array-like, sparse matrix}, shape = [n_samples, n_features]
-    def model_svm_train_sk(self,X, Y):
-        timer = Timer()
-        timer.mark()
-        lin_clf = svm.LinearSVC()
-        lin_clf.fit(X, Y)
-        with open('res/tmp.model', 'wb') as modelfile:
-            model = pickle.dump(lin_clf, modelfile)
+                Returns
+                -------
+                y_pred : array, shape = [n_samples]
+                    Class labels for samples in X.
+        """
+        if model is None:
+            if self.model != None:
+                model = self.model
+            else:
+                with open('res/svm_sk.model', 'rb') as modelfile:
+                    model = pickle.load(modelfile)
-        timer.report()
+        return model.predict(feat)
-        return lin_clf
+    def _test_sk(self, X, Y, model=None):
+        if model is None:
+            if self.model != None:
+                model = self.model
+            else:
+                with open('res/svm_sk.model', 'rb') as modelfile:
+                    model = pickle.load(modelfile)
+        result_Y = np.array(self._predict_sk(X, model))
-    def model_svm_predict_sk(self,image, clf=None):
-        if clf is None:
-            with open('res/tmp.model', 'rb') as modelfile:
-                clf = pickle.load(modelfile)
-        desc = feat_HOG(image, size=(48, 48))
-        return clf.predict(desc)
+        print result_Y == Y
+        return np.mean(Y == result_Y)
-    def model_svm_train_cv(self,X, Y):
+    def _train_cv(self, X, Y):
         svm_params = dict(kernel_type=cv2.SVM_LINEAR,
                           svm_type=cv2.SVM_C_SVC,
                           C=2.67, gamma=5.383)
+        X, Y = np.array(X, dtype=np.float32), np.array(Y, dtype=np.float32)
+
         timer = Timer()
         timer.mark()
         svm = cv2.SVM()
         svm.train(X, Y, params=svm_params)
-        svm.save('res/svm_data.model')
+        svm.save('res/svm_cv.model')
+
+        self.model = svm
         return svm
-    def model_svm_predict_cv(self,image, svm=None):
-        if svm is None:
-            svm = cv2.SVM()
-            svm.load('res/svm_data.model')
+    def _predict_cv(self, feat, model=None):
+        if model is None:
+            if self.model != None:
+                model = self.model
+            else:
+                with open('res/svm_cv.model', 'rb') as modelfile:
+                    model = pickle.load(modelfile)
+        feat = np.array(feat, dtype=np.float32)
+
+        return model.predict(feat)
+
-        desc = feat_HOG(image, size=(48, 48))
-        desc = np.float32(np.asarray(desc))
-        return svm.predict(desc)
+    def _test_cv(self, X, Y, model=None):
+        if model is None:
+            if self.model != None:
+                model = self.model
+            else:
+                with open('res/svm_cv.model', 'rb') as modelfile:
+                    model = pickle.load(modelfile)
+        X, Y = np.array(X, dtype=np.float32), np.array(Y, dtype=np.float32)
-    def test_sk(self):
-        X, Y = load_data()
+        # result_Y = np.array([self._predict_cv(x, model) for x in X])
+        result_Y = np.array(model.predict_all(X)).ravel()
-        clf = model_svm_train_sk(X, Y)
-        for path, subdirs, files in os.walk('data/467/'):
-            for name in files:
-                imgpath = os.path.join(path, name)
-                print name, model_svm_predict_sk(imgpath, clf)
-        print clf.coef_.shape, clf.coef_
+        return np.mean(Y == result_Y)
-    def test_cv(self):
-        X, Y = load_data()
-        X, Y = np.float32(np.asarray(X)), np.float32(np.asarray(Y))
-        print X, Y
-        svm = model_svm_train_cv(X, Y)
-        for path, subdirs, files in os.walk('data/467/'):
-            for name in files:
-                imgpath = os.path.join(path, name)
-                print name, model_svm_predict_cv(imgpath, svm)
+    def train(self, X, Y):
+        return self._train_cv(X, Y)
+    def predict(self, feat, model=None):
+        return self._predict_cv(feat, model)
+    def test(self, X, Y, model=None):
+        return self._test_cv(X, Y, model)
@@ -2,19 +2,17 @@ __author__ = &#39;chunk&#39;
 __all__ = ['ModelBase', ]
+
 class ModelBase(object):
     def __init__(self):
-        pass
-
-    def load(self, mode, file):
-        pass
+        self.model = None
-    def train(self):
+    def train(self, X, Y):
         pass
-    def test(self):
+    def predict(self, feat, model=None):
         pass
-    def predict(self):
+    def test(self, X, Y, model=None):
         pass
@@ -0,0 +1,319 @@
+%YAML:1.0
+my_svm: !!opencv-ml-svm
+   svm_type: C_SVC
+   kernel: { type:LINEAR }
+   C: 2.6699999999999999e+00
+   term_criteria: { epsilon:1.1920928955078125e-07, iterations:1000 }
+   var_all: 900
+   var_count: 900
+   class_count: 2
+   class_labels: !!opencv-matrix
+      rows: 1
+      cols: 2
+      dt: i
+      data: [ 0, 1 ]
+   sv_total: 1
+   support_vectors:
+      - [ 1.00635447e-01, 3.80848616e-01, -3.61999944e-02,
+          6.16463959e-01, -2.13634253e-01, 5.80662847e-01,
+          1.32952228e-01, 6.20944738e-01, -1.50688276e-01,
+          -3.70861590e-01, -5.90423107e-01, 4.63727176e-01,
+          -2.05607280e-01, 2.41474081e-02, -1.48096114e-01,
+          -3.15087080e-01, -4.53733578e-02, -2.70828068e-01,
+          -2.03975633e-01, 2.82555878e-01, 9.47522465e-03,
+          -1.56084165e-01, 2.07339182e-01, -5.33106327e-01,
+          -1.25123873e-01, -2.21039921e-01, 2.36624721e-02,
+          1.96492359e-01, -3.15506011e-01, -8.20042118e-02,
+          -6.43781200e-02, -3.41593117e-01, 2.09613755e-01,
+          -3.63092989e-01, 5.35438620e-02, 4.83716935e-01,
+          2.24727899e-01, -1.06053203e-01, 2.59556502e-01,
+          1.01173162e-01, -1.20247312e-01, 6.69345260e-02,
+          3.92155111e-01, 6.04222454e-02, 2.99450070e-01, 1.96121454e-01,
+          -2.54563857e-02, 3.81424576e-01, 5.82436204e-01,
+          -3.16342831e-01, 1.58296034e-01, 1.53645635e-01,
+          2.66968310e-01, -2.09024653e-01, -1.76746130e-01,
+          -4.63609487e-01, 2.29493454e-01, -3.91426325e-01,
+          4.17520814e-02, -4.45464216e-02, -2.52456944e-02,
+          -1.75413117e-01, -1.89545355e-03, -4.60857272e-01,
+          6.53700605e-02, -3.92839074e-01, -1.48130864e-01,
+          3.35914567e-02, -4.86958064e-02, 7.31010810e-02,
+          5.15685491e-02, 4.33498286e-02, -6.18374608e-02,
+          4.24069226e-01, -1.12407561e-02, -5.09570315e-02,
+          -2.56250173e-01, -1.85945898e-01, -8.57309029e-02,
+          -6.62742183e-02, 1.88151091e-01, -1.12024345e-01,
+          8.67723599e-02, 4.68617022e-01, -4.08511579e-01,
+          4.94396180e-01, -2.02981289e-03, 1.70159534e-01,
+          -1.20491460e-01, 6.62366748e-02, 2.55558491e-01,
+          1.52796045e-01, 1.26943201e-01, 2.59416312e-01, 4.79911327e-01,
+          -5.69163524e-02, -2.66524225e-01, 1.56104997e-01,
+          -2.32822329e-01, 6.70313761e-02, -1.03652611e-01,
+          6.28767833e-02, -3.24732393e-01, -4.04358841e-02,
+          3.57844740e-01, -1.59993663e-01, 1.35739282e-01,
+          -1.66464984e-01, 2.23796099e-01, 1.07447878e-01,
+          -1.44861355e-01, -1.36439964e-01, -1.07676782e-01,
+          -2.58990467e-01, 1.71149686e-01, -6.26204908e-02,
+          2.75087565e-01, -1.36915579e-01, 3.37498523e-02,
+          -3.26198280e-01, -8.42690691e-02, 6.43373579e-02,
+          2.99615502e-01, 3.18146795e-01, 1.13480799e-01, 3.62246484e-01,
+          -4.33289975e-01, 1.07924186e-01, 3.01304795e-02,
+          -1.77692845e-01, 3.31654578e-01, 3.22302759e-01,
+          2.99436580e-02, 2.93032467e-01, 2.17150301e-01,
+          -6.47793710e-02, 6.06899321e-01, -2.77149528e-01,
+          3.97809565e-01, -1.31793424e-01, -5.68050444e-02,
+          -9.24519729e-03, -3.72854285e-02, 1.62220318e-02,
+          2.68669844e-01, -1.95947185e-01, 3.97543490e-01,
+          -3.82243127e-01, -1.73717774e-02, -2.06354544e-01,
+          1.30633742e-01, 1.71459280e-02, -2.50614416e-02,
+          -4.15923506e-01, -1.01500526e-01, 3.00851882e-01,
+          1.15127452e-01, 4.06373411e-01, -2.02237535e-02,
+          -3.17377858e-02, -4.38075781e-01, 2.82054126e-01,
+          2.44007245e-01, 1.02171618e-02, -4.09222037e-01,
+          -1.82712048e-01, 1.41949460e-01, 2.09071502e-01,
+          3.96968752e-01, -4.83931676e-02, 3.59820187e-01,
+          5.31860851e-02, -3.35021585e-01, -7.78729934e-03,
+          5.17243564e-01, 3.03447902e-01, 1.55488417e-01,
+          -4.34996575e-01, -1.17763869e-01, -2.11806029e-01,
+          -1.10544242e-01, -2.30027605e-02, -9.00872946e-02,
+          -4.62244079e-02, -8.72705132e-02, -9.02773887e-02,
+          4.49247569e-01, 5.96865341e-02, -3.21723908e-01,
+          -1.42104374e-02, 6.55051321e-02, -8.89712796e-02,
+          -8.20277706e-02, -8.21489915e-02, 1.18508145e-01,
+          1.59893055e-02, -6.40436560e-02, -2.71267563e-01,
+          -1.10220641e-01, -5.91016822e-02, -4.78215665e-02,
+          -5.37244856e-01, -1.69124529e-01, -5.39994657e-01,
+          2.37307459e-01, 3.48656982e-01, -2.44099542e-01,
+          -3.65893334e-01, -4.60833237e-02, 1.98111981e-01,
+          4.00575042e-01, -2.18251944e-01, -2.97822505e-01,
+          -1.58445835e-02, 1.24876015e-01, 2.21260786e-01,
+          -4.74443519e-03, -1.11860886e-01, 1.94061294e-01,
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+   decision_functions:
+      -
+         sv_count: 1
+         rho: 1.2788952397013760e+00
+         alpha: [ 1. ]
+         index: [ 0 ]
@@ -0,0 +1,176 @@
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+p69
+tp70
+bsS'class_weight'
+p71
+NsS'intercept_scaling'
+p72
+I1
+sb.
 \ No newline at end of file
 __author__ = 'chunk'
+from common import *
+
 from mdata import MSR, CV
+
 def test_MSR():
     dmsr = MSR.DataMSR()
     # msrd.format()
     # msrd.build_list()
-    dmsr.store_image()
-    dmsr.store_tag()
+    # dmsr.store_image()
+    # dmsr.store_tag()
+
+    # dmsr.extract_feat(feattype='ibd')
+    dmsr.store_feat(feattype='ibd')
+
 def test_CV():
     dcv = CV.DataCV()
-    #dcv.format()
-    #dcv.build_list()
+    # dcv.format()
+    # dcv.build_list()
     #dcv.get_feat()
-    dcv.store_feat()
+    # dcv.extract_feat()
+    print dcv.get_feat("/home/hadoop/data/HeadShoulder/dst/Train/Img/132/7c5fe33bd194fc1ae7b0023956ebd.jpg", 'ibd')
+    X, Y = dcv.load_data()
+    print len(X), len(Y)
+
 if __name__ == '__main__':
+    # test_MSR()
     test_CV()
-    print 'helllo '
+
+
+    print 'helllo'
 __author__ = 'chunk'
+from common import *
+
 from mfeat import HOG, IntraBlockDiff
@@ -0,0 +1,29 @@
+__author__ = 'chunk'
+
+from common import *
+
+from mdata import MSR, CV
+from mmodel import SVM
+from mfeat import HOG
+
+timer = Timer()
+
+
+def test_SVM_CV():
+    dcv = CV.DataCV()
+    X, Y = dcv.load_data()
+    msvm = SVM.ModelSVM()
+    msvm.train(X, Y)
+
+    for path, subdirs, files in os.walk('data/467/'):
+        for name in files:
+            imgpath = os.path.join(path, name)
+            feat = dcv.get_feat(imgpath, 'hog')
+            print name, msvm.predict(feat)
+
+    print msvm.test(X, Y)  # 0.948892561983 for svm_cv ,0.989024793388 for svm_sk
+
+
+if __name__ == '__main__':
+    test_SVM_CV()
+    print 'helllo'
1	__author__ = 'chunk'	1	__author__ = 'chunk'
2		2
3	from mdata import *	3	from mdata import *
4	-from mfeat import HOG	4	+from mfeat import HOG, IntraBlockDiff
5		5
6	import os, sys	6	import os, sys
7	from PIL import Image	7	from PIL import Image
	@@ -138,19 +138,40 @@ class DataCV(DataDumperBase):		@@ -138,19 +138,40 @@ class DataCV(DataDumperBase):
138	pass	138	pass
139		139
140		140
141	- def get_feat(self, feattype='hog'):	141	+ def get_feat(self, image, feattype='hog', **kwargs):
		142	+ size = kwargs.get('size', (48, 48))
142		143
143	- dict_tagbuf = {}	144	+ if feattype == 'hog':
		145	+ feater = HOG.FeatHOG(size=size)
		146	+ elif feattype == 'ibd':
		147	+ feater = IntraBlockDiff.FeatIntraBlockDiff()
		148	+ else:
		149	+ raise Exception("Unknown feature type!")
		150	+
		151	+ desc = feater.feat(image)
		152	+
		153	+ return desc
		154	+
		155	+ def extract_feat(self, feattype='hog'):
		156	+
		157	+ if feattype == 'hog':
		158	+ feater = HOG.FeatHOG(size=(48, 48))
		159	+ elif feattype == 'ibd':
		160	+ feater = IntraBlockDiff.FeatIntraBlockDiff()
		161	+ else:
		162	+ raise Exception("Unknown feature type!")
		163	+
		164	+ list_image = []
144	with open(self.list_file, 'rb') as tsvfile:	165	with open(self.list_file, 'rb') as tsvfile:
145	tsvfile = csv.reader(tsvfile, delimiter='\t')	166	tsvfile = csv.reader(tsvfile, delimiter='\t')
146	for line in tsvfile:	167	for line in tsvfile:
147	- dict_tagbuf[line[0] + '.jpg'] = line[1]	168	+ list_image.append(line[0])
148		169
149	dict_featbuf = {}	170	dict_featbuf = {}
150	- for imgname, imgtag in dict_tagbuf.items():	171	+ for imgname in list_image:
151	# if imgtag == 'True':	172	# if imgtag == 'True':
152	- image = self.img_dir + imgname[:3] + '/' + imgname[3:]
153	- desc = HOG.FeatHOG().feat(image, size=(48, 48))	173	+ image = self.img_dir + imgname[:3] + '/' + imgname[3:] + '.jpg'
		174	+ desc = feater.feat(image)
154	dict_featbuf[imgname] = desc	175	dict_featbuf[imgname] = desc
155		176
156	for imgname, desc in dict_featbuf.items():	177	for imgname, desc in dict_featbuf.items():
	@@ -184,3 +205,33 @@ class DataCV(DataDumperBase):		@@ -184,3 +205,33 @@ class DataCV(DataDumperBase):
184	raise	205	raise
185	pass	206	pass
186		207
		208	+ def load_data(self, mode='local', feattype='hog'):
		209	+ INDEX = []
		210	+ X = []
		211	+ Y = []
		212	+
		213	+ if mode == "local":
		214	+
		215	+ dict_tagbuf = {}
		216	+ with open(self.list_file, 'rb') as tsvfile:
		217	+ tsvfile = csv.reader(tsvfile, delimiter='\t')
		218	+ for line in tsvfile:
		219	+ imgname = line[0] + '.jpg'
		220	+ dict_tagbuf[imgname] = line[1]
		221	+
		222	+ dict_dataset = {}
		223	+ for path, subdirs, files in os.walk(self.feat_dir):
		224	+ for name in files:
		225	+ featpath = os.path.join(path, name)
		226	+ with open(featpath, 'rb') as featfile:
		227	+ imgname = path.split('/')[-1] + name.replace('.' + feattype, '.jpg')
		228	+ dict_dataset[imgname] = json.loads(featfile.read())
		229	+
		230	+ for imgname, tag in dict_tagbuf.items():
		231	+ tag = 1 if tag == 'True' else 0
		232	+ INDEX.append(imgname)
		233	+ X.append(dict_dataset[imgname])
		234	+ Y.append(tag)
		235	+
		236	+ return X, Y
		237	+
@@ -0,0 +1,319 @@		@@ -0,0 +1,319 @@
	1	+%YAML:1.0
	2	+my_svm: !!opencv-ml-svm
	3	+ svm_type: C_SVC
	4	+ kernel: { type:LINEAR }
	5	+ C: 2.6699999999999999e+00
	6	+ term_criteria: { epsilon:1.1920928955078125e-07, iterations:1000 }
	7	+ var_all: 900
	8	+ var_count: 900
	9	+ class_count: 2
	10	+ class_labels: !!opencv-matrix
	11	+ rows: 1
	12	+ cols: 2
	13	+ dt: i
	14	+ data: [ 0, 1 ]
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	241	+ 1.17361508e-01, -2.41528228e-01, -5.12285307e-02,
	242	+ -5.40408939e-02, 2.95093685e-01, -1.56089067e-01,
	243	+ -6.90469146e-01, -3.65456357e-03, 7.04098493e-02,
	244	+ -9.66299176e-02, -5.12030572e-02, 2.51964360e-01,
	245	+ 3.58488597e-02, 6.95013642e-01, 1.39234260e-01, 3.39805812e-01,
	246	+ -1.59381479e-01, 4.17184323e-01, -8.45175609e-02,
	247	+ 3.42082322e-01, 3.35495412e-01, -6.75573274e-02,
	248	+ -1.55734196e-01, 3.24536055e-01, -1.03536710e-01,
	249	+ 3.33302289e-01, -1.24088511e-01, 2.30881527e-01,
	250	+ -7.11121336e-02, -1.00211062e-01, 1.86521187e-01,
	251	+ 8.83157849e-02, 2.13061988e-01, -1.84731558e-01,
	252	+ 6.97695464e-02, 4.76718128e-01, -1.30899161e-01,
	253	+ -3.95139813e-01, 1.07154146e-01, -2.48354554e-01,
	254	+ -3.61816287e-01, 4.31138515e-01, -1.06623344e-01,
	255	+ -4.58101928e-01, -2.23259002e-01, 4.54964116e-02,
	256	+ -4.71347183e-01, 2.82200336e-01, -1.99851170e-01,
	257	+ 3.69985849e-02, 4.05309796e-01, -1.72780603e-02,
	258	+ 3.40874881e-01, -2.75685996e-01, 2.43859753e-01,
	259	+ 4.10324723e-01, 5.24578877e-02, 8.00840259e-02,
	260	+ -6.86981604e-02, 4.91635621e-01, -1.18908718e-01,
	261	+ -6.08145036e-02, -1.75886080e-01, 2.98066884e-01,
	262	+ -1.31822675e-01, 2.65567780e-01, -2.92782128e-01,
	263	+ -6.57639921e-01, -1.34164244e-01, 9.24002603e-02,
	264	+ -7.00423643e-02, 4.08357009e-02, -4.65526223e-01,
	265	+ -3.02779078e-02, -2.29174390e-01, -5.53770103e-02,
	266	+ 3.84121180e-01, -2.95284539e-01, -1.20111905e-01,
	267	+ -1.89169347e-01, -2.93912515e-02, 3.77128739e-03,
	268	+ 3.34948078e-02, -4.21449482e-01, 2.25277305e-01,
	269	+ -1.37642473e-01, -2.32760031e-02, -4.58599702e-02,
	270	+ -1.47618234e-01, -1.18895071e-02, -1.37809962e-01,
	271	+ 2.92905539e-01, -1.84893534e-02, -4.83157076e-02,
	272	+ 3.05987835e-01, -7.29814321e-02, -3.15960765e-01,
	273	+ -4.60345715e-01, -1.07046194e-01, 1.82802603e-01,
	274	+ -1.80641711e-01, 1.76112682e-01, 1.86231136e-02,
	275	+ 1.37297302e-01, -5.23265779e-01, 3.09553087e-01,
	276	+ 3.14518332e-01, -2.98973918e-01, 2.55388051e-01,
	277	+ 4.02834177e-01, -1.05708390e-01, 5.69513552e-02,
	278	+ -2.22940654e-01, 1.16365194e-01, -1.78536698e-02,
	279	+ -1.79674000e-01, 2.12301463e-01, 5.01181670e-02,
	280	+ 2.33651429e-01, 9.96237546e-02, -1.97195768e-01,
	281	+ 2.23892361e-01, -2.09036842e-01, 6.88388646e-02,
	282	+ 6.18318841e-02, -3.34334970e-01, -2.90876329e-01,
	283	+ 1.17357962e-01, 3.53356063e-01, -1.00525208e-01,
	284	+ 3.05819720e-01, 2.93604583e-01, 5.67990579e-02,
	285	+ -1.26378313e-01, -1.26170188e-01, 1.06249660e-01,
	286	+ -2.14072056e-02, -4.95804809e-02, -1.03100706e-02,
	287	+ 5.12418163e-04, -1.36731952e-01, -7.37648308e-02,
	288	+ 1.96228698e-01, -6.72558323e-02, -1.24358177e-01,
	289	+ -9.39830542e-02, 4.75961983e-01, 9.50917527e-02,
	290	+ 4.15876746e-01, 1.41114518e-01, 1.98907271e-01,
	291	+ -1.99507982e-01, 3.13885242e-01, -3.15521240e-01,
	292	+ 2.42021441e-01, 2.31652319e-01, -4.59780186e-01,
	293	+ 1.88241582e-02, -2.36623734e-01, 3.29642266e-01,
	294	+ -6.21061921e-02, 3.01684141e-01, 5.67733906e-02,
	295	+ 1.78491510e-02, 1.20198198e-01, -4.94834304e-01,
	296	+ -2.78854519e-01, 7.25529809e-03, -1.12130277e-01,
	297	+ 5.98911606e-02, 1.37148827e-01, -1.79479152e-01,
	298	+ 1.54982030e-01, 2.27190051e-02, 1.83249563e-01, 4.51547742e-01,
	299	+ 1.55428752e-01, -5.13685979e-02, 1.12153314e-01,
	300	+ 1.53186172e-01, 2.06783172e-02, -1.60872728e-01,
	301	+ -3.41741025e-01, 2.00449526e-01, 9.83034223e-02,
	302	+ 1.62472233e-01, -1.30619943e-01, -1.64210603e-01,
	303	+ 2.26896137e-01, -3.64758760e-01, -6.58841953e-02,
	304	+ 5.54984808e-01, 3.76383424e-01, 4.08599496e-01,
	305	+ -2.42465481e-01, -2.06514783e-02, 5.33783473e-02,
	306	+ 1.11794574e-02, -1.07440695e-01, 1.55651331e-01,
	307	+ -2.05175787e-01, -1.22273723e-02, -9.18614417e-02,
	308	+ -1.96771454e-02, -2.11976450e-02, 3.37859005e-01,
	309	+ 1.40858158e-01, -2.61175871e-01, -2.54496515e-01,
	310	+ -1.37110084e-01, 2.60234654e-01, 2.59515136e-01,
	311	+ -6.09289110e-02, 1.22784421e-01, -9.18693766e-02,
	312	+ 1.49260059e-01, 4.13678527e-01, 2.82856733e-01,
	313	+ -3.79875749e-01, -6.14341676e-01 ]
	314	+ decision_functions:
	315	+ -
	316	+ sv_count: 1
	317	+ rho: 1.2788952397013760e+00
	318	+ alpha: [ 1. ]
	319	+ index: [ 0 ]
1	__author__ = 'chunk'	1	__author__ = 'chunk'
2		2
		3	+from common import *
		4	+
3	from mfeat import HOG, IntraBlockDiff	5	from mfeat import HOG, IntraBlockDiff
4		6
5		7
@@ -0,0 +1,29 @@		@@ -0,0 +1,29 @@
	1	+__author__ = 'chunk'
	2	+
	3	+from common import *
	4	+
	5	+from mdata import MSR, CV
	6	+from mmodel import SVM
	7	+from mfeat import HOG
	8	+
	9	+timer = Timer()
	10	+
	11	+
	12	+def test_SVM_CV():
	13	+ dcv = CV.DataCV()
	14	+ X, Y = dcv.load_data()
	15	+ msvm = SVM.ModelSVM()
	16	+ msvm.train(X, Y)
	17	+
	18	+ for path, subdirs, files in os.walk('data/467/'):
	19	+ for name in files:
	20	+ imgpath = os.path.join(path, name)
	21	+ feat = dcv.get_feat(imgpath, 'hog')
	22	+ print name, msvm.predict(feat)
	23	+
	24	+ print msvm.test(X, Y) # 0.948892561983 for svm_cv ,0.989024793388 for svm_sk
	25	+
	26	+
	27	+if __name__ == '__main__':
	28	+ test_SVM_CV()
	29	+ print 'helllo'