Fixed bugs with job libs in decoders

Added a smaller background tester

.gitignore

@@ -2,7 +2,8 @@
 
 # CUSTOM FILES
 # We don't need to have the cell.dat file in our VCS, its auto-generated
-cell.dat
+back_dat/
+cell_dat/
 *.db
 
 .idea/

back_decoder.py

@@ -36,14 +36,24 @@ def load(self):
         self._load(pt + '/static', static)
 
     def train(self):
-        if os.path.isfile(back_recognizer):
-            self.svc = joblib.load(back_recognizer)
+        folder = 'back_dat'
+        pt = os.getcwd() + '/' + folder
+        if os.path.isdir(folder):
+            dat = os.listdir(pt)
+            if not int(len(dat)) == 0:
+                self.svc = joblib.load(pt + '/' + back_recognizer)
+            else:
+                self.learn_dat(pt)
         else:
-            self.load()
-            np_data = np.array(self.trainingData)
-            np_values = np.array(self.targetValues)
-            self.svc.fit(np_data, np_values)
-            joblib.dump(self.svc, back_recognizer)
+            os.mkdir(pt)
+            self.learn_dat(pt)
+
+    def learn_dat(self, path):
+        self.load()
+        np_data = np.array(self.trainingData)
+        np_values = np.array(self.targetValues)
+        self.svc.fit(np_data, np_values)
+        joblib.dump(self.svc, path + '/' + back_recognizer)
 
     def test(self):
         np_train_data = np.array(self.trainingData)

cell_decoder.py

@@ -55,14 +55,24 @@ def load(self):
     # import training data to scikit if it already exists
     # else, train the bot with the sample data
     def train(self):
-        if os.path.isfile(cell_recognizer):
-            self.svc = joblib.load(cell_recognizer)
+        folder = 'cell_dat'
+        pt = os.getcwd() + '/' + folder
+        if os.path.isdir(folder):
+            dat = os.listdir(pt)
+            if not int(len(dat)) == 0:
+                self.svc = joblib.load(pt + '/' + cell_recognizer)
+            else:
+                self.learn_dat(pt)
         else:
-            self.load()
-            np_data = np.array(self.trainingData)
-            np_values = np.array(self.targetValues)
-            self.svc.fit(np_data, np_values)
-            joblib.dump(self.svc, cell_recognizer)
+            os.mkdir(pt)
+            self.learn_dat(pt)
+
+    def learn_dat(self, path):
+        self.load()
+        np_data = np.array(self.trainingData)
+        np_values = np.array(self.targetValues)
+        self.svc.fit(np_data, np_values)
+        joblib.dump(self.svc, path + '/' + cell_recognizer)
 
     def test(self):
         np_train_data = np.array(self.trainingData)

utilities/tester.py

@@ -0,0 +1,28 @@
+import back_decoder
+from utils import *
+from PIL import Image
+from PIL import ImageGrab
+
+import time
+board = (77, 164, 652, 670)
+
+backdec = back_decoder.BackRecognizer()
+backdec.train()
+
+board_dic = {
+    curtain: 'curtain\n',
+    end: 'end\n',
+    intro: 'intro\n',
+    loading: 'loading\n',
+    move: 'move\n',
+    scoreboard: 'scoreboard\n',
+    shop: 'shop\n',
+    static: 'static\n'
+}
+
+i = 1
+while True:
+    img = ImageGrab.grab()
+    img = img.crop(board)
+    print board_dic[backdec.predict(img)]
+    time.sleep(0.4)