recognizing but training must be improved

.idea/workspace.xml

@@ -19,11 +19,14 @@
     <select />
   </component>
   <component name="ChangeListManager">
-    <list default="true" id="828778c9-9d97-422f-a727-18ddbd059b85" name="Default Changelist" comment="train.py">
+    <list default="true" id="828778c9-9d97-422f-a727-18ddbd059b85" name="Default Changelist" comment="fun created">
-      <change afterPath="$PROJECT_DIR$/coder/gr_test.png" afterDir="false" />
+      <change afterPath="$PROJECT_DIR$/coder/rocognizer.py" afterDir="false" />
-      <change afterPath="$PROJECT_DIR$/coder/image.py" afterDir="false" />
+      <change afterPath="$PROJECT_DIR$/coder/test1.jpg" afterDir="false" />
+      <change afterPath="$PROJECT_DIR$/coder/test3.png" afterDir="false" />
+      <change afterPath="$PROJECT_DIR$/coder/test9.png" afterDir="false" />
       <change beforePath="$PROJECT_DIR$/.idea/workspace.xml" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/workspace.xml" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/coder/test.jpg" beforeDir="false" afterPath="$PROJECT_DIR$/coder/test.jpg" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/coder/image.py" beforeDir="false" afterPath="$PROJECT_DIR$/coder/image.py" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/coder/train.py" beforeDir="false" afterPath="$PROJECT_DIR$/coder/train_nn.py" afterDir="false" />
     </list>
     <option name="SHOW_DIALOG" value="false" />
     <option name="HIGHLIGHT_CONFLICTS" value="true" />
@@ -81,7 +84,7 @@
       <recent name="C:\Users\Pawel Lukaszewicz\PycharmProjects\AL-2020\coder" />
     </key>
   </component>
-  <component name="RunManager" selected="Python.train">
+  <component name="RunManager" selected="Python.load_image">
     <configuration default="true" type="PythonConfigurationType" factoryName="Python">
       <module name="wozek" />
       <option name="INTERPRETER_OPTIONS" value="" />
@@ -158,7 +161,7 @@
       <option name="ADD_CONTENT_ROOTS" value="true" />
       <option name="ADD_SOURCE_ROOTS" value="true" />
       <EXTENSION ID="PythonCoverageRunConfigurationExtension" runner="coverage.py" />
-      <option name="SCRIPT_NAME" value="$PROJECT_DIR$/coder/image.py" />
+      <option name="SCRIPT_NAME" value="C:\Users\Pawel Lukaszewicz\PycharmProjects\AL-2020\coder\image.py" />
       <option name="PARAMETERS" value="" />
       <option name="SHOW_COMMAND_LINE" value="true" />
       <option name="EMULATE_TERMINAL" value="false" />
@@ -189,7 +192,7 @@
       <option name="INPUT_FILE" value="" />
       <method v="2" />
     </configuration>
-    <configuration name="train" type="PythonConfigurationType" factoryName="Python" temporary="true" nameIsGenerated="true">
+    <configuration name="train_nn" type="PythonConfigurationType" factoryName="Python" temporary="true" nameIsGenerated="true">
       <module name="wozek" />
       <option name="INTERPRETER_OPTIONS" value="" />
       <option name="PARENT_ENVS" value="true" />
@@ -199,7 +202,7 @@
       <option name="ADD_CONTENT_ROOTS" value="true" />
       <option name="ADD_SOURCE_ROOTS" value="true" />
       <EXTENSION ID="PythonCoverageRunConfigurationExtension" runner="coverage.py" />
-      <option name="SCRIPT_NAME" value="C:\Users\Pawel Lukaszewicz\PycharmProjects\AL-2020\coder\train.py" />
+      <option name="SCRIPT_NAME" value="C:\Users\Pawel Lukaszewicz\PycharmProjects\AL-2020\coder\train_nn.py" />
       <option name="PARAMETERS" value="" />
       <option name="SHOW_COMMAND_LINE" value="true" />
       <option name="EMULATE_TERMINAL" value="false" />
@@ -212,13 +215,13 @@
       <item itemvalue="Python.board" />
       <item itemvalue="Python.main" />
       <item itemvalue="Python.feature_hashing" />
-      <item itemvalue="Python.train" />
+      <item itemvalue="Python.train_nn" />
-      <item itemvalue="Python.image" />
+      <item itemvalue="Python.load_image" />
     </list>
     <recent_temporary>
       <list>
-        <item itemvalue="Python.train" />
+        <item itemvalue="Python.load_image" />
-        <item itemvalue="Python.image" />
+        <item itemvalue="Python.train_nn" />
         <item itemvalue="Python.main" />
         <item itemvalue="Python.feature_hashing" />
         <item itemvalue="Python.board" />
@@ -247,7 +250,7 @@
       <workItem from="1589814601057" duration="4208000" />
       <workItem from="1589844260514" duration="777000" />
       <workItem from="1589845066174" duration="177000" />
-      <workItem from="1589888090669" duration="16639000" />
+      <workItem from="1589888090669" duration="19744000" />
     </task>
     <task id="LOCAL-00001" summary="create Shelf">
       <created>1589815443652</created>
@@ -277,7 +280,21 @@
       <option name="project" value="LOCAL" />
       <updated>1589918562733</updated>
     </task>
-    <option name="localTasksCounter" value="5" />
+    <task id="LOCAL-00005" summary="img to 1d array">
+      <created>1589952733192</created>
+      <option name="number" value="00005" />
+      <option name="presentableId" value="LOCAL-00005" />
+      <option name="project" value="LOCAL" />
+      <updated>1589952733192</updated>
+    </task>
+    <task id="LOCAL-00006" summary="fun created">
+      <created>1589952979012</created>
+      <option name="number" value="00006" />
+      <option name="presentableId" value="LOCAL-00006" />
+      <option name="project" value="LOCAL" />
+      <updated>1589952979012</updated>
+    </task>
+    <option name="localTasksCounter" value="7" />
     <servers />
   </component>
   <component name="TypeScriptGeneratedFilesManager">
@@ -301,7 +318,9 @@
     <MESSAGE value="create addShelf method in Field class" />
     <MESSAGE value="changed CreateDataSweets to lowercase" />
     <MESSAGE value="train.py" />
-    <option name="LAST_COMMIT_MESSAGE" value="train.py" />
+    <MESSAGE value="img to 1d array" />
+    <MESSAGE value="fun created" />
+    <option name="LAST_COMMIT_MESSAGE" value="fun created" />
   </component>
   <component name="WindowStateProjectService">
     <state x="115" y="162" key="#com.intellij.refactoring.safeDelete.UnsafeUsagesDialog" timestamp="1589923610328">

coder/image.py

@@ -1,31 +1,63 @@
 import numpy as np
 from PIL import Image
 import matplotlib.pyplot as plt
+from sklearn import datasets
+from sklearn.metrics import accuracy_score
+from sklearn.neural_network import MLPClassifier
 import cv2
 
 
-def image():
+# training
-    img = cv2.cvtColor(cv2.imread('test.jpg'), cv2.COLOR_BGR2GRAY)
+# recznie napisane cyfry
-    img = cv2.GaussianBlur(img, (15, 15), 0)  # poprawia jakosc
+digits = datasets.load_digits()
-    img = cv2.resize(img, (8, 8), interpolation=cv2.INTER_AREA)
 
-    print(type(img))
+y = digits.target
-    print(img.shape)
+x = digits.images.reshape((len(digits.images), -1))
-    print(img)
-    plt.imshow(img, cmap='binary')
-    plt.show()
 
-    data = []
+x_train = x[:1000000]
+y_train = y[:1000000]
+x_test = x[1000:]
+y_test = y[1000:]
 
-    rows, cols = img.shape
+mlp = MLPClassifier(hidden_layer_sizes=(15,), activation='logistic', alpha=1e-4,
-    for i in range(rows):
+                    solver='sgd', tol=1e-4, random_state=1,
-        for j in range(cols):
+                    learning_rate_init=.1, verbose=True)
-            k = img[i, j]
-            if k > 200:
-                k = 0  # brak czarnego
-            else:
-                k = 1
 
-            data.append(k)
+mlp.fit(x_train, y_train)
+
+predictions = mlp.predict(x_test)
+print(accuracy_score(y_test, predictions))
+
+
+# image
+
+img = cv2.cvtColor(cv2.imread('test3.png'), cv2.COLOR_BGR2GRAY)
+img = cv2.GaussianBlur(img, (5, 5), 0) # poprawia jakosc
+img = cv2.resize(img, (8, 8), interpolation=cv2.INTER_AREA)
+
+print(type(img))
+print(img.shape)
+print(img)
+plt.imshow(img ,cmap='binary')
+plt.show()
+
+data = []
+
+rows, cols = img.shape
+for i in range(rows):
+    for j in range(cols):
+        k = img[i, j]
+        if k > 100:
+            k = 0 # brak czarnego
+        else:
+            k = 1
+
+        data.append(k)
+
+data = np.asarray(data, dtype=np.float32)
+print(data)
+
+predictions = mlp.predict([data])
+
+print("Liczba to:", predictions[0])
 
-    print(data)

coder/rocognizer.py

@@ -0,0 +1,13 @@
+import matplotlib.pyplot as plt
+import numpy as np
+from numpy import asarray
+from sklearn import datasets
+from sklearn.neural_network import MLPClassifier
+from sklearn.metrics import accuracy_score
+from PIL import Image
+import pygame
+import functions
+import sys
+import time
+
+

coder/train.py

@@ -1,29 +0,0 @@
-import matplotlib.pyplot as plt
-import numpy as np
-from numpy import asarray
-from sklearn import datasets
-from sklearn.neural_network import MLPClassifier
-from sklearn.metrics import accuracy_score
-from PIL import Image
-
-
-def train():
-    # recznie napisane cyfry
-    digits = datasets.load_digits()
-
-    y = digits.target
-    x = digits.images.reshape((len(digits.images), -1))
-
-    x_train = x[:1000000]
-    y_train = y[:1000000]
-    x_test = x[1000:]
-    y_test = y[1000:]
-
-    mlp = MLPClassifier(hidden_layer_sizes=(15,), activation='logistic', alpha=1e-4,
-                        solver='sgd', tol=1e-4, random_state=1,
-                        learning_rate_init=.1, verbose=True)
-
-    mlp.fit(x_train, y_train)
-
-    predictions = mlp.predict(x_test)
-    print(accuracy_score(y_test, predictions))

coder/train_nn.py

@@ -0,0 +1,30 @@
+import matplotlib.pyplot as plt
+import numpy as np
+from numpy import asarray
+import pygame
+from sklearn import datasets
+from sklearn.neural_network import MLPClassifier
+from sklearn.metrics import accuracy_score
+from PIL import Image
+
+
+# recznie napisane cyfry
+digits = datasets.load_digits()
+
+y = digits.target
+x = digits.images.reshape((len(digits.images), -1))
+
+x_train = x[:1000000]
+y_train = y[:1000000]
+x_test = x[1000:]
+y_test = y[1000:]
+
+mlp = MLPClassifier(hidden_layer_sizes=(15,), activation='logistic', alpha=1e-4,
+                    solver='sgd', tol=1e-4, random_state=1,
+                    learning_rate_init=.1, verbose=True)
+
+mlp.fit(x_train, y_train)
+
+predictions = mlp.predict(x_test)
+print(accuracy_score(y_test, predictions))
+print(x_test[1])